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RGMQL: scalable and interoperable computing of heterogeneous omics big data and metadata in R/Bioconductor

Simone Pallotta, Silvia Cascianelli, Marco Masseroli

BMC Bioinformatics. 2022; 23: 123. Published online 2022 Apr 7. doi: 10.1186/s12859-022-04648-4

PMCID:: PMC8991469

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Orchestrating Single-Cell Analysis with Bioconductor

Robert A. Amezquita, Aaron T. L. Lun, Etienne Becht, Vince J. Carey, Lindsay N. Carpp, Ludwig Geistlinger, Federico Marini, Kevin Rue-Albrecht, Davide Risso, Charlotte Soneson, Levi Waldron, Hervé Pagès, Mike L. Smith, Wolfgang Huber, Martin Morgan, Raphael Gottardo, Stephanie C. Hicks

Nat Methods. Author manuscript; available in PMC 2020 Aug 1.

Published in final edited form as: Nat Methods. 2020 Feb; 17(2): 137–145. Published online 2019 Dec 2. doi: 10.1038/s41592-019-0654-x

PMCID:: PMC7358058

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Select item 63818173.

Reproducible Bioconductor workflows using browser-based interactive notebooks and containers

Reem Almugbel, Ling-Hong Hung, Jiaming Hu, Abeer Almutairy, Nicole Ortogero, Yashaswi Tamta, Ka Yee Yeung

J Am Med Inform Assoc. 2018 Jan; 25(1): 4–12. Published online 2017 Oct 28. doi: 10.1093/jamia/ocx120

PMCID:: PMC6381817

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Select item 30405254.

An intuitive Python interface for Bioconductor libraries demonstrates the utility of language translators

Laurent Gautier

BMC Bioinformatics. 2010; 11(Suppl 12): S11. Published online 2010 Dec 21. doi: 10.1186/1471-2105-11-S12-S11

PMCID:: PMC3040525

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Orchestrating high-throughput genomic analysis with Bioconductor

Wolfgang Huber, Vincent J. Carey, Robert Gentleman, Simon Anders, Marc Carlson, Benilton S. Carvalho, Hector Corrada Bravo, Sean Davis, Laurent Gatto, Thomas Girke, Raphael Gottardo, Florian Hahne, Kasper D. Hansen, Rafael A. Irizarry, Michael Lawrence, Michael I. Love, James MacDonald, Valerie Obenchain, Andrzej K. Oleś, Hervé Pagès, Alejandro Reyes, Paul Shannon, Gordon K. Smyth, Dan Tenenbaum, Levi Waldron, Martin Morgan

Nat Methods. Author manuscript; available in PMC 2015 Jul 21.

Published in final edited form as: Nat Methods. 2015 Feb; 12(2): 115–121. doi: 10.1038/nmeth.3252

PMCID:: PMC4509590

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Select item 47868996.

The Bioconductor channel in F1000Research

Wolfgang Huber, Vincent Carey, Sean Davis, Kasper Daniel Hansen, Martin Morgan

Version 2. F1000Res. 2015; 4: 217. Published online 2016 Apr 20. doi: 10.12688/f1000research.6758.2

PMCID:: PMC4786899

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Multiomic Integration of Public Oncology Databases in Bioconductor

Marcel Ramos, Ludwig Geistlinger, Sehyun Oh, Lucas Schiffer, Rimsha Azhar, Hanish Kodali, Ino de Bruijn, Jianjiong Gao, Vincent J. Carey, Martin Morgan, Levi Waldron

JCO Clin Cancer Inform. 2020; 4: CCI.19.00119. Published online 2020 Oct 29. doi: 10.1200/CCI.19.00119

PMCID:: PMC7608653

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Curated single cell multimodal landmark datasets for R/Bioconductor

Kelly B. Eckenrode, Dario Righelli, Marcel Ramos, Ricard Argelaguet, Christophe Vanderaa, Ludwig Geistlinger, Aedin C. Culhane, Laurent Gatto, Vincent Carey, Martin Morgan, Davide Risso, Levi Waldron

PLoS Comput Biol. 2023 Aug; 19(8): e1011324. Published online 2023 Aug 25. doi: 10.1371/journal.pcbi.1011324

PMCID:: PMC10497156

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DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts

Marco Catoni, Jonathan MF Tsang, Alessandro P Greco, Nicolae Radu Zabet

Nucleic Acids Res. 2018 Nov 2; 46(19): e114. Published online 2018 Jul 9. doi: 10.1093/nar/gky602

PMCID:: PMC6212837

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SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor

Dario Righelli, Lukas M Weber, Helena L Crowell, Brenda Pardo, Leonardo Collado-Torres, Shila Ghazanfar, Aaron T L Lun, Stephanie C Hicks, Davide Risso

Bioinformatics. 2022 Jun 1; 38(11): 3128–3131. Published online 2022 Apr 28. doi: 10.1093/bioinformatics/btac299

PMCID:: PMC9154247

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GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data

Nathan Harmston, Elizabeth Ing-Simmons, Malcolm Perry, Anja Barešić, Boris Lenhard

BMC Genomics. 2015; 16: 963. Published online 2015 Nov 17. doi: 10.1186/s12864-015-2140-x

PMCID:: PMC4650858

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MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis

Vu T Chu, Raphael Gottardo, Adrian E Raftery, Roger E Bumgarner, Ka Yee Yeung

Genome Biol. 2008; 9(7): R118. Published online 2008 Jul 24. doi: 10.1186/gb-2008-9-7-r118

PMCID:: PMC2530872

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Select item 401512213.

The Risa R/Bioconductor package: integrative data analysis from experimental metadata and back again

Alejandra González-Beltrán, Steffen Neumann, Eamonn Maguire, Susanna-Assunta Sansone, Philippe Rocca-Serra

BMC Bioinformatics. 2014; 15(Suppl 1): S11. Published online 2014 Jan 10. doi: 10.1186/1471-2105-15-S1-S11

PMCID:: PMC4015122

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Bioconductor: open software development for computational biology and bioinformatics

Robert C Gentleman, Vincent J Carey, Douglas M Bates, Ben Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, Laurent Gautier, Yongchao Ge, Jeff Gentry, Kurt Hornik, Torsten Hothorn, Wolfgang Huber, Stefano Iacus, Rafael Irizarry, Friedrich Leisch, Cheng Li, Martin Maechler, Anthony J Rossini, Gunther Sawitzki, Colin Smith, Gordon Smyth, Luke Tierney, Jean YH Yang, Jianhua Zhang

Genome Biol. 2004; 5(10): R80. Published online 2004 Sep 15. doi: 10.1186/gb-2004-5-10-r80

PMCID:: PMC545600

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BiocMAP: a Bioconductor-friendly, GPU-accelerated pipeline for bisulfite-sequencing data

Nicholas J. Eagles, Richard Wilton, Andrew E. Jaffe, Leonardo Collado-Torres

BMC Bioinformatics. 2023; 24: 340. Published online 2023 Sep 13. doi: 10.1186/s12859-023-05461-3

PMCID:: PMC10498615

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Select item 963031016.

A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies

Luke Hoberecht, Pirunthan Perampalam, Aaron Lun, Jean-Philippe Fortin

Nat Commun. 2022; 13: 6568. Published online 2022 Nov 2. doi: 10.1038/s41467-022-34320-7

PMCID:: PMC9630310

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A step-by-step guide to analyzing CAGE data using R/Bioconductor

Malte Thodberg, Albin Sandelin

Version 1. F1000Res. 2019; 8: 886. Published online 2019 Jun 18. doi: 10.12688/f1000research.18456.1

PMCID:: PMC6613478

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Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

Michael I. Love, Charlotte Soneson, Rob Patro

Version 3. F1000Res. 2018; 7: 952. Published online 2018 Oct 1. doi: 10.12688/f1000research.15398.3

PMCID:: PMC6178912

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BeadArray Expression Analysis Using Bioconductor

Matthew E. Ritchie, Mark J. Dunning, Mike L. Smith, Wei Shi, Andy G. Lynch

PLoS Comput Biol. 2011 Dec; 7(12): e1002276. Published online 2011 Dec 1. doi: 10.1371/journal.pcbi.1002276

PMCID:: PMC3228778

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Select item 417269220.

CRISPRseek: A Bioconductor Package to Identify Target-Specific Guide RNAs for CRISPR-Cas9 Genome-Editing Systems

Lihua J. Zhu, Benjamin R. Holmes, Neil Aronin, Michael H. Brodsky

PLoS One. 2014; 9(9): e108424. Published online 2014 Sep 23. doi: 10.1371/journal.pone.0108424

PMCID:: PMC4172692

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PMC Images search for bioconductor

Figure 3:Bioconductor workflow for analyzing single-cell data.

A typical analytical workflow using Bioconductor leads to the creation and evolution of a SingleCellExperiment (or sce) object during data processing and downstream statistical analysis (left column). An example of a sce object evolving throughout the course of a workflow is shown, including visualization, analysis, and annotation (right column).

Orchestrating Single-Cell Analysis with Bioconductor

Nat Methods. ;17(2):137-145.
Figure 1:Number of Bioconductor packages for the analysis of high-throughput sequencing data over ten years.

Bioconductor software packages associated with the analysis of sequencing data were tracked by date of submission over the course of ten years. Software packages were uniquely defined by their primary sequencing technology association, with examples of specific terms used for annotation in parentheses.

Orchestrating Single-Cell Analysis with Bioconductor

Nat Methods. ;17(2):137-145.
Figure 1

Implementing a Bioconductor class in Python. Implementing a Bioconductor class can be performed by either declaring all class components explicitly (right), or by relying on meta-programming utilities found in the rpy2 package (left). Meta-programming helps reduce the amount of boilerplate code by automating part of the translation work.

An intuitive Python interface for Bioconductor libraries demonstrates the utility of language translators

BMC Bioinformatics. 2010;11(Suppl 12):S11-S11.
Figure 2

Heat map (produced by the Bioconductor function heatmap()) of the ALL leukemia data.

Bioconductor: open software development for computational biology and bioinformatics

Genome Biol. 2004;5(10):R80-R80.