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Related Concept Videos

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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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...
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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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.
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.
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Related Experiment Video

Updated: May 17, 2026

Pyrosequencing: A Simple Method for Accurate Genotyping
13:06

Pyrosequencing: A Simple Method for Accurate Genotyping

Published on: January 8, 2008

High Performance Multiple Sequence Alignment System for Pyrosequencing Reads from Multiple Reference Genomes.

Fahad Saeed1, Alan Perez-Rathke, Jaroslaw Gwarnicki

  • 1Department of Computer Science, University of Illinois at Chicago, IL USA.

Journal of Parallel and Distributed Computing
|November 6, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces P-Pyro-Align, a scalable parallel algorithm for aligning short reads from pyrosequencing. It accurately handles erroneous reads from single or multiple reference genomes, improving genome resequencing efficiency.

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Last Updated: May 17, 2026

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Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Short reads from pyrosequencing pose challenges for genome resequencing due to mapping limitations.
  • Existing multiple sequence alignment (MSA) methods are inefficient and do not account for read positioning in large-scale genome analysis.

Purpose of the Study:

  • To develop a scalable parallel algorithm for aligning short reads from single or multiple reference genomes.
  • To address the limitations of pairwise mapping and existing MSA methods for pyrosequencing data.

Main Methods:

  • Developed a parallel algorithm, P-Pyro-Align, utilizing domain decomposition for scalability.
  • Implemented the algorithm on a cluster of workstations using the Message Passing Interface (MPI) library.
  • Analyzed execution time, alignment quality, and performance with varying problem sizes and processor counts.

Main Results:

  • P-Pyro-Align accurately aligns erroneous reads from single or multiple reference genomes.
  • Achieved high-quality multiple alignments for up to 0.5 million reads.
  • Demonstrated high scalability with super-linear speedups as the number of processors increased.

Conclusions:

  • P-Pyro-Align offers an efficient and accurate solution for aligning large-scale pyrosequencing data.
  • The algorithm effectively handles reads from multiple haplotypes and reference genomes.
  • P-Pyro-Align significantly advances the capabilities of genome resequencing analysis.