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Related Concept Videos

Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
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.
Since the...
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: Jul 16, 2026

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Bam-readcount - rapid generation of basepair-resolution sequence metrics.

Ajay Khanna1, David E Larson2,3, Sridhar Nonavinkere Srivatsan1

  • 1Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO.

Journal of Open Source Software
|July 15, 2026
PubMed
Summary

Bam-readcount provides detailed nucleotide-level sequencing data. This utility aids in filtering genomic mutations and is valuable for variant detection and diverse research areas like tumor evolution and infectious disease tracking.

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Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Related Experiment Videos

Last Updated: Jul 16, 2026

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Variant calling in genomics requires accurate, low-level sequencing data.
  • Existing tools may have limitations in resolving ambiguities between different variant callers.
  • Diverse biological and ecological research necessitates detailed genomic insights.

Purpose of the Study:

  • To introduce and describe the bam-readcount utility.
  • To highlight its utility in processing sequencing data for variant analysis.
  • To showcase its broad applicability across various scientific disciplines.

Main Methods:

  • Utilizes bam-readcount to extract nucleotide-level information from sequencing data.
  • Applies the generated metrics as input for variant detection algorithms.
  • Leverages the tool for comparative analysis between different variant callers.

Main Results:

  • Bam-readcount generates essential low-level sequencing metrics.
  • These metrics effectively assist in filtering genomic mutation calls.
  • The utility proves valuable for resolving ambiguities in variant detection.

Conclusions:

  • Bam-readcount is a versatile tool for detailed genomic analysis.
  • Its outputs are crucial for enhancing variant detection accuracy.
  • The tool's applicability extends to fields such as cancer genomics, single-cell studies, and epidemiology.