دورية أكاديمية
Multiple model species selection for transcriptomics analysis of non-model organisms
| العنوان: | Multiple model species selection for transcriptomics analysis of non-model organisms |
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| المؤلفون: | Tun-Wen Pai, Kuan-Hung Li, Cing-Han Yang, Chin-Hwa Hu, Han-Jia Lin, Wen-Der Wang, Yet-Ran Chen |
| المصدر: | BMC Bioinformatics, Vol 19, Iss S9, Pp 53-66 (2018) |
| بيانات النشر: | BMC, 2018. |
| سنة النشر: | 2018 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Biology (General) |
| مصطلحات موضوعية: | RNA-seq, Reference model species, Differential expression analysis, Ultra-conserved orthologous gene, Gene ontology, Biological pathway, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5 |
| الوصف: | 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. 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2105 |
| Relation: | http://link.springer.com/article/10.1186/s12859-018-2278-z; https://doaj.org/toc/1471-2105 |
| DOI: | 10.1186/s12859-018-2278-z |
| URL الوصول: | https://doaj.org/article/f8acc630ccbe4efa86345369d7734dce |
| رقم الأكسشن: | edsdoj.f8acc630ccbe4efa86345369d7734dce |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14712105 |
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| DOI: | 10.1186/s12859-018-2278-z |