Introduction to Bioinformatics for UVA Cell Bio 8401

Introduction to
Bioinformatics
Stephen Turner, Ph.D.
Bioinformatics Core Director
bioinformatics@virginia.edu

Slides at bit.ly/intro-bioinfo

Contact
Web: bioinformatics.virginia.edu
E-mail: bioinformatics@virginia.edu
Blog: GettingGeneticsDone.com
Twitter: @genetics_blog

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 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.”

“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

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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.

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

Central Dogma

Reverse RNA
transcription Silencing Prions

DNA RNA Protein

Post-translational
modification

Methylation

Protein folding determines
molecular function

DNA provides assembly
instructions 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 biology
DNA provides assembly Biomarker ID'n
instructions for proteins
Networks of interacting
miRNA analysis proteins determine
Quantitative MS tissue/organ function
Proteomics

Sequence alignment, example 1
Outbreak: fever, characteristic skin lesions.

Culture, isolate DNA, sequence (sanger):
GTGAGTAATAATAATTCAAAACTGGAATTTGTACCTAATATACAGCTTAAAGAAGACTTAGGAGCTTTTAGCTATAAAGTCCAACTTTCT
CCTGTAGAAAAAGGTATGGCTCATATCCTTGGTAACTCTATTAGAAGGGTTTTATTATCTTCACTATCAGGTGCATCTATAATTAAAGTA
AACATCGCTAATGTACTACATGAGTATTCTACTTTAGAAGATGTAAAAGAAGATGTTGTTGAAATTGTTTCTAATTTGAAAAAGGTTGCG
ATAAAGCTTGATACAGGTATAGATAGACTAGATTTAGAACTATCTGTAAATAAATCAGGTGTAGTTAGCGCTGGAGATTTTAAGACGACT
CAAGGTGTAGAAATAATAAATAAAGATCAGCCAATAGCTACTTTGACAAACCAAAGAGCATTTAGCTTAACTGCTACAGTGAGTGTAGGT
AGAAATGTCGGAATACTTTCTGCGATACCAACCGAGCTTGAGAGAGTTGGTGATATAGCTGTAGATGCTGATTTTAATCCTATTAAAAGA
GTTGCTTTTGAGGTTTTTGATAATGGTGATAGTGAAACTTTAGAAGTATTTGTAAAGACAAATGGTACTATAGAACCACTAGCAGCTGTT
ACGAAAGCTTTAGAGTATTTCTGTGAGCAAATATCAGTATTTGTATCTCTAAGAGTACCTAGTAATGGTAAAACAGGTGATGTATTAATA
GATTCTAATATTGATCCTATCCTTCTTAAGCCGATTGATGATTTAGAGCTAACTGTCAGATCATCTAACTGTCTGCGTGCAGAAAACATT
AAGTATCTTGGTGATTTGGTACAGTATTCTGAATCACAGCTTATGAAGATACCTAACTTAGGTAAGAAATCTCTCAATGAGATCAAACAA
ATTTTAATAGATAATAACTTGTCTCTAGGTGTCCAAATTGACAATTTTAGAGAGCTAGTTGAAGGAAAATAA

● 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

● 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.

Comparative Genomics example
● Go to genome.ucsc.edu
● Search for POLR2A
● Turn on some conservation tracks

Sequence similarity
Evolutionary distance

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.

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

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/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

Gene expression pre-2008
PCR Microarrays

Exercise (Thursday)
● Download R: r-project.org
● Download Rstudio: rstudio.com
● Get data: http://people.virginia.edu/~sdt5z/GSE4107_RAW.zip
● Run code to download BioC packages:
○ source("http://bioconductor.org/biocLite.R")
○ biocLite()
○ biocLite(c("affy", "AnnotationDbi", "hgu133plus2cdf",
"hgu133plus2.db", "genefilter", "DBI", "annotate",
"arrayQualityMetrics", "limma", "GOstats",
"Category", "GO.db", "KEGG.db"))

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

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-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

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 sequencing
Expression
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 counting
Methylation 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 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

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.


Overlay 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.


Seebacher and Gavin (2011). Cell 144:1000-
1001
● “Essential” genes
k = degree ● Encode hubs
= # interaction partners ● Are expressed globally

● “Non-essential” disease genes
● Do not encode hubs
● Tissue specific expression

● 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!

Vidal et al, Cell 2011.

● 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.

Publicly Available Data: Databases
● 2013 Nucleic Acids Research Database Issue
○ http://nar.oxfordjournals.org/content/41/D1/D1.abstract
○ 176 articles describing new/updated molecular biology databases.
● NAR Molecular Biology Database Collection
○ http://www.oxfordjournals.org/nar/database/a/
○ 1,512 molecular biology databases
○ Categories: DNA/RNA/Protein sequences, structures,
metabolic/signaling pathways, genes & genomes, human diseases,
microarray/other gene expression data, proteomics, organelles, plants,
immunological, cell bio, …

Publicly Available Data: Webservers
● 2012 NAR Web Server Issue
○ http://nar.oxfordjournals.org/content/40/W1.toc
○ 102 articles/webservers featured
● Bioinformatics Links Directory
○ http://bioinformatics.ca/links_directory/
○ Includes all the NAR resources above.
○ 1,376 tools, 620 databases, 163 other resources
○ Topics: computer-related, DNA, education, expression,
genomics, literature, model organisms, RNA, protein, other
molecules, sequence comparison, …

Bioinformatics Core Mission:
help scientists publish their
work and obtain new funding
through service and training.

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.

Introduction to Bioinformatics for UVA Cell Bio 8401

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