Search Results for: HSP 110 / GP 100 Chaperone

Greg Caporaso, PhD Professor at Northern Arizona University A microbiome expert with 100+ related publications Lead developer of the QIIME 2 Platform Visit his lab website at https://caporasolab.us

What if we want to transform all of our counts spread across multiple columns in acount using scale() , which applies a z-score transformation? In this case we use across() within mutate() , which has replaced the Read More...

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...

get an interactive node sinteractive --cpus-per-task=12 --mem=30g --gres=lscratch:20 module load STAR mkdir -p bam/rnaseq_STAR GENOME=/fdb/STAR_current/UCSC/mm10/genes-100 and run STAR. STAR --runThreadN 12 --genomeDir $GENOME --sjdbOverhang 100 --readFilesIn filename. 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...

Biowulf is the high performance computing cluster at NIH. When you apply for a Biowulf account you will be issued two primary storage spaces: 1) /home/$User and 2) /data/$USER , with 16 GB and 100 GB of default Read More...

If you do not already have a Biowulf account, you can obtain one by following the instructions here . NIH HPC accounts are available to all NIH employees and contractors listed in the NIH Enterprise Directory. Read More...

Used for data transfers and file management on a large scale. 48 core system with 1.5 TB of main memory direct internet connection Helix should be used when you are transferring >100 GB using scp gzipping a Read More...

Biowulf is the high performance computing cluster at NIH. To work on Biowulf, you will need to use the command line interface, which requires some knowledge of unix commands. When you apply for a Biowulf Read More...

#Plot ggplot(pcaData) + aes(PC1, PC2, color = Species, shape = Species) + geom_point(size = 2) + coord_fixed() + xlab("PC1: 73%")+ #x axis label text ylab("PC2: 23%") # y axis label text Automating % Variance in axis Read More...

Scaling is important during cluster analysis because it reduces the influence that variables with high magnitude values will have on distance. (https://medium.com/analytics-vidhya/why-is-scaling-required-in-knn-and-k-means-8129e4d88ed7). A common method for scaling is Read More...

Load the data For these exercises, you will explore the titanic data from kaggle.com , which was downloaded from here . You will need to download the data and load into R. As this is a Read More...

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...

/*color variables main= #1E1E1E secondery= #333333 highlight= #073254 */ * { box-sizing: border-box; } body, html { font-family: "Open Sans", sans-serif; } .clearfix:before, .clearfix:after { content: " "; display: table; } .clearfix:after { clear: both; } h1, h2, h3, h4, h5, h6 { font-weight: 300; } body Read More...

The AgingResearchBiobank was officially launched in January 2019 with a mission to provide a state-of-the-art inventory system for the storage, maintenance, and distribution of de-identified biospecimens and associated phenotypic, clinical, and imaging data from numerous NIA-funded Read More...

06/06/2025 - The la t decade ha demon trated the power of genomic to unravel the etiology of complex trait and di ea e . The va t majority of genomic tudie have been ba ed on Read More...

11/20/2024 - ggplot2 is a popular R package for data visualization that uses layers to build high quality plots. There are over 100 packages that extend the functionality of ggplot2. This session of the BTEP Coding Club Read More...

Due to limits on computational resources, you may be interested in running your analysis on an HPC. Biowulf is the NIH high performance compute cluster. It has greater than 90k processors, and can easily perform 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...

Let's use some functions. a. Use sum() to add the numbers from 1 to 10. {{Sdet}} Solution{{Esum}} sum ( 1 : 10 ) {{Edet}} b. Compute the base 10 logarithm of the elements in the following vector and save to an Read More...

Lesson 2 Exercise Questions: Base R syntax, objects, and data types Let's use some functions. a. Use sum() to add the numbers from 1 to 10. {{Sdet}} Solution{{Esum}} sum ( 1 : 10 ) {{Edet}} b. Compute the base 10 logarithm of Read More...

How many rows per sample are in the scaled_counts data frame? scaled_counts |> group_by(dex, sample) |> summarize(n=n()) #there are multiple functions that can be used here `summarise()` has grouped Read More...

How many rows per sample are in the scaled_counts data frame? ::: {.cell} scaled_counts |> group_by ( dex , sample ) |> summarize ( n = n ()) #there are multiple functions that can be used here ::: {.cell-output .cell-output-stderr} ` Read More...

mutate() adds new variables and preserves existing ones; transmute() adds new variables and drops existing ones. New variables overwrite existing variables of the same name. --- dplyr.tidyverse.org Let's create a column in 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...

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...

Lesson 1: Toward fully reproducible microbiome multi-omics bioinformatics with QIIME 2 Lesson 1 does not include a hands on component, but rather includes an introduction to QIIME2 by guest speaker, Dr. Greg Caporaso, a leading developer of the 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...

The recommended method involves applying a scikit-learn multinomial naive Bayes classifier. This uses machine learning to train a classifier on a referene database and then use the trained classifier to classify your ASVs. These classifiers 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...

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...

samtools flagstat SRR1972739.bwa.bam produces this 20740 + 0 in total (QC-passed reads + QC-failed reads) 0 + 0 secondary 740 + 0 supplementary 0 + 0 duplicates 15279 + 0 mapped (73.67% : N/A) 20000 + 0 paired in sequencing 10000 + 0 read1 10000 + 0 read2 14480 + 0 properly paired (72.40% : N/A) 14528 + 0 with itself and mate mapped 11 + 0 singletons (0.05% : N/ Read More...

samtools flagstat SRR1972739.bwa.bam produces this 20740 + 0 in total (QC-passed reads + QC-failed reads) 0 + 0 secondary 740 + 0 supplementary 0 + 0 duplicates 15279 + 0 mapped (73.67% : N/A) 20000 + 0 paired in sequencing 10000 + 0 read1 10000 + 0 read2 14480 + 0 properly paired (72.40% : N/A) 14528 + 0 with itself and mate mapped 11 + 0 singletons (0.05% : N/ Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Learn: FASTQC for assaying quality of sequence reads MultiQC for combining multiple FASTQC reports into one report Trimmomatic for removing sequence data based Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Learn: FASTQC for assaying quality of sequence reads MultiQC for combining multiple FASTQC reports into one report Trimmomatic for removing sequence data based Read More...

There are instructions for running SRA-Toolkit on Biowulf here ([https://hpc.nih.gov/apps/sratoolkit.html])(https://hpc.nih.gov/apps/sratoolkit.html). To start with, we will start up an interactive node using the & 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...

STAR 2-pass mode --sjdbGTFfile is the path to the file with annotated transcripts in standard GTF format, STAR extracts splice junctions from this file, improves accuracy of mapping. Using annotations is highly recommended whenever they Read More...

Now that our HISAT2 indices have been built and the text file with the sample IDs has been generated, we can do the actual alignment. First, change into the ~/biostar_class/snidget/snidget_hisat2 folder 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...

We previously stored FASTQC results for the HBR and UHR raw sequencing data in the ~/biostar_class/hbr_uhr/QC directory (recall that ~ denotes home directory). So before getting started, change into this folder. cd ~/ Read More...

How to download data from the Sequence Read Archive (NCBI/SRA) to your account on NIH HPC Biowulf You will need: active, unlocked Biowulf account (hpc.nih.gov) active Globus account for transferring files OR 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...

06/28/2023 - This talk will focus on the FAIR principles and the other aspects of data and workflow management we believe are necessary for reproducible research. Mr. Smith will discuss how the Arvados platform helps you & Read More...

Visualizing clusters with heatmaps Objectives Introduce the heatmap and dendrogram as tools for visualizing clusters in data. Learn to construct cluster heatmap using the package pheatmap . Learn how to save a non-ggplot2 plot. Introduce ggplotify Read More...

The idea behind cluster analysis is to calculate some sort of distance between objects in order to identify the ones that are closer together. When two objects have a small distance, we can conclude they Read More...

03/31/2023 - Alejandro Schäffer, Ph.D., was born in Montevideo, Uruguay, and emigrated with his parents to the United States. He received his B.S. in Applied Mathematics and his M.S. in Mathematics from Read More...

03/10/2023 - Dr. Melissa Haendel and Dr. Jakob Seidlitz will present "2022 DataWorks! Grand Prize Winners: Innovations in Data Sharing and Reuse" at the monthly Data Sharing and Reuse Seminar on March 10, 2023 at 12 p.m. Read More...

Employing spatial biology techniques enables acquisition of transcript and protein data from intact tissue sections, and in turn, spatial distribution information and cellular interaction patterns are revealed.

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...

What is Xenium? Xenium is a high-resolution, imaging-based in situ spatial profiling technology from 10x Genomics that allows for simultaneous expression analysis of RNA targets (currently in range of 100’s) within the same tissue section. Read More...

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Back Services: Biophysics Facility offers ITC calorimeters as open-access instruments.  First-time users must complete a short training session before gaining access to the instrument reservation calendar.  Training includes performing a test experiment and 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...

The mission of Human Brain Collection Core (HBCC) within the National Institute of Mental Health, Division of Intramural Programs (NIMH IRP) is to conduct and support research on brain and behavior, with the goal of Read More...

The Antibody Engineering Program (AEP) is located at the Laboratory of Molecular Biology, which is part of the Center for Cancer Research (CCR), an intramural program at the National Cancer Institute (NCI). AEP focuses on 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...

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 research conducted within the Synthetic Biologics Core (SBC) Facility has a dual role: Generate chemical biology tools and drug candidates for molecular targets identified by NCI research groups, Develop novel effective methods and tools Read More...

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...

The LCBG Microscopy Core offers imaging technologies and training. The Core has established instrumentation for for 2D and 3D imaging of both fixed and living specimens.

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...

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...

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

The NIH Center for Human Immunology, Inflammation, and Autoimmunity (CHI) is a trans-NIH resource whose mission is to provide a collaborative hub of advanced translational immunology for NIH clinical and pre-clinical studies. This uniquely structured Read More...

Services: Biophysics Facility offers CD as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument reservation calendar. Location: Building 50, room 3123 Description: CD spectroscopy measures the difference Read More...

Back Services: Biophysics Facility offers CD as an open-access instrument.  First-time users must complete a short training session before gaining access to the instrument reservation calendar. Location: Building 50, room 3123 Description: CD spectroscopy measures the Read More...

The Pan-Microbial Serology Facility (PMSF) is part of the Center for Cancer Research (CCR) at the National Cancer Institute (NCI). The PMSF focuses on determining individualized pan-microbial immune profiles associated with human diseases including immunological Read More...

The CCR Genomics Core is located in Building 41 on the NIH Bethesda campus. The primary goal of the Core is to provide investigators from CCR/NCI and other NIH Institutes access to genomic technologies and Read More...

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...

Here, we will start with the data stored in a Seurat object. For instructions on data import and creating the object, see an Introduction to scRNA-Seq with R (Seurat) and Getting Started with Seurat: QC 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...

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...

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...

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...

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...

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...

dplyr : joining, tranforming, and summarizing data frames Objectives Today we will continue to wrangle data using the tidyverse package, dplyr . We will learn: how to join data frames using dplyr how to transform and create 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...

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...

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...

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...

This page uses content directly from the Biostar Handbook by Istvan Albert. Remember to activate the bioinformatics environment and create a directory for today's work. conda activate bioinfo mkdir blast cd blast What is Read More...

Let's align an RNA-Seq sample using the "splice aware" aligner hisat2. First we will need to create the indices. Use this format: hisat2-build REFERENCE_GENOME INDEX_PREFIX Like this: hisat2-build Read More...

Let's align an RNA-Seq sample using the "splice aware" aligner hisat2. First we will need to create the indices. Use this format: hisat2-build REFERENCE_GENOME INDEX_PREFIX Like this: hisat2-build Read More...

Let's align an RNA-Seq sample using the "splice aware" aligner hisat2. First we will need to create the indices. Use this format: hisat2-build REFERENCE_GENOME INDEX_PREFIX Like this: hisat2-build Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Learn: using trimmomatic to remove low-quality bases from a sequence Always remember to activate the bioinformatics environment. conda activate bioinfo We will be Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Learn: using trimmomatic to remove low-quality bases from a sequence Always remember to activate the bioinformatics environment. conda activate bioinfo We will be Read More...

This page uses content directly from the Biostars Handbook by Istvan Albert (https://www.biostarhandbook.com). Always remember to load the bioinformatics environment. conda activate bioinfo SAM files SAM format is TAB-delimited, line-oriented, human-readable text Read More...

This page uses content directly from the Biostars Handbook by Istvan Albert (https://www.biostarhandbook.com). Always remember to load the bioinformatics environment. conda activate bioinfo SAM files SAM format is TAB-delimited, line-oriented, human-readable text 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...

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 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...

The bowtie2-build indexer builds a Bowtie index from a set of DNA sequences ([ref]. "bowtie2-build" builds a Bowtie index from a set of DNA sequences. "bowtie2-build" outputs a 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 material. conda activate bioinfo Retrieving a FASTA genome from NCBI/GenBank Read More...

Lesson 11: Merging FASTQ quality reports and data cleanup Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 10 Review In the previous lesson, we learned about the structure of the FASTQ Read More...

Lesson 4: Useful Unix For this lesson, you will need to login to the GOLD environment on DNAnexus. Lesson 3 Review Biowulf is the high performance computing cluster at NIH. When you apply for a Biowulf account 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...

This page contains content taken directly from the Biostar Handbook (Istvan Albert). Always remember to activate the class bioinformatics environment. conda activate bioinfo For this data analysis, we will be using: Two commercially available RNA Read More...

This page contains content taken directly from the Biostar Handbook (Istvan Albert). Always remember to activate the class bioinformatics environment. conda activate bioinfo For this data analysis, we will be using: Two commercially available RNA 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...

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...

Learning Objectives Understand the components of an HPC system. How does this compare to your local desktop? Learn about Biowulf, the NIH HPC cluster. Learn about the command line interface and resources for learning. What Read More...

Unix, what is it, and why should biologists take the time to learn it? The Unix operating system forms the basis of many bioinformatics analyses resources, such as the NIH High Performance Cluster (HPC) Biowulf/ Read More...

High resolution single cell profiling assays have provided an unprecedented view of many biological systems and processes, but the spatial context in which this biology is occurring is often crucial. Spatial profiling, including spatial transcriptomic Read More...

Excel is a great program for visualizing and manipulating small data sets. However, it isn't great for working with "big data", and resulting plots are generally not publishable. Learning R and associated Read More...

Visualizing clusters with heatmaps Objectives Introduce the heatmap and dendrogram as tools for visualizing clusters in data. Learn how to work with the package pheatmap . Learn how to save a non-ggplot2 plot. Introduce ggplotify to Read More...

Visualizing clusters with heatmaps Objectives Introduce the heatmap and dendrogram as tools for visualizing clusters in data. Learn to construct cluster heatmap using the package pheatmap . Learn how to save a non-ggplot2 plot. Introduce ggplotify Read More...

Scatter plots and plot customization Objectives Learn to customize your ggplot with labels, axes, text annotations, and themes. Learn how to make and modify scatter plots to make fairly different overall plot representations. Load a Read More...

Introduction to ggplot2 Objectives Learn how to import spreadsheet data. 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 Read More...

Below, we assign the heatmap to the R object hm_ph. hm_ph {"x":{"data":[{"x":[4.875,2.875,null,2.875,2.875,null,2.875,1.75,null,1.75,1.75,null,1.75,1,null,1,1,null,1.75,2.5,null,2.5,2.5,null,2.5,2,null,2,2,null,2.5,3,null,3,3,null,2.875,4,null,4,4, Read More...

Below, we assign the heatmap to the R object hm_ph and we can import this back to R in the future. hm_ph {"x":{"data":[{"x":[4.875,2.875,null,2.875,2.875,null,2.875,1.75, Read More...