Search Results for: mRNA levels

The majority of mRNA derived from eukaryotes is the result of splicing together discontinuous exons, and this creates specific challenges for the alignment of RNASEQ data.

class ( a1 $ dose ) ## [1] "numeric" As it turns out, dose is really an experimental factor, so if we specify factor(dose) it will be interpreted as categorical or discrete. Before fixing the x axis Read More...

class(a1$dose) ## [1] "numeric" As it turns out, dose is really an experimental factor, so if we specify factor(dose) it will be interpreted as a categorical or discrete. Before fixing the x Read More...

10/12/2021 - Speaker :  Tali Mazor, Ph.D., Scientist, Knowledge Systems Group, Dana-Farber Cancer Institute Tali Mazor, Ph.D., of the Dana-Farber Cancer Institute will discuss the functions and features of the cBioPortal for Cancer Genomics. This Read More...

06/13/2024 - The schedule for this week consists of one presentation: Lorenz Adlung, UMC Hamburg-Eppendorf, will discuss: scMod: Marrying machine learning and deterministic modelling of longitudinal single-cell data Single-cell-based methods such as flow cytometry or single-cell Read More...

To add to the mRNA mapping problem is the existance of alternate splicing events. Attempting to identify alternate splicing in RNASEQ data is not something for the novice to attempt! .... get professional help

Remember ~90% of RNA is ribosomal RNA. Therefore enrich your total RNA sample by: polyA selection (oligodT affinity) of mRNA (eukaryote), or rRNA depletion - RiboZero is typically used (costs extra)

05/17/2023 - Join the next ScHARe Think-a-Thon on May 17. This 2-hour interactive webinar will continue to introduce researchers to the numerous social determinants of health and population science datasets available through ScHARe. NIH staff will provide Read More...

Before diving into the construction of bar plot, box & whisker plot, and histogram, we should do a quick review of the types of variables that we commonly work with in data analysis. Categorical variables Read More...

01/07/2016 - REGISTRATION IS FULL FOR THIS WORKSHOP (25 ATTENDEES; 6 WAITLIST) The Cancer Genome Atlas (TCGA) is a large-scale study that has cataloged genomic data accumulated for many different types of cancers, and includes mutations, copy number Read More...

04/01/2025 - Whether you are measuring mRNA expression, protein expression, DNA methylation, expressed miRNAs, protein binding to DNA or RNA, etc., you will likely end up with a list of genes or gene products from which 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...

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...

In addition to the Heatmap and the FeaturePlot shown previously, two other options easily accessible through Seurat are the Dot Plot and Violin Plot. The dot plot can visualize relative expression level and expression fraction. Read More...

The Bioinformatics Training and Education Program (BTEP) was established by the Office of Science and Technology Resources (OSTR) for the purpose of providing training enabling scientists to understand and analyze their own experimental data, at Read More...

Factors are an important data structure in statistical computing. They are specialized vectors (ordered or unordered) for the storage of categorical data. While they appear to be character vectors, data in factors are stored as 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...

01/17/2024 - Join us for the next ScHARe Think-a-Thon on January 17. This interactive webinar will help attendees unlock the power of data science by demystifying the process of choosing computational data science tools and techniques. Participants Read More...

Factors can be thought of as vectors which are specialized for categorical data. Given R’s specialization for statistics, this make sense since categorial and continuous variables are usually treated differently. Sometimes you may want Read More...

First, notice that you can easily access columns from the sample metadata ( colData() ) using $ . Using brackets to subset: se$SampleName ## [1] GSM1275862 GSM1275863 GSM1275866 GSM1275867 GSM1275870 GSM1275871 GSM1275874 ## [8] GSM1275875 ## 8 Levels: GSM1275862 GSM1275863 GSM1275866 GSM1275867 ... GSM1275875 se$ Read More...

We can access a column of our data frame using [] , [[]] , or using the $ . We can use colnames() and rownames() to access the column names and row names of a data frame. For example: df[[" Read More...

The QIIME2 platform can be used for different types of -omics data. For this course, we will be focusing on targeted amplicon sequencing of the 16S rRNA gene. The 16S rRNA gene (~1500 bp) codes for 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...

09/19/2023 - The NIH is home to a number of groups aimed at supporting bioinformatics, genomics, computational biology and various related topics. Many of these groups meet regularly to host talks or provide training opportunities, while Read More...

09/18/2023 - The NIH is home to a number of groups aimed at supporting bioinformatics, genomics, computational biology and various related topics. Many of these groups meet regularly to host talks or provide training opportunities, while Read More...

Factor in at least 3 replicates (absolute minimum), but 4 if possible (optimum minimum). Biological replicates are recommended rather than technical replicates. Always process your RNA extractions at the same time. Extractions done at different times lead Read More...

Basic RNASEQ Quality Control (QC) examines the technical characteristics of the data produced by the sequencer. ( It tells us nothing about whether the experiment worked . It answer the questions: Is the data of sufficiently high Read More...

There are a number of specific solutions that have been devised to address the issues created by attempting to map mRNA to DNA genomes. Each of these has its advantages and disadvantages. Align against the Read More...

Before diving into the construction of bar plot, box & whisker plot, and histogram, we should do a quick review of the types of variables that we commonly work with in data analysis. Categorical variables Read More...

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The Genomics and Pharmacology Facility is part of the NCI's Center for Cancer Research (CCR), within the Developmental Therapeutics Branch. Its mission is to manage and assess molecular interaction data obtained through multiple platforms, increase 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...

We are a bioinformatics team within the Center for Biomedical Informatics and Information Technology’s (CBIIT’s) Cancer Informatics Branch (CIB)—soon to be referred to as the Informatics and Data Science (IDS) Program. Headed Read More...

The introduction of DNA sequencing instruments capable of producing millions of DNA sequence reads in a single run has profoundly altered the landscape of genetics and cancer biology. Complex questions can now be answered at Read More...

06/19/2017 - Harvesting the Wealth of TCGA Data The Cancer Genome Atlas (TCGA) is a large-scale study that has catalogued genomic data accumulated from more than 20 different types of cancer including mutations, copy number variation, mRNA 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...

06/20/2024 - Clustered heat maps are widely used for visualizing patterns in molecular profiling data. But traditional, static heat maps have significant limitations when applied to large datasets (1000s of elements per axis). Presented in this 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) . adp

Look at the distribution of nCount_RNA with a Violin plot: # set colors cnames% ggplot(aes(color=orig.ident, x=nCount_RNA, fill= orig.ident)) + geom_density(alpha = 0.2) + theme_classic() + scale_x_log10() + geom_vline( Read More...

Now that we have loaded the package, we can import our data. Generally, in R programming, functions that involve data import begin with "read / Read". Seurat includes a number of read functions for Read More...

#Setting a theme my_theme

You do not need to load a package to visually explore data. Rather, you can use base R graphics for plotting (from the graphics package). This plotting is fairly different from ggplot2 , which is based Read More...

Now let's filter the rows based on a condition. Let's look at only the treated samples in scaled_counts using the function filter() . filter() requires the df as the first argument followed by 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...

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

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

The data type of an R object affects how that object can be used or will behave. Examples of base R data types include numeric, integer, complex, character, and logical. R objects can also have 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...

Luckily, there is a q2-longitudinal plugin to handle dependent longitudinal data. The q2-longitudinal plugin includes: interactive plotting (e.g., volatility plots) linear mixed effects models paired differences and distances non-metric microbial interdependence testing ( Read More...

NCI scientists have many choices available to them for bioinformatic analyses of Next Generation Sequencing (NGS) data. While some require expertise in programming, others provide a more user-friendly, point-and-click interface. These options include programs for Read More...

Alignment RNASeq Mapping Challenges The majority of mRNA derived from eukaryotes is the result of splicing together discontinuous exons, and this creates specific challenges for the alignment of RNASEQ data. Mapping Challenges Reads not perfect 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...

This page uses content directly from the Biostar Handbook by Istvan Albert. Start by activating the bioinfo environment. conda activate bioinfo Create a new directory for the multiqc data. mkdir multi cd multi Retrieve the Read More...

This page uses content directly from the Biostar Handbook by Istvan Albert. Start by activating the bioinfo environment. conda activate bioinfo Create a new directory for the multiqc data. mkdir multi cd multi Retrieve the Read More...

Data Analysis Here are a pair of examples of RNASEQ complete workflows RNASEQ Pipeline from NCI CCBR https://github.com/CCBR/Pipeliner/blob/master/RNASeqDocumentation.pdf RNASEQ Nextflow Pipeline from nf-core https://nf-co.re/rnaseq Read More...

Can you speed up your code with parallelization? Considerations: levels of parallelization: multiprocessing vs multithreads The most common form of parallelism in R is multiprocessing. This is usually explicitly done by you or package you Read More...

06/13/2023 - Many cancer-related independent studies that employ bulk and single cell RNA-seq can be found in the Gene Expression Omnibus (GEO). While some studies provide aligned read files, these are processed non-uniformly. This shortcoming makes 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...

05/09/2023 - T he  C ancer  P roteome  A tlas (TCPA) : a major bioinformatics resource for cancer proteomics data using  reverse-phase protein arrays (RPPAs)   In contrast to the recent exploration of next-generation sequencing at both DNA Read More...

05/02/2023 - T he  C ancer  P roteome  A tlas (TCPA) : a major bioinformatics resource for cancer proteomics data using  reverse-phase protein arrays (RPPAs) In contrast to the recent exploration of next-generation sequencing at both DNA 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...

When you have a lot of colors and you want to keep these colors consistent, you can use the following convenient functions to set a name attribute for a vector of colors. Let's do 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...

Stat Transformations: Bar plots, box plots, and histograms Objectives Review the grammar of graphics template Learn about the statistical transformations inherent to geoms Review data types Create bar plots, box & whisker plots, and histograms. Read More...

/* Whole document: */ body{ font-family: Times; font-size: 16pt; } Stat Transformations: Bar plots, box plots, and histograms Objectives Review the grammar of graphics template Learn about the statistical transformations inherent to geoms Review data types Create bar Read More...

CREx News & Updates July 2021   Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More NIH Collaborative Research Exchange (CREx) News Site Spotlight FACILITY HIGHLIGHTS Learn more about services from the NHLBI 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 NCI High-Throughput Imaging Facility (HiTIF) works in a collaborative fashion with NCI/NIH Investigators by providing them with the necessary expertise, instrumentation, and software to develop and execute advanced High-Throughput Imaging (HTI) assays. These Read More...

The Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative The STRIDES Initiative aims to help NIH and its institutes, centers, and offices (ICOs) accelerate biomedical research by reducing barriers in utilizing Read More...

CREx News & Updates July 2022 Learn about the NIH Collaborative Research Exchange (CREx), Core Facilities, Webinars, & More Click below to learn how easy it is to navigate the CREx platform. These short videos will Read More...

The Genomics Technology Laboratory is an integrated, high-throughput molecular biology laboratory focusing on the development of genetics and genomics technologies, data analysis, and information management tools, in support of CCR Investigators. The laboratory develops integrated 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...

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...

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...

General questions or comments about the BTEP program or classes should be addressed to: NCIBTEP@nih.gov Desiree Tillo, Ph.D . Staff Scientist desiree.tillo@nih.gov I have a broad research background in bioinformatics Read More...

Listed below are the video recordings of past BTEP events (classes, seminars, workshops). Videos are hosted on various servers and may play slightly differently. Some videos may be downloaded for local viewing. Recorded Videos of 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...

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...

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...

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...

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...

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...

Objectives To explore Bioconductor, a repository for R packages related to biological data analysis. To better understand S4 objects as they relate to the Bioconductor core infrastructure. To learn more about a popular Bioconductor S4 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 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 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...

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...

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...

This page uses content directly from the Biostar Handbook by Istvan Albert. Remember to activate the bioinfo environment. conda activate bioinfo Then create a new directory for files we will be working with today in 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...

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...

Lesson 9: Reference genomes and genome annotations used in RNA sequencing Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 8 Review In Lesson 8, we learned about the basics of RNA sequencing, Read More...

Lesson 9: Reference genomes and genome annotations used in RNA sequencing Before getting started, remember to be signed on to the DNAnexus GOLD environment. Lesson 8 Review In Lesson 8, we learned about the basics of RNA sequencing, 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...

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: Submitting R Scripts via command line Learning Objectives Learn how to use R with less interaction Learn how to deploy sbatch R jobs, and learn about alternatives such as swarm . Learn about R job Read More...

Whether you are measuring mRNA expression, protein expression, DNA methylation, expressed miRNAs, protein binding to DNA or RNA, etc., you will likely end up with a list of genes or gene products from which you 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...