STRING - Cross-species integration of known and predicted protein-protein interactions

STRING Cross-species integration of known and predicted protein-protein interactions Lars Juhl Jensen EMBL Heidelberg

STRING provides a protein network based on integration of diverse types of evidence Genomic neighborhood Species co-occurrence Gene fusions Database imports Exp. interaction data Microarray expression data Literature co-mentioning

Inferring functional modules from gene presence/absence patterns T Resting protuberances Protracted protuberance Cellulose © Trends Microbiol, 1999 Cell Cell wall Anchoring proteins Cellulosomes Cellulose The “Cellulosome”

Genomic context methods © Nature Biotechnology, 2004

Formalizing the phylogenetic profile method Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps

Predicting functional and physical interactions from gene fusion/fission events Find in A genes that match a the same gene in B Exclude overlapping alignments Calibrate against KEGG maps Calculate all-against-all pairwise alignments

Inferring functional associations from evolutionarily conserved operons Identify runs of adjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in other species

Score calibration against a common reference

Many diverse types of evidence

The quality of each is judged by very different raw scores

Quality differences exist among data sets of the same type

Solved by calibrating all scores against a common reference

Scores are directly comparable

Probabilistic scores allow evidence to be combined

Requirements for the reference

Must represent a compromise of the all types of evidence

Broad species coverage

Integrating physical interaction screens Complex pull-down experiments Yeast two-hybrid data sets are inherently binary Calculate score from number of (co-)occurrences Calculate score from non-shared partners Calibrate against KEGG maps Infer associations in other species Combine evidence from experiments

Mining microarray expression databases Re-normalize arrays by modern method to remove biases Build expression matrix Combine similar arrays by PCA Calculate pairwise linear correlation coefficients Calibrate against KEGG maps Infer associations in other species

Evidence transfer based on “fuzzy orthology”

Orthology transfer is tricky

Correct assignment of orthology is difficult for distant species

Functional equivalence cannot be guaranteed for in-paralogs

These problems are addressed by our “fuzzy orthology” scheme

Confidence scores for functional equivalence are calculated from all-against-all alignment

Evidence is distributed across possible pairs according to confidence scores in the case of many-to-many relationships

? Source species Target species

Multiple evidence types from several species

Getting more specific – generally speaking

Benchmarking against one common reference allows integration of heterogeneous data

The different types of data do not all tell us about the same kind of functional associations

It should be possible to assign likely interaction types from supporting evidence types

The aim: to construct an accurate, qualitative models of biological systems or processes

The models should be accurate even at the level of individual interactions

This allows specific, testable hypotheses to be made based on high-throughput experimental data

Getting the parts list Yeast culture Microarrays Gene expression Expression profile 600 periodically expressed genes (with associated peak times) that encode “dynamic proteins” The parts list New analysis Cho & Spellman et al.

Constructing a reliable protein network

The stickiness of an interaction was scored based on its local network topology

We benchmarked these scores for each individual data set against a common reference

Impossible interactions were eliminated based on subcellular localization data

By restricting the network to a particular system the error rate is further reduced

Extracting a cell cycle interaction network Cell cycle microarray data Physical PPI interactions with confidence scores Expand the set of proteins to include non-periodic proteins that are strongly connected to periodic proteins Raw Data Node selection List of periodically expressed proteins with peak time Interactions Require compatible compartments and high confidence Extract cell cycle network

The temporal interaction network