CoChat
Sign Up
Cited by 1,357Genomics and Phylogenetic StudiesFractal and DNA sequence analysisGenetics, Bioinformatics, and Biomedical Research

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data

Open access
Cox, Murray P, Peterson, Daniel A, Biggs, Patrick J·2010·BMC Bioinformatics
DOI 10.1186/1471-2105-11-485PMC PMC2956736
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Illumina's second-generation sequencing platform is playing an increasingly prominent role in modern DNA and RNA sequencing efforts. However, rapid, simple, standardized and independent measures of run quality are currently lacking, as are tools to process sequences for use in downstream applications based on read-level quality data. RESULTS: We present SolexaQA, a user-friendly software package designed to generate detailed statistics and at-a-glance graphics of sequence data quality both quickly and in an automated fashion. This package contains associated software to trim sequences dynamically using the quality scores of bases within individual reads. CONCLUSION: The SolexaQA package produces standardized outputs within minutes, thus facilitating ready comparison between flow cell lanes and machine runs, as well as providing immediate diagnostic information to guide the manipulation of sequence data for downstream analyses.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/1471-2105-11-485

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Example heat map showing several commonly observed quality defects. Nucleotide positions 1-75 are plotted from left-to-right along the x-axis; tiles 1-100 are ranked from top-to-bottom along the y-axis. (These numbers may vary for other datasets). The scale depicts the mean probability of observing a base call error for each tile at each nucleotide position. The defects evident in this dataset (see text for details) are atypical of Illumina sequencing; this dataset was chosen specifically to illustrate the capabilities of SolexaQA.
Figure 1 Example heat map showing several commonly observed quality defects. Nucleotide positions 1-75 are plotted from left-to-right along the x-axis; tiles 1-100 are ranked from top-to-bottom along the y-axis. (These numbers may vary for other datasets). The scale depicts the mean probability of observing a base call error for each tile at each nucleotide position. The defects evident in this dataset (see text for details) are atypical of Illumina sequencing; this dataset was chosen specifically to illustrate the capabilities of SolexaQA.CC BY · PubMed Central
Figure 2 Distribution of mean quality (probability of error, y-axis) at each nucleotide position (x-axis) for each tile individually (dotted black lines) and the entire dataset combined (red circles). Note the considerable variance in data quality between tiles. The defects evident in this dataset (see text for details) are atypical of Illumina sequencing; this dataset was chosen specifically to illustrate the capabilities of SolexaQA.
Figure 2 Distribution of mean quality (probability of error, y-axis) at each nucleotide position (x-axis) for each tile individually (dotted black lines) and the entire dataset combined (red circles). Note the considerable variance in data quality between tiles. The defects evident in this dataset (see text for details) are atypical of Illumina sequencing; this dataset was chosen specifically to illustrate the capabilities of SolexaQA.CC BY · PubMed Central
Figure 3 Distribution of longest read segments passing a user-defined quality threshold (here, P = 0.05, or equivalently, Phred quality score Q ≈ 13, or a base call error rate of 1-in-20). Note that reads in this dataset would be trimmed on average to ~25 nucleotides (i.e., only approximately one-third of the initial 75 nucleotide read length). The defects evident in this dataset (see text for details) are atypical of Illumina sequencing; this dataset was chosen specifically to illustrate the capabilities of SolexaQA.
Figure 3 Distribution of longest read segments passing a user-defined quality threshold (here, P = 0.05, or equivalently, Phred quality score Q ≈ 13, or a base call error rate of 1-in-20). Note that reads in this dataset would be trimmed on average to ~25 nucleotides (i.e., only approximately one-third of the initial 75 nucleotide read length). The defects evident in this dataset (see text for details) are atypical of Illumina sequencing; this dataset was chosen specifically to illustrate the capabilities of SolexaQA.CC BY · PubMed Central
Figure 4 Effect of dynamically trimmed versus untrimmed reads on de novo assembly with the Velvet assembler. Dynamically trimmed reads (solid symbols) relative to untrimmed reads (open symbols) yield improved N50 values (red squares) and maximum contig sizes (blue triangles). Summary statistics were averaged across de novo assemblies for 20 isolates of Campylobacter coli and C. jejuni, and normalized by the total number of reads employed in each assembly.
Figure 4 Effect of dynamically trimmed versus untrimmed reads on de novo assembly with the Velvet assembler. Dynamically trimmed reads (solid symbols) relative to untrimmed reads (open symbols) yield improved N50 values (red squares) and maximum contig sizes (blue triangles). Summary statistics were averaged across de novo assemblies for 20 isolates of Campylobacter coli and C. jejuni, and normalized by the total number of reads employed in each assembly.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

Sorted by citations

See all 1,357

Related

See all

Metadata

Authors
Cox, M. P., Peterson, D. A., & Biggs, P. J.
Affiliations
Massey University (NZ)
Corresponding author
Cox, Murray P
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2010-09-27
Volume
11
Issue
1
Pages
485-485
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/1471-2105-11-485
PMC
PMC2956736
OpenAlex
W2097205777
Cited by
1,357
References
9
Citations by year
2026: 29 · 2025: 50 · 2024: 57 · 2023: 86 · 2022: 86 · 2021: 131 · 2020: 118 · 2019: 118 · 2018: 135 · 2017: 105 · 2016: 148 · 2015: 95 · 2014: 97 · 2013: 56 · 2012: 37
FWCI
13.30
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Royal Society
  • Royal Society Te Apārangi (MAU0802)
  • Massey University
  • Marsden Fund (MAU0802)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
Keywords
sequence trimming, quality control software
MeSH terms
Base Sequence, Software, Sequence Analysis, DNA, Sequence Analysis, RNA, Internet

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review