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Ramos, M, et al. (2017) Software for the Integration of Multiomics Experiments in Bioconductor Cancer Research 77:e39-e42. doi:10.1158/0008-5472.CAN-17-0344

Pasolli, E, et al. (2017) Accessible, curated metagenomic data through ExperimentHub Nature Methods 14, 1023–1024. doi:10.1038/nmeth.4468

Huber W, et al. (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods 12:115-121; doi:10.1038/nmeth.3252 (full-text free with registration).

Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, et al. (2013) Software for Computing and Annotating Genomic Ranges. PLoS Comput Biol 9(8): e1003118. doi: 10.1371/journal.pcbi.1003118

Irizarry R, et al. (2015) Biomedical Data Science. Course Notes, EdX PH525.1x.

Recent

20 most recent PubMed and PubMed Central citations mentioning "Bioconductor". For a complete list, go to PubMed and PubMed Central. Last updated 2019-05-26T10:00:09-04:00.

Gonzalo Sanz R, Sánchez-Pla A. Statistical Analysis of Microarray Data. Methods Mol Biol, 1986, pp. 87-121. doi:10.1007/978-1-4939-9442-7_5 (23 May 2019)

Trino S, Zoppoli P, Carella AM, Laurenzana I, Weisz A, Memoli D, Calice G, La Rocca F, Simeon V, Savino L, Del Vecchio L, Musto P, Caivano A, De Luca L. DNA methylation dynamic of bone marrow hematopoietic stem cells after allogeneic transplantation. Stem Cell Res Ther, 10(1), pp. 138. doi:10.1186/s13287-019-1245-6 (20 May 2019)

Orjuela S, Huang R, Hembach KM, Robinson MD, Soneson C. ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data. G3 (Bethesda). doi:10.1534/g3.119.400185 (14 May 2019)

Wang TT, Lee CY, Lai LC, Tsai MH, Lu TP, Chuang EY. anamiR: integrated analysis of MicroRNA and gene expression profiling. BMC Bioinformatics, 20(1), pp. 239. doi:10.1186/s12859-019-2870-x (14 May 2019)

Holding AN, Giorgi FM, Donnelly A, Cullen AE, Nagarajan S, Selth LA, Markowetz F. VULCAN integrates ChIP-seq with patient-derived co-expression networks to identify GRHL2 as a key co-regulator of ERa at enhancers in breast cancer. Genome Biol, 20(1), pp. 91. doi:10.1186/s13059-019-1698-z (13 May 2019)

Zhang J, Liu L, Xu T, Xie Y, Zhao C, Li J, Le TD. miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC Bioinformatics, 20(1), pp. 235. doi:10.1186/s12859-019-2861-y (10 May 2019)

Ah Kim S, Brossard M, Roshandel D, Paterson AD, Bull SB, Yoo YJ. gpart: human genome partitioning and visualization of high-density SNP data by identifying haplotype blocks. Bioinformatics. doi:10.1093/bioinformatics/btz308 (9 May 2019)

Acosta JP, Restrepo S, Henao JD, López-Kleine L. Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments. J Comput Biol. doi:10.1089/cmb.2018.0013 (7 May 2019)

Liu F, Wu Y, Mi Y, Gu L, Sang M, Geng C. Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis. Pathol Res Pract, pp. 152436. doi:10.1016/j.prp.2019.152436 (4 May 2019)

Wang Z, Hu J, Johnson WE, Campbell JD. scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data. BMC Bioinformatics, 20(1), pp. 222. doi:10.1186/s12859-019-2797-2 (2 May 2019)

Goksuluk D, Zararsiz G, Korkmaz S, Eldem V, Zararsiz GE, Ozcetin E, Ozturk A, Karaagaoglu AE. MLSeq: Machine learning interface for RNA-sequencing data. Comput Methods Programs Biomed, 175, pp. 223-231. doi:10.1016/j.cmpb.2019.04.007 (29 April 2019)

Graw S, Henn R, Thompson JA, Koestler DC. pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS). BMC Bioinformatics, 20(1), pp. 218. doi:10.1186/s12859-019-2804-7 (29 April 2019)

Brewster AL. Human Microglia Seize the Chance to be Different. Epilepsy Curr, pp. 1535759719843299. doi:10.1177/1535759719843299 (29 April 2019)

Li X, Li B, Jiang H. Identification of time‑series differentially expressed genes and pathways associated with heart failure post‑myocardial infarction using integrated bioinformatics analysis. Mol Med Rep, 19(6), pp. 5281-5290. doi:10.3892/mmr.2019.10190 (24 April 2019)

Cole MB, Risso D, Wagner A, DeTomaso D, Ngai J, Purdom E, Dudoit S, Yosef N. Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq. Cell Syst, 8(4), pp. 315-328.e8. doi:10.1016/j.cels.2019.03.010 (24 April 2019)

Sherman TD, Kagohara LT, Cao R, Cheng R, Satriano M, Considine M, Krigsfeld G, Ranaweera R, Tang Y, Jablonski SA, Stein-O'Brien G, Gaykalova DA, Weiner LM, Chung CH, Fertig EJ. CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer. PLoS Comput Biol, 14(4), pp. e1006935. doi:10.1371/journal.pcbi.1006935 (19 April 2019)

Crook OM, Breckels LM, Lilley KS, Kirk PDW, Gatto L. A Bioconductor workflow for the Bayesian analysis of spatial proteomics. F1000Res, 8, pp. 446. doi:10.12688/f1000research.18636.1 (11 April 2019)

Torabi Moghadam B, Etemadikhah M, Rajkowska G, Stockmeier C, Grabherr M, Komorowski J, Feuk L, Carlström EL. Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods. J Psychiatr Res, 114, pp. 41-47. doi:10.1016/j.jpsychires.2019.04.001 (2 April 2019)

Kaleb K, Vesztrocy AW, Altenhoff A, Dessimoz C. Expanding the Orthologous Matrix (OMA) programmatic interfaces: REST API and the OmaDB packages for R and Python. F1000Res, 8, pp. 42. doi:10.12688/f1000research.17548.2 (10 January 2019)

Mah CK, Mesirov JP, Chavez L. An accessible GenePattern notebook for the copy number variation analysis of Illumina Infinium DNA methylation arrays. F1000Res, 7. doi:10.12688/f1000research.16338.1 (5 December 2018)