PHANG LAB TALK

Tzu L Phang Ph.D.Assistant Professor

Department of MedicineDivision of Pulmonary Sciences & Critical Care Medicine

What I do:• Perform high-throughput data analysis for the scientific community;

microarray and Next Generation Sequencing datasets

• Provide analysis solution for experts and novice users alike

• Develop multi-media approaches to disseminate translational science education

• Studying the role of long non-coding RNA; second talk

• Establishing the Bioinformatics Consultation and Analysis Core to help researchers and scientists design, analyze and interpret their experiments.

Today’s Talk Layout

• The center of my universe:– R and Bioconductor

• Collaboration with Biologists• 5x5; simple way to teach and contribute• Next Generation Sequencing (NGS)

Today’s Talk Layout

• The center of my universe:– R and Bioconductor

• Collaboration with Biologists• 5x5; simple way to teach and contribute• Next Generation Sequencing (NGS)

R

r-project.org

R is hot

http://blog.revolutionanalytics.com/r-is-hot/

R in the media

Bioconductor• www.bioconductor.org• Statistical tools in R for high-throughput data analysis• 6 month update cycle. Release 2.10 with 554 software

package (45 new)• Analysis workflow

– Oligonucleotide Arrays– Sequence Analysis– Variants– Accessing Annotation Data– High-throughput Assays

The Websitewww.bioconductor.org

Categories

Categoriescont …

• Typical Analysis Routine

R is easy

Result output

Other Resourceshttp://www.rseek.org/ http://www.statmethods.net/

http://crantastic.org/ http://stackoverflow.com/

Today’s Talk Layout

• The center of my universe:– R and Bioconductor

• Collaboration with Biologists• 5x5; simple way to teach and contribute• Next Generation Sequencing (NGS

Collaboration

• >1000 microarray chips / year• Affymetrix & Illumina platforms• Next Generation Sequencing 25 free Pilot

Projects.• Serve the rocky mountain region scientific

community

Collaboration - tips

• Don’t be a data analyst – be a co-investigator• Suggest analysis approaches that are not

obvious• Focus on the result, not method• Always looks for grant writing opportunity• Understand the technical & biological system

as thoroughly as possible – you will be surprise what biologists missed informatically

Exmaple 1: Classification of Pituitary Tumors

• Pituitary tumors are the most common type of brain tumor in 20% at autopsy and 1/10,000 persons clinically. Based upon 2010 figures of a veteran population of 22.7 million, this translates into >225,000 veterans with pituitary tumors.

• Currently no medical therapies exist for these tumors and surgical resection is the treatment of choice. Recurrence rates approach 40%.

• Understanding of the pathways to tumorigenesis and markers of aggressiveness and risk of recurrence would alter the intensity and cost of clinical care and may provide novel candidates and pathways to explore for new treatment options for these patients

Principle Component Analysis

Potential markers

Outputs

Example 2: Explore the artistic side!

Example 3: Unconventional Usage

Introduction• Crohn’s Disease (CD) is an Inflammatory Bowel Disease

(IBD) that affecting up to one million Americans (15 to 30 ages).

• Discordance between monozygotic twins affected by CD provide evidence for epigenetic role in etiology of disease.

• We combined 2 microarray technologies to study these roles– CHARM array (Comprehensive High-throughout Array for

Relative Methylation)– Gene Expression (Affymetix Gene 1.0 ST)

Research Informatics Integrated Core (RIIC)

Michael G. Kahn MD, PhDCCTSI Co-Director & RIIC Core Director

Michael.Kahn@ucdenver.edu

RIIC Organizational ModelMichael Kahn

ThomasYaeger

Web site

Portal application

s

Virtual server farm

Research LIS

implementation

Desktop support

Jessica Bondy

(Cancer Center Informatics

Core Director)REDCap, REDCap Survey

Data Manageme

nt Best Practices

MichaelKahnThird

Thursday @ Three

Thirty Three

InformaticsSeminar

Series

Secondary database

and analysis service

TzuPhang

5x5s

Video Tutorials

Bioinformatics Tools Tutorials

SteveRoss

Community Engagemen

tInformatics

Liaison

http://cctsi.ucdenver.edu/RIIC/Pages/ConsultationDataAnalysis.aspx

5X5http://cctsi.ucdenver.edu/5x5

Demonstration

http://gcrc.ucdenver.edu/Videos/Informatics/5x5/SocialNetworking5x5.wmv

Tools

Podcast

TIES – Translational Informatics Education Support (TIES)

• Bridging the gap in translational research through education

• Training biologist informatics• Enhance collaboration through education and

knowledge exchange• Bring awareness in latest technical advances• Disseminate knowledge through innovation

Next Generation Sequencing

The future is here ….

High Throughput Parallel Sequencing

• http://www.youtube.com/watch?v=77r5p8IBwJk

Paradigm Shift

• Standard “Sanger” sequencing– 96 sample/day– Read length ~650 bp– Total = 450,000 bases of sequence data

• 454 – the game changer!– ~400,000 different templates (reads)/day– Read length ~ 250 (at that time)– Total = 100,000,000 bases of sequence data

The second generationRoche (454) http://454.com/

– First on the market– Emulsion PCR and pyrosequencing

Illumina (Solexa) http://www.illumina.com/– Second on the market– Bridge PCR and polymerase based SBS

Abi (Solid) http://solid.appliedbiosystems.com/– Third on the market– Emulsion PCR and ligase based sequencing

Single molecule sequencingHelicos Biosciences

http://helicosbio.comtrue Single Molecule Sequencing technology

Pacific Biosciences http://www.pacificbiosciences.comSingle Molecule Real Time sequencing

Portable Sequencer

• Ion Torrent

OthersPolonator http://www.polonator.org

Emulsion PCR and ligase based sequencingUsed in the Personal Genome ProjectOpen platform, open sourceCheap/affordable

Complete Genomics http://www.completegenomics.comSpecializing in human genome sequencing

Type of read data

• Base Space or Color Space• Paired end or single end• Stranded or Unstranded

Short Reads

• Short reads from NGS are challenging (Solexa ~36 bp, now HiSeq 100 bp single pass)– Very hard to assemble whole genome– Especially on repeat regions

• Requires many fold coverage• New and faster algorithm for many traditional

bioinformatics operations• Reads are getting longer – another moving

target. (2x250)

Applications

• An explosion of scientific innovation!!• New usages not directly foreseen by the

original developers of the technology• Some envision the beginning of next

revolution – such as PCR – NGS machine in every lab!!

• Cheap high-volume sequencing – revisiting data collection and management system

RNA Sequencing• “Digital Gene Expression” or “RNA-Seq”• Truly accurate gene expression measurements– Can replace gene expression microarrays • 25% more sensitive• Does not rely on hybridization (no %GC bias, no cross-

hybridization between related genes)

• Discover novel genes (and other kinds of RNA

molecules) – one experiment found that 34% of human transcripts were

not from known genes• Sultan et al, Science. 2008 Aug 15;321(5891):956-60.

Why RNAseq better then microarray?

• Not predefine gene annotation — make discovering novel transcripts possible

• Low, if any, background• Large dynamic range of expression levels, no

upper limit for quantification• Reveal sequence variation, such as SNP, in the

transcript region• In Helico — single molecule sequencing — no

PCR step, remove amplification bias

More information from RNA

• Can capture true alternative splicing information– Sequence of splice-junctions• One study found 4,096 previously unknown splice

junctions in 3,106 human genes– Different transcription start and end points for

RNA molecules• Allelic variation (SNPs) • Small RNAs

Bottleneck: Data Analysis

Informatics is the Bottleneck

• Scientists are currently able to generate sequence data much faster/more easily than they are able to analyze it

• Customized analysis / Bioinformatics consulting is needed for every project

Bioinformatics Challenges• Need for large amount of CPU power– Informatics groups must manage compute clusters– Challenges in parallelizing existing software or redesign of

algorithms to work in a parallel environment– Another level of software complexity and challenges to

interoperability• VERY large text files (million lines long)– Can’t do ‘business as usual’ with familiar tools such as

Microsoft Excel.– Impossible memory usage and execution time

• Sequence Quality filtering

Auer P. Statistical design and analysis of RNA sequencing data. Genetics. 2010.

Data formats

• Images• “raw” basecalls with quality scores• Sequence reads aligned to reference genomes• Assemblies (contigs)• Variants (SNPs, indels, copy number variants)

Hexadecimal mode

Decimal mode

FASTQ

Raw

SAM format

Example

Pileup format

QNAME

FLAG

RNAME

POS

MAPQ

CIGAR

MRNM

MPOS

ISIZE

SEQ

QUAL

CIGAR

• M : match/mismatch• I : Insertion compared with reference• D : Deletion compared with reference• N : Skipped bases on reference• S : soft clipping (unaligned)• H : hard clipping• P : padding

File Size

• s_1_ILS4_sequence.txt [5.2 GB]• s_1_ILS4_sequence.fastq [3.3 GB]• s_1_ILS4_sequence.sam [4.5 GB]• s_1_ILS4_sequence.bam [995 MB]• s_1_ILS4_sequence.sorted.bam [696 MB]

The Bible

Utility Tools

• SamTools• Picard• Useq• Etc …

Bioconductor Solution

A demonstration

Secondary Tools

• Laboratory Management• Data mining and visualization• Project management for genome assembly• Pathway mapping (functional analysis of

groups of genes)• Motif finding (for Chip-Seq)

Integration

• Integrate information from different technologies on a single genome map– Genetic variation– Gene expression (mRNA levels)– Alternative splicing– Transcription factor binding– Methylation/histone status– Small RNA levels (gene regulatory molecules)– Non-coding RNA levels!

Speed/Efficiency

• New emphasis on efficient data structures and algorithms

• Use of “old style” tools such as grep/sed/awk• Machine language programming• Currently a huge burst of programming creativity in

an “anything goes” environment• A desperate scramble for tools that work• Huge duplication of effort in programming, but also

in evaluating new software

Amazon Web Serviceshttp://aws.amazon.com/education/

Future Directions

• Sequencing will continue to get much faster and cheaper, by 4-10x per year for several more years.

• Affordable complete human genome sequencing will be available as a clinical diagnostic tool within 2-3 years.

• Data storage and analysis bottleneck• Data security/privacy issues

Move to 1:52

Field Trip