Phylogenetic inference using transcriptomic data

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

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

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]

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.

Orthology or paralogy 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]

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:

• large set of characters
• cost-effective
• not dependent upon a reference genome

• 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]