Bioinformatics Origins:Rooted in sequence analysis.Driven by the need to:● Collect● Annotate● Analyze
Margaret Dayhoff (1925-1983)● Collected all known protein structures & sequences● Published Atlas in 1965● Pioneered algorithm development for: ○ Comparing protein sequences ○ Deriving evolutionary history from alignments“In this paper we shall describe a completedcomputer program for the IBM 7090, which toour knowledge is the first successful attemptat aiding the analysis of the amino acid chainstructure of protein.”
“There is a tremendous amount of informationregarding evolutionary history and biochemical function implicit in each sequence and the number of known sequences is growing explosively. We feel it is important to collect this significant information, correlate it into a unified whole and interpret it.” M. Dayhoff, February 27, 1967
modified from @drewconway
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DefinitionFrom Wikipedia: Bioinformatics is a branch of biological science which deals with the study of methods for storing,retrieving and analyzing biological data, such as nucleic acid (DNA/RNA) and protein sequence, structure, function,pathways and genetic interactions. It generates new knowledge that is useful in such fields as drug design anddevelopment of new software tools to create that knowledge. Bioinformatics also deals with algorithms, databases andinformation systems, web technologies, artificial intelligence and soft computing, information and computation theory,structural biology, software engineering, data mining, image processing, modeling and simulation, discretemathematics, control and system theory, circuit theory, and statistics.Our definition: using computer science andstatistics to answer biological questions.
Central Dogma Reverse RNA transcription Silencing PrionsDNA RNA Protein Post-translational modification Methylation
Protein folding determines molecular functionDNA provides assemblyinstructions for proteins Networks of interacting proteins determine tissue/organ function
Protein folding determines molecular function DNA variant analysis Gene expression analysis Genome annotation Pathway analysis Epigenetics Systems biologyDNA provides assembly Biomarker IDninstructions for proteins Networks of interacting miRNA analysis proteins determine Quantitative MS tissue/organ function Proteomics
Sequence alignment, example 1● BLAST (Basic Local Alignment Search Tool)● Go to blast.ncbi.nlm.nih.gov● Click "Nucleotide BLAST" (blastn)● Under "Choose Search Set", click the "Others" button, then search the entire nr/nt collection (you dont know what it is) GTGAGTAATAATAATTCAAAACTGGAATTTGTACCTAATATACAGCTTAAAGAAGACTTAGGAGCTTTTAGCTATAAAGTCCAACTTTCT CCTGTAGAAAAAGGTATGGCTCATATCCTTGGTAACTCTATTAGAAGGGTTTTATTATCTTCACTATCAGGTGCATCTATAATTAAAGTA AACATCGCTAATGTACTACATGAGTATTCTACTTTAGAAGATGTAAAAGAAGATGTTGTTGAAATTGTTTCTAATTTGAAAAAGGTTGCG ATAAAGCTTGATACAGGTATAGATAGACTAGATTTAGAACTATCTGTAAATAAATCAGGTGTAGTTAGCGCTGGAGATTTTAAGACGACT CAAGGTGTAGAAATAATAAATAAAGATCAGCCAATAGCTACTTTGACAAACCAAAGAGCATTTAGCTTAACTGCTACAGTGAGTGTAGGT AGAAATGTCGGAATACTTTCTGCGATACCAACCGAGCTTGAGAGAGTTGGTGATATAGCTGTAGATGCTGATTTTAATCCTATTAAAAGA GTTGCTTTTGAGGTTTTTGATAATGGTGATAGTGAAACTTTAGAAGTATTTGTAAAGACAAATGGTACTATAGAACCACTAGCAGCTGTT ACGAAAGCTTTAGAGTATTTCTGTGAGCAAATATCAGTATTTGTATCTCTAAGAGTACCTAGTAATGGTAAAACAGGTGATGTATTAATA GATTCTAATATTGATCCTATCCTTCTTAAGCCGATTGATGATTTAGAGCTAACTGTCAGATCATCTAACTGTCTGCGTGCAGAAAACATT AAGTATCTTGGTGATTTGGTACAGTATTCTGAATCACAGCTTATGAAGATACCTAACTTAGGTAAGAAATCTCTCAATGAGATCAAACAA ATTTTAATAGATAATAACTTGTCTCTAGGTGTCCAAATTGACAATTTTAGAGAGCTAGTTGAAGGAAAATAA
Sequence alignment, example 2● Illumina HiSeq 2500: ○ 600,000,000,000 bases sequenced in single run. ○ 6,000,000,000 x 100-bp (short) reads● BLAST way too slow.● BWA: burrows wheeler aligner (fast)● Bowtie: fast, memory-efficient (aligns 25,000,000 35-bp reads per hour per CPU).● Many others... MAQ, Eland, RMAP, SOAP, SHRiMP, BFAST, Mosaik, Novoalign, BLAT, GMAP, GSNAP, MOM, QPalma, SeqMap, VelociMapper, Stampy, mrFAST, etc.
Genetic EpidemiologyEpidemiology: the study of the patterns,causes, and effects of health and diseaseconditions in defined populations.Genetic epidemiology: the study of geneticfactors in determining health and disease infamilies and populations.
Protein folding determines molecular functionDNA provides assemblyinstructions for proteins Networks of interacting proteins determine tissue/organ function
Genetic epidemiology● Linkage: finding genetic loci that segregate with the disease in families.● Association: finding alleles that co-occur with disease in populations. ○ Common disease - common variant hypothesis: ■ Common variants (e.g. >1-5% in the population) contribute to common, complex disease). ○ Common disease - rare variant hypothesis: ■ Polymorphisms that cause disease are under purifying selection, and will thus be rare. ○ Really, its a mix of both
Candidate gene study ● Select candidate genes based on: ○ Known biology ○ Previous linkage/association evidence ○ Pathways ○ Evidence from model organisms ● Genotype variants (SNPs) in those genes ● Statistical associationGenotype at position rs12345: A/A Genotype at position rs12345: A/T Genotype at position rs12345: T/T
Genome-wide association study● Genotype >500,000 SNPs● Statistical test at each one● Manhattan plot of results● GWAS does not inform: ○ Which gene affected ○ How gene function perturbed ○ How biological function altered
RNA sequencing (RNA-seq) Isolate RNAs Generate cDNA, fragment, size Samples of interest select, add linkers Condition 1 Condition 2(normal colon) (colon tumor) Sequence ends Image: www.bioinformatics.ca Align to Genome Downstream analysis 100s of millions of paired reads 10s of billions bases of sequence
RNA-seq advantages● No reference necessary● Low background (no cross-hybridization)● Unlimited dynamic range (FC 9000 Science 320:1344)● Direct counting (microarrays: indirect – hybridization)● Can characterize full transcriptome ○ mRNA and ncRNA (miRNA, lncRNA, snoRNA, etc) ○ Differential gene expression ○ Differential coding output ○ Differential TSS usage ○ Differential isoform expression
Isoform level data
Isoform level data
Differential splicing & TSS use
RNA-seq challenges● Library construction ○ Size selection (messenger, small) ○ Strand specificity?● Bioinformatic challenges ○ Spliced alignment ○ Transcript deconvolution● Statistical Challenges ○ Highly variable abundance ○ Sample size: never, ever, plan n=1● Normalization (RPKM) ○ Compare features of different lengths ○ Compare conditions with different sequence depth
Common question #1: Depth● Question: how much sequence do I need?● Answer: it’s complicated.● Depends on: ○ Size & complexity of transcriptome ○ Application: differential gene expression, transcript discovery, aberrant splicing, etc. ○ Tissue type, RNA quality, library preparation ○ Sequencing type: length, single-/paired-end, etc.● Find publication in your field w/ similar goals.● Good news: 1 GA or ½ HiSeq lane is sufficient for most applications
Common question #2: Sample Size● Question: How many samples should I sequence?● Oversimplified Answer: At least 3 biological replicates per condition.● Depends on: ○ Sequencing depth ○ Application ○ Goals (prioritization, biomarker discovery, etc.) ○ Effect size, desired power, statistical significance● Find a publication with similar goals
Common question #3: Workflow● How do I analyze the data?● No standards! ○ Unspliced aligners: BWA, Bowtie, Stampy, SHRiMP ○ Spliced aligners: Tophat, MapSplice, SpliceMap, GSNAP, QPALMA ○ Reference builds & annotations: UCSC, Entrez, Ensembl ○ Assembly: Cufflinks, Scripture, Trinity, G.Mor.Se, Velvet, TransABySS ○ Quantification: Cufflinks, RSEM, MISO, ERANGE, NEUMA, Alexa-Seq ○ Differential expression: Cuffdiff, DegSeq, DESeq, EdgeR, Myrna● Like early microarray days: lots of excitement, lots of tools, little knowledge of integrating tools in pipeline!● Benchmarks● Microarray: Spike-ins (Irizarry)● RNA-Seq: ???, simulation, ???
Phases of NGS analysis● Primary ○ Conversion of raw machine signal into sequence and qualities● Secondary ○ Alignment of reads to reference genome or transcriptome ○ De novo assembly of reads into contigs● Tertiary ○ SNP discovery/genotyping ○ Peak discovery/quantification (ChIP, MeDIP) ○ Transcript assembly/quantification (RNA-seq)● Quaternary ○ Differential expression ○ Enrichment, pathways, correlation, clustering, visualization, etc.
Extra credit (not really): RNA-seqhttp://bit.ly/galaxy-rnaseq● #1: learn to use galaxy: bit.ly/uva-galaxy● #2: Run through an RNA-seq exercise in 1 hour: ○ Read some background material on RNA-seq ○ Read the tophat/cufflinks method paper ○ Get some data (Illumina BodyMap) ○ QC / trim your reads ○ Map to hg19 with tophat ○ Visualize where reads map ○ Assemble with cufflinks ○ Differential expression with cuffdiff
How are genes regulated?● Transcription factors (ChIP-seq)● Micro-RNAs (RNA-seq)● Chromatin accessibility (DNAse-Seq)● DNA Methylation (RRBS-seq, MeDIP-seq)● RNA processing● RNA transport● Translation● Post-translational modification
Importance of DNA methylation● Occurs most frequently at CpG sites● High methylation at promoters ≈ silencing● Methylation perturbed in cancer● Methylation associated with many other complex diseases: neural, autoimmune, response to env.● Mapping DNA methylation → new disease genes & drug targets.
DNA Methylation Challenges● Dynamic and tissue-specific● DNA → Collection of cells which vary in 5meC patterns → 5meC pattern is complex.● Further, uneven distribution of CpG targets● Multiple classes of methods: ○ Bisulfite, sequence-based: Assay methylated target sequences across individual DNAs. ○ Affinity enrichment, count-based: Assay methylation level across many genomic loci.● Many methods● Many algorithms
Many methylation methods Gene RNA-Seq High-throughput cDNA sequencingExpression BS-Seq Whole-genome bisulfite sequencing RRBS-Seq Reduced representation bisulfite sequencing BC-Seq Bisulfite capture sequencing BSPP Bisulfite specific padlock probes Methyl-Seq Restriction enzyme based methyl-seq DNA MSCC Methyl sensitive cut countingMethylation HELP-Seq HpaII fragment enrichment by ligation PCR MCA-Seq Methylated CpG island amplification MeDIP-Seq Methylated DNA immunoprecipitation MBP-Seq Methyl-binding protein sequencing MethylCap-seq Methylated DNA capture by affinity purification MIRA-Seq Methylated CpG island recovery assay
Methylation methods:Features & biases
Methylation: Bioinformatics ResourcesResource Purpose URL RefsBatman MeDIP DNA methylation analysis tool http://td-blade.gurdon.cam.ac.uk/software/batmanBDPC DNA methylation analysis platform http://biochem.jacobs-university.de/BDPCBSMAP Whole-genome bisulphite sequence mapping http://code.google.com/p/bsmapCpG Analyzer Windows-based program for bisulphite DNA -CpGcluster CpG island identification http://bioinfo2.ugr.es/CpGclusterCpGFinder Online program for CpG island identification http://linux1.softberry.comCpG Island Explorer Online program for CpG Island identification http://bioinfo.hku.hk/cpgieintro.htmlCpG Island Searcher Online program for CpG Island identification http://cpgislands.usc.eduCpG PatternFinder Windows-based program for bisulphite DNA -CpG Promoter Large-scale promoter mapping using CpG islands http://www.cshl.edu/OTT/html/cpg_promoter.htmlCpG ratio and GC content Plotter Online program for plotting the observed:expected ratio of CpG http://mwsross.bms.ed.ac.uk/public/cgi-bin/cpg.plCpGviewer Bisulphite DNA sequencing viewer http://dna.leeds.ac.uk/cpgviewerCyMATE Bisulphite-based analysis of plant genomic DNA http://www.gmi.oeaw.ac.at/en/cymate-index/EMBOSS CpGPlot/ CpGReport Online program for plotting CpG-rich regions http://www.ebi.ac.uk/Tools/emboss/cpgplot/index.htmlEpigenomics Roadmap NIH Epigenomics Roadmap Initiative homepage http://nihroadmap.nih.gov/epigenomicsEpinexus DNA methylation analysis tools http://epinexus.net/home.htmlMEDME Software package (using R) for modelling MeDIP experimental data http://espresso.med.yale.edu/medmemethBLAST Similarity search program for bisulphite-modified DNA http://medgen.ugent.be/methBLASTMethDB Database for DNA methylation data http://www.methdb.deMethPrimer Primer design for bisulphite PCR http://www.urogene.org/methprimermethPrimerDB PCR primers for DNA methylation analysis http://medgen.ugent.be/methprimerdbMethTools Bisulphite sequence data analysis tool http://www.methdb.deMethyCancer Database Database of cancer DNA methylation data http://methycancer.psych.ac.cnMethyl Primer Express Primer design for bisulphite PCR http://www.appliedbiosystems.com/Methylumi Bioconductor pkg for DNA methylation data from Illumina http://www.bioconductor.org/packages/bioc/html/Methylyzer Bisulphite DNA sequence visualization tool http://ubio.bioinfo.cnio.es/Methylyzer/main/index.htmlmPod DNA methylation viewer integrated w/ Ensembl genome browser http://www.compbio.group.cam.ac.uk/Projects/PubMeth Database of DNA methylation literature http://www.pubmeth.orgQUMA Quantification tool for methylation analysis http://quma.cdb.riken.jpTCGA Data Portal Database of TCGA DNA methylation data http://cancergenome.nih.gov/dataportal
One gene, one enzyme, one function? Zhu X. et al. (2007). Genes & Dev 21:1010-1024. Jeong, H. et al.. (2001) Nature 411:41–42. Ptacek, J. et al. (2005) Nature 438:679–684. Guimera and Amaral. (2005). Nature 433:895-900. Tong, A.H. et al. (2001). Science 294:2364-2368.
Distribution of disease genes Diseases connected if same gene implicated in both. Genes connected if implicated in the same disorder. Goh et al. (2007). PNAS 104:8685.
Distribution of disease genesOverlay with PPI data Genes contributing to a common disease interact through protein- protein interactions. Genes connected if implicated in the same disorder. Goh et al. (2007). PNAS 104:8685.
Distribution of disease genesSeebacher and Gavin (2011). Cell 144:1000-1001 ● “Essential” genesk = degree ● Encode hubs = # interaction partners ● Are expressed globally ● “Non-essential” disease genes ● Do not encode hubs ● Tissue specific expression
Distribution of disease genes● Disease genes at functional periphery of cellular networks (Goh PNAS 2007).● Genes contributing to a common disease interact through protein-protein interactions (Goh PNAS 2007).● Diseaseome analysis: Pt 2x likely to develop another disease if that disease shares gene with pt’s primary disease (Park et al. 2009. The Impact of Cellular Networks on Disease Comorbidity. Mol Syst Biol 5:262).● miRNA analysis: If connect diseases with associated genes regulated by common miRNA, get disease-class segregation. E.g. cancers share similar associations at miRNA level (Lu et al. 2009. An analysis of human microRNA and disease associations. PLoS ONE 3:e3420). Nonrandom placement of disease genes in interactome!
Distribution of disease genes Vidal et al, Cell 2011.
Distribution of disease genes● Data is cheap and diverse. ○ Genetic variation: GWAS, next-gen sequencing ○ Gene expression: Microarray, RNA-seq ○ Proteomics: Y2H, CoAP/MS● Cellular components interact in a network with other cellular components.● Disease is the result of an abnormality in that network.● Integrate multiple data types, understand network, understand disease.
Pathway Analysis● You’ve done your microarray/RNA-Seq experiment ○ You have a list of genes ○ Want to put these into functional context ○ What biological processes are perturbed? ○ What pathways are being dysregulated? ○ Data reduction: hundreds or thousands of genes can be reduced to 10s of pathways ○ Identifying active pathways = more explanatory power● “Pathway analysis” encompasses many, many techniques: ○ 1st Generation: Overrepresentation Analysis (E.g. GO ORA) ○ 2nd Generation: Functional Class Scoring (e.g. GSEA) ○ 3rd Generation (in development): Pathway Topology (E.g. SPIA)● http://gettinggeneticsdone.com/2012/03/pathway-analysis-for-high-throughput.html
Pathway Analysis: Over-representation analysis● Many variations on the same theme: statistically evaluates the fraction of genes in particular pathway that show changes in expression.● Algorithm: ○ Create input list (e.g. “significant at p<0.05”) ○ For each gene set: ■ Count number of input genes ■ Count number of “background” genes (e.g. all genes on platform). ○ Test each pathway for over-representation of input genes● Gene Set: typically gene ontology (GO) term.
Pathway analysis: over-representation analysis● Ontology = formal representation of a knowledge domain.● Gene ontology = cell biology.● GO represented by directed acyclic graph (DAG). ○ Terms are nodes, relationships are edges. ○ Parent terms are more general than their child terms. ○ Unlike a simple tree, terms can have multiple parents. Rhee, S. Y., Wood, V., Dolinski, K., & Draghici, S. (2008). Use and misuse of the gene ontology annotations. Nature Reviews Genetics, 9(7), 509-15.
Pathway analysis:Over-representation analysis● Algorithm: ○ Create input list (e.g. “significant at p<0.05”) ○ For each gene set: ■ Count number of input genes ■ Count number of “background” genes (e.g. all genes on platform). ○ Test each pathway for over-representation of input genes● Ex: GO “Purine Ribonucleotide Biosynthetic Process” ○ 1% of input (significant) genes are annotated with this term. ○ 1% of genes on the chip are annotated with this term. ○ Not significantly overrepresented.● Ex: GO “V(D)J Recombination” ○ 20% of input (significant) genes are annotated with this term. ○ 1% of genes on the chip are annotated with this term. ○ Highly significantly over-represented!
Pathway analysis● Pathway analysis gives you more biological insight than staring at lists of genes.● Pathway analysis is complex, and has many limitations.● Pathway analysis is still more of an exploratory procedure rather than a pure statistical endpoint.● The best conclusions are made by viewing enrichment analysis results through the lens of the investigator’s expert biological knowledge.
Resources: Online community &discussion forum● Seqanswers ○ http://SEQanswers.com ○ Twitter: @SEQquestions ○ Format: Forum ○ Li et al. SEQanswers : An open access community for collaboratively decoding genomes. Bioinformatics (2012).● BioStar: ○ http://biostar.stackexchange.com ○ Twitter: @BioStarQuestion ○ Format: Q&A ○ Parnell et al. BioStar: an online question & answer resource for the bioinformatics community. PLoS Comp Bio (2011) 7:e1002216.
Resources: further education stephenturner.us/p/edu Regularly updated, comprehensive list of over 20 in- person and free online workshops in bioinformatics, programming, statistics, genetics, etc.
Publicly Available Data: NCBI● Genbank: http://www.ncbi.nlm.nih.gov/genbank/ ○ Collection of all publicly available DNA sequences. ○ Feb 2013: 150,141,354,858 bases from 162,886,727 sequences.● NCBI Genomes: http://www.ncbi.nlm.nih.gov/genome/ ○ Public repository for sequenced genomes. ○ March 2013: 3,005 eukaryotes, 19,125 prokaryotes, 3,570 viruses.● NCBI Taxonomy: http://www.ncbi.nlm.nih.gov/taxonomy ○ Publicly available classification and nomenclature database for all organisms in the public sequences database. ○ Phylogenetic lineages for >160,000 organisms (est. ~10% life on the planet)● GEO: http://www.ncbi.nlm.nih.gov/geo/ ○ Public repository of sequence- and array-based gene expression data, free for the taking. ○ 900,000+ samples, 3,200+ datasets.● dbGaP: http://www.ncbi.nlm.nih.gov/gap ○ Public repository for genetic studies. ○ 2,500+ datasets, 100,000+ variables.● SRA: http://www.ncbi.nlm.nih.gov/sra ○ Public repository for raw sequencing data from NGS platforms. ○ 3,500,000,000,000,000 bases sequenced.