ArticleBMC bioinformatics2018
Multiple model species selection for transcriptomics analysis of non-model organisms.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Protocol for Efficient Ribodepletion of Euglena gracilis RNA.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Exploring the molecular landscape of environmental responses in the Antarctic plantFrontiers in plant science · 2026Article
- Comparative Transcriptome Analyses of DifferentGenes · 2022Article
- Insights into the species evolution ofEcology and evolution · 2022Article
- Juxtapose: a gene-embedding approach for comparing co-expression networks.BMC bioinformatics · 2021Article
- Blast2Fish: a reference-based annotation web tool for transcriptome analysis of non-model teleost fish.BMC bioinformatics · 2020Article
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Authors and funding
7 authors.
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Abstract
backgroundTranscriptomic 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.
methodsWe 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.
resultsUsing 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.
conclusionsSelection 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.
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