NGS Bioinformatics Workshop 2.5 Meta-Analysis of Genomic Data May 30 th, 2012 IRMACS 10900...
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Transcript of NGS Bioinformatics Workshop 2.5 Meta-Analysis of Genomic Data May 30 th, 2012 IRMACS 10900...
NGS Bioinformatics Workshop2.5 Meta-Analysis of
Genomic Data
May 30th, 2012IRMACS 10900
Facilitator: Richard BruskiewichAdjunct Professor, MBB
Acknowledgment:Several slides courtesy of Professor Fiona Brinkman, MBB
Today’s AgendaA brief overview of the bioinformatics for
SNP detection softwareProteinsSystems biologyMetagenomics (some resources; very brief…)
Group feedback: bioinformatics needs at SFU?
NGS-based SNP Analysis Programs
From: Nielsen et al. 2011. Nature Reviews Genetics 12:443-451
BIOINFORMATICS OF PROTEINS
NGS Bioinformatics Workshop2.5 Meta-Analysis of Genomic Data
5
From DNA to Protein to Systems
ATGGAATTC…
Amino Acid Properties – Venn Diagram
Polypeptides
O
R3HNH
O
R4HH3N+
O
R1HNH
O
R2HNH
O
Ramachandran Plot
Secondary Structure (SS) Prediction
Note major assumptions in all– Entire information for forming ss is contained in the primary sequence– Side groups of residues will determine structure
• Pattern recognition – Looks for patterns in common ss’s like amphipathic alpha-helices (e.g. pattern
of polar and non-polar residues)
• Homology– Predict ss of the central residue of a given segment from homologous segments
(neighbors)– Based on alignments of homologous residues from a protein family– Assumption: homologous proteins = similar structure– Extension: Use BLOSUM to detect similarity, or, better, use Position Specific
Scoring Matrix (PSSM)
SS Prediction Programs• PredictProtein-PHD (72%)
– http://www.predictprotein.org/ • PREDATOR (75%)
– http://www-db.embl heidelberg.de/jss/servlet/ de.embl.bk.wwwTools.GroupLeftEMBL/argos/ predator/predator_info.html
• PSIpred (77%)
– http://bioinf.cs.ucl.ac.uk/psipred/ (PSSM generated by PSI-BLAST, better sequence database, won CASP competition for many years)
• Jpred (81%)
– http://www.compbio.dundee.ac.uk/jpred/
Tertiary Structure
Lactate Dehydrogenase: Mixed a / b
Immunoglobulin Fold: b
Hemoglobin B Chain: a
Tertiary Structure: Protein Folds
Holm, L. and Sander, C. (1996) Mapping the protein universe. Science, 273, 595-603.
Protein Folds
Folds: definition difficult and different criteria used for different classification systems– Normally formed around a separate hydrophobic core
Current protein fold taxonomy– Very roughly …– Approx. 1000-2000 different estimated folds,
depending on method of analysis – of which about half are estimated to be known (500-1000)
– Average domain size approx. 150 aa (50 – 250 aa approx std dev)
Protein Fold Major ClassesAll alpha proteins (all a)
All beta proteins (all b)
Alpha/beta proteins (a/b)- Parallel strands connected by helices (bab motifs)
Alpha plus beta proteins (a+b)- More irregular a and b combinations
“Other”- Often subclassified now
Protein Fold Classification• Curated/Semi Manual Classification
– SCOP (Structural Classification Of Proteins)
http://scop.mrc-lmb.cam.ac.uk/scop/
– CATH (Class, Architecture, Topology, Homologous superfamily)
http://www.cathdb.info/
SCOP classification Family: clear evolutionarily relationship
– Residue identities >= 30% – OR known similar functions and structures (example:
globins form family though some only 15% identical)
Superfamily: Probable common evolutionary origin– Low sequence identities, but structural and functional
features suggest common evolutionary origin. (example: actin, ATPase domain of heat shock proteins, and hexakinase form a superfamily).
Fold: major structural similarity– Same major ss in same arrangement with the same
topological connections– May occur by convergent evolution
17
SCOP example
18
CATH example
Protein Fold Classification• Automated Classification
– DALIhttp://ekhidna.biocenter.helsinki.fi/dali
– VAST (Vector Alignment Search Tool)http://www.ncbi.nlm.nih.gov/Structure/ VAST/vast.shtml
Domain Classification # (DC_l_m_n_p)
l: fold space attractor region
m: globular folding topology/fold type (clusters of structural neighbours in fold space with average pairwise Z-scores, by Dali, above 2)
n: functional family (PSI-Blast, clusters of identically conserved functional residues, E.C. numbers, Swissprot keywords)
p: sequence family (>25% identities)
DALI/FSSP – Automated classificationExhaustive all-against-all 3D structure comparison of protein structures currently in the PDB
http://www.ncbi.nlm.nih.gov/Structure/VAST/vasthelp.html
All against all BLAST comparison of NCBI’s MMDB (database of known protein structure at NCBI, derived from the PDB)
Clustered into groups by a neighbor joining procedure, using BLAST p-value cutoffs of C or less (where C=10e-7, 10e-40 or 10e-80, to reflect three different levels of redundancy). A fourth level of classification is based on sequence identity
VAST – Automated classification
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Motif and Domain Searching• InterPro – an integration of tools (PROSITE,
PFAM, PRINTS, PRODOM)– http://www.ebi.ac.uk/interpro/
• Expasy Tools has more…– PATTINPROT, to search for patterns in proteins yourself, etc…
But first… Check if the analysis you want to do has already been done!
i.e. www.ebi.ac.uk/proteome/ db.psort.org
Phylofacts
PhyloFacts includes hidden Markov models for classification of user-submitted protein sequences to protein families across the Tree of Life.
http://phylogenomics.berkeley.edu/phylofacts/
Subcellular Localization Prediction – Example of the benefit of integrating results with a Baysian approach
Localization Prediction - methods
Several programs analyze single features:
TargetP
Initially one program analyzed multiple features:
PSORT I (eukaryotes and prokaryotes)
Developed in 1990
PSORT I prediction method: Rule based
Nakai & Kanehisa, Proteins: Structure, Function, Genetics (1991)
27
Compositional Analysis
• Molecular Weight• Amino Acid Frequency• Isoelectric Point• UV Absorptivity• Solubility, Size, Shape
SYSTEMS BIOLOGY
NGS Bioinformatics Workshop2.1 Meta-Analysis of Genomic Data
Systems Biology
What is systems biology?
① Considers all (or many) of the proteins and genes in the system
② Links proteins and genes using interactions and functions
③ Uses computational models to study system
④ Provides insights into mechanisms, system dynamics, global properties
Molecular Interaction (MI) Network
Nodes = Gene / Protein Edge = Interaction Possible interactions:
phosphorylation physical binding transcriptional regulation others?
Cytoscape
http://www.cytoscape.org/
Cytoscape supports many use cases in molecular and systems biology, genomics, and proteomics:
Load molecular and genetic interaction data sets in many formats
Project and integrate global datasets and functional annotations
Establish powerful visual mappings across these data
Perform advanced analysis and modeling using Cytoscape plugins
Visualize and analyze human-curated pathway datasets such as Reactome or KEGG.
Cytoscape
Attributes for highlighted nodes / edges
Change visible attributes
Network navigation
Visible networks
Search for nodes
Control tabs: Network, VizMapper, plugin tabs
Data Files:1. Network (Simple Interaction Format)2. Node attributes (tab-delimited)3. Gene expression (tab-delimited)
Cytoscape – Loading Data
1. Network (Simple Interaction Format)• Format:
gene1 interaction_type gene2
• E.g.:
Cytoscape – Loading Data
C1QB pp C1RC1R pp C2C2 pp C4
…
2. Gene Attribute (tab-delimited table)• Maps data values to nodes
Cytoscape – Loading Data
Load File
Check off “Show Text File Import Options”
Check off “Transfer first line as attribute names..”
Preview
3. Gene expression (tab-delimited table)• Format:
gene1 exp_cond1 exp_cond2 … sig_cond1 sig_cond2 …
• Expression value: fold-change or intensity from microarray
• Significance value: P-value indicating how likely the expression value is different between conditions.
Cytoscape – Loading Data
Cytoscape – Network Style
Can change color by double-clicking on arrows
Select “Continuous Mapping” as mapping type
Select expression fold-change values (CMexp)
Double-click “Node color”
In “Vizmapper” tab…
1. Differentially-expressed subnetworks• jActiveModules
2. Functional enrichment• BiNGO
Systems Biology Analyses
Search for sub-networks that contain a significant number differentially-expressed genes (nodes)
All genes in sub-network interact… SO these highly differentially-expressed sub-networks
may represent a critical pathway or complex involved in a condition of interest
Differentially-Expressed Subnetworks
jActive algorithm: Searches for sub-networks that contain a significant
number differentially-expressed genes (or nodes) Heuristic – won’t always find the optimum result Z-score signifies how likely to find a subnetwork
with a similar number of DE genes.
Differentially-Expressed Subnetworks
Search from highlighted nodes
Select expression significance (p-values)
jActive - Inputs
Highlight result and click “Create Network”
Subnetworks listed here
jActive - Results
Functional Enrichment: Also called over-representation analysis
Searches for common or related functions in a gene set Is there a common annotation (e.g. pathway, GO term)
for a set of genes that is more frequent than you would expect by chance?
Functional Enrichment
Gene Ontology• Controlled vocabulary describing functions, processes and cell
components• Consistency between organisms and gene products• GO terms linked by relationships (is-a, part-of) and have
hierarchy (parent – child)
is-apart-of
[other protein complexes]
[other organelles]
protein complex organelle
mitochondrion
fatty acid beta-oxidation multienzyme complex
BiNGO: Looks for GO terms that are over-represented in a set of
genes. Displays the results in two ways
A table with p-values A graph showing relationships between terms
Uses the hypergeometric test to statistically test for over-representation of each GO term.
Performs multiple hypothesis correction (since we are testing multiple GO terms for over-representation).
Functional Enrichment
BiNGO - Inputs
Click Start BiNGO
Select “Custom” and then load go.annot file
Lower significance level
Fill in Name
BiNGO - Results
BiNGO - Results
General GO Terms
Specific GO Terms
Significance
EGAN: Exploratory Gene Association Networks
http://akt.ucsf.edu/EGAN/
METAGENOMICS
NGS Bioinformatics Workshop2.5 Meta-Analysis of Genomic Data
What is Metagenomics? The culture-independent isolation and characterization of
DNA from uncultured microorganism communities Nice reading list on the topic:
http://www.cbcb.umd.edu/confcour/CMSC828G-materials/reading-list.html
See also: Torsten Thomas Jack Gilbert and Folker Meyer. 2012. Metagenomics - a guide from sampling to data analysis. Microb. Inform. Exp. doi:10.1186/2042-5783-2-3 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351745/
I will just mention a few relevant bioinformatics tools here (no specific endorsements implied).
MG-RAST server
http://metagenomics.nmpdr.org/
Meyer, F. et al. 2008. The metagenomics RAST server – a public resource for the automatic phylogenetic and
functional analysis of metagenomes. BMC Bioinformatics. 9:386 doi:10.1186/1471-2105-9-386
MEGAN - MEtaGenome ANalyzerhttp://ab.inf.uni-tuebingen.de/software/megan/
Huson DH et al. 2007. MEGAN analysis of metagenomic data. Genome Res. 17: 377-386