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Bioinformatics, Translational Bioinformatics, Personalized Medicine

Bioinformatics, Translational Bioinformatics, Personalized Medicine. Uma Chandran, MSIS, PhD Department of Biomedical Informatics University of Pittsburgh chandran@pitt.edu 412-648-9326 07/17/2013. Outline of lecture. What is Bioinformatics ? Examples of bioinformatics Past to present

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Bioinformatics, Translational Bioinformatics, Personalized Medicine

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  1. Bioinformatics, Translational Bioinformatics, Personalized Medicine Uma Chandran, MSIS, PhD Department of Biomedical Informatics University of Pittsburgh chandran@pitt.edu 412-648-9326 07/17/2013

  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? • http://en.wikipedia.org/wiki/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 http://blog.openhelix.eu/?p=1078

  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 30000 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;15:709-714 doi:10.1197

  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 • 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

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

  19. 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

  20. Northern Sensitivity and dynamic range low

  21. How are these changes measured • Example: Northern blot (measure RNA) • http://www.youtube.com/watch?v=KfHZFyADnNg • 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

  22. 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

  23. Affymetrix

  24. 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

  25. How to analyze? Tumor Normal Noise reduction Background subtraction Normalization Samples GENES Data Analysis

  26. 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

  27. Pathway Analysis

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

  29. SNPs to detect Copy Number changes amplification amplification diploid deletion

  30. Hagenkord et al; Modern Pathology, 21:599

  31. 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

  32. Personalized Medicine From ageofpersonalizedmedicine.org

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

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

  35. Personalized Medicine • Examples of personalized medicine resulted from studies that generate • Lots of data • Rely on bioinformatics methods to discover these associations • OncotypeDx: • 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

  36. 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

  37. 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

  38. Era of Personalized medicine • Shift from microarrays to Next Gen Sequencing

  39. Central dogma • DNA is transcribed to RNA • RNA is translated to protein • Many regulatory processes control these steps

  40. 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

  41. Next Gen Sequencing

  42. 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

  43. 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

  44. 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

  45. 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

  46. Sequence variation

  47. 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

  48. 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 • 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

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