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CREMSA: compressed indexing of (ultra) large multiple sequence alignments.

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We developed CREMSA, a novel compression method for large viral genome alignments. This tool significantly reduces file size and speeds up data access for enhanced genomic analysis.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Viral outbreaks necessitate rapid collection and analysis of pathogenic genomes.
  • Large Multiple Sequence Alignments (MSAs) of viral genomes pose storage and analysis challenges due to their size and local variations.
  • Existing sequential compression algorithms are inefficient for MSAs.

Purpose of the Study:

  • To introduce CREMSA (Column-wise Run-length Encoding for MSAs), an efficient compression method for large MSAs.
  • To enable accelerated querying of compressed MSAs without decompression.
  • To facilitate comprehensive statistical analyses, including covariation studies, on massive genomic datasets.

Main Methods:

  • Developed CREMSA, utilizing sparse bitvector representations for compression.
  • Implemented a resorting strategy to improve MSA compressibility.
  • Benchmarked CREMSA performance on a large SARS-CoV-2 MSA.

Main Results:

  • CREMSA compressed a 65 GB MSA (1.9M SARS-CoV-2 genomes) to 22 MB using <0.5 GB RAM.
  • Query access times were reduced to approximately 100 ns.
  • The proposed resorting strategy significantly increased compression ratios with minimal computational overhead.

Conclusions:

  • CREMSA provides a highly efficient solution for compressing and querying large viral MSAs.
  • The method enables faster and more comprehensive genomic data analysis.
  • CREMSA is freely accessible, promoting its adoption in pathogen surveillance and research.