NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL

A. May, S. Abeln, M.J. Buijs, J. Heringa, W. Crielaard, B.W. Brandt

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Massively parallel sequencing of microbial genetic markers (MGMs) is used to uncover the species composition in a multitude of ecological niches. These sequencing runs often contain a sample with known composition that can be used to evaluate the sequencing quality or to detect novel sequence variants. With NGS-eval, the reads from such (mock) samples can be used to (i) explore the differences between the reads and their references and to (ii) estimate the sequencing error rate. This tool maps these reads to references and calculates as well as visualizes the different types of sequencing errors. Clearly, sequencing errors can only be accurately calculated if the reference sequences are correct. However, even with known strains, it is not straightforward to select the correct references from databases. We previously analysed a pyrosequencing dataset from a mock sample to estimate sequencing error rates and detected sequence variants in our mock community, allowing us to obtain an accurate error estimation. Here, we demonstrate the variant detection and error analysis capability of NGS-eval with Illumina MiSeq reads from the same mock community. While tailored towards the field of metagenomics, this server can be used for any type of MGM-based reads. NGS-eval is available at http://www.ibi.vu.nl/programs/ngsevalwww/.
Original languageEnglish
JournalNucleic Acids Research
DOIs
Publication statusPublished - 2015

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Microbial Genetics
Genetic Markers
Sequence Analysis
High-Throughput Nucleotide Sequencing
Metagenomics
Databases

Cite this

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title = "NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL",
abstract = "Massively parallel sequencing of microbial genetic markers (MGMs) is used to uncover the species composition in a multitude of ecological niches. These sequencing runs often contain a sample with known composition that can be used to evaluate the sequencing quality or to detect novel sequence variants. With NGS-eval, the reads from such (mock) samples can be used to (i) explore the differences between the reads and their references and to (ii) estimate the sequencing error rate. This tool maps these reads to references and calculates as well as visualizes the different types of sequencing errors. Clearly, sequencing errors can only be accurately calculated if the reference sequences are correct. However, even with known strains, it is not straightforward to select the correct references from databases. We previously analysed a pyrosequencing dataset from a mock sample to estimate sequencing error rates and detected sequence variants in our mock community, allowing us to obtain an accurate error estimation. Here, we demonstrate the variant detection and error analysis capability of NGS-eval with Illumina MiSeq reads from the same mock community. While tailored towards the field of metagenomics, this server can be used for any type of MGM-based reads. NGS-eval is available at http://www.ibi.vu.nl/programs/ngsevalwww/.",
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NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL. / May, A.; Abeln, S.; Buijs, M.J.; Heringa, J.; Crielaard, W.; Brandt, B.W.

In: Nucleic Acids Research, 2015.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL

AU - May, A.

AU - Abeln, S.

AU - Buijs, M.J.

AU - Heringa, J.

AU - Crielaard, W.

AU - Brandt, B.W.

PY - 2015

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AB - Massively parallel sequencing of microbial genetic markers (MGMs) is used to uncover the species composition in a multitude of ecological niches. These sequencing runs often contain a sample with known composition that can be used to evaluate the sequencing quality or to detect novel sequence variants. With NGS-eval, the reads from such (mock) samples can be used to (i) explore the differences between the reads and their references and to (ii) estimate the sequencing error rate. This tool maps these reads to references and calculates as well as visualizes the different types of sequencing errors. Clearly, sequencing errors can only be accurately calculated if the reference sequences are correct. However, even with known strains, it is not straightforward to select the correct references from databases. We previously analysed a pyrosequencing dataset from a mock sample to estimate sequencing error rates and detected sequence variants in our mock community, allowing us to obtain an accurate error estimation. Here, we demonstrate the variant detection and error analysis capability of NGS-eval with Illumina MiSeq reads from the same mock community. While tailored towards the field of metagenomics, this server can be used for any type of MGM-based reads. NGS-eval is available at http://www.ibi.vu.nl/programs/ngsevalwww/.

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