Name
Title | ||
|---|---|---|
OrthoReD: a rapid and accurate orthology prediction tool with low computational requirement. |
Abstract | ||
|---|---|---|
Identifying orthologous genes is an initial step required for phylogenetics, and it is also a common strategy employed in functional genetics to find candidates for functionally equivalent genes across multiple species. At the same time, in silico orthology prediction tools often require large computational resources only available on computing clusters. Here we present OrthoReD, an open-source orthology prediction tool with accuracy comparable to published tools that requires only a desktop computer. The low computational resource requirement of OrthoReD is achieved by repeating orthology searches on one gene of interest at a time, thereby generating a reduced dataset to limit the scope of orthology search for each gene of interest.The output of OrthoReD was highly similar to the outputs of two other published orthology prediction tools, OrthologID and/or OrthoDB, for the three dataset tested, which represented three phyla with different ranges of species diversity and different number of genomes included. Median CPU time for ortholog prediction per gene by OrthoReD executed on a desktop computer was <15 min even for the largest dataset tested, which included all coding sequences of 100 bacterial species.With high-throughput sequencing, unprecedented numbers of genes from non-model organisms are available with increasing need for clear information about their orthologies and/or functional equivalents in model organisms. OrthoReD is not only fast and accurate as an orthology prediction tool, but also gives researchers flexibility in the number of genes analyzed at a time, without requiring a high-performance computing cluster. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s12859-017-1726-5 | BMC Bioinformatics |
Keywords | Field | DocType |
Gene evolution,Gene orthology,Genome,Phylogenetics,Transcriptome | Genome,OrthoDB,Biology,CPU time,Coding (social sciences),Computational biology,Bioinformatics,Computer cluster,Computational resource,DNA microarray,In silico | Journal |
Volume | Issue | ISSN |
18 | 1 | 1471-2105 |
Citations | PageRank | References |
1 | 0.36 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Battenberg | 1 | 1 | 0.36 |
| Ernest K. Lee | 2 | 47 | 2.72 |
| Joanna C. Chiu | 3 | 17 | 2.18 |
| Alison M Berry | 4 | 1 | 0.36 |
| Daniel Potter | 5 | 1 | 0.36 |