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Related Concept Videos

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
Although all next-generation methods use different technologies, they all share a set of standard features.
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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...
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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Related Experiment Video

Updated: Jun 17, 2026

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Next-generation bioinformatics: using many-core processor architecture to develop a web service for sequence

Sergio Gálvez1, David Díaz, Pilar Hernández

  • 1Department Lenguajes y Ciencias de la Computación, Universidad de Málaga 29071 Málaga, Spain. galvez@uma.es

Bioinformatics (Oxford, England)
|January 19, 2010
PubMed
Summary
This summary is machine-generated.

This study presents a new parallelized Needleman-Wunsch algorithm for faster sequence alignment on multi-core processors. The optimized bioinformatics approach significantly accelerates data analysis, saving valuable research time.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • Bioinformatics algorithms and computing power are critical bottlenecks in analyzing large datasets from next-generation sequencing.
  • Many-core microprocessors offer significant computational power but require parallelized algorithms for effective utilization.

Purpose of the Study:

  • To develop a novel parallelization of the Needleman-Wunsch (NW) sequence alignment algorithm.
  • To leverage the power of many-core processors for accelerating bioinformatics analyses.

Main Methods:

  • Developed a new parallelization of the Needleman-Wunsch algorithm from scratch.
  • Targeted the 64-core Tile64 microprocessor for implementation.
  • Evaluated performance on a standalone personal computer (PC).

Main Results:

  • Achieved optimal sequence alignment up to 20 times faster than the non-parallelized version.
  • Demonstrated unprecedented performance for a standalone PC.
  • Successfully parallelized a core bioinformatics algorithm for multi-core architectures.

Conclusions:

  • The parallelized NW algorithm significantly enhances the speed of sequence alignment.
  • This advancement addresses computational bottlenecks in processing large-scale biological data.
  • The developed algorithm offers a valuable tool for the scientific community, available as a free web service and open-source code.