GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient
Abstract Background Gene is a key step in genome annotation. Ab initio gene prediction enables gene annotation of new genomes regardless of availability of homologous sequences. There exist a number of ab initio gene prediction tools and they have been widely used for gene annotation for various spe...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2019-12-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-019-3047-3 |
id |
doaj-6c3784b70a1c4679879b31b813d1442d |
---|---|
record_format |
Article |
spelling |
doaj-6c3784b70a1c4679879b31b813d1442d2020-12-27T12:21:42ZengBMCBMC Bioinformatics1471-21052019-12-0120S1511510.1186/s12859-019-3047-3GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradientPrapaporn Techa-Angkoon0Kevin L. Childs1Yanni Sun2Department of Computer Science and Engineering, Michigan State UniversityDepartment of Plant Biology, Michigan State UniversityDepartment of Electronic Engineering, City University of Hong KongAbstract Background Gene is a key step in genome annotation. Ab initio gene prediction enables gene annotation of new genomes regardless of availability of homologous sequences. There exist a number of ab initio gene prediction tools and they have been widely used for gene annotation for various species. However, existing tools are not optimized for identifying genes with highly variable GC content. In addition, some genes in grass genomes exhibit a sharp 5 ′- 3′ decreasing GC content gradient, which is not carefully modeled by available gene prediction tools. Thus, there is still room to improve the sensitivity and accuracy for predicting genes with GC gradients. Results In this work, we designed and implemented a new hidden Markov model (HMM)-based ab initio gene prediction tool, which is optimized for finding genes with highly variable GC contents, such as the genes with negative GC gradients in grass genomes. We tested the tool on three datasets from Arabidopsis thaliana and Oryza sativa. The results showed that our tool can identify genes missed by existing tools due to the highly variable GC contents. Conclusions GPRED-GC can effectively predict genes with highly variable GC contents without manual intervention. It provides a useful complementary tool to existing ones such as Augustus for more sensitive gene discovery. The source code is freely available at https://sourceforge.net/projects/gpred-gc/.https://doi.org/10.1186/s12859-019-3047-3Gene findingPlant genome gene predictionHidden Markov modelGC contentsGrass genomes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Prapaporn Techa-Angkoon Kevin L. Childs Yanni Sun |
spellingShingle |
Prapaporn Techa-Angkoon Kevin L. Childs Yanni Sun GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient BMC Bioinformatics Gene finding Plant genome gene prediction Hidden Markov model GC contents Grass genomes |
author_facet |
Prapaporn Techa-Angkoon Kevin L. Childs Yanni Sun |
author_sort |
Prapaporn Techa-Angkoon |
title |
GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient |
title_short |
GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient |
title_full |
GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient |
title_fullStr |
GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient |
title_full_unstemmed |
GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient |
title_sort |
gpred-gc: a gene prediction model accounting for 5 ′- 3′ gc gradient |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2019-12-01 |
description |
Abstract Background Gene is a key step in genome annotation. Ab initio gene prediction enables gene annotation of new genomes regardless of availability of homologous sequences. There exist a number of ab initio gene prediction tools and they have been widely used for gene annotation for various species. However, existing tools are not optimized for identifying genes with highly variable GC content. In addition, some genes in grass genomes exhibit a sharp 5 ′- 3′ decreasing GC content gradient, which is not carefully modeled by available gene prediction tools. Thus, there is still room to improve the sensitivity and accuracy for predicting genes with GC gradients. Results In this work, we designed and implemented a new hidden Markov model (HMM)-based ab initio gene prediction tool, which is optimized for finding genes with highly variable GC contents, such as the genes with negative GC gradients in grass genomes. We tested the tool on three datasets from Arabidopsis thaliana and Oryza sativa. The results showed that our tool can identify genes missed by existing tools due to the highly variable GC contents. Conclusions GPRED-GC can effectively predict genes with highly variable GC contents without manual intervention. It provides a useful complementary tool to existing ones such as Augustus for more sensitive gene discovery. The source code is freely available at https://sourceforge.net/projects/gpred-gc/. |
topic |
Gene finding Plant genome gene prediction Hidden Markov model GC contents Grass genomes |
url |
https://doi.org/10.1186/s12859-019-3047-3 |
work_keys_str_mv |
AT prapaporntechaangkoon gpredgcagenepredictionmodelaccountingfor53gcgradient AT kevinlchilds gpredgcagenepredictionmodelaccountingfor53gcgradient AT yannisun gpredgcagenepredictionmodelaccountingfor53gcgradient |
_version_ |
1724369052906815488 |