Bethesda, MD
Trans NIH Facility
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...
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[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...
Charlottesville, VA
Repositories
The Mid-Atlantic CHTN is one of six participating divisions of the Cooperative Human Tissue Network (CHTN) and is based in the Pathology Department at the University of Virginia. While primarily a resource for cancer researchers, Read More...
Frederick, MD
Core Facility
Clinical Support Laboratory – Flow Cytometry Section is a laboratory specializing in providing immunophenotyping support of NCI intramural clinical trials, though assessments may also be performed using cells from Non-human primates and other species. The CSL Read More...
Federick, MD
Core Facility
The Mass Cytometry Core (MCC) provides access to cutting-edge platforms and expert technical support for high-dimensional single-cell analysis and spatial proteomic profiling. A central expertise of the core is Imaging Mass Cytometry (IMC), which allows Read More...
Frederick, MD
Core Facility
Molecular Cytogenetics Core Facility facilitates the assessment of structural and numerical genomic changes in pre-cancer and cancer research models. This core provides comprehensive support for the cytogenetic analysis of cells from human and research animal Read More...
Frederick, Maryland
Core Facility
Repositories
The Biological Products Core provides the AIDS research community with high-quality purified preparations of various strains of Human Immunodeficiency Virus (HIV) and Simian Immunodeficiency Virus (SIV), economically prepared by leveraging the economy of scale. Materials Read More...
Frederick, Maryland
Core Facility
CLIA-Certified Technologies Offered: Fragment Analysis for Micro-satellite Instability Detection, Pharmacoscan Array for Pharmacogenomics, Mutation Detection for PCR and Sanger Sequencing, DNA extraction from whole blood, saliva, FFPE tissues, buccal swabs, nails, hair, PBMCs, buffy coats, Read More...
Bethesda, MD
Collaborative
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...
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Bioinformatics
DESeq2 and another popular differential expression analysis package edgeR model expression data generated from RNA sequencing using the negative binomial distribtution. "We assume that the number of reads in sample j that are assigned Read More...
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Bioinformatics
10/19/2020 - Register
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Bioinformatics
To change the y axis scale, we will need a specific function. These functions generally start with scale_y... . In our case we want to reverse our axis so that increasingly negative is going in Read More...
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Bioinformatics
Add a column to hcc1395_deg_chr22_1 that contains the negative log10 of the PAdj value. {{Sdet}}{{Ssum}}Solution{{Esum}} import numpy hcc1395_deg_chr22_1["-log10PAdj"]=numpy.negative(numpy.log10(hcc1395_deg_ Read More...
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Bioinformatics
Add a column to hcc1395_deg_chr22_1 that contains the negative log10 of the PAdj value. {{Sdet}}{{Ssum}}Solution{{Esum}} import numpy hcc1395_deg_chr22_1["-log10PAdj"]=numpy.negative(numpy.log10(hcc1395_deg_ Read More...
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Bioinformatics
To change the y axis scale, we will need a specific function. These functions generally start with scale_y.... In our case we want to reverse our axis so that increasingly negative is going in Read More...
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Bioinformatics
The confusion matrix is a technique used for summarizing the performance of a classification algorithm i.e. it has binary outputs. A confusion matrix is a table that describes the performance of a classification model. Read More...
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Bioinformatics
The confusion matrix is a technique used for summarizing the performance of a classification algorithm i.e. it has binary outputs. A confusion matrix is a table that describes the performance of a classification model. Read More...
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Bioinformatics
Two Statistical Components:(Remember all statistical methods rely on various assumptions regarding the characteristics of the data...if they are not true all bets are off). Normalization of counts - the process of ensuring that Read More...
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Bioinformatics
05/19/2021 - Register Kinase inhibitors have been intensively studied and used effectively for cancer treatment for decades. Yet, despite our progress in understanding kinases in oncology, more needs to be known to better predict how and Read More...
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Bioinformatics
These sets of exercises will create the following plots from the hbr_uhr_deg_chr22 and hbr_uhr_top_deg_normalized_counts data tables. Volcano plot for hbr_uhr_deg_chr22 using Python's Seaborn Read More...
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Bioinformatics
These sets of exercises will create the following plots from the hbr_uhr_deg_chr22 and hbr_uhr_top_deg_normalized_counts data tables. Volcano plot for hbr_uhr_deg_chr22 using Python's Seaborn Read More...
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Bioinformatics
Imported using R and Python Contains RNA sequencing differential expression analysis results. The columns are: name: contains gene names log2FoldChange: gene expression change between two experimental conditions on the log2 scale PAdj: adjusted p-value Read More...
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Bioinformatics
Imported using R and Python Contains RNA sequencing differential expression analysis results. The columns are: name: contains gene names log2FoldChange: gene expression change between two experimental conditions on the log2 scale PAdj: adjusted p-value Read More...
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Bioinformatics
What are the 3 required components needed to create a plot? Data data - the data should include our differential expression results (dexp_sigtrnsc). 1 or more geoms All data points are plotted using an x and Read More...
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Bioinformatics
What are the 3 required components needed to create a plot? the data data - the data should include our differential expression results (dexp_sigtrnsc). at least 1 geom function All data points are plotted using an Read More...
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Bioinformatics
Enrichment Score and normalized enrichment score. Enrichment Score - represents the degree to which a set is over-represented at the top or bottom of the ranked list. normalized enrichment score (NES) - allows comparability across Read More...
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Bioinformatics
Definition "The goal of GSEA is to determine whether members of a gene set S tend to occur toward the top (or bottom) of the list L, in which case the gene set is Read More...
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Bioinformatics
Diabetes is a chronic health condition affecting millions worldwide. Early prediction of diabetes can help in timely management and prevention of complications. In this article, we will walk through a Python-based machine learning project for Read More...
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Bioinformatics
Diabetes is a chronic health condition affecting millions worldwide. Early prediction of diabetes can help in timely management and prevention of complications. In this article, we will walk through a Python-based machine learning project for Read More...
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Confocal
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...
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Bioinformatics
The primary means of running differential expression in Seurat is through the FindMarkers function. The main usage for this function is as follows: FindMarkers(object,ident.1= ..., ident.2=..., test.use="wilcox", min.pct = 0.01, logfc. Read More...
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Bioinformatics
There are data sets available in R to practice with or showcase different packages. For today's lesson and the remainder of this course, we will use data from the Bioconductor package airway to showcase Read More...
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Bioinformatics
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...
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Bioinformatics
In tab delimited files, data columns are separated by tabs. To import tab-delimited files there are several options. There are base R functions such as read.delim() and read.table() as well as the readr Read More...
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Bioinformatics
Differential Expression Differential expression involves the comparison of normalized expression counts of different samples and the application of statistical measures to identify quantitative changes in gene expression between the different samples. Normalization and Statistical Significance Read More...
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Bioinformatics
Plots condense complex and busy tabular data into a form that is easier to interpret. An expression heatmap is a common visualization used in RNA sequencing analysis. A heatmap shows numerical data on a color Read More...
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Bioinformatics
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...
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Bioinformatics
Let's draw an image on here, just to show that we can add one. There are two options. We can add an image as if it was another figure or we can simply draw Read More...
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Bioinformatics
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...
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Bioinformatics
There are data sets available in R to practice with or showcase different packages. For today's lesson and the remainder of this course, we will use data from the Bioconductor package airway to showcase Read More...
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Bioinformatics
05/19/2022 - THIS EVENT HAS BEEN CANCELLED Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood and are subdivided into three major histomorphologic subtypes: alveolar (ARMS), embryonal (ERMS), or spindle/sclerosing (SSRMS). Patients with Read More...
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Bioinformatics
05/19/2021 - Register/Join Kinase inhibitors have been intensively studied and used effectively for cancer treatment for decades. Yet, despite our progress in understanding kinases in oncology, more needs to be known to better predict how Read More...
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Bioinformatics
03/17/2021 - Presenter: Himel Mallick, PhD Senior Scientist, Biostatistics Merck Research Laboratories Abstract Identifying clinically actionable features that display differential abundance and expression patterns across experimental conditions is an important first step toward characterizing the multi-omics Read More...
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Bioinformatics
09/04/2020 - Speakers: Dave Clements, Galaxy Community Manager, Johns Hopkins University, Steven Weaver, Senior Programmer Analyst, Temple University Galaxy is an open web-based platform for data integration and analysis in the life sciences. Galaxy makes sophisticated Read More...
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Bioinformatics
The example data we will use for plotting are from a bulk RNA-Seq experiment described by Himes et al. (2014) and available in the Bioconductor package airway. In this experiment, the authors were comparing transcriptomic differences Read More...
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Bioinformatics
Here we will use bulk RNA-Seq data available in the R package airway, which is from an experiment published by Himes et al. (2014). These data, which are available in R as a RangedSummarizedExperiment object, are Read More...
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Bioinformatics
These example data are from a bulk RNA-Seq experiment described by Himes et al. (2014) and available in the Bioconductor package airway. ::: {.cell} #data import from excel exdata<-readxl::read_xlsx("./data/RNASeq_ Read More...
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Bioinformatics
We also need some data to plot, so if you haven't already, let's load the data we will need for this lesson. #scaled_counts #We used this in lesson 2 so you may not Read More...
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Bioinformatics
Users will be working with data tables during analyses. These can be imported into the a Jupyter Notebook although the approach for doing so will be language dependent. The example below will use a Python Read More...
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Bioinformatics
Users will be working with data tables during analyses. These can be imported into the a Jupyter Notebook although the approach for doing so will be language dependent. The example below will use a Python Read More...
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Bioinformatics
This exercise will use the differential gene expression analysis table from the hbr and uhr study. hbr_uhr_deg_chr22=pandas.read_csv("./hbr_uhr_deg_chr22.csv") The info() attribute will retrieve Read More...
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Bioinformatics
For this exercise, stay in the /data/username/pies_2023 folder, which should be the present working directory (use pwd to check). If not in the /data/username/pies_2023 folder, change into it. Copy the hbr_ Read More...
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Bioinformatics
We also need some data to plot, so if you haven't already, let's load the data we will need for this lesson. Getting the Data If you have not already done so, please Read More...
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Bioinformatics
For today's lesson, we will use data derived from the Bioconductor package airway. The airway data is from Himes et al. (2014). These data, which are contained within a RangedSummarizedExperiment, object are from a bulk Read More...
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Bioinformatics
Plots condense complex and busy tabular data into a form that is easier to interpret. An expression heatmap is a common visualization used in RNA sequencing analysis. A heatmap shows numerical data on a color Read More...
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Bioinformatics
Step 1: Import the data my_file <- paste0(data_path,"/","airway_deg_results.csv") airway_deg <- read.csv(my_file,check.names=FALSE,row.names=1) EnhancedVolcano Read More...
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Confocal
2024 Date: Tuesday, October 15, 2024 Time and Location: 11 am EST, ZOOM (INVITATION BY LMIG LIST SERVER) Speaker: Dr. Diego Presman (U Buenos Aires) Title: “Insights on Glucocorticoid Receptor Activity Through Live Cell Imaging” Summary: Eucaryotic transcription factors ( Read More...
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Bioinformatics
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...
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Bioinformatics
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...
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Bioinformatics
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...
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Bioinformatics
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...
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Bioinformatics
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...
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Bioinformatics
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...
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Bioinformatics
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...
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Bioinformatics
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...
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Bioinformatics
Multi-figure panel Objectives Combine multiple plots into a single figure Learn how to use patchwork and cowplot The primary purpose of this lesson is to learn how to combine multiple figures into a single multi-panel Read More...
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Bioinformatics
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...
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Bioinformatics
Data frames Objectives To be able to load, explore, and access data in a tabular format. To this end, students should understand the following: 1. how to import and export data 2. how to create, summarize, and Read More...
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Bioinformatics
The next exercise is to practice creating a scatter plot on a biologically relevant dataset. Namely, the differential expression results from the hbr and uhr RNA sequencing study will be used to create a scatter Read More...
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Bioinformatics
In tab delimited files, data columns are separated by tabs. To import tab-delimited files there are several options. There are base R functions such as read.delim() and read.table() as well as the readr Read More...
Web Page
Bioinformatics
The next exercise is to practice creating a scatter plot on a biologically relevant dataset. Namely, the differential expression results from the hbr and uhr RNA sequencing study will be used to create a scatter Read More...
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Bioinformatics
Tasks to do at the Analysis Wizard: Provide an input gene list (either copy paste or upload as a text file) Specify the gene identifier type. Gene identifiers can be gene symbol, Ensembl IDs, Entrez Read More...
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Bioinformatics
To start learning how to track changes using Git, a text file called mars will be created in the directory /Users/tillodc/teaching/planets. This file will contain notes about the planet mars. Note that Read More...
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Bioinformatics
The resulting object eh is a gseaResult object. This object contains the results (eh@result) and other information that went into the analysis, for example, @organism type, @setType, the @geneSets used, the genes in our @ Read More...