title: Parallel-META 2.0: enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization.
abstract: The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.
authors.affiliation: Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao, People's Republic of China.
authors.forename: Xiaoquan
authors.initials: X
authors.lastname: Su
authors.affiliation: School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui Province, People's Republic of China.
authors.forename: Weihua
authors.initials: W
authors.lastname: Pan
authors.affiliation: Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao, People's Republic of China.
authors.forename: Baoxing
authors.initials: B
authors.lastname: Song
authors.affiliation: Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao, People's Republic of China.
authors.forename: Jian
authors.initials: J
authors.lastname: Xu
authors.affiliation: Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and BioEnergy Genome Center, Computational Biology Group of Single Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences. Qingdao, People's Republic of China.
authors.forename: Kang
authors.initials: K
authors.lastname: Ning
mesh_terms: D019295:Computational Biology; D056186:Metagenomics; D012984:Software