RNA-clique: a method for computing genetic distances from RNA-seq data

Andrew C. Tapia, Jerzy Jaromczyk, Neil Moore, Christopher L. Schardl

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Although RNA-seq data are traditionally used for quantifying gene expression levels, the same data could be useful in an integrated approach to compute genetic distances as well. Challenges to using mRNA sequences for computing genetic distances include the relatively high conservation of coding sequences and the presence of paralogous and, in some species, homeologous genes. Results: We developed a new computational method, RNA-clique, for calculating genetic distances using assembled RNA-seq data and assessed the efficacy of the method using biological and simulated data. The method employs reciprocal BLASTn followed by graph-based filtering to ensure that only orthologous genes are compared. Each vertex in the graph constructed for filtering represents a gene in a specific sample under comparison, and an edge connects a pair of vertices if the genes they represent are best matches for each other in their respective samples. The distance computation is a function of the BLAST alignment statistics and the constructed graph and incorporates only those genes that are present in some complete connected component of this graph. As a biological testbed we used RNA-seq data of tall fescue (Lolium arundinaceum), an allohexaploid plant (2n=14Gb), and bluehead wrasse (Thalassoma bifasciatum), a teleost fish. RNA-clique reliably distinguished individual tall fescue plants by genotype and distinguished bluehead wrasse RNA-seq samples by individual. In tests with simulated RNA-seq data, the ground truth phylogeny was accurately recovered from the computed distances. Moreover, tests of the algorithm parameters indicated that, even with stringent filtering for orthologs, sufficient sequence data were retained for the distance computations. Although comparisons with an alternative method revealed that RNA-clique has relatively high time and memory requirements, the comparisons also showed that RNA-clique’s results were at least as reliable as the alternative’s for tall fescue data and were much more reliable for the bluehead wrasse data. Conclusion: Results of this work indicate that RNA-clique works well as a way of deriving genetic distances from RNA-seq data, thus providing a methodological integration of functional and genetic diversity studies.

Original languageEnglish
Article number205
JournalBMC Bioinformatics
Volume25
Issue number1
DOIs
StatePublished - Dec 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Keywords

  • Genetic distance
  • Graph algorithms
  • Phylogenetics
  • RNA-seq

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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