Multiple model species selection for transcriptomics analysis of non-model organisms

Autor: Chin-Hwa Hu, Cing-Han Yang, Han-Jia Lin, Kuan-Hung Li, Tun-Wen Pai, Wen-Der Wang, Yet-Ran Chen
Jazyk: angličtina
Rok vydání: 2018
Předmět:
0301 basic medicine
Computer science
Reference model species
ved/biology.organism_classification_rank.species
RNA-Seq
Biochemistry
Transcriptome
Structural Biology
RefSeq
Biological pathway
lcsh:QH301-705.5
Regulation of gene expression
Genome
Applied Mathematics
High-Throughput Nucleotide Sequencing
Genomics
Plants
Reference Standards
Computer Science Applications
Ultra-conserved orthologous gene
lcsh:R858-859.7
UniProt
DNA microarray
Computational biology
lcsh:Computer applications to medicine. Medical informatics
Models
Biological
03 medical and health sciences
Species Specificity
Animals
Humans
KEGG
Model organism
Differential expression analysis
Molecular Biology
Gene
Selection (genetic algorithm)
030102 biochemistry & molecular biology
Bacteria
ved/biology
Gene Expression Profiling
Research
Computational Biology
Molecular Sequence Annotation
Gene Annotation
030104 developmental biology
lcsh:Biology (General)
Gene ontology
ComputingMethodologies_GENERAL
RNA-seq
Zdroj: BMC Bioinformatics
BMC Bioinformatics, Vol 19, Iss S9, Pp 53-66 (2018)
ISSN: 1471-2105
Popis: 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. Methods 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. Results 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. Conclusions 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. Electronic supplementary material The online version of this article (10.1186/s12859-018-2278-z) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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