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Related Concept Videos

Introduction to R01:11

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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?

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.
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RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
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Ribosome Profiling02:24

Ribosome Profiling

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Related Experiment Video

Updated: Jun 30, 2026

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

R/parallel--speeding up bioinformatics analysis with R.

Gonzalo Vera1, Ritsert C Jansen, Remo L Suppi

  • 1Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona, Bellaterra, Spain. gonzalo.vera@rparallel.org

BMC Bioinformatics
|September 24, 2008
PubMed
Summary
This summary is machine-generated.

Bioinformaticians can now speed up data analysis using R/parallel, an R add-on package. This tool simplifies parallel computing, reducing processing and development time on multicore processors without altering existing algorithms.

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Last Updated: Jun 30, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Published on: May 28, 2021

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Computing

Background:

  • R is a popular tool for bioinformaticians due to its extensive analytical methods.
  • High-throughput screening generates large datasets, leading to long processing times in R.
  • Existing parallel computing tools for R require significant modifications and expertise.

Purpose of the Study:

  • To develop a user-friendly R add-on package for parallel computing.
  • To enable bioinformaticians to leverage multicore processors for faster data analysis.
  • To reduce the time and skill barrier for implementing parallel computations in R.

Main Methods:

  • Designed and implemented the R/parallel add-on package for R.
  • Integrated parallel execution capabilities into R through a simple function.
  • Enabled direct integration with existing R packages without algorithm modification.

Main Results:

  • R/parallel provides user-friendly parallel computing capabilities for R.
  • Automates parallel execution of loops, utilizing multicore processor power.
  • Achieved approximate N-fold reduction in processing time, where N is the number of processor cores.

Conclusions:

  • R/parallel significantly reduces bioinformaticians' data analysis time.
  • Shortens development time by avoiding reimplementation of existing R methods.
  • Speeds up computations on standard desktop computers, enhancing efficiency.