- About BiocNYC
- Meet-ups
- Absolute copy number analysis from tumor-only whole-exome sequencing by Sehyun Oh
- Methods for modeling protein-DNA interaction based on high-throughput SELEX data by Harmen J. Bussemaker
- Creating and maintaining an R package by Davide Risso
- Workflow for Multi-omics Data Analysis by Levi Waldron
- ChIPSeqSpike: ChIP-Seq data scaling according to spike-in control by Dr. Nicolas Descoste
- Gene set analysis for RNA-seq and microarray gene expression data by Ludwig Geistlinger
- Analysis of ATAC-seq data in R/Bioconductor by Thomas Carroll
- Microbiome data analysis by the Waldron lab
- RNA-seq differential expression with Bioconductor by Davide Risso
- Multi-omics infrastructure and data for R/Bioconductor by Levi Waldron
BiocNYC is a group of people with an interest in R/Bioconductor for genomics, based in New York City. It is currently organized primarily by Levi Waldron of CUNY SPH, Davide Risso of Weill Cornell Medical College, Archana Iyer of MSKCC, and Thomas Carroll of Rockefeller University. See https://www.meetup.com/BiocNYC for more information.
We will attempt to post all materials presented at these meetings, see below for links. It's our intention to post videos as well, although technical issues have gotten in the way of several past meetings.
Absolute copy number analysis requires simultaneous inference of purity, ploidy, and loss of heterozygosity. Commonly used algorithms rely on high quality genome-wide data with matched normal profiles, limiting their applicability in clinical settings. In this workshop, I will introduce a benchmark example of absolute copy number variation (CNV) analysis from tumor-only whole exome sequencing (WES) data, followed by a step-by-step tutorial on the analysis workflow. The workflow is based on PureCN, a R/Bioconductor package.
- PureCN paper: https://scfbm.biomedcentral.com/articles/10.1186/s13029-016-0060-z
- PureCN R/Bioconductor: https://bioconductor.org/packages/release/bioc/html/PureCN.html
About the speaker: Dr. Sehyun Oh is a molecular biologist by training, specialized in DNA double-strand break repair mechanism. As a post-doctoral fellow in the Waldron lab, she is investigating intra-tumoral heterogeneity of ovarian carcinoma subtypes through analysis of DNA and RNA sequencing data.
Materials: https://github.com/shbrief/CNVWorkflow_Meetup
Methods for modeling protein-DNA interaction based on high-throughput SELEX data by Harmen J. Bussemaker
October 26, 2018
Accurate models for predicting transcription factor (TF) binding are indispensable for analyzing non-coding genomic DNA. In vitro binding assays coupled with deep sequencing (SELEX-seq and HT-SELEX) have been applied on a large scale to profile the binding specificities of hundreds of human TFs. In this talk, we wil discuss a number of R/Bioconductor tools that our lab has developed to analyze such data. First, we will demonstrate how the SELEX package can be used to efficiently construct tables of relative k-mer enrichment. Next, we will show how generalized linear models can be used to model binding specificity over TF footprints of unprecedented length using the SelexGLM package. Finally, we will demonstrate how the NRLBtools package can be used to construct genomic binding affinity landscapes using ultra-accurate sequence-to-affinity models built using our feature-based maximum-likelihood algorithm No Read Left Behind (NRLB; Rastogi et al., PNAS, 2018).
About the speaker:
Harmen J. Bussemaker is a Professor in the Department of Biological Sciences and Department of Systems Biology at Columbia University. His credentials include a Lenfest Distinguished Columbia Faculty Award and a John Simon Guggenheim Foundation Fellowship. Dr. Bussemaker is known for his work aimed at understanding gene regulatory networks based on the integration of genome sequence, transcription factor binding, and gene expression data.
Recording: https://youtu.be/Rp_OU_mMwt8
Materials: http://bussemakerlab.org/tutorials/BiocNYC2018.html
September 6, 2018
Do you have a collection of R functions and scripts scattered across different project folders? Do you ever find yourself using over and over the same function that you created? Why not turn your function collection into an R package? Most people think about R packages as a publishing unit, but R packages are a really good way to keep your functions organized, up-do-date, and to enforce reproducibility. Even when you want to keep the package for yourself without ever publishing it! In this hands-on workshop I will show you how to create and maintain your first R package. No previous experience with package development is needed. To get the best out of the workshop, please bring your laptop with the latest version of R already installed.
About the speaker:
Davide Risso is an Assistant Professor in the Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine. Prof Risso is author and/or maintainer of six Bioconductor packages for the analysis of (single-cell) RNA-seq data.
Recording: https://youtu.be/Io2P7fe88eE
July 20, 2018
- https://www.meetup.com/BiocNYC/events/251719874/
- Materials: https://github.com/waldronlab/MultiAssayExperimentWorkshop
This workshop describes an ecosystem of packages and databases for multi-omics data analysis. This includes: 1) MultiAssayExperiment for the representation of multi-omics experiments, 2) SummarizedExperiment for matrix-like datasets, 3) RaggedExperiment for non matrix-like datasets such as SNPs, different types of somatic variants, and segmented copy number, 4) curatedTCGAData, which provides unrestricted 'omics data with merged clinical, pathological, specimen, and subtype data of TCGA as MultiAssayExperiment objects customizable to contain only what you need, 5) TCGAUtils for simplifying common tasks of working TCGA data such as ID-mapping and specimen-type lookup, and 6) MultiAssayExperimentData, under development to provide other integrated multi-omics cancer data starting with 213 cBioPortal datasets.
April 19, 2018
- https://github.com/lwaldron/BiocNYC-ChIPSeqSpike
- Materials at https://github.com/descostesn/BiocNYC-ChIPSeqSpike
Chromatin Immuno-Precipitation followed by Sequencing (ChIP-Seq) is used to determine the binding sites of any protein of interest, such as transcription factors or histones with or without a specific modification, at a genome scale. The many steps of the protocol can introduce biases that make ChIP-Seq more qualitative than quantitative. For instance, it was shown that global histone modification differences are not caught by traditional downstream data normalization techniques. A case study reported no differences in histone H3 lysine-27 trimethyl (H3K27me3) upon Ezh2 inhibitor treatment. To tackle this problem, external spike-in control were used to keep track of technical biases between conditions. Exogenous DNA from a different non-closely related species was inserted during the protocol to infer scaling factors that enabled an accurate normalization, thus revealing the inhibitor effect. ChIPSeqSpike offers tools for ChIP-Seq spike-in normalization. Ready to use scaled bigwig files and scaling factors values are obtained as output. ChIPSeqSpike also provides tools for ChIP-Seq spike-in assessment and analysis through a versatile collection of graphical functions. ChIPSeqSpike is newly released in the development version of Bioconductor, with a pre-print available at https://www.biorxiv.org/content/early/2018/02/22/269118.
About the speaker:
This workshop will be led by Dr. Nicolas Descoste, author of the ChIPSeqSpike Bioconductor package. Dr. Descoste earned a PhD in Bioinformatics and Genomics at the Centre d'Immunologie de Marseille-Luminy, France. During his PhD, Dr. Descoste studied fundamental transcriptional processes focusing on RNA Polymerase II c-terminal domain. To better understand the inner workings of the genome in terms of mechanistic expression of genes, he joined Danny Reinberg's research group in 2015 as a postdoctoral fellow at New York University. His research focuses on bioinformatic solutions to the study epigenetic mechanisms and transcriptional regulation.
March 29, 2018
- https://www.meetup.com/BiocNYC/events/248199742/
- Materials are in this repo: Rmd and built html
Gene set analysis encompasses a broad range of methods differing greatly in objectives and interpretation of results. This workshop will help participants understand the distinctions between assumptions and hypotheses of existing methods for enrichment analysis of gene expression data. It will provide code and hands-on practice of all necessary steps for differential expression analysis, gene set- and network-based enrichment analysis, and identification of enriched genomic regions and regulatory elements, along with visualization and exploration of results.
About the speaker: Dr. Ludwig Geistlinger is a post-doctoral fellow in the Waldron lab, specializing in enrichment methods and analysis.
Feb 16, 2018
- https://www.meetup.com/BiocNYC/events/247121601/
- Materials at https://github.com/ThomasCarroll/ATAC_Workshop
The use of ChIP-seq, DNA-seq and MNAse-seq to identify transcription factor binding, open chromatin and nucleosome positions respectively has led to a broader understanding of epigenetic events across the genome. ATAC-seq (Assay for Transposase Accessible Chromatin with high-throughput sequencing) offers a method to rapidly and simultaneously identify openchromatin, nucleosome positioning and transcription factor binding at a genome scale. In this session, we will review the alignment, pre-processing and peak calling of ATAC-seq data in R/Bioconductor and will perform quality control, identification of replicated peaks, annotation of peaks to genes and visualization of ATAC-seq data in IGV.
Dec 15, 2017
Audrey Renson, Lucas Schiffer, Levi Waldron
- https://www.meetup.com/BiocNYC/events/243813429/
- Materials at https://github.com/waldronlab/MicrobiomeWorkshop/tree/BiocNYC-2017-12-15
- short-cut gists to install needed packages and download essential data.
Bioconductor provides significant resources for microbiome data acquisition, analysis, and visualization. This workshop introduces the common analyses of differential abundance and ordination using the phyloseq, edgeR, and DESeq2. It will utilize the curatedMetagenomicData package, a resource providing uniformly processed taxonomic and metabolic functional profiles for more than 6,000 whole metagenome shotgun sequencing samples from 26 publicly available studies, including the Human Microbiome Project. At the end of this workshop, users will be able to access publicly available metagenomic data and to perform differential abundance tests, ordination, and visualization of microbiome data in R/Bioconductor.
I will briefly introduce the topic and scope of the workshop, and brief (10-minute) talks by two active microbiome researchers in my lab:
- Audrey Renson will summarize results from an analysis of sociodemographic patterning of the oral microbiome in the New York City Health and Nutrition Examination Study (NYC-HANES). https://www.biorxiv.org/content/early/2017/10/25/189225
- Lucas Schiffer will demonstrate the use of curatedMetagenomicData for meta-analysis of health outcomes. Free link to article at Nature Methods: http://rdcu.be/A27v
Then we will supervise a hands-on workshop on analyzing microbiome data in R/Bioconductor.
Oct 27, 2017
- https://www.meetup.com/BiocNYC/events/244210806/
- Materials at: https://github.com/drisso/rnaseq_meetup
In this hands-on workshop I will show you how to perform exploratory data analysis, normalization, and differential expression of RNA-seq data using popular Bioconductor packages, such as edgeR and DESeq2. To get the best out of the workshop, please bring your laptop with the latest version of R and Bioconductor already installed. I will follow up with a list of packages that we will use during the workshop.
About the speaker:
Davide Risso is an Assistant Professor in the Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine. Prof Risso is author and/or maintainer of six Bioconductor packages for the analysis of (single-cell) RNA-seq data.
Sept 29, 2017
- https://www.meetup.com/BiocNYC/events/243180166/
- Materials at: https://www.slideshare.net/LeviWaldron/multiomics-infrastructure-and-data-for-rbioconductor/1
Multi-omics experiments are increasingly commonplace in biomedical research, and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multi-omics experiments. This talk introduces the recent MultiAssayExperiment class and methods, with integrated datasets and analyses of The Cancer Genome Atlas. It also introduces curatedMetagenomicData, a curated resource of taxonomic, gene, and metabolic functional profiles for thousands of human microbiome samples.