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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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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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DNA isolation protocols can be fast and straightforward or complex and time-consuming depending on the type and quality of DNA required for further processing. For example, plasmid DNA extraction is a bit more complicated than genomic DNA extraction because of the need for an appropriate lysis method to separate plasmid DNA from gDNA during isolation. However, for specific applications, such as long-range DNA sequencing that require a good yield of high- quality DNA samples, we need to follow...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Related Experiment Video

Updated: Jul 3, 2025

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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A randomized optimal k-mer indexing approach for efficient parallel genome sequence compression.

Subhankar Roy1, Anirban Mukhopadhyay2

  • 1Department of Computer Science and Engineering, Academy of Technology, Adisaptagram, Hooghly 712121, West Bengal, India.

Gene
|February 11, 2024
PubMed
Summary

Efficient genome compression is crucial for handling massive Next Generation Sequencing (NGS) data. Our new algorithm, RGCOK, significantly reduces the time needed to find optimal k-mer lengths for compressing large genome sequences.

Keywords:
FASTAGenome sequence formatsHashingNext-generation sequencingOptimal k-mer lengthRandomization approachReference-basedk-mer indexing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Next Generation Sequencing (NGS) generates vast amounts of genomic data, necessitating efficient compression methods.
  • Current compression algorithms face challenges with processing, storage, transmission, and analysis of large-scale genome sequences.
  • Existing k-mer hash indexing systems can be time-consuming due to their decision-making processes.

Purpose of the Study:

  • To propose an efficient two-phase reference genome compression algorithm using optimal k-mer length (RGCOK).
  • To leverage inter-chromosomal similarity for improved reference-based compression.
  • To reduce the computational time required for finding optimal k-mer lengths in genome compression.

Main Methods:

  • Developed a two-phase reference genome compression algorithm (RGCOK).
  • Employed a randomization method and hashing to identify the optimal k-mer length for sequence matching.
  • Evaluated performance on diverse datasets including SARS-CoV-2, Homo sapiens, and other species sequences using cloud computing.

Main Results:

  • RGCOK significantly reduces the time for optimal k-mer length determination.
  • Optimal k-mer finding time with RGCOK is approximately 45.28 minutes.
  • This is substantially faster than existing state-of-the-art algorithms (HiRGC, SCCG, HRCM), which range from 58 minutes to 8.97 hours.

Conclusions:

  • RGCOK offers a more time-efficient approach to reference genome compression.
  • The algorithm effectively utilizes optimal k-mer length identification for faster compression.
  • RGCOK presents a promising solution for managing and analyzing large-scale genomic datasets.