Search Results for: print collateral

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...

Which of the following will NOT print the "Run" column from scaled_counts? a. scaled_counts$Run b. scaled_counts["Run"] c. scaled_counts[8,] d. scaled_counts[8] {{Sdet}} Solution{{Esum}} C {{ Read More...

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

pwd (print working directory) ls (list) nano (basic editor for creating small text files) rm (remove files) mkdir (make a directory) cd (change directory) mv (rename or move files) less (view files) man (manual) cp ( Read More...

You can use the up arrow on your keyboard when using the console to pull up previously used commands. Certain symbols in R always come in pairs, for example, parentheses and quotation marks. If you Read More...

When loading tabular data with readr , the default object created will be a tibble . Tibbles are like data frames with some small but apparent modifications. For example, they can have numbers for column names, and Read More...

We will use the R helper scripts that we used before. cat 22simple_counts.txt | Rscript deseq2.r 3x3 > 22results_deseq2.txt The file 22results_deseq2.txt contains the genes sorted by their adjusted Read More...

We will use the R helper scripts that we used before. cat 22simple_counts.txt | Rscript deseq2.r 3x3 > 22results_deseq2.txt The file 22results_deseq2.txt contains the genes sorted by their adjusted Read More...

We will use the R helper scripts that we used before. cat 22simple_counts.txt | Rscript deseq2.r 3x3 > 22results_deseq2.txt The file 22results_deseq2.txt contains the genes sorted by their adjusted Read More...

pwd (print working directory) ls (list) cd (change directory), by itself will take you home, cd .. (will take you up one directory), cd /results_dir/exp1 (go directly to this directory)

Used to perform specific tasks. R: product

Used to perform specific tasks. R: ::: {.cell} product

R: Data types: integer, numeric, character, and logical Data structures: vectors, lists, data frames, matrices. x

Now we are ready to work with some of our first R commands. We are going to run commands directly from our R script rather than typing into the R console. Our first command will Read More...

Which of the following functions is used to print your working directory in R? a. pwd b. Setwd() c. getwd() d. wkdir() {{Sdet}} Solution{{Esum}} C {{Edet}} Which of the following can be used to Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More NIH Research Festival The NIH Research Festival highlights the groundbreaking science and the vibrant NIH community driving our Read More...

10/15/2024 - This one-hour online training will cover techniques on locating biomedical research articles, patents, NIH-funded research projects, genetic information, and print and electronic books related to animal models and model organisms. This training will also Read More...

#Run one step at a time with intermediate objects. #We've done this a few times above #select gene, logFC, FDR dexp_s 1 TSPAN6 -0.390 0.00283 2 DPM1 0.198 0.0770 Or we could nest a function within a function.

::: {.cell} #Run one step at a time with intermediate objects. #We've done this a few times above #select gene, logFC, FDR dexp_s 1 TSPAN6 -0.390 0.00283 2 DPM1 0.198 0.0770 ::: ::: Or we could nest a function within a Read More...

Now that we know what a function is, let's use them to navigate our directories. Our first function will be getwd() . This simply prints your working directory (our default directory for saving files) and Read More...

09/12/2023 - Animal models are used to study the development and progression of diseases and to test new treatments. Model organisms are a subset of research organisms that serve as a proxy for understanding human biology. Read More...

pwd (print working directory) ls (list) touch (creates an empty file) nano (basic editor for creating small text files) using the rm command to remove files. Be careful! mkdir (make a directory) and rmdir (remove Read More...

ls (list) pwd (print working directory) touch (creates an empty file) nano (basic editor for creating small text files) using the "rm" command to remove files. Be careful! mkdir (make a directory) and Read More...

ls (list) pwd (print working directory) touch (creates an empty file) nano (basic editor for creating small text files) using the "rm" command to remove files. Be careful! mkdir (make a directory) and Read More...

ls (list) pwd (print working directory) touch (creates an empty file) nano (basic editor for creating small text files) using the rm command to remove files. Be careful! mkdir (make a directory) and rmdir (remove Read More...

ls (list) pwd (print working directory) touch (creates an empty file) nano (basic editor for creating small text files) using the rm command to remove files. Be careful! mkdir (make a directory) and rmdir (remove Read More...

To help us align all of the FASTQ files in one go, we should create in the reads directory a file with the sample IDs names for the Golden Snidget. First, change into the ~/biostar_ Read More...

This page contains content taken directly from the Biostar Handbook by Istvan Albert. Activate the bioinformatics environment. conda activate bioinfo First let's make a place to store today's work. In your biostar_class Read More...

Filtering is done to keep or remove variants to satisfy certain constraints. Make a new directory and download the example data (VCF file and index). This file contains variants in chromosome 19:400kb-500kb mkdir hg19 Read More...

This page contains content taken directly from the Biostar Handbook by Istvan Albert. Always remember to start the bioinformatics environment. conda activate bioinfo We will be analyzing differential expression of genes on Chr22 from the Read More...

This page contains content taken directly from the Biostar Handbook by Istvan Albert. Always remember to start the bioinformatics environment. conda activate bioinfo We will be analyzing differential expression of genes on Chr22 from the Read More...

This is a very helpful command used for moving around the directory structure. It can be used to go to a specific directory. Let's "go to" the directory we just made, and Read More...

Can you print the first sequencing read (from header line to quality score line) in hcc1395_normal_rep1_r1.fastq.gz? {{Sdet}} Solution{{Esum}} zcat hcc1395_normal_rep1_r1.fastq.gz | head -4 {{Edet}} How Read More...

Let's now create a gene expression heatmap for results generated using the classification based approach. We have our results.csv and design.csv file in our salmon directory so we just need to do Read More...

Lesson 7: Downloading the RNA-Seq Data and Dataset Overview Lesson Review pwd (print working directory) ls (list) touch (creates an empty file) nano (basic editor for creating small text files) using the rm command to remove Read More...

First we will retrieve the Ebola reference genome and put it in the "refs" directory. Next we need to create the index for the aligner (bwa index) and for IGV (samtools faidx). Let' Read More...

To align FASTQ files for one sample, we construct the HISAT2 command with the following options. The "-x" flag prompts us to enter the base name (ie. without extension) of genome index. The Read More...

This page contains content taken directly from the Biostars Handbook by Istvan Albert. Remember to activate the bioinformatics environment. conda activate bioinfo Install the statistical packages we will need for the analysis, curl http://data. Read More...

Before we can align the HBR and UHR raw sequencing data to human chromosome 22 transcriptome, we need to create an index of this transcriptome (like we did with the genome). This will make the alignment Read More...

The bulk RNA-Seq test data we've been working with is in FASTQ format. We'd like to do a BLAST search on a couple of these sequences. Data must be in FASTA format to Read More...

After the merged expression counts table has been created, we can proceed with differential expression analysis. Let's use DESeq2 again for this. But first, let's move counts.csv (the merged salmon expression table) Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Always remember to activate the bioinfo environment when working on Biostar class materials. conda activate bioinfo The bulk RNA-Seq test data we've 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...

R: Data types: integer, numeric, character, and logical Data structures: vectors, lists, data frames, matrices. ::: {.cell} x ::: :::

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...

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...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New CREx User Survey The CREx Team is carrying out a CREx User Survey. If you haven’t Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More New NIH Resource Spotlight The NIH Lab Managers Working Group have developed a new NIH-wide database of cold Read More...

CREx News & Updates December 2021 Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Site Spotlight FACILITY HIGLIGHTS Learn more about services from the STRIDES Initiative. VIEW STRIDES INITIATIVE NIH Cores Read More...

CREx News & Updates July 2022 Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Click below to learn how easy it is to navigate the CREx platform. These short videos will 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...

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...

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...

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...

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...

Lesson 2: Getting Started with QIIME2 Lesson Objectives Obtain sequence data and sample metadata Import data and metadata Discuss other useful QIIME2 features including view QIIME2, provenance tracking, and the QIIME2 forum. DNAnexus DNAnexus provides a 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...

Prior to differential expression analysis, we need to generate a design.csv file that contains the samples and their corresponding treatment conditions. Note that csv stands for comma separated value so the columns in these 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...

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...

Why Learn Bioinformatics? Analyze your own data Expand scientific training and skills Provide a path to a new career Have a better understanding of how other people analyze data What is Unix? an operating system, Read More...

Why Learn Bioinformatics? Analyze your own data Expand scientific training and skills Provide a path to a new career Have a better understanding of how other people analyze data What is Unix? an operating system, Read More...

Let's now take a look at our final differential analysis results table (results_with_gene_names_labeled.txt), using the SLC2A11 gene as an example and below we use the column command to Read More...

Why Learn Bioinformatics? Analyze your own data Expand scientific training and skills Provide a path to a new career Have a better understanding of how other people analyze data What is Unix? an operating system, Read More...

Why Learn Bioinformatics? Analyze your own data Expand scientific training and skills Provide a path to a new career Have a better understanding of how other people analyze data What is Unix? an operating system, Read More...

Step 1 for generating bigWig files is to convert the BAM alignment results to a bedGraph file that contains coverage along genomic regions. Enchancing your vocabulary: BED file - this is also known as Browser Extensible 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...

Lesson 13: Aligning raw sequences to reference genome Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 11 Review In Lesson 11 we learned to aggregate multiple FASTQC reports into one using MultiQC, Read More...

Lesson 2: Navigating file systems with Unix Quick review Unix is an operating system We use a unix shell (typically bash) to run many bioinformatics programs We need to learn unix to use non-GUI based tools Read More...

Lesson 12 Practice Objectives In this practice session, we will work with something new, which is a dataset from the Griffith lab RNA sequencing tutorial. Here, we will have a chance to practice what we have Read More...

Lesson 13 Practice Objectives In this lesson we learned how to align raw sequencing reads to reference and to process alignment results for downstream analysis. Here, we will test our knowledge by continuing with the Golden Read More...

Generating VCF Files (Simulated data) 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 Read More...

Here, let's change back in the ~/biostar_class/hbr_uhr/hbr_uhr_hisat2 folder. cd $hbr_uhr_hisat2 To align FASTQ files for one sample, we construct the HISAT2 command with the following options Read More...

First Unix command (ls) ls You may see something like this: public reads.tar sample.fasta sample.fastq The "ls" command "lists" the contents of the directory you are in. You Read More...

First Unix command (ls) ls You may see something like this: public reads.tar sample.fasta sample.fastq The "ls" command "lists" the contents of the directory you are in. You Read More...

Now that we have downloaded the HBR and UHR dataset and know where analysis tools are, let's start learning about RNA sequencing, by first learning about our reference genome and annotation files. Let's Read More...

Now that we have downloaded the HBR and UHR dataset and know where analysis tools are, let's start learning about RNA sequencing, by first learning about our reference genome and annotation files. Let's Read More...

First Unix command (ls) ls You may see something like this: public reads.tar sample.fasta sample.fastq The "ls" command "lists" the contents of the directory you are in. You Read More...

First Unix command (ls) ls You may see something like this: public reads.tar sample.fasta sample.fastq The "ls" command "lists" the contents of the directory you are in. You 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 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...

“Gene set enrichment analysis” refers to the process of discovering the common characteristics potentially present in a list of genes. When these characteristics are GO terms, the process is called “functional enrichment.” Warning Overall GO 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 14: Visualizing alignment results Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 13 Review Previously, we used the application HISAT2 to align the raw sequencing data from the Human Brain 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...

This page uses content directly from the Biostar Handbook by Istvan Albert. Obtain RNA-seq test data. The test data consists of two commercially available RNA samples: Universal Human Reference (UHR) and Human Brain Reference (HBR) . Read More...

In lesson 9, we learned that reference genomes came in the form of FASTA files, which essentially store nucleotide sequences. In this lesson, we will learn about the FASTQ file, which is the file format that Read More...

Lesson 10: Introducing the FASTQ file and assessing sequencing data quality Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 9 Review In the previous lesson, we explored the reference genomes and Read More...

Lesson 13: Aligning raw sequences to reference genome Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 11 Review In Lesson 11 we learned to aggregate multiple FASTQC reports into one using MultiQC, Read More...

Lesson 9: Reference genomes and genome annotations used in RNA sequencing Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 8 Review In Lesson 8, we learned about the basics of RNA sequencing, Read More...

Lesson 9: Reference genomes and genome annotations used in RNA sequencing Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 8 Review In Lesson 8, we learned about the basics of RNA sequencing, 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...

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...