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LSA specifications


Unique identifier OMICS_19019
Name LSA
Alternative name Latent Strain Analysis
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Input data A collection of multiple sequenced metagenomes.
Operating system Unix/Linux
Programming languages Python
License MIT License
Computer skills Advanced
Stability Stable
NumPy, SciPy, Gensim, GNU Parallel, Pyro4
Maintained Yes




No version available



  • person_outline Eric Alm

Publication for Latent Strain Analysis

LSA citations


A Review of Bioinformatics Tools for Bio Prospecting from Metagenomic Sequence Data

Front Genet
PMCID: 5337752
PMID: 28321234
DOI: 10.3389/fgene.2017.00023

[…] enomes and present them as distant to the pathogenic ones. Thus, CONCOCT has been suggested for extracting biologically important information and could possibly contribute to recovery after infection.Latent strain analysis is a pre-assembly algorithm which aims to bin short sequenced reads into microbial categories. This method is based on the assumption that reads which belong to the same organis […]


2015 Brainhack Proceedings

PMCID: 5103253
DOI: 10.1186/s13742-016-0147-0

[…] ctly load Nifti images at client-side and support some AFNI features, such as voxel clustering. Availability of supporting data More information about this project can be found at: Competing interests None. Author’s contributions ASH wrote the software, and ASH, FM, ARF, and AB wrote the report. Acknowledgements Report from 2015 Brainhack Americas (MX). The […]


Tracking Strains in the Microbiome: Insights from Metagenomics and Models

Front Microbiol
PMCID: 4871868
PMID: 27242733
DOI: 10.3389/fmicb.2016.00712

[…] mes do not yet exist. Several methods overcome this limitation, enabling de novo assembly of genomes across metagenomic samples (Boisvert et al., ; Pell et al., ; Howe et al., ; Cleary et al., ). The Latent Strain Analysis method (Cleary et al., ) is notable because species of very low abundance (as low as 0.00001% in one case) distributed across many samples can be successfully assembled.Both ass […]


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LSA institution(s)
Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
LSA funding source(s)
Supported in part by the Rasmussen Family Foundation; by the National Human Genome Research Institute, grant number U54HG003067; the Center for Environmental Health Sciences at MIT; the Fijian Ministry of Health and by the Earth Institute at Columbia University

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