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MEGAN / MEtaGenome ANalyzer
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Allows users to taxonomically and functionally explore and analyze large-scale microbiome sequencing data. MEGAN is a comprehensive microbiome analysis toolbox for metagenome, meta-transcriptome, amplicon and from other sources data. Users can perform taxonomic, functional or comparative analysis, map reads to reference sequences, reference-based multiple alignments and reference-guided assembly and integrate their own classifications.
Provides a comprehensive easy-to-use framework for automating data retrieval and functional annotation for meta-genomic approaches. The biomartr package implements straightforward functions for bulk retrieval of all genomic data or data for selected genomes, proteomes, coding sequences and annotation files present in databases hosted by the National Center for Biotechnology Information (NCBI) and European Bioinformatics Institute (EMBL-EBI). In addition, biomartr communicates with the BioMart database for functional annotation of retrieved sequences.
A method to detect and annotate novel classes of qnr antibiotic resistance genes in nucleotide sequence data. Qnr-search uses a hidden Markov model with a fragment length-dependent classification rule and has a high sensitivity and specificity, even for sequences as short at 100 nucleotides. This makes the method directly applicable to the immense amount of data generated by the next-generation DNA sequencing techniques. Based on sequence data currently available in the repositories, the method was able to identify all previously reported plasmid-mediated qnr genes as well as the vast majority of the previously reported chromosomal variants. In addition, the method predicted several novel putative qnr genes and some of these were discovered in shotgun metagenomes, which may indicate a large and unknown diversity of qnr genes in uncultured environmental bacteria.
A-GAME / A GAlaxy suite for functional MEtagenomics
Incorporates tools and workflows for the analysis of environmental DNA (eDNA) sequence data. A-GAME is a general bioinformatics workflow management system implemented within Galaxy. The software contains pre-designed workflows that utilize standard tools for data pre-processing, sequence assembly and annotation; as well as custom utilities dedicated to the analysis of functional metagenomics data. It allows the incorporation of most widely used bioinformatics tools. A-GAME can be used to build and customize bioinformatics workflows.
An orthology-based functional classifier by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and >97% on two real metagenomic datasets. In addition, it performed >87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets.
A powerful tool in accurate profiling functions in a metagenomic sample. metaFunction detects all possible functional roles at the low level from a metagenomic sample/community. In the first step a statistical mixture model is proposed at the base of gene codons to estimate the abundances for the candidate functional roles, with sequencing error being considered. As a gene could be involved in multiple biological processes the functional assignment is therefore adjusted by utilizing an error distribution in the second step.
A software platform for automated taxonomic and functional analysis of metagenome data. MetaSAMS includes a pipeline consisting of three different classifiers that perform taxonomic profiling of metagenome sequences. In addition, MetaSAMS implements a functional pipeline based on contigs that automatically assigns functions to predicted coding sequences. MetaSAMS provides tools for statistical and comparative analyses based on taxonomic and functional annotations. It has been successfully applied for the analysis of a biogas-producing microbial community from a biogas-production plant.
ICoVeR / Interactive Contig-bin Verification and Refinement
Allows to visualize genome bins. ICoVeR allows to curate bin assignments based on multiple binning algorithms. It was tested on the refinement disparate of genome bins automatically generated by other binning algorithms for an anaerobic digestion metagenomic dataset. The tool renders the bin refinement process faster and more replicable. It permits to capture the provenance of changes derived in the course of an exploratory task.
MAGpy / Metagenome-Assembled Genomes in Python
Allows characterization of metagenome-assembled genomes (MAGs) using open source and freely available bioinformatics software. MAGpy is a pipeline that enables reproducible analyses, extensibility, integration with high-performance-compute clusters (HPC) and restart capabilities. The software annotates the genomes, predicts putative protein sequences, compares the MAGs to multiple genomic, proteomic and protein family databases, produces several reports and draws a taxonomic tree.
Infers protein-coding genes, and their putative functions from metagenomic datasets. Fun4Me is a pipeline for functional annotation of metagenomes. It is built upon several computational tools developed first for metagenomic sequence analyses: (i) a predictor for protein coding genes, (ii) a software that achieves fast similarity search against reference protein database for metagenomic sequences, and (iii) a pipeline that implements a parsimony approach to biological pathway reconstruction/inference for metagenomes.
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A multi-modular package that is based on the above assumption and automates inferring and/ or comparing the functional characteristics of an environment using taxonomic abundance generated from one or more environmental sample datasets. Vikodak is based upon the assumption that the overall metabolic/functional potential of any given environmental niche is a function of the sum total of genes/proteins/enzymes that are encoded and expressed by various interacting microbes residing in that niche. Vikodak is expected to be an important value addition to the family of existing tools for 16S based function prediction.
A comprehensive stand-alone annotation framework which enables end-users to functionally annotate sequences constituting metagenomic datasets. The COGNIZER framework provides multiple workflow options. A subset of these options employs a novel directed-search strategy which helps in reducing the overall compute requirements for end-users. Multiple search options in COGNIZER provide end-users the flexibility of choosing a homology search protocol based on available compute resources. The COGNIZER framework includes a cross-mapping database that enables end-users to simultaneously derive/infer KEGG, Pfam, GO, and SEED subsystem information from the COG annotations.
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