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PANDAseq: paired-end assembler for illumina sequences
Open accessAbstract
BACKGROUND: Illumina paired-end reads are used to analyse microbial communities by targeting amplicons of the 16S rRNA gene. Publicly available tools are needed to assemble overlapping paired-end reads while correcting mismatches and uncalled bases; many errors could be corrected to obtain higher sequence yields using quality information. RESULTS: PANDAseq assembles paired-end reads rapidly and with the correction of most errors. Uncertain error corrections come from reads with many low-quality bases identified by upstream processing. Benchmarks were done using real error masks on simulated data, a pure source template, and a pooled template of genomic DNA from known organisms. PANDAseq assembled reads more rapidly and with reduced error incorporation compared to alternative methods. CONCLUSIONS: PANDAseq rapidly assembles sequences and scales to billions of paired-end reads. Assembly of control libraries showed a 4-50% increase in the number of assembled sequences over naïve assembly with negligible loss of "good" sequence.
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![Figure 4 Rank abundance curves for control libraries. Rank-abundance curves for defined multi-organism libraries [1] assembled at two different quality thresholds using PANDAseq and naïve assembly followed by clustering with CD-HIT into OTUs of 97% identity.](/paper-figures/c7aa17d75a5648cb9be4d664599fd049/pandaseq-paired-end-assembler-for-illumina-sequences-figure-4.jpg)
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“However, it assumes that all paired-end reads can be merged.”
From PEAR: a fast and accurate Illumina Paired-End reAd mergeR (2014) · cites this paper as Masella et al., 2012
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“PANDAseq merges fragments by maximizing the probability of true sequence matches, given the observed sequences.”
From PEAR: a fast and accurate Illumina Paired-End reAd mergeR (2014) · cites this paper as Masella et al., 2012
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“It combines quality scores with sequence matches and thereby improves merging quality.”
From PEAR: a fast and accurate Illumina Paired-End reAd mergeR (2014) · cites this paper as Masella et al., 2012
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“In contrast to FLASH, PANDAseq works well with short overlap regions and does not require prior knowledge of the target DNA fragment size.”
From PEAR: a fast and accurate Illumina Paired-End reAd mergeR (2014) · cites this paper as Masella et al., 2012
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Metadata
- Authors
- Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G., & Neufeld, J. D.
- Affiliations
- University of Waterloo (CA)
- Corresponding author
- Neufeld, Josh D
- Venue
- BMC Bioinformatics
- Published
- 2012-02-14
- Volume
- 13
- Issue
- 1
- Pages
- 31-31
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC3471323
- OpenAlex
- W2132548846
- Cited by
- 2,132
- References
- 12
- Citations by year
- 2026: 61 · 2025: 89 · 2024: 90 · 2023: 115 · 2022: 187 · 2021: 254 · 2020: 277 · 2019: 264 · 2018: 251 · 2017: 204 · 2016: 152 · 2015: 108 · 2014: 56 · 2013: 21 · 2012: 3
- FWCI
- 24.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Government of Ontario
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- 16S rRNA gene, microbial community analysis, error correction, amplicon sequencing, sequencing errors
- MeSH terms
- Bacteria, RNA, Bacterial, RNA, Ribosomal, 16S, Software, Metagenomics
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.
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