177 related articles for article (PubMed ID: 38755121)
1. Benchmarking of methods for DNA methylome deconvolution.
De Ridder K; Che H; Leroy K; Thienpont B
Nat Commun; 2024 May; 15(1):4134. PubMed ID: 38755121
[TBL] [Abstract][Full Text] [Related]
2. Cell-Type Deconvolution of Bulk DNA Methylation Data with EpiSCORE.
Zhu T; Teschendorff AE
Methods Mol Biol; 2023; 2629():23-42. PubMed ID: 36929072
[TBL] [Abstract][Full Text] [Related]
3. Systematic evaluation of cell-type deconvolution pipelines for sequencing-based bulk DNA methylomes.
Jeong Y; de Andrade E Sousa LB; Thalmeier D; Toth R; Ganslmeier M; Breuer K; Plass C; Lutsik P
Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35794707
[TBL] [Abstract][Full Text] [Related]
4. The SEQC2 epigenomics quality control (EpiQC) study.
Foox J; Nordlund J; Lalancette C; Gong T; Lacey M; Lent S; Langhorst BW; Ponnaluri VKC; Williams L; Padmanabhan KR; Cavalcante R; Lundmark A; Butler D; Mozsary C; Gurvitch J; Greally JM; Suzuki M; Menor M; Nasu M; Alonso A; Sheridan C; Scherer A; Bruinsma S; Golda G; Muszynska A; Łabaj PP; Campbell MA; Wos F; Raine A; Liljedahl U; Axelsson T; Wang C; Chen Z; Yang Z; Li J; Yang X; Wang H; Melnick A; Guo S; Blume A; Franke V; Ibanez de Caceres I; Rodriguez-Antolin C; Rosas R; Davis JW; Ishii J; Megherbi DB; Xiao W; Liao W; Xu J; Hong H; Ning B; Tong W; Akalin A; Wang Y; Deng Y; Mason CE
Genome Biol; 2021 Dec; 22(1):332. PubMed ID: 34872606
[TBL] [Abstract][Full Text] [Related]
5. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.
Decamps C; Arnaud A; Petitprez F; Ayadi M; Baurès A; Armenoult L; ; Escalera S; Guyon I; Nicolle R; Tomasini R; de Reyniès A; Cros J; Blum Y; Richard M
BMC Bioinformatics; 2021 Oct; 22(1):473. PubMed ID: 34600479
[TBL] [Abstract][Full Text] [Related]
6. From Methylome to Integrative Analysis of Tissue Specificity.
Dugé de Bernonville T; Daviaud C; Chaparro C; Tost J; Maury S
Methods Mol Biol; 2022; 2505():223-240. PubMed ID: 35732948
[TBL] [Abstract][Full Text] [Related]
7. Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0.
Groth EE; Weber M; Bahmer T; Pedersen F; Kirsten A; Börnigen D; Rabe KF; Watz H; Ammerpohl O; Goldmann T
Respir Res; 2020 Oct; 21(1):274. PubMed ID: 33076907
[TBL] [Abstract][Full Text] [Related]
8. Base resolution methylome profiling: considerations in platform selection, data preprocessing and analysis.
Sun Z; Cunningham J; Slager S; Kocher JP
Epigenomics; 2015 Aug; 7(5):813-28. PubMed ID: 26366945
[TBL] [Abstract][Full Text] [Related]
9. Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data.
Tanner G; Westhead DR; Droop A; Stead LF
Nat Commun; 2021 Nov; 12(1):6396. PubMed ID: 34737285
[TBL] [Abstract][Full Text] [Related]
10. Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data.
Gervin K; Salas LA; Bakulski KM; van Zelm MC; Koestler DC; Wiencke JK; Duijts L; Moll HA; Kelsey KT; Kobor MS; Lyle R; Christensen BC; Felix JF; Jones MJ
Clin Epigenetics; 2019 Aug; 11(1):125. PubMed ID: 31455416
[TBL] [Abstract][Full Text] [Related]
11. Dataset including whole blood gene expression profiles and matched leukocyte counts with utility for benchmarking cellular deconvolution pipelines.
O'Connell GC
BMC Genom Data; 2024 May; 25(1):45. PubMed ID: 38714942
[TBL] [Abstract][Full Text] [Related]
12. Computational deconvolution of DNA methylation data from mixed DNA samples.
Ferro Dos Santos MR; Giuili E; De Koker A; Everaert C; De Preter K
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38762790
[TBL] [Abstract][Full Text] [Related]
13. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis.
Down TA; Rakyan VK; Turner DJ; Flicek P; Li H; Kulesha E; Gräf S; Johnson N; Herrero J; Tomazou EM; Thorne NP; Bäckdahl L; Herberth M; Howe KL; Jackson DK; Miretti MM; Marioni JC; Birney E; Hubbard TJ; Durbin R; Tavaré S; Beck S
Nat Biotechnol; 2008 Jul; 26(7):779-85. PubMed ID: 18612301
[TBL] [Abstract][Full Text] [Related]
14. Cell-type deconvolution from DNA methylation: a review of recent applications.
Titus AJ; Gallimore RM; Salas LA; Christensen BC
Hum Mol Genet; 2017 Oct; 26(R2):R216-R224. PubMed ID: 28977446
[TBL] [Abstract][Full Text] [Related]
15. A comprehensive evaluation of alignment software for reduced representation bisulfite sequencing data.
Sun X; Han Y; Zhou L; Chen E; Lu B; Liu Y; Pan X; Cowley AW; Liang M; Wu Q; Lu Y; Liu P
Bioinformatics; 2018 Aug; 34(16):2715-2723. PubMed ID: 29579198
[TBL] [Abstract][Full Text] [Related]
16. Comparison of methylation capture sequencing and Infinium MethylationEPIC array in peripheral blood mononuclear cells.
Shu C; Zhang X; Aouizerat BE; Xu K
Epigenetics Chromatin; 2020 Nov; 13(1):51. PubMed ID: 33228774
[TBL] [Abstract][Full Text] [Related]
17. Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.
Lei H; Lyu B; Gertz EM; Schäffer AA; Shi X; Wu K; Li G; Xu L; Hou Y; Dean M; Schwartz R
J Comput Biol; 2020 Apr; 27(4):565-598. PubMed ID: 32181683
[TBL] [Abstract][Full Text] [Related]
18. Comparison and imputation-aided integration of five commercial platforms for targeted DNA methylome analysis.
Tanić M; Moghul I; Rodney S; Dhami P; Vaikkinen H; Ambrose J; Barrett J; Feber A; Beck S
Nat Biotechnol; 2022 Oct; 40(10):1478-1487. PubMed ID: 35654977
[TBL] [Abstract][Full Text] [Related]
19. Epigenomics: sequencing the methylome.
Hirst M
Methods Mol Biol; 2013; 973():39-54. PubMed ID: 23412782
[TBL] [Abstract][Full Text] [Related]
20. Comprehensive Whole DNA Methylome Analysis by Integrating MeDIP-seq and MRE-seq.
Xing X; Zhang B; Li D; Wang T
Methods Mol Biol; 2018; 1708():209-246. PubMed ID: 29224147
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]