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Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers
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AC: A Compression Tool for Amino Acid Sequences.

Morteza Hosseini1, Diogo Pratas2, Armando J Pinho2

  • 1IEETA/DETI, University of Aveiro, Aveiro, Portugal. seyedmorteza@ua.pt.

Interdisciplinary Sciences, Computational Life Sciences
|February 6, 2019
PubMed
Summary
This summary is machine-generated.

We developed AC, a new lossless compression method for amino acid sequences. AC achieves superior bit-rates and faster compression speeds, outperforming existing protein compressors.

Keywords:
CompressionFinite-context modelKolmogorov complexityProteinSubstitutional tolerant Markov model

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Protein sequencing technologies generate vast amounts of data.
  • Efficient storage and transmission of this data are critical challenges.
  • Existing compression methods may not be optimal for biological sequence data.

Purpose of the Study:

  • To introduce AC, a novel lossless compression algorithm for amino acid sequences.
  • To evaluate the performance of AC against existing compression methods.
  • To analyze the compressibility of protein sequences from various biological domains.

Main Methods:

  • AC utilizes a combination of finite-context models and substitutional tolerant Markov models.
  • The algorithm was compared with general-purpose and specialized protein compressors.
  • Compressibility analysis was performed on a large dataset of protein sequences.

Main Results:

  • AC achieves superior bit-rates compared to other protein compressors.
  • AC demonstrates a nine-fold increase in compression speed over the paq8l competitor.
  • Sequence compressibility varies across domains: viruses are hardest to compress, followed by archaea/bacteria, with eukaryota being easiest.

Conclusions:

  • AC offers a state-of-the-art solution for lossless compression of protein sequence data.
  • The method provides significant improvements in both compression efficiency and speed.
  • Understanding sequence compressibility across different domains can inform data management strategies.