Search Results for: macromolecular particle size

#Setting a theme my_theme

Normalization - the process of scaling data to account for uncontrolled factors affecting variation. Effect size - "the quantitative measure of the magnitude of a phenomenon" (Biostar Handbook). P-value - "the probability Read More...

Biophysics Core Facility assists NIH investigators in measuring molecular interactions and in characterizing macromolecular properties. This includes binding studies of proteins, DNA, RNA, and their ligands in buffers, cell lysate, plasma, and other media. We 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...

These steps can be used to create a publish worthy figure. For example, let's create a volcano plot of our differential expression results. A volcano plot is a type of scatterplot that shows statistical Read More...

Lesson 6 Exercise Questions: ggplot2 Putting what we have learned to the test: The following questions synthesize several of the skills you have learned thus far. It may not be immediately apparent how you would go Read More...

Differential abundance testing examines which taxa are significantly different in abundance between conditions. However, challenges such as sparsity, compositionality, and library size differences make this challenging to determine.

This lesson will provide an overview of Database for Annotation, Visualization and Integrated Discovery (DAVID) . We will Provide some background on DAVID, including what it does statistical methods that it uses some expected outputs data 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...

Query: the sequence that we are looking to match. Target: the database (collection of sequences) that we are searching for matches. Subject: the sequence entry in the target that produced an alignment. Score: the alignment Read More...

The Biophysics Resource (BR) was established in January 2001. Our mission is to provide CCR investigators with access to both the latest instrumentation and expertise in characterizing the biophysical aspects of systems under structural investigation. The Read More...

Protein Characterization Laboratory (PCL) offers various technologies to CCR investigators to characterize proteins and metabolites. The laboratory develops and applies state-of-the-art analytical technologies, primarily mass spectrometry, liquid chromatography, and Surface Plasmon Resonance (SPR), to advance Read More...

Home About the Biophysics Core Biophysics Core Services [tabby title="Instrumentation"] NHLBI Biophysics Core The Biophysics Core Facility: Overview Core Facilities provide scientific resources, cutting-edge technologies and novel approaches to support DIR scientists. Availability of Read More...

The Crystallization Facility provides an automated environment for setting up crystallization experiments in a high-throughput format, storing the resulting plates under controlled conditions, and monitoring the status of prepared droplets remotely. The Facility is in Read More...

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The Biophysics Core’s mission is to provide support in the study of macromolecular interactions, dynamics, and stability by offering consultations, training, professional collaborations, and instrument access. General Services Multi-technique molecular interaction studies, Kinetic and Read More...

The Biomedical Engineering and Physical Science (BEPS) shared resource supports NIH’s intramural basic and clinical scientists on applications of engineering, physics, imaging, measurement, and analysis. BEPS is centrally located on the main NIH campus Read More...

Protein Characterization Laboratory (PCL) offers various technologies to CCR investigators to characterize proteins and metabolites. The laboratory develops and applies state-of-the-art analytical technologies, primarily mass spectrometry, liquid chromatography, and Surface Plasmon Resonance (SPR), to advance 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...

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Back Services: Biophysics Facility offers MP as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument training calendar.  Training includes mass distribution analysis of a Read More...

2024 Coutinho, L. L., Femino, E. L., Gonzalez, A. L., Moffat, R. L., Heinz, W. F., Cheng, R. Y. S., Lockett, S. J., Rangel, M. C., Ridnour, L. A. & Wink, D. A. NOS2 and 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...

05/07/2025 - Join Dr. Eytan Ruppin, NCI investigator in the Center for Cancer Research, as he discusses Path2Space, a new and unpublished deep learning approach that predicts spatial gene expression directly from histopathology slides. Spatial Read More...

In this tutorial, we will continue to use data from Nanduri et al. 2022, Epigenetic regulation of white adipose tissue plasticity and energy metabolism by nucleosome binding HMGN proteins , published in Nature Communications . As a reminder, Read More...

In this tutorial, we will continue to use data from Nanduri et al. 2022, Epigenetic regulation of white adipose tissue plasticity and energy metabolism by nucleosome binding HMGN proteins , published in Nature Communications . As a reminder, 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...

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...

The Seurat v5 object doesn’t require all assays have the same cells. In this case, Cells() can be used to return the cell names of the default assay while colnames() can be used to Read More...

03/29/2024 - This first of five webinars will introduce NIH’s All of Us Research Program, including the program’s mission and core values. Learn about the current size and diversity of Read More...

How do we ultimately get our figures to a publishable state? The bread and butter of pretty plots really falls to the additional non-data layers of our ggplot2 code. These layers will include code to Read More...

ggplot2 will automatically assign colors to the categories in our data. Colors are assigned to the fill and color aesthetics in aes() . We can change the default colors by providing an additional layer to our Read More...

Size: nrow() - number of rows ncol() - number of columns Content: head() - returns first 6 rows by default tail() - returns last 6 rows by default Names: colnames() - returns column names rownames() - returns Read More...

The geom functions require a mapping argument. The mapping argument includes the aes() function, which "describes how variables in the data are mapped to visual properties (aesthetics) of geoms" (ggplot2 R Documentation). If Read More...

Last lesson we discussed the three basic components of creating a ggplot2 plot: the data , one or more geoms , and aesthetic mappings . ggplot(data = ) + (mapping = aes()) But, we also learned of other features that greatly Read More...

There is an approach to data analysis known as "split-apply-combine", in which the data is split into smaller components, some type of analysis is applied to each component, and the results are combined. Read More...

There is an approach to data analysis known as "split-apply-combine", in which the data is split into smaller components, some type of analysis is applied to each component, and the results are combined. 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...

What geoms would you use to draw each of the following named plots? a. Scatterplot b. Line chart c. Histogram d. Bar chart e. Pie chart (Question taken from https://ggplot2-book.org/individual-geoms.html .) {{ Read More...

Lesson 5 Exercise Questions: ggplot2 What geoms would you use to draw each of the following named plots? a. Scatterplot b. Line chart c. Histogram d. Bar chart e. Pie chart (Question taken from https://ggplot2 Read More...

ggplot2 will automatically assign colors to the categories in our data. Colors are assigned to the fill and color aesthetics in aes() . We can change the default colors by providing an additional layer to our Read More...

Rarefaction is the process of subsampling reads without replacement to a defined sequencing depth, thereby creating a standardized library size across samples. Any sample with a total read count less than the defined sequencing depth Read More...

Using the boxplot you created above, reorder the x-axis so that color is organized from worst (J) to best (D). There are multiple possible solutions. Hint: Check out functions in the forcats package (a tidyverse Read More...

From the login screen, login with the username/passwd " rstudio/rstudio ", and proceed from there. Splitting the window - If you wish to integrate the class notes into the same window as the Read More...

The geom functions require a mapping argument. The mapping argument includes the aes() function, which "describes how variables in the data are mapped to visual properties (aesthetics) of geoms" (ggplot2 R Documentation). If Read More...

To rarefy or not to rarefy? Feature tables are composed of sparse and compositional data. Measuring microbial diversity using 16S rRNA sequencing is dependent on sequencing depth. By chance, a sample that is more deeply 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...

How do we ultimately get our figures to a publishable state? The bread and butter of pretty plots really falls to the additional non-data layers of our ggplot2 code. These layers will include code to Read More...

Again, rarefaction is used to eliminate issues due to differences in library size prior to beta diversity. This method is built-in to QIIME 2 core metrics pipelines. We can examine the stability of a beta diversity Read More...

Remember, to create a plot all you you need are the data , geom_function(s) , and mapping arguments. However, there are additional components that can be added to our core components to enable us to Read More...

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 Cell Cancer Read More...

For the following plots, let's use the diamonds data ( ?diamonds ). The diamonds dataset comes in ggplot2 and contains information about ~54,000 diamonds, including the price, carat, color, clarity, and cut of each diamond. --- R4 Read More...

In lesson 3, we learned how to read and save excel spreadsheet data to a R object using the tidyverse package readxl . Today we will use some example data from an excel spreadsheet to learn the Read More...

Help Session Lesson 4 Plotting with ggplot2 For the following plots, let's use the diamonds data ( ?diamonds ). The diamonds dataset comes in ggplot2 and contains information about ~54,000 diamonds, including the price, carat, color, clarity, and Read More...

Finally, we want to obtain summary information from our feature table and feature data (representative sequences). Our feature table includes count data of our ASVs in each sample, while the feature data provides the sequence Read More...

Bray-Curtis dissimilarity quantitative Takes into consideration abundance and presence absence Jaccard - qualitative - presence / absence - percentage of taxa not found in both samples Weighted UniFrac quantitative similar to Bray-Curtis but takes into consideration Read More...

DNAnexus Basics Setting up the R-Studio environment outside of Class hours These instructions should be followed if you are setting up the R-Studio Web environment outside the normal class hours. Log into DNAnexus Each student Read More...

09/28/2023 - This is Lesson 4 of the Fall 2023 Introduction to Unix on Biowulf Series . Registering for this class will get you signed up for all four sessions in this course. Please make sure that you can Read More...

09/21/2023 - This is Lesson 3 of the Fall 2023 Introduction to Unix on Biowulf Series . Registering for this class will get you signed up for all four sessions in this course. Please make sure that you can Read More...

09/14/2023 - This is Lesson 2 of the Fall 2023 Introduction to Unix on Biowulf Series . Registering for this class will get you signed up for all four sessions in this course. Please make sure that you can Read More...

09/07/2023 - This is Lesson 1 of the Fall 2023 Introduction to Unix on Biowulf Series. Registering for this class will get you signed up for all four sessions in this course. Please make sure that you can Read More...

Compare the output from these commands: ls ls -S ls -lh ls -h (when used with -l option, prints file sizes in a human readable format with the unit suffixes: Byte, Kilobyte, Megabyte, Gigabyte, Terabyte. Read More...

This lesson will provide an overview of the Database for Annotation, Visualization and Integrated Discovery (DAVID) . We will Provide some background on DAVID, including what it does statistical methods that it uses some expected outputs Read More...

Ignoring these simple guidelines will greatly increase the chances that your data will be unanalysable and/or your experiment unpublishable. Prepare all samples at the same time or as close as possible. The same person Read More...

The instructions that follow were designed to test the skills you learned in Lesson 2. Thus, the primary focus will be navigating directories and manipulating files. Let's navigate our files using the command line. Begin Read More...

Lesson 2 Practice The instructions that follow were designed to test the skills you learned in Lesson 2. Thus, the primary focus will be navigating directories and manipulating files. Let's navigate our files using the command Read More...

When analyzing high throughput sequencing data, we will need to trim away adapters. Adapters help anchor the unknown sequencing template to the Illumina flow cell and can interfere with alignment. We may also want to Read More...

Below, you will find questions and answers brought up in the course polls for the BTEP Bioinformatics for Beginners course series that took place from September 13th, 2022 to December 13th, 2022. Question 1 : Normalization - when to Read More...

BTEP Bioinformatics for Beginners (September 13th, 2022 - December 13th, 2022) Questions and Answers Below, you will find questions and answers brought up in the course polls for the BTEP Bioinformatics for Beginners course series that took Read More...

Before diving into differential expression analysis, it is important to take a brief look at the concept of normalization. After obtaining expression counts, we will need to normalize the counts before performing differential expression analysis. Read More...

Generating the Data General Rules for Sample Preparation Ignoring these simple guidelines will greatly increase the chances that your data will be unanalysable and/or your experiment unpublishable. Prepare all samples at the same time Read More...

The SRA (Sequence Read Archive) at NCBI is a large, public database of DNA sequencing data. The repository holds "short reads" generated by high-throughput next-generation sequencing, usually less than 1,000 bp. We will download Read More...

fastq-dump and fasterq-dump can be used to download FASTQ-formatted data. Both download the data in SRA format and convert it to FASTQ format. fastq-dump SRR1553607 creates the file: SRR1553607.fastq Check the file to make Read More...

fastq-dump and fasterq-dump can be used to download FASTQ-formatted data. Both download the data in SRA format and convert it to FASTQ format. fastq-dump SRR1553607 creates the file: SRR1553607.fastq Check the file to make Read More...

First we will obtain the SRA data from the biostar handbook web site curl http://data.biostarhandbook.com/sra/sra-runinfo-2019-01.tar.gz --output sra-runinfo-2019-01.tar.gz Now we can unpack the data. tar 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...

This page uses content directly from the Biostar Handbook by Istvan Albert. Review: * cd * mkdir * curl * tar * cat * grep * wc * outputting data * piping data from one command to another * cut Learn: * du * pip * csvkit * datamash Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More NIH Extramural Common Fund Resources Metabolomics Workbench Developed by the NIH Metabolomics Common Fund's National Metabolomics Data Repository ( Read More...

Electron Microscopy Laboratory (EML) The EML offers investigators access to unique expertise and EM technologies that allow our partners to explore new avenues of research to enhance the knowledge of biological systems. To assist our Read More...

CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More NIH Intramural CryoEM (NICE) Consortium NICE provides NCI, NIAID, NIEHS, NICHD, NIDCR, NEI, and NIA investigators with Read More...

Back Services: This instrument is not user accessible.  We provide both data collection and data analysis services. Location: Building 50, room 3331 Description: An analytical ultracentrifuge is equipped with absorption and interference optical systems that monitor Read More...

The OSTR offers cutting-edge technology platforms to the CCR scientific community through centralized facilities. The videos accessed through this page are designed to introduce the various scientific methodologies OSTR makes available through the cores on Read More...

Back Services: Biophysics Facility offers MDS as an open-access instrument. First-time users must complete a short training session before gaining access to the instrument reservation calendar. Training includes the KD determination of a standard molecular 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...

September 8, 2022 crex.nih.gov CREx Monthly Newsletter Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Science & Technology Seminars and Training Events Upcoming Seminars and Educational Opportunities The following 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. We would greatly appreciate 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...

Back Services: Biophysics Facility offers fluorometers as open-access instruments.  First-time users must complete a short training session before gaining access to the instrument reservation calendar. Location: Building 50, room 3226 Description: Some substances reemit light after Read More...

The centrally funded Statistics team within the Advanced Biomedical Computational Science group at the Frederick National Lab provides statistical consultation and data analysis support for NCI laboratories. We have broad-range expertise in biomedically relevant areas Read More...

The facilities at AIM are available for use by the entire NIH intramural research community. While we welcome users with any size imaging project, AIM specializes in large, yearlong (or longer), collaborative research efforts with Read More...

What is Visium FFPE v2 with CytAssist? Visium FFPE v2 is sequencing-based spatial profiling technology developed by 10x Genomics. This assay can take mouse or human tissue sections on normal glass slides as input and Read More...

The National Cancer Institute (NCI) is developing a national repository of Patient-Derived Models (PDMs) comprised of patient-derived xenografts (PDXs), patient-derived organoids (PDOrg), and in vitro patient-derived tumor cell cultures (PDCs) and cancer-associated fibroblasts (CAFs). These Read More...

The Clinical Image Processing Service (CIPS) offers timely and accurate advanced image processing of diagnostic radiology images for clinical care, research, and training. CIPS’ functions include clinical services and scientific researches. Established Technologies: CIPS can Read More...

Back Services: Biophysics Facility offers MST as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument reservation calendar.  Training includes the KD determination of a Read More...

The Spatial Imaging Technology Resource (formerly the Nanoscale Protein Analysis Section of the Collaborative Protein Technology Resource or CPTR) provides expertise and service in state-of-the-art protein analysis technologies to advance CCR research in basic discovery 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...

Differential expression analysis is the process of identifying genes that have a significant difference in expression between two or more groups. For many sequencing experiments, regardless of methodology, differential analysis lays the foundation of the 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...

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...

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...

The object that we imported, scaled_counts , is a data frame. Let's learn a bit more about our data frame. First, we can learn more about the structure of our data using str() . We 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...

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...

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...

Load in the comma separated file "./data/countB.csv" and save to an object named gcounts . {{Sdet}} Solution } gcounts `...1` colnames ( gcounts )[ 1 ] ## 1 Tspan6 703 567 867 71 970 242 ## 2 TNMD 490 482 18 342 935 469 ## 3 DPM1 921 797 622 661 8 500 ## 4 SCYL3 335 216 222 774 979 793 ## 5 FGR 574 574 515 584 941 344 ## 6 CFH 577 792 672 104 192 936 ## 7 FUCA2 798 766 995 27 756 546 ## 8 GCLC 822 874 923 705 667 522 ## 9 NFYA 622 793 918 868 334 64 {{Edet}} Plot the Read More...

Lesson 6 . Learning Objectives Introduce several beta diversity metrics Discover different ordination methods Learn about statistical methods that are applicable Beta diversity Beta diversity is between sample diversity. This is useful for answering the question, how 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 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...

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...

Lesson 5: Microbial diversity, alpha rarefaction, alpha diversity Learning Objectives Understand the difference between alpha and beta diversity Introduce several alpha diversity metrics Understand what rarefaction is and why it is important Introduce the debate regarding 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...

Let's start our exploration of sequencing read alignment by discussing the reference genome for human chromosome 22. For this, change into our ~/biostar_class/hbr_uhr/refs folder. In Lesson 9, we discussed why we need Read More...

Let's start our exploration of sequencing read alignment by discussing the reference genome for human chromosome 22. For this, change into our ~/biostar_class/hbr_uhr/refs folder. In Lesson 9, we discussed why we need Read More...

Now that we have downloaded the Golden Snidget reference files let's take a moment to get to know the references. First, change into the refs folder. How do we do this from the ~/biostar_ 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...

Database for Annotation, Visualization and Integrated Discovery (DAVID) - an overview Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 17 review In the previous class, we got an overview of 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...

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...

Lesson 1: Introduction to Unix and the Shell Lesson Objectives Course overview. Introduce Unix and describe how it differs from other operating systems. Introduce and get set up on DNAnexus and the GOLD system. Discuss ways 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...

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...

We will build a database out of all features of the 2014 Ebola genome under accession number KM233118. This data will go into a new directory named "db_2014". mkdir -p db_2014 # Get the 2014 Ebola Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Review: * downloading data from SRA * decompressing tar files * e-utilities * fastq-dump Learn: * sra-stat * XML format * automating SRA downloads * working with comma-separated values (csv) format * Read More...