Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data.

ABSTRACT:

Background

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.

Results

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 MAKE

SUBMITTER: Chan KL 

PROVIDER: S-EPMC5333190 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature