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LotuS2

Updated LotuS pipeline, Dada2 + Unoise clustering, improved speed, LULU and crosstalk postprocessing, offtarget removal, updated tax assignment, better phylogenies and a more rounded user experience.

Available from lotus2.earlham.ac.uk

LotuS

Fast, easy-to-use and highly accurate amplicon sequencing pipeline. Choose between several highly accurate OTU calling algorithms, choose from general or spezialized databases to annotate these taxonomically and create a phylogeny of OTUs - all in on single command.

Available with self-installer from http://psbweb05.psb.ugent.be/lotus/

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MG-TK

Pipeline to process metagenomic data from raw reads to taxonomic and functional abudnance matrices. MG-TK offers assembly-dependent (gene catalog) and assembly-independent (direct mapping to appropriate databases) workflows. Previously named “MATAFILER”.

https://github.com/hildebra/MG-TK

RTK

RTK (rarefaction toolkit) provides fast and scalable rarefaction and diversity estimation for numerical ecology data (e.g. species abundance tables). RTK uses significantly (~100x) less memory while being ~10x faster than competing software. Accessible through R interface - install.package(“rtk”) - or C++ binary:

https://github.com/hildebra/Rarefaction

KSGP

KSGP 16S database for Bacteria, Eukaryotes and Archaeae. Has a significantly higher taxonomic coverage for Archaea and uses GTDB conform taxonomy. Fully integrated with LotuS2.

Database webpage:

KSGP Database (earlham.ac.uk)

AdhesiomeR

AdhesiomeR can be used to classify adhesion related genes in E coli genomes. Available in R or through webserver to directly upload and explore genomes:

https://adhesiomer.quadram.ac.uk/

ksidorczuk/adhesiomeR (github.com)

clusterMAGs

An algorithm to cluster MAGs (metagenomic assembled genomes) into metagenomic species (MGS), based on a gene catalogue and universal marker genes. In our benchmarks, this approach is more robust to (biological/artificial) differences in pangenomes and chimeric missassemblies.

clusterMAGs on github

cvaNMF

cvaNMF (chiva-enemef) is an implementation of bicrossvalidation for Non-negative Matrix Factorisation (NMF) rank selection, along with methods for analysis and visualisation of NMF decomposition.

cvaNMF