Published genomes frequently contain erroneous gene models that represent issues associated with identification of open reading frames,start sites,splice sites,and related structural features.The source of these incon...Published genomes frequently contain erroneous gene models that represent issues associated with identification of open reading frames,start sites,splice sites,and related structural features.The source of these inconsistencies is often traced back to integration across text file formats designed to describe long read alignments and predicted gene structures.In addition,the majority of gene prediction frameworks do not provide robust downstream filtering to remove problematic gene annotations,nor do they represent these annotations in a format consistent with current file standards.These frameworks also lack consideration for functional attributes,such as the presence or absence of protein domains that can be used for gene model validation.To provide oversight to the increasing number of published genome annotations,we present a software package,the Gene Filtering,Analysis,and Conversion(gFACs),to filter,analyze,and convert predicted gene models and alignments.The software operates across a wide range of alignment,analysis,and gene prediction files with a flexible framework for defining gene models with reliable structural and functional attributes.gFACs supports common downstream applications,including genome browsers,and generates extensive details on the filtering process,including distributions that can be visualized to further assess the proposed gene space.gFACs is freely available and implemented in Perl with support from BioPerl libraries at https://gitlab.com/PlantGenomicsLab/gFACs.展开更多
Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low le...Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly se- quenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http：//bio.scu.edu.cn/SCGPred/.展开更多
基金supported by the National Science Foundation Plant Genome Research Program of the United States(Grant No.1444573)
文摘Published genomes frequently contain erroneous gene models that represent issues associated with identification of open reading frames,start sites,splice sites,and related structural features.The source of these inconsistencies is often traced back to integration across text file formats designed to describe long read alignments and predicted gene structures.In addition,the majority of gene prediction frameworks do not provide robust downstream filtering to remove problematic gene annotations,nor do they represent these annotations in a format consistent with current file standards.These frameworks also lack consideration for functional attributes,such as the presence or absence of protein domains that can be used for gene model validation.To provide oversight to the increasing number of published genome annotations,we present a software package,the Gene Filtering,Analysis,and Conversion(gFACs),to filter,analyze,and convert predicted gene models and alignments.The software operates across a wide range of alignment,analysis,and gene prediction files with a flexible framework for defining gene models with reliable structural and functional attributes.gFACs supports common downstream applications,including genome browsers,and generates extensive details on the filtering process,including distributions that can be visualized to further assess the proposed gene space.gFACs is freely available and implemented in Perl with support from BioPerl libraries at https://gitlab.com/PlantGenomicsLab/gFACs.
基金This work was partially supported by the National Natural Science Foundation of China (No.30470984)
文摘Predicting protein-coding genes still remains a significant challenge. Although a variety of computational programs that use commonly machine learning methods have emerged, the accuracy of predictions remains a low level when implementing in large genomic sequences. Moreover, computational gene finding in newly se- quenced genomes is especially a difficult task due to the absence of a training set of abundant validated genes. Here we present a new gene-finding program, SCGPred, to improve the accuracy of prediction by combining multiple sources of evidence. SCGPred can perform both supervised method in previously well-studied genomes and unsupervised one in novel genomes. By testing with datasets composed of large DNA sequences from human and a novel genome of Ustilago maydi, SCGPred gains a significant improvement in comparison to the popular ab initio gene predictors. We also demonstrate that SCGPred can significantly improve prediction in novel genomes by combining several foreign gene finders with similarity alignments, which is superior to other unsupervised methods. Therefore, SCGPred can serve as an alternative gene-finding tool for newly sequenced eukaryotic genomes. The program is freely available at http：//bio.scu.edu.cn/SCGPred/.
