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Everything about Bioconductor
Software project for the analysis of genomic data
Bioconductor is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: BioconductorA 5-minute orientation
- Skim a recent paper: microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualizationInfluential work since 2022
- Pick up a classic book: Bioconductor case studiesThe most-reprinted book on the subject
- Read the landmark paper: edgeR : a Bioconductor package for differential expression analysis of digital gene expression dataCited 41,310 times
- Go sideways: List of RNA-Seq bioinformatics toolsA closely related topic
- Go sideways: RNA-SeqA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
edgeR : a Bioconductor package for differential expression analysis of digital gene expression data
Bioconductor: open software development for computational biology and bioinformatics
ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization
Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt
Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data
Orchestrating high-throughput genomic analysis with Bioconductor
GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor
🆕 Influential since 2022
microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization
SimplifyEnrichment : A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results
rGREAT : an R/bioconductor package for functional enrichment on genomic regions
spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data
SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor
📚 Books worth reading
Bioconductor case studiesWolfgang Huber, Robert Gentleman · 2008
Bioinformatics and computational biology solutions using R and BioconductorRobert Gentleman · 2005
Statistics and data analysis for microarrays using R and BioconductorSorin Drăghici · 2012
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- List of RNA-Seq bioinformatics tools RNA-Seq is a technique that allows transcriptome studies (see also Transcriptomics technologies) based on next-generation sequencing technologies. This…
- RNA-Seq RNA-Seq (short for RNA sequencing) is a next-generation sequencing (NGS) technique used to quantify and identify RNA molecules in a biological sample,…
- Transcriptomics technologies Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content…
- MA plot Within computational biology, an MA plot is an application of a Bland–Altman plot for visual representation of genomic data. The plot visualizes the differences…
- Metatranscriptomics Metatranscriptomics is the set of techniques used to study gene expression of microbes within natural environments, i.e., the metatranscriptome. While…
- Alicia Oshlack Alicia Yinema Kate Nungarai Oshlack is an Australian bioinformatician and is Co-Head of Computational Biology at the Peter MacCallum Cancer Centre in Melbourne…
- Sequence analysis In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand…
- DESeq2 DESeq2 is a software package in the field of bioinformatics and computational biology for the statistical programming language R. It is primarily employed…