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Hubert HacklBiocenter, Institute of Bioinformatics

Medical University of Innsbruck Innrain 80, 6020 Innsbruck, Austria

Tel: +43-512-9003-71403Email: hubert.hackl@i-med.ac.at

URL: http://icbi.at

104540 VO/2 Bioinformatik SS2020

High throughput methods

Biological databases

Data integration methods

VI Data Integration

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Whole genome sequencing

• Few large contigs are better than many small contigs• N50 = length of smallest contig in the set of largest contigs covering 50% of assembly• Maximal and average length of contigs

• Few scaffolds with high number of contigs are better than many with few• S50 (according to N50)• Maximal and average length of contigs

Green et al., Nature Genet, 2001

• Assembly in O(n2) where n is number of reads• Sequencing errors (e.g. homopolymers)• Repeats in different length• Areas without or limited coverages• Finishing gap closure

Greedy algorithm• Find shortest common substring T for reads {s1,s2,…}• Solution is very time consuming (NP hard) but can approximated by

greedy algorithm• Successful used for small genomes (e.g. bacteria)• CAP3, SSAKE, VCAKE, SHARCGS

Overlap-layout-consensus• Graph, where the nodes represent each of the reads and an edge

connects two nodes if the corresponding reads overlap• Identifying a path through the graph that contains all the nodes - a

Hamiltonian path• Arachne, Celera Assembler,

newbler, Minimus, Edena

Genome assembly

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De Brujin graphs• Break up each read into a collection of overlapping k-mers.• Each k-mer is represented in a graph as an edge connecting two nodes

corresponding to its k-1 bp prefix and suffix respectively.• A graph that uses all the edges containing the information obtain from

all the reads is a solution to the assembly problem (Eulerian path).• Repeats • Euler, Velvet,

Allpath, ABySS

Genome assembly

Compeau et al., Nature Biotech, 2011

Personal genomes

Sequencing of the genomes of fraternal twins diagnosed with a movement disorder

6000m nucleotides (diploid human genome)1.63m single-base variants shared by twins that differ from reference

human genome9531 variants that code for proteins4605 variants that change amino-acid sequence77 rare variants (which are more likely to cause disease)3 candidate genes1 gene linked to disorder

Bainbridge et al., Sci Transl Med, 2011Maher, Nature, 2011

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Exome sequencing

• 85% of disease causing genes are in the exome• exome ~1% of the genome

Bioinformatics analysis

• Sequence quality• Alignment (e.g. BWA-mem)• Filter (mapped read, duplicate, exome, local alignment around DIP)• Variant detection (SNP, DIP, homo/heterozygous splitter)• Annotation and visualization (IGV)

Somatic variant calling (Mutetct2)

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Copy number variation

Bisulfite sequencing (DNA methylation)

• Lower sequence complexity can make problems (e.g. primer design)• Incomplete conversions• Degradation of DNA during bisulfite treatment

HT sequencing (PCR)

• Bisulfite converts cytosine (C) residues to uracil (U) • Leaves 5-methylcytosine (5mC) residues unaffected

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Yeast two-hybrid (Y2H)

Protein-protein interaction (Y2H)

TF-DNA interaction (Y1H)

Synthetic lethal interactions

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eQTL

• An expression profile can be mapped to gene expression Quantitative Trait Loci by linkage or association method.

• QTLs are stretches of DNA containing or linked to the genes that underlie a quantitative trait (phenotype, charcateristics)

hot spots

LC-MS/MS

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Spectrum

Peptide fragment fingerprinting (PFF)

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Quantitative proteomics

ICAT

Isotope-Coded Affinity TagsStable isotope labeling with amino

acids in cell culture

SILAC

Tissue microarray

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FACS

Examples of public databases

• National Center for Biotechnology Information (NCBI)

GenBank

• European Bioinformatics Institute (EBI) and Sanger Center

Ensembl

• The Molecular Biology Database Collection

http://www3.oup.co.uk/nar/database/c/

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National Institutes of Health

National Library of Medicine (NLM)National Center for Biotechnology Information (NCBI)

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Databases at the NCBI

• Pubmed• Protein• Nucleotides• Structure• Genome• Books• CancerChromosomes• Conserved Domains• 3D Domains• Gene• Genome Project• dbGAP• GEO Profiles• GEO Datasets• GeneSat

• HomoloGene• Journals• MeSH• NLM Catalogs• OMIA• OMIM• PMC• PopSet• Probe• Protein Cluster• SNP• Taxonomy• UniGene• UniSTS

Linking within Entrez

GenBank

GenBank is the NIH genetic sequence database of all publicly available DNA and derived protein sequences,with annotations describing the biological information these records contain.

• Full release of GenBank every 2 months.

• Incremental and cumulative releases: daily.

• GenBank is only available from the Internet.

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GenBank

GenBank Flat file

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RefSeq

• Best, comprehensive, non-redundant set of sequences

• For genomic DNA, transcript (RNA), and protein

• For major research organisms (2645 organisms)

• Based on GenBank derived sequences

• Ongoing curation by NCBI staff and collaborators, with review status indicated on each record

• Identifiers: NT_ Genomic contig

NM_ mRNA

NP_ protein

NR_ None-coding RNA

XM_ mRNA

XP_ protienautomatic annotation

Gene

• A record represents a single gene from an organism• A gene-specific information such as map, sequence,

expression, structure, function, homology and publications

• Includes data for all organisms that have RefSeqgenome records

• Official gene symbol and gene name are used

Gene ID 5091

Official Symbol PC

Official Full Name pyruvate carboxylase

For human provided fromHUGO Gene NomenclatureCommitee (HGNC)

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PubMed

• The database was designed to provide access to citations (with abstracts) from biomedical journals.

• PubMed has more than 15 million MEDLINE journal article references and abstracts (~1960-2008)

• 700 million searches per year over the web

• Linking feature to provide access to full-text journal articles at web sites of participating publishers, as well as to other related web resources.

Online Mendelian Inheritance in Man (OMIM)

• Is a timely, authoritative compendium of bibliographic material and observations on inherited disorders and human genes.

• Curation of the database and editorial decisions take place at The Johns Hopkins University School of Medicine.

• OMIM provides authoritative free text overviews of genetic disorders and gene loci that can be used by clinicians, researchers, students, and educators.

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Gene expression databases

• Public microarray data repositories

ArrayExpress (AE) Gene Expression Omnibus

www.ebi.ac.uk/arrayexpress/ www.ncbi.nlm.nih.gov/geo/

Protein sequences

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Three-dimensional Protein Structures

Protein-protein interaction

• centralized platform to visualize domain architecture, post-translational

modifications, interaction networks and disease association (golden standard for PPI)

• 36,500 unique PPIs annotated for 25,000 proteins (2007).

• > 50% of molecules annotated in HPRD have at least one PPI

• 10% have more than 10 PPIs.

• 3 categories of experiments for PPIs:

in vitro, in vivo and yeast two hybrid (Y2H).

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Genome Browsers

Ensembl

– Normalized

– Each data point stored only once

– Quick updates

– Minimal storage requirements

– BUT: Many tables

Many joins for complicated queries

Slow for data mining questions

– De-normalized– Tables with ‘redundant’ information– Query-optimized– Fast and flexible

Core database

Mart database (EnsMart)

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Comparative Genomics

Genomes change over time

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Definitions

Homologs: A – B – C

Orthologs: B1 – C1

Paralogs: C1 – C2 –C3

Inparalogs: C2 – C3

Outparalogs: B2 – C1

Xenologs: A1 – AB1

Protein A

Orthologues prediction

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Ortholog databases

• YOGY (eukarYotic OrtholoGY) is a web-based resource and integrates 5 independent resources (Sanger)

• COG Cluster of ortholog groups of proteins and KOG for 7 eukaryotic genomes (NCBI),

• Inparanoid (Center Stockholm Bioinformatics)

• HomoloGene (NCBI)

• OrthoMCL use Markov Clustering algorithm (University of Pennsylvania)

Rhodes et al., Nat Biotechnol, 2005

Probabilistic model for data integration

DIP Coexpression GO Interpro

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HPRD(Gold standard)

Y N

Example protein-protein interaction network

Naïve Bayes model

LR (f1..fn)P(f1..fn|pos)

P(f1..fn|neg)= ∏i=1

n

LR (fi) ∏i=1

n

=

Evidence (data sets) f1..fn

Prior odds Oprior = P(pos)/P(neg)

Posterior odds Opost = P(pos|f1..fn)/P(neg|f1..fn))

Likelihood ratio LR (f1..fn) = P(f1..fn|pos)/P(f1..fn|neg)

Opost = Oprior* LR (f1..fn)

Log-likelihood score LLS=log LR (f1..fn)

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Integration of datasets

Fraser AG, Marcotte EM. Nat Genet (2004)

Fisher’s χ2 :

Mudholkar-George’s T:

Liptak-Stouffer’s Z:

Meta analysis: combination of p-values

• Get list of significant p-values from different type of experiments (data)

• There are a number of (weighted) statistical measures to combine p-values from

k datasets

χ2 = -2 ∑ wi*log(pi) (df=2k)

T = f(k) ∑ wi*log(pi/(1-pi)

Z = (1/sqrt(∑wi2))∑ wi*Ф-1(1-pi)

i=1

k

• Derive networks based on overall p-values

Intersection min (pi)

Union max (pi)k

k

i=1

i=1

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Technologies for data integration

data sources (databases)

Presentation layer (user)

mediator

wrapper

direct links

data warehouse

data martsOLAP tools (cubes)data mining

extract, transform, load (ETL)

databases

Linking (relational database, SQL)

Mediator-based approach(federated databases)

Data warehouse

Semantic integration Agents, RDF, OWL, SPARQL, Ontologies