Beyond the Human Genome- Transcriptomics

Beyond the Human Genome: Transcriptomics Dr Jen Taylor Henry Wellcome Centre for Gene Function Bioinformatics Department of Statistics taylor@stats.ox.ac.uk Beyond the Human Genome: 1995 Human Genome sequencing begins in earnest “Mapping the Book of Life” 1999 Human Genome = approx 140, 000 genes 2000 - First Draft Human Genome = 30, 000 – 40,000 genes ?? 2003 - Essential Completion Human Genome = 24, 195 genes !!!??? Commemorative stained glass window for F.C. Crick, designed by Maria McClafferty.(Photograph: Paul Forster) Gonville & Caius College, Cambridge, UK. Beyond the Human Genome: Gene Number ≠ Complexity Complexity Gene Regulation Transcriptome Commemorative stained glass window for F.C. Crick, designed by Maria McClafferty.(Photograph: Paul Forster) Gonville & Caius College, Cambridge, UK. Introduction: The scope of transcriptomics – a definition of the transcriptome Part I: Observing the transcriptome Experimental methodology Data analysis Part II: Using the transcriptome The regulation of the trancriptome The transcriptome and the genome The transcriptome and the proteome Beyond the Human Transcriptome Transcriptome: “transcriptome, the mRNAs expressed by a genome at any given time..” (Abbott, 1999) Central Dogma of Molecular Biology  mRNA – single stranded RNA molecule  Complementary to DNA Processed (spliced and polyadenylated) RNA transcript Carries the sequence of a gene out of the nucleus into the cytoplasm where it can be translated into a protein structure   Image: Access Excellence, National Institutes of Heath Transcriptome: An evolving definition  (the population of) mRNAs expressed by a genome at any given time (Abbott, 1999) The complete collection of transcribed elements of the genome. (Affymetrix, 2004) mRNAs: 35, 913 transcripts (including alternative spliced variants) Non-coding RNAs  tRNAs (497 genes)  rRNAs (243 genes)  snmRNAs (small non-messenger RNAs)  microRNAs and siRNAs (small interferring RNAs)  snoRNAs (small nucleolar RNAs)  snRNAs (small nuclear RNAs)  Pseudogenes (~ 2,000)    The human transcriptome Nucleotides High density oligonucleotide arrays across 11 different cell lines ~ 70% of transcripts non-coding ~79-88% have multiple transcripts Kapranov et al., 2002 ~ 90% of transcribed nucleotides outside annotated exons The dimensions of the unique transcriptome?? >>> current 40,000 estimate Kampa et al., Novel RNAs identified from an in-depth analysis of the transcriptome of human chromosomes 21 and 22. Genome Research. 2004 Transcriptomics Scope    the population of functional RNA transcripts. the mechanisms that regulate the production of RNA transcripts dynamics of the trancriptome (time, cell type, genotype, external stimuli) Definition The study of characteristics and regulation of the functional RNA transcript population of a cell/s or organism at a specific time. Introduction: The scope of transcriptomics – a definition of the transcriptome Part I: Observing the transcriptome Experimental methodology Data analysis Part II: Using the transcriptome The regulation of the trancriptome The transcriptome and the genome The transcriptome and the proteome Beyond the Human Transcriptome Observing the transcriptome High-throughput friendly Genome Predicts Biology ** Regulatory network Transcriptome Context dependent and dynamic Proteome **Li et al., 2004 Publications: Expression Profiling vs Proteomics Expression Profiling 3500 3000 2500 2000 1500 1000 500 0 19 95 19 96 19 97 19 98 19 99 Proteomics Quantitative monitoring of gene expression patterns with a complementary DNA arrays themselves, but “ The challenge is no longer in the expression microarray. in developing experimental designs to exploit the full power of a Schena M, Shalon D, Davis RW, Brown PO. global perspective.” Stanford University Medical Center, CA. Eric Lander Data from PubMed 20 00 20 01 20 02 20 03 Observing the transcriptome? Classic Human Transcriptome Profiling Studies: Trancriptome reflects Biology Golub et al., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999. ALL – acute lymphoblastic leukemia AML – acute myeloid leukemia Scherf et al., A gene expression database for the molecular pharmacology of cancer. Nature Genetics 2000 60 human cancer cell lines Observing the transcriptome Focussed Experimental Approaches:  Northern Blotting Analysis  Real time PCR (quantitative or semi-quantitative) Highthroughput Approaches:  Closed System Profiling: Microarray expression profiling  Open System Profiling: Serial analysis of gene expression (SAGE) Massively Parallel Signature Sequencing (MPSS) Red – increase of Cy5 sample transcripts Green – increase of Cy3 sample transcripts Yellow – equal abundance Limit of Detection: 1 in 30,000 transcripts ~ 20 transcripts/cell Experimental overview: Cell population A Cell population B RNA extraction A A B B Quantify pixel intensities. Reverse transcription A A B B “Overlay images” Klenow label incorporation Sample A labelled with cy5 dye Sample B labelled with cy3 dye Scan cy5 channel Scan cy3 channel Washing Hybridisation Red – increase of Cy5 sample transcripts Green – increase of Cy3 sample transcripts Yellow – equal abundance Limit of Detection: 1 in 30,000 transcripts ~ 20 transcripts/cell Platforms and Formats  Isotope  Nylon – cDNA (300-900 nt)  Two-colour  Glass  cDNA or Oligo (80 nt)  500 – 11,000 elements  Affymetrix  Silicone – oligo (20 nt)  22 ,000 elements  Tissue Arrays  Glass  Tissue Discs (20-150) Affymetrix GeneChip® Limits: 1: 100,000 transcripts ~ 5 transcripts/cell Affymetrix GeneChip® http://www.affymetrix.com Affymetrix: Gene Expression Arrays Arabidopsis Genome C. elegans Genome Drosophila Genome E. coli Genome Human Genome U133 Plus Mouse Genome Yeast Genome Rat Genome Zebrafish Plasmodium/Anopheles Transcripts/Genes 24,000 22,500 18, 500 20, 366 47,000 39, 000 5, 841 (S. cerevisiae) & 5, 031 (S. pombe) 30, 000 14, 900 4,300 (P. falciparum) & 14,900 (A. gambiae) Barley (25,500), Soybean (37,500 + 23,300 pathogen), Grape (15,700) Canine (21,700), Bovine (23,000) B.subtilis (5,000), S. aureus (3,300 ORFS), Xenopus (14, 400) Microarray and GeneChip Approaches Advantages:     Rapid Method and data analysis well described and supported Robust Convenient for directed and focussed studies Disadvantages:  Closed system approach  Difficult to correlate with absolute transcript number  Sensitive to alternative splicing ambiguities Serial Analysis of Gene Expression (SAGE) The principles:  Velculescu et al., Science 1995  A transcript (new or novel) can be recognised by a small subset (e.g. 14) of its nucleotides – a tag  Linking tags allows for rapid sequencing.  Open system for transcript profiling 14 nt Modified SAGE methods LongSAGE (21 nt) SAGE-lite, micro-SAGE, mini-SAGE TAG TAG TAG AAAAAAAAA – 3‟ AAAAAAAAA – 3‟ AAAAAAAAA – 3‟ TAG RASL/DASL methods (5‟ and 3‟ Tags) AAAAAAAAA – 3‟ TAG TAG TAG TAG Sequence AGCTTGAACCGTGACATCA TGGCCATTGGCCCCAATTG AGACAGTGAGTTCAATGC SAGE Advantages:     Potential „open‟ system method – new transcripts can be identified Accuracy of unambiguous transcript observation Digital output of data Quantitative and qualitative information Disadvantages:  Characterising novel transcripts is often computationally difficult from short tag sequences  Tag specificity (recently increased length to 21 bp)  Length of tags can vary (RE enzyme activity variable with temperature)  A subset of transcripts do not contain enzyme recognition sequence  Sensitive to a subset of alternative splice variants Introduction: The scope of transcriptomics – a definition of the transcriptome Part I: Observing the transcriptome Experimental methodology Data analysis Part II: Using the transcriptome The regulation of the trancriptome The transcriptome and the genome The transcriptome and the proteome Beyond the Human Transcriptome Biological question Sample Attributes Experimental design Platform Choice Microarray experiment 16-bit TIFF Files Image analysis (Rspot, Rbkg), (Gspot, Gbkg) Normalization Clustering Statistical Analysis Classification Data Mining Pattern Discovery Biological verification and interpretation Analysis 47,000 x 2 x 2 Liver datapoints 188, 000 Brain 47,000 x 2 x 2 datapoints 188, 000 Lymphocyte 47,000 x 2 x 2 datapoints 188, 000 Analysis Essential problem: Given a large dataset with technical and biological noise: Find: A) Transcripts: patterns (common themes or differences) measures of robustness or some idea of uncertainty B) Sample: similarities or differences between samples on global/multi-gene level Analysis Liver Brain Lymphocytes Which transcripts are different? What are the patterns? Biologists Nightmare: Statisticians Playground Characteristics of the expression profiling data:   High dimensionality Sample number (n) low and observation number high (p) Non-independence of observations Complex patterns: visualisation and extraction Incorporation of contextual information Standardisation and data sharing Integration of & with other data types      Analysis Methods  Classical parametric & non-parametric statistical tests for hypothesis testing Unsupervised clustering algorithms Hierarchical clustering Kmeans and Self-Organising Maps Classification e.g. Machine learning and Linear discriminant analysis Dimensionality Reduction or Principal Component Analysis e.g. Gene Shaving and Multi-dimensional Scaling Probabilistic Modelling Dynamic Bayesian Networks Markov Models     Analysis Methods Classical Parametric Statistical Analysis: H 0 (GeneA)   AL   AB   ALy  AL Fold Change Tools: T-test  ALy ANOVA Mann Whitney U Test  AB Liver Brain Lymphocyte Analysis Methods Classical Parametric Statistical Analysis: H 0   L   B   Ly (P=0.01) 20,000 transcripts = 200 transcripts Difficulties ???  Assumes that observations are normally distributed and independent  ‘Statistical significance’ does not equal biological significance  Appropriate multiple testing corrections are difficult Analysis Methods Clustering Approaches: Divides or groups genes/samples into groups “clusters”, based on similarities and differences Number of groups is user defined Algorithms: Hierarchical clustering Kmeans clustering Self organising maps Distance Metrics log2(cy5/cy3) 2 0 -2 Time Distance between 2 expression vectors Euclidean Pearson(r*-1) 1.4 4.2 -0.90 -1.00 to to Distance Metric log2(cy5/cy3) 2 0 -2 Transcription Factor Transcript Target Transcript 1 Target Transcript 2 Pearson Distance Euclidean Distance Hierarchical Clustering g1 g2 g3 g4 g5 g6 g7 g8 g1 is most like g8 g1 g8 g2 g3 g4 g5 g6 g7 g4 is most like {g1, g8} g1 g8 g4 g2 g3 g5 g6 g7 Hierarchical Tree g1 g8 g4 g5 g7 g2 g3 g6 Clustering: Case Study Sorlie et al., 2001 Breast tissue subtypes Hierarchical clustering K-means clustering Partition or centroid algorithms Step 1: User specifies K clusters x Brain Expression Level K=3 x x Liver Expression Level K-means clustering Step 2 – Using Euclidean distance nearest points assigned to clusters (k) Step 3 – New centroids calculated x K=3 x x K-means clustering Step 4 – Points re-assigned to nearest centroid Step 5 – New centroids calculated Iterates until centroids don‟t move K=3 Classification Transcript B Transcript A K-nearest neighbour methods (KNN) Linear Discriminant Analysis (LDA) Machine Learning: Support Vector Machines Neural Network Analysis Adapted from Florian Markowetz Classification Training Set 2/3 sample set Test Set 1/3 sample set Define Classification Rule Linear Discriminant Analysis Gene B KNN Gene A Classification More complex classifiers Gene B Gene A KNN – Voting scheme – (k=3) Use three closest points to classify Adapted from Florian Markowetz Probabilistic Modelling  Incorporate dependencies and prior knowledge into the identification of patterns/clusters: - relationships in time between samples - relationships between genes  Handle measures of uncertainty well  Conceptually simple, consideration needed on implementation  Markov modelling  Dynamic bayesian networks Analysis Methods  Classical parametric & non-parametric statistical tests for hypothesis testing Unsupervised clustering algorithms Hierarchical clustering Kmeans and Self-Organising Maps Classification Machine learning and Linear discriminant Analysis Dimensionality Reduction or Principal Component Analysis Gene Shaving and Multi-dimensional Scaling Probabilistic Modelling Dynamic Bayesian Networks and Pattern recognition Markov Models     Introduction: The scope of transcriptomics – a definition of the transcriptome Part I: Observing the transcriptome Experimental methodology Data curation and analysis pipelines Part II: Using the transcriptome The regulation of the trancriptome The transcriptome and the genome The transcriptome and the proteome Beyond the Human Transcriptome …. to be continued. Introduction: The scope of transcriptomics – a definition of the transcriptome Part I: Observing the transcriptome Experimental methodology Data curation and analysis pipelines Part II: Using the transcriptome The regulation of the trancriptome The transcriptome and the genome The transcriptome and the proteome Beyond the Human Transcriptome Regulation of Gene Expression Abundance (transcript) = Rate of Transcription – Rate of Decay Transcription Decay Protein/DNA interactions  cis and trans regulatory sequence motifs Protein/RNA interactions  cis-acting regulatory motifs  secondary structure  chromatin structure  Methylation Regulation of Transcription Wray et al., 2003 Regulation of Decay Stabilisation – facilitates rapid increase in potential protein production Destabilisation – facilitates precise time and dose control of transcripts Abundance Abundance Stabile Decay Time Time Sequence-mediated mRNA decay – AU rich elements (AREs)  3‟ UTR, 50 – 150 nucleotides     usually multiple copies (e.g. AUUUA x 5) protein recruitment for destabilisation size and content variation (functionally critical motif unknown) >30% of vertebrate homologous mRNAs have highly conserved elements in the 3‟UTR - often sequence & position The importance of the decay process  BMP2 (bone morphogenetic protein 2) developmentally critical, highly conserved protein in vertebrates (Fritz et al., 2004) 3‟ UTR conservation: - 73% /100 nucleotides, 450 myr evolution - 95% within mammals  Cancer related genes: C-fos, C-myc, C-jun, MMP-13, Cyclooxygenase-2, Cyclin D, Cyclin E, Cyclins A and B, Cdk inhibitors, DNA methyltransferase 1………. (Review: Audic and Hartley, 2004) Regulation of Transcription Wray et al., 2003 Regulation of Trancription Diverse orientations, structure and functional properties of regulatory modules Wray et al., 2003 Regulation of the transcriptome  Finding regulatory elements using co-abundant transcripts Assumption: shared abundance profile = same cluster = shared regulatory machinery Penacchio and Rubin, 2001 Introduction: The scope of transcriptomics – a definition of the transcriptome Part I: Observing the transcriptome Experimental methodology Data analysis Part II: Using the transcriptome The regulation of the trancriptome The transcriptome and the genome The transcriptome and the proteome Beyond the Human Transcriptome The transcriptome & the genome Using the genome to infer/observe the transcriptome:  Construction of whole genome/transcriptome arrays and SAGE tags Using sequence features to predict gene expression: Beer and Tavazoie. Predicting gene expression from sequence. Cell 2004 Using chromatin structure to predict regulation of gene expression: Sabo et al. Genome-wide identification of DNaseI hypersenstive sites. PNAS 2004 Quantitative trait loci mapping Morley et al., Genetic analysis of genome-wide variation in human gene expression. Nature 2004 Schadt et al., Genetics of gene expression surveyed in mouse, human and maize. Nature 2003    Transcriptome & Genome Beer and Tavazoie, Cell. 2004 Abundance profile  Transcription factor binding site Predict potential gene expression patterns Transcriptome & Genome Beer and Tavazoie, Cell. 2004 AND Logic: AND Logic, OR Logic: OR Logic, NOT Logic: Combinatorial patterns help identify groups of transcripts predicted to show similar abundance profiles Solid: Actual expression Dashed: Predicted Introduction: The scope of transcriptomics – a definition of the transcriptome Part I: Observing the transcriptome Experimental methodology Data analysis Part II: Using the transcriptome The regulation of the trancriptome The transcriptome and the genome The transcriptome and the proteome Beyond the Human Transcriptome The transcriptome & the proteome  Functional annotations of co-abundant genes Yang et al., 2003 Decay rates of human mRNAs: Correlation with functional characteristics and sequence attributes. Genome Research. Co-ordinated patterns of decay rates within functional classes of transcripts Transcription factor functional classes have “fast-decaying” mRNAs (<2 hr half lives). Transcripts of multi-subunit proteins have correlated decay patterns and rates The transcriptome & the proteome Do they agree? Studies of direct correlation between mRNA abundance and protein abundances ( r = 0.6) (Hegde et al., 2003) Biological Issues: Post-translational modifications Protein stability and folding Alternative splicing products Technical Issues: Inter-platform variability (microarray and RT PCR: r = 0.8) Protein abundance measures – 2D gel electrophoresis The transcriptome & the proteome The integration of transcriptomics and proteomics Hegde et al., 2003 Synergistic approaches to biological problems using both transcriptomics and proteomics Beyond the Human Transcriptome Challenges for the Future: (short and long term)  Integration of different datatypes - sequence, exon structure, transcript abundance, protein abundance and function Dealing with alternative splice variants The regulatory processes behind any given RNA abundance Dealing with gene ontologies in a quantitative manner    Beyond the Human Transcriptome Future Directions:  „Open‟ systems for comprehensively cataloguing the transcriptome - between tissues/cells/developmental time points - between individuals Variation of transcriptome between individuals - coding variants, epigenetic variation and inheritance Clinical deployment of transcriptome profiling approaches in diagnostics and pharmacogenetics Human Regulatory Network Resources for Tissues    Acknowledgements OX-FORD BIOINFORMATICS GROUP Genomes, Sequences and Function Oxford Centre for Gene Function Jotun Hein Chris Holmes Gerton Lunter Lizhong Hao Ben Holtom Karen Lees http://www.stats.ox.ac.uk/~taylor/Presentations