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On this page
  • Apps
  • Inputs
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  • Tutorial webinar
  • SFA Collaboration Page

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  1. Community Developed Workflows and Tools

Functional and Taxonomic Profiling of MAGs

These tools allow users to examine the genetic potential of a community and investigate the strain frequencies of subpopulations in those communities.

Apps

  • Meta-Decoder Call Variants: Identify polymorphisms within microbiome read sequences against reference genomes.

    • Map Reads to Reference Sequence: Map reads to a reference genome.

    • Call Microbial SNPs: identify single nucleotide polymorphism (SNP) variants based on mapped reads.

  • StrainFinder v1: Determine strain genome sequences from sub-populations of allele frequency.

    • strainFinder Repository

  • FamaProfiling: Generate a functional profile of nitrogen cycle genes or universal single-copy markers for metagenomic read libraries and assembled genomes.

    • Fama Read Profiling

    • Fama Genome Profiling

Inputs

These apps take metagenome-assembled genomes (MAGs) as Genome objects input. See Metagenomic and Community Analysis for an overview of metagenomic workflows.

Narratives

  • Groundwater MAGs from Tian et al., 2020 – provides an example of the workflow using MAGs

    • Tian et al. (2020) Small and mighty: adaptation of superphylum Patescibacteria to groundwater environment drives their genome simplicity. Microbiome 8:51. https://doi.org/10.1186/s40168-020-00825-w

    • Narrative created and made public by Alexey Kazakov, Lawrence Berkeley National Laboratory

  • Metagenome-Assembled Genome Extraction from a Compost Microbiome Enrichment – guidance for creating MAGs.

Tutorial webinar

SFA Collaboration Page

These apps were developed as part of the Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA) Science Focus Area (SFA). To learn more about this collaboration, see: https://www.kbase.us/research/adams-sfa/.

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Last updated 8 months ago

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Tutorial webinar presented by by John-Marc Chandonia (Lawrence Berkeley National Laboratory), Alexey Kazakov (Lawrence Berkeley National Laboratory), and Anni Zhang (Massachusetts Institute of Technology)