Search Results for: step sectioning

Back Services: We offer a limited sample processing service using standard SEC-MALS and FFF protocols.  This service is intended for the occasional users of this system.  Researchers who expect to use this instrument Read More...

Back Services: We offer a limited sample processing service using standard SEC-MALS and FFF protocols.  This service is intended for the occasional users of this system.  Researchers who expect to use this instrument 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, we can either use our filtering parameters directly with subset() or provide a cells argument. # use different parameters; established above adp_filt 350 & nCount_RNA >650 & percent.mt

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...

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...

Step 1 : Login to DNAnexus Step 2 : Once you login, you should see the Projects page. If you have used DNAnexus previously, you may see more than one project listed. If this is your first time using Read More...

To import we will need to keep in mind that our samples are paired-end with quality information and that we are using a manifest format. Note: Phred 64 quality scores are associated with older data, so Read More...

Download the data using prefetch and fasterq-dump . {{Sdet}} Solution{{Esum}} cd raw_data cat ../sra_id.txt | while read sra_id; do prefetch $sra_id; fasterq-dump $sra_id; gzip ${sra_id}*.fastq;done {{Edet}} What Read More...

We will need to use a manifest file to import. See the Import tutorial . Note: The manifest file can be comma separated depending on the format that you use at import , despite what is written Read More...

Notice that the column names each begin with a number (e.g., 1_MB231_RNA1). Place an "S" at the beginning of each sample name using rename_with() , which is similar to rename() , but Read More...

Many of the packages that handle RNASeq count data do not work correctly with decimal numbers. We need to convert these numbers to integers using mutate() . Save your transformed data frame to an object named Read More...

According to the data availability statement, the data can be found in PRJNA803155 . Change to the Practice directory created above or make it now. Then make a new directory named raw_data . {{Sdet}} Solution{{Esum}} 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...

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...

mkdir rnaseq cd rnaseq curl -s http://data.biostarhandbook.com/rnaseq/projects/griffith/griffith-data.tar.gz | tar zxv Directories: * "reads" contains the sequencing reads * "refs" contains genome and annotation information using Read More...

mkdir rnaseq cd rnaseq curl -s http://data.biostarhandbook.com/rnaseq/projects/griffith/griffith-data.tar.gz | tar zxv Directories: * "reads" contains the sequencing reads * "refs" contains genome and annotation information using Read More...

mkdir rnaseq cd rnaseq curl -s http://data.biostarhandbook.com/rnaseq/projects/griffith/griffith-data.tar.gz | tar zxv Directories: * "reads" contains the sequencing reads * "refs" contains genome and annotation information using Read More...

06/03/2025 - Class Description Introduction to RNA-Seq data analysis Step-by-step live demonstration of RNA-Seq analysis using the Galaxy platform   What You’ll Learn: How to independently carry out the basic gene expression Read More...

09/20/2024 - In this course, participants will integrate the knowledge gained from previous sessions on data wrangling, data visualization, and data analysis to undertake a comprehensive demonstration project analyzing real-world data. Through a step-by-step walkthrough of Read More...

There are several built in functions for visualizing data with Seurat. We can use violin plots and scatter plots to check out the individual distributions and correlations between metrics.

Because of the different layers, you will need to break down the object to apply different thresholds by group or sample. The easiest way to do this is to work with the metadata and use Read More...

Separate the gene abbreviation from the Ensembl ID in column 1 using separate() . Save the output to a new object named dmat2 . {{Sdet}} Solution }. #separate gene abbreviation from ensembl id dmat2% select(1:2) dmat2% select(!gene_abb) {{ Read More...

The object class used by the DESeq2 package to store the read counts and the intermediate estimated quantities during statistical analysis is the DESeqDataSet. --- Analyzing RNA-seq data with DESeq2 Constructing this object from a Read More...

The data is available in the Sequence Read Archive (BioProject PRJNA803155 ), so the first step is to grab the data from the SRA. For your convenience, we have also created a compressed archive of the Read More...

In order to create a ggplot2 bar plot we will need to reshape the data. The sample names should be in a single column named Sample and the gene counts in a single column named Read More...

Begin by loading the data and saving to an object named dmat . {{Sdet}} Solution } library(tidyverse) dmat ## 1 ENSG00000001630.… 6877. 6614 7058. 11305. ## 2 ENSG00000002016.… 283. 287. 287. 265. ## 3 ENSG00000002330.… 1946 1662 2121 608 ## 4 ENSG00000002834.… 17636 19333 18917 4583 ## 5 ENSG00000003056.… 3874 4107 4005 5741 ## 6 ENSG00000003393.… 2041 2150 2141 1687 ## 7 ENSG00000003989.… 279 345 305 18586 ## 8 ENSG00000004534.… 2695. 3031 2871 1948 ## 9 ENSG00000004838.… 42 52 39 61 ## 10 ENSG00000004848.… 1 0 0 18 ## # ℹ 9,990 more rows ## # ℹ 2 more variables: `5_Cell2_Rep2` , `6_Cell2_Rep3`

Step 1 : Login to DNAnexus Step 2 : Once you login, you should see the Projects page. If you have used DNAnexus previously, you may see more than one project listed. If this is your first time using Read More...

To proceed with converting the bedGraph files to bigWig, we need to first create an index of our genome using SAMTOOLS and it's faidx feature. Where faidx will index/extract FASTA. samtools faidx refs/22. Read More...

To proceed with converting the bedgraph files to bigWig, we need to first create an index of our genome using SAMTOOLS. samtools faidx refs/22.fa Listing the contents of our refs directory, we now see Read More...

Navigating the NCBI website can be challenging. From a user perspective, nothing is as straight forward as you would expect. For the sequence read archive (SRA), there are fortunately some options. There are the convuluted Read More...

After the index file for the genome has been created, we can go ahead and run the following to create the bigWig files for both the HISAT2 and Bowtie2 alignments. Generate bw files for the Read More...

After the index file for the genome has been created, we will use a tool called bedGraphToBigWig to generate bigWig (bw) files from bedGraph (bg). Again, we use cat and parallel where cat reads/ids. Read More...

11/19/2024 - This one hour and a half online training in the NIH Library Evidence Synthesis Review series provides an overview of the data collection process for your review. The training will cover how to clean Read More...

10/24/2024 - Recent advances in artificial intelligence (AI) have revolutionized the use of hematoxylin and eosin (H&E)-stained tumor slides for precision oncology, enabling data-driven approaches to predict molecular characteristics and therapeutic outcomes. In Read More...

07/18/2024 - This class will provide an overview of the data collection process for your review – whether scoping or systematic. The importance of data cleaning for consistency to ensure accurate identification of comparable outcome Read More...

Following normalization, the next step is to find variable features. In most scRNA-seq experiments only a small proportion of the genes will be informative and biologically variable. A subset of cells with high cell to Read More...

Let's explore how piping streamlines this. Piping (using |> ) allows you to employ multiple functions consecutively, while improving readability. The output of one function is passed directly to another without storing the intermediate steps Read More...

Let's explore how piping streamlines this. Piping (using |> ) allows you to employ multiple functions consecutively, while improving readability. The output of one function is passed directly to another without storing the intermediate steps Read More...

Finally, we have a quality plot ready to publish. The next step is to save our plot to a file. The easiest way to do this with ggplot2 is ggsave() . This function will save the Read More...

01/24/2024 - Documenting your data analysis is a crucial step toward making your research reproducible. In this session of the BTEP Coding Club, we will learn how to get started using Quarto with RStudio for report 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...

Let's use our practice data set to run ANCOM. Step 1: Filter out low abundance / low prevalent ASVs. Note: this will shift the composition of the samples, and thus could bias results. mkdir ancom qiime Read More...

Finally, we have a quality plot ready to publish. The next step is to save our plot to a file. The easiest way to do this with ggplot2 is ggsave() . This function will save the Read More...

QIIME2 is a platform for the processing and analysis of microbiome sequencing data. A general amplicon workflow in QIIME2 may look like the following: Image adapted from QIIME2 documentation (Conceptual overview of QIIME2) The first Read More...

As mentioned previously, the first step of any QIIME 2 analysis will be to import the data. Each type of data will be stored in its own QIIME2 artifact. For example, sample metadata, ASV / OTU tables, Read More...

10/17/2023 - Labeling signal data is a very important step in creating AI-based signal processing solutions.  However, this step can be very time consuming and manual. Read More...

Access the NIH network; this will require you to VPN if off campus. Connect to Biowulf ssh user_name@biowulf.nih.gov where user_name is your NIH username. Use sinteractive to work on an Read More...

The first step to working on Biowulf is getting a Biowulf account. If you intend to analzye your own data, most of you will need a Biowulf account some time in the future. All NIH Read More...

Tasks to do at the Analysis Wizard : provide our input gene list (either copy paste or upload as a text file) specify gene identifier type (gene identifiers could be gene symbol, Ensembl IDs, Entrez IDs, Read More...

The first step in using IGV is to load our reference genome. Take some time to see if you recall how to do this. {{Sdet}} Solution{{Esum}} {{Edet}} After loading the genome, let's view Read More...

One of the challenges in analyzing high throughput sequencing is to reconstruct the genome of the unknown by using a knonw (ie. reference). The next step in analysis is to align our sequencing data to Read More...

Analyzing next generation sequencing data requires a large number of computational steps. As you work, you should ALWAYS keep a record of what you are doing. Just as you keep a laboratory notebook, you should Read More...

A typical RNASEQ experiment involves several steps, only one of which falls within the realm of bioinformatics. Namely the Data Analysis step. Experimental Design What question am I asking How should I do it (does Read More...

09/18/2025 - This one-hour online training, provided by SAS, will demonstrate the basics of the Structured Query Language (SQL) procedure in SAS.   By the end of this training, attendees will be able Read More...

07/17/2025 - NIDDK Biostats Seminar Series: From Research Study Design to Collecting, Managing, and Analyzing Data. Learning Objectives 1. The learner should know the difference between observational studies, clinical trials (drug and non-drug studies), and secondary data ( Read More...

03/13/2025 - This session will introduce participants to visualizing alignment results from Next Generation Sequences using the Integrative Genomics Viewer (IGV). This step is important as scientists may want to highlight results from certain genomic locations Read More...

02/20/2025 - This class will introduce the first step in analyzing NGS data using bulk RNA sequencing as an example. After attending, participants will be able to describe the file format in which NGS data is Read More...

01/22/2025 - Join us for a one-hour talk investigating tumor signatures in the BRCA dataset by utilizing the CCBR Single-Cell RNA-seq Workflow on NIDAP. This talk will take you through an analysis of a publicly available Read More...

11/06/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/29/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/23/2024 - This one-hour online training will cover tips and tricks to run your processing against large datasets more efficiently in SAS.   By the end of this training, attendees will be able Read More...

10/22/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions Read More...

10/15/2024 - Join Our Training on Spatial Omics Data Analysis with MAWA (Multiplex Analysis Web Apps).   Want to learn how to process and analyze your spatial proteomics/transcriptomics data? Join us for four virtual hour-long sessions 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...

In Seurat (since version 4), differential analysis requires a preprocessing step to appropriately scale the normalized SCTransform assay across samples: adp = PrepSCTFindMarkers(adp) Found 8 SCT models. Recorrecting SCT counts using minimum median counts: 8146 As covered earlier, Read More...

It is next standard to scale and center the features in the data set prior to dimension reduction or visualization via heatmap. Scaling the data will keep highly expressed genes from dominating our analysis. This Read More...

03/22/2024 - This 3-hour seminar is tailored for biologists, data analysts, and researchers who are eager to dive into the essentials of computational flow cytometry analysis using R. Flow cytometry is a crucial technique in cell Read More...

Step 1: Goto https://www.globus.org to log in by clicking on the "LOG IN" icon at the top right of the pages. After logging in, select organziational affiliation, which is National Institutes Read More...

02/16/2024 - Deep sequencing has emerged as the primary tool for transcriptome profiling in cancer research. Like other high-throughput profiling technologies, sequencing is susceptible to systematic non-biological artifacts stemming from inconsistent experimental handling. A critical initial Read More...

Before diving into subsetting with dplyr , let's take a step back and learn to subset with base R. Subsetting a data frame is similar to subsetting a vector; we can use bracket notation [] . However, Read More...

02/05/2024 - The fifth lesson in the Introduction to Unix on Biowulf, January 2024 series teaches participants to submit scripts to the Biowulf batch system, which enables automation of multi-step analyses. Meeting link: https://cbiit.webex.com/ Read More...

Practice Lesson 2 For the help sessions, we will work on processing sequences generated in Zhang Z, Feng Q, Li M, Li Z, Xu Q, Pan X, Chen W. Age-Related Cancer-Associated Microbiota Potentially Promotes Oral Squamous Read More...

If all R could do was function as a calculator, it wouldn't be very useful. R can be used for powerful analyses and visualizations. As we learn more about R and begin implementing our Read More...

learn how to apply different types of filtering to your ASV table and representative sequence data. classify your ASVs. Generate a phylogenetic tree. Now that we have imported and denoised, let's move on to Read More...

The two methods used for denoising on QIIME 2 include: DADA2 - Uses a run specific error profile - Unclear how an incomplete run profile would impact results - There is a method available for Pacbio Read More...

The following represents the basic ggplot2 template. ggplot(data = ) + (mapping = aes()) The only required components to begin plotting are the data we want to plot, geom function(s), and mapping aesthetics. Notice the + symbol following 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...

Training We recommend that users become familiar with the principals of fluorescence labeling and optical microscopy before arranging for training. We recommend the following sites for learning about microscopy: Introductions to Fluorescence Microscopy Fluorescence Labeling Read More...

Cancer research is a complex and data-intensive field. Cloud computing offers a powerful solution for researchers to store, analyze, and share large datasets efficiently. In this month’s topic spotlight, we will explore cloud resources 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...

When using SingleR, the 3 primary parameters are the experimental dataset, the reference dataset, and the labels being used. Continuing with the main labels of the MouseRNASeq dataset on the full dataset looks like this: annot = Read More...

Once we have read in the matrices, the next step is to create a Seurat object. The Seurat object will be used to store the raw count matrices, sample information, and processed data (normalized counts, 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...

Now, that we have clusters, we can use differential expression analysis to uncover markers that define our clusters. These markers can be used to assign cell types to our clusters. First, because we are working 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...

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...

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...

Accessing Partek Flow at NIH and tips for data transfer Learning objectives Instructions for accessing Partek Flow NCI researchers can find instructions for accessing Partek Flow at https://bioinformatics.ccr.cancer.gov/btep/partek-flow-bulk-and-single-cell-rna-seq-data-analysis/ . But Read More...

The following represents the basic ggplot2 template: ggplot(data = ) + (mapping = aes()) We need three basic components to create a plot: the data we want to plot , geom function(s) , and mapping aesthetics . Notice the + symbol 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...

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...

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...

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...

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...

Lesson 7: Course Wrap-Up Learning Objectives Introduce the QIIME2 microbiome workflow for Biowulf Review key concepts Showcase additional plugins QIIME 2 on Biowulf As mentioned previously, QIIME 2 is installed on Biowulf. To see available versions use module 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...

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...

Lesson 4: Feature table filtering, taxonomic classification, and phylogeny Learning objectives learn how to apply different types of filtering to your ASV table and representative sequence data. classify your ASVs. Generate a phylogenetic tree. Now that 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...

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...