Abstract:The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs). NeLS has been in produc… Show more
“… Figure 4 KMAP workflow and Comparative overview of features in KMAP and other most relevant pipelines for metagenomic data analysis. The pipelines included in comparison are MG-RAST 23 , EBI Metagenomics 10 , Meta-Pipe 24 , MGX 25 , MetaRep 21 and IMG/M 26 . A score of 1 is assigned for features present (green color), 0 for absent (orange) and with limited availability a score of 0.5 is assigned (white).…”
Section: Results and Analysesmentioning
confidence: 99%
“…Currently, several metagenomic data analysis pipelines offering a breadth of useful features already exists. A comparison of KMAP is performed with a few relevant platforms such as MG-RAST 23 , EBI Metagenomics 10 , Meta-Pipe 24 , MGX 25 , MetaRep 21 and IMG/M 26 . Considering presence/absence and extent of implementation of different features categorized as types of input, gene prediction, gene annotation, visualization, comparison of samples and available computational resources, shows KMAP to be the most comprehensive platform thus far available (see Fig.…”
Exponential rise of metagenomics sequencing is delivering massive functional environmental genomics data. However, this also generates a procedural bottleneck for on-going re-analysis as reference databases grow and methods improve, and analyses need be updated for consistency, which require acceess to increasingly demanding bioinformatic and computational resources. Here, we present the KAUST Metagenomic Analysis Platform (KMAP), a new integrated open web-based tool for the comprehensive exploration of shotgun metagenomic data. We illustrate the capacities KMAP provides through the re-assembly of ~ 27,000 public metagenomic samples captured in ~ 450 studies sampled across ~ 77 diverse habitats. A small subset of these metagenomic assemblies is used in this pilot study grouped into 36 new habitat-specific gene catalogs, all based on full-length (complete) genes. Extensive taxonomic and gene annotations are stored in Gene Information Tables (GITs), a simple tractable data integration format useful for analysis through command line or for database management. KMAP pilot study provides the exploration and comparison of microbial GITs across different habitats with over 275 million genes. KMAP access to data and analyses is available at https://www.cbrc.kaust.edu.sa/aamg/kmap.start.
“… Figure 4 KMAP workflow and Comparative overview of features in KMAP and other most relevant pipelines for metagenomic data analysis. The pipelines included in comparison are MG-RAST 23 , EBI Metagenomics 10 , Meta-Pipe 24 , MGX 25 , MetaRep 21 and IMG/M 26 . A score of 1 is assigned for features present (green color), 0 for absent (orange) and with limited availability a score of 0.5 is assigned (white).…”
Section: Results and Analysesmentioning
confidence: 99%
“…Currently, several metagenomic data analysis pipelines offering a breadth of useful features already exists. A comparison of KMAP is performed with a few relevant platforms such as MG-RAST 23 , EBI Metagenomics 10 , Meta-Pipe 24 , MGX 25 , MetaRep 21 and IMG/M 26 . Considering presence/absence and extent of implementation of different features categorized as types of input, gene prediction, gene annotation, visualization, comparison of samples and available computational resources, shows KMAP to be the most comprehensive platform thus far available (see Fig.…”
Exponential rise of metagenomics sequencing is delivering massive functional environmental genomics data. However, this also generates a procedural bottleneck for on-going re-analysis as reference databases grow and methods improve, and analyses need be updated for consistency, which require acceess to increasingly demanding bioinformatic and computational resources. Here, we present the KAUST Metagenomic Analysis Platform (KMAP), a new integrated open web-based tool for the comprehensive exploration of shotgun metagenomic data. We illustrate the capacities KMAP provides through the re-assembly of ~ 27,000 public metagenomic samples captured in ~ 450 studies sampled across ~ 77 diverse habitats. A small subset of these metagenomic assemblies is used in this pilot study grouped into 36 new habitat-specific gene catalogs, all based on full-length (complete) genes. Extensive taxonomic and gene annotations are stored in Gene Information Tables (GITs), a simple tractable data integration format useful for analysis through command line or for database management. KMAP pilot study provides the exploration and comparison of microbial GITs across different habitats with over 275 million genes. KMAP access to data and analyses is available at https://www.cbrc.kaust.edu.sa/aamg/kmap.start.
“…The Nodes guide the direction of ELIXIR in alignment with their own national priorities, as has been described, e.g. by ELIXIR Netherlands ( Eijssen et al , 2015 ), ELIXIR Switzerland ( Baillie Gerritsen et al , 2019 ) and ELIXIR Norway ( Tekle et al , 2018 ). ELIXIR aims to ensure that all life science projects, irrespective of their membership status with respect to ELIXIR, have access to advanced, scalable and long-term sustainable infrastructure across Europe.…”
Section: Building a Stable And Sustainable Infrastructure For Biological Information Across Europementioning
“…The DNA thus obtained is sequenced and analyzed for expression (De Sousa et al, 2018). This appeal is now becoming an extension of all life sciences research (Tekle et al, 2018). Metaproteomics is the protein study derived from environmental samples (Biswas and Sarkar, 2018; Bhatt, 2018).…”
Bioremediation is the degradation potential of microorganisms to dissimilate the complex chemical compounds from the surrounding environment. The genetics and biochemistry of biodegradation processes in datasets opened the way of systems biology. Systemic biology aid the study of interacting parts involved in the system. The significant keys of system biology are biodegradation network, computational biology, and omics approaches. Biodegradation network consists of all the databases and datasets which aid in assisting the degradation and deterioration potential of microorganisms for bioremediation processes. This review deciphers the bio-degradation network, i.e., the databases and datasets (UM-BBD, PAN, PTID, etc.) aiding in assisting the degradation and deterioration potential of microorganisms for bioremediation processes, computational biology and multi omics approaches like metagenomics, genomics, transcriptomics, proteomics, and metabolomics for the efficient functional gene mining and their validation for bioremediation experiments. Besides, the present review also describes the gene editing tools like CRISPR Cas, TALEN, and ZFNs which can possibly make design microbe with functional gene of interest for degradation of particular recalcitrant for improved bioremediation.
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