Dataset Information

Iterative Correction of Reference Nucleotides (iCORN) using second generation sequencing technology.

ABSTRACT:

Motivation

The accuracy of reference genomes is important for downstream analysis but a low error rate requires expensive manual interrogation of the sequence. Here, we describe a novel algorithm (Iterative Correction of Reference Nucleotides) that iteratively aligns deep coverage of short sequencing reads to correct errors in reference genome sequences and evaluate their accuracy.

Results

Using Plasmodium falciparum (81% A + T content) as an extreme example, we show that the algorithm is highly accurate and corrects over 2000 errors in the reference sequence. We give examples of its application to numerous other eukaryotic and prokaryotic genomes and suggest additional applications.

Availability

The software is available at http://icorn.sourceforge.net

SUBMITTER: Otto TD

PROVIDER: S-EPMC2894513 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Publications

Iterative Correction of Reference Nucleotides (iCORN) using second generation sequencing technology.

Otto Thomas D TD Sanders Mandy M Berriman Matthew M Newbold Chris C

Bioinformatics (Oxford, England) 20100618 14

<h4>Motivation</h4>The accuracy of reference genomes is important for downstream analysis but a low error rate requires expensive manual interrogation of the sequence. Here, we describe a novel algorithm (Iterative Correction of Reference Nucleotides) that iteratively aligns deep coverage of short sequencing reads to correct errors in reference genome sequences and evaluate their accuracy.<h4>Results</h4>Using Plasmodium falciparum (81% A + T content) as an extreme example, we show that the algo ...[more]

PMID: 20562415

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Dataset Information

Iterative Correction of Reference Nucleotides (iCORN) using second generation sequencing technology.

Motivation

Results

Availability

Publications

Iterative Correction of Reference Nucleotides (iCORN) using second generation sequencing technology.

Similar Datasets

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