Introduction

Orthology characterizes genes of different organisms that arose from a single ancestral gene via speciation, in contrast to paralogy, which is assigned to genes that arose via gene duplication. An accurate orthology assignment is a crucial step for comparative genomic studies. Orthologous genes in two organisms can be identified by applying a so-called reciprocal search strategy, given that complete information of the organisms' gene repertoire is available. In many investigations, however, only a fraction of the gene content of the organisms under study is examined (e.g., RNA sequencing). Here, identification of orthologous nucleotide or amino acid sequences can be achieved using a graph-based approach that maps nucleotide sequences to genes of known orthology. Existing implementations of this approach, however, suffer from algorithmic issues that may cause problems in downstream analyses.We present a new software pipeline, Orthograph, that addresses and solves the above problems and implements useful features for a wide range of comparative genomic and transcriptomic analyses. Orthograph applies a best reciprocal hit search strategy using profile hidden Markov models and maps nucleotide sequences to the globally best matching cluster of orthologous genes, thus enabling researchers to conveniently and reliably delineate orthologs and paralogs from transcriptomic and genomic sequence data. We demonstrate the performance of our approach on de novo-sequenced and assembled transcript libraries of 24 species of apoid wasps (Hymenoptera: Aculeata) as well as on published genomic datasets.With Orthograph, we implemented a best reciprocal hit approach to reference-based orthology prediction for coding nucleotide sequences such as RNAseq data. Orthograph is flexible, easy to use, open source and freely available at https://mptrsen.github.io/Orthograph . Additionally, we release 24 de novo-sequenced and assembled transcript libraries of apoid wasp species.

Publications

  1. Orthograph: a versatile tool for mapping coding nucleotide sequences to clusters of orthologous genes.
    Cite this
    Petersen M, Meusemann K, Donath A, Dowling D, Liu S, Peters RS, Podsiadlowski L, Vasilikopoulos A, Zhou X, Misof B, Niehuis O, 2017-02-01 - BMC bioinformatics

Credits

  1. Malte Petersen
    Developer

    Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Germany

  2. Karen Meusemann
    Developer

    Department for Evolutionary Biology & Ecology, Institute for Biology I (Zoology), Germany

  3. Alexander Donath
    Developer

    Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Germany

  4. Daniel Dowling
    Developer

    Institute of Molecular Biology (IMB), Ackermannweg 4, Germany

  5. Shanlin Liu
    Developer

    China National GeneBank, BGI-Shenzhen, China

  6. Ralph S Peters
    Developer

    Arthropod Department, Zoological Research Museum Alexander Koenig, Germany

  7. Lars Podsiadlowski
    Developer

    Institute of Evolutionary Biology and Ecology, Zoology and Evolutionary Biology, Germany

  8. Alexandros Vasilikopoulos
    Developer

    Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Germany

  9. Xin Zhou
    Developer

    College of Food Science and Nutritional Engineering, China Agricultural University, China

  10. Bernhard Misof
    Developer

    Center for Molecular Biodiversity Research, Zoological Research Museum Alexander Koenig, Germany

  11. Oliver Niehuis
    Investigator

    Department for Evolutionary Biology & Ecology, Institute for Biology I (Zoology), Germany

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT000351
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesPerl
User InterfaceTerminal Command Line
Download Count0
Country/RegionGermany
Submitted ByOliver Niehuis