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Related Concept Videos

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...
RNA-seq03:21

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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...
Sanger Sequencing01:57

Sanger Sequencing

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Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
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DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: May 14, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

A data parallel strategy for aligning multiple biological sequences on multi-core computers.

Xiangyuan Zhu1, Kenli Li, Ahmad Salah

  • 1College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China. hnzxy@hnu.edu.cn

Computers in Biology and Medicine
|February 19, 2013
PubMed
Summary
This summary is machine-generated.

This study accelerates large-scale biological sequence alignment using data parallelism on multi-core computers. The proposed strategy significantly reduces execution time with minimal accuracy loss, benefiting computational biology.

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Last Updated: May 14, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

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Published on: June 28, 2018

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

  • Computational Biology
  • Bioinformatics
  • High-Performance Computing

Background:

  • Large-scale biological sequence alignment is crucial in computational biology.
  • Existing methods face challenges with increasing data volumes.
  • Efficient parallel processing is needed for modern biological sequence analysis.

Purpose of the Study:

  • To develop a general strategy for accelerating multiple sequence alignment methods.
  • To leverage the data parallelism paradigm for high-performance computing.
  • To improve the efficiency of large-scale biological sequence alignment.

Main Methods:

  • Implementation of a data parallelism strategy on multi-core computers.
  • Integration of five different clustering algorithms.
  • Rigorous testing using traditional benchmarks and artificial sequences on an 8-core system.

Main Results:

  • Achieved up to 151-fold improvements in execution time.
  • Reported an average accuracy loss of 2.19%.
  • Demonstrated significant speedup for multiple sequence alignment.

Conclusions:

  • The proposed multi-core-based strategy effectively accelerates large-scale biological sequence alignment.
  • The data parallelism approach offers a viable solution for computational biology challenges.
  • The method provides a significant speedup with acceptable accuracy trade-offs.