Detail
Článek
Článek online
FT
Medvik - BMČ
  • Je něco špatně v tomto záznamu ?

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

I. Vogel, RC. Blanshard, ER. Hoffmann,

. 2019 ; 35 (23) : 5055-5062. [pub] 20191201

Jazyk angličtina Země Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc20025421

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. AVAILABILITY AND IMPLEMENTATION: The implementation of SureTypeSC in Python and sample data are available in the GitHub repository: https://github.com/puko818/SureTypeSC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc20025421
003      
CZ-PrNML
005      
20201222153911.0
007      
ta
008      
201125s2019 xxk f 000 0|eng||
009      
AR
024    7_
$a 10.1093/bioinformatics/btz412 $2 doi
035    __
$a (PubMed)31116387
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxk
100    1_
$a Vogel, Ivan $u DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark. Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.
245    10
$a SureTypeSC-a Random Forest and Gaussian mixture predictor of high confidence genotypes in single-cell data / $c I. Vogel, RC. Blanshard, ER. Hoffmann,
520    9_
$a 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. AVAILABILITY AND IMPLEMENTATION: The implementation of SureTypeSC in Python and sample data are available in the GitHub repository: https://github.com/puko818/SureTypeSC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
650    _2
$a Bayesova věta $7 D001499
650    _2
$a genotyp $7 D005838
650    _2
$a normální rozdělení $7 D016011
650    12
$a jednonukleotidový polymorfismus $7 D020641
650    _2
$a sekvenování celého genomu $7 D000073336
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Blanshard, Robert C $u Illumina Cambridge Ltd., Fulbourn, UK. Genome Damage and Stability Centre, School of Life Sciences, University of Sussex, Brighton, UK.
700    1_
$a Hoffmann, Eva R $u DNRF Center for Chromosome Stability, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark. Genome Damage and Stability Centre, School of Life Sciences, University of Sussex, Brighton, UK.
773    0_
$w MED00008115 $t Bioinformatics (Oxford, England) $x 1367-4811 $g Roč. 35, č. 23 (2019), s. 5055-5062
856    41
$u https://pubmed.ncbi.nlm.nih.gov/31116387 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y a $z 0
990    __
$a 20201125 $b ABA008
991    __
$a 20201222153907 $b ABA008
999    __
$a ok $b bmc $g 1599566 $s 1116107
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2019 $b 35 $c 23 $d 5055-5062 $e 20191201 $i 1367-4811 $m Bioinformatics $n Bioinformatics $x MED00008115
LZP    __
$a Pubmed-20201125

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...