AlgaePath: comprehensive analysis of metabolic pathways using transcript abundance data from next-generation sequencing in green algae
Open accessAbstract
BACKGROUND: 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. DESCRIPTION: AlgaePath is a web-based database that integrates gene information, biological pathways, and NGS datasets for the green algae Chlamydomonas reinhardtii and Neodesmus sp. UTEX 2219-4. Users can search this database to identify transcript abundance profiles and pathway information using five query pages (Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-expression Analysis). The transcript abundance data of 45 and four samples from C. reinhardtii and Neodesmus sp. UTEX 2219-4, respectively, can be obtained directly on pathway maps. Genes that are differentially expressed between two conditions can be identified using Folds Search. The Gene Group Analysis page includes a pathway enrichment analysis, and can be used to easily compare the transcript abundance profiles of functionally related genes on a map. Finally, the Co-expression Analysis page can be used to search for co-expressed transcripts of a target gene. The results of the searches will provide a valuable reference for designing further experiments and for elucidating critical mechanisms from high-throughput data. CONCLUSIONS: AlgaePath is an effective interface that can be used to clarify the transcript response mechanisms in different metabolic pathways under various conditions. Importantly, AlgaePath can be mined to identify critical mechanisms based on high-throughput sequencing. To our knowledge, AlgaePath is the most comprehensive resource for integrating numerous databases and analysis tools in algae. The system can be accessed freely online at http://algaepath.itps.ncku.edu.tw.
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Three web-based resources for algal genomics (pico-PLAZA, AlgaePath, ALCOdb) provide genomic information, intuitive tools for functional genomics, gene co-expression data, and details of gene expression prediction based on metabolic pathways in some microalgae.
“Besides, there are three web-based resources available for algal genomics, including pico-PLAZA (http://bioinformatics.psb.ugent.be/pico-plaza/), AlgaePath (http://algaepath.itps.ncku.edu.tw), and ALCOdb (http://alcodb.jp). These genomics resources provide genomic information, intuitive tools for functional genomics, gene co-expression data, and details of gene expression prediction based on metab”
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Self-citation
- Authors
- Zheng, H.-Q., Chiang-Hsieh, Y.-F., Chien, C.-H., Hsu, B.-K. J., Liu, T.-L., Chen, C.-N. N., & Chang, W.-C.
- Affiliations
- National Cheng Kung University (TW)
- National Sun Yat-sen University (TW)
- Corresponding author
- Chen, Ching-Nen Nathan
- Chang, Wen-Chi
- Venue
- BMC Genomics
- Published
- 2014-03-14
- Volume
- 15
- Issue
- 1
- Pages
- 196-196
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC4028061
- OpenAlex
- W1978858718
- Cited by
- 53
- References
- 38
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 8 · 2023: 5 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 4 · 2018: 3 · 2017: 6 · 2016: 6 · 2015: 3 · 2014: 2
- FWCI
- 2.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Council (NSC99)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Keywords
- metabolic pathway analysis, mRNA levels, green algae, Chlamydomonas reinhardtii, gene expression database, pathway enrichment, co-expression analysis, differentially expressed genes, online databases
- MeSH terms
- Chlorophyta, Biological Evolution, Gene Expression Regulation, Software, Databases, Factual, Computational Biology, Internet, Metabolic Networks and Pathways, High-Throughput Nucleotide Sequencing, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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