Introduction

Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion.We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

Publications

  1. Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data.
    Cite this
    Chan KL, Rosli R, Tatarinova TV, Hogan M, Firdaus-Raih M, Low EL, 2017-01-01 - BMC Bioinformatics

Credits

  1. Kuang-Lim Chan
    Developer

    Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Malaysia

  2. Rozana Rosli
    Developer

    Advanced Biotechnology and Breeding Center, Malaysian Palm Oil Board, Malaysia

  3. Tatiana V Tatarinova
    Developer

    Center for Personalized Medicine and Spatial Sciences Institute, University of Southern California, United States of America

  4. Michael Hogan
    Developer

    Orion Genomics, 4041 Forest Park Avenue, United States of America

  5. Mohd Firdaus-Raih
    Developer

    Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Malaysia

  6. Eng-Ti Leslie Low
    Investigator

    Advanced Biotechnology and Breeding Center, Malaysian Palm Oil Board, Malaysia

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Summary
AccessionBT001883
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesPerl
User InterfaceTerminal Command Line
Download Count0
Country/RegionMalaysia
Submitted ByEng-Ti Leslie Low