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Related Concept Videos

Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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. 
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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...
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Overview
Mismatch Repair01:20

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Next-generation Sequencing

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Error correction of high-throughput sequencing datasets with non-uniform coverage.

Paul Medvedev1, Eric Scott, Boyko Kakaradov

  • 1Department of Computer Science and Engineering, University of California, San Diego, CA, USA. pmedvedev@cs.ucsd.edu

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary

Hammer is a new method for sequencing error correction that works on non-uniform datasets, like those from single-cell sequencing. This adaptable algorithm improves accuracy on challenging data while performing comparably on standard datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput sequencing (HTS) generates vast amounts of data, necessitating accurate read processing.
  • Existing error correction tools perform well on uniform datasets but struggle with non-uniform data, such as single-cell sequencing.
  • Addressing error correction in non-uniform datasets is crucial for advancing applications like single-cell genomics.

Purpose of the Study:

  • To develop a novel method for robust sequencing error correction.
  • To specifically address the challenge of correcting reads from non-uniform datasets, including single-cell data.
  • To provide an adaptable algorithm that improves upon existing tools for challenging sequencing data.

Main Methods:

  • Developed Hammer, a method for sequencing error correction.
  • Utilized a combination of a Hamming graph and a probabilistic model for sequencing errors.
  • Designed Hammer to operate without uniformity assumptions on sequencing read distributions.

Main Results:

  • Hammer demonstrates improved performance on non-uniform single-cell sequencing data.
  • The method achieves comparable results to existing tools on standard, multi-cell datasets.
  • Hammer provides an effective solution for error correction in diverse sequencing data types.

Conclusions:

  • Hammer offers a significant advancement in sequencing error correction, particularly for non-uniform data.
  • The algorithm's adaptability makes it suitable for a range of HTS applications.
  • This method enhances the reliability of genomic data analysis from challenging sources like single cells.