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Cluster Orfs Specifications

Process Details

  • Name: cluster_orfs
  • Process UUID: f9312a02gvnn1a91fkjl8d0ga9czec
  • Process Group: Annotation

Overview

This process clusters Open Reading Frame (ORF) sequences using MMseqs2, a fast and sensitive sequence clustering tool. The clustering process groups similar ORF sequences together based on sequence identity and coverage thresholds, which is essential for reducing redundancy in large sequence datasets and identifying sequence families.

This process is implemented in Bash, which invokes MMseqs2 for sequence clustering analysis.

Key Functionality

  • Sequence Clustering: Groups similar ORF sequences into clusters based on configurable identity and coverage thresholds
  • Redundancy Reduction: Eliminates redundant sequences from large ORF datasets to create representative sequence sets
  • Flexible Clustering Modes: Supports both standard clustering (easy-cluster) and fast linear clustering (easy-linclust) for different dataset sizes

Input/Output Specification

Inputs

Required Inputs

  • FASTA File
    • Description: Input file containing ORF sequences to be clustered
    • Format: FASTA

Outputs

  • FASTA File

    • Description: Clustered sequences in FASTA format containing representative sequences from each cluster
    • Format: FASTA

  • TSV File

    • Description: Tab-separated file containing cluster membership information and clustering statistics
    • Format: TSV

  • Log File

    • Description: Process execution log containing MMseqs2 output and clustering statistics
    • Format: OUT

Parameters & Settings

These parameters can be adjusted in the Foundry UI when running this process.

  • MMseqs Mode

    • Description: Choose easy-cluster or easy-linclust for larger gene catalogs
    • Available options: easy-cluster (default), easy-linclust

  • Cluster Percent Identity

    • Description: Minimum sequence identity
    • Default value: 0.3

  • Min Cluster Coverage

    • Description: Minimum coverage
    • Default value: 0.9

References & Resources

  • Tool Documentation: MMseqs2 official documentation available at https://github.com/soedinglab/MMseqs2
  • Related Papers: Steinegger, M., & Söding, J. (2017). MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, 35(11), 1026-1028. https://doi.org/10.1038/nbt.3988