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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
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Algorithms designed for compressed-gene-data transformation among gene banks with different references.

Qiuming Luo1, Chao Guo2, Yi Jun Zhang1

  • 1NHPCC/Guangdong Key Laboratory of popular HPC and College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.

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Gene data is exploding, but merging compressed data from different banks is slow. New transformation algorithms (TDM, TPI, TGI) significantly speed up this process, outperforming traditional methods.

Keywords:
DNA sequence compressionGene data transformationReference-based compression

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Science

Background:

  • Gene sequencing costs have decreased, leading to a surge in genomic data.
  • Efficient storage, transmission, and analysis of large-scale genomic data are critical research areas.
  • Reference-based compression is common but hinders data merging from different gene banks due to varied references.

Purpose of the Study:

  • To address the challenge of merging and efficiently sharing genomic data compressed with different references.
  • To develop novel algorithms that accelerate the transformation of data between disparate gene banks.

Main Methods:

  • Analysis of existing compression algorithms to identify similarities and differences.
  • Development of a naive transformation method (TDM).
  • Optimization of TDM to create improved methods: TPI and TGI.

Main Results:

  • Proposed algorithms (TDM, TPI, TGI) are an order of magnitude faster than traditional decompression-recompression workflows.
  • All three algorithms demonstrate excellent time performance.
  • TDM and TPI are suitable for small-scale transformations, while TGI excels with large-scale data.

Conclusions:

  • The developed transformation algorithms offer significant speed improvements for inter-gene bank data sharing.
  • Algorithm selection (TDM, TPI, TGI) depends on the scale of the genomic data being transformed.
  • These methods overcome limitations of traditional data handling in the era of genomic data outbreaks.