Introduction to Bioinformatics for UVA Cell Bio 8401

Introduction to Bioinformatics for UVA Cell Bio 8401

Transcript

1. Introduction to Bioinformatics Stephen Turner, Ph.D. Bioinformatics Core Director bioinformatics@virginia.edu Slides at bit.ly/phs7070-bioinfo
2. Contact Web: bioinformatics.virginia.edu E-mail: bioinformatics@virginia.edu Blog: GettingGeneticsDone.com Twitter: @genetics_blog
3. Bioinformatics Origins: Rooted in sequence analysis. Driven by the need to: • Collect • Annotate • Analyze
4. 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 completed computer program for the IBM 7090, which to our knowledge is the first successful attempt at aiding the analysis of the amino acid chain structure of protein.”
5. IBM 7090
6. “There is a tremendous amount of information regarding 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
7. modified from @drewconway
8. 1960 1970 1980 1990 2000 2010 D ayhoff Atlas Sanger Sequencing G enBank EBI-EM BL N ext-G en Sequencing Internet invented AR PAnet W W W invented
10. Definition From 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 and development of new software tools to create that knowledge. Bioinformatics also deals with algorithms, databases and information systems, web technologies, artificial intelligence and soft computing, information and computation theory, structural biology, software engineering, data mining, image processing, modeling and simulation, discrete mathematics, control and system theory, circuit theory, and statistics. Our definition: using computer science and statistics to answer biological questions.
11. Subdisciplines • Sequence alignment (DNA, RNA, Protein) • Genome annotation • Evolutionary biology / comparative genomics • Analysis of gene expression • Analysis of gene regulation • Genotype-phenotype association • Mutation analysis • Structural biology • Biomarker identification • Pathway analysis / "systems biology" • Literature analysis / text-mining
12. Central Dogma DNA RNA Protein Post-translational modification Prions Reverse transcription Methylation RNA Silencing
13. DNA provides assembly instructions for proteins Protein folding determines molecular function Networks of interacting proteins determine tissue/organ function
14. DNA provides assembly instructions for proteins Protein folding determines molecular function Networks of interacting proteins determine tissue/organ function DNA variant analysis Gene expression analysis Genome annotation Epigenetics Pathway analysis Systems biology Biomarker ID'n miRNA analysis Quantitative MS Proteomics
15. Subdisciplines • Sequence alignment (DNA, RNA, Protein) • Genome annotation • Evolutionary biology / comparative genomics • Analysis of gene expression • Analysis of gene regulation • Genotype-phenotype association • Mutation analysis • Structural biology • Biomarker identification • Pathway analysis / "systems biology" • Literature analysis / text-mining
16. Outbreak: fever, characteristic skin lesions. Culture, isolate DNA, sequence (sanger): GTGAGTAATAATAATTCAAAACTGGAATTTGTACCTAATATACAGCTTAAAGAAGACTTAGGAGCTTTTAGCTATAAAGTCCAACTTTCT CCTGTAGAAAAAGGTATGGCTCATATCCTTGGTAACTCTATTAGAAGGGTTTTATTATCTTCACTATCAGGTGCATCTATAATTAAAGTA AACATCGCTAATGTACTACATGAGTATTCTACTTTAGAAGATGTAAAAGAAGATGTTGTTGAAATTGTTTCTAATTTGAAAAAGGTTGCG ATAAAGCTTGATACAGGTATAGATAGACTAGATTTAGAACTATCTGTAAATAAATCAGGTGTAGTTAGCGCTGGAGATTTTAAGACGACT CAAGGTGTAGAAATAATAAATAAAGATCAGCCAATAGCTACTTTGACAAACCAAAGAGCATTTAGCTTAACTGCTACAGTGAGTGTAGGT AGAAATGTCGGAATACTTTCTGCGATACCAACCGAGCTTGAGAGAGTTGGTGATATAGCTGTAGATGCTGATTTTAATCCTATTAAAAGA GTTGCTTTTGAGGTTTTTGATAATGGTGATAGTGAAACTTTAGAAGTATTTGTAAAGACAAATGGTACTATAGAACCACTAGCAGCTGTT ACGAAAGCTTTAGAGTATTTCTGTGAGCAAATATCAGTATTTGTATCTCTAAGAGTACCTAGTAATGGTAAAACAGGTGATGTATTAATA GATTCTAATATTGATCCTATCCTTCTTAAGCCGATTGATGATTTAGAGCTAACTGTCAGATCATCTAACTGTCTGCGTGCAGAAAACATT AAGTATCTTGGTGATTTGGTACAGTATTCTGAATCACAGCTTATGAAGATACCTAACTTAGGTAAGAAATCTCTCAATGAGATCAAACAA ATTTTAATAGATAATAACTTGTCTCTAGGTGTCCAAATTGACAATTTTAGAGAGCTAGTTGAAGGAAAATAA Sequence alignment, example 1
17. 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 don't know what it is) GTGAGTAATAATAATTCAAAACTGGAATTTGTACCTAATATACAGCTTAAAGAAGACTTAGGAGCTTTTAGCTATAAAGTCCAACTTTCT CCTGTAGAAAAAGGTATGGCTCATATCCTTGGTAACTCTATTAGAAGGGTTTTATTATCTTCACTATCAGGTGCATCTATAATTAAAGTA AACATCGCTAATGTACTACATGAGTATTCTACTTTAGAAGATGTAAAAGAAGATGTTGTTGAAATTGTTTCTAATTTGAAAAAGGTTGCG ATAAAGCTTGATACAGGTATAGATAGACTAGATTTAGAACTATCTGTAAATAAATCAGGTGTAGTTAGCGCTGGAGATTTTAAGACGACT CAAGGTGTAGAAATAATAAATAAAGATCAGCCAATAGCTACTTTGACAAACCAAAGAGCATTTAGCTTAACTGCTACAGTGAGTGTAGGT AGAAATGTCGGAATACTTTCTGCGATACCAACCGAGCTTGAGAGAGTTGGTGATATAGCTGTAGATGCTGATTTTAATCCTATTAAAAGA GTTGCTTTTGAGGTTTTTGATAATGGTGATAGTGAAACTTTAGAAGTATTTGTAAAGACAAATGGTACTATAGAACCACTAGCAGCTGTT ACGAAAGCTTTAGAGTATTTCTGTGAGCAAATATCAGTATTTGTATCTCTAAGAGTACCTAGTAATGGTAAAACAGGTGATGTATTAATA GATTCTAATATTGATCCTATCCTTCTTAAGCCGATTGATGATTTAGAGCTAACTGTCAGATCATCTAACTGTCTGCGTGCAGAAAACATT AAGTATCTTGGTGATTTGGTACAGTATTCTGAATCACAGCTTATGAAGATACCTAACTTAGGTAAGAAATCTCTCAATGAGATCAAACAA ATTTTAATAGATAATAACTTGTCTCTAGGTGTCCAAATTGACAATTTTAGAGAGCTAGTTGAAGGAAAATAA
20. 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.
21. Subdisciplines • Sequence alignment (DNA, RNA, Protein) • Genome annotation • Evolutionary biology / comparative genomics • Analysis of gene expression • Analysis of gene regulation • Genotype-phenotype association • Mutation analysis • Structural biology • Biomarker identification • Pathway analysis / "systems biology" • Literature analysis / text-mining
22. Comparative Genomics example • Go to genome.ucsc.edu • Search for POLR2A • Turn on some conservation tracks
23. Sequence similarity Evolutionary distance
24. Subdisciplines • Sequence alignment (DNA, RNA, Protein) • Genome annotation • Evolutionary biology / comparative genomics • Analysis of gene expression • Analysis of gene regulation • Genotype-phenotype association • Mutation analysis • Structural biology • Biomarker identification • Pathway analysis / "systems biology" • Literature analysis / text-mining
25. Genetic Epidemiology Epidemiology: the study of the patterns, causes, and effects of health and disease conditions in defined populations. Genetic epidemiology: the study of genetic factors in determining health and disease in families and populations.
26. DNA provides assembly instructions for proteins Protein folding determines molecular function Networks of interacting proteins determine tissue/organ function
27. 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, it's a mix of both
28. 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 association Genotype at position rs12345: A/T Genotype at position rs12345: A/A Genotype at position rs12345: T/T
29. 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
30. Subdisciplines • Sequence alignment (DNA, RNA, Protein) • Genome annotation • Evolutionary biology / comparative genomics • Analysis of gene expression • Analysis of gene regulation • Genotype-phenotype association • Mutation analysis • Structural biology • Biomarker identification • Pathway analysis / "systems biology" • Literature analysis / text-mining
31. Gene expression pre-2008 PCR Microarrays
32. RNA sequencing (RNA-seq) Condition 1 (normal colon) Condition 2 (colon tumor) Isolate RNAs Sequence ends 100s of millions of paired reads 10s of billions bases of sequence Generate cDNA, fragment, size select, add linkers Samples of interest Align to Genome Downstream analysis Image: www.bioinformatics.ca
33. 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
34. Isoform level data
35. Isoform level data
36. Differential splicing & TSS use
37. 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
38. 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
39. 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
40. 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, ???
41. 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.
42. Extra credit (not really): RNA-seq http://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
43. Subdisciplines • Sequence alignment (DNA, RNA, Protein) • Genome annotation • Evolutionary biology / comparative genomics • Analysis of gene expression • Analysis of gene regulation • Genotype-phenotype association • Mutation analysis • Structural biology • Biomarker identification • Pathway analysis / "systems biology" • Literature analysis / text-mining
44. 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
45. 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.
46. 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
47. Many methylation methods 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 MSCC Methyl sensitive cut counting 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 RNA-Seq High-throughput cDNA sequencing DNA Methylation Gene Expression
48. Methylation methods: Features & biases
49. Methylation: Bioinformatics Resources Resource Purpose URL Refs Batman MeDIP DNA methylation analysis tool http://td-blade.gurdon.cam.ac.uk/software/batman BDPC DNA methylation analysis platform http://biochem.jacobs-university.de/BDPC BSMAP Whole-genome bisulphite sequence mapping http://code.google.com/p/bsmap CpG Analyzer Windows-based program for bisulphite DNA - CpGcluster CpG island identification http://bioinfo2.ugr.es/CpGcluster CpGFinder Online program for CpG island identification http://linux1.softberry.com CpG Island Explorer Online program for CpG Island identification http://bioinfo.hku.hk/cpgieintro.html CpG Island Searcher Online program for CpG Island identification http://cpgislands.usc.edu CpG PatternFinder Windows-based program for bisulphite DNA - CpG Promoter Large-scale promoter mapping using CpG islands http://www.cshl.edu/OTT/html/cpg_promoter.html CpG 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.pl CpGviewer Bisulphite DNA sequencing viewer http://dna.leeds.ac.uk/cpgviewer CyMATE 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.html Epigenomics Roadmap NIH Epigenomics Roadmap Initiative homepage http://nihroadmap.nih.gov/epigenomics Epinexus DNA methylation analysis tools http://epinexus.net/home.html MEDME Software package (using R) for modelling MeDIP experimental data http://espresso.med.yale.edu/medme methBLAST Similarity search program for bisulphite-modified DNA http://medgen.ugent.be/methBLAST MethDB Database for DNA methylation data http://www.methdb.de MethPrimer Primer design for bisulphite PCR http://www.urogene.org/methprimer methPrimerDB PCR primers for DNA methylation analysis http://medgen.ugent.be/methprimerdb MethTools Bisulphite sequence data analysis tool http://www.methdb.de MethyCancer Database Database of cancer DNA methylation data http://methycancer.psych.ac.cn Methyl 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.html mPod 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.org QUMA Quantification tool for methylation analysis http://quma.cdb.riken.jp TCGA Data Portal Database of TCGA DNA methylation data http://cancergenome.nih.gov/dataportal
50. Subdisciplines • Sequence alignment (DNA, RNA, Protein) • Genome annotation • Evolutionary biology / comparative genomics • Analysis of gene expression • Analysis of gene regulation • Genotype-phenotype association • Mutation analysis • Structural biology • Biomarker identification • Pathway analysis / "systems biology" • Literature analysis / text-mining
51. 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. Zhu X. et al. (2007). Genes & Dev 21:1010-1024. One gene, one enzyme, one function?
52. 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.
53. Distribution of disease genes Genes connected if implicated in the same disorder. Goh et al. (2007). PNAS 104:8685. Overlay with PPI data Genes contributing to a common disease interact through protein- protein interactions.
54. Distribution of disease genes Seebacher and Gavin (2011). Cell 144:1000- 1001 k = degree = # interaction partners • “Essential” genes • Encode hubs • Are expressed globally • “Non-essential” disease genes • Do not encode hubs • Tissue specific expression
55. 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!
56. Distribution of disease genes Vidal et al, Cell 2011.
57. 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.
58. 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
59. 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.
60. 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.
61. 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!
62. 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.
63. Subdisciplines • Sequence alignment (DNA, RNA, Protein) • Genome annotation • Evolutionary biology / comparative genomics • Analysis of gene expression • Analysis of gene regulation • Genotype-phenotype association • Mutation analysis • Structural biology • Biomarker identification • Pathway analysis / "systems biology" • Literature analysis / text-mining
64. • 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: Online community & discussion forum
65. Bioinformatics Core Mission: help scientists publish their work and obtain new funding through service and training.
66. Services • Gene expression: Microarray Analysis • Gene expression: RNA-seq Analysis • Pathway analysis • DNA Variation (GWAS, NGS) • DNA Binding / ChIP-Seq • DNA Methylation • Metagenomics • Grant / Manuscript support • Custom development (computing & stats) • ... etc.
67. Contact Web: bioinformatics.virginia.edu E-mail: bioinformatics@virginia.edu Blog: GettingGeneticsDone.com Twitter: @genetics_blog