Ontology-based Expansion of Genomic Metadata and Genometric Queries

Transcription

1 Ontology-based Expansion of Genomic Metadata and Genometric Queries Joint work with Javier Fernandez and Maurizio Lenzerini (Università Sapienza, Roma)

2 Motivation. Genomic Metadata Genomic experiment meta-information: Each experiment has meta-information defined by free lists of attribute-value pairs (e.g., experimental condition, cell line, biological sample). Experiment 1 (from ENCODE) Experiment 2 (from TCGA) Different sources Different A:V pairs Different semantics EXP_SUBTYPE_DESC: Chromatin IP Sequencing CELL_DESCRIPTION: initial_pathologic_diagnosis_method: hepatocellular carcinoma Cytology (e.g. Peritoneal or pleural fluid) icd_o_3_histology: ANTIBODY_DESCRIPTION 8490/3 : Goat polyclonal IgG HRT1 (C-20) Epitope mapping near the C-terminus of HRT1 lymph_node_examined_count: of human origin. Peptibody 16 Target: HRT1 ANTIBODY_TARGET_DESCRIPTION her2_positivity_method_text: dextran : This coated gene encodes charcoal a nuclear protein belonging to the hairy and enhancer : of split-related (HESR) family of basic helix-loop-helix (bhlh)-type transcriptional repressors. : Data Base [e 1 ], e 2 ]

3 Motivation. Current System Query, Q SOLUTION: {Processed Data Files} Retrieve Candidate Experiments {e1,e2, ek} Evaluate Candidates {keywords} Similarity score (syntactic!) LUCENE HADOOP Data Base

4 Motivation. Syntactic Matching Query, Q HM_TF_rep_broad = SELECT(exp_subtype_desc == 'Chrom. IP Sequencing' AND view == 'Peaks' AND settype == 'exp' AND Immunoglobulins _description=='capra HEY1 wt wt Allele Epitope mapping. Antibody. target: HEY1 HEY1 wt Allele' wt Allele' AND AND treatment == 'BPA_100nM' == OR OR treatment == 'DEX_100nM' == OR OR treatment == 'DEX_500pM' OR treatment == 'DEX_50nM' OR Syntantic similarity? [e 1, e 2 ] Experiment 1 (from ENCODE) EXP_SUBTYPE_DESC: Chromatin IP Sequencing CELL_DESCRIPTION: hepatocellular carcinoma ANTIBODY_DESCRIPTION : : Goat polyclonal IgG HRT1 (C-20) Epitope mapping near the C-terminus of HRT1 of human origin. Peptibody Target: HRT1 ANTIBODY TARGET DESCRIPTION : This gene encodes a nuclear protein belonging to the hairy and

5 EXP_SUBTYPE_DESC: Chromatin IP Sequencing CELL_DESCRIPTION: hepatocellular carcinoma ANTIBODY_DESCRIPTION : Goat polyclonal IgG HRT1 (C-20) Epitope mapping near the C-terminus of HRT1 of human origin. Peptibody Target: HRT1 ANTIBODY TARGET DESCRIPTION : This gene encodes a nuclear protein belonging to the hairy and Our Motivation. goal. Semantic Syntactic Matching Query, Q HM_TF_rep_broad = SELECT(exp_subtype_desc == 'Chrom. IP Sequencing' AND view == 'Peaks' AND settype == 'exp' AND Immunoglobulins _description=='capra HEY1 wt Allele Epitope mapping. Antibody target: HEY1 wt Allele' AND treatment == 'BPA_100nM' OR treatment == 'DEX_100nM' OR treatment == 'DEX_500pM' OR treatment == 'DEX_50nM' OR Semantic similarity? why? [synonyms] Immunoglobulins = Antibody [synonyms] Goat = Capra [synonyms] Hey1 wt Allele = HRT1 [subtype] Peptibody is a subtype of Antibody Knowledge Base [e 1, e 2 ] Experiment 1 (from ENCODE)

6 Semantic Matching by Ontology-based Expansion Formally: (one pair) Given an attribute-value pair (A 1, V 1 ), retrieve a set of experiments: {x Exp(x) ^ A (x, z) ^ V (z), A A 1, V V 1 }. Two main strategies: Online-focused workload: At query resolving time: Recognize the concepts in the query, i.e., in Attribute:Value pairs For each concept, find the synonyms and subtypes using standard ontologies. Expand A:V pairs (Metamap, Mgrep) Note: we can get ontological information easily (Bioportal, UMLS, EBI, etc.) Note: exponential in #concepts Match expandes pairs againts the Database Offline-focused workload: Compute the Closure of the Knowledge Base Recognize the concepts in the Knowledge Base Enhance the knowledge Base with the concept hierarchy (paths to root) At query time: match the query keywords with the KB (if possible) get a semantic similarity score to retrieve the top-k results

7 Semantic Matching by Ontology-based Expansion. First conclusions Online-focused workload: Huge expansion and time demanding (minutes), preventing a real-time exploratory use We test several improvements: Incremental output setting several parameters (max. hierarchy levels, max. retrieved synonyms, etc.), but affects the final results Detecting and assuring Extensional Constrains Inter and intra-experiment extensional constrains Offline-focused workload: Our on-going work! Size is suitable in a Big Data paradigm Greatly improves the query response time Apply several concepts from Information Retrieval