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Pizza club - March 2017 - Gaia

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A Tenyu et al, ChainRank, a chain prioritisation method for contextualisation of biological networks, BMC Bioinformatics 2016 17:17, DOI: 10.1186/s12859-015-0864-x

Published in: Science
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Pizza club - March 2017 - Gaia

  1. 1. 22 March 2017
  2. 2. Background & Aim • There is more and more (genome-wide) data available that is still not optimally used • Genome-wide networks are too big and complex to be interpreted in a meaningful way • Knowledge-based networks are in general non specific: e.g. canonical pathways, PPI networks… Develop a flexible method to identify context-specific subnetworks
  3. 3. Approach • Model the flow of information using chains of interactions • Chains = simple paths: sequence of interactions (e.g. protein modifications) that connect one start and one ending point. • Multiple chains can exist between a couple of start and end protein: what is the best meaningful subnetwork? • Prioritization of the chains based on many possible scores: gene expression, functional module identification, … • Here they present a general tool for combining multiple biological information as chain scores: ChainRank
  4. 4. Methods 1. Search for all chains among user-defined start and end nodes in the network 2. Annotate the nodes with scores in order to calculate chains score and p-value
  5. 5. Subnetwork Restrict the network by heuristic breadth-first search from the fixed initial proteins to the final one with 2 criteria: 1. Maximal length allowed = length of the shortest path between initial and final node 2. Prefer the integration of highly connected proteins (canonical signaling interactors)
  6. 6. Scoring scheme • Chain score = 𝑛𝑜𝑑𝑒 𝑠𝑐𝑜𝑟𝑒𝑠 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑜𝑑𝑒𝑠 • Node scores used 1. Localisation: mean expression variability across studied tissue vs. mean expression variability across all others -> gene expression 2. Relevance: occurrence of each protein among the significant ones across studies -> gene expression, protein modifications, metabolism… 3. Connectivity: degree centrality -> topology • Combination of scores 1. Weighted product of normalized scores 2. Filtering: pre-filter chains by score S1 and rank them by score S2 3. Intersection: keep only chains that pass filter on all scores
  7. 7. Results • Application to chronic obstructive pulmonary disease (COPD) • Network used: experimental interactions from different public databases + COPD knowledge base (10k nodes, 62k interactions) • Significance: comparison to chains in random networks • Evaluation: enrichment of the top ranked chains in gold standard pathways proteins • Improvement metric: 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑜𝑓 𝑟𝑎𝑛𝑘𝑖𝑛𝑔 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑜𝑓 𝑟𝑎𝑛𝑑𝑜𝑚 𝑟𝑎𝑛𝑘𝑖𝑛𝑔
  8. 8. Localisation: expression variability across studied tissue vs. across all others Relevance: occurrence of each protein among the significant ones across COPD-related studies Connectivity: degree centrality Combination by weighted product: no improvement Filtering: connectivity<0.05, ranked by localization Intersection: connectivity and localization Filtering: top quartile localization, ranked by relevance Intersection: localization and relevance IGF-Akt proximity subnetwork MAPK proximity subnetwork
  9. 9. Results for the best 50 chains Other methods: recall 50-85% Precision 18-42% Here (max): recall 67%, precision 30%
  10. 10. Conclusions and claims • 50% improvement in finding gold standard proteins (compared to random), and combining scores even better (x2.5) • 11% improvement of the AUC (compared to random) • Generic tool applicable to different network types (GRN, metabolic networks) • Importance of selected scores based on scientific question • Applications • Causal, mechanistic connection? • Common mechanisms driving different diseases • Reduce the computational models • Synthetic lethality

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