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 splicingorgene 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.
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:
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 structureorresidue substitution rates may be preferable for translated RNA sequence data.
^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. PMC3013733. PMID21097890.
