Phylogenetic inference using transcriptomic data
 | This is a user sandbox of Phylogenetic inference using transcriptomic data. You can use it for testing or practicing edits. This is not the place where you work on your assigned article for a dashboard.wikiedu.org course. Visit your Dashboard course page and follow the links for your assigned article in the My Articles section. |
This template should only be used in the user namespace.This template should only be used in the user namespace.
WORKING TITLE: Phylogenetic inference using transcriptomic data
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. 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.
Transcript acquisition
RNA-Seq data may be directly assembled into transcripts using sequence assembly. Two main categories of sequence assembly are often distinguished:
- de novo transcriptome assembly - especially important when a reference genome is not available for a given species.
- Mapping assembly (sometimes genome-guided assembly) - is capable of using a pre-existing reference to guide the assembly of transcripts
Public databases
RNA may also be acquired from public databases, such as GenBank or RefSeq. 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
Computationally inferring orthologs
A variety of BLAST methods are often used to detect orthologs between species, such as MegaBLAST, BLASTALL, or other forms of all-versus-all BLAST and may be nucleotide- or protein-based alignments[1][2]. RevTrans[3] 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.
Accessing public orthology data
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:
- DIOPT
- Ensembl Compara
- GreenPhylDB
- InParanoid
- OMA
- OrthoDB
- OrthoMCL
- OrtholugeDB
- PhylomeDB
- TreeFam
- eggNOG
- metaPhOrs
Opportunities and limitations
Advantages
- large set of characters
- cost-effective
- not dependent upon a reference genome
Disadvantages
- expenses of extensive taxon sampling
- difficulty in identification of full-length, single-copy transcripts and orthologs
- missing data as a product of the transcriptome representing a snapshot of expression or incomplete lineage sorting[4]
Minimizing bias
Bias in estimating phylogenetic relationships can be ameliorate in several ways:
- Synonymous substitution rate (Ks value) normalization can account differences in Ks values between species. However, to avoid complications with saturation and codon usage bias, only select Ks values may be normalizaed.[5]
- The use of UniGenes and single-copy genes can limit difficulties associated with comparing genes derived from duplications or recently diverged gene families. They may also be used to annotate a transcriptome and limit analysis to gene sets that can be unambiguously identified as orthologs.[6]
- 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.[7][8]
As such, characterizing gene family evolution is vital for both systematic and functional purposes.[9]
Inferring phylogenetic relationships
Importance of multiple sequence alignment for transcripts (spliced forms and incomplete) Tree building methods -- software list in see also
See also
- BLAST
- Coding region
- Computational phylogenetics
- De novo transcriptome assembly
- Exome
- Exome sequencing
- Expressed sequence tag
- Gene expression
- Homology
- List of phylogenetics software
- Phylogenetics
- Phylogenetic tree
- RNA
- RNA-Seq
- Sequence alignment
- Synonymous substitution
- Systematics
- Transcriptome
- UniGene
References
- ^ 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.
- ^ 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) - ^ 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.
- ^ 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) - ^ McKain, M. R.; Wickett, N.; Zhang, Y.; Ayyampalayam, S.; McCombie, W. R.; Chase, M. W.; Pires, J. C.; dePamphilis, C. W.; Leebens-Mack, J. (1 February 2012). "Phylogenomic analysis of transcriptome data elucidates co-occurrence of a paleopolyploid event and the origin of bimodal karyotypes in Agavoideae (Asparagaceae)". American Journal of Botany. 99 (2): 397–406. doi:10.3732/ajb.1100537.
- ^ Franssen, Susanne U; Shrestha, Roshan P; Bräutigam, Andrea; Bornberg-Bauer, Erich; Weber, Andreas PM (11 May 2011). "Comprehensive transcriptome analysis of the highly complex Pisum sativum genome using next generation sequencing". BMC Genomics. 12 (1). doi:10.1186/1471-2164-12-227.
{{cite journal}}: CS1 maint: unflagged free DOI (link) - ^ Yang, Ya; Moore, Michael J.; Brockington, Samuel F.; Soltis, Douglas E.; Wong, Gane Ka-Shu; Carpenter, Eric J.; Zhang, Yong; Chen, Li; Yan, Zhixiang; Xie, Yinlong; Sage, Rowan F.; Covshoff, Sarah; Hibberd, Julian M.; Nelson, Matthew N.; Smith, Stephen A. (August 2015). "Dissecting Molecular Evolution in the Highly Diverse Plant Clade Caryophyllales Using Transcriptome Sequencing". Molecular Biology and Evolution. 32 (8): 2001–2014. doi:10.1093/molbev/msv081.
- ^ Yang, Y.; Smith, S. A. (25 August 2014). "Orthology Inference in Nonmodel Organisms Using Transcriptomes and Low-Coverage Genomes: Improving Accuracy and Matrix Occupancy for Phylogenomics". Molecular Biology and Evolution. 31 (11): 3081–3092. doi:10.1093/molbev/msu245.
- ^ Liberles, David A.; Dittmar, Katharina (December 2008). "Characterizing gene family evolution". Biological Procedures Online. 10 (1): 66–73. doi:10.1251/bpo144.