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Bioinformatics, Translational Bioinformatics, Personalized Medicine Uma Chandran, MSIS, PhD Department of Biomedical Informatics University of Pittsburgh.

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Presentation on theme: "Bioinformatics, Translational Bioinformatics, Personalized Medicine Uma Chandran, MSIS, PhD Department of Biomedical Informatics University of Pittsburgh."— Presentation transcript:

1 Bioinformatics, Translational Bioinformatics, Personalized Medicine Uma Chandran, MSIS, PhD Department of Biomedical Informatics University of Pittsburgh /17/2013

2 Outline of lecture What is Bioinformatics? – Examples of bioinformatics – Past to present What is translational bioinformatics? Personalized Medicine – Bioinformatics and Personalized Medicine

3 What is Bioinformatics? iki/Bioinformatics iki/Bioinformatics Application of information technology to molecular biology Databases Algorithms Statistical techniques

4 Bioinformatics examples Sequence analysis Genome annotation Evolutionary biology Literature analysis Analysis of Gene Expression Analysis of regulation Analysis of protein expression Analysis of mutations in cancer Comparative genomics Systems Biology Image analysis Protein structure prediction From Wikipedia

5 Early Bioinformatics Robert Ledley and Margaret Dayhoff – First bioinformaticians – Using IBM 7090 and punch card analyzed amino acid structure of proteins – Created amino acid scoring matrix – Protein evolution – Protein sequence alignment

6 Sequence analysis Databases to store sequence info – Phage Φ-X174 sequenced in 1977 – GenBank 30, 000 organisms 143 billion base pairs – BLAST program for sequence searching Algorithms, databases, software tools

7 Evolutionary biology Compare relationships between organism by comparing – DNA sequences – Now whole genomes Can even find single base changes, duplication, insertions, deletions Uses advanced algorithms, programs and computational resources

8 Literature mining Millions of articles in the literature How to find meaningful information – Natural language processing techniques Example – Type in p53 or PTEN in Pubmed – will retrieve 1000s of publications – How to summarize all the information for a particular gene – Function, disease, mutations, drugs – IHOP database creates network between genes and proteins for genes

9 Genome annotation Marking genes and other features in DNA Algorithms, software

10 Bioinformatics Interdisciplinary discipline – Gene/proteins/function/ - Biologist – In Cancer – Physician/Scientist/Biologist – Algorithms, for example, BLAST – Math/CS – Separate Signal from Noise, Diff gene expression, correlation with disease – Statistician – Tools, Software, Databases – Software developers, programmers Aim to make sense of biological data

11 Translational bioinformatics Translational = benchside to bedside – Bringing discoveries made at the benchside to clinical use the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health. Translational bioinformatics includes research on the development of novel techniques for the integration of biological and clinical data and the evolution of clinical informatics methodology to encompass biological observations. The end product of translational bioinformatics is newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders, including biomedical scientists, clinicians, and patients.” Translational = benchside to bedside Atul Butte, JAMIA 2008;15: doi:

12 Central dogma DNA is transcribed to RNA RNA is translated to protein Many regulatory processes control these steps

13 Molecular Biology Primer 20, 000 genes Many transcripts, many proteins More than 20, 000 proteins Southern, Northern, Western Blots

14 Biological questions DNA – Are there any mutations sickle cell anemia Cystic fibrosis Hemophilia Other diseases such as diabetes, cancer ?? – Polymorphisms Variation in the population Mutation

15 DNA amplification Are there regions of amplification or deletions that correlate with disease – If so, what genes are present in these regions – HER2 amplification in breast cancer – EGFR mutations in lung cancer

16 RNA – DNA is transcribed to RNA – Approximately 20K genes RNA levels will differ in different conditions – Liver, kidney, cancer, normal, treatment etc – Diagnosis or prognostic – microRNAs level – lnncRNAs – Splicing differences mRNA

17 Clinical questions DNA level – Are there mutations or polymorphism between different cancer patient groups Good outcome v bad outcome Early stage vs late stage Therapy responders v non-responders Examples: Renal cell, prostate cancer etc RNA – Are there specific transcripts – mRNA, microRNA - that are up or down and are signature for outcome, disease and response – 1000s of studies – Consortia projects TCGA – The Cancer Genome Atlas projects Profile 500 samples of each cancer for DNA, RNA changes

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20 Molecular Biology Primer 20, 000 genes Many transcripts, many proteins More than 20, 000 proteins Southern, Northern, Western Blots

21 Base pairing Microarray and Northern/Southern blots – Exploit the ability of nucleotides to hybridize to each other – Base pairing – Complementary bases A :T (U) G: C

22 Northern Sensitivity and dynamic range low

23 How are these changes measured Example: Northern blot (measure RNA) – – Workflow of Northern blot Key points – mRNA run on gel – separated by size – transferred to a membrane – immobilized – Have a hypothesis – for example studying RNA level for BRCA in normal and cancer – Only probe for a mRNA or transcript is labeled or tagged – probe is prepared and labeled with radioactivity – Hybridized to X-ray film – Only that mRNA is detected and quantitated

24 Microarrays Solid surface – Many different technologies Affy, Illumina, Agilent – Probes are synthesized on the solid surface Synthesized using proprietary technology – Probe are selected using proprietary algorithms – RNA (or DNA) is in solutions – RNA is labeled or tagged – Hybridized to the chip – Tagged RNA is quantitated – Compare between conditions

25 Affymetrix

26 Need for computational methods Data Management – Each file for a chip experiment is large 100MG x 10 = 1G Generates Gigabytes of data Data preprocessing – Convert raw image into signal values Data analysis – 1000s of genes (or SNPs) and few samples – How to find differences between samples – What statistical methods to use? – Like finding needle in a haystack

27 How to analyze? Samples GENESGENES Normal Tumor Noise reduction Background subtraction Normalization Data Analysis

28 Data analysis Class discovery – Are there novel subclasses within data? Class comparison – How are tumor and normal different in expression? – Which SNPs are different? Class prediction – Predict class of new sample Advanced pathway Analysis

29 Pathway Analysis

30 Analytic methods – many studies, many methods Dupuy and Simon, JNCI; 2007

31 SNPs to detect Copy Number changes diploid deletion amplification

32 Hagenkord et al; Modern Pathology, 21:599

33 What is personalized medicine Personalized medicine is the tailoring of medical treatment to the individual characteristics of each patient. Based on scientific breakthroughs in understanding of how a person’s unique molecular and genetic profile makes them susceptible to certain diseases. ability to predict which medical treatments will be safe and effective for each patient, and which ones will not be. From ageofpersonalizedmedicine.org

34 Personalized Medicine From ageofpersonalizedmedicine.org

35 Personalized Medicine From Fernald et al; Bioinformatics, 13: 1741

36 Examples of personalized medicine Breast cancer – 30% of patients over express HER2 – Treated with Herceptin – Oncotype Dx: gene expression predicting recurrence Cardiovascular – Patients response to Warfarin, the blood thinner – Response determined by polymorphism in a CYP genes

37 Personalized Medicine Examples of personalized medicine resulted from studies that generate – Lots of data – Rely on bioinformatics methods to discover these associations Oncotype Dx: – Gene expression studies of large number of patients CYP polymorphisms – Discover single nucleotide polymorphisms in patient polulations and association with response » Initial studies done with PCR methods

38 Personalized Medicine Current examples are few in numbers Making personalized medicine a reality – Generate the data – Discover the associations – Find targeted therapies – Genome sequences prices are dropping – Large scale genome information is coming: 1000 genome TCGA ICGC Also possible to commercially sequence a person’s genome Processing all this data into translating these discoveries into medical practice has many challenges

39 Bioinformatics challenges in personalized medicine Processing large scale robust genomic data Interpreting the functional impact of variants Integrating data to relate complex interactions with phenotypes Translating into medical practice Fernald et al; Bioinformatics: 13: 1741

40 Era of Personalized medicine Shift from microarrays to Next Gen Sequencing

41 Central dogma DNA is transcribed to RNA RNA is translated to protein Many regulatory processes control these steps

42 Next Gen Sequencing Directly sequence DNA to determine – SNP – CN – Expression, mRNA, microRNA – Protein binding sites – Methylation Initial steps depend not on hybridization but also on base pairing or complementarity and DNA synthesis Bioinformatics is extremely challenging

43 Next Gen Sequencing

44 NGS in personalized medicine Whole genome sequencing – Sequence genomes and find variants (1000 genome project) Find variants associated with disease phenotype Sequence exomes only – Find coding region variants associated with phenotypes RNA seq – RNA sequence signatures associated with phenotype

45 Microarrays v NGS RNA Seq Restricted to probes on chips Only transcripts with probes File sizes in MBs to GB Algorithms, methods Typically done on PCs Storage on hard drives No – predetermined probes Can detect everything that is sequenced More applications than microarray Very large file sizes Computationally very intensive Clusters, supercomputers Large scale storage solutions

46 Microarrays v RNA seq Expression Analysis Dynamic range is low Statistic to determine expression based on signal Many methods in the last 10 years Dynamic range is high – Based on reads Statistics based on counts – Affected by read length, total number of transcripts, lack of replicates

47 Read mapping Alignment Denovo assembly Mapping to reference genome – Based on complementarity of a given 35 nucleotide to the entire genome – Computationally intensive Million of 35 bp reads has to search for alignment against the reference and align spefically to a given regions – Large file sizes Sequence files in the TB Aligned file BAM files – Several hundred GB Reference genome

48 Sequence variation

49 Bioinformatics challenges in personalized medicine Processing large scale robust genomic data – Suppose we want to identify DNA variants associated with disease Which technology How much data How to analyze the data How to identify variants Each genome can have millions of variants 300, 000 new variants – i.e, not in existing databases – Will have to separate error from true variants – 1 error per 100 kb can lead to 30,000 errors in a single experiment Why do these errors happen? Fernald et al; Bioinformatics: 13: 1741

50 Bioinformatics Challenges Data Which technology to use – Each technology has different error rates, Ion Torrent (higher error rate), SOLID, Illumina – Speed of generation of data – Ion Torrent is faster Application – Whole genome or exome or targeted exome Analysis – Algorithms, speed, accuracy – BLAST is not good for WGS – Other new algorithms Speed of analysis – Alignment can take days Alignment relies on matches between sequence and reference genome – How much mismatches to tolerate – True mismatch or error – sequencing error, true mismatch – is it a SNP Quality of reference genome Large amounts of data – Each whole genome sequencing experiment can generate TB of data Where to store – patient privacy – Servers, locations, networking Sample sizes – how many samples to sequence to discover the association with disease

51 Bioinformatics Challenges Technology – Ion Torrent, SoLiD, Illumina – Each has its own error rates – Speed of data generation – Dependent on application – WGS or exome Analysis – Algorithms, speed, accuracy Speed of analysis – Alignment can take days Alignment relies on matches between sequence and reference genome – How much mismatches to tolerate – True mismatch or error – sequencing error, true mismatch – is it a SNP Quality of reference genome

52 From Mark Boguski’s presentation at the IOM, July 19, 2011

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55 Molecular Diagnostics using NGS From Mark Boguski’s presentation at the IOM, July 19, 2011

56 NGS Bioinformatics - medicine Infrastructure – Storage, backup, archive – Where – HIPAA compliant? – Network How to move data Analysis – Methods – statistics, annotation – Computing resources – How many samples can be handled at a time? – Time to report

57 NGS and bioinformatics

58 Next Gen Sequencing From Mark Boguski’s presentation at the IOM, July 19, 2011


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