In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 1 ( 2023-1-10), p. e1010743-
Abstract:
Interspecific gene comparisons are the keystones for many areas of biological research and are especially important for the translation of knowledge from model organisms to economically important species. Currently they are hampered by the low resolution of methods based on sequence analysis and by the complex evolutionary history of eukaryotic genes. This is especially critical for plants, whose genomes are shaped by multiple whole genome duplications and subsequent gene loss. This requires the development of new methods for comparing the functions of genes in different species. Here, we report ISEEML ( I nterspecific S imilarity of E xpression E valuated using M achine L earning )–a novel machine learning-based algorithm for interspecific gene classification. In contrast to previous studies focused on sequence similarity, our algorithm focuses on functional similarity inferred from the comparison of gene expression profiles. We propose novel metrics for expression pattern similarity–expression score (ES)–that is suitable for species with differing morphologies. As a proof of concept, we compare detailed transcriptome maps of Arabidopsis thaliana , the model species, Zea mays (maize) and Fagopyrum esculentum (common buckwheat), which are species that represent distant clades within flowering plants. The classifier resulted in an AUC of 0.91; under the ES threshold of 0.5, the specificity was 94%, and sensitivity was 72%.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010743
DOI:
10.1371/journal.pcbi.1010743.g001
DOI:
10.1371/journal.pcbi.1010743.g002
DOI:
10.1371/journal.pcbi.1010743.g003
DOI:
10.1371/journal.pcbi.1010743.g004
DOI:
10.1371/journal.pcbi.1010743.g005
DOI:
10.1371/journal.pcbi.1010743.s001
DOI:
10.1371/journal.pcbi.1010743.s002
DOI:
10.1371/journal.pcbi.1010743.s003
DOI:
10.1371/journal.pcbi.1010743.s004
DOI:
10.1371/journal.pcbi.1010743.s005
DOI:
10.1371/journal.pcbi.1010743.s006
DOI:
10.1371/journal.pcbi.1010743.s007
DOI:
10.1371/journal.pcbi.1010743.s008
DOI:
10.1371/journal.pcbi.1010743.s009
DOI:
10.1371/journal.pcbi.1010743.s010
DOI:
10.1371/journal.pcbi.1010743.s011
DOI:
10.1371/journal.pcbi.1010743.s012
DOI:
10.1371/journal.pcbi.1010743.s013
DOI:
10.1371/journal.pcbi.1010743.s014
DOI:
10.1371/journal.pcbi.1010743.s015
DOI:
10.1371/journal.pcbi.1010743.s016
DOI:
10.1371/journal.pcbi.1010743.s017
DOI:
10.1371/journal.pcbi.1010743.s018
DOI:
10.1371/journal.pcbi.1010743.r001
DOI:
10.1371/journal.pcbi.1010743.r002
DOI:
10.1371/journal.pcbi.1010743.r003
DOI:
10.1371/journal.pcbi.1010743.r004
DOI:
10.1371/journal.pcbi.1010743.r005
DOI:
10.1371/journal.pcbi.1010743.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2193340-6