186 related articles for article (PubMed ID: 34551708)
1. SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data.
Prashant NM; Alomran N; Chen Y; Liu H; Bousounis P; Movassagh M; Edwards N; Horvath A
BMC Genomics; 2021 Sep; 22(1):689. PubMed ID: 34551708
[TBL] [Abstract][Full Text] [Related]
2. Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments.
N M P; Liu H; Dillard C; Ibeawuchi H; Alsaeedy T; Chan H; Horvath AD
Genes (Basel); 2021 Sep; 12(10):. PubMed ID: 34680953
[TBL] [Abstract][Full Text] [Related]
3. scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets.
Liu H; Prashant NM; Spurr LF; Bousounis P; Alomran N; Ibeawuchi H; Sein J; Słowiński P; Tsaneva-Atanasova K; Horvath A
BMC Genomics; 2021 Jan; 22(1):40. PubMed ID: 33419390
[TBL] [Abstract][Full Text] [Related]
4. Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data.
M PN; Liu H; Bousounis P; Spurr L; Alomran N; Ibeawuchi H; Sein J; Reece-Stremtan D; Horvath A
Genes (Basel); 2020 Feb; 11(3):. PubMed ID: 32106453
[TBL] [Abstract][Full Text] [Related]
5. Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data.
Liu F; Zhang Y; Zhang L; Li Z; Fang Q; Gao R; Zhang Z
Genome Biol; 2019 Nov; 20(1):242. PubMed ID: 31744515
[TBL] [Abstract][Full Text] [Related]
6. ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data.
Spurr LF; Alomran N; Bousounis P; Reece-Stremtan D; Prashant NM; Liu H; Słowiński P; Li M; Zhang Q; Sein J; Asher G; Crandall KA; Tsaneva-Atanasova K; Horvath A
Bioinformatics; 2020 Mar; 36(5):1351-1359. PubMed ID: 31589315
[TBL] [Abstract][Full Text] [Related]
7. SNV identification from single-cell RNA sequencing data.
Schnepp PM; Chen M; Keller ET; Zhou X
Hum Mol Genet; 2019 Nov; 28(21):3569-3583. PubMed ID: 31504520
[TBL] [Abstract][Full Text] [Related]
8. Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA.
Paul MR; Levitt NP; Moore DE; Watson PM; Wilson RC; Denlinger CE; Watson DK; Anderson PE
BMC Genomics; 2016 Mar; 17():263. PubMed ID: 27029813
[TBL] [Abstract][Full Text] [Related]
9. SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.
Edwards N; Dillard C; Prashant NM; Hongyu L; Yang M; Ulianova E; Horvath A
Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36448703
[TBL] [Abstract][Full Text] [Related]
10. RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data.
Movassagh M; Alomran N; Mudvari P; Dede M; Dede C; Kowsari K; Restrepo P; Cauley E; Bahl S; Li M; Waterhouse W; Tsaneva-Atanasova K; Edwards N; Horvath A
Nucleic Acids Res; 2016 Dec; 44(22):e161. PubMed ID: 27576531
[TBL] [Abstract][Full Text] [Related]
11. Cell-level somatic mutation detection from single-cell RNA sequencing.
Vu TN; Nguyen HN; Calza S; Kalari KR; Wang L; Pawitan Y
Bioinformatics; 2019 Nov; 35(22):4679-4687. PubMed ID: 31028395
[TBL] [Abstract][Full Text] [Related]
12. CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones.
Müller S; Cho A; Liu SJ; Lim DA; Diaz A
Bioinformatics; 2018 Sep; 34(18):3217-3219. PubMed ID: 29897414
[TBL] [Abstract][Full Text] [Related]
13. Inconsistency and features of single nucleotide variants detected in whole exome sequencing versus transcriptome sequencing: A case study in lung cancer.
O'Brien TD; Jia P; Xia J; Saxena U; Jin H; Vuong H; Kim P; Wang Q; Aryee MJ; Mino-Kenudson M; Engelman JA; Le LP; Iafrate AJ; Heist RS; Pao W; Zhao Z
Methods; 2015 Jul; 83():118-27. PubMed ID: 25913717
[TBL] [Abstract][Full Text] [Related]
14. Learning deep features and topological structure of cells for clustering of scRNA-sequencing data.
Wang H; Ma X
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35302164
[TBL] [Abstract][Full Text] [Related]
15. SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing.
Rozhoňová H; Danciu D; Stark S; Rätsch G; Kahles A; Lehmann KV
Bioinformatics; 2022 Sep; 38(18):4293-4300. PubMed ID: 35900151
[TBL] [Abstract][Full Text] [Related]
16. De novo detection of somatic variants in long-read single-cell RNA sequencing data.
Dondi A; Borgsmüller N; Ferreira PF; Haas BJ; Jacob F; Heinzelmann-Schwarz V; ; Beerenwinkel N
bioRxiv; 2024 Mar; ():. PubMed ID: 38496441
[TBL] [Abstract][Full Text] [Related]
17. SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.
Liu Y; Loewer M; Aluru S; Schmidt B
BMC Syst Biol; 2016 Aug; 10 Suppl 2(Suppl 2):47. PubMed ID: 27489955
[TBL] [Abstract][Full Text] [Related]
18. De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data.
Zhang T; Jia H; Song T; Lv L; Gulhan DC; Wang H; Guo W; Xi R; Guo H; Shen N
Genome Med; 2023 Dec; 15(1):115. PubMed ID: 38111063
[TBL] [Abstract][Full Text] [Related]
19. CAISC: A software to integrate copy number variations and single nucleotide mutations for genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.
Kannan J; Mathews L; Wu Z; Young NS; Gao S
BMC Bioinformatics; 2022 Mar; 23(Suppl 3):98. PubMed ID: 35313800
[TBL] [Abstract][Full Text] [Related]
20. Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.
Tang W; Jørgensen ACS; Marguerat S; Thomas P; Shahrezaei V
Bioinformatics; 2023 Jul; 39(7):. PubMed ID: 37354494
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]