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04/26/2024 - Dear Colleagues,    In this webinar, you'll get an introduction to WebMeV. WebMeV aims to democratize bioinformatics analysis for biological sciences researchers.   The maturation of many bioinformatics processes Read More...

02/29/2024 - This class provides an overview of the methods used to visualize the association among two or more quantitative variables. This class will focus on scatterplots, scatterplot matrix, and visualizing paired data. Upon completion of Read More...

To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures.

Learning R and associated plotting packages is a great way to generate publishable figures in a reproducible fashion. With R you can: 1. Create simple or complex figures. 2. Create high resolution figures. 3. Generate scripts that can Read More...

For Further Reading Books and / or Book Chapters of Interest R for Data Science Hands-on Programming with R Statistical Inference via Data Science: A ModernDive into R and the Tidyverse The R Graphics Cookbook ggplot2: Read More...

readRDS("./data/diffexp_results_edger_airways.rds") |> #read data select(transcript, logFC, FDR) |> #select columns of interest filter(transcript == "TSPAN6" | transcript=="DPM1" ) |> #filter ggplot(aes(x= Read More...

::: {.cell} readRDS ( "./data/diffexp_results_edger_airways.rds" ) |> #read data select ( transcript , logFC , FDR ) |> #select columns of interest filter ( transcript == "TSPAN6" | transcript == "DPM1" ) |> #filter ggplot ( aes ( Read More...

To create an R object, you need a name, a value, and an assignment operator (e.g., or option + - on a mac. Let's create a simple object and run our code. There are Read More...

Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective figures.

In lesson 3, we will learn how to import simple and complex data and how to avoid common mistakes. We will also learn how to reshape data, for example, from wide to long format, with tidyr .

The R community is extensive and getting help is now easier than ever with a simple web search. If you can't figure out how to plot something, give a quick web search a try. Read More...

12/05/2023 - This class provides an overview of the methods used to visualize the association among two or more quantitative variables. This class will focus on scatterplots, scatterplot matrix, and visualizing paired data. Upon completion of Read More...

12/04/2023 - In this lesson, we will learn how to import simple and complex data and how to avoid common mistakes. We will also learn how to reshape data, for example, from wide to long format, Read More...

Before diving into differential expression analysis, it is important to take a brief look at the concept of normalization. After obtaining expression counts, we will need to normalize the counts before performing differential expression analysis. Read More...

We can get similar information from our downloaded files using a program called seqkit . First, let's see what options are available? seqkit --help We can see that seqkit stats provides "simple statistics of Read More...

We can get similar information from our downloaded files using a program called seqkit . First, let's see what options are available? seqkit --help We can see that seqkit stats provides "simple statistics of Read More...

Introduction to Unix from the Bioinformatics Workbook Unix from Happy Belly Bioinformatics Brandies PA, Hogg CJ (2021) Ten simple rules for getting started with command-line bioinformatics. PLoS Comput Biol 17(2): e1008645. https://doi.org/10.1371/journal.pcbi.1008645 Linux Read More...

Additional Resources Working with Unix Introduction to Unix from the Bioinformatics Workbook Unix from Happy Belly Bioinformatics Brandies PA, Hogg CJ (2021) Ten simple rules for getting started with command-line bioinformatics. PLoS Comput Biol 17(2): e1008645. https:// Read More...

Ignoring these simple guidelines will greatly increase the chances that your data will be unanalysable and/or your experiment unpublishable. Prepare all samples at the same time or as close as possible. The same person Read More...

As with any language, the learning curve for Unix can be quite steep. However, to get started analyzing data you really need to understand the following: Directory navigation: what the directory tree is, how to Read More...

As with any language, the learning curve for Unix can be quite steap. However, to get started analyzing data you really need to understand the following: Directory navigation: what the directory tree is, how to Read More...

We can create a "for" loop to do iterative actions in Unix. For each commands all on one line or separate lines: (“i” can be any variable name). These steps can be saved Read More...

As with any language, the learning curve for Unix can be quite steep. However, to work on Biowulf you really need to understand the following: Directory navigation: what the directory tree is, how to navigate Read More...

A syntax comparison from Dataquest: https://www.dataquest.io/blog/python-vs-r/ . ::: {.callout-note appearance="simple"} R code can be run using python with the rpy2 library. Python code can be executed through R using Read More...

To work on Biowulf you really need to understand the following: Directory navigation: what the directory tree is, how to navigate and move around with cd Absolute and relative paths: how to access files located Read More...

We use a command line interface and a Secure shell protocol (SSH) to establish a remote connection to the login node / head node HPCs are remote resources that require connections using slow or intermitten interfaces ( Read More...

Learn how to import spreadsheet data. Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective figures.

Make a simple scatter plot. Is there a relationship between the age of the passenger and the passenger fare? {{Sdet}} Possible Solution{{Esum}} ggplot(titanic) + geom_point(aes(x=Age, y=Fare)) {{Edet}}

Let's use some other geoms. Plot the number of passengers (a simple count) that survived by ticket class and facet by sex. {{Sdet}} Possible Solution{{Esum}} ggplot(titanic) + geom_bar(aes(x=Pclass, fill= Read More...

Let's take another look at a simple scatter plot using the iris data. We can look at the relationship between petal length and petal width (i.e., variable association) for the various Iris species. Read More...

The R community is extensive and getting help is now easier than ever with a simple web search. If you can't figure out how to plot something, give a quick web search a try. Read More...

Below we generate the basic heatmap using the pheatmap package. It is simple, just use the pheatmap command and include the data that we want to construct a heatmap of as the argument. In the Read More...

CREx is an easy-to-use platform that catalogues research resources available across the NIH. It also includes an expansive database of services offered by the biotechnology industry. All resources are organized by research areas and keywords Read More...

The Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative The STRIDES Initiative aims to help NIH and its institutes, centers, and offices (ICOs) accelerate biomedical research by reducing barriers in utilizing Read More...

The BRB Preclinical Biologics Repository is a central repository that supplies reagents to the broad research community. This NCI-sponsored repository is managed by NCI's Biological Resources Branch (BRB) in the Development Therapeutics Program (DTP) of Read More...

DTP maintains a repository of synthetic compounds and pure natural products that are available to investigators for non-clinical research purposes. The Repository collection is a uniquely diverse set of more than 200,000 compounds that have been Read More...

The Functional Genomics Laboratory (formerly, the RNAi Screening Facility) of the National Center for Advancing Translational Sciences (NCATS) assist investigators with all stages of project planning and execution, beginning with assay development through genome-wide siRNA Read More...

03/19/2025 - This one hour and a half online training provides an accessible introduction to artificial intelligence (AI) using MATLAB. Designed for beginners, the session covers fundamental concepts in AI and machine learning, introduces intuitive tools Read More...

03/17/2025 - In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering a several trainings that cover general concepts behind statistics and epidemiology. These trainings will help Read More...

12/03/2024 - In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will Read More...

09/30/2024 - In this webinar, you'll gain insights into the Electronic Medical Record Search Engine (EMERSE). EMERSE is a simple, powerful tool to help researchers like you identify key data within free text clinical notes Read More...

08/13/2024 - This session will cover the basics of linear mixed-effects modeling as a method of regression analysis for clustered data. Special emphasis will be on the ideas behind random-intercepts and random-slopes modeling, and the discussion Read More...

03/20/2024 - Dear Colleagues,   cBioPortal is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from >200,000 tumor samples collected from >400 published cancer Read More...

(Inspired by Visualizing Data in the Tidyverse, a Coursera lesson) Consider whether the plot type you have chosen is the best way to convey your message Make your plot visually appealing Careful color selection - Read More...

The data type of an R object affects how that object can be used or will behave. Examples of base R data types include numeric, integer, complex, character, and logical. R objects can also have Read More...

How many rows per sample are in the scaled_counts data frame? scaled_counts |> group_by(dex, sample) |> summarize(n=n()) #there are multiple functions that can be used here `summarise()` has grouped Read More...

To create an R object, you need a name, a value, and an assignment operator (e.g.,

S4 objects store complex information that isn't necessarily simple to save. If you intend to work with the object further, try using saveRDS . saveRDS(se, "airways.rds") #save the object Note saveRDS() Read More...

Often we will apply multiple functions to wrangle a data frame into the state that we need it. For example, maybe you want to select and filter. What are our options? We could run one Read More...

Course Overview Welcome to the Data Wrangling with R course series The purpose of this course is to introduce you to essential R packages and functions that will make your life easier when it comes Read More...

12/12/2023 - In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will Read More...

09/08/2023 - Hybrid Seminar Friday, September 8, 2023 • 9:00-10:00 a.m. Building 549 Auditorium (In-person attendance encouraged)   Speaker: Brian Kelsall, M.D. Senior Investigator, Mucosal Immunobiology Section Laboratory of Molecular Immunology National Institutes of Allergy Read More...

This page contains content directly from the Biostar Handbook by Istvan Albert. Always remember to activate your bioinformatics environment. conda activate bioinfo What is a sequence pattern? A sequence pattern is a sequence of bases Read More...

Generating the Data General Rules for Sample Preparation Ignoring these simple guidelines will greatly increase the chances that your data will be unanalysable and/or your experiment unpublishable. Prepare all samples at the same time Read More...

Genomic variations are typically categorized into different classes and are often denoted with a shortened acronym: SNP, a single nucleotide polymorphism - A change of a single base. INDEL, an insertion or a deletion - Read More...

Thus far we have: Downloaded raw RNA-Seq data (.fastq files). Examined raw data quality using fastqc and multiqc . Performed adapter and quality trimming using Trimmomatic . Aligned the raw sequences to a reference genome (human chromosome 22 Read More...

R scripts can be run from the command line with command line arguments. Here is a great resource from software carpentry explaining command line arguments. To use command line arguments with an R script, we Read More...

07/24/2023 - In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will Read More...

The cowplot package provides various features that help with creating publication-quality figures, such as a set of themes, functions to align plots and arrange them into complex compound figures, and functions that make it easy Read More...

Add a variable to the data frame called age_cat (child = % mutate(age_cat= case_when(Age = 12 & Age = 18 ~ "adult" )) %>% ggplot() + geom_bar(aes(x=age_cat, fill=factor(Sex)), position=position_ Read More...

Load the data For these exercises, you will explore the titanic data from kaggle.com , which was downloaded from here . You will need to download the data and load into R. As this is a Read More...

The CHTN is a unique NCI-supported resource that provides human tissues and fluids from routine procedures to investigators who utilize human biospecimens in their research. Unlike tissue banks, the CHTN works prospectively with each investigator Read More...

Protein Expression Laboratory The Protein Expression Laboratory develops, improves, and delivers protein-centric services. Our goal is to help client investigators achieve their research goals with the lowest possible cost in the shortest time. All PEL Read More...

Protein Characterization Laboratory (PCL) offers various technologies to CCR investigators to characterize proteins and metabolites. The laboratory develops and applies state-of-the-art analytical technologies, primarily mass spectrometry, liquid chromatography, and Surface Plasmon Resonance (SPR), to advance Read More...

This is part II of the article highlighting nf-core pipelines and specifically addresses the use of these pipelines in the DNAnexus cloud environment. Part I of the article can be found in the October 2023 topic Read More...

nf-core is a community effort to generate a curated set of standardized, best-practice, reproducible, documented, NGS analysis pipelines. All these workflows are built using the versatile workflow manager, Nextflow , and have been released under the Read More...

Listed below are the video recordings of past BTEP events (classes, seminars, workshops). Videos are hosted on various servers and may play slightly differently. Some videos may be downloaded for local viewing. Recorded Videos of Read More...

How many rows per sample are in the scaled_counts data frame? ::: {.cell} scaled_counts |> group_by ( dex , sample ) |> summarize ( n = n ()) #there are multiple functions that can be used here ::: {.cell-output .cell-output-stderr} ` Read More...

Objectives To understand some of the most basic features of the R language including: Creating R objects and understanding object types Using mathematical operations Using comparison operators Creating, subsetting, and modifying vectors By the end Read More...

In this lesson, attendees will learn how to transform, summarize, and reshape data using functions from the tidyverse. Learning Objectives Continue to wrangle data using tidyverse functionality. To this end, you should understand: how to Read More...

Data visualization with ggplot2 Objectives To learn how to create publishable figures using the ggplot2 package in R. By the end of this lesson, learners should be able to create simple, pretty, and effective figures. Read More...

In this lesson, attendees will learn how to transform, summarize, and reshape data using functions from the tidyverse. Learning Objectives Continue to wrangle data using tidyverse functionality. To this end, you should understand: how to Read More...

Objectives Review the grammar of graphics template. Learn about the statistical transformations inherent to geoms. Learn more about fine tuning figures with labels, legends, scales, and themes. Learn how to save plots with ggsave() . Review Read More...

R is both a computational language and open-source environment for statistical computing and graphics. While R programming was ranked 20 th in popularity when compared with other programming languages in December 2023, it remains a favorite among Read More...

There is an approach to data analysis known as "split-apply-combine", in which the data is split into smaller components, some type of analysis is applied to each component, and the results are combined. Read More...

Introduction to ggplot2 Objectives Learn the ggplot2 syntax. Build a ggplot2 general template. By the end of the course, students should be able to create simple, pretty, and effective figures. Data Visualization in the tidyverse Read More...

R Crash Course: A few things to know before diving into wrangling Learning the Basics Objectives 1. Learn about R objects 3. Learn how to recognize and use R functions 4. Learn about data types and accessors Console Read More...

Objectives To explore Bioconductor, a repository for R packages related to biological data analysis. To better understand S4 objects as they relate to the Bioconductor core infrastructure. To learn more about a popular Bioconductor S4 Read More...

dplyr : joining, tranforming, and summarizing data frames Objectives Today we will continue to wrangle data using the tidyverse package, dplyr . We will learn: how to join data frames using dplyr how to transform and create Read More...

Introduction to dplyr and the %>% Objectives Today we will begin to wrangle data using the tidyverse package, dplyr . To this end, you will learn: how to filter data frames using dplyr how to employ Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Learn * What are sequence adapters? * Do we need to trim them before alignment? * How can I trim with a new adapter sequence? Be Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Learn * What are sequence adapters? * Do we need to trim them before alignment? * How can I trim with a new adapter sequence? Be Read More...

When analyzing high throughput sequencing data, we will need to trim away adapters. Adapters help anchor the unknown sequencing template to the Illumina flow cell and can interfere with alignment. We may also want to Read More...

Lesson 15: Finding differentially expressed genes Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 14 review In the previous lesson, we learned to visualize RNA sequencing alignment results in the Integrative Read More...

Lesson 1: Introduction to Unix and the Shell Lesson Objectives Review the course syllabus and general structure of lessons to come. Introduce Unix and describe how it differs from other operating systems. Introduce and get set Read More...

Note that we now have differential expression by transcripts and our first column contains the transcript IDs. But what genes do these transcripts map to? We will need to do some data wrangling to find Read More...

This page uses context taken directly from the Biostar Handbook by Istvan Albert. Remember to activate the class bioinformatics environment. conda activate bioinfo Introduction to Genomic Variation Genomic variations are typically categorized into different classes Read More...

More useful Unix Flags and command options - making programs do what they do Use of wildcards Using tab complete for less typing Access your history with the "up" and "down" Read More...

Gene ontology and pathway analysis Objectives Determine potential next steps following differential expression analysis. Tour geneontology.org and understand the three main ontologies. Learn about different methods and tools related to functional enrichment and pathway Read More...

More useful Unix Flags and command options - making programs do what they do Use of wildcards Using tab complete for less typing Access your history with the "up" and "down" Read More...

More useful Unix Flags and command options - making programs do what they do Use of wildcards Using tab complete for less typing Access your history with the "up" and "down" Read More...

Lesson 4: Useful Unix For this lesson, you will need to login to the GOLD environment on DNAnexus. Lesson 3 Review Biowulf is the high performance computing cluster at NIH. When you apply for a Biowulf account Read More...

Lesson 1: Introduction to Unix and the Shell Lesson Objectives Course overview. Introduce Unix and describe how it differs from other operating systems. Introduce and get set up on DNAnexus and the GOLD system. Discuss ways Read More...

Lesson 6: sra-tools, e-utilities, and parallel This page uses some content directly from the Biostar Handbook by Istvan Albert. Lesson 5 Review: The majority of computational tasks on Biowulf should be submitted as jobs: sbatch or swarm Read More...

Lesson 6: Downloading data from the SRA For this lesson, you will need to login to the GOLD environment on DNAnexus. Lesson 5 Review: The majority of computational tasks on Biowulf should be submitted as jobs: sbatch Read More...

Lesson 16: RNA sequencing review and classification based analysis Before getting started, remember to be signed on to the DNAnexus GOLD environment. Review In the previous classes, we learned about the steps involved in RNA sequencing Read More...

Lesson 4: Submitting R Scripts via command line Learning Objectives Learn how to use R with less interaction Learn how to deploy sbatch R jobs, and learn about alternatives such as swarm . Learn about R job Read More...

Lesson 1: Introduction to Biowulf, Unix, and R Learning Objectives Learn about why you may want to use R on Biowulf. Refresh Unix and R skills. This lesson will not be hands on. Why use R Read More...

Learning Objectives Understand the components of an HPC system. How does this compare to your local desktop? Learn about Biowulf, the NIH HPC cluster. Learn about the command line interface and resources for learning. What Read More...

Learning Objectives Learn about popular programming languagues in bioinformatics Compare advantages and disadvantages of Python and R Discuss what you will need to learn to use these languages Discuss learning resources Choosing a programming language Read More...

Objectives Combine multiple plots into a single figure Learn how to use aspects of cowplot and patchwork The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel Read More...