Do you use excel's VLOOKUP function often to merge tables or search for subsets of data in large NGS data files? If so, you may be interested in a more programmatic solution. Join us for a lesson on performing VLOOKUP in excel followed by a more reproducible solution with R programming. Whether you are interested in merging a list of gene ids with a table of functional annotations or searching for unique matches of known T-Cell Receptor sequences among output from a 10X TCR sequencing run, this tutorial will likely be useful to you.  

This tutorial will kick off the BTEP Coding Club, which features monthly 1-hour tutorials of bioinformatics tools, software, or skills. Email us at ncibtep@nih.gov if you would like to see a topic featured by the BTEP Coding Club. 

This BTEP coding club will introduce beginners to Jupyter Notebook, a platform to organize code and analysis steps in one place. Jupyter Notebook can be easily installed or run in a web browser, and supports several languages such as R and Python. It provides a way to keep track of all steps in an analysis and a place for collaboration. Come learn what Jupyter Notebook can do for you. This class will not be hands-on so need to install anything to attend.

Functional enrichment analysis is used to understand the biological context of gene lists or differential expression results. There are a multitude of tools available for this purpose. clusterProfiler is a popular R / Bioconductor package supporting over-representation analysis (ORA) and gene set enrichment analysis (GSEA) using up-to-date biological knowledge of genes and biological processes (GO and KEGG) and support for thousands of organisms. The latest version of clusterProfiler (v. 4.6.2) also provides a tidy interface for visualizing resulting output.

This May 2023 session of the BTEP Coding Club will provide an overview and demo of many of the key features of the clusterProfiler R package. 

Biowulf is the high-performance computing cluster (HPC) at NIH. In addition to its vast compute power, Biowulf has hundreds of bioinformatics tools and databases for analyzing Next Generation Sequencing (NGS) data. This coding club will provide participants the foundations for harnessing Biowulf’s computing power to analyze NGS data. Participants will learn to request computing resources on and to submit scripts to the Biowulf system. This class is not hands-on so no need to obtain a Biowulf account prior to attending.

 

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m39e6aa973e1500fbac8d3516e23cfaf8

Meeting number:
2317 419 7733
Password:
yKZJuSQ*983
Host key:
520526

Join by video system
Dial 23174197733@cbiit.webex.com
You can also dial 173.243.2.68 and enter your meeting number.

Join by phone
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Access code: 2317 419 7733
Host PIN: 2784

Global call-in numbers:
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This session of the BTEP Coding Club will focus on strategies for overcoming errors, warnings, and other common problems with R code. In this 1-hour tutorial targeting beginner R users, we will discuss commonly observed errors, how to find help, and how to approach and debug R code. 

Heatmaps and volcano plots are common data visualizations in bioinformatic analyses of genomic data, such as bulk RNA-seq. While both plot types can be used to visualize gene expression, heatmaps can be used to examine expression data across samples, and in combination with clustering techniques, reveal potential patterns in the data. Volcano plots demonstrate the direction, distribution, and statistical significance of gene expression between experimental conditions (example tumor vs. non-tumor, or drug treated vs. non-treated). In this coding club, we will demonstrate how to construct these plots using the R/Bioconductor tools ComplexHeatmap and EnhancedVolcano.

 

 

This session of the BTEP Coding Club will focus on the tool rMATS for differential alternative splicing event detection from RNA-Seq data. This 1-hour demo will provide a detailed overview of rMATS including why you may want to use it, how to use it, and how to interpret and further use resulting outputs. 

https://rnaseq-mats.sourceforge.io/

Multivariate Analysis of Transcript Splicing (MATS)

MATS is a computational tool to detect differential alternative splicing events from RNA-Seq data. The statistical model of MATS calculates the P-value and false discovery rate that the difference in the isoform ratio of a gene between two conditions exceeds a given user-defined threshold. From the RNA-Seq data, MATS can automatically detect and analyze alternative splicing events corresponding to all major types of alternative splicing patterns. MATS handles replicate RNA-Seq data from both paired and unpaired study design.

This October session of the BTEP Coding club will feature a tutorial on how to access data from GEO as well as how to submit data to GEO. 

 

 

This session introduces two versions of the R/ Bioconductor package OmicCircos to generate high-quality circular plots for visualizing multi-dimensional omics data:

1. coding in the R environment for programmers;
2. point-and-click OmicCircos R Shiny app on the Cancer Genomics Cloud (CGC) for non-programmers. 

 

Meeting number:2310 050 3184

Password:3sfNDMBq*66

Join by phone

1-650-479-3207 Call-in number (US/Canada)

Access code: 2310 050 3184

NIDAP, the NIH Integrated Data Analysis Platform, is a cloud-based and collaborative data aggregation and analysis platform. The NIDAP platform hosts user-friendly bioinformatics workflows (Bulk RNA-Seq, scRNA-Seq, Digital Spatial Profiling) and other component analysis and visualization tools that have been created and maintained by the NCI developer community based on open-source tools.   In this BTEP Coding Club session, Alexei Lobanov, bioinformatics analyst with CCBR, will demonstrate how to create NIDAP templates, GUI-like environments that allow users to run the same code on new datasets using a point-and-click approach, from source code (R or python).   Why create a NIDAP template? 1) “Templatizing” your code is easy and allows users / collaborators with no coding skills to efficiently use your code. 2) Pre-made templates encourage efficiency and reproducibility. Templates allow the user to easily create custom workflows and pipelines that can be shared with collaborators and/or applied to future data sets.