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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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></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">30654736</PMID><DateCompleted><Year>2019</Year><Month>02</Month><Day>14</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>20</Volume><Issue>1</Issue><PubDate><Year>2019</Year><Month>Jan</Month><Day>17</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>Barcode identification for single cell genomics.</ArticleTitle><Pagination><MedlinePgn>32</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-019-2612-0</ELocationID><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></Author><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></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>01</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>9007-49-2</RegistryNumber><NameOfSubstance UI="D004247">DNA</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D004247" MajorTopicYN="N">DNA</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="Y">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D023281" MajorTopicYN="N">Genomics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D059014" MajorTopicYN="N">High-Throughput Nucleotide Sequencing</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017422" MajorTopicYN="N">Sequence Analysis, DNA</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList><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></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>05</Month><Day>23</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>01</Month><Day>07</Day></PubMedPubDate><PubMedPubDate 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