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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03244naa a2200349 4500 | |
| 001 | oai:DiVA.org:su-207866 | |
| 003 | SwePub | |
| 008 | 220815s2022 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-2078662 URI |
| 024 | 7 | a https://doi.org/10.1093/bioinformatics/btac3952 DOI |
| 040 | a (SwePub)su | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Tavakolian, Niku Stockholms universitet,Matematiska institutionen4 aut0 (Swepub:su)nita6475 |
| 245 | 1 0 | a Shepherd :b accurate clustering for correcting DNA barcode errors |
| 264 | c 2022-06-16 | |
| 264 | 1 | b Oxford University Press (OUP),c 2022 |
| 338 | a print2 rdacarrier | |
| 520 | a Motivation: DNA barcodes are short, random nucleotide sequences introduced into cell populations to track the relative counts of hundreds of thousands of individual lineages over time. Lineage tracking is widely applied, e.g. to understand evolutionary dynamics in microbial populations and the progression of breast cancer in humans. Barcode sequences are unknown upon insertion and must be identified using next-generation sequencing technology, which is error prone. In this study, we frame the barcode error correction task as a clustering problem with the aim to identify true barcode sequences from noisy sequencing data. We present Shepherd, a novel clustering method that is based on an indexing system of barcode sequences using k-mers, and a Bayesian statistical test incorporating a substitution error rate to distinguish true from error sequences.Results: When benchmarking with synthetic data, Shepherd provides barcode count estimates that are significantly more accurate than state-of-the-art methods, producing 10–150 times fewer spurious lineages. For empirical data, Shepherd produces results that are consistent with the improvements seen on synthetic data. These improvements enable higher resolution lineage tracking and more accurate estimates of biologically relevant quantities, e.g. the detection of small effect mutations.Availability and implementation: A Python implementation of Shepherd is freely available at: https://www.github.com/Nik-Tavakolian/Shepherd. | |
| 650 | 7 | a NATURVETENSKAPx Biologi0 (SwePub)1062 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Biological Sciences0 (SwePub)1062 hsv//eng |
| 700 | 1 | a Frazão, João Guilhermeu Stockholms universitet,Zoologiska institutionen4 aut0 (Swepub:su)jofr7431 |
| 700 | 1 | a Bendixsen, Devinu Stockholms universitet,Zoologiska institutionen4 aut0 (Swepub:su)debe7010 |
| 700 | 1 | a Stelkens, Rike,d 1978-u Stockholms universitet,Zoologiska institutionen4 aut0 (Swepub:su)rstel |
| 700 | 1 | a Li, Chun-Biuu Stockholms universitet,Matematiska institutionen4 aut0 (Swepub:su)chunl |
| 710 | 2 | a Stockholms universitetb Matematiska institutionen4 org |
| 773 | 0 | t Bioinformaticsd : Oxford University Press (OUP)g 38:15, s. 3710-3716q 38:15<3710-3716x 1367-4803x 1367-4811x 1460-2059 |
| 856 | 4 | u https://doi.org/10.1093/bioinformatics/btac395y Fulltext |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-207866 |
| 856 | 4 8 | u https://doi.org/10.1093/bioinformatics/btac395 |
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