Home
Search results “Data mining pathway analysis microarray”
Bioinformatics platforms for analyzing global gene expression (MBCO AACR 2018) Pt2: STRING
 
13:53
This video series was recorded on the last day of the RNA-seq Analysis in Cancer Biomarker Discovery and Network Interpretation laboratory from the 2018 American Association for Cancer Research Molecular Biology in Clinical Workshop. This section of the course concerned the interpretation of the final gene list from an RNA-seq analysis of 7 ovarian tumors (OTs) vs. 7 tumor-associated macrophages (TAMs) (Reinartz et al. Genome Biology 2016, 17:108) produced in the initial section of the lab by the workshop participants. Part 1 in this video series gives an introduction to the types of bioinformatics platforms available for pathway mining, network building, data mining and clinical interpretation. Part 2 demonstrates the pathway/network platform STRING using the top 200 genes (by fold change) upregulated in the TAMs vs. OTs. Part 3 introduces the Illumina pathway/data miner, Correlation Engine, by using a larger list of genes upregulated (log2 fold change greater than 2) in the TAMs vs. the OTs. Part 4 deals with using clinical information with our analysis by introducing some of the functionality of Illumina’s Cohort Analyzer. I would like to thank my co-instructors for this course, Drs. Tzu Phang and Robert Stearman from the University of Colorado Denver and Indiana University School of Medicine, respectively. I would also like to thank Illumina Informatics for donating the use of their platform during and after this course and the American Association for Cancer Research for once again putting on a fantastic workshop. All opinions expressed in the video are the speaker’s and do not necessarily reflect those of the companies whose platforms are being demonstrated. Any uses of the products and platforms described in this presentation may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body.
Views: 384 Michael Edwards
Bioinformatics platforms for analyzing global gene expression (MBCO AACR 2018) Pt 1: Introduction
 
14:44
This video series was recorded on the last day of the RNA-seq Analysis in Cancer Biomarker Discovery and Network Interpretation laboratory from the 2018 American Association for Cancer Research Molecular Biology in Clinical Workshop. This section of the course concerned the interpretation of the final gene list from an RNA-seq analysis of 7 ovarian tumors (OTs) vs. 7 tumor-associated macrophages (TAMs) (Reinartz et al. Genome Biology 2016, 17:108) produced in the initial section of the lab by the workshop participants. Part 1 in this video series gives an introduction to the types of bioinformatics platforms available for pathway mining, network building, data mining and clinical interpretation. Part 2 demonstrates the pathway/network platform STRING using the top 200 genes (by fold change) upregulated in the TAMs vs. OTs. Part 3 introduces the Illumina pathway/data miner, Correlation Engine, by using a larger list of genes upregulated (log2 fold change greater than 2) in the TAMs vs. the OTs. Part 4 deals with using clinical information with our analysis by introducing some of the functionality of Illumina’s Cohort Analyzer. I would like to thank my co-instructors for this course, Drs. Tzu Phang and Robert Stearman from the University of Colorado Denver and Indiana University School of Medicine, respectively. I would also like to thank Illumina Informatics for donating the use of their platform during and after this course and the American Association for Cancer Research for once again putting on a fantastic workshop. All opinions expressed in the video are the speaker’s and do not necessarily reflect those of the companies whose platforms are being demonstrated. Any uses of the products and platforms described in this presentation may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body.
Views: 159 Michael Edwards
Analyzing NimbleGen Human Methylation Array Data (or MetSeq)
 
15:22
This is a demo of methylation array data analysis. Though the starting files of this tutorial are GFF format, it's totally same if they're BED format files. So this process is completely applicable to MetSeq data based on any sequencing technologies, too. Data import ~ Creating intervals ~ Extracting differentially methylated intervals ~ Biological analysis All information and movies are organized at our web site. Please visit https://www.subioplatform.com
Views: 158 subiosupport
ImaGEO: Integrative Gene Expression Meta-Analysis from GEO database (User's Guide)
 
07:30
Tutorial to learn how to use Imageo (Integrative Gene Expression Meta-Analysis from GEO data) web tool. http://bioinfo.genyo.es/imageo/ More details could be found in their Help section
Views: 166 Daniel Toro
Integrate Analysis and Interactive Exploration of Data from TCGA - Ilya Shmulevich
 
36:52
November 17-18, 2011 - The Cancer Genome Atlas' 1st Annual Scientific Symposium More: http://www.genome.gov/27546242
ExpressionSet v1
 
09:50
Bioconductor for Genomic Data Science: http://kasperdanielhansen.github.io/genbioconductor/
Views: 899 Kasper Hansen
Discovering Cellular Mechanisms and Markers in Anti-PD1 Non-Responders Through Ingenuity Pathway..
 
52:30
Presented At: Cancer Research & Oncology 2018 Presented By: Devendra Mistry, PhD - Senior Field Application Scientist, Bioinformatics, QIAGEN Speaker Biography: Devendra (Dev) received his PhD from University of California San Diego(UCSD) Biomedical Sciences graduate program and did postdoctoral studies under both academic and pharmaceutical settings. During his post-doctoral studies, he focused on cellular mechanisms regulating stem cells and cancer stem cells through next-gen sequencing data generation, analysis, interpretation and mining. Dev joined QIAGEN as a field application scientist in 2015. In the past 3 years, he has provided trainings to many pharma, biotech, academic and government investigators for different QIAGEN bioinformatics software. In addition, he has helped many of these users troubleshoot problems with their existing workflows and design new workflows. Webinar: Discovering Cellular Mechanisms and Markers in Anti-PD1 Non-Responders Through Ingenuity Pathway Analysis and Oncoland Webinar Abstract: In the last two decades, large amount of next-generation sequencing (NGS) and -omics data has been generated in the field of immuno-oncology. Generating hypotheses by analyzing hundreds if not thousands of differentially expressed genes from expression studies and mining information from large amount of publicly available NGS and -omics data can be a very daunting task. QIAGEN’s Oncoland/Arraystudio (from Omicsoft) and Ingenuity Pathway Analysis (IPA) software provide with a set of tools and functionalities to do analysis and interpretation of NGS data to generate meaningful hypotheses and the ability to mine and compare information across a very large number of datasets curated from publicly available from sources such as GEO, SRA, TCGA, GTEX and others. In this webinar, we use gene expression data from a clinical study (GSE67501) focused on understanding the mechanism underlying anti-PD-1 therapy failure in advanced renal cell carcinoma patients. Using this data and the data curated from TCGA and other sources, it will be demonstrated how Arraystudio and Ingenuity Pathway Analysis can be used to generate hypotheses for mechanism of action and to discover potential targets and biomarkers. Learning Objectives: 1. Introduction to databases backing Ingenuity Pathway Analysis and Oncoland 2. Studying potential biomarkers and targets through Oncoland’s data mining and comparison tools 3. Generation of peer-reviewed literature backed hypotheses through Ingenuity Pathway Analysis Earn PACE/CME Credits: 1. Make sure you’re a registered member of LabRoots (https://www.labroots.com/virtual-event/cancer-research-oncology-2018) 2. Watch the webinar on YouTube above or on the LabRoots Website (https://www.labroots.com/virtual-event/cancer-research-oncology-2018) 3. Click Here to get your PACE (Expiration date – October 11, 2020 09:00 AM)– https://www.labroots.com/credit/pace-credits/3096/third-party LabRoots on Social: Facebook: https://www.facebook.com/LabRootsInc Twitter: https://twitter.com/LabRoots LinkedIn: https://www.linkedin.com/company/labroots Instagram: https://www.instagram.com/labrootsinc Pinterest: https://www.pinterest.com/labroots/ SnapChat: labroots_inc
Views: 69 LabRoots
A fully integrated and user-friendly microarray data tool: Goober - Part 2
 
01:58
Goober: A fully integrated and user-friendly microarray data management and analysis solution for core labs and bench biologists. Part 2 Manuscript Published on Journal of Integrated Bioinformatics: http://www.ncbi.nlm.nih.gov/pubmed/20134074
Views: 131 wenl888
Small RNA Discovery & Analysis - Microarray Service
 
00:31
http://www.LCsciences.com Non-coding (ncRNAs) are a class of RNAs that do not encode proteins but instead possess regulatory function at the level of RNA in the cell. Custom microarrays have been shown to be a effective experimental approach to discovery of novel ncRNAs1 as well as a method to validate candidate ncRNAs2. LC Sciences provides a small non-coding RNA discovery service using innovative µParaflo® technology and proprietary probe design, which enable highly sensitive and specific direct detection of small RNAs in your sample. This service is comprehensive; from array design to sample to data, allowing the most efficient novel discovery of small regulatory/functional RNA embedded within non-coding RNAs (ncRNAs) or genomic RNAs.
Views: 1295 LC Sciences
Hierarchical clustering of TCGA expression data in Gitools
 
01:39
The example shows how to cluster data in Gitools interactive heatmaps. The dendrogram is coded as color bars which give access to sub-dendrograms (subclusters) or the entire clustering tree. To know more about hierarchical clustering in Gitools read the blog post on how to interpret the Gitools hierarchical clustering: http://bg.upf.edu/blog/2014/03/how-to-perform-a-hierarchical-clustering-in-gitools/ The example data used in the video are cancer genomics data from the TCGA. In this video particularly we are clustering expression data of the cancer samples. All data is available (link below) The application: http://www.gitools.org The TCGA datasets: http://www.gitools.org/datasets
BioJupies Tutorial Part 4 of 8 - Analyzing GEO Data
 
01:43
This video shows how users can analyze over 8,000 publicly available RNA-seq datasets published in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) and processed by ARCHS4 (http://archs4.cloud). BioJupies is a web server application that enables automated creation, storage, and deployment of Jupyter Notebooks containing RNA-seq data analyses. Through an intuitive interface, novice users can rapidly generate tailored reports to analyze and visualize their own raw sequencing files, their gene expression tables, or fetch data from published studies. Generated notebooks have executable code of the entire pipeline, rich narrative text, interactive data visualizations, and differential expression and enrichment analyses. The notebooks are permanently stored in the cloud and made available online through a persistent URL. The notebooks are downloadable, customizable, and can run within a Docker container. In this video we provide an introduction to the BioJupies project. BioJupies is freely available as a web-based application from: http://biojupies.cloud The BioJupies Chrome extension is available from the Chrome Web Store at: https://chrome.google.com/webstore/detail/biojupies-generator/picalhhlpcjhonibabfigihelpmpadel A pre-print article that describes the project is available at: https://www.biorxiv.org/content/early/2018/06/20/352476 Playlist for BioJupies Video Tutorial - Automatically Generated Jupyter Notebook Reports for RNA-seq Data Analysis: https://www.youtube.com/playlist?list=PLae3onrutp8MEIdYYI8_mKG4Vv457Nlib BioJupies Tutorial Part 1 of 8 - Introduction to BioJupies https://youtu.be/kIzHdsCqssU BioJupies Tutorial Part 2 of 8 - Analyzing Raw RNA-seq Data https://youtu.be/5r2o6aoRXMo BioJupies Tutorial Part 3 of 8 - Analyzing Processed RNA-seq Data https://youtu.be/5U-YyObo_-Q BioJupies Tutorial Part 4 of 8 - Analyzing GEO Data https://youtu.be/lFIGceUB1M4 BioJupies Tutorial Part 5 of 8 - Generating a Jupyter Notebook https://youtu.be/mu-EQFYHWDI BioJupies Tutorial Part 6 of 8 - Exploring a Jupyter Notebook https://youtu.be/vY7WZ8jr2TE BioJupies Tutorial Part 7 of 8 - Reusing Notebooks https://youtu.be/L5fmFed9mz4 BioJupies Tutorial Part 8 of 8 - Using the Chrome Extension https://youtu.be/Ew3KBfAx7kM
Views: 27 Denis Torre
Import MSigDB Gene Sets / Find Measurement Lists or Pathways
 
04:51
The Molecular Signatures Database (MSigDB) is a collection of annotated gene sets for use with GSEA software. The MSigDB gene sets are divided into eight major collections; - Hall mark gene sets, representing well-defined biological states or processes. - Positional gene sets, representing chromosome and cytoband. - Curated gene sets, representing online pathways, publications in PubMed, and knowledge of domain experts. - Motif gene sets, representing conserved cis-regulatory motifs of transcription factors and miRNAs. - Computational gene sets defined by mining large collections of cancer-oriented microarray data. - GO gene sets, representing gene annotation of gene ontology terms. - Oncogenic gene sets defined directly from microarray gene expression data from cancer gene perturbations. - Immunologic gene sets defined directly from microarray gene expression data from immunologic studies. Learn more at http://software.broadinstitute.org/gsea/msigdb You can make use of the gene sets by importing as measurement lists. After that, you can execute the powerful enrichment analysis on them. "Find Measurement Lists or Pathways tool" is useful to find them from the name or contents gene. https://www.subioplatform.com/info_technical/319
Views: 78 subiosupport
Webinar: Introduction to the Genomatix Pathway System
 
01:06:28
Do you work with large scale gene expression / regulation data, such as RNA-Seq or microarrays? Then you'll want to reveal the hidden biology and regulatory networks in your gene sets, using pathway analysis. This webinar recording gives an introduction how to perform pathway and network analyses using the Genomatix Pathway System (GePS). GePS integrates multiple lines of evidence to generate information-rich gene interaction networks: - Canonical signal transduction pathways - Automated and manual literature mining - Annotated biology of genes (GO, MeSH, and more) - In silico promoter analysis - Experimental support for transcription factor binding The presented examples show how to - generate networks from gene lists based on common biology - access and review the underlying literature and transcription factor data - edit networks using literature information - extend networks with transcription factors and miRNAs - work with transcription factor filters and summaries
Views: 298 Genomatix
Analyzing TCGA's RNA-seq data of 240 prostate cancer patients
 
16:31
The Cancer Genome Atlas (TCGA) is a large data repository of multi-omics and clinical data of cancers.This movie tutorial shows how to get RNA-seq data and analyze with Subio Platform on your computer. For more information. https://www.subioplatform.com/info_casestudy/7/an-integrated-analysis-of-tcga-prad-rna-seq-&-dna-methylation-data
Views: 17575 subiosupport
Bioinformatics and Biospecimen Workshop 2013 - Gene Expression Data Analysis by Dr. Kimberly Bussey
 
01:26:55
A brief introduction to the sources of gene expression data, including RNA-Seq and reverse phase protein arrays (RPPA), followed by a brief tour of selected on-line tools including Biosig, cBio, MSigDB, UCSC Cancer Genomics Browser, Cancer Genome Workbench and IGV.
Views: 3495 nmsuaces
20180308 Sun Bioinfo D Gene Expression
 
01:35:09
Slides for this talk are available here: https://drive.google.com/open?id=1-4slHopK2dcOBJOCQ4vH_9h98gtoZQhV This is the fourth lecture of eight for the B.Sc. Honours bioinformatics module at Stellenbosch University Faculty of Medicine and Health Sciences, Department of Biomedical Sciences. The lecture covers the technologies we use to measure gene expression or the transcriptome: microarrays and RNA-Seq. We discuss the difference between microarrays produced through "spotting" or through photolithography (drawing parallels between ink-jet printers and the process for manufacturing CPUs from silicon wafers). We discuss the conversion from mRNA to cDNA through reverse-transcription and polymerase chain reaction. We discuss some of the added steps that become necessary when we switch from a microarray to RNA-Seq. How much will we use an annotated genome? Not at all (de novo sequence assembly), aligning to genomic DNA, or aligning to gene models are all possibilities. Once data have been produced, we see bioinformatics continuing to carry the load. Normalization may be necessary to ensure that inter-experimental differences do not hinder comparison of data among arrays or among sequencing experiments; we want the distributions to be comparable. We discussed the value of Bland-Altman plots to ensure that fold-change biases do not appears as a function of signal intensity. Biclustering, the technique used to group together samples with similar gene expression and to group together genes with similar expression across samples, is explained in connection with their characteristic "checkerboard" patterns. The lecture strongly emphasized the role of significance testing via Student's T-Test and the Mann-Whitney (or Wilcoxon) U-Test. We talked about the differences between these tests, and I argued in favor of defaulting to five replicates rather than three. From there, we passed to a discussion of multiple testing correction using the Bonferroni adjustment (as we saw before in Manhattan plots) or adapting the more lenient Benjamini-Hochberg method to control the False Discovery Rate.
Views: 37 David Tabb
Biostatistics NCBI GEO data collection Database creation
 
15:02
In this 15 min video I demonstrate how to get a gene annotation table and transcriptomic data set from NCBI GEO. The we make a quick gene list of our 'virtual' biomarkers. I show you how to open them in Excel and save in a format for import into Access. We then import these three tables to create our database. I got to show a bit on how to set up a query just at the end. Will complete that and a 'virtual' data analysis in the next video. The second video for this demonstration is titled, "Biostatistics database QUERY and analysis in Excel" https://www.youtube.com/watch?v=4kbFefje-xk
Views: 8762 Clyde Phelix
Data Mining from the New PCBC Genomics Expression Atlas
 
01:15:04
PCBC Webinar: Data Mining from the New Progenitor Cell Biology Consortium (PCBC) Genomics Expression Atlas When: Monday, July 29, 2013, 1:00 -- 2:00 PM, EDT (12 Noon -- 1:00 PM, CDT; 11:00 AM -- 12 Noon, MDT; 10:00 AM -- 11:00 AM, PDT) Presenter: Bruce Aronow, Ph.D. Topics for discussion: •Cell, RNA, and DNA samples in the PCBC Cell Characterization Core •Cell phenotype and genomics analyses have been performed (e.g., cell samples analyzed by RNAseq, miRseq, DNA methylation) •Finding what a gene of interest is doing in the different samples •Pathways and interactions that are active in different cells or involve genes of interest •Gene lists for similarity to signatures in the PCBC database •Splice forms of the RNAseq data from PCBC samples
Views: 450 Ling Tang
MLG 18/07/2016 - TCGAbiolinks: data, analyses & applications (Network Stability) by Catharina Olsen
 
18:35
In this talk we first present our recently published R/Bioconductor package TCGAbiolinks [1]. We give a short introduction into how to use the package to query The Cancer Genome Atlas (TCGA) database, download the data and carry out a number of standard analyses using genomic, epigenomic & transcriptomic data [2]. Then we will discuss two ongoing projects using TCGA data. Stability: The inference of gene regulatory networks from gene expression data aims to shed light on the biological mechanisms underlying diseases such as cancer. The difficulty in extracting these mechanisms from observational data stems from multiple factors: high variable to sample ratio and high amounts of noise from the data generation. In this project, we investigate the stability of gene regulatory network inference algorithms with respect to data type (microarray versus RNAseq), sample size, and cancer type. Moonlight: In order to make light of cancer development, it is crucial to understand which genes play a role in the mechanisms linked to this disease and moreover which role that this (so called ‘drivers’). In this project we present a framework that allows us to identify tumor suppressor genes (TSGs), oncogenes (OCGs) and possibly genes that have a ‘dual role’, i.e. genes that act as TSG in one cancer type and OCG in another.
Differential network analysis using gene expression data
 
41:59
Susmita Datta, Univ. of Louisville, gave a talk entitled "Differential network analysis using gene expression data" at the Spring Opportunities Workshop for Women in the Mathematical Sciences, held April 9-11, 2014. To read more about the workshop, click the following link: http://www.nimbios.org/education/WS_opportunities
Views: 600 NIMBioS
Patient-Specific Pathway Analysis Using PARADIGM Identifies Key Activities... - Josh Stuart
 
17:38
November 17-18, 2011 - The Cancer Genome Atlas' 1st Annual Scientific Symposium More: http://www.genome.gov/27546242
Retrieve and analyze a gene expression data set from NCBI GEO in R
 
16:39
R script is available at: https://github.com/hongqin/RCompBio/blob/master/ncbigeo/ncbiGEO2012Nov14-demo-youtube.R SBIO386, Spelman College, Fall 2012
Views: 24481 Hong Qin
139686_Microarray and bioinformatics analysis of ixazomib on colorectal cancer
 
03:31
Video abstract of Original Research paper “Identification of the anticancer effects of a novel proteasome inhibitor, ixazomib, on colorectal cancer using a combined method of microarray and bioinformatics analysis” published in the open access journal OncoTargets and Therapy by authors Fan and Liu. Purpose: The study aimed to explore the anticancer effects of a novel proteasome inhibitor, ixazomib, on colorectal cancer (CRC) using a combined method of microarray and bioinformatics analysis. Materials and methods: Cell proliferation was tested by Cell Counting Kit-8 (CCK-8) assay for SW620 cells treated with different concentrations of ixazomib and different treatment times. The microarray analysis was conducted for six samples, including three samples of SW620 cells untreated with ixazomib and three samples of SW620 cells treated with ixazomib. The differentially expressed genes (DEGs) between untreated and treated samples were identified by the Linear Models for Microarray data (LIMMA) package in R language. The Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the DEGs using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and KEGG Orthology-Based Annotation System (KOBAS) online tool. The protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and module analysis was performed for the PPI network. Results: Ixazomib could inhibit the proliferation of SW620 cells in a dose-dependent and time-dependent manner. A total of 743 DEGs, including 203 upregulated DEGs such as HSPA6 and 540 downregulated DEGs such as APCDD1, were identified. Eighty-three GO terms were enriched for DEGs, which were mainly related to protein folding, apoptotic process, transcription factor activity, and proteasome. Thirty-seven KEGG pathways were perturbed, including pathway of apoptosis and cell cycle. Forty-six hub genes, such as TP53, JUN, and ITGA2, were screened out, and three modules with important functions were mined from the PPI network. Conclusion: The novel proteasome inhibitor ixazomib significantly inhibited the proliferation of human CRC SW620 cells. It exerted anticancer effects through targeting the expression of DEGs, such as HSPA6, APCDD1, TP53, and JUN, and affecting the signaling pathways including apoptosis and cell cycle pathway, which demonstrated the promising potential of ixazomib for CRC therapy. Read the Original Research article here: https://www.dovepress.com/identification-of-the-anticancer-effects-of-a-novel-proteasome-inhibit-peer-reviewed-article-OTT
Views: 107 Dove Medical Press
TCGA PRAD Integrated Analysis of RNA seq and miRNA seq  (part 2)
 
13:16
This demonstration shows how to extract potential controller miRNAs of genes which are differentially expressed between normal and tumor samples in the former movie (part 1) We extracted the following miRNAs as potential controller miRNAs of Prostate Adenocarcinoma hsa-mir-10a, hsa-mir-15b, hsa-mir-17, hsa-mir-20a, hsa-mir-20b, hsa-mir-25, hsa-mir-30b, hsa-mir-32, hsa-mir-93, hsa-mir-96, hsa-mir-106a, hsa-mir-130b, hsa-mir-141, hsa-mir-148a, hsa-mir-182, hsa-mir-183, hsa-mir-191, hsa-mir-200c, hsa-mir-203, hsa-mir-204, hsa-mir-363, hsa-mir-375, hsa-mir-451, hsa-mir-484, hsa-mir-573, hsa-mir-629, hsa-mir-708, hsa-mir-888, hsa-mir-890, hsa-mir-891b, hsa-mir-892a, hsa-mir-892b For more information. https://www.subioplatform.com/info_casestudy/2/an-integrated-analysis-of-tcga-prad-rna-seq-&-mirna-seq
Views: 1019 subiosupport
The Science of Impact Analysis - Part 1
 
13:53
In this video, Advaita's founder and CEO, Dr. Sorin Draghici, walks you through why the current methods for gene expression pathway analysis don't work and introduces a new method, Impact Analysis.
Analysis of Paired Tumor and Normal Data in TCGA - Andrew Gross
 
16:49
May 12, 2015 - The Cancer Genome Atlas 4th Annual Scientific Symposium More: http://www.genome.gov/27561703
Gene Expression Data Analysis
 
05:49
This movie will demonstrate a few workflows and algorithms for gene expression data analysis.
Views: 4551 AriadneGenomics
Wenjie Xu - Gene Expression Profiling During Infection NanoString® nCounter...
 
57:41
Watch this webinar on Labroots at: http://www.labroots.com/virtual-event/microbiology-immunology-2016 Our speaker, Wenjie Xu, Ph.D., will present his publication data demonstrating how nCounter® technology can advance your infectious disease research faster and more accurately with unprecedented sensitivity, reproducibility and ease of use. nCounter has been successfully used for identification and profiling of viruses, bacteria and fungi from real life infection samples. One single RNA isolation can generate paired data for both pathogen and host. Thanks to the automated and streamlined workflow, it takes less than 48 hours from sample collection to publication grade figures.
Views: 230 LabRoots
CaseStudy1_AGTR1.mp4
 
08:36
In this Oncomine case study, Dr. Daniel Rhodes uses ERBB2 and AGTR1 to tell a breast cancer target discovery story. Within this case study, he demonstrates gene searches, using the gene summary map to understand where genes are differentially expressed, using meta-analysis to discover new genes that have interesting expression patterns, and cancer sub-type analyses to identify the particular sub-types showing differential expression.
Views: 916 Oncomine Platform
Download for Advanced Analysis
 
03:20
Download a list of genes and associated data to the "Local Analysis" tab of Array Studio. Attach the meta data, do a Log2 transformation on the gene-level data, followed by a 1-way ANOVA comparing TP53 status to WT.
Views: 257 OmicsoftCorporation
20180314 SUN Bioinfo H Biological Pathways Networks
 
01:17:53
Slides for this lecture can be found here: https://drive.google.com/open?id=1ZVLSxAHPF80jHMO1W5Pjt6KmZRfy2__J This is the eighth lecture of eight for the B.Sc. Honours bioinformatics module at Stellenbosch University Faculty of Medicine and Health Sciences, Department of Biomedical Sciences. The lecture covers topics in biological pathways and networks. We started with a quick assessment of the experimental sources that lead to our understanding of gene-gene, protein-protein, and gene-protein relationships. We contrasted approaches that enumerate a set of genes as associated with each activity (such as the genes in a given GO category) with those that infer collections of tightly connected components of genes in point-to-point networks. Using collective descriptions of genes improves our sensitivity for changes, our robustness against missing real differences, and improves the interpretability of our findings. We particularly examined Gene Ontology (GO), explaining that it marries a controlled vocabulary with defined relationships to create categories that have a hierarchical relationship described by annotated evidence. We looked at KEGG for another categorical grouping of genes, noting that the reactions leading from metabolite to metabolite were frequently marked by Enzyme Commission numbers. We used the WebGestalt interface for over-representation analysis: which pathways explain disproportionate numbers of our genes of interest? We discussed the use of the hypergeoemtric distribution, a la Fisher Exact Test, for computing the probability of a particular overlap between lists. We also ventured into Gene Set Enrichment Analysis as a way to find the pathways of greatest representation in a list of genes ordered by fold change / p-value. We spent a bit of time defining the graph theory terms that we frequently borrow for biological networks, and we defined the scale-free, small-world, and hierarchical modular properties of biological networks. We mentioned several tools for network visualization but emphasized the powerful Cytoscape environment and NetGestalt framework for both visualization and integration of OMICs data.
Views: 82 David Tabb
Network Analysis of Gene Expression in the Human Brain (Pt.3) by Mike Oldham, UCLA
 
58:31
(6/29/15) 2015 Network Analysis Short Course - Systems Biology Analysis Methods for Genomic Data This five full-day intensive course will cover network analysis methods widely used in systems biologic and systems genetic applications. The goal of the network analysis workshop is to familiarize researchers with network methods and software for integrating genomic data sets with complex phenotype data. Students will learn how to integrate disparate data sets (genetic variation, gene expression, epigenetic, protein interaction networks, complex phenotypes, gene ontology information) and use networks for identifying disease genes, pathways and key regulators. A companion Statistical Genetics Short Course will be held in August 2015. Speaker Mike Oldham is a genetics researcher and neuroscientist at UCLA.
Bioinformatics and Biospecimen Workshop 2013 - Online Tools to Analyze TCGA Data by Dr. Rehan Akbani
 
50:46
A brief tour of selected on-line tools to analyze TCGA data. The primary portal for these tools is found at Analytical Tools for The Cancer Genome Atlas https://tcga-data.nci.nih.gov/tcga/tcgaAnalyticalTools.jsp Examples are presented for tools at cBioPortal for Cancer Genomics; Integrative Genomics Viewer; Broad GDAC Firehose; MD Anderson GDAC MBatch; as well as tools still in development.
Views: 6844 nmsuaces
The Cancer Genome Atlas: The Power of TCGA Data
 
04:06
This video is one in a series of videos from The Cancer Genome Atlas (TCGA) project, explaining TCGA's approach to determining the important genomic changes that lead to cancer. TCGA researchers, Drs. Joe Gray, Chris Sander, Doug Levine and Richard Gibbs explain how TCGA is set up to collect and share genomic data, the importance of bioinformatics in sifting through the data to find important features, and how TCGA is changing the way we think of cancer. The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort supported by the National Cancer Institute and the National Human Genome Research Institute to accelerate our understanding of the genetics of cancer using innovative genome analysis technologies. To learn more about The Power of TCGA Data, please visit http://cancergenome.nih.gov/newsevents/multimedialibrary/videos/thepoweroftcgadata
Views: 3033 NCIresearchfunding
An Integrative Systems Biology Approach for Prioritizing Biomarkers and Drug Targets in Diseases
 
01:00:20
Systems biology or pathway-based data analysis approaches allow the identification of networks of biological entities that may collectively define mechanisms and phenotypes, especially as they relate to disease. Herein, we applied an integrative systems biology workflow to hypothesize clinically relevant biomarkers and drugs targets for Alzheimer’s disease.1 Our workflow included several in silico approaches that integrate the prioritization of disease gene signatures, the analysis of disease-gene pathways and networks, and the ranking of putative drug targets based on their novelty scores (i.e., evaluating complete novelty, condition novelty or evidence of early development). We foresee this workflow as a universal tool for the prioritization of drug targets and biomarkers in complex diseases including, cancer, diabetes and many neurodiseases. In this session, we will be using MetaCore to compare and analyze the differentially expressed genes (DEGs) in 6 brain regions of Alzheimer’s disease patients calculated from the gene expression profiles reported in Gene Expression Omnibus (GEO) dataset GSE5281. Next, we will apply Causal Reasoning in MetaCore Key Pathway Advisor (MetaCore KPA) to identify upstream regulatory hubs that could be prioritized as drug targets and/or biomarkers. The gene and protein hits identified from the upstream key hub predictions and downstream enrichment analyses will be integrated and analyzed using the network building tools available in MetaCore to understand the underlying mechanisms. Finally, the prioritized hypotheses will be evaluated and putative drug targets will be ranked based on their novelty scores, using the Drug Research Advisor-Target Druggability (DRA-TD).2 At the end of this session we will be able to answer the following key questions: • What pathways and process networks are potentially disrupted in Alzheimer’s disease? • What upstream key regulatory hubs are potentially activated or inhibited in Alzheimer’s disease? • How to integrate results from upstream and downstream analyses to generate higher confidence, clinically-relevant hypotheses about drug targets and biomarkers? • How to evaluate the resulting hypotheses and score putative drug targets? References: 1) Hajjo, R & Willis, C. Systems biology approaches to omics data analysis in complex diseases. 253rd Am Chem Soc (ACS) Natl Meet (April 2-6, San Francisco) 2017, Abst BIOT 461. 2) Drug research Advisor, https://projectne.thomsonreuters.com/dra/, 2017.
Omics data sharing via Subio Series Archives
 
01:47
Subio Platform is a free, technology-independent omics data browser and software platform for sharing analysis results. Its integrated visualization tools greatly help handling complex omics data and revealing biological insight. After you analyze omics data with wide variety of biological information, export all data (e.g. gene annotations, sample information, experimental data, result of statistical analysis, PDFs and so on) into one archive (ssa) file. Your collaborators can completely reconstruct what you saw on their PCs just by drag-and-drop the ssa. This is the easiest way of omics data sharing at the lowest cost. All information and movies are organized at our web site. Please visit https://www.subio.jp
Views: 316 subiosupport
GSE32958 Integrative analysis of Gene and miRNA expression data 2
 
15:30
Subio Platform and plug-ins are very useful for integrative analysis of gene-expression and miRNA-expression data sets. Please see the demo of analyzing GSE32958. GSE15387 is a similar study and it's interesting to compare to know the characteristics of platforms, because the quality of data is crucial. Viewing public data before planning your study is very important to avoid preventable failure. 1. Extracting differentially expressed genes. 2. Extracting negatively correlated miRNAs. 3. Importing other miRNA lists to compare results. 4. Extracting differentially expressed miRNAs. 5. Extracting negatively correlated genes. 6. Biological analysis of the resulting gene lists. All information and movies are organized at our web site. Please visit https://www.subio.jp
Views: 395 subiosupport
TCGA: Detection, Diagnosis and Correction of Batch Effects in TCGA Data - Rehan Akbani
 
14:21
November 27-28, 2012 - The Cancer Genome Atlas' 2nd Annual Scientific Symposium: Enabling Cancer Research Through TCGA More: http://www.genome.gov/27551851
CaseStudy2_signatureanalysis.mp4
 
08:10
In this Oncomine case study, Dr. Daniel Rhodes uses a pathway activation signature analysis to demonstrate the use of Oncomine Concepts. The use of signatures in combination with concepts allows you to learn about the biology of the signature, cancer types and sub-types where it is over-expressed, drug sensitivity associations, and Oncomine clusters to reveal co-expression with cancer types.
Views: 507 Oncomine Platform
Comparison of Protein and mRNA expression profiles
 
41:14
From Advaita's May 24, 2016 webinar. In this webinar Dr. Sidra Ahsan walks you through a comparison of mRNA and protein expression data from metastatic and non-metastatic bladder cancer cell lines.
20180412 UWC NGS A Genome Assembly Annotation
 
01:27:57
Slides for this lecture appear here: https://drive.google.com/open?id=1shbLd7AWECDk-J4udft5FQ2ETrPKb2TP This year I contribute three lectures for the Next-Generation Sequencing module, taught for the B.Sc. Honours program at the University of the Western Cape Department of Biotechnology. The first has considerable ground to cover as it details de novo assembly of sequencing reads and annotation of the resulting sequence! For Assembly, we emphasize the key topics of paired-end sequencing, the repetitive sequences such as VNTRs and transposable elements, as well as k-mer catalogs and de Bruijn graphs. Annotation has a similar diverse discussion, beginning with Hidden Markov Models for gene finding, continuing with sequence alignment algorithms such as BLAST that employ the BLOSUM62 substitution matrix, and passing through conserved domains and the InterPro motif, domain, and superfamily database.
Views: 139 David Tabb
cBio Cancer Genomics Portal: A Platform for Exploring Cancer Genomics Data: Dr. Ethan Cerami
 
48:12
Dr. Ethan Cerami from Memorial Sloan-Kettering Cancer Center, delivered a presentation titled "cBio Cancer Genomics Portal." This presentation provides an introduction to the portal and description on how to mine data generated by The Cancer Genome Atlas (TCGA) project.
Views: 2049 NCIwebinars
Large-Scale Expression Analysis - Paul Meltzer (2012)
 
01:08:58
March 28, 2012 - Current Topics in Genome Analysis More: http://www.genome.gov/COURSE2012
Developing and Benchmarking Gene Set Enrichment Analysis Methods
 
08:06
This presentation is by Damon Pham, an undergraduate student from Indiana University. Damon describes his summer research project with the BD2K-LINCS DCIC in the Ma'ayan Lab at the Icahn School of Medicine at Mount Sinai. http://lincs-dcic.org/summer-research-app#nav http://icahn.mssm.edu/labs/maayan https://github.com/MaayanLab/Enrichment_Sandbox Abstract Most gene set enrichment analysis methods that treat both the input genes and the gene sets in gene-set libraries as sets, do not consider the rich correlation structure that exists among genes. The purpose of this project is to evaluate new algorithms for enrichment analysis: the process of identifying known gene sets whose genes are over-represented within a given sample of genes. This is done by performing enrichment analysis with either transcription factor or drug target libraries. The benchmarking process is as follows: Two libraries, A and B, are chosen. Terms which correspond to transcription factors (or drugs and their known targets) which are in both libraries are identified. Then, the gene sets of those common terms from library A are used to perform enrichment into library B. For each common term in library A, the algorithm returns a ranking of all terms in library B, from most enriched to least enriched. Since these libraries should have some agreement with one another, we expect that the library B term(s) corresponding to the same transcription factor (or drug/drug-target) as the input term from library A will appear towards the top of the rankings. So a good algorithm will consistently rank matching terms highly, while a bad algorithm will not. Bridge plots are created to qualitatively evaluate different existing and new enrichment analysis algorithms using this bronze standard.
Views: 2425 Avi Ma'ayan