RNA spike-in controls & analysis methods for trustworthy genome-scale measurements
|
|
|
- Joel Moody
- 6 years ago
- Views:
Transcription
1 RNA spike-in controls & analysis methods for trustworthy genome-scale measurements Sarah A. Munro, Ph.D. Genome-Scale Measurements Group ABRF Meeting March 29, 2015
2 Overview External RNA Controls Consortium (ERCC) RNA spike-in controls erccdashboard analysis tool ERCC 2.0: Building an updated suite of RNA controls
3 Overview External RNA Controls Consortium (ERCC) RNA spike-in controls erccdashboard analysis tool ERCC 2.0: Building an updated suite of RNA controls
4 How can we have trustworthy We re simultaneously measuring thousands of RNA molecules in gene expression experiments But are we getting it right? gene expression results?
5 External RNA Controls Consortium (ERCC) initiated by industry, hosted by NIST Initiated by Janet Warrington, VP Clinical Genomics at Affymetrix Open to all interested parties Voluntary More than 90 participants Industry, Academia, Government All major microarray technology developers Other gene expression assay developers Spikeins
6 ERCC control sequences are in NIST Standard Reference Material 2374 DNA sequence library 96 unique control sequences in DNA plasmids Controls intended to mimic mammalian mrna In vitro transcription to make RNA controls NIST SRM 2374 and related data files are available directly from
7 Making ERCC ratio mixtures with true positive and true negative ratios NIST Plasmid DNA Library RNA transcripts Mixtures with known abundance ratios in vitro transcription Pooling
8 Using ERCC ratio mixtures Treated (n>3) Control (n>3)
9 Using ERCC ratio mixtures Treated (n>3) Control (n>3)
10 Using ERCC ratio mixtures Treated (n>3) Control (n>3)
11 Using ERCC ratio mixtures Treated (n>3) Control (n>3) Measurement process Expression Measures Multiple steps Many people & labs Takes days to weeks Statistical Analysis
12 Example gene expression data Treated Control
13 Are the RNA molecule ratios statistically different across the samples? Treated Control
14 Evaluate technical performance with ERCC true positive and true negative ratios Treated Control
15 Overview External RNA Controls Consortium (ERCC) RNA spike-in controls erccdashboard analysis tool ERCC 2.0: Building an updated suite of RNA controls
16 Use erccdashboard to produce standard performance metrics for any experiment R package is available from: Bioconductor NIST GitHub Site Open source and open access for use in Other analysis tools and pipelines Commercial software
17 Gauge technical performance with 4 erccdashboard figures Developed as part of SEQC study, with ABRF partners Technology-independent ratio performance measures Assessed differences in performance across Experiments Laboratories Measurement processes Munro, S. A. et al. Nature Communications 5:5125 doi: /ncomms6125 (2014).
18 Ambion ERCC Ratio Mixtures 23 Controls per Subpool Design abundance spans 2 20 range within each Subpool
19 Spike-in design for SEQC RNA Sequencing Experiments Samples replicates for sequencing Rat Experiment Treated and Control Rat RNA Biological Replicates Interlaboratory Experiment Human Reference RNA Samples Technical Replicates
20 What is the dynamic range of my experiment? Rat Experiment Interlaboratory Experiment Log2 Normalized ERCC Counts Log2 Normalized ERCC Counts Log2 ERCC Spike Amount (attomol nt µg -1 total RNA) Log2 ERCC Spike Amount (attomol nt µg -1 total RNA)
21 What is the dynamic range of my experiment? Rat Experiment Interlaboratory Experiment Log2 Normalized ERCC Counts Typical Sequencing ~40 million sequence reads per replicate Log2 Normalized ERCC Counts Deep Sequencing ~260 million sequence reads per replicate Log2 ERCC Spike Amount (attomol nt µg -1 total RNA) Log2 ERCC Spike Amount (attomol nt µg -1 total RNA)
22 What was the diagnostic power? Rat Experiment Interlaboratory Experiment True Positive Rate True Positive Rate False Positive Rate False Positive Rate
23 What was the diagnostic power? Rat Experiment Interlaboratory Experiment True Positive Rate Area Under the Curve (AUC) depends on the number of controls detected! True Positive Rate False Positive Rate False Positive Rate
24 AUC is a reasonable summary statistic But we d like to evaluate our diagnostic performance as a function of abundance
25 Rat Experiment MA Plot Log2 Normalized Ratio of Counts Log2 Normalized Average Counts
26 LODR: Limit of Detection of Ratios DE Test P-values Rat Experiment Average Counts Reference RNA Model P-values as a function of average signal Find P-value threshold based on chosen false discovery rate Here FDR = 0.1 Default is FDR = 0.05 Estimate LODR from intersection of model confidence interval upper bound and P-value threshold
27 LODR: Limit of Detection of Ratios DE Test P-values Rat Experiment Average Counts Reference RNA LODR provides Specified confidence in the differentially expressed transcripts above LODR (90% chance of <10% FDR) Guidance for experimental design increase signal for transcripts above LODR estimate
28 Rat Experiment MA Plot 4:1 LODR Log2 Ratio of Normalized Counts Log2 Normalized Average Counts
29 Rat Experiment ** MA Plot * 4:1 LODR Log2 Ratio of Normalized Counts Log2 Normalized Average Counts
30 Log2 Ratio of Normalized Counts Rat Experiment MA Plot 4:1 LODR ** * Increased sequencing depth shifts endogenous transcript ratio measurements above LODR Log2 Normalized Average Counts
31 What are the LODR estimates for my experiment? Rat Experiment Interlaboratory Experiment DE Test P-values DE Test P-values Average Counts Average Counts
32 How do the endogenous samples relate to LODR? Rat Experiment Interlaboratory Experiment Log2 Ratio of Normalized Counts Log2 Ratio of Normalized Counts 4:1 LODR 4:1 LODR Log2 Normalized Average Counts Log2 Normalized Average Counts
33 How much technical variability & bias is there? Rat Experiment Interlaboratory Experiment Log2 Ratio of Normalized Counts Log2 Ratio of Normalized Counts Decreased Variability Significant Ratio Bias
34 mrna Fraction Differences Between Samples Contributes to Bias in ERCC Ratios Spike-in Spike-in mrna Total RNA mrna enrichment mrna rrna Sample 1 Sample 2 Sample 1 Sample 2 The RNA fractions are exaggerated for illustration purposes
35 Dynamic Range AUC Diagnostic performance Variability Bias LODR & Sample Transcripts LODR Limit of Detection of Ratios
36 EVALUATE REPRODUCIBILITY ACROSS LABORATORIES
37 Good Performance Poor Performance
38 Interlaboratory Analysis Using erccdashboard performance metrics Lab 1-6 Illumina + poly-a selection (Illumina kit) Lab 7-9 Life Tech + poly-a selection (Life Tech kit) Lab Illumina + ribosomal RNA depletion
39 Consistent LODR across 11 of 12 Labs Diagnostic performance was consistent within and amongst measurement processes Lab 7 was an outlier for diagnostic performance LODR (Average Counts) LODR agreement with AUC Laboratory
40 Ratio bias is highly variable amongst experiments Ratio bias (r m ) can be attributed to mrna fraction difference between samples: Shippy et al mrna fraction Difference R s = nominal subpool ratio (E 1 /E 2 ) s = empirical ratio Log(r m ) Large standard errors indicate that mrna fraction isn t the only factor contributing to ERCC ratio bias mrna enrichment protocol is a factor Laboratory
41 Protocol-dependent bias from poly-a selection affects ERCC controls due to short poly-a tails Lab 1-6 ILM Poly-A Lab 7-9 LIF Poly- Lab ILM Ribo
42 mrna enrichment protocol biases vary across individual ERCCs but are consistent for a protocol
43 mrna enrichment protocol biases vary across individual ERCCs but are consistent for a protocol
44
45
46
47 Results of the erccdashboard Publication Ratio performance measures for any technology platform and any experiment Diagnostic Power Novel LODR metric Technical Variability & Bias Comparison across experiments Quantification of mrna fraction differences between samples Show protocol-dependent bias
48 Overview External RNA Controls Consortium (ERCC) RNA spike-in controls erccdashboard analysis tool ERCC 2.0: Building an updated suite of RNA controls
49 ERCC 2.0: A New Suite of RNA Controls Approached by industry and academia to build new RNA controls NIST-hosted open, public ERCC 2.0 workshop Workshop report and presentations available: slideshare.net/ercc-workshop All interested parties are welcome to participate Sequence contributions Interlaboratory analysis New and Improved mrna Mimics Transcript Isoforms mirna
50 New and Improved mrna Mimics Additional controls Expand distributions of RNA control properties Length (> 2kb) GC content Poly-A tail length
51 Transcript Isoform Controls Transcript Design Non-cognate Spike-in RNA Variants (SIRVs) developed by Lexogen Cognate sequence selection in progress Schizosaccharomyces pombe Mixture design Dynamic Range 2 4 Design Ratios < 2:1 Lukas Paul, Lexogen
52 Small and mirna Controls Needed for validation of clinical applications Early Detection Research Network Tgen Other applications relevant to bacterial RNA-Seq Non-cognate mirna controls Include some pre-mirna Direct RNA control synthesis by Agilent no need for DNA templates Karol Thompson, FDA
53 Recap External RNA Controls Consortium (ERCC) RNA spike-in controls erccdashboard analysis tool ERCC 2.0: Building an updated suite of RNA controls
54 Acknowledgements All External RNA Controls Consortium participants NIST Marc Salit Steve Lund P. Scott Pine Justin Zook David Duewer Jerod Parsons Jennifer McDaniel Margaret Klein Empa Matthias Roesslein SEQC study participants Co-authors on erccdashboard manuscript: S. P. Lund, P. S. Pine, H. Binder, D. Clevert, A. Conesa, J. Dopazo, M. Fasold, S. Hochreiter, H. Hong, N. Jafari, D. P. Kreil, P. P. Łabaj, S. Li, Y. Liao, S. M. Lin, J. Meehan, C. E. Mason, J. Santoyo-Lopez, R. A. Setterquist, L. Shi, W. Shi, G. K. Smyth, N. Stralis-Pavese, Z. Su, W. Tong, C. Wang, J. Wang, J. Xu, Z. Ye, Y. Yang, Y. Yu, & M. Salit For more information contact:
Assessing Technical Performance in Differential Gene Expression Experiments with External Spike- in RNA Control Ratio Mixtures
Assessing Technical Performance in Differential Gene Expression Experiments with External Spike- in RNA Control Ratio Mixtures Sarah A. Munro 1*, Steve P. Lund 1, P. Scott Pine 1, Hans Binder 2, Djork-
Best Practice for RNA-seq Analysis
MAQC2017 Conference Program Reproducible Genomics Best Practice for RNA-seq Analysis Boku University Paweł Łabaj, David Kreil Fudan University Ying Yu, Chen Suo, Luyao Ren, Yuanting Zheng, Leming Shi April
Novel methods for RNA and DNA- Seq analysis using SMART Technology. Andrew Farmer, D. Phil. Vice President, R&D Clontech Laboratories, Inc.
Novel methods for RNA and DNA- Seq analysis using SMART Technology Andrew Farmer, D. Phil. Vice President, R&D Clontech Laboratories, Inc. Agenda Enabling Single Cell RNA-Seq using SMART Technology SMART
SMARTer Ultra Low RNA Kit for Illumina Sequencing Two powerful technologies combine to enable sequencing with ultra-low levels of RNA
SMARTer Ultra Low RNA Kit for Illumina Sequencing Two powerful technologies combine to enable sequencing with ultra-low levels of RNA The most sensitive cdna synthesis technology, combined with next-generation
SO YOU WANT TO DO A: RNA-SEQ EXPERIMENT MATT SETTLES, PHD UNIVERSITY OF CALIFORNIA, DAVIS
SO YOU WANT TO DO A: RNA-SEQ EXPERIMENT MATT SETTLES, PHD UNIVERSITY OF CALIFORNIA, DAVIS SETTLES@UCDAVIS.EDU Bioinformatics Core Genome Center UC Davis BIOINFORMATICS.UCDAVIS.EDU DISCLAIMER This talk/workshop
Top 5 Lessons Learned From MAQC III/SEQC
Top 5 Lessons Learned From MAQC III/SEQC Weida Tong, Ph.D Division of Bioinformatics and Biostatistics, NCTR/FDA Weida.tong@fda.hhs.gov; 870 543 7142 1 MicroArray Quality Control (MAQC) An FDA led community
Wet-lab Considerations for Illumina data analysis
Wet-lab Considerations for Illumina data analysis Based on a presentation by Henriette O Geen Lutz Froenicke DNA Technologies and Expression Analysis Cores UCD Genome Center Complementary Approaches Illumina
Experimental Design. Dr. Matthew L. Settles. Genome Center University of California, Davis
Experimental Design Dr. Matthew L. Settles Genome Center University of California, Davis settles@ucdavis.edu What is Differential Expression Differential expression analysis means taking normalized sequencing
Introduction to RNA-Seq. David Wood Winter School in Mathematics and Computational Biology July 1, 2013
Introduction to RNA-Seq David Wood Winter School in Mathematics and Computational Biology July 1, 2013 Abundance RNA is... Diverse Dynamic Central DNA rrna Epigenetics trna RNA mrna Time Protein Abundance
RNA standards v May
Standards, Guidelines and Best Practices for RNA-Seq: 2010/2011 I. Introduction: Sequence based assays of transcriptomes (RNA-seq) are in wide use because of their favorable properties for quantification,
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium SEQC/MAQC-III Consortium* 214 Nature America, Inc. All rights reserved.
Parts of a standard FastQC report
FastQC FastQC, written by Simon Andrews of Babraham Bioinformatics, is a very popular tool used to provide an overview of basic quality control metrics for raw next generation sequencing data. There are
Transcriptome Assembly and Evaluation, using Sequencing Quality Control (SEQC) Data
Transcriptome Assembly and Evaluation, using Sequencing Quality Control (SEQC) Data Introduction The US Food and Drug Administration (FDA) has coordinated the Sequencing Quality Control project (SEQC/MAQC-III)
Integrated NGS Sample Preparation Solutions for Limiting Amounts of RNA and DNA. March 2, Steven R. Kain, Ph.D. ABRF 2013
Integrated NGS Sample Preparation Solutions for Limiting Amounts of RNA and DNA March 2, 2013 Steven R. Kain, Ph.D. ABRF 2013 NuGEN s Core Technologies Selective Sequence Priming Nucleic Acid Amplification
Obtain superior NGS library performance with lower input amounts using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina
be INSPIRED drive DISCOVERY stay GENINE TECHNICAL NOTE Directional rrna depletion Obtain superior NGS library performance with lower input amounts using the NEBNext ltra II Directional RNA Library Prep
Obtain superior NGS library performance with lower input amounts using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina
be INSPIRED drive DISCOVERY stay GENINE TECHNICAL NOTE Directional rrna depletion Obtain superior NGS library performance with lower input amounts using the NEBNext ltra II Directional RNA Library Prep
TECH NOTE Pushing the Limit: A Complete Solution for Generating Stranded RNA Seq Libraries from Picogram Inputs of Total Mammalian RNA
TECH NOTE Pushing the Limit: A Complete Solution for Generating Stranded RNA Seq Libraries from Picogram Inputs of Total Mammalian RNA Stranded, Illumina ready library construction in
TECH NOTE Stranded NGS libraries from FFPE samples
TECH NOTE Stranded NGS libraries from FFPE samples Robust performance with extremely degraded FFPE RNA (DV 200 >25%) Consistent library quality across a range of input amounts (5 ng 50 ng) Compatibility
Increased transcription detection with the NEBNext Single Cell/Low Input RNA Library Prep Kit
be INSPIRED drive DISCOVERY stay GENUINE TECHNICAL NOTE Increased transcription detection with the NEBNext Single Cell/Low Input RNA Library Prep Kit Highly sensitive, robust generation of high quality
RNA-SEQUENCING ANALYSIS
RNA-SEQUENCING ANALYSIS Joseph Powell SISG- 2018 CONTENTS Introduction to RNA sequencing Data structure Analyses Transcript counting Alternative splicing Allele specific expression Discovery APPLICATIONS
Application Note Selective transcript depletion
Application Note Selective transcript depletion Sample Authors Laura de Jager RED Scientist Michael Berry Bioinformatics Scientist Luke Esau RED Senior Scientist Ross Wadsworth RED Team Lead Roche Sequencing
Simultaneous profiling of transcriptome and DNA methylome from a single cell
Additional file 1: Supplementary materials Simultaneous profiling of transcriptome and DNA methylome from a single cell Youjin Hu 1, 2, Kevin Huang 1, 3, Qin An 1, Guizhen Du 1, Ganlu Hu 2, Jinfeng Xue
Sequence Analysis 2RNA-Seq
Sequence Analysis 2RNA-Seq Lecture 10 2/21/2018 Instructor : Kritika Karri kkarri@bu.edu Transcriptome Entire set of RNA transcripts in a given cell for a specific developmental stage or physiological
Deep Sequencing technologies
Deep Sequencing technologies Gabriela Salinas 30 October 2017 Transcriptome and Genome Analysis Laboratory http://www.uni-bc.gwdg.de/index.php?id=709 Microarray and Deep-Sequencing Core Facility University
High Throughput Sequencing the Multi-Tool of Life Sciences. Lutz Froenicke DNA Technologies and Expression Analysis Cores UCD Genome Center
High Throughput Sequencing the Multi-Tool of Life Sciences Lutz Froenicke DNA Technologies and Expression Analysis Cores UCD Genome Center Complementary Approaches Illumina Still-imaging of clusters (~1000
Overcome limitations with RNA-Seq
Buyer s Guide Simple, customized RNA-Seq workflows Evaluating options for next-generation RNA sequencing Overcome limitations with RNA-Seq Next-generation sequencing (NGS) has revolutionized the study
Bio-Reference Standards/Materials. Carole Foy LGC Presentation to EuroGentest 15 th May 2007
Bio-Reference Standards/Materials Carole Foy LGC Presentation to EuroGentest 15 th May 2007 Overview of Presentation Role of Horizontal Standards in International Bio- Metrology Bio-reference standards
less sensitive than RNA-seq but more robust analysis pipelines expensive but quantitiatve standard but typically not high throughput
Chapter 11: Gene Expression The availability of an annotated genome sequence enables massively parallel analysis of gene expression. The expression of all genes in an organism can be measured in one experiment.
QPCR ASSAYS FOR MIRNA EXPRESSION PROFILING
TECH NOTE 4320 Forest Park Ave Suite 303 Saint Louis, MO 63108 +1 (314) 833-9764 mirna qpcr ASSAYS - powered by NAWGEN Our mirna qpcr Assays were developed by mirna experts at Nawgen to improve upon previously
Technical Note. GeneChip 3 IVT PLUS Reagent Kit vs. GeneChip 3 IVT Express Reagent Kit Comparison. Introduction:
Technical Note GeneChip 3 IVT PLUS Reagent Kit vs. GeneChip 3 IVT Express Reagent Kit Comparison Introduction: Affymetrix has launched a new 3 IVT PLUS Reagent Kit which creates hybridization ready target
Wheat CAP Gene Expression with RNA-Seq
Wheat CAP Gene Expression with RNA-Seq July 9 th -13 th, 2018 Overview of the workshop, Alina Akhunova http://www.ksre.k-state.edu/igenomics/workshops/ RNA-Seq Workshop Activities Lectures Laboratory Molecular
David M. Rocke Division of Biostatistics and Department of Biomedical Engineering University of California, Davis
David M. Rocke Division of Biostatistics and Department of Biomedical Engineering University of California, Davis Outline RNA-Seq for differential expression analysis Statistical methods for RNA-Seq: Structure
Outline. Analysis of Microarray Data. Most important design question. General experimental issues
Outline Analysis of Microarray Data Lecture 1: Experimental Design and Data Normalization Introduction to microarrays Experimental design Data normalization Other data transformation Exercises George Bell,
High performance sequencing and gene expression quantification
High performance sequencing and gene expression quantification Ana Conesa Genomics of Gene Expression Lab Centro de Investigaciones Príncipe Felipe Valencia aconesa@cipf.es Next Generation Sequencing NGS
Successful gene expression studies using validated qpcr assays. Jan Hellemans, CEO Biogazelle webinar October 28 th, 2015
Successful gene expression studies using validated qpcr assays Jan Hellemans, CEO Biogazelle webinar October 28 th, 2015 Agenda Requirements for high quality qpcr assays Approaches for qpcr assay validation
Non-Organic-Based Isolation of Mammalian microrna using Norgen s microrna Purification Kit
Application Note 13 RNA Sample Preparation Non-Organic-Based Isolation of Mammalian microrna using Norgen s microrna Purification Kit B. Lam, PhD 1, P. Roberts, MSc 1 Y. Haj-Ahmad, M.Sc., Ph.D 1,2 1 Norgen
Basics of RNA-Seq. (With a Focus on Application to Single Cell RNA-Seq) Michael Kelly, PhD Team Lead, NCI Single Cell Analysis Facility
2018 ABRF Meeting Satellite Workshop 4 Bridging the Gap: Isolation to Translation (Single Cell RNA-Seq) Sunday, April 22 Basics of RNA-Seq (With a Focus on Application to Single Cell RNA-Seq) Michael Kelly,
Overcome ligation-induced bias and skewed mirna representation in microrna-seq
Home Learning centers Next-generation sequencing Technical notes Accurate mirna representation in microrna-seq TECH NOTE Overcome ligation-induced bias and skewed mirna representation in microrna-seq SMARTer
Certificate of Analysis Standard Reference Material 2374
National Institute of Standards & Technology Certificate of Analysis Standard Reference Material 2374 DNA Sequence Library for External RNA Controls This Standard Reference Material (SRM) is intended for
Gene Regulation Solutions. Microarrays and Next-Generation Sequencing
Gene Regulation Solutions Microarrays and Next-Generation Sequencing Gene Regulation Solutions The Microarrays Advantage Microarrays Lead the Industry in: Comprehensive Content SurePrint G3 Human Gene
the mirna quality control study mirqc Jo Vandesompele Pieter Mestdagh
the mirna quality control study mirqc Jo Vandesompele Pieter Mestdagh The mirqc Consortium dr. Toumy Guettouche, University of Miami Novartis AIM Objective assessment of mirna profiling platforms qpcr
Experimental Design. Sequencing. Data Quality Control. Read mapping. Differential Expression analysis
-Seq Analysis Quality Control checks Reproducibility Reliability -seq vs Microarray Higher sensitivity and dynamic range Lower technical variation Available for all species Novel transcript identification
User Guide. SIRV-Set 2. SIRV-Set 3. Spike-In RNA Variant Controls. Spike-In RNA Variant Controls with Isoforms (Iso Mix E0)
Spike-In RNA Variant Controls SIRV-Set 2 Spike-In RNA Variant Controls with Isoforms (Iso Mix E0) SIRV-Set 3 Spike-In RNA Variant Controls with Isoforms and ERCCs (Iso Mix E0 / ERCC) User Guide Catalog
RNA-Seq. Joshua Ainsley, PhD Postdoctoral Researcher Lab of Leon Reijmers Neuroscience Department Tufts University
RNA-Seq Joshua Ainsley, PhD Postdoctoral Researcher Lab of Leon Reijmers Neuroscience Department Tufts University joshua.ainsley@tufts.edu Day five Alternative splicing Assembly RNA edits Alternative splicing
ChIP-seq and RNA-seq. Farhat Habib
ChIP-seq and RNA-seq Farhat Habib fhabib@iiserpune.ac.in Biological Goals Learn how genomes encode the diverse patterns of gene expression that define each cell type and state. Protein-DNA interactions
Analysis of RNA-seq Data. Feb 8, 2017 Peikai CHEN (PHD)
Analysis of RNA-seq Data Feb 8, 2017 Peikai CHEN (PHD) Outline What is RNA-seq? What can RNA-seq do? How is RNA-seq measured? How to process RNA-seq data: the basics How to visualize and diagnose your
Automation of Lexogen s QuantSeq 3 mrna-seq Library Prep Kits on the Biomek FX p NGS Workstation
Automation of Lexogen s QuantSeq 3 mrna-seq Library Prep Kits on the Biomek FX p NGS Workstation The Lexogen QuantSeq 3 mrna-seq Library Prep Kits for Illumina (FWD and REV) produce ready-to-sequence libraries
Gene expression microarrays and assays. Because your results can t wait
Gene expression microarrays and assays Because your results can t wait A simple path from data to decision-making The power of expression microarrays Transcriptome-wide analysis can be complex. Matching
Supplementary Information
Supplementary Information Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing Rui Zhang¹, Xin Li², Gokul Ramaswami¹, Kevin S Smith², Gustavo Turecki 3, Stephen B Montgomery¹, ²,
Motivation From Protein to Gene
MOLECULAR BIOLOGY 2003-4 Topic B Recombinant DNA -principles and tools Construct a library - what for, how Major techniques +principles Bioinformatics - in brief Chapter 7 (MCB) 1 Motivation From Protein
DNA METHYLATION RESEARCH TOOLS
SeqCap Epi Enrichment System Revolutionize your epigenomic research DNA METHYLATION RESEARCH TOOLS Methylated DNA The SeqCap Epi System is a set of target enrichment tools for DNA methylation assessment
Gene Expression Profiling and Validation Using Agilent SurePrint G3 Gene Expression Arrays
Gene Expression Profiling and Validation Using Agilent SurePrint G3 Gene Expression Arrays Application Note Authors Bahram Arezi, Nilanjan Guha and Anne Bergstrom Lucas Agilent Technologies Inc. Santa
Transcriptomics analysis with RNA seq: an overview Frederik Coppens
Transcriptomics analysis with RNA seq: an overview Frederik Coppens Platforms Applications Analysis Quantification RNA content Platforms Platforms Short (few hundred bases) Long reads (multiple kilobases)
Development of quantitative targeted RNA-seq methodology for use in differential gene expression
Development of quantitative targeted RNA-seq methodology for use in differential gene expression Dr. Jens Winter, Market Development Group Biological Biological Research Content EMEA QIAGEN Universal Workflows
sherwood - UltramiR shrna Collections
sherwood - UltramiR shrna Collections Incorporating advances in shrna design and processing for superior potency and specificity sherwood - UltramiR shrna Collections Enabling Discovery Across the Genome
DNA Microarray Technology
CHAPTER 1 DNA Microarray Technology All living organisms are composed of cells. As a functional unit, each cell can make copies of itself, and this process depends on a proper replication of the genetic
FFPE in your NGS Study
FFPE in your NGS Study Richard Corbett Canada s Michael Smith Genome Sciences Centre Vancouver, British Columbia Dec 6, 2017 Our mandate is to advance knowledge about cancer and other diseases and to use
Bi 8 Lecture 4. Ellen Rothenberg 14 January Reading: from Alberts Ch. 8
Bi 8 Lecture 4 DNA approaches: How we know what we know Ellen Rothenberg 14 January 2016 Reading: from Alberts Ch. 8 Central concept: DNA or RNA polymer length as an identifying feature RNA has intrinsically
Comparative Analysis using the Illumina DASL assay with FFPE tissue. Wendell Jones, PhD Vice President, Statistics and Bioinformatics
TM Comparative Analysis using the Illumina DASL assay with FFPE tissue Wendell Jones, PhD Vice President, Statistics and Bioinformatics Background EA has examined several protocol assay possibilities for
Digital RNA allelotyping reveals tissue-specific and allelespecific gene expression in human
nature methods Digital RNA allelotyping reveals tissue-specific and allelespecific gene expression in human Kun Zhang, Jin Billy Li, Yuan Gao, Dieter Egli, Bin Xie, Jie Deng, Zhe Li, Je-Hyuk Lee, John
ChIP-Seq Tools. J Fass UCD Genome Center Bioinformatics Core Wednesday September 16, 2015
ChIP-Seq Tools J Fass UCD Genome Center Bioinformatics Core Wednesday September 16, 2015 What s the Question? Where do Transcription Factors (TFs) bind genomic DNA 1? (Where do other things bind DNA or
ChIP-Seq Data Analysis. J Fass UCD Genome Center Bioinformatics Core Wednesday 15 June 2015
ChIP-Seq Data Analysis J Fass UCD Genome Center Bioinformatics Core Wednesday 15 June 2015 What s the Question? Where do Transcription Factors (TFs) bind genomic DNA 1? (Where do other things bind DNA
Next Generation Sequencing
Next Generation Sequencing Complete Report Catalogue # and Service: IR16001 rrna depletion (human, mouse, or rat) IR11081 Total RNA Sequencing (80 million reads, 2x75 bp PE) Xxxxxxx - xxxxxxxxxxxxxxxxxxxxxx
RNA-Sequencing analysis
RNA-Sequencing analysis Markus Kreuz 25. 04. 2012 Institut für Medizinische Informatik, Statistik und Epidemiologie Content: Biological background Overview transcriptomics RNA-Seq RNA-Seq technology Challenges
Direct RNA sequencing of human transcripts using the Oxford Nanopore sequencing platform. Rachael Workman The Nanopore RNA Consortium AGBT 2018
Direct RNA sequencing of human transcripts using the Oxford Nanopore sequencing platform Rachael Workman The Nanopore RNA Consortium AGBT 2018 Direct RNA sequencing opens new frontiers for transcriptome
ChIP-Seq Data Analysis. J Fass UCD Genome Center Bioinformatics Core Wednesday December 17, 2014
ChIP-Seq Data Analysis J Fass UCD Genome Center Bioinformatics Core Wednesday December 17, 2014 What s the Question? Where do Transcription Factors (TFs) bind genomic DNA 1? (Where do other things bind
Differential gene expression analysis using RNA-seq
https://abc.med.cornell.edu/ Differential gene expression analysis using RNA-seq Applied Bioinformatics Core, March 2018 Friederike Dündar with Luce Skrabanek & Paul Zumbo Day 1: Introduction into high-throughput
Introducing QIAseq. Accelerate your NGS performance through Sample to Insight solutions. Sample to Insight
Introducing QIAseq Accelerate your NGS performance through Sample to Insight solutions Sample to Insight From Sample to Insight let QIAGEN enhance your NGS-based research High-throughput next-generation
Joint RuminOmics/Rumen Microbial Genomics Network Workshop
Joint RuminOmics/Rumen Microbial Genomics Network Workshop Microbiome analysis - Amplicon sequencing Dr. Sinéad Waters Animal and Bioscience Research Department, Teagasc Grange, Ireland Prof. Leluo Guan
Guidelines Analysis of RNA Quantity and Quality for Next-Generation Sequencing Projects
Title: Protocol number: Guidelines Analysis of RNA Quantity and Quality for Next-Generation Sequencing Projects GAF S002 Version: Version 4 Date: December 17 th 2015 Author: P. van der Vlies, C.C. van
Lecture #1. Introduction to microarray technology
Lecture #1 Introduction to microarray technology Outline General purpose Microarray assay concept Basic microarray experimental process cdna/two channel arrays Oligonucleotide arrays Exon arrays Comparing
Exploring of microrna markers for body fluid identification using NGS
Exploring of microrna markers for body fluid identification using NGS Zheng Wang, Yiping Hou Institute of Forensic Medicine Sichuan University, China Barcelona May, 11, 2016 Outline Introduction of Institute
Low input RNA-seq library preparation provides higher small non-coding RNA diversity and greatly reduced hands-on time
TECHNICAL NOTE Low input RNA-seq library preparation provides higher small non-coding RNA diversity and greatly reduced hands-on time INTRODUCTION RNA-seq is a next-generation sequencing technique that
G E N OM I C S S E RV I C ES
GENOMICS SERVICES ABOUT T H E N E W YOR K G E NOM E C E N T E R NYGC is an independent non-profit implementing advanced genomic research to improve diagnosis and treatment of serious diseases. Through
Standardized Next Generation Sequencing Abundance Measurements (StarSeq) using Competitive Template Mixtures. AccuGenomics Inc.
Standardized Next Generation Sequencing Abundance Measurements (StarSeq) using Competitive Template Mixtures AccuGenomics Inc. Need for NGS Standardization * Nature Biotechnology November 2012 Targeted
G-MAKE, a Make-based Infrastructure for Rapid Genome Characterization and the Genomes in a Bottle Consortium
G-MAKE, a Make-based Infrastructure for Rapid Genome Characterization and the Genomes in a Bottle Consortium Dec 10 th, 2013 Sheng Li Christopher E. Mason lab Weill Cornell Medical College STRATEGIES FOR
Next-generation sequencing technologies
Next-generation sequencing technologies Illumina: Summary https://www.youtube.com/watch?v=fcd6b5hraz8 Illumina platforms: Benchtop sequencers https://www.illumina.com/systems/sequencing-platforms.html
Applied Biosystems SOLiD 3 Plus System. RNA Application Guide
Applied Biosystems SOLiD 3 Plus System RNA Application Guide For Research Use Use Only. Not intended for any animal or human therapeutic or diagnostic use. TRADEMARKS: Trademarks of Life Technologies Corporation
Assay Standards Working Group Nov 2012 Assay Standards Working Group Recommendations, November 2012
Assay Standards Working Group Recommendations, November 2012 Contents Assay Standards Working Group Recommendations, August 2012... 1 Contents... 1 Introduction... 2 1: Reference Epigenome Criteria...
Simultaneous multiplexed amplicon sequencing and transcriptome profiling in single cells
SUPPLEMENTARY Brief Communication INFORMATION https://doi.org/10.1038/s41592-018-0259-9 In the format provided by the authors and unedited. Simultaneous multiplexed amplicon sequencing and transcriptome
RAPID, ROBUST & RELIABLE
Roche Sample Prep Solutions for RNA-Seq Sequence what matters RAPID, ROBUST & RELIABLE Sample P le Samp Quant ifi /QC tion ca As the first step in the NGS workflow continuum, sample prep holds the key
QIAGEN s NGS Solutions for Biomarkers NGS & Bioinformatics team QIAGEN (Suzhou) Translational Medicine Co.,Ltd
QIAGEN s NGS Solutions for Biomarkers NGS & Bioinformatics team QIAGEN (Suzhou) Translational Medicine Co.,Ltd 1 Our current NGS & Bioinformatics Platform 2 Our NGS workflow and applications 3 QIAGEN s
Full-length single-cell RNA-seq applied to a viral human. cancer: Applications to HPV expression and splicing analysis. Supplementary Information
Full-length single-cell RNA-seq applied to a viral human cancer: Applications to HPV expression and splicing analysis in HeLa S3 cells Liang Wu 1, Xiaolong Zhang 1,2, Zhikun Zhao 1,3,4, Ling Wang 5, Bo
IMGM Laboratories GmbH. Sales Manager
IMGM Laboratories GmbH Dr. Jennifer K. Kuhn Sales Manager About IMGM Laboratories IMGM Laboratories was founded in 2001 IMGM operates as professional provider of advanced genomic services from research
High Throughput Sequencing the Multi-Tool of Life Sciences. Lutz Froenicke DNA Technologies and Expression Analysis Cores UCD Genome Center
High Throughput Sequencing the Multi-Tool of Life Sciences Lutz Froenicke DNA Technologies and Expression Analysis Cores UCD Genome Center DNA Technologies & Expression Analysis Cores HT Sequencing (Illumina
RNA-Seq Analysis. Simon Andrews, Laura v
RNA-Seq Analysis Simon Andrews, Laura Biggins simon.andrews@babraham.ac.uk @simon_andrews v2018-10 RNA-Seq Libraries rrna depleted mrna Fragment u u u u NNNN Random prime + RT 2 nd strand synthesis (+
rnaseqcore.vet.cornell.edu
Center for Reproductive Genomics Department of Biomedical Sciences College of Veterinary Medicine Cornell University Jen Grenier jgrenier@cornell.edu Compute power Software/parameters Reference genome
Introduction to RNA-Seq in GeneSpring NGS Software
Introduction to RNA-Seq in GeneSpring NGS Software Dipa Roy Choudhury, Ph.D. Strand Scientific Intelligence and Agilent Technologies Learn more at www.genespring.com Introduction to RNA-Seq In a few years,
Deep Sequencing QC: An Component Study of the FDA-led Sequencing Quality Control Project Phase 2 (SEQC2)
Deep Sequencing QC: An Component Study of the FDA-led Sequencing Quality Control Project Phase 2 (SEQC2) Joshua Xu, Ph.D. Division of Bioinformatics and Biostatistics, NCTR/FDA zhihua.xu@fda.hhs.gov World
RNA-Seq with the Tuxedo Suite
RNA-Seq with the Tuxedo Suite Monica Britton, Ph.D. Sr. Bioinformatics Analyst September 2015 Workshop The Basic Tuxedo Suite References Trapnell C, et al. 2009 TopHat: discovering splice junctions with
Technical Review. Real time PCR
Technical Review Real time PCR Normal PCR: Analyze with agarose gel Normal PCR vs Real time PCR Real-time PCR, also known as quantitative PCR (qpcr) or kinetic PCR Key feature: Used to amplify and simultaneously
ChIP-seq and RNA-seq
ChIP-seq and RNA-seq Biological Goals Learn how genomes encode the diverse patterns of gene expression that define each cell type and state. Protein-DNA interactions (ChIPchromatin immunoprecipitation)
Technical note: Molecular Index counting adjustment methods
Technical note: Molecular Index counting adjustment methods By Jue Fan, Jennifer Tsai, Eleen Shum Introduction. Overview of BD Precise assays BD Precise assays are fast, high-throughput, next-generation
Combining Techniques to Answer Molecular Questions
Combining Techniques to Answer Molecular Questions UNIT FM02 How to cite this article: Curr. Protoc. Essential Lab. Tech. 9:FM02.1-FM02.5. doi: 10.1002/9780470089941.etfm02s9 INTRODUCTION This manual is
CBC Data Therapy. Metatranscriptomics Discussion
CBC Data Therapy Metatranscriptomics Discussion Metatranscriptomics Extract RNA, subtract rrna Sequence cdna QC Gene expression, function Institute for Systems Genomics: Computational Biology Core bioinformatics.uconn.edu
Assay Validation Services
Overview PierianDx s assay validation services bring clinical genomic tests to market more rapidly through experimental design, sample requirements, analytical pipeline optimization, and criteria tuning.
Single-Cell Whole Transcriptome Profiling With the SOLiD. System
APPLICATION NOTE Single-Cell Whole Transcriptome Profiling Single-Cell Whole Transcriptome Profiling With the SOLiD System Introduction The ability to study the expression patterns of an individual cell
New Statistical Algorithms for Monitoring Gene Expression on GeneChip Probe Arrays
GENE EXPRESSION MONITORING TECHNICAL NOTE New Statistical Algorithms for Monitoring Gene Expression on GeneChip Probe Arrays Introduction Affymetrix has designed new algorithms for monitoring GeneChip
AGILENT S BIOINFORMATICS ANALYSIS SOFTWARE
ACCELERATING PROGRESS IS IN OUR GENES AGILENT S BIOINFORMATICS ANALYSIS SOFTWARE GENESPRING GENE EXPRESSION (GX) MASS PROFILER PROFESSIONAL (MPP) PATHWAY ARCHITECT (PA) See Deeper. Reach Further. BIOINFORMATICS
Functional Genomics Research Stream. Research Meeting: November 15, 2011 Developments in the Field of Functional Genomics
Functional Genomics Research Stream Research Meeting: November 15, 2011 Developments in the Field of Functional Genomics Are you excited about your FRI research and interested in telling others about your