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Maxam-Gilbert Sequencing01:05

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
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Related Experiment Video

Updated: Jun 3, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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A lossless reference-free sequence compression algorithm leveraging grammatical, statistical, and substitution rules.

Subhankar Roy1,2, Dilip Kumar Maity1, Anirban Mukhopadhyay2

  • 1Department of Computer Science & Engineering, Academy of Technology, Adisaptagram, Hooghly-712121, India.

Briefings in Functional Genomics
|January 8, 2025
PubMed
Summary

A new lossless sequence compressor, GraSS, enhances compression for DNA and RNA data by utilizing sequence-specific features. This method outperforms existing algorithms, offering efficient data compression for genomic research.

Keywords:
DNA and RNA Sequence CompressionFASTAGrammar RulesReference-free Lossless CompressionStatistical RulesSubstitution Rules

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • General-purpose compressors like Gzip and Zstd show suboptimal performance for DNA/RNA sequences due to lack of sequence-specific feature utilization.
  • Existing compressors require time-consuming parameter tuning for effective molecular sequence compression.

Purpose of the Study:

  • To introduce GraSS, a novel reference-free, lossless sequence compressor designed for DNA and RNA data.
  • To leverage grammatical, statistical, and substitution rule-based methods for improved sequence compression efficiency.

Main Methods:

  • GraSS processes raw, FASTA, and multi-FASTA formats, common in molecular sequence databases.
  • The compressor exploits inherent characteristics of DNA and RNA sequences for effective compression.

Main Results:

  • GraSS achieved weighted average compression ratios (WACR) of 4.5 for DNA and 19.6 for RNA sequences.
  • The total compression time (TCT) for the DNA sequence corpus was 246.8 seconds.
  • GraSS demonstrated superior performance compared to advanced algorithms, especially for repetitive sequences, with competitive decompression times and resource usage.

Conclusions:

  • GraSS offers a significant advancement in lossless compression for molecular sequences.
  • The proposed method provides an efficient and effective solution for handling large-scale genomic data.