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TaF:一个基于分类学概况的真菌基因预测网络平台。

TaF: a web platform for taxonomic profile-based fungal gene prediction.

作者信息

Park Sin-Gi, Ryu DongSung, Lee Hyunsung, Ryu Hojin, Ahn Yong Ju, Yoo Seung Il, Ko Junsu, Hong Chang Pyo

机构信息

TheragenEtex Bio Institute, Suwon, 16229, Republic of Korea.

Department of Biology, Chungbuk National University, Cheongju, 28644, Republic of Korea.

出版信息

Genes Genomics. 2019 Mar;41(3):337-342. doi: 10.1007/s13258-018-0766-1. Epub 2018 Nov 19.

Abstract

INTRODUCTION

The accurate prediction and annotation of gene structures from the genome sequence of an organism enable genome-wide functional analyses to obtain insight into the biological properties of an organism.

OBJECTIVES

We recently developed a highly accurate filamentous fungal gene prediction pipeline and web platform called TaF. TaF is a homology-based gene predictor employing large-scale taxonomic profiling to search for close relatives in genome queries.

METHODS

TaF pipeline consists of four processing steps; (1) taxonomic profiling to search for close relatives to query, (2) generation of hints for determining exon-intron boundaries from orthologous protein sequence data of the profiled species, (3) gene prediction by combination of ab inito and evidence-based prediction methods, and (4) homology search for gene models.

RESULTS

TaF generates extrinsic evidence that suggests possible exon-intron boundaries based on orthologous protein sequence data, thus reducing false-positive predictions of gene structure based on distantly related orthologs data. In particular, the gene prediction method using taxonomic profiling shows very high accuracy, including high sensitivity and specificity for gene models, suggesting a new approach for homology-based gene prediction from newly sequenced or uncharacterized fungal genomes, with the potential to improve the quality of gene prediction.

CONCLUSION

TaF will be a useful tool for fungal genome-wide analyses, including the identification of targeted genes associated with a trait, transcriptome profiling, comparative genomics, and evolutionary analysis.

摘要

引言

从生物体的基因组序列中准确预测和注释基因结构,能够进行全基因组功能分析,从而深入了解生物体的生物学特性。

目的

我们最近开发了一种高度准确的丝状真菌基因预测流程和网络平台,称为TaF。TaF是一种基于同源性的基因预测工具,采用大规模分类分析来在基因组查询中搜索近亲。

方法

TaF流程包括四个处理步骤;(1)分类分析以搜索与查询的近亲,(2)从分析物种的直系同源蛋白质序列数据生成用于确定外显子-内含子边界的提示,(3)通过从头预测和基于证据的预测方法相结合进行基因预测,以及(4)对基因模型进行同源性搜索。

结果

TaF基于直系同源蛋白质序列数据生成表明可能的外显子-内含子边界的外在证据,从而减少基于远缘直系同源数据的基因结构假阳性预测。特别是,使用分类分析的基因预测方法显示出非常高的准确性,包括对基因模型的高敏感性和特异性,这表明一种从新测序或未表征的真菌基因组进行基于同源性的基因预测的新方法,具有提高基因预测质量的潜力。

结论

TaF将成为真菌全基因组分析的有用工具,包括鉴定与性状相关的靶向基因、转录组分析、比较基因组学和进化分析。

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