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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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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Related Experiment Video

Updated: Jun 10, 2026

Metagenomic Analysis of Silage
08:43

Metagenomic Analysis of Silage

Published on: January 13, 2017

Parallel algorithm research on several important open problems in bioinformatics.

Bei-Fang Niu1, Xian-Yu Lang, Zhong-Hua Lu

  • 1Super Computing Center, Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China. niubf@sccas.cn

Interdisciplinary Sciences, Computational Life Sciences
|July 20, 2010
PubMed
Summary
This summary is machine-generated.

High performance computing accelerates bioinformatics research by enabling complex biological simulations. New parallel algorithms and software are developed for analyzing large datasets, aiding in gene discovery and RNA sequence alignment.

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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Related Experiment Videos

Last Updated: Jun 10, 2026

Metagenomic Analysis of Silage
08:43

Metagenomic Analysis of Silage

Published on: January 13, 2017

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
07:38

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • High-performance computing (HPC) facilitates exploration of complex biological data and large-scale simulations.
  • The development of novel algorithms, particularly parallel ones, is crucial for extracting biological insights from extensive datasets.
  • Supercomputing centers are increasingly involved due to the significant potential impact in this domain.

Purpose of the Study:

  • To design and develop parallel algorithms and software for addressing key bioinformatics challenges.
  • To enhance the analysis of large biological datasets and complex biological systems.
  • To make advanced bioinformatics tools accessible to a broader scientific community.

Main Methods:

  • Development of parallel algorithms for tasks such as RNA sequence structure alignment.
  • Creation of software for gene finding, alternative splicing analysis, and gene expression clustering.
  • Deployment of grid computing service interfaces for developed software on the China National Grid (CNGrid).

Main Results:

  • Successfully designed and developed parallel algorithms and software for critical bioinformatics problems.
  • Enabled efficient analysis of complex biological data, including RNA sequences and gene expression patterns.
  • Provided grid-enabled access to advanced bioinformatics tools via CNGrid.

Conclusions:

  • Parallel computing and bioinformatics are powerful tools for understanding biological systems.
  • The developed software addresses significant challenges in biological data analysis.
  • Future research directions include further algorithm refinement and broader application of these computational tools.