SureTypeSC-a Random Forest and Gaussian mixture predictor of high confidence genotypes in single-cell data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU136007" target="_blank" >RIV/00216305:26230/19:PU136007 - isvavai.cz</a>
Result on the web
<a href="https://academic.oup.com/bioinformatics/article-abstract/35/23/5055/5497252?redirectedFrom=fulltext" target="_blank" >https://academic.oup.com/bioinformatics/article-abstract/35/23/5055/5497252?redirectedFrom=fulltext</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/bioinformatics/btz412" target="_blank" >10.1093/bioinformatics/btz412</a>
Alternative languages
Result language
angličtina
Original language name
SureTypeSC-a Random Forest and Gaussian mixture predictor of high confidence genotypes in single-cell data
Original language description
Motivation Accurate genotyping of DNA from a single cell is required for applications such as de novo mutation detection, linkage analysis and lineage tracing. However, achieving high precision genotyping in the single-cell environment is challenging due to the errors caused by whole-genome amplification. Two factors make genotyping from single cells using single nucleotide polymorphism (SNP) arrays challenging. The lack of a comprehensive single-cell dataset with a reference genotype and the absence of genotyping tools specifically designed to detect noise from the whole-genome amplification step. Algorithms designed for bulk DNA genotyping cause significant data loss when used for single-cell applications. Results In this study, we have created a resource of 28.7 million SNPs, typed at high confidence from whole-genome amplified DNA from single cells using the Illumina SNP bead array technology. The resource is generated from 104 single cells from two cell lines that are available from the Coriell repository. We used mother-father-proband (trio) information from multiple technical replicates of bulk DNA to establish a high quality reference genotype for the two cell lines on the SNP array. This enabled us to develop SureTypeSC-a two-stage machine learning algorithm that filters a substantial part of the noise, thereby retaining the majority of the high quality SNPs. SureTypeSC also provides a simple statistical output to show the confidence of a particular single-cell genotype using Bayesian statistics.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
BIOINFORMATICS
ISSN
1367-4803
e-ISSN
1460-2059
Volume of the periodical
35
Issue of the periodical within the volume
23
Country of publishing house
GB - UNITED KINGDOM
Number of pages
8
Pages from-to
5055-5062
UT code for WoS article
000506808900024
EID of the result in the Scopus database
2-s2.0-85076331032