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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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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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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...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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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
Although all next-generation methods use different technologies, they all share a set of standard features.

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Related Experiment Video

Updated: May 17, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin1, Carrie A Davis, Felix Schlesinger

  • 1Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. dobin@cshl.edu

Bioinformatics (Oxford, England)
|October 30, 2012
PubMed
Summary
This summary is machine-generated.

We developed STAR, a fast and accurate RNA-seq aligner that significantly improves mapping speed and precision for large datasets. STAR enables sensitive detection of splice junctions and fusion transcripts.

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AQRNA-seq for Quantifying Small RNAs
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AQRNA-seq for Quantifying Small RNAs

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Last Updated: May 17, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

AQRNA-seq for Quantifying Small RNAs
05:12

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate alignment of high-throughput RNA-sequencing (RNA-seq) data is critical but challenging due to transcript complexity and sequencing technology limitations.
  • Existing RNA-seq aligners often exhibit high error rates, slow speeds, and biases, hindering comprehensive transcriptome analysis.

Purpose of the Study:

  • To develop a novel algorithm and software for highly accurate and efficient alignment of large-scale RNA-seq datasets.
  • To address the limitations of current RNA-seq aligners in terms of speed, accuracy, and detection of complex splicing events.

Main Methods:

  • Developed the Spliced Transcripts Alignment to a Reference (STAR) software, employing a novel algorithm based on sequential maximum mappable seed search, seed clustering, and stitching.
  • Implemented STAR as standalone C++ code, making it freely available as open-source software.

Main Results:

  • STAR demonstrates over 50-fold improvement in mapping speed compared to other aligners, processing 550 million 2x76 bp paired-end reads per hour on a 12-core server.
  • Achieved enhanced alignment sensitivity and precision, enabling unbiased de novo detection of canonical and non-canonical splice junctions, as well as chimeric transcripts.
  • Experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, confirming STAR's high mapping precision.

Conclusions:

  • STAR provides a significant advancement in RNA-seq data analysis, offering unprecedented speed and accuracy for large datasets.
  • The software facilitates comprehensive transcriptome profiling, including the discovery of novel splicing events and fusion transcripts.