Title |
AlgaePath: comprehensive analysis of metabolic pathways using transcript abundance data from next-generation sequencing in green algae
|
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Published in |
BMC Genomics, March 2014
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DOI | 10.1186/1471-2164-15-196 |
Pubmed ID | |
Authors |
Han-Qin Zheng, Yi-Fan Chiang-Hsieh, Chia-Hung Chien, Bo-Kai Justin Hsu, Tsung-Lin Liu, Ching-Nen Nathan Chen, Wen-Chi Chang |
Abstract |
Algae are important non-vascular plants that have many research applications, including high species diversity, biofuel sources, and adsorption of heavy metals and, following processing, are used as ingredients in health supplements. The increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes has made the development of an integrated resource for retrieving gene expression data and metabolic pathway essential for functional analysis and systems biology. In a currently available resource, gene expression profiles and biological pathways are displayed separately, making it impossible to easily search current databases to identify the cellular response mechanisms. Therefore, in this work the novel AlgaePath database was developed to retrieve transcript abundance profiles efficiently under various conditions in numerous metabolic pathways. |
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Spain | 1 | 100% |
Demographic breakdown
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
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Japan | 2 | 3% |
Brazil | 1 | 1% |
France | 1 | 1% |
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Unknown | 73 | 92% |
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Student > Ph. D. Student | 18 | 23% |
Student > Bachelor | 11 | 14% |
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Student > Doctoral Student | 4 | 5% |
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Unknown | 6 | 8% |
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Decision Sciences | 1 | 1% |
Other | 2 | 3% |
Unknown | 8 | 10% |