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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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Modern Molecular Taxonomy

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Related Experiment Video

Updated: Jul 13, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Clustered sequence representation for fast homology search.

Michael Cameron1, Yaniv Bernstein, Hugh E Williams

  • 1School of Computer Science and Information Technology, RMIT University, Melbourne, Australia. mcam@cs.rmit.edu.au

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|August 9, 2007
PubMed
Summary

Managing sequence databank redundancy is improved with a novel clustering method. This approach reduces collection size and search time by 27% and 22% respectively, with no accuracy loss.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Information Retrieval

Background:

  • Sequence databanks like GenBank contain significant redundancy.
  • Efficiently managing and searching large sequence datasets is a challenge.

Purpose of the Study:

  • To develop a novel method for managing redundancy in sequence databanks.
  • To improve search efficiency and reduce storage requirements without compromising accuracy.

Main Methods:

  • Storing clusters of near-identical sequences as a representative union-sequence and edits.
  • Utilizing fingerprinting for efficient clustering, adapted from Information Retrieval.
  • Integrating the approach into FSA-BLAST, an Open Source version of BLAST.

Main Results:

  • A 27% reduction in collection size and a 22% decrease in search time when using BLAST.
  • Clustering method is ten times faster than CD-HIT on the GenBank nonredundant protein database.
  • FSA-BLAST demonstrates twice the speed of NCBI-BLAST with comparable accuracy.

Conclusions:

  • The novel approach effectively manages sequence databank redundancy.
  • Fingerprinting-based clustering offers significant speed advantages.
  • FSA-BLAST provides a faster and efficient alternative for sequence database searching.