title: AlgaePath: comprehensive analysis of metabolic pathways using transcript abundance data from next-generation sequencing in green algae.
abstract: 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.
  
  
  METHODS
  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.
authors.forename: Han-Qin
authors.initials: HQ
authors.lastname: Zheng
authors.forename: Yi-Fan
authors.initials: YF
authors.lastname: Chiang-Hsieh
authors.forename: Chia-Hung
authors.initials: CH
authors.lastname: Chien
authors.forename: Bo-Kai Justin
authors.initials: BK
authors.lastname: Hsu
authors.forename: Tsung-Lin
authors.initials: TL
authors.lastname: Liu
authors.affiliation: Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan. nathanc@mail.nsysu.edu.tw.
authors.forename: Ching-Nen Nathan
authors.initials: CN
authors.lastname: Chen
authors.forename: Wen-Chi
authors.initials: WC
authors.lastname: Chang
mesh_terms: D005075:Biological Evolution; D000460:Chlorophyta / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D005786:Gene Expression Regulation; D059014:High-Throughput Nucleotide Sequencing; D020407:Internet; D053858:Metabolic Networks and Pathways; D012984:Software; D059467:Transcriptome