RNA-Seq analysis using R: Differential expression and transcriptome assembly
|
|
|
- Evan Pitts
- 6 years ago
- Views:
Transcription
1 RNA-Seq analysis using R: Differential expression and transcriptome assembly Beibei Chen Ph.D BICF 12/7/2016
2 Agenda Brief about RNA-seq and experiment design Gene oriented analysis Gene quantification Gene differential analysis Comparison model Astrocyte introduction Transcript oriented analysis Transcripts assembly and quantification Transcripts differential expression
3 Everything's connected slide by Dündar et al. (2015)
4 General RNA-seq Workflow
5 RNASeq Analysis Pipeline
6 Experimental Design Affecting Your Analysis Whole transcriptome vs mrna Single-end vs paired-end Paired-end produces more accurate alignments Paired-end allows for transcript-level analysis Single-end is cheaper Number of Reads 10-50M is a good range Aim at least 20M Read Length Longer reads produce better alignments, min 50 bp paired or 100bp single for gene quantification ChIP-seq, smallrna-seq, RIP-seq, CLIP-seq: 50nt single-end
7 Experimental Design Affecting Your Analysis Number of Samples Your power to detect an effect depends on Effect size (difference between group means) Within group variance Sample size More samples the better, min 3 per group Five samples sequenced to 20M reads each offer more power than 2 samples sequenced to 50M reads Stranded Can distinguish expression of overlapping genes
8 Strand-specific RNA-seq image from GATC Biotech
9 How to decide strand Reverse stranded Stranded
10 Agenda Brief about RNA-seq and experiment design Gene oriented analysis Gene quantification Gene differential analysis Comparison model Astrocyte introduction Transcript oriented analysis Transcripts assembly and quantification Transcripts differential expression
11 Gene quantification In RNA-Seq, the abundance level of a gene is measured by the number of reads that map to that gene/exon. Tool to use: featurecounts
12 Differential expressed gene detection Normalization Explore your data
13 Why normalize To smooth out technical variations among the samples Sequencing depth: genes have more reads in a deeper sequenced library Gene length: longer genes are likely to have more reads than the shorter genes
14 Effects of different normalization methods Assuming reads count distribution should be the same Total count (TC): Gene counts are divided by the total number of mapped reads Upper Quartile (UQ):Gene counts are divided by the upper quartile of counts Median (Med): Gene counts are divided by the median counts Quantile (Q): Matching distributions of gene counts across samples (limma) Reads Per Kilobase per Million mapped reads (RPKM): Re-scales gene counts to correct for differences in both library sizes and gene length Assuming most gene are not differentially expressed DESeq Trimmed Mean of M-values (TMM): edger doi: /bib/bbs046
15 Trimmed Mean M values (TMM) Applied in edger package Rationale: TMM is the weighted mean of log ratios between this test and the reference. TMM should be close to 0 according to the hypothesis of low DE. If it is not, its value provides an estimate of the correction factor that must be applied to the library sizes (and not the raw counts) in order to fulfill the hypothesis Reference sample can be assigned or the sample whose upper quartile is closest to the mean upper quartile is used
16 Trimmed Mean M values (TMM) Gene-wise log-fold-changes Absolute expression levels By default, trim the M g values by 30% and the A g values by 5% (can be tailored in program) Weights are from the delta method on Binomial data Normalization factor for sample k using reference sample r is calculated as:
17 Median-of-ratios normalization Applied in DESeq and DESeq2 Rationale: Calculate the ratio of between a test and a pseudosample (For each gene, the geometric mean of all samples) Non-DE genes should have similar read counts across samples, leading to a ratio of 1. Assuming most genes are not DE, the median of this ratio for the lane provides an estimate of the correction factor that should be applied to all read counts of this lane to fulfill the hypothesis
18 Median-of-ratios normalization Pseudo sample Get the median of log ratio of test comparing to pseudo sample:
19 Data exploration Use log transformed normalized gene reads count Check if replicates from the same group are well concordance and grouped together Hierarchy clustering PCA plot
20 Hierarchy clustering Raw data hierarchical clustering dendrogram Distance calculation Euclidean distance:
21 Hierarchy plot example Samples prepared a year ago
22 Principal component analysis (PCA) A mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in the data set It identifies directions, called principal components, along which the variation in the data is maximal By using a few components, each sample can be represented by relatively few numbers instead of by values for thousands of variables.
23 Simple example of PCA Separate breast cancer ER+ from ER- : get profiles with only two genes (a) Each dot represents a breast cancer sample plotted against its expression levels for two genes. (ER +, red; ER -, black). (b) (b) PCA identifies the two directions (PC1 and PC2) along which the data have the largest spread. (c) (c) Samples plotted in one dimension using their projections onto the first principal component (PC1) for ER +, ER - and all samples separately. Nature Biotechnology 26, (2008)
24 Real example
25 Test for differential expressed genes General liner model: negative binomial distribution For each gene, Typical commands >glmfit() >design <- model.matrix(~group) >fit <- glmfit(y, design) >res<-glmlrt(fit, coef=2)
26 Make a model and extract results Basic: treatment vs control Question: what s the difference between monocytes and neutrophils design <- model.matrix(~samplegroup) Set control as baseline (intercept) and compare treatment to it relevel(factor(samplegroup),ref= monocyotes ) Use coefficient to extract results res<-glmlrt(fit, coef=2)
27 Make a model and extract results Model without interception: model.matrix(~0+samplegroup) Use contrast vector Res<-glmLRT(fit, contrast=c(-1,1)) Use a contrast function My.contrast <- makecontrasts(samplegroupneutrophils- SampleGroupmonocytes, level=design)
28 Comparison model Batch effect (additive model) Question: I want to account for the individual since I think individual difference will affect resultsmodel.matrix(~samplegroup+subjectid)
29 More complicated comparison models Time series: treatment and control, 5 time points design<- model.matrix (~Treat+Time+Treat:Time, data=coldata)
30 More complicated comparison models Intercept: (control at time 0) Coef=2 baseline comparison of treat and control Coef=7 difference between treat and control at 2h Coef=3:6 difference at any time of control comparing to control baseline Coef=7:10 difference at any time of treat comparing to control at that time
31 Test for differential expressed genes After GLMs are fit for each gene Wald test: whether each model treatment coefficient differs significantly from zero Multiple testing adjust For a genome with 10,000 gene, using p<=0.05 as cutoff, there are 500 genes are significant by chance BH method
32 Define differential expressed genes FDR and/or logfc cutoff
33 Agenda Brief about RNA-seq and experiment design Gene oriented analysis Gene quantification Gene differential analysis Comparison model Astrocyte introduction Transcript oriented analysis Transcripts assembly and quantification Transcripts differential expression
34 Astrocyte BioHPC Workflow Platform Allows groups to give easy-access to their analysis pipelines via the web Standardized Workflows Simple Web Forms Online documentation & results visualization* Workflows run on HPC cluster without developer or user needing cluster knowledge astrocyte.biohpc.swmed.edu Slide contribution: David
35 Browse workflows
36 Create a new project
37 Add data to your project
38 Add data to your project For NGS experiment, this is recommended.
39 Make your design file
40 Make your design file Use tab as delimiter Excel save as Text (tab delimited) If no SubjectID, use same number/character for all rows If no FqR2, leave them empty For all contents, no - For all contents, no spaces Columns names MUST be exactly the same as documented
41 Comparisons Comparisons are based on SampleGroup All pair-wise comparisons Could be identified by file name A_B.edgeR.txt Log fold change will be A/B If you want B/A, -1*logFC
42 Select your data files and submit SELECT YOUR FILES
43 Download/visualize your results Vizapp need about 30s to start if there is no queue. You need to refresh the page. You can also choose individual files to download to your local computer
44 Agenda Brief about RNA-seq and experiment design Gene oriented analysis Gene quantification Gene differential analysis Comparison model Astrocyte introduction Transcript oriented analysis Transcripts assembly and quantification Transcripts differential expression
45 Transcript oriented analysis Transcripts assembly and quantification Stringtie Transcripts differential expression Ballgown
46 Pair-end and single-endsequencing Single-end reads Isoform 1 Isoform 2 Pair-end reads
47 StringTie workflow Nature Biotechnology 33, (2015)
48 Ballgown Bridged the gap of transcripts assembly and differential expression analysis RSEM + edger Statistical methods are conceptual similar to limma Super fast
49 Ballgown visualization
50 Ballgown: transcripts clustering Expression estimates are unreliable for very similar transcripts of a same gene Jaccard distance
51 Astrocyte Vizapp demo and workshop
Whole Transcriptome Analysis of Illumina RNA- Seq Data. Ryan Peters Field Application Specialist
Whole Transcriptome Analysis of Illumina RNA- Seq Data Ryan Peters Field Application Specialist Partek GS in your NGS Pipeline Your Start-to-Finish Solution for Analysis of Next Generation Sequencing Data
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.
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
Differential gene expression analysis using RNA-seq
https://abc.med.cornell.edu/ Differential gene expression analysis using RNA-seq Applied Bioinformatics Core, August 2017 Friederike Dündar with Luce Skrabanek & Ceyda Durmaz Day 4 overview (brief) theoretical
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
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
measuring gene expression December 11, 2018
measuring gene expression December 11, 2018 Intervening Sequences (introns): how does the cell get rid of them? Splicing!!! Highly conserved ribonucleoprotein complex recognizes intron/exon junctions and
1. Introduction Gene regulation Genomics and genome analyses
1. Introduction Gene regulation Genomics and genome analyses 2. Gene regulation tools and methods Regulatory sequences and motif discovery TF binding sites Databases 3. Technologies Microarrays Deep sequencing
A normalization method based on variance and median adjustment for massive mrna polyadenylation data
ISSN : 0974-7435 Volume 8 Issue 4 A normalization method based on variance and median adjustment for massive mrna polyadenylation data Guoli Ji, Ying Wang, Mingchen Wu, Yangzi Zhang, Xiaohui Wu* Department
Differential gene expression analysis using RNA-seq
https://abc.med.cornell.edu/ Differential gene expression analysis using RNA-seq Applied Bioinformatics Core, August 2017 Friederike Dündar with Luce Skrabanek & Ceyda Durmaz Day 3 QC of aligned reads
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
Analysis of RNA-seq Data. Bernard Pereira
Analysis of RNA-seq Data Bernard Pereira The many faces of RNA-seq Applications Discovery Find new transcripts Find transcript boundaries Find splice junctions Comparison Given samples from different experimental
Transcriptome analysis
Statistical Bioinformatics: Transcriptome analysis Stefan Seemann seemann@rth.dk University of Copenhagen April 11th 2018 Outline: a) How to assess the quality of sequencing reads? b) How to normalize
measuring gene expression December 5, 2017
measuring gene expression December 5, 2017 transcription a usually short-lived RNA copy of the DNA is created through transcription RNA is exported to the cytoplasm to encode proteins some types of RNA
RNA-sequencing. Next Generation sequencing analysis Anne-Mette Bjerregaard. Center for biological sequence analysis (CBS)
RNA-sequencing Next Generation sequencing analysis 2016 Anne-Mette Bjerregaard Center for biological sequence analysis (CBS) Terms and definitions TRANSCRIPTOME The full set of RNA transcripts and their
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
RNA-Seq Analysis. August Strand Genomics, Inc All rights reserved.
RNA-Seq Analysis August 2014 Strand Genomics, Inc. 2014. All rights reserved. Contents Introduction... 3 Sample import... 3 Quantification... 4 Novel exon... 5 Differential expression... 12 Differential
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 (+
Bioinformatics Monthly Workshop Series. Speaker: Fan Gao, Ph.D Bioinformatics Resource Office The Picower Institute for Learning and Memory
Bioinformatics Monthly Workshop Series Speaker: Fan Gao, Ph.D Bioinformatics Resource Office The Picower Institute for Learning and Memory Schedule for Fall, 2015 PILM Bioinformatics Web Server (09/21/2015)
Comparative analysis of RNA-Seq data with DESeq and DEXSeq
Comparative analysis of RNA-Seq data with DESeq and DEXSeq Simon Anders EMBL Heidelberg Discovery Two applications of RNA-Seq find new transcripts find transcript boundaries find splice junctions Comparison
Single-cell sequencing
Single-cell sequencing Harri Lähdesmäki Department of Computer Science Aalto University December 5, 2017 Contents Background & Motivation Single cell sequencing technologies Single cell sequencing data
Canadian Bioinforma3cs Workshops
Canadian Bioinforma3cs Workshops www.bioinforma3cs.ca Module #: Title of Module 2 1 Module 3 Expression and Differen3al Expression (lecture) Obi Griffith & Malachi Griffith www.obigriffith.org ogriffit@genome.wustl.edu
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)
RNA
RNA sequencing Michael Inouye Baker Heart and Diabetes Institute Univ of Melbourne / Monash Univ Summer Institute in Statistical Genetics 2017 Integrative Genomics Module Seattle @minouye271 www.inouyelab.org
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
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
Exercises: Analysing ChIP-Seq data
Exercises: Analysing ChIP-Seq data Version 2018-03-2 Exercises: Analysing ChIP-Seq data 2 Licence This manual is 2018, Simon Andrews. This manual is distributed under the creative commons Attribution-Non-Commercial-Share
Applications of short-read
Applications of short-read sequencing: RNA-Seq and ChIP-Seq BaRC Hot Topics March 2013 George Bell, Ph.D. http://jura.wi.mit.edu/bio/education/hot_topics/ Sequencing applications RNA-Seq includes experiments
BICF Variant Analysis Tools. Using the BioHPC Workflow Launching Tool Astrocyte
BICF Variant Analysis Tools Using the BioHPC Workflow Launching Tool Astrocyte Prioritization of Variants SNP INDEL SV Astrocyte BioHPC Workflow Platform Allows groups to give easy-access to their analysis
Integrative Genomics 1a. Introduction
2016 Course Outline Integrative Genomics 1a. Introduction ggibson.gt@gmail.com http://www.cig.gatech.edu 1a. Experimental Design and Hypothesis Testing (GG) 1b. Normalization (GG) 2a. RNASeq (MI) 2b. Clustering
Sequencing applications. Today's outline. Hands-on exercises. Applications of short-read sequencing: RNA-Seq and ChIP-Seq
Sequencing applications Applications of short-read sequencing: RNA-Seq and ChIP-Seq BaRC Hot Topics March 2013 George Bell, Ph.D. http://jura.wi.mit.edu/bio/education/hot_topics/ RNA-Seq includes experiments
Single Cell Transcriptomics scrnaseq
Single Cell Transcriptomics scrnaseq Matthew L. Settles Genome Center Bioinformatics Core University of California, Davis settles@ucdavis.edu; bioinformatics.core@ucdavis.edu Purpose The sequencing of
Session 8. Differential gene expression analysis using RNAseq data
Functional and Comparative Genomics 2018 Session 8. Differential gene expression analysis using RNAseq data Tutors: Hrant Hovhannisyan, PhD student, email: grant.hovhannisyan@gmail.com Uciel Chorostecki,
The first thing you will see is the opening page. SeqMonk scans your copy and make sure everything is in order, indicated by the green check marks.
Open Seqmonk Launch SeqMonk The first thing you will see is the opening page. SeqMonk scans your copy and make sure everything is in order, indicated by the green check marks. SeqMonk Analysis Page 1 Create
Introduction to RNAseq Analysis. Milena Kraus Apr 18, 2016
Introduction to RNAseq Analysis Milena Kraus Apr 18, 2016 Agenda What is RNA sequencing used for? 1. Biological background 2. From wet lab sample to transcriptome a. Experimental procedure b. Raw data
Comparative analysis of RNA-Seq data with DESeq2
Comparative analysis of RNA-Seq data with DESeq2 Simon Anders EMBL Heidelberg Two applications of RNA-Seq Discovery find new transcripts find transcript boundaries find splice junctions Comparison Given
Quan9fying with sequencing. Week 14, Lecture 27. RNA-Seq concepts. RNA-Seq goals 12/1/ Unknown transcriptome. 2. Known transcriptome
2015 - BMMB 852D: Applied Bioinforma9cs Quan9fying with sequencing Week 14, Lecture 27 István Albert Biochemistry and Molecular Biology and Bioinforma9cs Consul9ng Center Penn State Samples consists of
Quantifying gene expression
Quantifying gene expression Genome GTF (annotation)? Sequence reads FASTQ FASTQ (+reference transcriptome index) Quality control FASTQ Alignment to Genome: HISAT2, STAR (+reference genome index) (known
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
Gene Expression Data Analysis
Gene Expression Data Analysis Bing Zhang Department of Biomedical Informatics Vanderbilt University bing.zhang@vanderbilt.edu BMIF 310, Fall 2009 Gene expression technologies (summary) Hybridization-based
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
Deep sequencing of transcriptomes
1 / 40 Deep sequencing of transcriptomes An introduction to RNA-seq Michael Dondrup UNI BCCS 2. november 2010 2 / 40 Transcriptomics by Ultra-Fast Sequencing Microarrays have been the primary transcriptomics
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
Sanger vs Next-Gen Sequencing
Tools and Algorithms in Bioinformatics GCBA815/MCGB815/BMI815, Fall 2017 Week-8: Next-Gen Sequencing RNA-seq Data Analysis Babu Guda, Ph.D. Professor, Genetics, Cell Biology & Anatomy Director, Bioinformatics
Analysis of a Tiling Regulation Study in Partek Genomics Suite 6.6
Analysis of a Tiling Regulation Study in Partek Genomics Suite 6.6 The example data set used in this tutorial consists of 6 technical replicates from the same human cell line, 3 are SP1 treated, and 3
Analysis of data from high-throughput molecular biology experiments Lecture 6 (F6, RNA-seq ),
Analysis of data from high-throughput molecular biology experiments Lecture 6 (F6, RNA-seq ), 2012-01-26 What is a gene What is a transcriptome History of gene expression assessment RNA-seq RNA-seq analysis
Bioinformatics. Microarrays: designing chips, clustering methods. Fran Lewitter, Ph.D. Head, Biocomputing Whitehead Institute
Bioinformatics Microarrays: designing chips, clustering methods Fran Lewitter, Ph.D. Head, Biocomputing Whitehead Institute Course Syllabus Jan 7 Jan 14 Jan 21 Jan 28 Feb 4 Feb 11 Feb 18 Feb 25 Sequence
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,
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
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)
Background Wikipedia Lee and Mahadavan, JCB, 2009 History (Platform Comparison) P Park, Nature Review Genetics, 2009 P Park, Nature Reviews Genetics, 2009 Rozowsky et al., Nature Biotechnology, 2009
V3 differential gene expression analysis
differential gene expression analysis - What is measured by microarrays? - Microarray normalization - Differential gene expression (DE) analysis based on microarray data - Detection of outliers - RNAseq
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
METHOD Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data Open Access Franck Rapaport 1, Raya Khanin 1, Yupu Liang 1, Mono Pirun 1, Azra Krek 1, Paul Zumbo 2,3,
Genomic data visualisation
Genomic data visualisation Dr Jason Wong Prince of Wales Clinical School Introductory bioinformatics for human genomics workshop, UNSW Day 1 Thursday 29 th January 2016 Structure of human genome Consist
Normalization of RNA-Seq data in the case of asymmetric dierential expression
Senior Thesis in Mathematics Normalization of RNA-Seq data in the case of asymmetric dierential expression Author: Ciaran Evans Advisor: Dr. Jo Hardin Submitted to Pomona College in Partial Fulllment of
Next-Generation Sequencing Gene Expression Analysis Using Agilent GeneSpring GX
Next-Generation Sequencing Gene Expression Analysis Using Agilent GeneSpring GX Technical Overview Introduction RNA Sequencing (RNA-Seq) is one of the most commonly used next-generation sequencing (NGS)
Introduction to RNA sequencing
Introduction to RNA sequencing Bioinformatics perspective Olga Dethlefsen NBIS, National Bioinformatics Infrastructure Sweden November 2017 Olga (NBIS) RNA-seq November 2017 1 / 49 Outline Why sequence
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,
VM origin. Okeanos: Image Trinity_U16 (upgrade to Ubuntu16.04, thanks to Alexandros Dimopoulos) X2go: LXDE
VM origin Okeanos: Image Trinity_U16 (upgrade to Ubuntu16.04, thanks to Alexandros Dimopoulos) X2go: LXDE NGS intro + Genome-Based Transcript Reconstruction and Analysis Using RNA-Seq Data Based on material
Measuring gene expression
Measuring gene expression Grundlagen der Bioinformatik SS2018 https://www.youtube.com/watch?v=v8gh404a3gg Agenda Organization Gene expression Background Technologies FISH Nanostring Microarrays RNA-seq
Benchmarking of RNA-seq data processing pipelines using whole transcriptome qpcr expression data
Benchmarking of RNA-seq data processing pipelines using whole transcriptome qpcr expression data Jan Hellemans 7th international qpcr & NGS Event - Freising March 24 th, 2015 Therapeutics lncrna oncology
Ganatum: a graphical single-cell RNA-seq analysis pipeline
Ganatum: a graphical single-cell RNA-seq analysis pipeline User Manual February 28, 2017 University of Hawaii 2017 Contents 1. Introduction... 1 2. Upload... 1 3. Batch-effect removal... 4 4. Outlier removal...
Introduction of RNA-Seq Analysis
Introduction of RNA-Seq Analysis Jiang Li, MS Bioinformatics System Engineer I Center for Quantitative Sciences(CQS) Vanderbilt University September 21, 2012 Goal of this talk 1. Act as a practical resource
Statistical Genomics and Bioinformatics Workshop. Genetic Association and RNA-Seq Studies
Statistical Genomics and Bioinformatics Workshop: Genetic Association and RNA-Seq Studies RNA Seq and Differential Expression Analysis Brooke L. Fridley, PhD University of Kansas Medical Center 1 Next-generation
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
Shuji Shigenobu. April 3, 2013 Illumina Webinar Series
Shuji Shigenobu April 3, 2013 Illumina Webinar Series RNA-seq RNA-seq is a revolutionary tool for transcriptomics using deepsequencing technologies. genome HiSeq2000@NIBB (Wang 2009 with modifications)
From reads to results: differential. Alicia Oshlack Head of Bioinformatics
From reads to results: differential expression analysis with ihrna seq Alicia Oshlack Head of Bioinformatics Murdoch Childrens Research Institute Benefits and opportunities ii of RNA seq All transcripts
Optimal Calculation of RNA-Seq Fold-Change Values
International Journal of Computational Bioinformatics and In Silico Modeling Vol. 2, No. 6 (2013): 285-292 Research Article Open Access ISSN: 2320-0634 Optimal Calculation of RNA-Seq Fold-Change Values
Post-assembly Data Analysis
Assembled transcriptome Post-assembly Data Analysis Quantification: the expression level of each gene in each sample DE genes: genes differentially expressed between samples Clustering/network analysis
edger: differential expression analysis of digital gene expression data User s Guide Mark Robinson, Davis McCarthy, Yunshun Chen, Gordon K.
edger: differential expression analysis of digital gene expression data User s Guide Mark Robinson, Davis McCarthy, Yunshun Chen, Gordon K. Smyth First edition 17 September 2008 Last revised 27 October
Nature Genetics: doi: /ng Supplementary Figure 1. H3K27ac HiChIP enriches enhancer promoter-associated chromatin contacts.
Supplementary Figure 1 H3K27ac HiChIP enriches enhancer promoter-associated chromatin contacts. (a) Schematic of chromatin contacts captured in H3K27ac HiChIP. (b) Loop call overlap for cohesin HiChIP
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
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
DNA Microarrays and Clustering of Gene Expression Data
DNA Microarrays and Clustering of Gene Expression Data Martha L. Bulyk mlbulyk@receptor.med.harvard.edu Biophysics 205 Spring term 2008 Traditional Method: Northern Blot RNA population on filter (gel);
Bioinformatics for Biologists
Bioinformatics for Biologists Functional Genomics: Microarray Data Analysis Fran Lewitter, Ph.D. Head, Biocomputing Whitehead Institute Outline Introduction Working with microarray data Normalization Analysis
SCALABLE, REPRODUCIBLE RNA-Seq
SCALABLE, REPRODUCIBLE RNA-Seq SCALABLE, REPRODUCIBLE RNA-Seq Advances in the RNA sequencing workflow, from sample preparation through data analysis, are enabling deeper and more accurate exploration
RNA-Seq data analysis course September 7-9, 2015
RNA-Seq data analysis course September 7-9, 2015 Peter-Bram t Hoen (LUMC) Jan Oosting (LUMC) Celia van Gelder, Jacintha Valk (BioSB) Anita Remmelzwaal (LUMC) Expression profiling DNA mrna protein Comprehensive
RNA-seq differential expression analysis. bioconnector.org/workshops
RNA-seq differential expression analysis bioconnector.org/workshops Agenda Our data: source, pre-processing, structure Importing & exploring data Processing and analysis with DESeq2 - Structuring the count
Transcriptome Assembly, Functional Annotation (and a few other related thoughts)
Transcriptome Assembly, Functional Annotation (and a few other related thoughts) Monica Britton, Ph.D. Sr. Bioinformatics Analyst June 23, 2017 Differential Gene Expression Generalized Workflow File Types
NGS Data Analysis and Galaxy
NGS Data Analysis and Galaxy University of Pretoria Pretoria, South Africa 14-18 October 2013 Dave Clements, Emory University http://galaxyproject.org/ Fourie Joubert, Burger van Jaarsveld Bioinformatics
Post-assembly Data Analysis
Assembled transcriptome Post-assembly Data Analysis Quantification: get expression for each gene in each sample Genes differentially expressed between samples Clustering/network analysis Identifying over-represented
TSSpredator User Guide v 1.00
TSSpredator User Guide v 1.00 Alexander Herbig alexander.herbig@uni-tuebingen.de Kay Nieselt kay.nieselt@uni-tuebingen.de June 3, 2013 1 Getting Started TSSpredator is a tool for the comparative detection
RNAseq Differential Gene Expression Analysis Report
RNAseq Differential Gene Expression Analysis Report Customer Name: Institute/Company: Project: NGS Data: Bioinformatics Service: IlluminaHiSeq2500 2x126bp PE Differential gene expression analysis Sample
Supplementary Figures
Supplementary Figures Supplementary Figure 1. Multiplet rate and sensitivity of the GemCode single cell platform from scrna-seq of 50:50 mixing of 293Ts and 3T3s. (a) Inferred multiplet rate as a function
RNA Sequencing Analyses & Mapping Uncertainty
RNA Sequencing Analyses & Mapping Uncertainty Adam McDermaid 1/26 RNA-seq Pipelines Collection of tools for analyzing raw RNA-seq data Tier 1 Quality Check Data Trimming Tier 2 Read Alignment Assembly
Next Generation Sequencing Data Analysis with BioHPC. Updated for
Next Generation Sequencing Data Analysis with BioHPC 1 Updated for 2015-04-15 Next Generation Sequencing Genomic, transcriptomic sequencing now commonplace in projects. Now very cheap! UTSW McDermott Core
ChIP-seq data analysis with Chipster. Eija Korpelainen CSC IT Center for Science, Finland
ChIP-seq data analysis with Chipster Eija Korpelainen CSC IT Center for Science, Finland chipster@csc.fi What will I learn? Short introduction to ChIP-seq Analyzing ChIP-seq data Central concepts Analysis
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
SHAMAN : SHiny Application for Metagenomic ANalysis
SHAMAN : SHiny Application for Metagenomic ANalysis Stevenn Volant, Amine Ghozlane Hub Bioinformatique et Biostatistique C3BI, USR 3756 IP CNRS Biomics CITECH Ribosome ITS (1) : located between 18S and
Supplementary Materials for De-novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity
Supplementary Materials for De-novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity Sections: S1. Evaluation of transcriptome assembly completeness S2. Comparison
Corset: enabling differential gene expression analysis for de novo assembled transcriptomes
Davidson and Oshlack Genome Biology 2014, 15:410 METHOD Open Access : enabling differential gene expression analysis for de novo assembled transcriptomes Nadia M Davidson 1 and Alicia Oshlack 1,2* Abstract
RNA Sequencing: Experimental Planning and Data Analysis. Nadia Atallah September 12, 2018
RNA Sequencing: Experimental Planning and Data Analysis Nadia Atallah September 12, 2018 A Next Generation Sequencing (NGS) Refresher Became commercially available in 2005 Construction of a sequencing
Gene-Level Analysis of Exon Array Data using Partek Genomics Suite 6.6
Gene-Level Analysis of Exon Array Data using Partek Genomics Suite 6.6 Overview This tutorial will demonstrate how to: Summarize core exon-level data to produce gene-level data Perform exploratory analysis
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
RNAseq Applications in Genome Studies. Alexander Kanapin, PhD Wellcome Trust Centre for Human Genetics, University of Oxford
RNAseq Applications in Genome Studies Alexander Kanapin, PhD Wellcome Trust Centre for Human Genetics, University of Oxford RNAseq Protocols Next generation sequencing protocol cdna, not RNA sequencing
Introduction to transcriptome analysis using High Throughput Sequencing technologies. D. Puthier 2012
Introduction to transcriptome analysis using High Throughput Sequencing technologies D. Puthier 2012 A typical RNA-Seq experiment Library construction Protocol variations Fragmentation methods RNA: nebulization,
mrna Sequencing Quality Control (V6)
mrna Sequencing Quality Control (V6) Notes: the following analyses are based on 8 adult brains sequenced in USC and Yale 1. Error Rates The error rates of each sequencing cycle are reported for 120 tiles
Long and short/small RNA-seq data analysis
Long and short/small RNA-seq data analysis GEF5, 4.9.2015 Sami Heikkinen, PhD, Dos. Topics 1. RNA-seq in a nutshell 2. Long vs short/small RNA-seq 3. Bioinformatic analysis work flows GEF5 / Heikkinen
A comparison of methods for differential expression analysis of RNA-seq data
Soneson and Delorenzi BMC Bioinformatics 213, 14:91 RESEARCH ARTICLE A comparison of methods for differential expression analysis of RNA-seq data Charlotte Soneson 1* and Mauro Delorenzi 1,2 Open Access
Welcome to the NGS webinar series
Welcome to the NGS webinar series Webinar 1 NGS: Introduction to technology, and applications NGS Technology Webinar 2 Targeted NGS for Cancer Research NGS in cancer Webinar 3 NGS: Data analysis for genetic