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Phylogenetic inference using transcriptomic data

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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

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. Mapping assembly (sometimes genome-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 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, MultiParanoid, OrthoMCL, HomoloGene and OMA. Tree-based algorithms include programs such as OrthologID or RIO.[3][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[4][5]. RevTrans[6] 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.

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

DNA vs RNA vs Protein

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]

Character alignment

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 sequence, whereas an aligner that considers protein structure or residue substitution rates may be preferable for proteins.

Inferring phylogenetic relationships

Approaches

There are two main approaches to species tree construction using sequence data

  1. Multi-gene concatenated framework
  2. Gene-tree centric species tree[1]

Proponents for concatenation suggest that larger datasets are more liable to find true phylogenetic relationships than individual gene-level analyses.[7] 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 deep coalescence, horizontal gene transfer, hybridization or incomplete lineage sorting.[1]

Tools

There are a number of publicly available tools for phylogenetic analysis. The methods these tools use may be generally classified as:

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[8]

Minimizing bias

Bias in estimating phylogenetic relationships can be ameliorated in several ways:

  1. Synonymous substitution rate (Ks value) normalization can account for differences in Ks values between species. However, to avoid complications with saturation and codon usage bias, only select Ks values may be normalized.[9]
  2. 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.[10]
  3. 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.[11][12]

As such, characterizing gene family evolution is vital for both systematic and functional purposes.[13]

See also

References

  1. ^ a b c d e 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. ^ 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)
  4. ^ 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.
  5. ^ 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)
  6. ^ 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.
  7. ^ Salichos, Leonidas; Rokas, Antonis (8 May 2013). "Inferring ancient divergences requires genes with strong phylogenetic signals". Nature. 497 (7449): 327–331. doi:10.1038/nature12130.
  8. ^ 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)
  9. ^ 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.
  10. ^ 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)
  11. ^ 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.
  12. ^ 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.
  13. ^ Liberles, David A.; Dittmar, Katharina (December 2008). "Characterizing gene family evolution". Biological Procedures Online. 10 (1): 66–73. doi:10.1251/bpo144.
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