NGLess is a domain-specific language for next-generation sequencing data processing with a focus on metagenomics. It implements the NG-meta-profiler workflow, which efficiently and reproducibly performs sequence preprocessing, mapping to reference databases, filtering of the mapping results, and profiling.

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Version: 1.0.1

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Coelho et al., NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language. 1. 2019. PMID: 31159881

proGenomes is a database providing consistent taxonomic and functional annotations for 87,920 bacterial and archaeal genomes belonging to over 12,000 species. These can be interactively explored and downloaded, whereby subsets can be customized, e.g. taxonomic clades, representatives of each species or habitat-specific organisms.

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Version: 2.0

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Mende et al., proGenomes2: an improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes. D1. 2020. PMID: 31647096

Mende et al., proGenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes. D1. 2017. PMID: 28053165

Metagenomic operational taxonomic units (mOTUs) enable high-accuracy taxonomic profiling of known (sequenced) and unknown microorganisms at species-level resolution from shotgun metagenomic or metatranscriptomic data. The method clusters single-copy phylogenetic marker gene sequences from metagenomes and reference genomes into mOTUs to quantify their abundances in meta-omics data with very high precision and recall.

Homepage Biological data Source code Container Documentation

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Version: 2.5.1

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Milanese et al., Microbial abundance, activity and population genomic profiling with mOTUs2. 1. 2019. PMID: 30833550

Sunagawa et al., Metagenomic species profiling using universal phylogenetic marker genes. 12. 2013. PMID: 24141494

eggNOG is a database of nested orthologous gene groups (NOGs) inferred using unsupervised clustering applied to >5,000 complete genomes followed by comprehensive characterization and analysis of the resulting gene families. eggNOG provides orthologous group assignments at 379 different taxonomic levels as well as multiple sequence alignments, maximum-likelihood trees and broad functional annotations for each group accessible via a web interface or through bulk download.

Homepage Biological data Documentation

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Version: 5.0

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Huerta-Cepas et al., eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. D1. 2019. PMID: 30418610

Huerta-Cepas et al., eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. D1. 2016. PMID: 26582926

Powell et al., eggNOG v4.0: nested orthology inference across 3686 organisms. Database issue. 2014. PMID: 24297252

Powell et al., eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Database issue. 2012. PMID: 22096231

Muller et al., eggNOG v2.0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations. Database issue. 2010. PMID: 19900971

Jensen et al., eggNOG: automated construction and annotation of orthologous groups of genes. Database issue. 2008. PMID: 17942413

iPath is a web-based tool for the visualization and analysis of cellular pathways. Based on functional annotations (such as KEGG), it provides pathway maps for primary cellular metabolism as well as for some additional secondary metabolite synthesis and regulatory pathways. Users can map their own data onto these pathway maps. Due to its navigation and customization functions, iPATH allows users to easily explore and analyze the functional and metabolic capabilities of their (meta-)genomic data sets.

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Version: 3.0

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Darzi et al., iPath3.0: interactive pathways explorer v3. W1. 2018. PMID: 29718427

Yamada et al., iPath2.0: interactive pathway explorer. Web Server issue. 2011. PMID: 21546551

Letunic et al., iPath: interactive exploration of biochemical pathways and networks. 3. 2008. PMID: 18276143

Interactive Tree Of Life (iTOL) is an online tool for the display and manipulation of phylogenetic trees. It provides a large variety of tree layouts, drawing and annotation features including circular tree layout. iTOL is well-suited for a wide range of tree sizes up to several thousand leaves. Tree displays can be exported in several graphical formats, both bitmap and vector based.

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Version: 5.0

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Letunic & Bork, Interactive Tree Of Life (iTOL) v4: recent updates and new developments. W1. 2019. PMID: 30931475

Letunic & Bork, Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. W1. 2016. PMID: 27095192

Letunic & Bork, Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy. Web Server issue. 2011. PMID: 21470960

Letunic & Bork, Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. 1. 2007. PMID: 17050570

Enterotypes are densely populated regions in a high-dimensional space of microbiome community composition, by which human individuals can be stratified (Arumugam, Raes et al. Nature 2011). Computational methods to detect and characterise enterotypes in any dataset, either to reproduce previous reports or determine enterotypes in new studies, are provided and explained.

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Version: 2.0

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Costea et al., Enterotypes in the landscape of gut microbial community composition. 1. 2018. PMID: 29255284

Arumugam et al., Enterotypes of the human gut microbiome. Nature 7346. 2011. PMID: 21508958

SIAMCAT is a modular R toolbox, which offers machine learning and statistical testing workflows enabling the user to associate microbial community profiles with host phenotypes, such as disease states in clinical case-control studies. SIAMCAT combines statistical rigor with flexible customization for training and evaluating several machine learning classifiers. From these, microbiome biomarkers or signatures can be extracted and validated.

Homepage Bioconductor Source code Vignette

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Version: 1.9.0

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Wirbel et al., SIAMCAT: user-friendly and versatile machine learning workflows for statistically rigorous microbiome analyses bioRxiv. 2020. doi:10.1101/2020.02.06.931808