List of RNA-Seq bioinformatics tools
||This article's use of external links may not follow Wikipedia's policies or guidelines. (August 2015) (Learn how and when to remove this template message)|
RNA-Seq   is a technique that allows transcriptome studies (see also Transcriptomics technologies) based on next-generation sequencing technologies. This technique is largely dependent on bioinformatics tools developed to support the different steps of the process. Here are listed some of the principal tools commonly employed and links to some important web resources.
- 1 Design
- 2 Quality control, trimming, error correction and pre-processing of data
- 3 Alignment tools
- 4 Normalization, quantitative analysis and differential expression
- 5 Workbench (analysis pipeline / integrated solutions)
- 6 Alternative splicing analysis
- 7 Fusion genes/chimeras/translocation finders/structural variations
- 8 Copy number variation identification
- 9 Single cell RNA-Seq
- 10 RNA-Seq simulators
- 11 Transcriptome assemblers
- 12 Co-expression networks
- 13 miRNA prediction and analysis
- 14 Visualization tools
- 15 Functional, network and pathway analysis tools
- 16 Further annotation tools for RNA-Seq data
- 17 RNA-Seq databases
- 18 External links
- 19 References
Design is a fundamental step of a particular RNA-Seq experiment. Some important questions like sequencing depth/coverage or how many biological or technical replicates must be carefully considered. Design review.
- PROPER PROPER: PROspective Power Evaluation for RNAseq.
- RNAtor RNAtor Android Application to calculate optimal parameters for popular tools and kits available for DNA sequencing projects.
- Scotty Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression.
- ssizeRNA ssizeRNA: Sample Size Calculation for RNA-Seq Experimental Design.
Quality control, trimming, error correction and pre-processing of data
Quality assessment of raw data  is the first step of the bioinformatics pipeline of RNA-Seq. Often, is necessary to filter data, removing low quality sequences or bases (trimming), adapters, contaminations, overrepresented sequences or correcting errors to assure a coherent final result. Articles about common next-generation sequencing problems.
- AfterQC AfterQC - Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data.
- dupRadar dupRadar. An R package which provides functions for plotting and analyzing the duplication rates dependent on the expression levels.
- FastQC FastQC is a quality control tool for high-throughput sequence data (Babraham Institute) and is developed in Java. Import of data is possible from FastQ files, BAM or SAM format. This tool provides an overview to inform about problematic areas, summary graphs and tables to rapid assessment of data. Results are presented in HTML permanent reports. FastQC can be run as a stand-alone application or it can be integrated into a larger pipeline solution.
- fastqp fastqp. Simple FASTQ quality assessment using Python.
- Kraken kraken: A set of tools for quality control and analysis of high-throughput sequence data.
- HTSeq HTSeq. The Python script htseq-qa takes a file with sequencing reads (either raw or aligned reads) and produces a PDF file with useful plots to assess the technical quality of a run.
- mRIN mRIN - Assessing mRNA integrity directly from RNA-Seq data.
- MultiQC MultiQC - Aggregate and visualise results from numerous tools (FastQC, HTSeq, RSeQC, Tophat, STAR, others..) across all samples into a single report.
- NGSQC NGSQC: cross-platform quality analysis pipeline for deep sequencing data.
- NGS QC Toolkit NGS QC Toolkit A toolkit for the quality control (QC) of next generation sequencing (NGS) data. The toolkit comprises user-friendly stand alone tools for quality control of the sequence data generated using Illumina and Roche 454 platforms with detailed results in the form of tables and graphs, and filtering of high-quality sequence data. It also includes few other tools, which are helpful in NGS data quality control and analysis.
- PRINSEQ PRINSEQ is a tool that generates summary statistics of sequence and quality data and that is used to filter, reformat and trim next-generation sequence data. It is particular designed for 454/Roche data, but can also be used for other types of sequence.
- QC-Chain QC-Chain is a package of quality control tools for next generation sequencing (NGS) data, consisting of both raw reads quality evaluation and de novo contamination screening, which could identify all possible contamination sequences.
- QC3 QC3 a quality control tool designed for DNA sequencing data for raw data, alignment, and variant calling.
- qrqc qrqc. Quickly scans reads and gathers statistics on base and quality frequencies, read length, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected.
- RNA-SeQC RNA-SeQC is a tool with application in experiment design, process optimization and quality control before computational analysis. Essentially, provides three types of quality control: read counts (such as duplicate reads, mapped reads and mapped unique reads, rRNA reads, transcript-annotated reads, strand specificity), coverage (like mean coverage, mean coefficient of variation, 5’/3’ coverage, gaps in coverage, GC bias) and expression correlation (the tool provides RPKM-based estimation of expression levels). RNA-SeQC is implemented in Java and is not required installation, however can be run using the GenePattern web interface. The input could be one or more BAM files. HTML reports are generated as output.
- RSeQC RSeQC analyzes diverse aspects of RNA-Seq experiments: sequence quality, sequencing depth, strand specificity, GC bias, read distribution over the genome structure and coverage uniformity. The input can be SAM, BAM, FASTA, BED files or Chromosome size file (two-column, plain text file). Visualization can be performed by genome browsers like UCSC, IGB and IGV. However, R scripts can also be used to visualization.
- SAMStat SAMStat  identifies problems and reports several statistics at different phases of the process. This tool evaluates unmapped, poorly and accurately mapped sequences independently to infer possible causes of poor mapping.
- SolexaQA SolexaQA calculates sequence quality statistics and creates visual representations of data quality for second-generation sequencing data. Originally developed for the Illumina system (historically known as “Solexa”), SolexaQA now also supports Ion Torrent and 454 data.
- Trim galore! Trim_galore is a wrapper script to automate quality and adapter trimming as well as quality control, with some added functionality to remove biased methylation positions for RRBS sequence files (for directional, non-directional (or paired-end) sequencing).
Improving the quality
Improvement of the RNA-Seq quality, correcting the bias is a complex subject. Each RNA-Seq protocol introduces specific type of bias, each step of the process (such as the sequencing technology used) is susceptible to generate some sort of noise or type of error. Furthermore, even the species under investigation and the biological context of the samples are able to influence the results and introduce some kind of bias. Many sources of bias were already reported – GC content and PCR enrichment, rRNA depletion, errors produced during sequencing, priming of reverse transcription caused by random hexamers.
Different tools were developed to attempt to solve each of the detected errors.
Trimming and adapters removal
- BBDuk BBDuk. Ultrafast, multithreaded tool to trim adapters and filter or mask contaminants based on kmer-matching, allowing a hamming- or edit-distance, as well as degenerate bases. Also performs optimal quality-trimming and filtering, format conversion, contaminant concentration reporting, gc-filtering, length-filtering, entropy-filtering, chastity-filtering, and generates text histograms for most operations. Interconverts between fastq, fasta, sam, scarf, interleaved and 2-file paired, gzipped, bzipped, ASCII-33 and ASCII-64. Keeps pairs together. Open-source, written in pure Java; supports all platforms with no recompilation and no other dependencies.
- clean_reads clean_reads cleans NGS (Sanger, 454, Illumina and solid) reads. It can trim bad quality regions, adaptors, vectors, and regular expressions. It also filters out the reads that do not meet a minimum quality criteria based on the sequence length and the mean quality.
- condetri condetri is a method for content dependent read trimming for Illumina data using quality scores of each base individually. It is independent from sequencing coverage and user interaction. The main focus of the implementation is on usability and to incorporate read trimming in next-generation sequencing data processing and analysis pipelines. It can process single-end and paired-end sequencing data of arbitrary length.
- cutadapt  cutadapt removes adapter sequences from next-generation sequencing data (Illumina, SOLiD and 454). It is used especially when the read length of the sequencing machine is longer than the sequenced molecule, like the microRNA case.
- Deconseq Deconseq. Detect and remove contaminations from sequence data.
- Erne-Filter  Erne is a short string alignment package whose goal is to provide an all-inclusive set of tools to handle short (NGS-like) reads. ERNE comprises ERNE-FILTER (read trimming and continamination filtering), ERNE-MAP (core alignment tool/algorithm), ERNE-BS5 (bisulfite treated reads aligner), and ERNE-PMAP/ERNE-PBS5 (distributed versions of the aligners).
- FastqMcf FastqMcf. Fastq-mcf attempts to: Detect & remove sequencing adapters and primers; Detect limited skewing at the ends of reads and clip; Detect poor quality at the ends of reads and clip; Detect Ns, and remove from ends; Remove reads with CASAVA 'Y' flag (purity filtering); Discard sequences that are too short after all of the above; Keep multiple mate-reads in sync while doing all of the above.
- FASTX FASTX Toolkit is a set of command line tools to manipulate reads in files FASTA or FASTQ format. These commands make possible preprocess the files before mapping with tools like Bowtie. Some of the tasks allowed are: conversion from FASTQ to FASTA format, information about statistics of quality, removing sequencing adapters, filtering and cutting sequences based on quality or conversion DNA/RNA.
- Flexbar Flexbar performs removal of adapter sequences, trimming and filtering features.
- FreClu FreClu improves overall alignment accuracy performing sequencing-error correction by trimming short reads, based on a clustering methodology.
- htSeqTools htSeqTools is a Bioconductor package able to perform quality control, processing of data and visualization. htSeqTools makes possible visualize sample correlations, to remove over-amplification artifacts, to assess enrichment efficiency, to correct strand bias and visualize hits.
- NxTrim NxTrim. Adapter trimming and virtual library creation routine for Illumina Nextera Mate Pair libraries.
- PRINSEQ PRINSEQ generates statistics of your sequence data for sequence length, GC content, quality scores, n-plicates, complexity, tag sequences, poly-A/T tails, odds ratios. Filter the data, reformat and trim sequences.
- Sabre sabre. A barcode demultiplexing and trimming tool for FastQ files.
- Scythe scythe. A 3'-end adapter contaminant trimmer.
- SEECER seecer SEECER is a sequencing error correction algorithm for RNA-seq data sets. It takes the raw read sequences produced by a next generation sequencing platform like machines from Illumina or Roche. SEECER removes mismatch and indel errors from the raw reads and significantly improves downstream analysis of the data. Especially if the RNA-Seq data is used to produce a de novo transcriptome assembly, running SEECER can have tremendous impact on the quality of the assembly.
- Sickle Sickle. A windowed adaptive trimming tool for FASTQ files using quality.
- SnoWhite SnoWhite. Snowhite is a pipeline designed to flexibly and aggressively clean sequence reads (gDNA or cDNA) prior to assembly. It takes in and returns fastq or fasta formatted sequence files.
- ShortRead ShortRead is a package provided in the R (programming language) / BioConductor environments and allows input, manipulation, quality assessment and output of next-generation sequencing data. This tool makes possible manipulation of data, such as filter solutions to remove reads based on predefined criteria. ShortRead could be complemented with several Bioconductor packages to further analysis and visualization solutions (BioStrings, BSgenome, IRanges, and so on).
- SortMeRNA SortMeRNA is a program tool for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data. The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering ribosomal RNA from metatranscriptomic data.
- TagCleaner TagCleaner. The TagCleaner tool can be used to automatically detect and efficiently remove tag sequences (e.g. WTA tags) from genomic and metagenomic datasets. It is easily configurable and provides a user-friendly interface.
- Trimmomatic  Trimmomatic performs trimming for Illumina platforms and works with FASTQ reads (single or pair-ended). Some of the tasks executed are: cut adapters, cut bases in optional positions based on quality thresholds, cut reads to a specific length, converts quality scores to Phred-33/64.
Detection of chimeric reads
Recent sequencing technologies normally require DNA samples to be amplified via polymerase chain reaction (PCR). Amplification often generates chimeric elements (specially from ribosomal origin) - sequences formed from two or more original sequences joined together.
- UCHIME UCHIME. UCHIME is an algorithm for detecting chimeric sequences.
- ChimeraSlayer ChimeraSlayer is a chimeric sequence detection utility, compatible with near-full length Sanger sequences and shorter 454-FLX sequences (~500 bp).
High-throughput sequencing errors characterization and their eventual correction.
- Acacia Acacia. Error-corrector for pyrosequenced amplicon reads.
- AllPathsLG error correction.
- AmpliconNoise AmpliconNoise. AmpliconNoise is a collection of programs for the removal of noise from 454 sequenced PCR amplicons. It involves two steps the removal of noise from the sequencing itself and the removal of PCR point errors. This project also includes the Perseus algorithm for chimera removal.
- BayesHammer BayesHammer. Bayesian clustering for error correction. We introduce several novel algorithms based on Hamming graphs and Bayesian subclustering in our new error correction tool BAYES HAMMER. While BAYES HAMMER was designed for single-cell sequencing, we demonstrate that it also improves on existing error correction tools for multi-cell sequencing data while working much faster on real-life datasets. We benchmark BAYES HAMMER on both k-mer counts and actual assembly results with the SPADES genome assembler.
- Bless Bless. A bloom filter-based error correction solution for high-throughput sequencing reads.
- Blue Blue. Blue is a fast, accurate short-read error-correction tool based on k-mer consensus and context.
- bfc bfc. A free, fast and easy-to-use sequencing error corrector designed for Illumina short reads. It uses a non-greedy algorithm but still maintains a speed comparable to implementations based on greedy methods.
- Denoiser Denoiser. Denoiser is designed to address issues of noise in pyrosequencing data. Denoiser is a heuristic variant of PyroNoise. Developers of denoiser report a good agreement with PyroNoise on several test datasets.
- Echo Echo. A reference-free short-read error correction algorithm.
- Lighter. A fast and memory-efficient sequencing error correction without counting.
- LSC LSC. LSC uses short Illumina reads to corrected errors in long reads.
- Karect Karect. Karect: accurate correction of substitution, insertion and deletion errors for next-generation sequencing data.
- NoDe NoDe. NoDe: a fast error-correction algorithm for pyrosequencing amplicon reads.
- PyroTagger Pyrotagger. PyroTagger: A fast, accurate pipeline for analysis of rRNA amplicon pyrosequence data.
- Quake Quake is a tool to correct substitution sequencing errors in experiments with deep coverage for Illumina sequencing reads.
- QuorUM QuorUM: An Error Corrector for Illumina Reads.
- Rcorrector. Error correction for Illumina RNA-seq reads.
- Reptile Reptile is a software developed in C++ for correcting sequencing errors in short reads from next-gen sequencing platforms.
- Seecer Seecer. SEquencing Error CorrEction for Rna reads.
- SOAP denovo.
- Alpine Alpine. Modeling and correcting fragment sequence bias for RNA-seq.
- cqn cqn  is a normalization tool for RNA-Seq data, implementing the conditional quantile normalization method.
- EDASeq EDASeq  is a Bioconductor package to perform GC-Content Normalization for RNA-Seq Data.
- GeneScissors GeneScissors. A comprehensive approach to detecting and correcting spurious transcriptome inference due to RNAseq reads misalignment.
- Peer Peer  is a collection of Bayesian approaches to infer hidden determinants and their effects from gene expression profiles using factor analysis methods. Applications of PEER have: a) detected batch effects and experimental confounders, b) increased the number of expression QTL findings by threefold, c) allowed inference of intermediate cellular traits, such as transcription factor or pathway activations.
- RUV RUVseq  is a R package that implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.
- sva sva Surrogate Variable Analysis.
- svaseq svaseq removing batch effects and other unwanted noise from sequencing data.
- SysCall SysCall  is a classifier tool to identification and correction of systematic error in high-throughput sequence data.
Other tasks/pre-processing data
Further tasks performed before alignment, namely paired-read mergers.
- BamHash BamHashis a checksum based method to ensure that the read pairs in FASTQ files match exactly the read pairs stored in BAM files, regardless of the ordering of reads. BamHash can be used to verify the integrity of the files stored and discover any discrepancies. Thus, BamHash can be used to determine if it is safe to delete the FASTQ files storing raw sequencing reads after alignment, without the loss of data.
- BBMerge BBMergeMerges paired reads based on overlap to create longer reads, and an insert-size histogram. Fast, multithreaded, and yields extremely few false positives. Open-source, written in pure Java; supports all platforms with no recompilation and no other dependencies. Distributed with BBMap.
- Biopieces Biopieces are a collection of bioinformatics tools that can be pieced together in a very easy and flexible manner to perform both simple and complex tasks. The Biopieces work on a data stream in such a way that the data stream can be passed through several different Biopieces, each performing one specific task: modifying or adding records to the data stream, creating plots, or uploading data to databases and web services.
- COPE COPE. COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly.
- DeconRNASeq DeconRNASeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data.
- FastQ Screen FastQ Screen screens FASTQ format sequences to a set of databases to confirm that the sequences contain what is expected (such as species content, adapters, vectors, etc.).
- FLASH FLASH is a read pre-processing tool. FLASH combines paired-end reads which overlap and converts them to single long reads.
- IDCheck IDCheck.
- PANDASeq. PANDASeq is a program to align Illumina reads, optionally with PCR primers embedded in the sequence, and reconstruct an overlapping sequence.
- PEAR Pear. PEAR: A fast and accurate Illumina Paired-End reAd mergeR.
- qRNASeq script qRNASeq script. The qRNAseq tool can be used to accurately eliminate PCR duplicates from RNA-Seq data if Molecular Indexes™ or other stochastic labels have been used during library prep.
- SHERA Shera  a SHortread Error-Reducing Aligner.
- XORRO Xorro: Rapid Paired-End Read Overlapper.
After quality control, the first step of RNA-Seq analysis involves alignment (RNA-Seq alignment) of the sequenced reads to a reference genome (if available) or to a transcriptome database. See also  and List of sequence alignment software.
Short (unspliced) aligners
Short aligners are able to align continuous reads (not containing gaps result of splicing) to a genome of reference. Basically, there are two types: 1) based on the Burrows-Wheeler transform method such as Bowtie and BWA, and 2) based on Seed-extend methods, Needleman-Wunsch or Smith-Waterman algorithms. The first group (Bowtie and BWA) is many times faster, however some tools of the second group tend to be more sensitive, generating more correctly aligned reads. See a comparative study of short aligners - comparative study.
- BFAST BFAST aligns short reads to reference sequences and presents particular sensitivity towards errors, SNPs, insertions and deletions. BFAST works with the Smith-Waterman algorithm. See also seqanwers/BFAST.
- Bowtie Bowtie is a fast short aligner using an algorithm based on the Burrows-Wheeler transform and the FM-index. Bowtie tolerates a small number of mismatches.
- Burrows-Wheeler Aligner (BWA) BWA. BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.
- Short Oligonucleotide Analysis Package (SOAP) SOAP.
- GNUMAP GNUMAP performs alignment using a probabilistic Needleman-Wunsch algorithm. This tool is able to handle alignment in repetitive regions of a genome without losing information. The output of the program was developed to make possible easy visualization using available software.
- Maq Maq first aligns reads to reference sequences and after performs a consensus stage. On the first stage performs only ungapped alignment and tolerates up to 3 mismatches.
- Mosaik Mosaik. Mosaik is able to align reads containing short gaps using Smith-Waterman algorithm, ideal to overcome SNPs, insertions and deletions.
- NovoAlign (commercial) NovoAlign is a short aligner to the Illumina platform based on Needleman-Wunsch algorithm. Novoalign tolerates up to 8 mismatches per read, and up to 7bp of indels. It is able to deal with bisulphite data. Output in SAM format.
- PerM PerM is a software package which was designed to perform highly efficient genome scale alignments for hundreds of millions of short reads produced by the ABI SOLiD and Illumina sequencing platforms. PerM is capable of providing full sensitivity for alignments within 4 mismatches for 50bp SOLID reads and 9 mismatches for 100bp Illumina reads.
- RazerS RazerS.
- SEAL SEAL uses a MapReduce model to produce distributed computing on clusters of computers. Seal uses BWA to perform alignment and Picard MarkDuplicates to detection and duplicate read removal.
- segemehl segemehl.
- SeqMap SeqMap.
- SHRiMP SHRiMP employs two techniques to align short reads. Firstly, the q-gram filtering technique based on multiple seeds identifies candidate regions. Secondly, these regions are investigated in detail using Smith-Waterman algorithm.
- SMALT Smalt.
- Stampy Stampy combines the sensitivity of hash tables and the speed of BWA. Stampy is prepared to alignment of reads containing sequence variation like insertions and deletions. It is able to deal with reads up to 4500 bases and presents the output in SAM format.
- ZOOM (commercial) ZOOM is a short aligner of the Illumina/Solexa 1G platform. ZOOM uses extended spaced seeds methodology building hash tables for the reads, and tolerates mismatches and insertions and deletions.
- WHAM Wham. WHAM is a high-throughput sequence alignment tool developed at University of Wisconsin-Madison. It aligns short DNA sequences (reads) to the whole human genome at a rate of over 1500 million 60bit/s reads per hour, which is one to two orders of magnitudes faster than the leading state-of-the-art techniques.
Many reads span exon-exon junctions and can not be aligned directly by Short aligners, thus specific aligners were necessary - Spliced aligners. Some Spliced aligners employ Short aligners to align firstly unspliced/continuous reads (exon-first approach), and after follow a different strategy to align the rest containing spliced regions - normally the reads are split into smaller segments and mapped independently. See also.
Aligners based on known splice junctions (annotation-guided aligners)
In this case the detection of splice junctions is based on data available in databases about known junctions. This type of tools cannot identify new splice junctions. Some of this data comes from other expression methods like expressed sequence tags (EST).
- Erange Erange is a tool to alignment and data quantification to mammalian transcriptomes.
- IsoformEx IsoformEx.
- MapAL MapAL.
- OSA OSA.
- RNA-MATE RNA-MATE is a computational pipeline for alignment of data from Applied Biosystems SOLID system. Provides the possibility of quality control and trimming of reads. The genome alignments are performed using mapreads and the splice junctions are identified based on a library of known exon-junction sequences. This tool allows visualization of alignments and tag counting.
- RUM RUM performs alignment based on a pipeline, being able to manipulate reads with splice junctions, using Bowtie and Blat. The flowchart starts doing alignment against a genome and a transcriptome database executed by Bowtie. The next step is to perform alignment of unmapped sequences to the genome of reference using BLAT. In the final step all alignments are merged to get the final alignment. The input files can be in FASTA or FASTQ format. The output is presented in RUM and SAM format.
- RNASEQR RNASEQR.
- SAMMate SAMMate.
- SpliceSeq SpliceSeq.
- X-Mate X-Mate.
De novo splice aligners
De novo Splice aligners allow the detection of new Splice junctions without need to previous annotated information (some of these tools present annotation as a suplementar option). See also De novo Splice Aligners.
- ABMapper ABMapper.
- BBMap BBMap Uses short kmers to align reads directly to the genome (spanning introns to find novel isoforms) or transcriptome. Highly tolerant of substitution errors and indels, and very fast. Supports output of all SAM tags needed by Cufflinks. No limit to genome size or number of splices per read. Supports Illumina, 454, Sanger, Ion Torrent, PacBio, and Oxford Nanopore reads, paired or single-ended. Does not use any splice-site-finding heuristics optimized for a single taxonomic branch, but rather finds optimally-scoring multi-affine-transform global alignments, and thus is ideal for studying new organisms with no annotation and unknown splice motifs. Open-source, written in pure Java; supports all platforms with no recompilation and no other dependencies.
- ContextMap ContextMap was developed to overcome some limitations of other mapping approaches, such as resolution of ambiguities. The central idea of this tool is to consider reads in gene expression context, improving this way alignment accuracy. ContextMap can be used as a stand-alone program and supported by mappers producing a SAM file in the output (e.g.: TopHat or MapSplice). In stand-alone mode aligns reads to a genome, to a transcriptome database or both.
- CRAC CRAC propose a novel way of analyzing reads that integrates genomic locations and local coverage, and detect candidate mutations, indels, splice or fusion junctions in each single read. Importantly, CRAC improves its predictive performance when supplied with e.g. 200 nt reads and should fit future needs of read analyses.
- GSNAP GSNAP.
- GMAP GMAP A Genomic Mapping and Alignment Program for mRNA and EST Sequences.
- HISAT HISAT. HISAT is a fast and sensitive spliced alignment program for mapping RNA-seq reads. In addition to one global FM index that represents a whole genome, HISAT uses a large set of small FM indexes that collectively cover the whole genome (each index represents a genomic region of ~64,000 bp and ~48,000 indexes are needed to cover the human genome). These small indexes (called local indexes) combined with several alignment strategies enable effective alignment of RNA-seq reads, in particular, reads spanning multiple exons. The memory footprint of HISAT is relatively low (~4.3GB for the human genome). We have developed HISAT based on the Bowtie2 implementation to handle most of the operations on the FM index.
- HMMSplicer HMMSplicer can identify canonical and non-canonical splice junctions in short-reads. Firstly, unspliced reads are removed with Bowtie. After that, the remaining reads are one at a time divided in half, then each part is seeded against a genome and the exon borders are determined based on the Hidden Markov Model . A quality score is assigned to each junction, useful to detect false positive rates.
- MapSplice MapSplice.
- PALMapper PALMapper.
- Pass Pass  aligns gapped, ungapped reads and also bisulfite sequencing data. It includes the possibility to filter data before alignment (remotion of adapters). Pass uses Needleman-Wunsch and Smith-Waterman algorithms, and performs alignment in 3 stages: scanning positions of seed sequences in the genome, testing the contiguous regions and finally refining the alignment.
- PASSion PASSion.
- PASTA PASTA.
- QPALMA QPALMA predicts splice junctions supported on machine learning algorithms. In this case the training set is a set of spliced reads with quality information and already known alignments.
- RAZER RAZER: reads aligner for SNPs and editing sites of RNA.
- SeqSaw SeqSaw.
- SoapSplice SoapSplice - A tool for genome-wide ab initio detection of splice junction sites from RNA-Seq, a method using new generation sequencing technologies to sequence the messenger RNA.
- SpliceMap SpliceMap.
- SplitSeek SplitSeek.
- SuperSplat SuperSplat was developed to find all type of splice junctions. The algorithm splits each read in all possible two-chunk combinations in an iterative way, and alignment is tried to each chunck. Output in "Supersplat" format.
- Subread Subread is a superfast, accurate and scalable read aligner. It uses the seed-and-vote mapping paradigm to determine the mapping location of the read by using its largest mappable region. It automatically decides whether the read should be globally mapped or locally mapped. For RNA-seq data, Subread should be used for the purpose of expression analysis. Subread is very powerful in mapping gDNA-seq reads as well.
- Subjunc Subjunc is a specialized version of Subread. It uses all mappable regions in an RNA-seq read to discover exons and exon-exon junctions. It uses the donor/receptor signals to find the exact splicing locations. Subjunc yields full alignments for every RNA-seq read including exon-spanning reads, in addition to the discovered exon-exon junctions. Subjunc should be used for the purpose of junction detection and genomic variation detection in RNA-seq data.
De novo splice aligners that also use annotation optionally
- MapNext MapNext.
- OLego OLego.
- STAR STAR is an ultrafast tool that employs "sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure", detects canonical, non-canonical splices junctions and chimeric-fusion sequences. It is already adapted to align long reads (third-generation sequencing technologies) and can reach speeds of 45 million paired reads per hour per processor.
- TopHat TopHat is prepared to find de novo junctions. TopHat aligns reads in two steps. Firstly, unspliced reads are aligned with Bowtie. After, the aligned reads are assembled with Maq resulting islands of sequences. Secondly, the splice junctions are determined based on the initially unmapped reads and the possible canonical donor and acceptor sites within the island sequences.
Other spliced aligners
- G.Mo.R-Se G.Mo.R-Se is a method that uses RNA-Seq reads to build de novo gene models.
Evaluation of alignment tools
- AlignerBoost AlignerBoost is a generalized software toolkit for boosting Next-Gen sequencing mapping precision using a Bayesian-based mapping quality framework.
- CADBURE CADBURE:Bioinformatics tool for evaluating aligner performance on your RNA-Seq dataset.
- QualiMap QualiMap: Evaluating next generation sequencing alignment data.
- RNAseqEVAL RNAseqEval A collection of tools for evaluating RNA seq mapping.
- Teaser Teaser: Individualized benchmarking and optimization of read mapping results for NGS data.
Normalization, quantitative analysis and differential expression
These tools perform normalization and calculate the abundance of each gene expressed in a sample. RPKM, FPKM and TPMs are some of the units employed to quantification of expression (RPKM-FPKM-TPMs video). Some software are also designed to study the variability of genetic expression between samples (differential expression). Quantitative and differential studies are largely determined by the quality of reads alignment and accuracy of isoforms reconstruction. Several studies are available comparing differential expression methods.
- ABSSeq ABSSeq -a new RNA-Seq analysis method based on modelling absolute expression differences.
- ALDEx2 ALDEx2 is a tool for comparative analysis of high-throughput sequencing data. ALDEx2 uses compositional data analysis and can be applied to RNAseq, 16S rRNA gene sequencing, metagenomic sequencing, and selective growth experiments.
- Alexa-Seq Alexa-Seq is a pipeline that makes possible to perform gene expression analysis, transcript specific expression analysis, exon junction expression and quantitative alternative analysis. Allows wide alternative expression visualization, statistics and graphs.
- ARH-seq – identification of differential splicing in RNA-seq data.
- ASC ASC.
- Ballgown Ballgown.
- BaySeq BaySeq is a Bioconductor package to identify differential expression using next-generation sequencing data, via empirical Bayesian methods. There is an option of using the "snow" package for parallelisation of computer data processing, recommended when dealing with large data sets.
- BBSeq BBSeq.
- BitSeq BitSeq. (Bayesian Inference of Transcripts from Sequencing Data) is an application for inferring expression levels of individual transcripts from sequencing (RNA-Seq) data and estimating differential expression (DE) between conditions.
- CEDER CEDER. Accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq.
- CPTRA CPTRA. The CPTRA package is for analyzing transcriptome sequencing data from different sequencing platforms. It combines advantages of 454, Illumina GAII, or other platforms and can perform sequence tag alignment and annotation, expression quantification tasks.
- casper casper is a Bioconductor package to quantify expression at the isoform level. It combines using informative data summaries, flexible estimation of experimental biases and statistical precision considerations which (reportedly) provide substantial reductions in estimation error.
- Cufflinks/Cuffdiff Cufflinks is appropriate to measure global de novo transcript isoform expression. It performs assembly of transcripts, estimation of abundances and determines differential expression (Cuffdiff) and regulation in RNA-Seq samples.
- DESeq DESeq is a Bioconductor package to perform differential gene expression analysis based on negative binomial distribution.
- DEGSeq DEGSeq.
- Derfinder Derfinder Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach.
- DEXSeq DEXSeq is Bioconductor package that finds differential differential exon usage based on RNA-Seq exon counts between samples. DEXSeq employs negative binomial distribution, provides options to visualization and exploration of the results.
- DEXUS dexus is a Bioconductor package that identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. In contrast to other methods, DEXUS does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript.
- DiffSplice DiffSplice is a method for differential expression detection and visualization, not dependent on gene annotations. This method is supported on identification of alternative splicing modules (ASMs) that diverge in the different isoforms. A non-parametric test is applied to each ASM to identify significant differential transcription with a measured false discovery rate.
- EBSeq EBSeq is a Bioconductor package for identifying genes and isoforms differentially expressed (DE) across two or more biological conditions in an RNA-seq experiment. It also can be used to identify DE contigs after performing de novo transcriptome assembly. While performing DE analysis on isoforms or contigs, different isoform/contig groups have varying estimation uncertainties. EBSeq models the varying uncertainties using an empirical Bayes model with different priors.
- EdgeR EdgeR is a R package for analysis of differential expression of data from DNA sequencing methods, like RNA-Seq, SAGE or ChIP-Seq data. edgeR employs statistical methods supported on negative binomial distribution as a model for count variability.
- EdgeRun edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test.
- EQP EQP - The exon quantification pipeline (EQP): a comprehensive approach to the quantification of gene, exon and junction expression from RNA-seq data.
- ESAT ESAT The End Sequence Analysis Toolkit (ESAT) is specially designing to be applied for quantification of annotation of specialized RNA-Seq gene libraries that target the 5' or 3' ends of transcripts.
- eXpress eXpress performance includes transcript-level RNA-Seq quantification, allele-specific and haplotype analysis and can estimate transcript abundances of the multiple isoforms present in a gene. Although could be coupled directly with aligners (like Bowtie), eXpress can also be used with de novo assemblers and thus is not needed a reference genome to perform alignment. It runs on Linux, Mac and Windows.
- ERANGE ERANGE performs alignment, normalization and quantification of expressed genes.
- featureCounts featureCounts an efficient general-purpose read quantifier. It is part of the SourceForge Subread package and Bioconductor Rsubread package.
- FDM FDM.
- FineSplice FineSplice - Enhanced splice junction detection and estimation from RNA-Seq data.
- GFOLD GFOLD: Generalized fold change for ranking differentially expressed genes from RNA-seq data.
- globalSeq globalSeq. Testing for association between RNA-Seq and high-dimensional data.
- GPSeq GPSeq This is a software tool to analyze RNA-seq data to estimate gene and exon expression, identify differentially expressed genes, and differentially spliced exons.
- IsoDOT – Differential RNA-isoform Expression.
- Limma Limma. Limma powers differential expression analyses for RNA-sequencing and microarray studies.
- LPEseq LPEseq – accurately test differential expression with a limited number of replicates.
- Kallisto Kallisto. "Kallisto is a program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build."
- MATS MATS. Multivariate Analysis of Transcript Splicing (MATS).
- MetaDiff MetaDiff. Differential isoform expression analysis using random-effects meta-regression.
- metaseqR metaseqR is a Bioconductor package that detects differentially expressed genes from RNA-Seq data by combining six statistical algorithms using weights estimated from their performance with simulated data estimated from real data, either public or user-based. In this way, metaseqR optimizes the tradeoff between precision and sensitivity. In addition, metaseqR creates a detailed and interactive report with a variety of diagnostic and exploration plots and auto-generated text.
- MMSEQ MMSEQ is a pipeline for estimating isoform expression and allelic imbalance in diploid organisms based on RNA-Seq. The pipeline employs tools like Bowtie, TopHat, ArrayExpressHTS and SAMtools. Also, edgeR or DESeq to perform differential expression.
- MultiDE multiDE
- Myrna Myrna is a pipeline tool that runs in a cloud environment (Elastic MapReduce) or in a unique computer for estimating differential gene expression in RNA-Seq datasets. Bowtie is employed for short read alignment and R algorithms for interval calculations, normalization, and statistical processing.
- NEUMA NEUMA is a tool to estimate RNA abundances using length normalization, based on uniquely aligned reads and mRNA isoform models. NEUMA uses known transcriptome data available in databases like RefSeq.
- NOISeq NOISeq. NOISeq is a non-parametric approach for the identification of differentially expressed genes from count data or previously normalized count data. NOISeq empirically models the noise distribution of count changes by contrasting fold-change differences (M) and absolute expression differences (D) for all the features in samples within the same condition.
- NPEBseq NPEBseq is a nonparametric empirical bayesian- based method for differential expression analysis.
- NSMAP NSMAP allows inference of isoforms as well estimation of expression levels, without annotated information. The exons are aligned and splice junctions are identified using TopHat. All the possible isoforms are computed by combination of the detected exons.
- NURD NURD an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data.
- PANDORA PANDORA. An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms.
- PennSeq PennSeq PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution.
- Quark Quark - Quark enables semi-reference-based compression of RNA-seq data.
- QuasR QuasR - Quantify and Annotate Short Reads in R.
- RapMap RapMap. A Rapid, Sensitive and Accurate Tool for Mapping RNA-seq Reads to Transcriptomes.
- RNAeXpress RNAeXpress Can be run with Java GUI or command line on Mac, Windows and Linux. Can be configured to perform read counting, feature detection or GTF comparison on mapped rnaseq data.
- Rcount Rcount. Rcount: simple and flexible RNA-Seq read counting.
- rDiff rDiff is a tool that can detect differential RNA processing (e.g. alternative splicing, polyadenylation or ribosome occupancy).
- RNASeqPower RNASeqPower. Calculating samples Size estimates for RNA Seq studies. R package version .
- RNA-Skim RNA-Skim. RNA-Skim: a rapid method for RNA-Seq quantification at transcript-level.
- rSeq rSeq. rSeq is a set of tools for RNA-Seq data analysis. It consists of programs that deal with many aspects of RNA-Seq data analysis, such as read quality assessment, reference sequence generation, sequence mapping, gene and isoform expressions (RPKMs) estimation, etc.
- RSEM RSEM.
- rQuant rQuant is a web service (Galaxy (computational biology) installation) that determines abundances of transcripts per gene locus, based on quadratic programming. rQuant is able to evaluate biases introduced by experimental conditions. A combination of tools is employed: PALMapper (reads alignment), mTiM and mGene (inference of new transcripts).
- Salmon Salmon is an software tool for computing transcript abundance from RNA-seq data using either an alignment-free (based directly on the raw reads) or an alignment-based (based on pre-computed alignments) approach. It uses an online stochastic optimization approach to maximize the likelihood of the transcript abundances under the observed data. The software itself is capable of making use of many threads to produce accurate quantification estimates quickly. It is part of the Sailfish suite of software, and is the successor to the Sailfish tool.
- SAJR SAJR is a java-written read counter and R-package for differential splicing analysis. It uses junction reads to estimate exon exclusion and reads mapped within exon to estimate its inclusion. SAJR models it by GLM with quasibinomial distribution and uses log likelihood test to assess significance.
- Scotty Scotty Performs power analysis to estimate the number of replicates and depth of sequencing required to call differential expression.
- Seal Seal Ultrafast, alignment-free algorithm to quantify sequence expression by matching kmers between raw reads and a reference transcriptome. Handles paired reads and alternate isoforms, and uses little memory. Accepts all common read formats, and outputs read counts, coverage, and FPKM values per reference sequence. Open-source, written in pure Java; supports all platforms with no recompilation and no other dependencies. Distributed with BBMap. (Seal - Sequence Expression AnaLyzer - is unrelated to the SEAL distributed short-read aligner.)
- Sleuth Sleuth is a program for analysis of RNA-Seq experiments for which transcript abundances have been quantified with kallisto.
- SplicingCompass SplicingCompass. SplicingCompass: differential splicing detection using RNA-Seq data.
- sSeq sSeq. The purpose of this R package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments.
- TIGAR TIGAR Transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference.
- TimeSeq TimeSeq - Detecting Differentially Expressed Genes in Time Course RNA-Seq Data.
- WemIQ WemIQis a software tool to quantify isoform expression and exon splicing ratios from RNA-seq data accurately and robustly.
Evaluation of quantification and differential expression
- CompcodeR CompcodeR— RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods.
- DEAR-O DEAR-O : Differential Expression Analysis based on RNA-seq data – Online.
- PROPER PROPER: comprehensive power evaluation for differential expression using RNA-seq.
- rnaseqcomp rnaseqcomp. Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with nessasery meta information should be organizd into numeric matrices in order to proceed the evaluation.
- DEB DEB is a web-interface/pipeline that permits to compare results of significantly expressed genes from different tools. Currently are available three algorithms: edgeR, DESeq and bayseq.
- SARTools SARTools: A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data.
Workbench (analysis pipeline / integrated solutions)
- ActiveSite by Cofactor Genomics ActiveSite
- Avadis NGS (currently Strand NGS) Strand NGS.
- BaseSpace by Illumina BaseSpace
- Biowardrobe Biowardrobe an integrated platform for analysis of epigenomics and transcriptomics data.
- CLC Genomics Workbench CLC Genomics Workbench
- DNASTAR DNASTAR
- ERGO ERGO
- GeneSpring GX GeneSpring GX
- Genevestigator Genevestigator
- geospiza geospiza
- Golden Helix Golden Helix
- Maverix Biomics Maverix Biomics
- NextGENe NextGENe
- OmicsOffice OmicsOffice
- Qlucore. Easy to use for analysis and visualization. One button import of BAM files.
Open (free) source solutions
- ArrayExpressHTS ArrayExpressHTS (and ebi_ArrayExpressHTS) is a BioConductor package that allows preprocessing, quality assessment and estimation of expression of RNA-Seq datasets. It can be run remotely at the European Bioinformatics Institute cloud or locally. The package makes use of several tools: ShortRead (quality control), Bowtie, TopHat or BWA (alignment to a reference genome), SAMtools format, Cufflinks or MMSEQ (expression estimation).
- BioWardrobe BioWardrobe is an integrated package that for analysis of ChIP-Seq and RNA-Seq datasets using a web-based user-friendly GUI. For RNA-Seq Biowardrobe performs mapping, quality control, RPKM estimation and differential expression analysis between samples (groups of samples). Results of differential expression analysis can be integrated with ChIP-Seq data to build average tag density profiles and heat maps. The package makes use of several tools open source tools including STAR and DESeq. See also.
- Chipster Chipster. Chipster is a user-friendly analysis software for high-throughput data. It contains over 350 analysis tools for next generation sequencing (NGS), microarray, proteomics and sequence data. Users can save and share automatic analysis workflows, and visualize data interactively using a built-in genome browser and many other visualizations.
- easyRNASeq easyRNASeq. Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package.
- ExpressionPlot ExpressionPlot.
- FX FX. FX is a user-Frendly RNA-Seq gene eXpression analysis tool, empowered by the concept of cloud-computing. With FX, you can simply upload your RNA-Seq raw FASTQ data on the cloud, and let the computing infra to do the heavy analysis.
- Galaxy: Galaxy is a general purpose workbench platform for computational biology. There are several publicly accessible Galaxy servers that support RNA-Seq tools and workflows, including NBIC's Andromeda, the CBIIT-Giga server, the Galaxy Project's public server, the GeneNetwork Galaxy server, the University of Oslo's Genomic Hyperbrowser, URGI's server (which supports S-MART), and many others.
- GENE-Counter GENE-Counter is a Perl pipeline for RNA-Seq differential gene expression analyses. Gene-counter performs alignments with CASHX, Bowtie, BWA or other SAM output aligner. Differential gene expression is run with three optional packages (NBPSeq, edgeR and DESeq) using negative binomial distribution methods. Results are stored in a MySQL database to make possible additional analyses.
- GenePattern GenePattern offers integrated solutions to RNA-Seq analysis (Broad Institute).
- GeneProf GeneProf: Freely accessible, easy to use analysis pipelines for RNA-seq and ChIP-seq experiments.
- GT-FAR GT-FAR is an RNA seq pipeline that performs RNA-seq QC, alignment, reference free quantification, and splice variant calling. It filters, trims, and sequentially aligns reads to gene models and predicts and validates new splice junctions after which it quantifies expression for each gene, exon, and known/novel splice junction, and Variant Calling.
- MultiExperiment Viewer (MeV) MeV is suitable to perform analysis, data mining and visualization of large-scale genomic data. The MeV modules include a variety of algorithms to execute tasks like Clustering and Classification, Student's t-test, Gene Set Enrichment Analysis or Significance Analysis. MeV runs on Java. See also seqanswers/MeV.
- NGSUtils NGSUtils. NGSUtils is a suite of software tools for working with next-generation sequencing datasets.
- Rail-RNA Rail-Rna. Scalable analysis of RNA-seq splicing and coverage.
- RAP - RNA-Seq Analysis Pipeline, a new cloud-based NGS web application.
- RSEQtools RSEQtools. "RSEQtools consists of a set of modules that perform common tasks such as calculating gene expression values, generating signal tracks of mapped reads, and segmenting that signal into actively transcribed regions. In addition to the anonymization afforded by this format it also facilitates the decoupling of the alignment of reads from downstream analyses."
- RobiNA RobiNA provides a user graphical interface to deal with R/BioConductor packages. RobiNA provides a package that automatically installs all required external tools (R/Bioconductor frameworks and Bowtie). This tool offers a diversity of quality control methods and the possibility to produce many tables and plots supplying detailed results for differential expression. Furthermore, the results can be visualized and manipulated with MapMan and PageMan. RobiNA runs on Java version 6.
- RseqFlow RseqFlow is an RNA-Seq analysis pipeline which offers an express implementation of analysis steps for RNA sequencing datasets. It can perform pre and post mapping quality control (QC) for sequencing data, calculate expression levels for uniquely mapped reads, identify differentially expressed genes, and convert file formats for ease of visualization.
- S-MART S-MART handles mapped RNA-Seq data, and performs essentially data manipulation (selection/exclusion of reads, clustering and differential expression analysis) and visualization (read information, distribution, comparison with epigenomic ChIP-Seq data). It can be run on any laptop by a person without computer background. A friendly graphical user interface makes easy the operation of the tools.
- Taverna Taverna. Taverna is an open source and domain-independent Workflow Management System – a suite of tools used to design and execute scientific workflows and aid in silico experimentation.
- TCW TCW. TCW is a Transcriptome Computational Workbench.
- TRAPLINE TRAPLINE: a standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation.
- ViennaNGS ViennaNGS. A toolbox for building efficient next- generation sequencing analysis pipelines.
- wapRNA wapRNA. This is a free web-based application for the processing of high-throughput RNA-Seq data (wapRNA) from next generation sequencing (NGS) platforms, such as Genome Analyzer of Illumina Inc. (Solexa) and SOLiD of Applied Biosystems (SOLiD). wapRNA provides an integrated tool for RNA sequence, refers to the use of High-throughput sequencing technologies to sequence cDNAs in order to get information about a sample's RNA content.
Alternative splicing analysis
- Alternative Splicing Analysis Tool Package(ASATP) ASATP. Alternative splicing analysis tool package (ASATP) includes a series of toolkits to analyze alternative splicing events, which could be used to detect and visualized alternative splicing events, check ORF changes, assess regulations of alternative splicing and do statistical analysis.
- Asprofile asprofile. ASprofile is a suite of programs for extracting, quantifying and comparing alternative splicing (AS) events from RNA-seq data.
- AStalavista AStalavista. The AStalavista web server extracts and displays alternative splicing (AS) events from a given genomic annotation of exon-intron gene coordinates. By comparing all given transcripts, AStalavista detects the variations in their splicing structure and identify all AS events (like exon skipping, alternate donor, etc.) by assigning to each of them an AS code.
- CLASS2 Class: accurate and efficient splice variant annotation from RNA-seq reads.
- Cufflinks/Cuffdiff Cufflinks.
- DEXseq DEXseq. Inference of differential exon usage in RNA-Seq.
- Diceseq Diceseq - Statistical modeling of isoform splicing dynamics from RNA-seq time series data.
- EBChangepoint EBChangepoint An empirical Bayes change-point model for identifying 3′ and 5′ alternative splicing by RNA-Seq.
- Eoulsan Eoulsan A versatile framework dedicated to high throughput sequencing data analysis. Allows automated analysis (mapping, counting and differencial analysis with DESeq2).
- GESS GESS – for de novo detection of exon-skipping event sites from raw RNA-seq reads.
- LeafCutter LeaffCutter, a suite of novel methods that allow identification and quantication of novel and existing alternative splicing events by focusing on intron excisions.
- LEMONS  A Tool for the Identification of Splice Junctions in Transcriptomes of Organisms Lacking Reference Genomes.
- MAJIC MAJIC. Modeling Alternative Junction Inclusion Quantification.
- MATS MATS. Multivariate Analysis of Transcript Splicing (MATS).
- MISO MISO quantifies the expression level of splice variants from RNA-Seq data and is able to recognize differentially regulated exons/isoforms across different samples. MISO uses a probabilistic method (Bayesian inference) to calculate the probability of the reads origin.
- Rail-RNA Rail-Rna. Scalable analysis of RNA-seq splicing and coverage.
- RPASuite  RPASuite. RPASuite (RNA Processing Analysis Suite) is a computational pipeline to identify differentially and coherently processed transcripts using RNA-seq data obtained from multiple tissue or cell lines.
- RSVP RSVP. RSVP is a software package for prediction of alternative isoforms of protein-coding genes, based on both genomic DNA evidence and aligned RNA-seq reads. The method is based on the use of ORF graphs, which are more general than the splice graphs used in traditional transcript assembly.
- SAJR SAJR calculates the number of the reads that confirms segment (part of gene between two nearest splice sites) inclusion or exclusion and then model these counts by GLM with quasibinomial distribution to account for biological variability.
- SGSeq SGSeq A R package to de novo prediction of splicing events.
- SplAdder SplAdder Identification, quantification and testing of alternative splicing events from RNA-Seq data.
- SpliceJumper SpliceJumper: a classification-based approach for calling splicing junctions from RNA-seq data.
- SplicePie SplicePieis a pipeline to analyze non-sequential and multi-step splicing. SplicePie contains three major analysis steps: analyzing the order of splicing per sample, looking for recursive splicing events per sample and summarizing predicted recursive splicing events for all analyzed sample (it is recommended to use more samples for higher reliability). The first two steps are performed individually on each sample and the last step looks at the overlap in all samples. However, the analysis can be run on one sample as well.
- SplicePlot SplicePlot. SplicePlot is a tool for visualizing alternative splicing and the effects of splicing quantitative trait loci (sQTLs) from RNA-seq data. It provides a simple command line interface for drawing sashimi plots, hive plots, and structure plots of alternative splicing events from .bam, .gtf, and .vcf files.
- SpliceR SpliceR. An R package for classification of alternative splicing and prediction of coding potential from RNA-seq data.
- SpliceSEQ SpliceSeq. SpliceViewer is a Java application that allows researchers to investigate alternative mRNA splicing patterns in data from high-throughput mRNA sequencing studies. Sequence reads are mapped to splice graphs that unambiguously quantify the inclusion level of each exon and splice junction. The graphs are then traversed to predict the protein isoforms that are likely to result from the observed exon and splice junction reads. UniProt annotations are mapped to each protein isoform to identify potential functional impacts of alternative splicing.
- SpliceTrap SpliceTrap is a statistical tool for the quantification of exon inclusion ratios from RNA-seq data.
- Splicing Express – a software suite for alternative splicing analysis using next-generation sequencing data.
- SUPPA Suppa. This tool generates different Alternative Splicing (AS) events and calculates the PSI ("Percentage Spliced In") value for each event exploiting the fast quantification of transcript abundances from multiple samples.
- SwitchSeq SwitchSeq identifies extreme changes in splicing (switch events).
- Portcullis Portcullis - identification of genuine splice junctions.
- TrueSight TrueSight A Self-training Algorithm for Splice Junction Detection using RNA-seq.
- Vast-tools vast-toolsA toolset for profiling alternative splicing events in RNA-Seq data.
Intron retention analysis
- IRcall / IRclassifier IRcall / IRclassifier. IRcall is a computational tool for IR event detection from RNA-Seq data. IRclassifier is a supervised machine learning-based approach for IR event detection from RNA-Seq data.
Differential isoform/transcript usage
- IsoformSwitchAnalyzeR IsoformSwitchAnalyzeR. IsoformSwitchAnalyzeR is an R package that enables statistical identification of isoform switches with predicted functional consequences where the consequences of interest can be chosen from a long list but includes gain/loss of protein domains, signal peptides changes in NMD sensitivity. IsoformSwitchAnalyzeR is made for post analysis of data from any full length isoform/transcript quantification tool but directly support Cufflinks/Cuffdiff, RSEM Kallisto and Salmon.
- DRIMSeq DRIMSeq. An R package that utilizes generalized linear modeling (GLM) to identify isoform switches from estimated isoform count data.
- BayesDRIMSeq BayesDRIMSeq. An R package conatining a Bayesian implementation of DRIMSeq.
- Cufflinks/Cuffdiff Cufflinks/Cuffdiff. Full length isoform/transcript quantification and differential analysis tool which amongst other test for changes in usage for isoform belonging to the same primary transcript (sharing a TSS) via a one-sided t-test based on the asymptotic of the Jensen-Shannon metric.
- rSeqNP rSeqNP. An R package that implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data.
- Isolator Isolator. Full length isoform/transcript quantification and differential analysis tool which analyses all samples in an experiment in unison using a simple Bayesian hierarchical model. Can identify differential isoform usage by testing for probability of monotonic splicing.
Fusion genes/chimeras/translocation finders/structural variations
Genome arrangements result of diseases like cancer can produce aberrant genetic modifications like fusions or translocations. Identification of these modifications play important role in carcinogenesis studies.
- BreakDancer BreakDancer.
- ChimeraScan ChimeraScan.
- DeFuse DeFuse. DeFuse is a software package for gene fusion discovery using RNA-Seq data.
- EBARDenovo EBARDenovo.
- FusionAnalyser FusionAnalyser. FusionAnalyser uses paired reads mapping to different genes (Bridge reads), generated through high-throughput whole transcriptome sequencing, to build a first dataset of candidate fusion events. Subsequently, a second dataset, built upon those reads where only one of the two sequences in a pair is successfully mapped to the reference genome (‘Half-mapped Anchor reads’), is generated. The mapped reads in the latter dataset are used as an anchor to tie each Half-mapped event to the corresponding Bridge region.
- FusionCatcher FusionCatcher. FusionCatcher searches for novel/known somatic fusion genes, translocations, and chimeras in RNA-seq data (stranded/unstranded paired-end reads from Illumina NGS platforms like Solexa/HiSeq/NextSeq/MiSeq) from diseased samples.
- FusionHunter FusionHunter identifies fusion transcripts without depending on already known annotations. It uses Bowtie as a first aligner and paired-end reads.
- FusionMap FusionMap. FusionMap is an efficient fusion aligner which aligns reads spanning fusion junctions directly to the genome without prior knowledge of potential fusion regions. It detects and characterizes fusion junctions at base-pair resolution. FusionMap can be applied to detect fusion junctions in both single- and paired-end dataset from either gDNA-Seq or RNA-Seq studies.
- FusionSeq FusionSeq.
- JAFFA JAFFA is based on the idea of comparing a transcriptome against a reference transcriptome rather than a genome-centric approach like other fusion finders.
- Oncomine Oncomine - NGS RNA-Seq Gene Expression Browser.
- PRADA prada.
- SOAPFuse SOAPFuse detects fusion transcripts from human paired-end RNA-Seq data. It outperforms other five similar tools in both computation and fusion detection performance using both real and simulated data.
- SOAPfusion Soapfusion.
- TopHat-Fusion TopHat-Fusion is based on TopHat version and was developed to handle reads resulting from fusion genes. It does not require previous data about known genes and uses Bowtie to align continuous reads.
- ViralFusionSeq ViralFusionSeq is high-throughput sequencing (HTS) tool for discovering viral integration events and reconstruct fusion transcripts at single-base resolution. See also hkbic/VFS.
Copy number variation identification
- CNVseq CNVseq detects copy number variations supported on a statistical model derived from array-comparative genomic hybridization. Sequences alignment are performed by BLAT, calculations are executed by R modules and is fully automated using Perl.
Single cell RNA-Seq
Single cell sequencing. The traditional RNA-Seq methodology is commonly known as "bulk RNA-Seq", in this case RNA is extracted from a group of cells or tissues, not from the individual cell like it happens in single cell methods. Some tools available to bulk RNA-Seq are also applied to single cell analysis, however to face the specificity of this technique new algorithms were developed. Comparative analysis of single-cell RNA-sequencing methods. A list of Single Cell tools can be found at Awesome Single Cell.
- CEL-Seq CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.
- Drop-Seq Drop-Seq: Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.
- FISSEQ FISSEQ Single cell transcriptome sequencing in situ, i.e. without dissociating the cells.
- Oscope: a statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq experiments.
- SCUBA SCUBA: Extracting lineage relationships and modeling dynamic changes associated with multi-lineage cell differentiation.
- scLVM scLVM. scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation.
- scM&T-Seq scM&T-Seq - Parallel single-cell sequencing.
- Sphinx Sphinx. SPHINX is a hybrid binning approach that achieves high binning efficiency by utilizing both 'compositional' and 'similarity' features of the query sequence during the binning process. SPHINX can analyze sequences in metagenomic data sets as rapidly as composition based approaches, but nevertheless has the accuracy and specificity of similarity based algorithms.
- TraCeR TraCeR: Paired T-cell receptor reconstruction from single-cell RNA-Seq reads.
- VDJPuzzle VDJPuzzle: T-cell receptor reconstruction from single-cell RNA-Seq reads and link the clonotype with the functional phenotype and transcriptome of individual cells.
- Monocle Monocle: Differential expression and time-series analysis for single-cell RNA-Seq and qPCR experiments.
- SCell SCell – integrated analysis of single-cell RNA-seq data.
- Sincell Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq.
- SINCERA SINCERA. A Pipeline for Single-Cell RNA-Seq Profiling Analysis.
Quality Control and Gene Filtering
- Celloline Celloline A pipeline for mapping and quality assessment single cell RNA-seq data.
- OEFinder OEFinder. A user interface to identify and visualize ordering effects in single-cell RNA-seq data.
- SinQC SinQC A Method and Tool to Control Single-cell RNA-seq Data Quality.
- BASICS BASICS. Understanding changes in gene expression at the single-cell level.
- GRM GRM - Normalization and noise reduction for single cell RNA-seq experiments.
- BPSC BPSC An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq.
- MAST MAST : a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.
- SCDE SCDE. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis.
These Simulators generate in silico reads and are useful tools to compare and test the efficiency of algorithms developed to handle RNA-Seq data. Moreover, some of them make possible to analyse and model RNA-Seq protocols.See also Genetic Simulation Resources and some discussion about simulation at Biostars.
- BEERS Simulator BEERS is formatted to mouse or human data, and paired-end reads sequenced on Illumina platform. Beers generates reads starting from a pool of gene models coming from different published annotation origins. Some genes are chosen randomly and afterwards are introduced deliberately errors (like indels, base changes and low quality tails), followed by construction of novel splice junctions.
- CuReSim Curesim: a customized read simulator.
- Flux simulator Flux Simulator implements a computer pipeline simulation to mimic a RNA-Seq experiment. All component steps that influence RNA-Seq are taken into account (reverse transcription, fragmentation, adapter ligation, PCR amplification, gel segregation and sequencing) in the simulation. These steps present experimental attributes that can be measured, and the approximate experimental biases are captured. Flux Simulator allows joining each of these steps as modules to analyse different type of protocols.
- PBSIM PBSIM - PacBio reads simulator - toward accurate genome assembly.
- Polyester Polyester. This bioconductor package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression.
- RandomReads RandomReads Generates synthetic reads from a genome with an Illumina or PacBio error model. The reads may be paired or unpaired, with arbitrary length and insert size, output in fasta or fastq, RandomReads has a wide selection of options for mutation rates, with individual settings for substitution, deletion, insertion, and N rates and length distributions, annotating reads with their original, unmutated genomic start and stop location. RandomReads is does not vary expression levels and thus is not designed to simulate RNA-seq experiments, but to test the sensitivity and specificity of RNA-seq aligners with de-novo introns. Includes a tool for grading and generating ROC curves from resultant sam files. Open-source, written in pure Java; supports all platforms with no recompilation and no other dependencies. Distributed with BBMap.
- rlsim rlsim is a software package for simulating RNA-seq library preparation with parameter estimation.
- RSEM Read Simulator rsem-simulate-reads. RSEM provides users the ‘rsem-simulate-reads’ program to simulate RNA-Seq data based on parameters learned from real data sets.
- RNASeqReadSimulator RNASeqReadSimulator contains a set of simple Python scripts, command line driven. It generates random expression levels of transcripts (single or paired-end), equally simulates reads with a specific positional bias pattern and generates random errors from sequencing platforms.
- RNA Seq Simulator RNA Seq Simulator . RSS takes SAM alignment files from RNA-Seq data and simulates over dispersed, multiple replica, differential, non-stranded RNA-Seq datasets.
- SimSeq SimSeq: A Nonparametric Approach to Simulation of RNA-Sequence Datasets.
- WGsim wgsim. Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.
The transcriptome is the total population of RNAs expressed in one cell or group of cells, including non-coding and protein-coding RNAs. There are two types of approaches to assemble transcriptomes. Genome-guided methods use a reference genome (if possible a finished and high quality genome) as a template to align and assembling reads into transcripts. Genome-independent methods does not require a reference genome and are normally used when a genome is not available. In this case reads are assembled directly in transcripts. Some important comparative studies  were already published.
- Bayesembler Baysembler Bayesian transcriptome assembly.
- CIDANE Cidane a comprehensive isoform discovery and abundance estimation.
- CLASS CLASS. CLASS is a program for assembling transcripts from RNA-seq reads aligned to a genome. CLASS produces a set of transcripts in three stages. Stage 1 uses linear programming to determine a set of exons for each gene. Stage 2 builds a splice graph representation of a gene, by connecting the exons (vertices) via introns (edges) extracted from spliced read alignments. Stage 3 selects a subset of the candidate transcripts encoded in the graph that can explain all the reads, using either a parsimonius (SET_COVER) or a dynamic programming optimization approach. This stage takes into account constraints derived from mate pairs and spliced alignments and, optionally, knowledge about gene structure extracted from known annotation or alignments of cDNA sequences.
- Cufflinks Cufflinks. Cufflinks assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. It accepts aligned RNA-Seq reads and assembles the alignments into a parsimonious set of transcripts. Cufflinks then estimates the relative abundances of these transcripts based on how many reads support each one, taking into account biases in library preparation protocols.
- iReckon iReckon. iReckon is an algorithm for the simultaneous isoform reconstruction and abundance estimation. In addition to modelling novel isoforms, multi-mapped reads and read duplicates, this method takes into account the possible presence of unspliced pre-mRNA and intron retention. iReckon only requires a set of transcription start and end sites, but can use known full isoforms to improve sensitivity. Starting from the set of nearly all possible isoforms, iReckon uses a regularized EM algorithm to determine those actually present in the sequenced sample, together with their abundances. iReckon is multi-threaded to increase efficiency in all its time consuming steps.
- IsoInfer IsoInfer. IsoInfer is a C/C++ program to infer isoforms based on short RNA-Seq (single-end and paired-end) reads, exon-intron boundary and TSS/PAS information.
- IsoLasso IsoLasso. IsoLasso is an algorithm to assemble transcripts and estimate their expression levels from RNA-Seq reads.
- Flipflop flipflop. FlipFlop implements a fast method for de novo transcript discovery and abundance estimation from RNA-Seq data. It differs from Cufflinks by simultaneously performing the identification and quantitation tasks using a convex penalized maximum likelihood approach, which leads to improved precision/recall.
- GIIRA GIIRA. GIIRA is a gene prediction method that identifies potential coding regions exclusively based on the mapping of reads from an RNA-Seq experiment. It was foremost designed for prokaryotic gene prediction and is able to resolve genes within the expressed region of an operon. However, it is also applicable to eukaryotes and predicts exon intron structures as well as alternative isoforms.
- MITIE MITIE. Simultaneous RNA-Seq-based Transcript Identification and Quantification in Multiple Samples.
- RNAeXpress RNAeXpress. RNA-eXpress was designed as a user friendly solution to extract and annotate biologically important transcripts from next generation RNA sequencing data. This approach complements existing gene annotation databases by ensuring all transcripts present in the sample are considered for further analysis.
- Scripture Scripture. Scripture is a method for transcriptome reconstruction that relies solely on RNA-Seq reads and an assembled genome to build a transcriptome ab initio. The statistical methods to estimate read coverage significance are also applicable to other sequencing data. Scripture also has modules for ChIP-Seq peak calling.
- SLIDE SLIDE - Sparse Linear modeling of RNA-Seq data for Isoform Discovery and abundance Estimation.
- Strawberry Strawberry- A program for fast and accurate genome-guided transcripts reconstruction and quantification from paired-end RNA-seq.
- StringTie StringTie. StringTie is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus. Its input can include not only the alignments of raw reads used by other transcript assemblers, but also alignments longer sequences that have been assembled from those reads.To identify differentially expressed genes between experiments, StringTie's output can be processed either by the Cuffdiff or Ballgown programs.
- TransComb TransComb a genome-guided transcriptome assembly via combing junctions in splicing graphs.
- Traph Traph A tool for transcript identification and quantification with RNA-Seq.
- Tiling Assembly for Annotation-independent Novel Gene Discovery.
Genome-independent (de novo) assemblers
- Bridger Bridger was developed at Shandong University, takes advantage of techniques employed in Cufflinks to overcome limitations of the existing de novo assemblers.
- CLC CLC de novo assembly algorithm of CLC Genomics Workbench.
- KISSPLICE KISSPLICE is a software that enables to analyse RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition.
- Oases Oases. De novo transcriptome assembler for very short reads.
- rnaSPAdes rnaSPAdes
- Rnnotator Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads.
- SAT-Assembler SAT-Assembler
- SOAPdenovo-Trans SOAPdenovo-trans.
- Scaffolding Translation Mapping STM.
- Trans-ABySS Trans-AByss.
- T-IDBA T-IDBA.
- Trinity Trinity was developed at the Broad Institute and the Hebrew University of Jerusalem, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads.
- Velvet Velvet (algorithm). Velvet(EMBL-EBI).
Assembly evaluation tools
- Busco Busco provides quantitative measures for the assessment of genome assembly, gene set, and transcriptome completeness, based on evolutionarily-informed expectations of gene content from near-universal single-copy orthologs selected from OrthoDB tool.
- Detonate Detonate. DETONATE (DE novo TranscriptOme rNa-seq Assembly with or without the Truth Evaluation) consists of two component packages, RSEM-EVAL and REF-EVAL. Both packages are mainly intended to be used to evaluate de novo transcriptome assemblies, although REF-EVAL can be used to compare sets of any kinds of genomic sequences.
- rnaQUAST rnaquast: Quality Assessment Tool for Transcriptome Assemblies.
- TransRate TransRate. Transrate is software for de-novo transcriptome assembly quality analysis. It examines your assembly in detail and compares it to experimental evidence such as the sequencing reads, reporting quality scores for contigs and assemblies. This allows you to choose between assemblers and parameters, filter out the bad contigs from an assembly, and help decide when to stop trying to improve the assembly.
- GeneNetWeaver GeneNetWeaver is an open-source tool for in silico benchmark generation and performance profiling of network inference methods.
- WGCNA Weighted Gene Coexpression Network Analysis is an R package for weighted correlation network analysis.
miRNA prediction and analysis
- iSRAP –  a one-touch research tool for rapid profiling of small RNA-seq data.
- miRDeep2 miRDeep2.
- MIReNA MIReNA.
- miRExpress miRExpress.
- miR-PREFeR miR-PREFeR.
- miRDeep-P For plants. miRDeep-P.
- miRDeep* mirdeepstar
- miRPlant mirplant
- MiRdup MiRdup
- ABrowse ABrowse - a customizable next-generation genome browser framework.
- Artemis Artemis. Artemis is a free genome browser and annotation tool that allows visualisation of sequence features, next generation data and the results of analyses within the context of the sequence, and also its six-frame translation.
- Apollo Apollo. Apollo is designed to support geographically dispersed researchers, and the work of a distributed community is coordinated through automatic synchronization: all edits in one client are instantly pushed to all other clients, allowing users to see annotation updates from collaborators in real-time during the editing process.
- BamView BamView. BamView is a free interactive display of read alignments in BAM data files. It has been developed by the Pathogen Group at the Sanger Institute.
- BrowserGenome BrowserGenome.org: web-based RNA-seq data analysis and visualization.
- Degust Degust. An interactive web tool for visualising Differential Gene Expression data.
- DensityMap DensityMap is a perl tool for the visualization of features density along chromosomes.
- EagleView EagleView. EagleView is an information-rich genome assembler viewer with data integration capability. EagleView can display a dozen different types of information including base qualities, machine specific trace signals, and genome feature annotations.
- expvip-web expvip-web a customisable RNA-seq data analysis and visualisation platform.
- GBrowse GBrowse.
- Integrated Genome Browser IGB.
- Integrative Genomics Viewer (IGV) IGV.
- GenomeView genomeview.
- MapView MapView.
- MicroScope Microscope: comprehensive genome analysis software suite for gene expression heatmaps.
- ReadXplorer ReadXplorer. ReadXplorer is a freely available comprehensive exploration and evaluation tool for NGS data. It extracts and adds quantity and quality measures to each alignment in order to classify the mapped reads. This classification is then taken into account for the different data views and all supported automatic analysis functions.
- RNASeqExpressionBrowser RNASeqExpressionBrowser is a web-based tool which provides means for the search and visualization of RNA-seq expression data (e.g. based on sequence-information or domain annotations). It can generate detailed reports for selected genes including expression data and associated annotations. If needed, links to (publicly available) databases can be easily integrated. The RNASeqExpressionBrowser allows password protection and thereby access restriction to authorized users only.
- Savant Savant. Savant is a next-generation genome browser designed for the latest generation of genome data.
- Samscope Samscope.
- SeqMonk SeqMonk.
- Tablet  Tablet. Tablet is a lightweight, high-performance graphical viewer for next generation sequence assemblies and alignments.
- Tbrowse- HTML5 Transcriptome Browser Tbrowse.
- TBro TBro – a transcriptome browser for de novo RNA-sequencing experiments.
- Vespa Vespa.
Functional, network and pathway analysis tools
- BioCyc BioCyc: Visualize RNA-seq data onto individual pathway diagrams, multi-pathway diagrams called pathway collages, and zoomable organism-specific metabolic map diagrams. Computes pathway enrichment. Also provides a tool called the Omics Dashboard for visualizing RNA-seq data within a hierarchy of cellular systems.
- BRANE Clust BRANE Clust: Biologically-Related Apriori Network Enhancement for Gene Regulatory Network Inference combined with clustering.
- BRANE Cut BRANE Cut: Biologically-Related Apriori Network Enhancement with Graph cuts for Gene Regulatory Network Inference.
- Enrichr Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.
- FunRich Funrich: Functional Enrichment analysis tool.
- GAGE is applicable independent of sample sizes, experimental design, assay platforms, and other types of heterogeneity. This Biocondutor package also provides functions and data for pathway, GO and gene set analysis in general. Tutorials describe both RNA-Seq pathway analysis workflows and microarray analysis workflows. The RNA-Seq workflows cover from preparation, reads counting, data preprocessing, gene set test, to pathway visualization in about 40 lines of codes.
- Gene Set Association Analysis for RNA-Seq (GSAASeq): GSAASeq are computational methods that assess the differential expression of a pathway/gene set between two biological states based on sequence count data.
- GeneSCF GeneSCF: a real-time based functional enrichment tool with support for multiple organisms. Complete tutorial for GeneSCF is available on BioStar and documentation on GeneSCF project home page. Advantages of using GeneSCF: real-time analysis, users do not have to depend on enrichment tools to get updated, easy for computational biologists to integrate GeneSCF with their NGS pipeline, it supports multiple organisms, enrichment analysis for multiple gene list using multiple source database in single run, retrieve or download complete GO terms/Pathways/Functions with associated genes as simple table format in a plain text file.
- GOexpress GOexpress. Visualise microarray and RNAseq data using gene ontology annotations.
- GOSeq GOSeq. Gene Ontology analyser for RNA-seq and other length biased data.
- GSAASEQSP GSAASeqSP: A Toolset for Gene Set Association Analysis of RNA-Seq Data.
- GSVA GSVA  gene set variation analysis for microarray and RNA-Seq data.
- Heat*Seq Heat*Seq: an interactive web tool for high-throughput sequencing experiment comparison with public data.
- Ingenuity Systems (commercial) iReport & IPA: Ingenuity’s IPA and iReport applications enable you to upload, analyze, and visualize RNA-Seq datasets, eliminating the obstacles between data and biological insight. Both IPA and iReport support identification, analysis and interpretation of differentially expressed isoforms between condition and control samples, and support interpretation and assessment of expression changes in the context of biological processes, disease and cellular phenotypes, and molecular interactions. Ingenuity iReport supports the upload of native Cuffdiff file format as well as gene expression lists. IPA supports the upload of gene expression lists.
- PathwaySeq  Pathway analysis for RNA-Seq data using a score-based approach.
- petal petalNet – Co-expression network modelling in R.
- ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data.
- RNA-Enrich RNA-Enrich: A cut-off free functional enrichment testing method for RNA-seq with improved detection power.
- TRAPID TRAPID. Rapid Analysis of Transcriptome Data.
- T-REx T-REx. RNA-seq expression analysis.
Further annotation tools for RNA-Seq data
- Frama Frama: From RNA-seq data to annotated mRNA assemblies.
- HLAminer HLAminer is a computational method for identifying HLA alleles directly from whole genome, exome and transcriptome shotgun sequence datasets. HLA allele predictions are derived by targeted assembly of shotgun sequence data and comparison to a database of reference allele sequences. This tool is developed in perl and it is available as console tool.
- pasa pasa. PASA, acronym for Program to Assemble Spliced Alignments, is a eukaryotic genome annotation tool that exploits spliced alignments of expressed transcript sequences to automatically model gene structures, and to maintain gene structure annotation consistent with the most recently available experimental sequence data. PASA also identifies and classifies all splicing variations supported by the transcript alignments.
- seq2HLA seq2HLA is an annotation tool for obtaining an individual's HLA class I and II type and expression using standard NGS RNA-Seq data in fastq format. It comprises mapping RNA-Seq reads against a reference database of HLA alleles using bowtie, determining and reporting HLA type, confidence score and locus-specific expression level. This tool is developed in Python and R. It is available as console tool or Galaxy module.
- ENA ENA. The European Nucleotide Archive (ENA) provides a comprehensive record of the world's nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation.
- ENCODE ENCODE.
- queryable-rna-seq-database queryable-rna-seq-database. Formally known as the Queryable RNA-Seq Database, this system is designed to simplify the process of RNA-seq analysis by providing the ability upload the result data from RNA-Seq analysis into a database, store it, and query it in many different ways.
- Brain RNA-Seq Brain RNA-Seq. An RNA-Seq transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex.
- FusionCancer  FusionCancer: a database of cancer fusion genes derived from RNA-seq data.
- Hipposeq Hipposeq a comprehensive RNA-seq database of gene expression in hippocampal principal neurons.
- Mitranscriptome Mitranscriptome is a systematic list of long poly-adenylated Human RNA transcripts based on RNA-Seq data from more than 6,500 samples associated with a variety of cancer and tissue types. The database contains detailed gene expression analysis of over 91,000 genes, most are uncharacterized long RNAs.
- RNA-Seq AtlasRNA-Seq Atlas a reference database for gene expression profiling in normal tissue by next-generation sequencing.
- SRA SRA. The Sequence Read Archive (SRA) stores raw sequence data from "next-generation" sequencing technologies including 454, IonTorrent, Illumina, SOLiD, Helicos and Complete Genomics. In addition to raw sequence data, SRA now stores alignment information in the form of read placements on a reference sequence.
Single species' RNA-Seq databases
- Aedes-albopictus Aedes albopictus Aedes albopictus database.
- Arabidopsis thaliana TraVa the database of gene expression profiles in Arabidopsis thaliana based on RNA-seq analysis.
- Barley morexGenes- Barley RNA-seq Database.
- Chickpea Chickpea transcriptome database (CTDB) has been developed with the view to provide most comprehensive information about the chickpea transcriptome, the most relevant part of the genome".
- Chilo suppressalis ChiloDB : a genomic and transcriptome database for an important rice insect pest Chilo suppressalis.
- Echinoderm EchinoDB – a repository of orthologous transcripts from echinoderms.
- Equine transcriptome (University of California, Davis). The data can be found at: https://github.com/dib-lab/horse_trans.
- Escherichia coli Ecomics – an omics normalized database for Escherichia coli.
- Fish Phylofish.
- Ginger Ginger Ginger transcriptome database.
- Lygodium japonicum Lygodium japonicum Transcriptome Database.
- Mammals Mammalian Transcriptomic Database.
- Oyster (Pacific) GigaTon: an extensive publicly searchable database providing a new reference transcriptome in the pacific oyster Crassostrea gigas.
- Mangrove Mangrove Transcriptome Database.
- Krill (Antarctic) KrillDB: a de novo Transcriptome Database for the Antarctic Krill.
- Mouse RNASeqMetaDB: a database and web server for navigating metadata of publicly available mouse RNA-Seq data sets.
- Rubus Rubus GDR RefTrans V1 - GDR Rubus RefTrans combines published RNA-Seq and EST data sets to create a reference transcriptome (RefTrans) for rubus and provides putative gene function identified by homology to known proteins.
- Sorghum MOROKOSHI Sorghum transcriptome database. RIKEN full-length cDNA clone and RNA-Seq data in Sorghum bicolor.
- S. purpuratus S. purpuratus Developmental Transcriptomes of S. purpuratus
- S. cerevisiae YeastMine transcriptome database.
- Wheat WheatExp – an RNA-seq expression database for polyploid wheat.
Webinars and presentations
- RNASeq-Blog Presentations
- RNA-Seq Workshop Documentation (UC Davis University)
- VIDEO: Strategies for Identifying Biologically Compelling Genes from Breast Cancer Subtype RNA-Seq Profiles with Accompanying Analysis
- Princeton Workshop
- NGS Leaders
- COFACTOR genomics
- RNA-Seq Presentations from GSK, University of Torino and University of Bath.
- Wang, Z., Gerstein, M., & Snyder, M. (2009). "RNA-Seq: a revolutionary tool for transcriptomics". Nature Reviews Genetics. 10 (1): 57–63. PMC . PMID 19015660. doi:10.1038/nrg2484.
- Kimberly R. Kukurba & Stephen B. Montgomery (2015). "RNA Sequencing and Analysis". Cold Spring Harb Protoc. 2015: pdb.top084970. PMC . PMID 25870306. doi:10.1101/pdb.top084970.
- Ana Conesa; Pedro Madrigal; Sonia Tarazona; David Gomez-Cabrero; Alejandra Cervera; Andrew McPherson; Michał Wojciech Szcześniak; Daniel J. Gaffney; Laura L. Elo; Xuegong Zhang; Ali Mortazavi (2016). "A survey of best practices for RNA-seq data analysis". Genome Biology. 17 (13). doi:10.1186/s13059-016-0881-8.
- "RNA Sequencing and analysis" (PDF). Canadian Bioinformatics Workshops. 2012.
- Alicia Poplawski, Harald Binder (2017). "Feasibility of sample size calculation for RNA-seq studies". Briefings in Bioinformatics: bbw144. doi:10.1093/bib/bbw144.
- Sheng Q, Vickers K, Zhao S, Wang J, Samuels DC, Koues O, Shyr Y, Guo Y (2016). "Multi-perspective quality control of Illumina RNA sequencing data analysis". Brief Funct Genomics: 1–11. PMID 27687708. doi:10.1093/bfgp/elw035.
- Sayols S & Klein H (2015). "dupRadar: Assessment of duplication rates in RNA-Seq datasets. R package version 1.1.0.".
- Matthew P.A. Davis; Stijn van Dongen; Cei Abreu-Goodger; Nenad Bartonicek; Anton J. Enright (2013). "Kraken: A set of tools for quality control and analysis of high-throughput sequence data". Methods. 63 (1): 41–49. PMC . PMID 23816787. doi:10.1016/j.ymeth.2013.06.027.
- S Anders; T P Pyl; W Huber (2015). "HTSeq — A Python framework to work with high-throughput sequencing data.". Bioinformatics. 31 (2): 166–169. PMC . PMID 25260700. doi:10.1093/bioinformatics/btu638.
- Feng, H., Zhang, X., Zhang, C. (2015). "mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA sequencing data". Nature Communications. 6 (7816). PMC . PMID 26234653. doi:10.1038/ncomms8816.
- Ewels, Philip; Magnusson, Måns; Lundin, Sverker; Käller, Max (2016-06-16). "MultiQC: summarize analysis results for multiple tools and samples in a single report". Bioinformatics: btw354. ISSN 1367-4803. PMID 27312411. doi:10.1093/bioinformatics/btw354.
- Deluca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M, Winckler W, Getz G (2012). "RNA-SeQC: RNA-seq metrics for quality control and process optimization". Bioinformatics. 28 (11): 1530–1532. PMC . PMID 22539670. doi:10.1093/bioinformatics/bts196.
- Wang, L., Wang, S., & Li, W. (2012). "RSeQC: Quality Control of RNA-seq experiments.". Bioinformatics. 28 (16): 2184–2185. PMID 22743226. doi:10.1093/bioinformatics/bts356.
- Lassmann T, Hayashizaki Y, Daub CO (2010). "SAMStat: monitoring biases in next generation sequencing data.". Bioinformatics. 27 (1): 130–131. PMC . PMID 21088025. doi:10.1093/bioinformatics/btq614.
- Nicholas F Lahens; Ibrahim Halil Kavakli; Ray Zhang; Katharina Hayer; Michael B Black; Hannah Dueck; Angel Pizarro; Junhyong Kim; Rafael Irizarry; Russell S Thomas; Gregory R Grant; John B Hogenesch (2014). "IVT-seq reveals extreme bias in RNA sequencing". Genome Biology. 15 (6): R86. PMC . PMID 24981968. doi:10.1186/gb-2014-15-6-r86.
- Li S, Łabaj PP, Zumbo P, Sykacek P, Shi W, Shi L, Phan J, Wu PY, Wang M, Wang C, Thierry-Mieg D, Thierry-Mieg J, Kreil DP, Mason CE (2014). "Detecting and correcting systematic variation in large-scale RNA sequencing data.". Nature Biotechnology. 32: 888–895. PMC . PMID 25150837. doi:10.1038/nbt.3000.
- Benjamini Y, Speed TP (2014). "Summarizing and correcting the GC content bias in high-throughput sequencing .". Nucleic Acids Res. 40 (10): e72. PMC . PMID 22323520. doi:10.1093/nar/gks001.
- Aird D, Ross MG, Chen WS, Danielsson M, Fennell T, Russ C, Jaffe DB, Nusbaum C, Gnirke A (2011). "Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries.". Genome Biol. 12 (2): 888–895. PMC . PMID 21338519. doi:10.1186/gb-2011-12-2-r18.
- Adiconis X, Borges-Rivera D, Satija R, DeLuca DS, Busby MA, Berlin AM, Sivachenko A, Thompson DA, Wysoker A, Fennell T, Gnirke A, Pochet N, Regev A, Levin JZ (2013). "Comparative analysis of RNA sequencing methods for degraded or low-input samples.". Nat Methods. 10 (7): 623–629. PMC . PMID 23685885. doi:10.1038/nmeth.2483.
- Nakamura K, Oshima T, Morimoto T, Ikeda S, Yoshikawa H, Shiwa Y, Ishikawa S, Linak MC, Hirai A, Takahashi H, Altaf-Ul-Amin M, Ogasawara N, Kanaya S (2011). "Sequence-specific error profile of Illumina sequencers.". Nucleic Acids Res. 39 (13): e90. PMC . PMID 21576222. doi:10.1093/nar/gkr344.
- Hansen KD, Brenner SE, Dudoit S (2010). "Biases in Illumina transcriptome sequencing caused by random hexamer priming.". Nucleic Acids Res. 38: e131. PMC . PMID 20395217. doi:10.1093/nar/gkq224.
- Smeds, Linnéa; Künstner, Axel; Donlin, Maureen J. (19 October 2011). "ConDeTri - A Content Dependent Read Trimmer for Illumina Data". PLoS ONE. 6 (10): e26314. PMC . PMID 22039460. doi:10.1371/journal.pone.0026314.
- Martin, Marcel (2 May 2011). "Cutadapt removes adapter sequences from high-throughput sequencing reads". EMBnet.journal. 17 (1): 10. doi:10.14806/ej.17.1.200.
- Spandow, O; Hellström, S; Schmidt, SH; De Paoli, Emanuale; Policriti, Alberto (2012). "ERNE-BS5: Aligning BS-treated Sequences by Multiple Hits on a 5-letters Alphabet". Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine. 12: 12–19. doi:10.1145/2382936.2382938.
- Schmieder, R.; Edwards, R. (28 January 2011). "Quality control and preprocessing of metagenomic datasets". Bioinformatics. 27 (6): 863–864. PMC . PMID 21278185. doi:10.1093/bioinformatics/btr026.
- Dlugosch KM, Lai Z, Bonin A, Hierro J, Rieseberg LH (2013). "Allele identification for transcriptome-based population genomics in the invasive plant Centaurea solstitialis.". G3. 3 (2): 359–367. PMC . PMID 23390612. doi:10.1534/g3.112.003871.
- Bolger, A. M.; Lohse, M.; Usadel, B. (1 April 2014). "Trimmomatic: a flexible trimmer for Illumina sequence data". Bioinformatics. 30 (15): 2114–2120. doi:10.1093/bioinformatics/btu170.
- David Laehnemann, Arndt Borkhardt & Alice Carolyn McHardy (2015). "Denoising DNA deep sequencing data—high-throughput sequencing errors and their correction". Briefings in Bioinformatics. 17: 1–26. PMC . PMID 26026159. doi:10.1093/bib/bbv029.
- Quince C, Lanzen A, Davenport RJ, Turnbaugh PJ (2011). "Removing noise from pyrosequenced amplicons.". BMC Bioinformatics. 12 (38). PMC . PMID 21276213. doi:10.1186/1471-2105-12-38.
- Heo Y, Wu XL, Chen D, Ma J, Hwu WM (2014). "BLESS: bloom filter-based error correction solution for high-throughput sequencing reads.". Bioinformatics. 30: 1354–1362. PMID 24451628. doi:10.1093/bioinformatics/btu030.
- Paul Greenfield; Konsta Duesing; Alexie Papanicolaou; Denis C. Bauer (2014). "Blue: correcting sequencing errors using consensus and context.". Bioinformatics. 30 (19): 2723–32. PMID 24919879. doi:10.1093/bioinformatics/btu368.
- Michael I Love; John B Hogenesch; Rafael A Irizarry (2015). "Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation". bioRxiv .
- Hansen KD, Irizarry RA, Wu Z (2012). "Removing technical variability in RNA-seq data using conditional quantile normalization.". Biostatistics. 13 (2): 204–16. PMC . PMID 22285995. doi:10.1093/biostatistics/kxr054.
- Risso D, Schwartz K, Sherlock G, Dudoit S (2011). "GC-Content Normalization for RNA-Seq Data.". BMC Bioinformatics. 12 (1): 480. doi:10.1186/1471-2105-12-480.
- Oliver Stegle; Leopold Parts; Matias Piipari; John Winn & Richard Durbin (2012). "Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses.". Nat Protoc. 7 (6): 500–507. PMC . PMID 22343431. doi:10.1038/nprot.2011.457.
- Risso D, Ngai J, Speed TP, Dudoit S (2014). "Normalization of RNA-seq data using factor analysis of control genes or samples". Nat. Biotechnol. 32 (9): 896–902. PMC . PMID 25150836. doi:10.1038/nbt.2931.
- Meacham, F., Boffelli, D., Dhahbi, J., Martin, D. I., Singer, M., & Pachter, L. (2011). "Identification and correction of systematic error in high-throughput sequence data". BMC Bioinformatics. 12 (1): 451. doi:10.1186/1471-2105-12-451.
- Liu B, Yuan J, Yiu SM, Li Z, Xie Y, Chen Y, Shi Y, Zhang H, Li Y, Lam TW, Luo R (2012). "COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly.". Bioinformatics. 28 (22): 2870–4. PMID 23044551. doi:10.1093/bioinformatics/bts563.
- J. Zhang; K. Kobert; T. Flouri; A. Stamatakis (2013). "PEAR: A fast and accurate Illumina Paired-End read mergeR.". Bioinformatics. 30: 614–620. PMC . PMID 24142950. doi:10.1093/bioinformatics/btt593.
- Sébastien Rodrigue; Arne C. Materna; Sonia C. Timberlake; Matthew C. Blackburn; Rex R. Malmstrom; Eric J. Alm; Sallie W. Chisholm (2010). "Unlocking Short Read Sequencing for Metagenomics". PLoS ONE. 5 (7): e11840. PMC . PMID 20676378. doi:10.1371/journal.pone.0011840.
- Nuno A. Fonseca; Johan Rung; Alvis Brazma; John C. Marioni (2012). "Tools for mapping high-throughput sequencing data". Bioinformatics. 28 (24): 3169–3177. doi:10.1093/bioinformatics/bts605.
- Alamancos GP, Agirre E, Eyras E (2014). "Methods to study splicing from high-throughput RNA sequencing data.". Methods Mol Biol. 1126: 357–97. PMID 24549677. doi:10.1007/978-1-62703-980-2_26.
- Baruzzo, Giacomo; Hayer, Katharina E.; Kim, Eun Ji; Di Camillo, Barbara; FitzGerald, Garret A.; Grant, Gregory R. (February 2017). "Simulation-based comprehensive benchmarking of RNA-seq aligners". Nature Methods. 14 (2): 135–139. ISSN 1548-7091. doi:10.1038/nmeth.4106.
- Campagna D, Telatin A, Forcato C, Vitulo N, Valle G (2013). "PASS-bis: a bisulfite aligner suitable for whole methylome analysis of Illumina and SOLiD reads.". Bioinformatics. 29 (2): 268–70. PMID 23162053. doi:10.1093/bioinformatics/bts675.
- Jaegyoon Ahn & Xinshu Xiao (2015). "RASER: reads aligner for SNPs and editing sites of RNA". Bioinformatics. 31: btv505. PMC . PMID 26323713. doi:10.1093/bioinformatics/btv505.
- Yang Liao, Gordon K Smyth and Wei Shi (2013). "The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote". Nucleic Acids Research. 41 (10): e108. PMC . PMID 23558742. doi:10.1093/nar/gkt214.
- Cole Trapnell, Lior Pachter and Steven Salzberg (2009). "TopHat: discovering splice junctions with RNA-Seq". Bioinformatics. 25 (9): 1105–1111. PMC . PMID 19289445. doi:10.1093/bioinformatics/btp120.
- Lior Pachter (2011). "Models for transcript quantification from RNA-Seq". arXiv: .
- Haijing Jin, Ying-Wooi Wan and Zhandong Liu (2017). "Comprehensive evaluation of RNA-seq quantification methods for linearity". BMC Bioinformatics. 18(Suppl 4) (117). doi:10.1186/s12859-017-1526-y.
- VANESSA M. KVAM; PENG LIU & YAQING SI (2012). "A COMPARISON OF STATISTICAL METHODS FOR DETECTING DIFFERENTIALLY EXPRESSED GENES FROM RNA-SEQ DATA". American Journal of Botany. 99 (2): 248–256. doi:10.3732/ajb.1100340.
- Marie-Agne's Dillies, Andrea Rau, Julie Aubert, Christelle Hennequet-Antier, and on behalf of The French StatOmique Consortium (2012). "A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis". Brief Bioinform. 14: 1–13. PMID 22988256. doi:10.1093/bib/bbs046.
- Evans, C., Hardin, J. and Stoebel, D. (2017). "Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions". Briefing in Bioinformatics. doi:10.1093/bib/bbx008.
- Zhijin Wu, corresponding author1 Bethany D Jenkins, Tatiana A Rynearson, Sonya T Dyhrman, Mak A Saito, Melissa Mercier, and LeAnn P Whitney (2010). "Empirical bayes analysis of sequencing-based transcriptional profiling without replicates.". BMC Bioinformatics. 11: 564. PMC . PMID 21080965. doi:10.1186/1471-2105-11-564.
- Cole Trapnell, Brian A Williams, Geo Pertea, Ali Mortazavi, Gordon Kwan, Marijke J van Baren, Steven L Salzberg, Barbara J Wold and Lior Pachter (2010). "Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms". Nature Biotechnology. 28 (5): 511–515. PMC . PMID 20436464. doi:10.1038/nbt.1621.
- Klambauer, G.; Unterthiner, T.; Hochreiter, S. (2013). "DEXUS: Identifying differential expression in RNA-Seq studies with unknown conditions". Nucleic Acids Research. 41 (21): e198. PMC . PMID 24049071. doi:10.1093/nar/gkt834.
- Feng J, Meyer CA, Wang Q, Liu JS, Liu XS, Zhang Y (2012). "GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data.". Bioinformatics. 28: 2782–2788. PMID 22923299. doi:10.1093/bioinformatics/bts515.
- Rauschenberger A, Jonker MA, van de Wiel MA, Menezes RX (2016). "Testing for association between RNA-Seq and high-dimensional data". BMC Bioinformatics. 17 (118). PMC . PMID 26951498. doi:10.1186/s12859-016-0961-5.
- Moulos, P; Hatzis, P (2015). "Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns". Nucleic Acids Research. 43 (4): e25. PMC . PMID 25452340. doi:10.1093/nar/gku1273.
- Kartashov AV, Barski A (2015). "BioWardrobe: an integrated platform for analysis of epigenomics and transcriptomics data". Genome Biology. 16 (1): 158. doi:10.1186/s13059-015-0720-3.
- evin L, Bar-Yaacov D, Bouskila A, Chorev M, Carmel L, Mishmar D (2015). "LEMONS – A Tool for the Identification of Splice Junctions in Transcriptomes of Organisms Lacking Reference Genomes.". PLoS ONE. 10 (11): e0143329. PMC . PMID 26606265. doi:10.1371/journal.pone.0143329.
- Pundhir S, Gorodkin J (2015). "Differential and coherent processing patterns from small RNAs.". Scientific Reports. 5: 12062. PMC . PMID 26166713. doi:10.1038/srep12062.
- Wu, J.; et al. (2011). "SpliceTrap: a method to quantify alternative splicing under single cellular conditions". Bioinformatics. 27 (21): 3010–3016. doi:10.1093/bioinformatics/btr508.
- Vitting-Seerup, Kristoffer; Sandelin, Albin (2017-01-01). "The Landscape of Isoform Switches in Human Cancers". Molecular Cancer Research: molcanres.0459.2016. ISSN 1541-7786. PMID 28584021. doi:10.1158/1541-7786.mcr-16-0459.
- Nowicka, Malgorzata; Robinson, Mark D. (2016-12-06). "DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics". F1000Research. 5: 1356. PMC . PMID 28105305. doi:10.12688/f1000research.8900.2.
- Papastamoulis, Panagiotis; Rattray, Magnus (2017-01-11). "Bayesian estimation of Differential Transcript Usage from RNA-seq data". arXiv: .
- Trapnell, Cole; Williams, Brian A.; Pertea, Geo; Mortazavi, Ali; Kwan, Gordon; van Baren, Marijke J.; Salzberg, Steven L.; Wold, Barbara J.; Pachter, Lior (May 2010). "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation". Nature Biotechnology. 28 (5): 511–515. ISSN 1546-1696. PMC . PMID 20436464. doi:10.1038/nbt.1621.
- Shi, Yang; Chinnaiyan, Arul M.; Jiang, Hui (2015-07-01). "rSeqNP: a non-parametric approach for detecting differential expression and splicing from RNA-Seq data". Bioinformatics. 31 (13): 2222–2224. ISSN 1367-4811. PMC . PMID 25717189. doi:10.1093/bioinformatics/btv119.
- Jones, Daniel C.; Kuppusamy, Kavitha T.; Palpant, Nathan J.; Peng, Xinxia; Murry, Charles E.; Ruohola-Baker, Hannele; Ruzzo, Walter L. (2016-11-20). "Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experiments". bioRxiv .
- Shailesh Kumar; Angie Duy Vo; Fujun Qin; Hui Li (2016). "Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data". Scientific Reports. 6 (21587): 21597. PMC . PMID 26862001. doi:10.1038/srep21597.
- Jia, W; Qiu, K; He, M; Song, P; Zhou, Q; Zhou, F; Yu, Y; Zhu, D; Nickerson, ML; Wan, S; Liao, X; Zhu, X; Peng, S; Li, Y; Wang, J; Guo, G (2013). "SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data". Genome Biology. 14: R12. PMC . PMID 23409703. doi:10.1186/gb-2013-14-2-r12.
- Christoph Ziegenhain; Swati Parekh; Beate Vieth; Martha Smets; Heinrich Leonhardt; Ines Hellmann; Wolfgang Enard (2016). "Comparative analysis of single-cell RNA-sequencing methods". bioRxiv .
- Tamar Hashimshony; Florian Wagner; Noa Sher; Itai Yanai (2012). "CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification". Cell Reports. 2 (3): 666–673. PMID 22939981. doi:10.1016/j.celrep.2012.08.003.
- Evan Z. Macosko; Anindita Basu; Rahul Satija; James Nemesh; Karthik Shekhar; Melissa Goldman; Itay Tirosh; Allison R. Bialas; Nolan Kamitaki; Emily M. Martersteck; John J. Trombetta; David A. Weitz; Joshua R. Sanes; Alex K. Shalek; Aviv Regev; Steven A. McCarroll (2015). "Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.". Cell. 161 (5): 1202–1214. PMC . PMID 26000488. doi:10.1016/j.cell.2015.05.002.
- Marco E, Karp RL, Guo G, Robson P, Hart AH, Trippa L, Yuan GC (2014). "Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape". PNAS. 111: E5643–50. PMC . PMID 25512504. doi:10.1073/pnas.1408993111.
- Buettner F; Natarajan KN; Casale FP; Proserpio V; Scialdone A; Theis FJ; Teichmann SA; Marioni JC; Stegle O (2015). "Computational analysis of cell-to-cell heterogeneity in single-cell RNA-Sequencing data reveals hidden subpopulation of cells". Nature Biotechnology. 33: 155–160. doi:10.1038/nbt.3102.
- Monzoorul Haque M; Tarini Shankar Ghosh; Nitin Kumar Singh; Sharmila S Mande (2011). "SPHINX - An algorithm for taxonomic binning of metagenomic sequences". Bioinformatics. 27: 22–30. PMID 21030462. doi:10.1093/bioinformatics/btq608.
- Stubbington, Michael JT; Lönnberg, Tapio; Proserpio, Valentina; Clare, Simon; Speak, Anneliese O; Dougan, Gordon; Teichmann, Sarah A. "T cell fate and clonality inference from single-cell transcriptomes". Nature Methods. 13: 329–332. PMC . PMID 26950746. doi:10.1038/nmeth.3800.
- Eltahla, Auda A; Rizzetto, Simone; Pirozyan, Mehdi R; Betz-Stablein, Brigid D; Venturi, Vanessa; Kedzierska, Katherine; Lloyd, Andrew R; Bull, Rowena A; Luciani, Fabio. "Linking the T cell receptor to the single cell transcriptome in antigen-specific human T cells". Immunology and Cell Biology. doi:10.1038/icb.2016.16.
- Minzhe Guo; Hui Wang; S. Steven Potter; Jeffrey A. Whitsett; Yan Xu (2015). "SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis". PLoS Comput Biol. 11 (11): e1004575. doi:10.1371/journal.pcbi.1004575.
- Emma Pierson & Christopher Yau (2015). "ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis". Genome Biology. 16 (241). PMC . PMID 26527291. doi:10.1186/s13059-015-0805-z.
- Hayer, Katharina E.; Pizarro, Angel; Lahens, Nicholas F.; Hogenesch, John B.; Grant, Gregory R. (3 September 2015). "Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data". Bioinformatics. 31: btv488. PMC . PMID 26338770. doi:10.1093/bioinformatics/btv488.
- Steijger T, Abril JF, Engström PG, Kokocinski F, Hubbard TJ, Guigó R, Harrow J, Bertone P; RGASP Consortium. (2013). "Assessment of transcript reconstruction methods for RNA-seq". Nat Methods. 10 (12): 1177–84. PMC . PMID 24185837. doi:10.1038/nmeth.2714.
- Chang, Zheng; Li, Guojun; Liu, Juntao; Zhang, Yu; Ashby, Cody; Liu, Deli; Cramer, Carole L.; Huang, Xiuzhen (2015-02-11). "Bridger: a new framework for de novo transcriptome assembly using RNA-seq data". Genome Biology. 16 (1): 30. ISSN 1465-6906. PMC . PMID 25723335. doi:10.1186/s13059-015-0596-2.
- Zerbino DR, Birney E (2008). "Velvet: Algorithms for de novo short read assembly using de Bruijn graphs". Genome Research. 18 (5): 821–829. PMC . PMID 18349386. doi:10.1101/gr.074492.107.
- Camelia Quek; Chol-hee Jung; Shayne A. Bellingham; Andrew Lonie; Andrew F. Hill (2015). "iSRAP - a one-touch research tool for rapid profiling of small RNA-seq data". Journal of Extracellular Vesicles. 4. doi:10.3402/jev.v4.29454.
- Schmid-Burgk JL, Hornung V (2015). "BrowserGenome.org: web-based RNA-seq data analysis and visualization". Nat Methods. 12 (11): 1001. doi:10.1038/nmeth.3615.
- Milne I, Stephen G, Bayer M, Cock PJ, Pritchard L, Cardle L, Shaw PD, Marshall D (2013). "Using Tablet for visual exploration of second-generation sequencing data". Briefings in Bioinformatics. 14 (2): 193–202. PMID 22445902. doi:10.1093/bib/bbs012.
- Aurelie Pirayre; Camille Couprie; Laurent Duval; Jean-Christophe Pesquet (2017). "BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement". IEEE/ACM Transactions on Computational Biology and Bioinformatics. PP: 1. PMID 28368827. doi:10.1109/TCBB.2017.2688355.
- Aurelie Pirayre; Camille Couprie; Frédérique Bidard; Laurent Duval; Jean-Christophe Pesquet (2015). "BRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference". BMC Bioinformatics. 16: 368. PMC . PMID 26537179. doi:10.1186/s12859-015-0754-2.
- Weijun Luo; Michael S Friedman; Kerby Shedden; Kurt D Hankenson; Peter J Woolf (2009). "GAGE: generally applicable gene set enrichment for pathway analysis". BMC Bioinformatics. 10 (161): 17. PMC . PMID 19473525. doi:10.1186/1471-2105-10-161.
- Santhilal Subhash; Chandrasekhar Kanduri (2016). "GeneSCF: a real-time based functional enrichment tool with support for multiple organisms". BMC Bioinformatics. 17 (1): 365. PMC . PMID 27618934. doi:10.1186/s12859-016-1250-z.
- Rue-Albrecht K (2014). "Visualise microarray and RNAseq data using gene ontology annotations. R package version 1.4.1".
- Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010). "Gene ontology analysis for RNA-seq: accounting for selection bias.". Genome Biology. 11 (2): 12. PMC . PMID 20132535. doi:10.1186/gb-2010-11-2-r14.
- Qing Xiong, Sayan Mukherjee & Terrence S. Furey (2014). "GSAASeqSP: A Toolset for Gene Set Association Analysis of RNA-Seq Data". Scientific Reports. 4 (6347). doi:10.1038/srep06347.
- Sonja Hänzelmann, Robert Castelo & Justin Guinney (2013). "GSVA: gene set variation analysis for microarray and RNA-Seq data.". BMC Bioinformatics. 14 (17): 7. PMC . PMID 23323831. doi:10.1186/1471-2105-14-7.
- Yi-Hui Zhou (2015). "Pathway analysis for RNA-Seq data using a score-based approach". Biometrics. doi:10.1111/biom.12372.
- Ivana Ihnatova & Eva Budinska (2015). "ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data". BMC Bioinformatics. 16 (350). PMC . PMID 26514335. doi:10.1186/s12859-015-0763-1.
- Van Bel M, Proost S, Van Neste C, Deforce D, Van de Peer Y, Vandepoele K (2013). "TRAPID, an efficient online tool for the functional and comparative analysis of de novo RNA-Seq transcriptomes.". Genome Biol. 14: R134. PMC . PMID 24330842. doi:10.1186/gb-2013-14-12-r134.
- Anne de Jong; Sjoerd van der Meulen; Oscar P. Kuipers; Jan Kok (2015). "T-REx: Transcriptome analysis webserver for RNA-seq Expression data". BMC Genomics. 16 (663). PMC . PMID 26335208. doi:10.1186/s12864-015-1834-4.
- Ye Zhang; Kenian Chen; Steven A Sloan; Mariko L Bennett; Anja R Scholze; Sean O'Keefe; Hemali P Phatnani; Paolo Guarnieri; Christine Caneda; Nadine Ruderisch; Shuyun Deng; Shane A Liddelow; Chaolin Zhang; Richard Daneman; Tom Maniatis; Ben A Barres; Jia Qian Wu (2014). "An RNA-Seq transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex.". Journal of Neuroscience. 34 (36): 11929–11947. doi:10.1523/JNEUROSCI.1860-14.2014.
- Wang, Y., Wu, N., Liu, J., Wu, Z., & Dong, D. (2015). "FusionCancer: a database of cancer fusion genes derived from RNA-seq data". Diagnostic Pathology. 10 (131): 39. PMC . PMID 26215638. doi:10.1186/s13000-015-0310-4.
- Krupp M; Marquardt JU; Sahin U; Galle PR; Castle J; Teufel A. (2012). "RNA-Seq Atlas--a reference database for gene expression profiling in normal tissue by next-generation sequencing". Bioinformatics. 28 (8): 1184–1185. PMID 22345621. doi:10.1093/bioinformatics/bts084.