LSA – Metagenomic Read Partitioning by Latent Factor analysis

LSA

:: DESCRIPTION

LSA (Latent Strain Analysis) partitions reads into sets likely belonging to the same genome, using the covariance of k-mers across many samples.

::DEVELOPER

The Alm lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

LSA

:: MORE INFORMATION

Citation

Cleary B, Brito IL, Huang K, Gevers D, Shea T, Young S, Alm EJ.
Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning.
Nat Biotechnol. 2015 Oct;33(10):1053-60. doi: 10.1038/nbt.3329. Epub 2015 Sep 14. PMID: 26368049; PMCID: PMC4720164.

MetaCRAM – Lossless Compression Tool for Metagenomic Reads

MetaCRAM

:: DESCRIPTION

MetaCRAM is a pipeline for taxonomy identification and lossless compression of FASTA-format metagenomic reads. It integrates algorithms for taxonomy identification, read alignment, assembly, and finally, a reference-based compression method in a parallel manner.

::DEVELOPER

MetaCRAM team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows/Linux/MacOsX
Perl
Java

:: DOWNLOAD

MetaCRAM

:: MORE INFORMATION

Citation

MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression.
Kim M, Zhang X, Ligo JG, Farnoud F, Veeravalli VV, Milenkovic O.
BMC Bioinformatics. 2016 Feb 19;17(1):94. doi: 10.1186/s12859-016-0932-x

GrammR 1.1.0 – Graphical Representation and Modeling of Metagenomic reads

GrammR 1.1.0

:: DESCRIPTION

GrammR represents metagenomic samples on the Euclidean space to examine similarity amongst samples by studying clusters in the model. Given the matrix of metagenomic counts for samples, this package (1) quantifies dissimilarity between samples using Kendall’s tau-distance, (2) constructs multidimensional models of different dimension, and (3) plots the models for visualization and comparison.

::DEVELOPER

Statistical Genetics and Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows /MacOs
R

:: DOWNLOAD

GrammR

:: MORE INFORMATION

Citation

Bioinformatics. 2015 Jan 20. pii: btv032. [Epub ahead of print]
GrammR: Graphical Representation and Modeling of Count Data with Application in Metagenomics.
Ayyala DN, Lin S

SKraken – Classification of Short Metagenomic Reads based on filtering uninformative k-mers

SKraken

:: DESCRIPTION

SKraken is an efficient approach to accurately classify metagenomic reads against a set of reference genomes, e.g. the NCBI/RefSeq database.

::DEVELOPER

Matteo Comin

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

SKraken

:: MORE INFORMATION

Citation

D. Marchiori, M. Comin
“SKraken: Fast and Sensitive Classification of Short Metagenomic Reads based on Filtering Uninformative k-mers“.
In Proceedings of the 10th International Conference on Bioinformatics Models, Methods and Algorithms (Bioinformatics 2017), pp. 59-67