Barcode identification for single cell genomics.
Identifieur interne : 000657 ( PubMed/Checkpoint ); précédent : 000656; suivant : 000658Barcode identification for single cell genomics.
Auteurs : Akshay Tambe [États-Unis] ; Lior Pachter [États-Unis]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2019.
Descripteurs français
- KwdFr :
- MESH :
- génétique : ADN.
- Analyse de séquence d'ADN, Génomique, Humains, Séquençage nucléotidique à haut débit.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : DNA.
- methods : Genomics, High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA.
- Humans.
Abstract
Single-cell sequencing experiments use short DNA barcode 'tags' to identify reads that originate from the same cell. In order to recover single-cell information from such experiments, reads must be grouped based on their barcode tag, a crucial processing step that precedes other computations. However, this step can be difficult due to high rates of mismatch and deletion errors that can afflict barcodes.
DOI: 10.1186/s12859-019-2612-0
PubMed: 30654736
Affiliations:
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pubmed:30654736Le document en format XML
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<author><name sortKey="Pachter, Lior" sort="Pachter, Lior" uniqKey="Pachter L" first="Lior" last="Pachter">Lior Pachter</name>
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<affiliation wicri:level="1"><nlm:affiliation>Division of Biology and Biological Engineering, California Institute of Technology, 116 Kerckhoff Laboratory, Pasadena, CA, 91125, USA.</nlm:affiliation>
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<author><name sortKey="Pachter, Lior" sort="Pachter, Lior" uniqKey="Pachter L" first="Lior" last="Pachter">Lior Pachter</name>
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<series><title level="j">BMC bioinformatics</title>
<idno type="eISSN">1471-2105</idno>
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<term>Genomics (methods)</term>
<term>High-Throughput Nucleotide Sequencing (methods)</term>
<term>Humans</term>
<term>Sequence Analysis, DNA (methods)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>ADN (génétique)</term>
<term>Analyse de séquence d'ADN ()</term>
<term>Génomique ()</term>
<term>Humains</term>
<term>Séquençage nucléotidique à haut débit ()</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Genomics</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Sequence Analysis, DNA</term>
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</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Analyse de séquence d'ADN</term>
<term>Génomique</term>
<term>Humains</term>
<term>Séquençage nucléotidique à haut débit</term>
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<front><div type="abstract" xml:lang="en">Single-cell sequencing experiments use short DNA barcode 'tags' to identify reads that originate from the same cell. In order to recover single-cell information from such experiments, reads must be grouped based on their barcode tag, a crucial processing step that precedes other computations. However, this step can be difficult due to high rates of mismatch and deletion errors that can afflict barcodes.</div>
</front>
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<Title>BMC bioinformatics</Title>
<ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation>
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<ArticleTitle>Barcode identification for single cell genomics.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Single-cell sequencing experiments use short DNA barcode 'tags' to identify reads that originate from the same cell. In order to recover single-cell information from such experiments, reads must be grouped based on their barcode tag, a crucial processing step that precedes other computations. However, this step can be difficult due to high rates of mismatch and deletion errors that can afflict barcodes.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">Here we present an approach to identify and error-correct barcodes by traversing the de Bruijn graph of circularized barcode k-mers. Our approach is based on the observation that circularizing a barcode sequence can yield error-free k-mers even when the size of k is large relative to the length of the barcode sequence, a regime which is typical single-cell barcoding applications. This allows for assignment of reads to consensus fingerprints constructed from k-mers.</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We show that for single-cell RNA-Seq circularization improves the recovery of accurate single-cell transcriptome estimates, especially when there are a high number of errors per read. This approach is robust to the type of error (mismatch, insertion, deletion), as well as to the relative abundances of the cells. Sircel, a software package that implements this approach is described and publically available.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Tambe</LastName>
<ForeName>Akshay</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Division of Biology and Biological Engineering, California Institute of Technology, 116 Kerckhoff Laboratory, Pasadena, CA, 91125, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Pachter</LastName>
<ForeName>Lior</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>Departments of Biology and Computing & Mathematical Sciences, California Institute of Technology, 116 Kerckhoff Laboratory, Pasadena, CA, 91125, USA. lpachter@caltech.edu.</Affiliation>
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<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Barcode identification</Keyword>
<Keyword MajorTopicYN="N">Barcodes</Keyword>
<Keyword MajorTopicYN="N">Circularization</Keyword>
<Keyword MajorTopicYN="N">K-mer counting</Keyword>
<Keyword MajorTopicYN="N">Single-cell</Keyword>
<Keyword MajorTopicYN="N">de Bruijn graph</Keyword>
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