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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51185

Title: Multiple model species selection for transcriptomics analysis of non-model organisms
Authors: Tun-Wen Pai
Kuan-Hung Li
Cing-Han Yang
Chin-Hwa Hu
Han-Jia Lin
Wen-Der Wang
Yet-Ran Chen
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: RNA-seq
Reference model species
Differential expression analysis
Ultra-conserved orthologous gene
Gene ontology
Biological pathway
Date: 2018-08
Issue Date: 2018-11-15T03:39:36Z
Publisher: BMC Bioinformatics
Abstract: Abstract: Background
Transcriptomic sequencing (RNA-seq) related applications allow for rapid explorations due to their high-throughput and relatively fast experimental capabilities, providing unprecedented progress in gene functional annotation, gene regulation analysis, and environmental factor verification. However, with increasing amounts of sequenced reads and reference model species, the selection of appropriate reference species for gene annotation has become a new challenge.

We proposed a novel approach for finding the most effective reference model species through taxonomic associations and ultra-conserved orthologous (UCO) gene comparisons among species. An online system for multiple species selection (MSS) for RNA-seq differential expression analysis was developed, and comprehensive genomic annotations from 291 reference model eukaryotic species were retrieved from the RefSeq, KEGG, and UniProt databases.

Using the proposed MSS pipeline, gene ontology and biological pathway enrichment analysis can be efficiently achieved, especially in the case of transcriptomic analysis of non-model organisms. The results showed that the proposed method solved problems related to limitations in annotation information and provided a roughly twenty-fold reduction in computational time, resulting in more accurate results than those of traditional approaches of using a single model reference species or the large non-redundant reference database.

Selection of appropriate reference model species helps to reduce missing annotation information, allowing for more comprehensive results than those obtained with a single model reference species. In addition, adequate model species selection reduces the computational time significantly while retaining the same order of accuracy. The proposed system indeed provides superior performance by selecting appropriate multiple species for transcriptomic analysis compared to traditional approaches.
Relation: 19(9) pp.284
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51185
Appears in Collections:[資訊工程學系] 期刊論文

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