Search Results for: Whole transcriptome

To help us apply what we learned during the lesson, we are going to use DAVID to obtain some functional annotation information on upregulated genes in the HCC1395 dataset. Recall that the HCC1395 dataset is 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...

Reference genome or transcriptome Annotation files (gff or gtf) that tells us the genomic features (ie. gene, transcript, etc.) Raw sequencing files in FASTQ (or fq) format

{{Sdet}} Solution{{Esum}} Reference genome or transcriptome Annotation files (gff or gtf) that tells us the genomic features (ie. gene, transcript, etc.) Raw sequencing data in FASTQ (or fq) format {{Edet}}

In this lesson, we learned about the classification based approach for RNA sequencing analysis. In this approach, we are aligning our raw sequencing reads to a reference transcriptome rather than a genome. Here, we will Read More...

Next, what application can we use to obtain an expression counts table? {{Sdet}} Answer{{Esum}} We can use featureCounts if aligned the reads to the genome. We will need our annotation file and either SAM Read More...

Reads not perfect Duplicate molecules (PCR artifacts skew quantitation) Multimapped reads - Some regions of the genome are thus classified as unmappable Aligners try very hard to align all reads, therefore fewest artifacts occur when 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.

Profile the entire transcriptome and more than 570 proteins on a single slide with the GeoMx Digital Spatial Profiler

Technical Replicates It’s generally accepted that they are not necessary because of the low technical variation in RNASeq experiments Biological Replicates (Always useful) Not strictly needed for the identification of novel transcripts and transcriptome Read More...

Getting the data Unpacking the data QA/QC with FASTQC and MULTIQC Adapter trimming Alignment Getting a Reference Genome Genome Indexing Spliced reads Non-spliced reads No Reference Genome? Working with a Transcriptome (non-model organisms) Pseudo-alignment ( Read More...

Lesson 16 Practice Objectives In this lesson, we learned about the classification based approach for RNA sequencing analysis. In this approach, we are aligning our raw sequencing reads to a reference transcriptome rather than a genome. Read More...

Overview of wet lab procedures Library preparation process, including Adapters and indices Single and paired end sequencing Strandedness Coverage and depth Spike-ins Replicates Batch effects Overview of analysis procedures References and annotation files needed for Read More...

The Golden Snidget reference genome is located at http://data.biostarhandbook.com/books/rnaseq/data/golden.genome.tar.gz. Can you download and extract? {{Sdet}} Solution{{Esum}} Download wget http://data.biostarhandbook.com/books/rnaseq/ Read More...

Database for Annotation, Visualization and Integrated Discovery (DAVID) - practicing what we learned Learning objectives In this practice session, we will practice using DAVID. Practicing what we learned To help us apply what we learned Read More...

This data set is from the Griffith lab RNA sequencing tutorial and are kept at http://genomedata.org/rnaseq-tutorial/practical.tar. This dataset is derived from a study that profiled the transcriptome of HCC1395 breast Read More...

Lesson 17: RNA sequencing review 2 Learning objectives This lesson will serve as comprehensive review of Module 2. We will spend roughly the first hour reviewing the Module 2 material the second hour answering specific questions from the poll Read More...

08/24/2022 - Precision oncology has made significant advances, mainly by targeting actionable mutations and fusion events involving cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome Read More...

11/17/2021 - The webinar will highlight how Chromium Single Cell Solutions and Visium Spatial Solutions can uncover molecular insights, dissect cell-type differences, investigate the adaptive immune system, detect novel subtypes and biomarkers, and map the epigenetic Read More...

08/25/2021 - This introductory course teaches the basics of deep learning and of different types of deep learning networks through a set of hands-on biological examples implemented in Keras, one example per class. Class #3 will focus Read More...

12/03/2020 - Meeting Link DNAnexus is a secure cloud-based platform designed for the analysis of genomic data. CCR has licensed this resource to allow CCR investigators easy access to intuitive bioinformatics workflows running on the Amazon Read More...

08/31/2020 - Alternative splicing (AS) and alternative back-splicing (ABS) are essential to understanding the development of cancer and may play a role as a target of personalized cancer therapeutics. However, the existing reference transcriptome annotation databases Read More...

05/07/2013 -   This 2 hour seminar will be an interactive discussion and demonstration of the types of applications and work-flows that can be performed on deep sequencing data generated by the latest instruments from Illumina, Life Technologies ( 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...

[embed]https://youtu.be/yTl1Q0D7aZ0[/embed] 10x Genomics' supports multiple cancer research areas from single nucleotide and structural variant detection to single cell whole transcriptome gene expression analysis of thousands of individual Read More...

Total end-to-end system for single-cell research [embed]https://youtu.be/vMzhSzg1rUw[/embed] The BD Rhapsody Single-Cell Analysis system empowers and streamlines your research with a complete system of tools, including reagents and analysis software, 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...

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

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

10/04/2023 - Our series of talks continues this month with two 20-minute presentations.  There will be light refreshments and we encourage attendees to stay and chat with colleagues after the presentations.   & Read More...

We were introduced to the hcc1395 RNA sequencing data in Lesson 12 practice session . This study compared the transcriptome of hcc1395 normal and cancer cell lines so it's a normal versus tumor comparison. This dataset Read More...

Lesson 9 Practice Objectives In this practice session, we will apply our knowledge to learn about the reference genome and annotation file for the Golden Snidget dataset visualize the Golden Snidget genome using the Integrative Genome Read More...

Course setup Intro to DNAnexus and the GOLD learning system Learning Objectives Unix Bootcamp Brief Review of R (for R scripts in later analyses) Introduction to RNA Sequencing Central Dogma of Molecular Biology What is 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...

We are using the datasets below in the Qiagen IPA course. Human Brain Reference (HBR) and Univeral Human Reference (UHR) dataset Using this for the one hour lecture (from 1 - 2 pm) Find more information about Read More...

Throughout this module, we will be running various tools, including helper R scripts on the Unix command line to analyze the HBR and UHR RNA sequencing dataset. The helper R scripts were developed by the Read More...

Throughout this module, we will be running various tools, including helper R scripts on the Unix command line to analyze the HBR and UHR RNA sequencing dataset. The helper R scripts were developed by the 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...

Lesson 12 Practice Objectives In this practice session, we will work with something new, which is a dataset from the Griffith lab RNA sequencing tutorial. Here, we will have a chance to practice what we have Read More...

Lesson 12: RNA sequencing review 1 Learning objectives Here, we will do a quick review of what we have learned about RNA sequencing in Lessons 8 through 11. Accessing the Biostar handbook The URL for the Biostar handbook is Read More...

Bioinformatics for beginners Module 2: Introduction to RNA sequencing In this module, we will use the Human Brain Reference and Universal Human Reference RNA sequencing datasets to learn about RNA sequencing. Each lesson will be followed Read More...

Lesson 14 Practice Objectives Here, we will practice using the Integrative Genome Viewer (IGV) to visualize the hcc1395 RNA sequencing alignment results. About the data and launching IGV We were introduced to the hcc1395 RNA sequencing 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...

The data that we will be working with comes from the airway study that profiled the transcriptome of several airway smooth muscle cell lines under either control or dexamethasone treatment Himes et al 2014 . The dataset Read More...

The data that we will be working with comes from the airway study that profiled the transcriptome of several airway smooth muscle cell lines under either control or dexamethasone treatment Himes et al 2014 . The dataset Read More...

The data that we will be working with comes from the airway study that profiled the transcriptome of several airway smooth muscle cell lines under either control or dexamethasone treatment Himes et al 2014 and the Read More...

02/17/2023 - Dr. Brendan Miller is a post-doctoral research fellow at Johns Hopkins University in the Department of Biomedical Engineering. On Friday Feb 17, 1:00-2:00 PM, he will be discussing some tools he has recently helped develop Read More...

06/29/2022 - During this webinar, Computational Biologist Dr. Eytan Ruppin will present on “SELECT,” a computational approach that aims to identify clinically relevant synthetic lethal interactions, thereby harnessing them to predict patient response to cancer therapy Read More...

05/25/2022 - Dr. Peng Jiang of NCI’s Center for Cancer Research will discuss  CytoSig , a software-based platform that is designed to provide both a  database  of target genes modulated by cytokines (i.e., proteins secreted Read More...

04/22/2021 - Abstract: Single-cell RNA-sequencing has emerged as a popular technique for dissecting temporal processes such as tumor development and cell differentiation from snapshots of asynchronous ensembles of cells. Ongoing efforts in this area are now Read More...

01/27/2021 - Please plan to attend the Earl Stadtman Investigator Program search seminar by: Qian Zhu, Ph.D. Dana Farber Cancer Institute/Boston Children's Hospital Dr. Zhu's research interests include: single-cell genomics; spatial transcriptomics; Read More...

05/20/2020 - Advances in technology have reduced the cost of single cell sequencing, opening the doors to many new areas of study including transcriptome, DNA genomics, epigenomics and microbial systems. This workshop, provided by experts from Read More...

03/20/2017 - BTEP Workshop on RNA-Seq Data Analysis (2-day) This 2-day workshop, which includes both lecture and hands-on components, will cover the fundamentals of and best practices for RNA-Seq Data Analysis. Learn everything from experimental design Read More...

02/19/2015 - This 2-day course, which includes both lecture and hands-on components, will teach the basic concepts and practical aspects of RNA-Seq Data Analysis. Learn everything from experimental design to statistical analysis. This workshop will include Read More...

Normalization of gene expression estimates obtained from the quantification step is important as this will remove technical or non-biological variants in the data such as: Differences in sequencing depth between samples (ie. not all samples Read More...

Jupyter Notebook utilizes markdown for text editing. "Markdown is is a lightweight markup language for creating formatted text using a" -- https://en.wikipedia.org/wiki/Markdown We can use different size headings Read More...

Data collected for a specific case in TCGA may have differed according to sample quality and quantity, cancer type, or technology available at the time of analysis. --- https://www.cancer.gov/ccg/research/genome-sequencing/ Read More...

Our operational objectives are to provide state-of-the-art OMICS technologies in support of the Genetics Branch (GB) investigators and collaborators. Research Services Wet Lab Single cell isolation from fresh, frozen, and FFPE tissue, DNA/RNA extractions 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...

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...

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...

The Center for Advanced Preclinical Research (CAPR) specializes in evaluating the efficacy of preclinical compounds, existing drugs, or biologics (therapeutics) in genetically engineered mouse models, GEM-derived allograft (GDA) models, or patient-derived mouse xenografts (PDX). We 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...

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...

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...

As you can see from the image, there are several accessor functions to access the data from the object: assays() - access matrix-like experimental data (e.g., count data). Rows are genomic features (e.g., 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...

This page contains content directly from The Biostar Handbook . Always remember to start the bioinformatics environment. conda activate bioinfo Pseudoalignment-based methods identify locations in the genome using patterns rather than via alignment type algorithms. It Read More...

This page contains content directly from The Biostar Handbook . Always remember to start the bioinformatics environment. conda activate bioinfo Pseudoalignment-based methods identify locations in the genome using patterns rather than via alignment type algorithms. It Read More...

RNA-SEQ Overview What is RNASEQ ? RNA-Seq (RNA sequencing), uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment. (Wikipedia) Strictly speaking this could be any Read More...

Lesson 16: RNA sequencing review and classification based analysis Before getting started, remember to be signed on to the DNAnexus GOLD environment. Review In the previous classes, we learned about the steps involved in RNA sequencing Read More...

“Gene set enrichment analysis” refers to the process of discovering the common characteristics potentially present in a list of genes. When these characteristics are GO terms, the process is called “functional enrichment.” Warning Overall GO 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...

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. 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...

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...

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...

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...

01/19/2022 - For our next CDSL Webinar we will have a guest lecture by  Dr. Russell Rockne from Beckman Research Institute, City of Hope National Medical Center. Abstract: Temporal dynamics of gene expression inform cellular and Read More...

Step 1: Import the data This exercise will use the package ComplexHeatmap and the normalized (log2 counts per million or log2 CPM) count values from the top 20 differential expressed genes from the Airway study to reproduce Read More...

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