Bioinformatics Training & Education Program

Learn how scientists are using ddPCR technology for absolute quantification of copy number variation, pathogen detection, detection of rare mutations, genome editing, and NGS data validation. We will go over ddPCR basics, and workflow followed by ddPCR application areas.  Presentation will include  30 minutes of QX Manager software training (software introduction + data analysis)  followed by Q&A

The upcoming presentations will showcase research from different Cancer Moonshot initiatives that support the 10 recommendations of the Blue Ribbon Panel report. They will inform the community about the progress of Cancer Moonshot-funded projects, provide outreach related to Cancer Moonshot projects, enhance discussions and collaborations related to Cancer Moonshot research, and promote the sharing of data from Cancer Moonshot initiatives. Speaker: Sandro Santagata, M.D., Ph.D., Brigham and Women’s Hospital

This afternoon workshop will give attendees an understanding of how single cell technologies are used to study cancer.

To start us off, Mike Kelly, head of the Single Cell Analysis Facility (SCAF), will give an overview of the current and emerging high-value single cell sequencing and closely associated spatial transcriptional profiling methods along with some example applications in cancer research. He will discuss some of the strengths and limitations of these existing technologies, and preview some of the most promising upcoming assays that may be of interest to the community.

Following this we will have Kimia Dadkhah, bioinformatics analyst (SCAF), who will talk about the essential quality control metrics in single cell data analysis and important factors to keep in mind in interpretation of the data.

Next up is Abdalla Abdelmaksound (CCBR), [topic to be announced], and Stefan Cordes (NHLBI) – who will review trajectory inference – with and without the incorporation of RNA velocity – as a tool to reconstruct cell state dynamics from single cell genomics data.  He will show that the inclusion of single cell lineage-tracing indices to follow heritable barcodes can improve reconstruction.

Building a Mind for Cancer

Cancer neurodegenerative disorders and other diseases have multiple subtypes, each with their different causes, effective treatments, and clinical outcomes. Tumor genome sequences provide a rich new source of data for uncovering these subtypes, but they have proven difficult to compare, as two tumors rarely share the same somatic or germline mutations.

The Ideker Lab previously introduced the concept and method of network-based stratification (NBS), which integrates tumor genomes with knowledge of hallmark cancer pathways encoded in gene networks [Chuang et al. Mol Sys Biol 2007 and Hofree et al. Nature Methods 2013; first translated to the clinic in Chuang et al. Blood 2012]. This approach allows for stratification of cancer neurodegenerative disorders and other diseases into informative subtypes by clustering together patients with molecular alteration in similar network regions (e.g. distinct mutations in the same hallmark pathway). These network-defined subtypes have turned out to be predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. Thus far the evidence suggests that network biomarkers, which aggregate together mutations in multiple genes, will not just be useful in clinical interpretation of cancer genomes, in many cases they may be necessary. – Ideker Lab, https://idekerlab.ucsd.edu/research/cancer/

Azimuth is a web application that uses an annotated reference dataset to automate the processing, analysis, and interpretation of a new single-cell RNA-seq experiment. Azimuth leverages a 'reference-based mapping' pipeline that inputs a counts matrix of gene expression in single cells, and performs normalization, visualization, cell annotation, and differential expression (biomarker discovery). All results can be explored within the app, and easily downloaded for additional downstream analysis. - Satija Lab

The development of Azimuth is led by the New York Genome Center Mapping Component as part of the NIH Human Biomolecular Atlas Project (HuBMAP).

Wondering why you should use R for data visualization? Lesson 1 of the Data Visualization with R course series will address this question and introduce the various plot types that will be generated throughout the course. Lesson 1 will also showcase related plots that you will be able to create in the future using the foundational skills gained over the next 5 lessons.

This will not be a hands-on lesson so no coding just yet. The hands-on portion of this series will start with lesson 2, Getting Started with ggplot2. 

This lesson is the first lesson of a multi-lesson course series. Registering here will register you for the entire course series.

IMPORTANT: You do not need to download or install any software to participate in the course. This course will be taught on the DNAnexus platform. Every learner will need to create a free DNAnexus account at https://dnanexus.com. After you have created your DNAnexus account, please complete this form. If you fail to complete the form, we will not be able to give you access to the course on DNAnexus.

Lesson 2 of the Data Visualization with R course series will focus on the basics of ggplot2, including the grammar of graphics philosophy and its application. This lesson will provide a hands on introduction to the ggplot2 syntax, geom functions, mapping and aesthetics, and plot layering.

Registering for lesson 1 of this course series will enroll you in the entire course series.