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Search Results for: base calling

Total Results Found: 112

Total Results Found: 112

R Introductory Series: R base graphics

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Bioinformatics

You do not need to load a package to visually explore data. Rather, you can use base R graphics for plotting (from the graphics package). This plotting is fairly different from ggplot2 , which is based Read More...

R Introductory Series: Base R and data frames

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Bioinformatics

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...

R Introductory Series: Deleting objects

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Bioinformatics

# 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

CCR Collaborative Bioinformatics Resource (CCBR)
Bethesda, MD

Collaborative

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...

ChIP-Seq Analysis Using Galaxy

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Bioinformatics

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...

Integration of SAS with Open-Source Tools

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Bioinformatics

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...

Advanced Coding Macros in SAS

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Bioinformatics

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...

ChIP Sequencing Data Analysis

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Bioinformatics

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...

Introduction to Geneious Prime

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Bioinformatics

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...

R Introductory Series: Subsetting with dplyr

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Bioinformatics

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 .

R Introductory Series: Learning Objectives

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Bioinformatics

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 .

R Introductory Series: Test your learning

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Bioinformatics

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...

R Introductory Series: Test your learning

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Bioinformatics

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...

Data Frames and Data Wrangling (part 1)

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Bioinformatics

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

Data Wrangling with R: Things to note

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Bioinformatics

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...

Data Wrangling with R: Lesson 3

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Bioinformatics

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...

NIH Intramural Sequencing Center (NISC)
Rockville, MD

Trans NIH Facility

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...

Bioinformatics Resources

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

Large STARS Submission Instructions

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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...

Nanotechnology Characterization Lab
Frederick, MD

Collaborative

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...

R Introductory Series: Basics of R Programming

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Bioinformatics

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...

Introduction to R: Part 2 – Data Visualization

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Bioinformatics

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 Introductory Series: What is R?

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Bioinformatics

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...

R Introductory Series: Using functions

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Bioinformatics

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...

R Introductory Series: Course Overview

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Bioinformatics

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...

Data Wrangling with R: Why ggplot2?

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Bioinformatics

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...

Data Wrangling with R: Get the data

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Bioinformatics

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...

Bioinformatics for Beginners 2022: Module2 outline

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Bioinformatics

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...

CCR Sequencing Facility
Frederick, MD

Core Facility

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...

NIH Clinical Center Positron Emission Tomography (PET)
Bethesda, MD

Core Facility

Trans NIH Facility

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...

All Scientific Resources

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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...

STARS Request

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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...

R Introductory Series: Introduction to R and RStudio

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Bioinformatics

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...

R Introductory Series 2023: Introduction to R and RStudio

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Bioinformatics

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...

BTEP Video Archive of Past Classes

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Bioinformatics

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 Quick Start Guide

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Bioinformatics

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...

The Who, What, Why of BTEP

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Bioinformatics

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...

R Introductory Series: Object data types

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Bioinformatics

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...

R Introductory Series: Course Overview

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Bioinformatics

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...

R Introductory Series: Facets

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Bioinformatics

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...

Data Wrangling with R: Challenge data load

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Bioinformatics

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...

Data Wrangling with R: Accessors

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Bioinformatics

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...

Data Wrangling with R: Facets

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Bioinformatics

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...

Data Wrangling with R: Help Session Lesson 6

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Bioinformatics

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...

Data Wrangling with R: Lesson 6: Continuing with Dplyr

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Bioinformatics

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...