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“BIG DATA” from RNA-Seq Experiments

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“BIG DATA” from RNA- Seq Experiments. Significance of RNA- Seq Approaches Reveals which genes are expressed and the levels at which they are expressed; a technique for the “post-genomic” era Of huge importance for biomedicine, toxicology, environmental biology, and basic research - PowerPoint PPT Presentation

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Page 1: “BIG DATA” from RNA- Seq Experiments

“BIG DATA” from RNA-Seq Experiments

Page 2: “BIG DATA” from RNA- Seq Experiments

Significance of RNA-Seq Approaches Reveals which genes are expressed and the levels at which they are expressed; a technique for the “post-genomic” era

Of huge importance for biomedicine, toxicology, environmental biology, and basic research

“Whole Genome Analysis: When Each Patient is a Big Data Problem”

“ … spectacular opportunities and immense challenges presented by the dawning era of "Big Data" in biomedical research … (NIH call for proposals)

Page 3: “BIG DATA” from RNA- Seq Experiments

Review:

“Central Dogma of Molecular Biology”

DNA -> RNA -> Protein

(Way more complex!)

http://biology200.gsu.edu/houghton/4595%20'04/figures/Lecture1/dogma.gif

Page 4: “BIG DATA” from RNA- Seq Experiments

http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/95777/DNA_alternative_splicing_LARGE_2.jpg

Alternative Splicing: Splice Variants or Isoforms

Page 5: “BIG DATA” from RNA- Seq Experiments

http://www.technologyreview.com/view/519851/the-big-data-conundrum-how-to-define-it/

1. Gartner. In 2001: Three-fold definition encompassing the “three Vs”: Volume, Velocity and Variety.sometimes includes a fourth V: veracity, to cover questions of trust and uncertainty.

2. Oracle. Big data is the derivation of value from traditional relational database-driven business decision making, augmented with new sources of unstructured data.

3. Intel: a median of 300 terabytes of data a week.

4. Microsoft. applying serious computing power— machine learning and artificial intelligence—To massive and often complex sets of information.”

5. The Method for an Integrated Knowledge Environment : not a function of the size of a data set but its complexity; high degree of permutations and interactions within a data set.

6. NIST : data which exceed(s) the capacity or capability of current or conventional methods and systems.

WHAT IS BIG DATA ???

Page 6: “BIG DATA” from RNA- Seq Experiments

“The Big Challenges of Big Data”: http://www.nature.com/nature/journal/v498/n7453/full/498255a.html

BIG DATA in BIOLOGY (Sequencing) : A BIG Problem

~150 GB per human genome/transcriptome

Page 7: “BIG DATA” from RNA- Seq Experiments

The “Science Question” in Our Lab: How do cells in the nervous system acquire and identity …

…. and maintain an identity on the face of a changing environment?

Page 8: “BIG DATA” from RNA- Seq Experiments

Genetic Perturbations: Embryos “compensate” following overexpression of Notch signaling pathway !

Neural beta-tubulin expression at different stages

Page 9: “BIG DATA” from RNA- Seq Experiments

Tailbud Embryo

Tadpole Embryo

Physical Perturbations

Page 10: “BIG DATA” from RNA- Seq Experiments

Environmental Perturbations

Hg-treated Xenopus embryos A new developmental model system: Zebra Finch Embryos

Page 11: “BIG DATA” from RNA- Seq Experiments

We use ION TORRENT PGM (Personal GenomeMachine) technology

1.Isolate RNA Isolate mRNA

2.Make Library

3.Prepare Template

4.Sequence

5.Analyze Data

6.Interpret Data

Page 12: “BIG DATA” from RNA- Seq Experiments

1. Isolate RNA 2. Library Preparation

Page 13: “BIG DATA” from RNA- Seq Experiments

http://sunnybrook.ca/uploads/PGM_flowchart_web2.png

3. Template Preparation

Page 14: “BIG DATA” from RNA- Seq Experiments

4. Sequencing

Page 15: “BIG DATA” from RNA- Seq Experiments

Sequencing (continued)

Page 16: “BIG DATA” from RNA- Seq Experiments

a. Align reads to genome/transcriptomeb. Identify expressed genes/isoforms

(transcriptome reconstruction)c. Estimation of abundanced. Differential expression analysis

5. Data Analysis:

http://www.informaticsblogs.com/author/rna-seq-blog/page/7/

Page 17: “BIG DATA” from RNA- Seq Experiments

Problems: repetitive sequences; which gene model to use; poor annotations/gene models; novel isoforms; intronic sequence; ambiguous reads; how to normalize!!!

Review article: Garber et al. (2011). “Computational methods for transcriptomeAnnotation and quantification using RNA-seq,” Nature Methods, 8: 469-477.

Data Analysis: continued

Page 18: “BIG DATA” from RNA- Seq Experiments

a. Alignment of Reads to Genome

Page 19: “BIG DATA” from RNA- Seq Experiments

b. Transcriptome Reconstruction Methods

http://gcat.davidson.edu/phast/debruijn.html

A simple de Bruijn graph with k = 4. The graph corresponds to a series of short reads for the consensus sequence "ACCCAACCAC“;

Assemblers must identify an Eulerian path through this graph.

Page 20: “BIG DATA” from RNA- Seq Experiments

c. Gene Quantification (appropriate normalization a huge issue)

Page 21: “BIG DATA” from RNA- Seq Experiments

d. Differential Expression Analysis

Dealing with the “n” problem in RNA-seq – these data represent ONE experiment.

Page 22: “BIG DATA” from RNA- Seq Experiments

6. Interpret Data !

Perhaps the biggest challenge ….

Grouping the expressed genes to producebiological meaningful data and visualization of the data

Page 23: “BIG DATA” from RNA- Seq Experiments

Domain-centric Gene Ontology (supfam.cs.bris.ac.uk )

Gene Ontology (GO Terms): Identifying Function

http://supfam.cs.bris.ac.uk/SUPERFAMILY/GO.html
http://supfam.cs.bris.ac.uk/SUPERFAMILY/GO.html
http://supfam.cs.bris.ac.uk/SUPERFAMILY/GO.html
Page 24: “BIG DATA” from RNA- Seq Experiments

https://www.google.com/search?q=pathway+analysis&client=firefox-a&hs=N6d&rls=org.

Pathway Analysis -> Modeling Data -> Making & TestingPredictions -> Heuristic Models

Page 25: “BIG DATA” from RNA- Seq Experiments

• “ … spectacular opportunities and immense challenges … presented by the dawning era of "Big Data" in biomedical research … “

• Sequencing platforms very different

• Substantial difference among different methods for detecting DE

• Often poor gene models – continually changing

• ULTRA-high dimensionality (unknown) – extremely low “n”

• Poor GO (gene ontology) assignments

• Multiclass/multiscale comparisons /modeling

Page 26: “BIG DATA” from RNA- Seq Experiments

http://www.rna-seqblog.com/methods-to-study-splicing-from-rna-seq/

Commonly Used Programs …


RNA-seq · RNA-seq Design of experiments. Experimental design. Introduction • An experiment is a process or study that results in the collection of data. • Statistical experiments

Biases in RNA- Seq data October 30, 2013 NBIC Advanced RNA- Seq course

RNA-seq data analysis - CSC · Analysis tool overview 250 NGS tools for • RNA-seq • single cell RNA-seq • miRNA-seq • exome/genome-seq • ChIP-seq • FAIRE/DNase-seq •

Analysis of RNA-seq Data - University of Hong Kongcgs.hku.hk/portal/files/GRC/Events/Seminars/2017/20170208/rna-seq.pdf · Outline • What is RNA-seq? • What can RNA-seq do? •

ERANGE RNA-Seq pipeline

RNA‐Seq: Methods and Applicaonsbarc.wi.mit.edu/education/hot_topics/RNAseq/RNA_Seq.pdf · Outline • Intro to RNA‐Seq Biological Quesons Comparison with Other Methods RNA‐Seq

RNA-Seq analysis workshop - · PDF fileOutline • Background of RNA-Seq • Application of RNA-Seq (what RNA-Seq can do?) • Available sequencing platforms and strategies and which

Rail-RNA: Scalable analysis of RNA-seq splicing and coverageMay 07, 2015  · RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Cur-rent spliced alignment

Discovering Regulatory Overlapping RNA Transcripts · has led to the recent adoption of RNA-seq as a measurement of genome wide transcription [9, 21]. RNA-seq experiments sequence

Bioinformatics for DNA-seq and RNA-seq experiments Li-San Wang Department of Pathology and Laboratory Medicine Penn Institute for Biomedical Informatics

RNA-Seq Module 1

BGi RNA-Seq Analysis

Bioinformatics for DNA - seq and RNA- seq experiments

RNA-Seq and Single-Cell RNA-Seq Tertiary Analysismed.stanford.edu/content/dam/sm/gbsc/YueZhang_2016_Genetics_R… · 3. Statistical Methods RNA-Seq and Single-Cell RNA-Seq Tertiary

RNA-seq co-expression analysis using mixture modelsjouy.inra.fr RNA-seq co-expression analysis 3 / 25 Introduction Co-expression analysis with RNA-seq data RNA-seq data, continued

Rna seq - PDX models

Introduction to RNA-Seq - University of California, Davis...Introduction to RNA-Seq Monica Britton, Ph.D. Bioinformatics Analyst December 2014 Workshop Overview of RNA-Seq Activities

RNA-seq data

ChIP-seq MBD-seq (MIRA-seq) BS-seq RNA-seq miRNA-seq

Getting the most out of RNA-seq data analysis · 2015-10-29 · large RNA-seq experiments remain the exception, rather than the rule at the moment. The count-based nature of RNA-seq

PROGRAM (60 ECTS) - Món UVic · DNA-seq experiments for variant calling: SNP and rare variant detection RNA-seq experiments for differential gene expression Gene set enrichment for

Design of “RNA-seq” Experiments - Columbia Universitysystemsbiology.columbia.edu/sites/default/files/Design_of_rnaseq... · Design of “RNA-seq” Experiments RabadanLab 06/29/2015

Analysing RNA-Seq data produced by Mars-Seq protocoldors.weizmann.ac.il/course/course2018/AnalysingRNA-Seq...Analysing RNA-Seq data produced by Mars-Seq protocol Dena Leshkowitz, Introduction

RNA-Seq - · PDF fileWhat is RNA-seq? • RNA-seq is the high-throughput sequencing of the cDNA! • It’s used to measure the RNA expression! • It’s the NGS equivalent of

RNA-seq experiments for bioinformaticians

RNA-seq from a bioinformatics perspective · 2017-11-07 · RNA-Seq - Stranded . Differential expression. Rakesh Kaundal et al. Normalization of RNA -seq . Total count (TC) : Gene

RNA-seq mixology: designing realistic control experiments ... · RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods Aliaksei Z. Holik1

How many biological replicates are needed in an RNA-seq ...thesammonslab.org/pdfs/links/rnasymp/Schurch.2016.RNA.pdf · RNA-seq experiments with a given number of replicates. RESULTS

RNA-seq analysis

Poster rna seq-molecular_medtriconf_2011_a_vladimirova

“BIG DATA” from RNA-Seq Experiments. Significance of RNA-Seq Approaches Reveals which genes are expressed and the levels at which they are expressed;

Practical RNAPractical RNA-Seq analysisbarc.wi.mit.edu/education/hot_topics/RNAseq_Feb2014/RNA-seq_Feb_2014.slides_color.pdfPractical RNAPractical RNA-Seq analysis BaRC Hot Topics

Introductiontodifferentialgeneexpressionanalysisusing RNA-seq · Figure 1 RNA-seq work flow. (a) Schematic diagram of RNA-seq library construction. Total RNA is extracted from 300,000

RNA-seq - Read mapping and · PDF fileRNA-seq (1) Peter N. Robinson Microarrays RNA-seq Alternative splicing mapping cu inks Bipartite RNA-seq Read mapping and Quanti cation Peter

RNA-Seq-BasedBreastCancerSubtypesClassificationUsing