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| PhD-SNP || SVM-based method using sequence information retrieved by BLAST algorithm. || UniRef90 || http://snps.biofold.org/phd-snp/ ||.
| PhD-SNP || SVM-based method using sequence information retrieved by BLAST algorithm. || UniRef90 || http://snps.biofold.org/phd-snp/ ||.
<ref>{{cite journal | journal=Bioinformatics| volume=22 | pages=2729-2734 | year=2012 | author=Capriotti E, Calabrese R, Casadio R. | title=Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.| url=http://biofold.org/emidio/pages/documents/papers/BioinfDis06.pdf | pmid=16895930}}</ref>
<ref>{{cite journal | journal=Bioinformatics| volume=22 | pages=2729-2734 | year=2006 | author=Capriotti E, Calabrese R, Casadio R. | title=Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.| url=http://biofold.org/emidio/pages/documents/papers/BioinfDis06.pdf | pmid=16895930}}</ref>
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| PolyPhen-2 || Suitable for predicting damaging effects of missense mutations. Use sequence conservation, structure to model position of amino acid substitution, and SWISS-PROT annotation || UniPort || http://genetics.bwh.harvard.edu/pph2/ ||.<ref>Adzhubei I., Jordan D.M., Sunyaev S.R. (2013). Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. Vol.(7):20. doi: 10.1002/0471142905.hg0720s76</ref>
| PolyPhen-2 || Suitable for predicting damaging effects of missense mutations. Use sequence conservation, structure to model position of amino acid substitution, and SWISS-PROT annotation || UniPort || http://genetics.bwh.harvard.edu/pph2/ ||.<ref>Adzhubei I., Jordan D.M., Sunyaev S.R. (2013). Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. Vol.(7):20. doi: 10.1002/0471142905.hg0720s76</ref>
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| SNAP || A neural network-based method for the prediction of the functional effects of non-synonymous SNPs || Ensembl, UCSC, Uniprot, UniProt, Pfam, DAS-CBS, MINT, BIND, KEGG, TreeFam || http://www.rostlab.org/services/SNAP ||.<ref>Bromberg, Y., & Rost, B. (2007). SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Research, 35(11), 3823–3835. doi:10.1093/nar/gkm238</ref>
| SNAP || A neural network-based method for the prediction of the functional effects of non-synonymous SNPs || Ensembl, UCSC, Uniprot, UniProt, Pfam, DAS-CBS, MINT, BIND, KEGG, TreeFam || http://www.rostlab.org/services/SNAP ||.<ref>Bromberg, Y., & Rost, B. (2007). SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Research, 35(11), 3823–3835. doi:10.1093/nar/gkm238</ref>
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| SNPs&GO || SVM-based method using sequence information, Gene Ontology annotation and when available protein structure. || UniRef90, GO, PANTHER, PDB || http://snps.biofold.org/snps-and-go/ ||.<ref>Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Human Mutation. 30: 1237-1244.</ref>
| SNPs&GO || SVM-based method using sequence information, Gene Ontology annotation and when available protein structure. || UniRef90, GO, PANTHER, PDB || http://snps.biofold.org/snps-and-go/ ||.
<ref>{{cite journal | journal=Human Mutation.| volume=30 | pages=1237-1244 | year=2009 | author=Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. | title=Functional annotations improve the predictive score of human disease-related mutations in proteins. | url=http://biofold.org/emidio/pages/documents/papers/Calabrese_HumMut09.pdf | pmid=19514061}}</ref>
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| LS-SNP || Maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models || UniProtKB, Genome Browser, dbSNP, PD || http://www.salilab.org/LS-SNP ||.<ref>Karchin R., Diekhans M., Kelly L., Thomas D.J., Pieper U., Eswar N., Haussler D., Sali A. (2005). LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources. Bioinformatics. Vol. 21:2814–2820</ref>
| LS-SNP || Maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models || UniProtKB, Genome Browser, dbSNP, PD || http://www.salilab.org/LS-SNP ||.<ref>Karchin R., Diekhans M., Kelly L., Thomas D.J., Pieper U., Eswar N., Haussler D., Sali A. (2005). LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources. Bioinformatics. Vol. 21:2814–2820</ref>
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| PANTHER || PANTHER relate protein sequence evolution to the evolution of specific protein functions and biological roles. The source of protein sequences used to build the protein family trees and used a computer-assisted manual curation step to better define the protein family clusters || STKE, KEGG, MetaCyc, FREX and Reactome || http://www.pantherdb.org/ ||.<ref>Mi, H., Guo, N., Kejariwal, A., & Thomas, P. D. (2007). PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic Acids Research, 35(Database issue), D247–D252. doi:10.1093/nar/gkl869</ref>
| PANTHER || PANTHER relate protein sequence evolution to the evolution of specific protein functions and biological roles. The source of protein sequences used to build the protein family trees and used a computer-assisted manual curation step to better define the protein family clusters || STKE, KEGG, MetaCyc, FREX and Reactome || http://www.pantherdb.org/ ||.<ref>Mi, H., Guo, N., Kejariwal, A., & Thomas, P. D. (2007). PANTHER version 6: protein sequence and function evolution data with expanded representation of biological pathways. Nucleic Acids Research, 35(Database issue), D247–D252. doi:10.1093/nar/gkl869</ref>
| Meta-SNP || SVM-based meta predictor including 4 different methods. || PhD-SNP, PANTHER, SIFT, SNAP || http://snps.biofold.org/meta-snp ||.

<ref>{{cite journal | journal=BMC Genomics.| volume=14 | pages=S2 | year=2013 | author=Capriotti E, Altman RB, Bromberg Y. | title=Collective judgment predicts disease-associated single nucleotide variants. | url=http://biofold.org/emidio/pages/documents/papers/Capriotti_MetaSNP_BMCGenomics13.pdf | pmid=23819846}}</ref>
|}
|}


Revision as of 14:04, 2 August 2015

SNP annotation
ClassificationBioinformatics
SubclassificationSingle-nucleotide polymorphism
Type of tools usedFunctional annotation tools
Other subjects relatedGenome project, Genomics

Single nucleotide polymorphism (SNP) annotation is the process to predict the effect or function of an individual SNP using SNP annotational tools. In SNP annotation the biological information is extracted, collected and displayed in a clear form amenable to query. SNP functional annotation is done base on the available information on nucleic acid and protein sequence.[1]

Introduction

Directed graph of relationships among SNP prediction webservers and their bioinformatics sources.[2]

Single nucleotide polymorphism plays an important role in genome wide association studies because they act as primary biomarker. SNPs are currently the marker of choice due to their large numbers in virtually all populations of individuals. The location of these biomarkers can be tremendously important in terms of predicting functional significance, genetic mapping and population genetics.[3] Each SNP represents a nucleotide change between two individuals at a defined location. SNPs are the most common genetic variant found in all individual with one SNP every 100–300 bp in some species.[4] Due to the tremendous number of SNPs on the genome to expedite genotyping and analysis, there is a clear need to prioritize SNPs according to their potential effect [5]

Annotating large numbers of SNPs is a difficult and complex process, which need computational method to handle such a large dataset. Many tools available have been developed for SNP annotation in different organism, some of them are optimized for use with organisms densely sampled for SNPs (such as humans), but there are currently few tools available that are species non-specific or support non-model organism data. The majority of SNPs annotation tools provide computationally predicted putative deleterious effects of SNPs. These tools examine whether a SNP resides in functional genomic regions such as exons, splice sites, or transcription regulatory sites, and predict the potential corresponding functional effects that the SNP may have using a variety of machine-learning approaches. But the tools and systems that prioritize functionally significant SNPs, suffer from few limitations: First, they examine the putative deleterious effects of SNPs with respect to a single biological function that provide only partial information about the functional significance of SNPs. Second, current systems classify SNPs into deleterious or neutral group.[6]

SNP annotation evidence

Different type of annotations in genomics

For SNP annotation many genetic and genomic information are used. Based on different feature used by the annotation tool, the SNP annotation can be classified into this category.

Gene based annotation

Genomic information from the surrounding genomic element is the most useful information for interpreting the biological function of the observed variants. The information from known gene use as the standard to locate the variant attribute to indicate whether the observed variant reside in or near a gene with the potential to disrupt the protein sequence and further its function. Gene base annotation use the concept that non-synonymous mutation altering the protein sequence and the splice site mutation disrupting the transcript splicing pattern.[7]

Knowledge based annotation

Knowledge base annotation is done based on the information of gene attribute, protein function and its metabolism. In this type of annotation more emphasis is given to genetic variation that disrupts the protein function domain, protein-protein interaction and biological pathway. The non-coding region of genome contain many important regulatory elements including promoter, enhancer and insulator, any kind of change in this regulatory region can change the functionality of that protein.[8] The mutation in DNA can change the RNA sequence and then influence the RNA secondary structure, RNA binding protein recognition and miRNA binding activity,.[9][10]

Functional annotation

This method mainly identifies variant function based on the information whether the variant loci are in the known functional region that harbor genomic or epigenomic signals. The function of non-coding variants are extensive in terms of the affected genomic region and they involve in almost all processes of gene regulation from transcriptional to post translational level [11]

Transcriptional gene regulation

Transcriptional gene regulation process depend on many spatial and temporal factor in the nucleus such as global or local chromatin states, nucleosome positioning, TF binding, enhancer/promoter activities. Variant that alter the function of any of these biological processes may alter the gene regulation and cause phenotypic abnormality.[12] Genetic variants that located in distal regulatory region can affect the binding motif of TFs, chromatin regulators and other distal transcriptional factors, which disturb the interaction between enhancer/silencer and its target gene.[13]

Alternative splicing

Alternative splicing is one of the most important components that show functional complexity of genome. Modified splicing has significant effect on the phenotype that is relevance to disease or drug metabolism. A change in splicing can be caused by modifying any of the components of the splicing machinery such as splice sites or splice enhancers or silencers.[14] Modification in the alternative splicing site can lead to a different protein form which will show a different function. Humans use an estimated 100,000 different proteins or more, so some genes must be capable of coding for a lot more than just one protein. Alternative splicing occurs more frequently than was previously thought and can be hard to control; genes may produce tens of thousands of different transcripts, necessitating a new gene model for each alternative splice.

RNA processing and post transcriptional regulation

Mutations in the untranslated region (UTR) affect many post-transcriptional regulation. Distinctive structural features are required for many RNA molecules and cis-acting regulatory elements to execute effective functions during gene regulation. SNVs can alter the secondary structure of RNA molecules and then disrupt the proper folding of RNAs, such as tRNA/mRNA/lncRNA folding and miRNA binding recognition regions.[15]

Translation and post translational modifications

Single nucleotide variant can also affect the cis-acting regulatory elements in mRNA’s to inhibit/promote the translation initiation. Change in the synonymous codons region due to mutation may affect the translation efficiency because of codon usage biases. The translation elongation can also be retarded by mutations along the ramp of ribosomal movement. In the post-translational level, genetic variants can contribute to proteostasis and amino acid modifications. However, mechanisms of variant effect in this field are complicated and there are only a few tools available to predict variant’s effect on translation related modifications.[16]

Protein function

Non-synonymous is the variant in exons that change the amino acid sequence encoded by the gene, including single base changes and non frameshift indels. It has been extremely investigated the function of non-synonymous variants on protein and many algorithms have been developed to predict the deleteriousness and pathogenesis of single nucleotide variants (SNVs). Classical bioinformatics tools, such as SIFT, Polyphen and MutationTaster, successfully predict the functional consequence of non-synonymous substitution.[17][18][19][20]

Evolutionary conservation and nature selection

Comparative genomics approaches were used to predict the function-relevant variants under the assumption that the functional genetic locus should be conserved across different species at an extensive phylogenetic distance. On the other hand, some adaptive traits and the population differences are driven by positive selections of advantageous variants, and these genetic mutations are functionally relevant to population specific phenotypes. Functional prediction of variants’ effect in different biological processes is pivotal to pinpoint the molecular mechanism of diseases/traits and direct the experimental validation.[21]

List of available SNP annotation tools

To annotate large number of available NGS data, currently a large number of SNPs annotation tools is available. Some of them are specific to some specific SNPs annotation. Some of the available SNPs annotation tools are as follows SNPeff, VEP, ANNOVAR, FATHMM, PhD-SNP, PolyPhen-2, SuSPect, F-SNP, AnnTools, SeattleSeq, SNPit, SCAN, Snap, SNPs&GO, LS-SNP, Snat, TREAT, TRAMS, Maviant, SNPdat, Snpranker, NGS – SNP, SVA, VARIANT, SIFT, PhD-SNP and FAST-SNP. Function and approach used in SNPs annotation tools are listed below

Tools Description External resources use WebsiteURL References
SNPeff SnpEff annotates variants based on their genomic locations and predicts coding effects. Use an interval forest approach ENSEMBL, UCSC and organism based e.g. FlyBase, WormBase and TAIR http://snpeff.sourceforge.net/SnpEff_manual.htm .[22]
VEP Provides the location of specific variants in individuals. Variants are calculated using sanger-style resequencing data dbSNP, Ensembl, UCSC and NCBI http://www.ensembl.org/ .[23]
ANNOVAR This tools is suitable for pinpoint a small subset of functionally important variant. Use mutation prediction approach for annotation UCSC, RefSe and Ensembl http://www.openbioinformatics.org/annovar/ .[24]
PhD-SNP SVM-based method using sequence information retrieved by BLAST algorithm. UniRef90 http://snps.biofold.org/phd-snp/ .

[25]

PolyPhen-2 Suitable for predicting damaging effects of missense mutations. Use sequence conservation, structure to model position of amino acid substitution, and SWISS-PROT annotation UniPort http://genetics.bwh.harvard.edu/pph2/ .[26]
SuSPect An SVM-trained predictor of the damaging effects of missense mutations. Use sequence conservation, structure and network (interactome) information to model phenotypic effect of amino acid substitution. Accepts VCF file UniProt, PDB, Phyre2 for predicted structures, DOMINE and STRING for interactome http://www.sbg.bio.ic.ac.uk/suspect/index.html .[27]
F-SNP Computationally predicts functional SNPs for disease association studies. PolyPhen, SIFT, SNPeffect, SNPs3D, LS-SNP, ESEfinder, RescueESE, ESRSearch, PESX, Ensembl, TFSearch, Consite, GoldenPath, Ensembl, KinasePhos, OGPET, Sulfinator, GoldenPath http://compbio.cs.queensu.ca/F-SNP/ .[28]
AnnTools Design to Identify novel and SNP/SNV, INDEL and SV/CNV. AnnTools searches for overlaps with regulatory elements, disease/trait associated loci, known segmental duplications and artifact prone regions dbSNP, UCSC, GATK refGene, GAD, published lists of common structural genomic variation, Database of Genomic Variants, lists of conserved TFBs, miRNA http://anntools.sourceforge.net/ .[29]
SNPit Analyses the potential functional significance of SNPs derived from genome wide association studies dbSNP, EntrezGene, UCSC Browser, HGMD, ECR Browser, Haplotter, SIFT -/- .[30]
SCAN Use Physical and functional based annotation to categorized according to their position relative to genes and according to linkage disequilibrium (LD) patterns and effects on expression levels -/- http://www.scandb.org/newinterface/about.html .[31]
SNAP A neural network-based method for the prediction of the functional effects of non-synonymous SNPs Ensembl, UCSC, Uniprot, UniProt, Pfam, DAS-CBS, MINT, BIND, KEGG, TreeFam http://www.rostlab.org/services/SNAP .[32]
SNPs&GO SVM-based method using sequence information, Gene Ontology annotation and when available protein structure. UniRef90, GO, PANTHER, PDB http://snps.biofold.org/snps-and-go/ .

[33]

LS-SNP Maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models UniProtKB, Genome Browser, dbSNP, PD http://www.salilab.org/LS-SNP .[34]
TREAT TREAT is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing -/- http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm .[35]
SNPdat Suitable for species non-specific or support non-model organism data. SNPdat does not require the creation of any local relational databases or pre-processing of any mandatory input files -/- https://code.google.com/p/snpdat/downloads/ .[36]
NGS – SNP Annotate SNPs comparing the reference amino acid and the non-reference amino acid to each orthologue Ensembl, NCBI and UniProt http://stothard.afns.ualberta.ca/downloads/NGS-SNP/ .[37]
SVA Predicted biological function to variants identified NCBI RefSeq, Ensembl, variation databases, UCSC, HGNC, GO, KEGG, HapMap, 1000 Genomes Project and DG http://www.svaproject.org/ .[38]
VARIANT VARIANT increases the information scope outside the coding regions by including all the available information on regulation, DNA structure, conservation, evolutionary pressures, etc. Regulatory variants constitute a recognized, but still unexplored, cause of pathologies dbSNP,1000 genomes, disease-related variants from GWAS,OMIM, COSMIC http://variant.bioinfo.cipf.es/ .[39]
SIFT SIFT is a program that predicts whether an amino acid substitution affects protein function. SIFT uses sequence homology to predict whether an amino acid substitution will affect protein function PROT/TrEMBL, or NCBI's http://blocks.fhcrc.org/sift/SIFT.html .[40]
FAST-SNP A web server that allows users to efficiently identify and prioritize high-risk SNPs according to their phenotypic risks and putative functional effects NCBI dbSNP, Ensembl , TFSearch , PolyPhen , ESEfinder , RescueESE , FAS-ESS , SwissProt , UCSC Golden Path , NCBI Blast and HapMap http://fastsnp.ibms.sinica.edu.tw/ .[41]
PANTHER PANTHER relate protein sequence evolution to the evolution of specific protein functions and biological roles. The source of protein sequences used to build the protein family trees and used a computer-assisted manual curation step to better define the protein family clusters STKE, KEGG, MetaCyc, FREX and Reactome http://www.pantherdb.org/ .[42] Meta-SNP SVM-based meta predictor including 4 different methods. PhD-SNP, PANTHER, SIFT, SNAP http://snps.biofold.org/meta-snp .

[43]

Algorithm used in annotation tools

Variant annotation tools use machine learning algorithms for prediction of variant. Different annotation tool use different algorithm to build their tools. The common algorithm used in annotation tools are

Comparison of variant annotation tools

A large number of variant annotation tools are available for variant annotation but in some cases the prediction by the tools does not agree since the way the rules have been defined differ slightly between each application. It is frankly impossible to Performing a perfect comparison of the tools. Not all the tools have same input and output and function. Here is a table of major annotation tools and it's functional area.

Tools Input file Output file SNP INDEL CNV WEB or Program Source
AnnoVar VCF, pileup,

CompleteGenomics, GFF3-SOLiD, SOAPsnp, MAQ, CASAVA

TXT Yes Yes Yes Program [44]
SNPeff VCF, pileup/TXT VCF, TXT, HTML EYes Yes No Program [45]
VEP VCF, pileup, HGVS,

TXT

TXT, VCF, HTML Yes Yes No Web/Program [46]
AnnTools VCF, pileup,TXT VCF Yes Yes No No [47]
SeattleSeq VVCF, MAQ, CASAVA,

GATK BED

VCF, SeattleSeq Yes Yes No Web [48]
VARIANT VCF,GFF2, BED web report, TXT Yes Yes Yes Web [49]

Source: S. Pabinger et al., 2012 [50]

Conclusions

The next generation of SNP annotation webservers can take advantage of the growing amount of data in core bioinformatics resources and use intelligent agents to fetch data from different sources as needed. From a user’s point of view, it is more efficient to submit a set of SNPs and receive results in a single step, which makes meta-servers the most attractive choice. However, if SNP annotation tools deliver heterogeneous data covering sequence, structure, regulation, pathways, etc., they must also provide frameworks for integrating data into a decision algorithm(s), and quantitative confidence measures so users can assess which data are relevant and which are not.

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  32. ^ Bromberg, Y., & Rost, B. (2007). SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Research, 35(11), 3823–3835. doi:10.1093/nar/gkm238
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