Phylogenetic inference using transcriptomic data: Difference between revisions

Add links
From Wikipedia, the free encyclopedia
Browse history interactively
← Previous editNext edit →
Content deleted Content added
VisualWikitext
added Category:Phylogenetics; removed {{uncategorized}} using HotCat
Lboat (talk | contribs)
67 edits
Addressed comments from Emilysessa
Line 4: Line 4:


==Sequence acquisition==
==Sequence acquisition==
===[[RNA-Seq]]===
RNA reads may be obtained using a variety of [[RNA-seq]] methods.
===Public databases===
There are a number of [[List_of_RNA-Seq_bioinformatics_tools#RNA-Seq_Databases|public databases]] that contain freely available RNA-Seq data.

==Assembly==
===[[Sequence assembly]]===
===[[Sequence assembly]]===
RNA-Seq data may be directly assembled into [[Messenger RNA|transcripts]] using [[sequence assembly]].
RNA-Seq data may be directly assembled into [[Messenger RNA|transcripts]] using [[sequence assembly]].
Two main categories of [[sequence assembly]] are often distinguished:
Two main categories of [[sequence assembly]] are often distinguished:
# [[De novo transcriptome assembly|''de novo'' transcriptome assembly]] - especially important when a [[reference genome]] is not available for a given [[List of sequenced eukaryotic genomes|species]].
# [[De novo transcriptome assembly|''de novo'' transcriptome assembly]] - especially important when a [[reference genome]] is not available for a given [[List of sequenced eukaryotic genomes|species]].
# Mapping assembly (sometimes genome-guided assembly) - is capable of using a pre-existing reference to guide the assembly of transcripts
# Genome-guided assembly (sometimes mapping or reference-guided assembly) - is capable of using a pre-existing reference to guide the assembly of transcripts
Both methods attempt to generate biologically representative isoform-level constructs from RNA-seq data and generally attempt to associate isoforms with a gene-level construct. However, proper identification of gene-level constructs may be complicated by recent [[Gene duplication|duplications]], [[Sequence homology#Paralogy|paralogs]] or [[Fusion gene|gene fusions]]. These complications may also cause downstream issues during ortholog inference. When selecting or generating sequence data, it is also vital to consider the tissue type, developmental stage and environmental conditions of the organisms. Since the [[transcriptome]] represents a snapshot of [[gene expression]], minor changes to these conditions may significantly affect which transcripts are expressed. This may detrimentally affect downstream ortholog detection.<ref name="book" />
Both methods attempt to generate biologically representative isoform-level constructs from RNA-seq data and generally attempt to associate isoforms with a gene-level construct. However, proper identification of gene-level constructs may be complicated by recent [[Gene duplication|duplications]], [[Sequence homology#Paralogy|paralogs]], [[alternative splicing]] or [[Fusion gene|gene fusions]]. These complications may also cause downstream issues during ortholog inference. When selecting or generating sequence data, it is also vital to consider the tissue type, developmental stage and environmental conditions of the organisms. Since the [[transcriptome]] represents a snapshot of [[gene expression]], minor changes to these conditions may significantly affect which transcripts are expressed. This may detrimentally affect downstream ortholog detection.<ref name="book" />


===Public databases===
===Public databases===
Line 16: Line 22:
==Inferring gene pair [[Sequence homology#Orthology|orthology]]/[[Sequence homology#Paralogy|paralogy]]==
==Inferring gene pair [[Sequence homology#Orthology|orthology]]/[[Sequence homology#Paralogy|paralogy]]==
===Approaches===
===Approaches===
Orthology inference requires an assessment of [[sequence homology]], usually via [[sequence alignment]]. [[Sequence alignment#Phylogenetic analysis|Phylogenetic analyses and sequence alignment]] are often considered jointly, as phylogenetic analyses using [[DNA]] or [[RNA]] require sequence alignment and alignments themselves often represent some hypothesis of [[homology]]. As proper ortholog identification is pivotal to phylogenetic analyses, there are a variety of methods available to infer [[Sequence homology#Orthology|orthologs]] and [[Sequence homology#Paralogy|paralogs]].<ref name="yeast">{{cite journal|last1=Salichos|first1=Leonidas|last2=Rokas|first2=Antonis|last3=Fairhead|first3=Cecile|title=Evaluating Ortholog Prediction Algorithms in a Yeast Model Clade|journal=PLoS ONE|date=13 April 2011|volume=6|issue=4|pages=e18755|doi=10.1371/journal.pone.0018755}}</ref>
Orthology inference requires an assessment of [[sequence homology]], usually via [[sequence alignment]]. [[Phylogenetics|Phylogenetic analyses]] and [[sequence alignment]] are often considered jointly, as phylogenetic analyses using [[DNA]] or [[RNA]] require sequence alignment and alignments themselves often represent some hypothesis of [[homology]]. As proper ortholog identification is pivotal to phylogenetic analyses, there are a variety of methods available to infer [[Sequence homology#Orthology|orthologs]] and [[Sequence homology#Paralogy|paralogs]].<ref name="yeast">{{cite journal|last1=Salichos|first1=Leonidas|last2=Rokas|first2=Antonis|last3=Fairhead|first3=Cecile|title=Evaluating Ortholog Prediction Algorithms in a Yeast Model Clade|journal=PLoS ONE|date=13 April 2011|volume=6|issue=4|pages=e18755|doi=10.1371/journal.pone.0018755}}</ref>


These methods are generally distinguished as either graph-based algorithms or tree-based algorithms. Some examples of graph-based methods include InParanoid<ref>{{cite journal|last1=Ostlund|first1=G.|last2=Schmitt|first2=T.|last3=Forslund|first3=K.|last4=Kostler|first4=T.|last5=Messina|first5=D. N.|last6=Roopra|first6=S.|last7=Frings|first7=O.|last8=Sonnhammer|first8=E. L. L.|title=InParanoid 7: new algorithms and tools for eukaryotic orthology analysis|journal=Nucleic Acids Research|date=5 November 2009|volume=38|issue=Database|pages=D196–D203|doi=10.1093/nar/gkp931}}</ref>, MultiParanoid<ref>{{cite journal|last1=Alexeyenko|first1=A.|last2=Tamas|first2=I.|last3=Liu|first3=G.|last4=Sonnhammer|first4=E. L.L.|title=Automatic clustering of orthologs and inparalogs shared by multiple proteomes|journal=Bioinformatics|date=27 July 2006|volume=22|issue=14|pages=e9–e15|doi=10.1093/bioinformatics/btl213}}</ref>, OrthoMCL<ref>{{cite journal|last1=Li|first1=L.|title=OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes|journal=Genome Research|date=1 September 2003|volume=13|issue=9|pages=2178–2189|doi=10.1101/gr.1224503}}</ref>, HomoloGene<ref>{{cite journal|last1=Sayers|first1=E. W.|last2=Barrett|first2=T.|last3=Benson|first3=D. A.|last4=Bolton|first4=E.|last5=Bryant|first5=S. H.|last6=Canese|first6=K.|last7=Chetvernin|first7=V.|last8=Church|first8=D. M.|last9=DiCuccio|first9=M.|last10=Federhen|first10=S.|last11=Feolo|first11=M.|last12=Fingerman|first12=I. M.|last13=Geer|first13=L. Y.|last14=Helmberg|first14=W.|last15=Kapustin|first15=Y.|last16=Landsman|first16=D.|last17=Lipman|first17=D. J.|last18=Lu|first18=Z.|last19=Madden|first19=T. L.|last20=Madej|first20=T.|last21=Maglott|first21=D. R.|last22=Marchler-Bauer|first22=A.|last23=Miller|first23=V.|last24=Mizrachi|first24=I.|last25=Ostell|first25=J.|last26=Panchenko|first26=A.|last27=Phan|first27=L.|last28=Pruitt|first28=K. D.|last29=Schuler|first29=G. D.|last30=Sequeira|first30=E.|last31=Sherry|first31=S. T.|last32=Shumway|first32=M.|last33=Sirotkin|first33=K.|last34=Slotta|first34=D.|last35=Souvorov|first35=A.|last36=Starchenko|first36=G.|last37=Tatusova|first37=T. A.|last38=Wagner|first38=L.|last39=Wang|first39=Y.|last40=Wilbur|first40=W. J.|last41=Yaschenko|first41=E.|last42=Ye|first42=J.|title=Database resources of the National Center for Biotechnology Information|journal=Nucleic Acids Research|date=21 November 2010|volume=39|issue=Database|pages=D38–D51|doi=10.1093/nar/gkq1172}}</ref> and OMA<ref>{{cite journal|last1=Altenhoff|first1=A. M.|last2=kunca|first2=N.|last3=Glover|first3=N.|last4=Train|first4=C.-M.|last5=Sueki|first5=A.|last6=Pili ota|first6=I.|last7=Gori|first7=K.|last8=Tomiczek|first8=B.|last9=Muller|first9=S.|last10=Redestig|first10=H.|last11=Gonnet|first11=G. H.|last12=Dessimoz|first12=C.|title=The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements|journal=Nucleic Acids Research|date=15 November 2014|volume=43|issue=D1|pages=D240–D249|doi=10.1093/nar/gku1158}}</ref>. Tree-based algorithms include programs such as OrthologID or RIO.<ref>{{cite journal|last1=Zmasek|first1=Christian M|last2=Eddy|first2=Sean R|title=RIO: Analyzing proteomes by automated phylogenomics using resampled inference of orthologs|journal=BMC Bioinformatics|date=2002|volume=3|issue=1|pages=14|doi=10.1186/1471-2105-3-14}}</ref><ref name="yeast" />
These methods are generally distinguished as either graph-based algorithms or tree-based algorithms. Some examples of graph-based methods include InParanoid, MultiParanoid, OrthoMCL, HomoloGene and OMA. Tree-based algorithms include programs such as OrthologID or RIO.<ref>{{cite journal|last1=Zmasek|first1=Christian M|last2=Eddy|first2=Sean R|title=RIO: Analyzing proteomes by automated phylogenomics using resampled inference of orthologs|journal=BMC Bioinformatics|date=2002|volume=3|issue=1|pages=14|doi=10.1186/1471-2105-3-14}}</ref><ref name="yeast" />


A variety of [[BLAST]] methods are often used to detect [[Sequence homology#Orthology|orthologs]] between [[species]] as a part of graph-based algorithms, such as MegaBLAST, BLASTALL, or other forms of all-versus-all BLAST and may be [[nucleotide]]- or [[Proteins|protein]]-based [[Sequence alignment|alignments]].<ref name="cleome">{{cite journal|last1=Barker|first1=M. S.|last2=Vogel|first2=H.|last3=Schranz|first3=M. E.|title=Paleopolyploidy in the Brassicales: Analyses of the Cleome Transcriptome Elucidate the History of Genome Duplications in Arabidopsis and Other Brassicales|journal=Genome Biology and Evolution|date=5 October 2009|volume=1|issue=0|pages=391–399|doi=10.1093/gbe/evp040}}</ref><ref name="eggplant">{{cite journal|last1=Yang|first1=Xu|last2=Cheng|first2=Yu-Fu|last3=Deng|first3=Cao|last4=Ma|first4=Yan|last5=Wang|first5=Zhi-Wen|last6=Chen|first6=Xue-Hao|last7=Xue|first7=Lin-Bao|title=Comparative transcriptome analysis of eggplant (Solanum melongena L.) and turkey berry (Solanum torvum Sw.): phylogenomics and disease resistance analysis|journal=BMC Genomics|date=2014|volume=15|issue=1|pages=412|doi=10.1186/1471-2164-15-412}}</ref> RevTrans<ref>{{cite journal|last1=Wernersson|first1=R.|title=RevTrans: multiple alignment of coding DNA from aligned amino acid sequences|journal=Nucleic Acids Research|date=1 July 2003|volume=31|issue=13|pages=3537–3539|doi=10.1093/nar/gkg609}}</ref> will even use protein data to inform DNA alignments, which can be beneficial for resolving more distant phylogenetic relationships. These approaches often assume that best-reciprocal-hits passing some threshold metric(s), such as identity, E-value, or percent alignment, represent [[Sequence homology#Orthology|orthologs]] and may be confounded by incomplete lineage sorting.
A variety of [[BLAST]] methods are often used to detect [[Sequence homology#Orthology|orthologs]] between [[species]] as a part of graph-based algorithms, such as MegaBLAST, BLASTALL, or other forms of all-versus-all BLAST and may be [[nucleotide]]- or [[Proteins|protein]]-based [[Sequence alignment|alignments]].<ref name="cleome">{{cite journal|last1=Barker|first1=M. S.|last2=Vogel|first2=H.|last3=Schranz|first3=M. E.|title=Paleopolyploidy in the Brassicales: Analyses of the Cleome Transcriptome Elucidate the History of Genome Duplications in Arabidopsis and Other Brassicales|journal=Genome Biology and Evolution|date=5 October 2009|volume=1|issue=0|pages=391–399|doi=10.1093/gbe/evp040}}</ref><ref name="eggplant">{{cite journal|last1=Yang|first1=Xu|last2=Cheng|first2=Yu-Fu|last3=Deng|first3=Cao|last4=Ma|first4=Yan|last5=Wang|first5=Zhi-Wen|last6=Chen|first6=Xue-Hao|last7=Xue|first7=Lin-Bao|title=Comparative transcriptome analysis of eggplant (Solanum melongena L.) and turkey berry (Solanum torvum Sw.): phylogenomics and disease resistance analysis|journal=BMC Genomics|date=2014|volume=15|issue=1|pages=412|doi=10.1186/1471-2164-15-412}}</ref> RevTrans<ref>{{cite journal|last1=Wernersson|first1=R.|title=RevTrans: multiple alignment of coding DNA from aligned amino acid sequences|journal=Nucleic Acids Research|date=1 July 2003|volume=31|issue=13|pages=3537–3539|doi=10.1093/nar/gkg609}}</ref> will even use protein data to inform DNA alignments, which can be beneficial for resolving more distant phylogenetic relationships. These approaches often assume that best-reciprocal-hits passing some threshold metric(s), such as identity, E-value, or percent alignment, represent [[Sequence homology#Orthology|orthologs]] and may be confounded by incomplete lineage sorting.<ref>{{cite journal|last1=Moreno-Hagelsieb|first1=G.|last2=Latimer|first2=K.|title=Choosing BLAST options for better detection of orthologs as reciprocal best hits|journal=Bioinformatics|date=26 November 2007|volume=24|issue=3|pages=319–324|doi=10.1093/bioinformatics/btm585}}</ref><ref>{{cite journal|last1=Castillo-Ramírez|first1=Santiago|last2=González|first2=Víctor|title=Factors affecting the concordance between orthologous gene trees and species tree in bacteria|journal=BMC Evolutionary Biology|date=2008|volume=8|issue=1|pages=300|doi=10.1186/1471-2148-8-300}}</ref>


===Databases and tools===
===Databases and tools===
Line 46: Line 52:


==[[Multiple sequence alignment]]==
==[[Multiple sequence alignment]]==
===DNA vs RNA vs Protein===
As [[eukaryotic transcription]] is a complex process by which multiple [[Messenger RNA|transcripts]] may be generated from a single [[gene]] through [[alternative splicing]] with variable [[Gene expression|expression]], the utilization of RNA is more complicated than DNA. However, [[transcriptomes]] are cheaper to sequence than complete genomes and may be obtained without the use of a pre-existing [[reference genome]].<ref name="book">{{cite book|last1=Hörandl|first1=Elvira|last2=Appelhans|first2=Mark|title=Next-generation sequencing in plant systematics|date=2015|publisher=Koeltz Scientific Books|isbn=9783874294928}}</ref>
As [[eukaryotic transcription]] is a complex process by which multiple [[Messenger RNA|transcripts]] may be generated from a single [[gene]] through [[alternative splicing]] with variable [[Gene expression|expression]], the utilization of RNA is more complicated than DNA. However, [[transcriptomes]] are cheaper to sequence than complete genomes and may be obtained without the use of a pre-existing [[reference genome]].<ref name="book">{{cite book|last1=Hörandl|first1=Elvira|last2=Appelhans|first2=Mark|title=Next-generation sequencing in plant systematics|date=2015|publisher=Koeltz Scientific Books|isbn=9783874294928}}</ref>


It is not uncommon to [[Translation (biology)|translate]] RNA sequence into protein sequence when using transcriptomic data, especially when analyzing highly diverged taxa. This is an intuitive step as many (but not all) transcripts are expected to code for [[protein isoform]]s. Potential benefits include the reduction of mutational biases and a reduced number of characters, which may speed analyses. However, this reduction in characters may also result in the loss of potentially informative characters.<ref name="book" />
It is not uncommon to [[Translation (biology)|translate]] RNA sequence into protein sequence when using transcriptomic data, especially when analyzing highly diverged taxa. This is an intuitive step as many (but not all) transcripts are expected to code for [[protein isoform]]s. Potential benefits include the reduction of mutational biases and a reduced number of characters, which may speed analyses. However, this reduction in characters may also result in the loss of potentially informative characters.<ref name="book" />


⚫
There are a number of [[List of sequence alignment software#Multiple sequence alignment|tools available for multiple sequence alignment]]. All of which possess their own strengths and weaknesses and may be specialized for distinct sequence types (DNA, RNA or protein). As such, a splice-aware aligner may be ideal for aligning RNA sequences, whereas an aligner that considers [[protein structure]] or [[Substitution model#Models of amino acid substitutions|residue substitution rates]] may be preferable for translated RNA sequence data.
===[[Multiple sequence alignment|Character alignment]]===
⚫
There are a number of [[List of sequence alignment software#Multiple sequence alignment|tools available for multiple sequence alignment]]. All of which possess their own strengths and weaknesses and may be specialized for distinct sequence types (DNA, RNA or protein). As such, a splice-aware aligner may be ideal for aligning RNA sequence, whereas an aligner that considers [[protein structure]] or [[Substitution model#Models of amino acid substitutions|residue substitution rates]] may be preferable for proteins.

==Inferring phylogenetic relationships==
===Approaches===
There are two main approaches to species tree construction using sequence data
# Multi-gene concatenated framework
# Gene-tree centric species tree<ref name="book" />
Proponents for concatenation suggest that larger datasets are more liable to find true phylogenetic relationships than individual gene-level analyses.<ref>{{cite journal|last1=Salichos|first1=Leonidas|last2=Rokas|first2=Antonis|title=Inferring ancient divergences requires genes with strong phylogenetic signals|journal=Nature|date=8 May 2013|volume=497|issue=7449|pages=327–331|doi=10.1038/nature12130}}</ref>
Proponents for gene-tree centric species trees suggest that the concatenation approach fails to account for potential discordance between gene trees and species trees by ignoring the evolutionary history of the genes. This may occur due to [[Coalescent theory|deep coalescence]], [[horizontal gene transfer]], [[Hybrid (biology)|hybridization]] or incomplete lineage sorting.<ref name="book" />

===Tools===
There are a number of publicly available [[List of phylogenetics software|tools for phylogenetic analysis]]. The methods these tools use may be generally classified as:
* [[Bayesian inference in phylogeny|Bayesian phylogenetic inference]]
* [[Distance matrices in phylogeny|distance matrix method]]
* [[maximum likelihood]]
* [[maximum parsimony]]
* [[neighbor-joining]]
* [[UPGMA]]


==Opportunities and limitations==
==Opportunities and limitations==
Using RNA for phylogenetic analysis comes with its own unique set of strengths and weaknesses.
Using RNA for phylogenetic analysis comes with its own unique set of strengths and weaknesses.

===Advantages===
===Advantages===
* large set of characters
* large set of characters
Line 84: Line 70:
* potential misassembly of transcripts (especially when duplicates are present)
* potential misassembly of transcripts (especially when duplicates are present)
* missing data as a product of the transcriptome representing a snapshot of expression or incomplete lineage sorting<ref name="grape">{{cite journal|last1=Wen|first1=Jun|last2=Xiong|first2=Zhiqiang|last3=Nie|first3=Ze-Long|last4=Mao|first4=Likai|last5=Zhu|first5=Yabing|last6=Kan|first6=Xian-Zhao|last7=Ickert-Bond|first7=Stefanie M.|last8=Gerrath|first8=Jean|last9=Zimmer|first9=Elizabeth A.|last10=Fang|first10=Xiao-Dong|last11=Candela|first11=Hector|title=Transcriptome Sequences Resolve Deep Relationships of the Grape Family|journal=PLoS ONE|date=17 September 2013|volume=8|issue=9|pages=e74394|doi=10.1371/journal.pone.0074394}}</ref>
* missing data as a product of the transcriptome representing a snapshot of expression or incomplete lineage sorting<ref name="grape">{{cite journal|last1=Wen|first1=Jun|last2=Xiong|first2=Zhiqiang|last3=Nie|first3=Ze-Long|last4=Mao|first4=Likai|last5=Zhu|first5=Yabing|last6=Kan|first6=Xian-Zhao|last7=Ickert-Bond|first7=Stefanie M.|last8=Gerrath|first8=Jean|last9=Zimmer|first9=Elizabeth A.|last10=Fang|first10=Xiao-Dong|last11=Candela|first11=Hector|title=Transcriptome Sequences Resolve Deep Relationships of the Grape Family|journal=PLoS ONE|date=17 September 2013|volume=8|issue=9|pages=e74394|doi=10.1371/journal.pone.0074394}}</ref>

==Minimizing bias==
[[Bias of an estimator|Bias]] in estimating phylogenetic relationships can be ameliorated in several ways:
# Synonymous substitution rate ([[Ka/Ks ratio|Ks value]]) normalization can account for differences in Ks values between species. However, to avoid [[Ka/Ks ratio#Complications|complications]] with saturation and codon usage bias, only select Ks values may be normalized.<ref name="yucca">{{cite journal|last1=McKain|first1=M. R.|last2=Wickett|first2=N.|last3=Zhang|first3=Y.|last4=Ayyampalayam|first4=S.|last5=McCombie|first5=W. R.|last6=Chase|first6=M. W.|last7=Pires|first7=J. C.|last8=dePamphilis|first8=C. W.|last9=Leebens-Mack|first9=J.|title=Phylogenomic analysis of transcriptome data elucidates co-occurrence of a paleopolyploid event and the origin of bimodal karyotypes in Agavoideae (Asparagaceae)|journal=American Journal of Botany|date=1 February 2012|volume=99|issue=2|pages=397–406|doi=10.3732/ajb.1100537}}</ref>
# The use of '''UniGenes''' and '''single-copy genes''' can limit difficulties associated with comparing genes derived from [[Gene duplication|duplications]] or recently diverged [[Gene family|gene families]]. They may also be used to annotate a [[transcriptome]] and limit analysis to gene sets that can be unambiguously identified as orthologs.<ref name="pisum">{{cite journal|last1=Franssen|first1=Susanne U|last2=Shrestha|first2=Roshan P|last3=Bräutigam|first3=Andrea|last4=Bornberg-Bauer|first4=Erich|last5=Weber|first5=Andreas PM|title=Comprehensive transcriptome analysis of the highly complex Pisum sativum genome using next generation sequencing|journal=BMC Genomics|date=11 May 2011|volume=12|issue=1|doi=10.1186/1471-2164-12-227}}</ref>
# Gene trees may also be built to infer orthology in non-model species, after which, species trees can be built using the newly derived orthologous gene sets.<ref name="caryo">{{cite journal|last1=Yang|first1=Ya|last2=Moore|first2=Michael J.|last3=Brockington|first3=Samuel F.|last4=Soltis|first4=Douglas E.|last5=Wong|first5=Gane Ka-Shu|last6=Carpenter|first6=Eric J.|last7=Zhang|first7=Yong|last8=Chen|first8=Li|last9=Yan|first9=Zhixiang|last10=Xie|first10=Yinlong|last11=Sage|first11=Rowan F.|last12=Covshoff|first12=Sarah|last13=Hibberd|first13=Julian M.|last14=Nelson|first14=Matthew N.|last15=Smith|first15=Stephen A.|title=Dissecting Molecular Evolution in the Highly Diverse Plant Clade Caryophyllales Using Transcriptome Sequencing|journal=Molecular Biology and Evolution|date=August 2015|volume=32|issue=8|pages=2001–2014|doi=10.1093/molbev/msv081}}</ref><ref name="orthology">{{cite journal|last1=Yang|first1=Y.|last2=Smith|first2=S. A.|title=Orthology Inference in Nonmodel Organisms Using Transcriptomes and Low-Coverage Genomes: Improving Accuracy and Matrix Occupancy for Phylogenomics|journal=Molecular Biology and Evolution|date=25 August 2014|volume=31|issue=11|pages=3081–3092|doi=10.1093/molbev/msu245}}</ref>

As such, characterizing gene family evolution is vital for both systematic and functional purposes.<ref name="gene_fam">{{cite journal|last1=Liberles|first1=David A.|last2=Dittmar|first2=Katharina|title=Characterizing gene family evolution|journal=Biological Procedures Online|date=December 2008|volume=10|issue=1|pages=66–73|doi=10.1251/bpo144}}</ref>


==See also==
==See also==

Revision as of 18:22, 17 April 2017

In molecular phylogenetics, relationships among individuals are determined using character traits, such as DNA, RNA or protein, which may be obtained using a variety of sequencing technologies. High-throughput next-generation sequencing has become a popular method for generating transcriptomes, which represent a snapshot of gene expression. In eukaryotes, making phylogenetic inferences using RNA is complicated by alternative splicing, which produces multiple transcripts from a single gene. As such, a variety of approaches may be used to improve phylogenetic inference using transcriptomic data obtained from RNA-Seq and processed using computational phylogenetics.

Sequence acquisition

RNA-Seq

RNA reads may be obtained using a variety of RNA-seq methods.

Public databases

There are a number of public databases that contain freely available RNA-Seq data.

Assembly

Sequence assembly

RNA-Seq data may be directly assembled into transcripts using sequence assembly. Two main categories of sequence assembly are often distinguished:

  1. de novo transcriptome assembly - especially important when a reference genome is not available for a given species.
  2. Genome-guided assembly (sometimes mapping or reference-guided assembly) - is capable of using a pre-existing reference to guide the assembly of transcripts

Both methods attempt to generate biologically representative isoform-level constructs from RNA-seq data and generally attempt to associate isoforms with a gene-level construct. However, proper identification of gene-level constructs may be complicated by recent duplications, paralogs, alternative splicing or gene fusions. These complications may also cause downstream issues during ortholog inference. When selecting or generating sequence data, it is also vital to consider the tissue type, developmental stage and environmental conditions of the organisms. Since the transcriptome represents a snapshot of gene expression, minor changes to these conditions may significantly affect which transcripts are expressed. This may detrimentally affect downstream ortholog detection.[1]

Public databases

RNA may also be acquired from public databases, such as GenBank, RefSeq, 1000 Plants (1KP) and 1KITE. Public databases potentially offer curated sequences which can improve inference quality and avoid the computational overhead associated with sequence assembly.

Inferring gene pair orthology/paralogy

Approaches

Orthology inference requires an assessment of sequence homology, usually via sequence alignment. Phylogenetic analyses and sequence alignment are often considered jointly, as phylogenetic analyses using DNA or RNA require sequence alignment and alignments themselves often represent some hypothesis of homology. As proper ortholog identification is pivotal to phylogenetic analyses, there are a variety of methods available to infer orthologs and paralogs.[2]

These methods are generally distinguished as either graph-based algorithms or tree-based algorithms. Some examples of graph-based methods include InParanoid[3], MultiParanoid[4], OrthoMCL[5], HomoloGene[6] and OMA[7]. Tree-based algorithms include programs such as OrthologID or RIO.[8][2]

A variety of BLAST methods are often used to detect orthologs between species as a part of graph-based algorithms, such as MegaBLAST, BLASTALL, or other forms of all-versus-all BLAST and may be nucleotide- or protein-based alignments.[9][10] RevTrans[11] will even use protein data to inform DNA alignments, which can be beneficial for resolving more distant phylogenetic relationships. These approaches often assume that best-reciprocal-hits passing some threshold metric(s), such as identity, E-value, or percent alignment, represent orthologs and may be confounded by incomplete lineage sorting.[12][13]

Databases and tools

It is important to note that orthology relationships in public databases typically represent gene-level orthology and do not provide information concerning conserved alternative splice variants.

Databases that contain and/or detect orthologous relationships include:

Multiple sequence alignment

As eukaryotic transcription is a complex process by which multiple transcripts may be generated from a single gene through alternative splicing with variable expression, the utilization of RNA is more complicated than DNA. However, transcriptomes are cheaper to sequence than complete genomes and may be obtained without the use of a pre-existing reference genome.[1]

It is not uncommon to translate RNA sequence into protein sequence when using transcriptomic data, especially when analyzing highly diverged taxa. This is an intuitive step as many (but not all) transcripts are expected to code for protein isoforms. Potential benefits include the reduction of mutational biases and a reduced number of characters, which may speed analyses. However, this reduction in characters may also result in the loss of potentially informative characters.[1]

There are a number of tools available for multiple sequence alignment. All of which possess their own strengths and weaknesses and may be specialized for distinct sequence types (DNA, RNA or protein). As such, a splice-aware aligner may be ideal for aligning RNA sequences, whereas an aligner that considers protein structure or residue substitution rates may be preferable for translated RNA sequence data.

Opportunities and limitations

Using RNA for phylogenetic analysis comes with its own unique set of strengths and weaknesses.

Advantages

Disadvantages

  • expenses of extensive taxon sampling
  • difficulty in identification of full-length, single-copy transcripts and orthologs
  • potential misassembly of transcripts (especially when duplicates are present)
  • missing data as a product of the transcriptome representing a snapshot of expression or incomplete lineage sorting[14]

See also

References

  1. ^ a b c Hörandl, Elvira; Appelhans, Mark (2015). Next-generation sequencing in plant systematics. Koeltz Scientific Books. ISBN 9783874294928.
  2. ^ a b Salichos, Leonidas; Rokas, Antonis; Fairhead, Cecile (13 April 2011). "Evaluating Ortholog Prediction Algorithms in a Yeast Model Clade". PLoS ONE. 6 (4): e18755. doi:10.1371/journal.pone.0018755.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  3. ^ Ostlund, G.; Schmitt, T.; Forslund, K.; Kostler, T.; Messina, D. N.; Roopra, S.; Frings, O.; Sonnhammer, E. L. L. (5 November 2009). "InParanoid 7: new algorithms and tools for eukaryotic orthology analysis". Nucleic Acids Research. 38 (Database): D196–D203. doi:10.1093/nar/gkp931.
  4. ^ Alexeyenko, A.; Tamas, I.; Liu, G.; Sonnhammer, E. L.L. (27 July 2006). "Automatic clustering of orthologs and inparalogs shared by multiple proteomes". Bioinformatics. 22 (14): e9–e15. doi:10.1093/bioinformatics/btl213.
  5. ^ Li, L. (1 September 2003). "OrthoMCL: Identification of Ortholog Groups for Eukaryotic Genomes". Genome Research. 13 (9): 2178–2189. doi:10.1101/gr.1224503.
  6. ^ Sayers, E. W.; Barrett, T.; Benson, D. A.; Bolton, E.; Bryant, S. H.; Canese, K.; Chetvernin, V.; Church, D. M.; DiCuccio, M.; Federhen, S.; Feolo, M.; Fingerman, I. M.; Geer, L. Y.; Helmberg, W.; Kapustin, Y.; Landsman, D.; Lipman, D. J.; Lu, Z.; Madden, T. L.; Madej, T.; Maglott, D. R.; Marchler-Bauer, A.; Miller, V.; Mizrachi, I.; Ostell, J.; Panchenko, A.; Phan, L.; Pruitt, K. D.; Schuler, G. D.; Sequeira, E.; Sherry, S. T.; Shumway, M.; Sirotkin, K.; Slotta, D.; Souvorov, A.; Starchenko, G.; Tatusova, T. A.; Wagner, L.; Wang, Y.; Wilbur, W. J.; Yaschenko, E.; Ye, J. (21 November 2010). "Database resources of the National Center for Biotechnology Information". Nucleic Acids Research. 39 (Database): D38–D51. doi:10.1093/nar/gkq1172.
  7. ^ Altenhoff, A. M.; kunca, N.; Glover, N.; Train, C.-M.; Sueki, A.; Pili ota, I.; Gori, K.; Tomiczek, B.; Muller, S.; Redestig, H.; Gonnet, G. H.; Dessimoz, C. (15 November 2014). "The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements". Nucleic Acids Research. 43 (D1): D240–D249. doi:10.1093/nar/gku1158. {{cite journal}}: no-break space character in |last6= at position 5 (help)
  8. ^ Zmasek, Christian M; Eddy, Sean R (2002). "RIO: Analyzing proteomes by automated phylogenomics using resampled inference of orthologs". BMC Bioinformatics. 3 (1): 14. doi:10.1186/1471-2105-3-14.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  9. ^ Barker, M. S.; Vogel, H.; Schranz, M. E. (5 October 2009). "Paleopolyploidy in the Brassicales: Analyses of the Cleome Transcriptome Elucidate the History of Genome Duplications in Arabidopsis and Other Brassicales". Genome Biology and Evolution. 1 (0): 391–399. doi:10.1093/gbe/evp040.
  10. ^ Yang, Xu; Cheng, Yu-Fu; Deng, Cao; Ma, Yan; Wang, Zhi-Wen; Chen, Xue-Hao; Xue, Lin-Bao (2014). "Comparative transcriptome analysis of eggplant (Solanum melongena L.) and turkey berry (Solanum torvum Sw.): phylogenomics and disease resistance analysis". BMC Genomics. 15 (1): 412. doi:10.1186/1471-2164-15-412.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  11. ^ Wernersson, R. (1 July 2003). "RevTrans: multiple alignment of coding DNA from aligned amino acid sequences". Nucleic Acids Research. 31 (13): 3537–3539. doi:10.1093/nar/gkg609.
  12. ^ Moreno-Hagelsieb, G.; Latimer, K. (26 November 2007). "Choosing BLAST options for better detection of orthologs as reciprocal best hits". Bioinformatics. 24 (3): 319–324. doi:10.1093/bioinformatics/btm585.
  13. ^ Castillo-Ramírez, Santiago; González, Víctor (2008). "Factors affecting the concordance between orthologous gene trees and species tree in bacteria". BMC Evolutionary Biology. 8 (1): 300. doi:10.1186/1471-2148-8-300.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  14. ^ Wen, Jun; Xiong, Zhiqiang; Nie, Ze-Long; Mao, Likai; Zhu, Yabing; Kan, Xian-Zhao; Ickert-Bond, Stefanie M.; Gerrath, Jean; Zimmer, Elizabeth A.; Fang, Xiao-Dong; Candela, Hector (17 September 2013). "Transcriptome Sequences Resolve Deep Relationships of the Grape Family". PLoS ONE. 8 (9): e74394. doi:10.1371/journal.pone.0074394.{{cite journal}}: CS1 maint: unflagged free DOI (link)

External links

Retrieved from "https://en.wikipedia.org/w/index.php?title=Phylogenetic_inference_using_transcriptomic_data&oldid=775891677"