SureTypeSC-a Random Forest and Gaussian mixture predictor of high confidence genotypes in single-cell data

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

SureTypeSC-a Random Forest and Gaussian mixture predictor of high confidence genotypes in single-cell data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

SureTypeSC-a Random Forest and Gaussian mixture predictor of high confidence genotypes in single-cell data

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

BIOINFORMATICS

ISSN

1367-4803

e-ISSN

1460-2059

Svazek periodika

35

Číslo periodika v rámci svazku

23

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

5055-5062

Kód UT WoS článku

000506808900024

EID výsledku v databázi Scopus

2-s2.0-85076331032