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RepeatMasker

repository·master·Indexed 43 days ago

https://github.com/dfam-consortium/repeatmasker

A tool for screening DNA sequences for interspersed repeats and low complexity sequences. It provides detailed annotations and masked sequence outputs using alignment engines such as RMBlast, crossmatch, NHMMER, and ABBLAST. It integrates with FamDB for automated, species-specific queries against the Dfam database.

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What's inside RepeatMasker

  1. What is RepeatMasker

    master

    RepeatMasker is a program used to screen DNA sequences for interspersed repeats and low complexity DNA sequences.

    It produces two main outputs:

    1. A detailed annotation of the repeats found in the query sequence.
    2. A modified version of the query sequence where annotated repeats are masked (by default, replaced with Ns).

    RepeatMasker performs sequence comparisons using one of several alignment engines:

    • RMBlast: A variant of NCBI blastn supporting substitution matrices, complexity-adjusted scoring, and masklevel filtering.
    • crossmatch: An efficient implementation of the Smith-Waterman-Gotoh algorithm.
    • NHMMER: A profile Hidden Markov Model (HMM) aligner.
    • ABBLAST: A blast variant.

  2. How RepeatMasker handles libraries

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    RepeatMasker supports different library formats depending on the search engine used:

    • FASTA files: Used with RMBlast, crossmatch, or ABBLAST via the -lib option.
    • Profile HMM files: Used with NHMMER.

    Automated Species-Specific Queries with FamDB

    For automated queries against the Dfam database, it is highly recommended to use FamDB. FamDB manages library partitions and can generate organism-specific consensus or HMM libraries on the fly.

    If you need to combine RepBase sequences with Dfam, this is handled entirely through the FamDB installation process.

  3. How the RepeatMasker library cache works

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    To speed up repeated searches, RepeatMasker creates a cache of species-specific libraries extracted from FamDB. It attempts to write to the first writable directory in this order:

    1. The Libraries/ subdirectory of the RepeatMasker installation.
    2. The .RepeatMaskerCache subdirectory of the user's home directory.
    3. The temporary processing directory RM_# created alongside the sequence file (this is removed at the end of the run).

    Note: If the cache cannot be written to the first two locations, libraries will be rebuilt on every run, which significantly slows down jobs on shorter sequences.

  4. Install RepeatMasker

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    Follow these steps to install RepeatMasker:

    1. Unpack the distribution: Extract the tarball into your desired location (e.g., /usr/local/). Warning: Do not extract into a directory that already contains a RepeatMasker subdirectory to avoid overwriting files.

    2. Set up Libraries: RepeatMasker does not ship with a TE (Transposable Element) library. You have two options:

      • Use a custom library via the -lib flag (e.g., -lib mylib.fa).
      • Install FamDB and Dfam library partitions for automated species-specific annotation. Download partitions from the FamDB releases page and install them into the RepeatMasker Libraries/famdb/ directory.

    3. Configure: Run the configuration script to link your search engines and dependencies.

    # 1. Unpack
% cp RepeatMasker-open-4-#-#.tar.gz /usr/local
% cd /usr/local
% gunzip RepeatMasker-open-4-#-#.tar.gz
% tar xvf RepeatMasker-open-4-#-#.tar

# 2. Configure
% perl ./configure

  5. Prerequisites for installing RepeatMasker

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    Before installing RepeatMasker, ensure your environment meets the following requirements:

    • Operating System: UNIX-based.
    • Perl: version 5.8.0 or higher.
    • Python: version 3.0 or higher.
    • TRF (Tandem Repeat Finder): version 4.09 or higher.
    • Search Engine: At least one of the following must be installed:
      • RMBlast
      • crossmatch
      • NHMMER (requires Dfam/FamDB)
      • ABBLAST
    • FamDB (Highly Recommended): For automated, species-specific queries against the Dfam database. FamDB manages Dfam library partitions and generates organism-specific consensus or HMM libraries on the fly.