Showing 5 open source projects for "learning linux command line"

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  • 1

    OpenM++

    OpenM++: open source microsimulation platform

    IMPORTANT: ========== Our project home page: https://openmpp.org Our latest source code and release available at: https://github.com/openmpp/main/releases/latest Our documentation available at: https://github.com/openmpp/openmpp.github.io/wiki SourceForge files still exist for historical reason. OpenM++ is an open source microsimulation platform inspired by and compatible with Modgen. OpenM++, compared to its closed source predecessor Modgen, has advantages like portability,...
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  • 2

    NGSbinningpytools

    Python tools for binning NGS scaffolds to various genomes.

    Next generation sequencing has opened new avenues for sequencing prokaryotes which were difficult or either impossible to culture. Now, it is possible to sequence all organisms present in a metagenomic community. However, a critical problem is to determine the host organism for each genomic fragment present in the dataset. This project provides the computational tools for assigning the organismal identity for each genomic scaffold in the given dataset. For more detailed documentation...
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  • 3
    GEPETTO - Gene Prioritization in Java

    GEPETTO - Gene Prioritization in Java

    GEPETTO (GEne Prioritization ExTended TOol)

    GEPETTO (GEne PrioriTization ExTended TOol) is an original open-source framework, distributed under the LGPL license, for gene selection and prioritization on a desktop computer that ensures confidentiality of personal data. It takes advantage of the data integration capabilities in the SM2PH-Central Framework(KD4v,MSV3d,BIRD,..), combined with in-house developed gene prioritization methods. It currently incorporates six prioritization modules, based on gene sequence, protein-protein...
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  • 4
    Hoea is a python module for hierarchical ontology enrichment analysis, which facilitated GO (Gene Ontology)/KO (KEGG Orthology) enrichment analysis at any desktop.
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  • 5
    clusterCons
    An R package implementation of a consensus clustering methodology. This package allows users to perform re-sampling statistics based clustering using multiple clustering algorithms to assess the robustness of both clusters and members of clusters.
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