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There are rules regarding the naming of objects. 1. Avoid spaces or special characters EXCEPT '_' and '.' 2. No numbers or underscores at the beginning of an object name. For example: 1a:1:2: unexpected symbol ## 1: 1a ## ^ Note It is Read More...

There are rules regarding the naming of objects. Avoid spaces or special characters EXCEPT '_' and '.' No numbers or symbols at the beginning of an object name. For example: 1a:1:2: unexpected symbol ## 1: 1a ## ^ In contrast: a

There are rules regarding the naming of objects. 1. Avoid spaces or special characters EXCEPT '_' and '.' 2. No numbers or underscores at the beginning of an object name. For example: 1a:1:2: unexpected symbol ## 1: 1a ## ^ Note: It is Read More...

11/18/2021 - In this class, we will explore the basics of R. Topics covered will include: Overview of R Studio IDE Checking R version and switching between different versions of R Working with directories Setting working Read More...

What is the difference between saving and committing. Saving just involves saving your work. For instance, suppose you are working on a Word document. You might save it at the end of the day on Read More...

In this coding club, we will Discuss commonly observed errors. How to approach and debug R code. How to find help. Debugging is the process of identifying and fixing errors or potential errors in your Read More...

In this coding club, we will Discuss commonly observed errors. How to approach and debug R code. How to find help. Debugging is the process of identifying and fixing errors or potential errors in your Read More...

Now that we know what we mean by denoising, let's apply it to our data. We will use DADA2 , which seems to be the more popular method. To use DADA2, we need to make Read More...

Which of the following will throw an error and why? 4 _ chr :1:2: unexpected input ## 1: 4_ ## ^ . 4 chr :1:3: unexpected symbol ## 1: .4chr ## ^ {{Edet}} Create the following objects; give each object an appropriate name (your best guess at what name to Read More...

This is our first coding help session. We have designed some practice problems to get you acquainted with using R before beginning to wrangle in our next lesson. Practice problems Which of the following will Read More...

Functional annotation clustering works to cluster annotations that share similar genes. If we click on Functional Annotation Clustering in the Annotation Summary Results page then we can see the functional annotation clusters that our input Read More...

Step 1 for generating bigWig files is to convert the BAM alignment results to a bedGraph (with extension bg) file that contains coverage along genomic regions. Enhancing your vocabulary: BED file - this is also known Read More...

There are rules regarding the naming of objects. Avoid spaces or special characters EXCEPT '_' and '.' No numbers or underscores at the beginning of an object name. For example: 1a<-"apples" # Read More...

The Functional Annotation Clustering tool groups similar annotations together to reduce the redundancy seen in the Functional Annotation Chart results. This eases the interpretation of the findings. The Functional Annotation Clustering integrates the same techniques Read More...

Note that each section of the report is marked by color coded flags (i.e., green, yellow, red). Yellow and red flags, which indicate "warning" and "fail" respectively, may indicate a Read More...

The first step for generating bigWig files is to convert the BAM alignment results to bedGraph (with extension bg) files that contains sequencing depth along genomic regions (ie. how many sequences mapped to a genomic Read More...

NIH Intramural CryoEM Consortium (NICE) serves intramural investigators in all NIH IC’s. NICE provides access to state-of-the-art Titan Krios cryo-electron microscopes for atomic-resolution structure determination of protein, macromolecular complexes, membrane receptors, cellular organelles, and Read More...

[tabby title="Home"] About NICE-NIH Intramural CryoEM Consortium  NIH Intramural CryoEM Consortium (NICE) serves intramural investigators in all NIH IC’s. NICE provides access to state-of-the-art Titan Krios cryo-electron microscopes for atomic-resolution structure determination of Read More...

1. Introduction and Learning Objectives This tutorial has been 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 that 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...

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 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 3: Creating a feature table Lesson Objectives Check for primers Generate an ASV count table and representative sequence file Understand the difference between OTU picking and denoising The two primary files that will be used 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...

Database for Annotation, Visualization and Integrated Discovery (DAVID) - an overview Lesson 17 review In the previous class, we got an overview of functional and pathway analysis, which help to put RNA sequencing results into biological 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 basics 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 Read More...

Nano is a built-in Unix text editor. We can use nano to create SRR1553606_fastqc.sh. The syntax here is nano file_to_edit so in this example do the following. nano SRR1553606_fastqc.sh Read More...

An important step in generating a heatmap is to scale the data by z-score (see equation below). Given a vector of numbers, the z-score informs how many standard deviations is each number in the vector Read More...