Search Results for: base calling

Variants are usually called from alignment (BAM) files. Here is the process: 1. Align reads to the reference. 2. Correct and refine alignments (optional). 3. Determine variants from the alignments. 4. Filter the resulting variants for the desired characteristics. 5. Read More...

Lesson 3 Exercise Questions: BaseR dataframe manipulation and factors The filtlowabund_scaledcounts_airways.txt includes normalized and non-normalized transcript count data from an RNAseq experiment. You can read more about the experiment here . We are going Read More...

They are typically much faster (~10x) than their base equivalents. Long running jobs have a progress bar, so you can see what’s happening. If you’re looking for raw speed, try data.table::fread(). Read More...

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The base data type (e.g., numeric, character, logical, etc.) and the class (dataframe, matrix, etc.) will be important for what you can do with an object. Learn more about an object with the following: Read More...

# delete the object 'gene_name' rm(gene_name) #the object no longer exists, so calling it will result in an error gene_name ## Error in eval(expr, envir, enclos): object 'gene_name' not found

Learn about the structure of FASTQ files Create a text file contains the base names of the HBR and UHR FASTQ files so that we can use those in the future (base names are file Read More...

The CCR Collaborative Bioinformatics Resource (CCBR) is a centrally funded resource group which provides a mechanism for CCR researchers to obtain many different types of bioinformatics assistance to further their research goals. The group has Read More...

The CCR Collaborative Bioinformatics Resource (CCBR) is a resource group which provides a mechanism for CCR researchers to obtain many different types of bioinformatics assistance to further their research goals. The group has expertise in Read More...

06/17/2025 - Explore tran cription factor binding ite analy i and peak calling. Thi training will introduce ChIP- eq data analy i followed by a tep-by- tep live demon tration of a ChIP- eq analy i Read More...

09/18/2024 - This one-hour online training will cover several integration points between SAS and open-source tools to empower the developer and the organization to integrate the benefits of both SAS and open source. & Read More...

06/12/2024 - Macros are ways to use code to substitute in a value, and using macros makes a code in SAS easier to read and edit, less prone to errors, and allows it to run more Read More...

06/04/2024 - Galaxy is a scientific workflow, data integration, data analysis, and publishing platform that makes computational biology accessible to research scientists that do not have computer programming experience. This training will introduce ChIP sequencing data Read More...

Base R cheat sheet Other cheat sheets can be found here . There is also a nice review here . There are a ton of free tutorials. There are at least 230 Git repositories that focus on R Read More...

There are 3 primary plotting systems with R: base R, ggplot2 , and lattice . Data visualization functions from Seurat primarily use ggplot2 and can easily be customized by adding additional ggplot2 layers. Check out the R Graph Read More...

03/20/2024 - Geneious Prime is a comprehensive software suite of molecular biology and NGS analysis tools. In this presentation, a Field Application Scientist with Geneious Prime, Dr. Evan Starr will discuss a general introduction to the Read More...

This lesson will introduce data wrangling with R. Attendees will learn to filter data using base R and tidyverse (dplyr) functionality.

We've seen how to select columns and rows using base R, but now let's look at a more intuitive way with functions ( select() and filter() ) from the tidyverse package dplyr .

To make scripting reproducible, you could avoid calling a specific number directly and use objects in logical evaluations like those above. If we use an object, the value itself could easily be replaced with whatever Read More...

Understand the concept of tidy data. Become familiar with the tidyverse packages. Be able to filter a data frame by rows and columns using base R and dplyr .

Using mutate apply a base 10 logarithmic transformation to the counts_scaled column of sscaled . Save the resulting data frame to an object called log10counts. Hint: see the function log10() . ::: {.cell} log10counts mutate ( logCounts = Read More...

Using mutate apply a base 10 logarithmic transformation to the counts_scaled column of sscaled . Save the resulting data frame to an object called log10counts. Hint: see the function log10() . {{Sdet}} Possible Solution{{Esum}} log10 Read More...

Try googling your problem or using some other search engine. rseek is an R specific search engine that searches several R related sites. If using google directly, make sure you use R to tag your Read More...

02/01/2024 - This lesson will introduce data wrangling with R. Attendees will learn to filter data using base R and tidyverse (dplyr) functionality.   

R doesn't care about spaces in your code. However, it can vastly improve readability if you include them. For example, "thisissohardtoread" but "this is fine". You can use tab completion Read More...

So far we have only worked with objects that we created in RStudio. We have not installed or loaded any packages. R packages extend the use of R programming beyond base R.

To reassign an object, simply overwrite the object. #object with gene named 'tp53' gene_name

Lesson 3 covered data import and reshaping. Data unavailable through base R or other R packages, can be downloaded here . The survey / species data sets were obtained from a data carpentry lesson Data Analysis and Visualization Read More...

Importing and exporting data into the R environment is done using base R and readR ( readxl in the case of excel files) functions. Most of these functions begin with read. and read_ for importing, or Read More...

We have learned how to import data using the read functions, but how can we export / write out data? We can use a series of write functions. Some examples from readr include write_csv() , write_ Read More...

12/07/2023 - This webinar will discuss and demonstrate experimental design considerations in variant analysis, including the origins of tissue samples (germline vs. somatic), whole exome (WES) or whole-genome sequencing (WGS), sample sizes and statistical power, quality Read More...

Introduction to classification base RNA sequencing Review of RNA sequencing

We will be downloading a FASTQ file from the Sequence Read Archive to learn about trimming. But first, go back to the ~/biostar_class folder and then create a new directory named trimming. cd ~/biostar_ Read More...

FASTQC to obtain quality metrics for individual FASTQ files. Recall that FASTQ files contain our sequencing data and each file has many sequencing reads. Each read is composed of four lines Header, that starts with @ 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...

This page uses content directly from the Biostar Handbook by Istvan Albert. Start by activating the bioinfo environment. conda activate bioinfo Create a new directory for the multiqc data. mkdir multi cd multi Retrieve the Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Start by activating the bioinfo environment. conda activate bioinfo Create a new directory for the multiqc data. mkdir multi cd multi Retrieve the Read More...

NISC’s role within NHGRI, and more broadly across NIH, aims to advance genome sequencing and its many applications, with a goal not simply to produce sequence data, but to produce the infrastructure required to Read More...

Discover expert help with analysis, processing applications, and licensed software packages.

Large STARS can be used to request supplemental funding exceeding $8,000 to offset experimental costs (e.g., core or vendor services or specialized reagents).  Applications require detailed scientific and budgetary justification and must be proposed in Read More...

NCI established the Nanotechnology Characterization Laboratory (NCL) to support the extramural research community to accelerate the progress of nanomedicine by providing preclinical characterization and safety testing of nanoparticles. It is a collaborative effort between NCI, Read More...

Long read sequencing was recently named 2022’s method of the year by Nature Methods . Long read sequencing technologies, those that generate sequence reads with lengths of 10s of kilobases or longer have several advantages over 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...

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...

There are a number of core facilities available to NCI researchers. See more information from the Office of Science and Technology Resources. We most commonly see data from the following cores: CCR Sequencing Facility (CCR-SF) Read More...

04/16/2025 - Updated Location: ATRF, Frederick MD, Main Auditorium What to bring:  Laptop capable of connecting to internet via NIH wifi For questions or to register, please contact Amy Stonelake ( amy.stonelake@nih.gov ) Read More...

04/15/2025 - What to bring:  Laptop capable of connecting to internet via NIH  wifi Are you looking to expand the reach of your sequencing to enable what long read technologies can provide?& Read More...

09/06/2024 - This course provides an introduction to data visualization using R. Participants will learn data visualization with base R and using the R package ggplot2 to explore various types of data visualizations, including scatter plots, Read More...

R is both a computational language and environment for statistical computing and graphics. It is open-source and widely used by scientists, not just bioinformaticians. Base packages of R are built into your initial installation, but Read More...

There are several metrics that can be used to assess overall quality. The base workflow from Seurat suggests the following: nCount_RNA - the absolute number of RNA molecules (UMIs) per cell (i.e., count Read More...

Now that we have loaded the package, we can import our data. Generally, in R programming, functions that involve data import begin with "read / Read". Seurat includes a number of read functions for Read More...

R is both a computational language and environment for statistical computing and graphics. It is open-source and widely used by scientists, not just bioinformaticians. Base packages of R are built into your initial installation, but Read More...

A function in R (or any computing language) is a short program that takes some input and returns some output. An R function has three key properties: Functions have a name (e.g. dir, getwd); Read More...

Lesson 1: Introduction to R and RStudio\ Lesson 2: The Basics of R Programming (syntax and base R)\ Lesson 3: R Data Structures: Introducing Data Frames\ Lesson 4: Data Frames and Data Wrangling (part 1)\ Lesson 5: Data Frames and Data Read More...

Before we can do anything with our data, we need to first import it into R. There are several ways to do this. First, the RStudio IDE has a dropdown menu for data import. Simply Read More...

To subset by column, we use the function select() . We can include and exclude columns, reorder columns, and rename columns using select() . Select a few columns from our differential expression results ( dexp ). We can select Read More...

Outside of base R plotting, one of the most popular packages used to generate graphics in R is ggplot2 , which is associated with a family of packages collectively known as the tidyverse. GGplot2 allows the Read More...

Before we can do anything with our data, we need to first import it into R. There are several ways to do this. First, the RStudio IDE has a drop down menu for data import. Read More...

R is generally thought of as a functional programming language, where the focus is on the functions rather than the object, and the output of the function is always the same given the same inputs. Read More...

Get the data Lesson 1 No data available. Lesson 2 No data available. Lesson 3 Lesson 3 covered data import and reshaping. Data unavailable through base R or other R packages, can be downloaded here . The survey / species data 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...

This page uses content directly from the Biostar Handbook by Istvan Albert. Remember to activate the bioinformatics environment. conda activate bioinfo The jellyfish program is dependent on a program called "gcc" which is Read More...

VCF files are produced by running a variant caller on one or more BAM alignment files. We will download the ebola genome (AF086833) into a "refs" directory, create a "bwa index" Read More...

Bioinformatics for beginners Module 2: Introduction to RNA sequencing In this module, we will use the Human Brain Reference and Universal Human Reference RNA sequencing datasets to learn about RNA sequencing. Each lesson will be followed Read More...

The introduction of DNA sequencing instruments capable of producing millions of DNA sequence reads in a single run has profoundly altered the landscape of genetics and cancer biology. Complex questions can now be answered at Read More...

The PET Department, CC, functions as a core facility that supports basic, translational, and clinical research using PET. It is a vertically integrated facility, with resources to produce positron-emitting radionuclides, manufacture PET radiopharmaceuticals in a Read More...

Cores & Facilities From centralized laboratories to collaborative resources and technologies available to CCR Investigators. NCI Cores Centralized laboratories providing broad access to cutting-edge technologies and specialized expertise. Browse NCI Cores Collaborative Resources are technologies and Read More...

The NIH Center for Human Immunology, Inflammation, and Autoimmunity (CHI) is a trans-NIH resource whose mission is to provide a collaborative hub of advanced translational immunology for NIH clinical and pre-clinical studies. This uniquely structured Read More...

Supplemental Technology Award Review System (STARS) Overview STARS Request Form STARS System The Supplemental Technology Award Review System (STARS) is a web-based interface for submission and review of S&S supplement requests by CCR Read More...

Learning Objectives This tutorial was designed to demonstrate common secondary analysis steps in a scRNA-Seq workflow. We will start with a merged Seurat Object with multiple data layers representing multiple samples. Throughout this tutorial we Read More...

Learning Objectives To understand: 1. the difference between R and RStudioIDE. 2. how to work within the RStudio environment including: creating an Rproject and Rscript navigating between directories using functions obtaining help how R can enhance data 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...

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...

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...

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...

Introduction to R and RStudio IDE Objectives To understand: 1. the difference between R and RStudioIDE. 2. how to work within the RStudio environment including: creating an Rproject and Rscript navigating between directories using functions obtaining help 3. 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...

Partek Flow enables scientists to build comprehensive workflows for analyzing multi-omics high throughput sequencing data including DNA and variant calling, bulk and single cell modalities for RNA, ChIP, and ATAC, spatial transcriptomics, CITE, and immune Read More...

Following Cell Ranger and/or other pre-processing tools, you will have a gene-by-cell counts table for each sample. The three most popular frameworks for analyzing these count matrices include: R ( Seurat ). Seurat, brought to you Read More...

Clustering is used to group cells by similar transcriptomic profiles. Seurat uses a graph based clustering method. You can read more about it here . The first step is to compute the nearest neighbors of each Read More...

This lesson provides an introduction to R in the context of single cell RNA-Seq analysis with Seurat. Learning Objectives Learn about options for analyzing your scRNA-Seq data. Learn about resources for learning R programming. Learn Read More...

What is BTEP? BTEP is the NCI CCR Bioinformatics Training and Education Program. The BTEP mission is to enable scientists to understand and analyze their own experimental data. At BTEP, we do this 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...

Course Overview Welcome to the R Introductory Series! A series of introductory lessons in R for scientists. This course will include a series of lessons for individuals new to R or with limited R experience . 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...

A way to add variables to a plot beyond mapping them to an aesthetic is to use facets or subplots. There are two primary functions to add facets, facet_wrap() and facet_grid() . If faceting Read More...

Welcome! Welcome to the R Introductory Series! Who: Novices and beginners\ What: A course series introducing R and RStudio. This course will introduce the foundational skills necessary to begin to analyze and visualize data in Read More...

This lesson will introduce data wrangling with R. Attendees will learn to filter data using base R and tidyverse (dplyr) functionality. Learning Objectives Understand the concept of tidy data. Become familiar with the tidyverse packages. 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...

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...

Learning Objectives Learn about data structures including factors, lists, data frames, and matrices. Load, explore, and access data in a tabular format (data frames) Learn to write out (export) data from the R environment Data Read More...

Load in a tab delimited file (file_path= "./data/WebexSession_report.txt") using read_delim() . You will need to troubleshoot the error message and modify the function arguments as needed. {{Sdet}} Solution } library ( Read More...

In tab delimited files, data columns are separated by tabs. To import tab-delimited files there are several options. There are base R functions such as read.delim() and read.table() as well as the readr Read More...

As you can see from the image, there are several accessor functions to access the data from the object: assays() - access matrix-like experimental data (e.g., count data). Rows are genomic features (e.g., Read More...

A way to add variables to a plot beyond mapping them to an aesthetic is to use facets or subplots. There are two primary functions to add facets, facet_wrap() and facet_grid() . If faceting Read More...

Let's grab some data. library ( tidyverse ) acount_smeta % dplyr :: rename ( "Feature" = "...1" ) acount #differential expression results dexp % filter ( ! Feature %in% dexp $ feature ) ## # A tibble: 48,176 × 9 ## Feature SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 ## ## 1 Read More...

Help Session Lesson 6 Let's grab some data. library ( tidyverse ) acount_smeta % dplyr :: rename ( "Feature" = "...1" ) acount #differential expression results dexp % filter ( ! Feature %in% dexp $ feature ) ## # A tibble: 48,176 × 9 ## Feature SRR1039508 SRR1039509 SRR1039512 Read More...

Data import and reshape Objectives 1. Learn to import multiple data types 2. Data reshape with tidyr : pivot_longer() , pivot_wider() , separate() , and unite() Installing and loading packages So far we have only worked with objects that 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...

Help Session Lesson 3 Loading data Import data from the sheet "iris_data_long" from the excel workbook (file_path = "./data/iris_data.xlsx"). Make sure the column names are unique and Read More...

Objectives Review important data wrangling functions Put our wrangling skills to use on a realistic RNA-Seq data set Data Wrangling Review Important functions by topic Importing / Exporting Data Importing and exporting data into the R 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...