Vocabulary Introduction to Bioinformatics and Gene Expression Technologies Utah State University Fall 2017 Statistical Bioinformatics (Biomedical Big Data) Notes 1 Gene: Genetics: Genome: Genomics: hereditary DNA sequence at a specific location on chromosome (that does something ) study of heredity & variation in organisms an organism s total genetic content (full DNA sequence) study of organisms in terms of their genome 1 2 Vocabulary Protein: sequence of amino acids that does something Vocabulary Proteomics: Phylogeny: study of all of the proteins that can come from an organisms genome the evolutionary or historical development of an organism (or its DNA sequence) Bioinformatics: the collection, organization, & analysis of largescale, complex biological data Statistical Bioinformatics: Phylogenetics: Phenotype: the study of an organism s phylogeny the physical characteristic of interest in each individual for example, plant height, disease status, or embryo type the application of statistical approaches to bioinformatics, especially in identifying significant changes (in sequences, expression patterns, etc.) that are biologically relevant (especially in affecting the phenotype) 3 4
Central Dogma of Molecular Biology A road map to bioinformatics Central Dogma Technology Gene Genome Sequencing Genomic Hypothesis Genotype QTL Type of Study or Analysis mrna transcript Protein Transcript Profiling Transcriptome Protein quantification and function Proteome Microarrays or Next-Gen Sequencing (Epigenetics / methylation) Protein Microarrays or Proteomics 5 Phenotype (From introductory lecture by RW Doerge at 2013 Joint Statistical Meetings) 6 Alphabets DNA sequences defined by nucleotides (4) DNA sequence mrna sequence Protein sequence Protein sequences defined by amino acids (20) General assumption of gene expression technology Use mrna transcript abundance level as a measure of the level of expression for the corresponding gene Proportional to degree of gene expression Side note: a methylated gene is silenced (no expression) 7 8
How to measure mrna abundance? Several different approaches with similar themes: Affymetrix GeneChip oligonucleotide Nimblegen array arrays Two-color cdna array More modern: next-generation sequencing (NGS) Representation of genes on slide Small portion of gene ( oligo ) Larger sequence of gene Blank slate (NGS) General DNA sequencing Sanger 1970 s today most reliable, but expensive Next-generation [high-throughput] (NGS): Genome Sequencer FLC (GS FLX, by 454 Sequencing) Illumina s Solexa Genome Analyzer Applied Biosystems SOLiD platform others Key aspect: sequence (and identify) all sequences present 9 10 Common features of NGS technologies (1) fragment prepared genomic material biological system s RNA molecules RNA-Seq DNA or RNA interaction regions ChIP-Seq, HITS-CLIP others sequence these fragments (at least partially) produces HUGE data files (~10 million fragments sequenced) Common features of NGS technologies (2) align sequenced fragments with reference sequence usually, a known target genome (gigo ) alignment tools: ELAND, MAQ, SOAP, Bowtie, others often done with command-line tools still a major computational challenge count number of fragments mapping to certain regions usually, genes these read counts linearly approximate target transcript abundance 11 12
Here, RNA-Seq: recall central dogma: DNA mrna protein action quantify [mrna] transcript abundance Isolate RNA from cells, fragment at random positions, and copy into cdna Attach adapters to ends of cdna fragments, and bind to flow cell (Illumina has glass slide with 8 such lanes so can process 8 samples on one slide) Amplify cdna fragments in certain size range (e.g., 200-300 bases) using PCR clusters of same fragment Sequence base-by-base for all clusters in parallel https://www.youtube.com/watch?v=-7gk1hxwcte 13 (originally illumina.com download) 14 (originally illumina.com download) 15 (orginally illumina.com download) 16
Then align and map For sequence at each cluster, compare to [align with] reference genome; file format: millions of clusters per lane approx. 1 GB file size per lane For regions of interest in reference genome (genes, here), count number of clusters mapping there requires well-studied and well-documented genome (orginally illumina.com download) 17 18 RNA-Seq Example: 8 patients, 56,621 genes 8 heart tissue samples 4 control (no heart disease) 4 cardiomyopathy (heart disease) 2 restrictive (contracts okay, relaxes abnormally) 2 dilated (enlarged left ventricle) These Naples data made public Nov 2015 by Institute of Genetics and Biophysics (Naples, Italy) Ctrl_3 RCM_3 Ctrl_4 DCM_4 Ctrl_5 RCM_5 Ctrl_6 DCM_6 ENSG00000000003 308 498 362 554 351 353 220 309 ENSG00000000005 3 164 2 43 13 83 22 16 ENSG00000000419 1187 1249 1096 1303 970 863 637 684 ENSG00000000457 163 239 168 195 153 194 44 117 ENSG00000000460 63 108 83 109 87 43 54 51 ENSG00000000938 369 328 272 669 1216 193 861 292... http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse71613 19 Common statistical research objectives Test each gene (row) for differential expression between conditions Ctrl vs. non-ctrl Dilated vs. Restrictive Restrictive vs. Ctrl etc. Test specific groups of genes (with a known common function) for overall expression differences between conditions Which functions are differentially active between Ctrl and non-ctrl, for example? 20
A short word on bioinformatic technologies Never marry a technology, because it will always leave you. Scott Tingey, Director of Genetic Discovery at DuPont (shared in RW Doerge 2013 introductory overview lecture at 2013 JSM) In this class, we will discuss only a couple of technologies, emphasizing their recurring statistical issues These are perpetual (and compounding) A Rough Timeline of Technologies (1995+) Microarrays require probes fixed in advance only set up to detect those (2005+) Next-Generation Sequencing (NGS) typically involves amplification of genomic material (PCR) (2010+) Third-Generation Sequencing next-next-generation Pac Bio, Ion Torrent no amplification needed can sequence single molecule longer reads possible; still (2013 ; 2016) showing high errors (2012+) Nanopore-Based Sequencing [very promising] Oxford Nanopore, Genia, others bases identified as whole molecule slips through nanoscale hole (like threading a needle); coupled with disposable cartridges; still (2013 ; 2016) under development (?+) more Differ in how sequencing done; subsequent postalignment statistical analysis basically same 21 (see 2016 Goodwin et al. paper on Canvas course page, in Files) 22 Affymetrix Technology GeneChip Affymetrix Technology Expression Each gene is represented by a unique set of probe pairs (usually 12-20 probe pairs per probe set) Each spot on array represents a single probe (with millions of copies) These probes are fixed to the array A tissue sample is prepared so that its mrna has fluorescent tags; wait for hybridization; scan to light tag (Image courtesy Affymetrix, www.affymetrix.com) (Images courtesy Affymetrix, www.affymetrix.com) 23 24
Affymetrix GeneChip Cartoon Representations (originally from Affymetrix outreach) Animation 1: GeneChip structure (1 min.) Animation 2: Measuring gene expression (2.5 min) Image courtesy Affymetrix, www.affymetrix.com 25 26 Images; Affymetrix data is probe intensity How to analyze data meaningfully? Consider (for any technology): Data quality Data distribution Data format & organization Appropriateness of measurement methods (& variance) Sources of variability (and their types) Appropriate models to account for sources of variability and address question of interest Meaning of P-values and appropriate tests of significance Statistical significance vs. biological relevance Appropriate and useful representation of results Full Array Image Close-up of Array Image Many useful tools available from Bioconductor Images courtesy Affymetrix, www.affymetrix.com 27 28
The Bioconductor Project Main Features of the Bioconductor Project Bioconductor is an open source and open development software project for the analysis and comprehension of genomic data Not just for RNA-Seq or microarray data Like a living family of software packages, changing with needs Core team mainly at Fred Hutchinson Cancer Research, plus many other U.S. and international institutions Use of R Documentation and reproducible research Statistical and graphical methods Annotation Short courses Open source Open development Source: www.bioconductor.org 29 Source: www.bioconductor.org 30 What will we do in this class? Learn basics of a few major Bioconductor tools Focus on statistical issues Discuss recent developments Learn to discuss all of this 31