Canadian Bioinformatics Workshops

1. Canadian Bioinformatics Workshops www.bioinformatics.ca

2. Module #: Title of Module 2

3. Module 1Introduction to Pathway and Network Analysis of Gene Lists Gary Bader Pathway and Network Analysis of –omic Data June 13, 2016 http://baderlab.org

4. Interpreting Gene Lists • My cool new screen worked and produced 1000 hits! …Now what? • Genome-Scale Analysis (Omics) • Genomics, Proteomics • Tell me what’s interesting about these genes Ranking or clustering ? GenMAPP.org

5. Interpreting Gene Lists • My cool new screen worked and produced 1000 hits! …Now what? • Genome-Scale Analysis (Omics) • Genomics, Proteomics • Tell me what’s interesting about these genes • Are they enriched in known pathways, complexes, functions Analysis tools Ranking or clustering Eureka! New heart disease gene! Prior knowledge about cellular processes

6. Pathway and network analysis • Save time compared to traditional approach my favorite gene

7. Pathway and Network Analysis • Helps gain mechanistic insight into ‘omics data • Identifying a master regulator, drug targets, characterizing pathways active in a sample • Any type of analysis that involves pathway or network information • Most commonly applied to help interpret lists of genes • Most popular type is pathway enrichment analysis, but many others are useful

8. Pathway analysis example 1 Autism Spectrum Disorder (ASD) • Genetics • highly heritable • monozygotic twin concordance 60-90% • dizygotic twin concordance 0-10% (depending on the stringency of diagnosis) • known genetics: • 5-15% rare single-gene disorders and chromosomal re-arrangements • de-novo CNV previously reported in 5-10% of ASD cases • GWA (Genome-wide Association Studies) have been able to explain only a small amount of heritability Pinto et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature. 2010 Jun 9.

9. Rare copy number variants in ASD • Rare Copy Number Variation screening (Del, Dup) • 889 Case and 1146 Ctrl (European Ancestry) • Illumina Infinium 1M-single SNP • high quality rare CNV (90% PCR validation) • identification by three algorithms required for detection • QuantiSNP, iPattern, PennCNV • frequency < 1%, length > 30 kb • Results • average CNV size: 182.7 kb, median CNVs per individual: 2 • > 5.7% ASD individuals carry at least one de-novo CNV • Top ~10 genes in CNVs associated to ASD

10. Pathways Enriched in Autism Spectrum

12. Pathway analysis example 2 Ependymoma Pathway Analysis • Ependymoma brain cancer - most common and morbid location for childhood is the posterior fossa (PF = brainstem + cerebellum) • Two classes: PFA - young, dismal prognosis, PFB - older, excellent prognosis. Determined by gene expression clustering. • Exome sequencing (42 samples), WGS (5 samples) showed almost no mutations, however methylation arrays showed clear clustering into PFA and PFB (79 samples) • PFA more transcriptionally silenced by CpG methylation Nature. 2014 Feb 27;506(7489):445-50 Witt et al., Cancer Cell 2011 Steve Mack, Michael Taylor, Scott Zuyderduyn

13. polycomb repressor complex 2 – inhibited by SAHA, DZNep, GSK343 – killed PFA cells No known treatment, so now going to clinical trial

14. Treatment of Metastatic PF ependymoma with Vidaza 9 yo with metastatic PF ependymoma to lung treated with azacytidine 2 months 3 months 3 cycles Vidaza Effect lasted 15 months

15. Benefits of Pathway Analysis vs. transcripts, proteins, SNPs… • Easier to interpret • Familiar concepts e.g. cell cycle • Identifies possible causal mechanisms • Predicts new roles for genes • Improves statistical power • Fewer tests, aggregates data from multiple genes into one pathway • More reproducible • E.g. gene expression signatures • Facilitates integration of multiple data types

16. Pathways vs. Networks - Detailed, high-confidence consensus - Biochemical reactions - Small-scale, fewer genes - Concentrated from decades of literature - Simplified cellular logic, noisy - Abstractions: directed, undirected - Large-scale, genome-wide - Constructed from omics data integration

17. Types of Pathway/Network Analysis

18. Types of Pathway/Network Analysis Are new pathways altered in this cancer? Are there clinically-relevant tumour subtypes? How are pathway activities altered in a particular patient? Are there targetable pathways in this patient? What biological processes are altered in this cancer?

19. Pathway analysis workflow overview

21. Where Do Gene Lists Come From? • Molecular profiling e.g. mRNA, protein • Identification  Gene list • Quantification  Gene list + values • Ranking, Clustering (biostatistics) • Interactions: Protein interactions, microRNA targets, transcription factor binding sites (ChIP) • Genetic screen e.g. of knock out library • Association studies (Genome-wide) • Single nucleotide polymorphisms (SNPs) • Copy number variants (CNVs) Other examples?

22. What Do Gene Lists Mean? • Biological system: complex, pathway, physical interactors • Similar gene function e.g. protein kinase • Similar cell or tissue location • Chromosomal location (linkage, CNVs) Data

24. Before Analysis • Normalization • Background adjustment • Quality control (garbage in, garbage out) • Use statistics that will increase signal and reduce noise specifically for your experiment • Gene list size • Make sure your gene IDs are compatible with software

26. Biological Questions • Step 1: What do you want to accomplish with your list (hopefully part of experiment design!  ) • Summarize biological processes or other aspects of gene function • Perform differential analysis – what pathways are different between samples? • Find a controller for a process (TF, miRNA) • Find new pathways or new pathway members • Discover new gene function • Correlate with a disease or phenotype (candidate gene prioritization) • Find a drug

27. Biological Answers • Computational analysis methods we will cover • Day 1: Pathway enrichment analysis: summarize and compare • Day 2: Network analysis: predict gene function, find new pathway members, identify functional modules (new pathways) • Day 3: Regulatory network analysis: find and analyze controllers

29. Pathway enrichment analysis Gene list from experiment: Genes down-regulated in drug- sensitive brain cancer cell lines Pathway information: All genes known to be involved in Neurotransmitter signaling p<0.05 ? Test many pathways Statistical test: are there more annotations in gene list than expected? Hypothesis: drug sensitivity in brain cancer is related to reduced neurotransmitter signaling

30. Pathway Enrichment Analysis • Gene identifiers • Pathways and other gene annotation • Gene Ontology • Ontology Structure • Annotation • BioMart + other sources

31. Gene and Protein Identifiers • Identifiers (IDs) are ideally unique, stable names or numbers that help track database records • E.g. Social Insurance Number, Entrez Gene ID 41232 • Gene and protein information stored in many databases •  Genes have many IDs • Records for: Gene, DNA, RNA, Protein • Important to recognize the correct record type • E.g. Entrez Gene records don’t store sequence. They link to DNA regions, RNA transcripts and proteins e.g. in RefSeq, which stores sequence.

32. Common Identifiers Gene Ensembl ENSG00000139618 Entrez Gene 675 Unigene Hs.34012 RNA transcript GenBank BC026160.1 RefSeq NM_000059 Ensembl ENST00000380152 Protein Ensembl ENSP00000369497 RefSeq NP_000050.2 UniProt BRCA2_HUMAN or A1YBP1_HUMAN IPI IPI00412408.1 EMBL AF309413 PDB 1MIU Species-specific HUGO HGNC BRCA2 MGI MGI:109337 RGD 2219 ZFIN ZDB-GENE-060510-3 FlyBase CG9097 WormBase WBGene00002299 or ZK1067.1 SGD S000002187 or YDL029W Annotations InterPro IPR015252 OMIM 600185 Pfam PF09104 Gene Ontology GO:0000724 SNPs rs28897757 Experimental Platform Affymetrix 208368_3p_s_at Agilent A_23_P99452 CodeLink GE60169 Illumina GI_4502450-S Red = Recommended

33. Identifier Mapping • So many IDs! • Software tools recognize only a handful • May need to map from your gene list IDs to standard IDs • Four main uses • Searching for a favorite gene name • Link to related resources • Identifier translation • E.g. Proteins to genes, Affy ID to Entrez Gene • Merging data from different sources • Find equivalent records

34. ID Mapping Services Input gene/protein/transcript IDs (mixed) • g:Convert • http://biit.cs.ut.ee/gprofiler/gconvert.cgi Type of output ID • Ensembl Biomart • http://www.ensembl.org

35. Beware of ambiguous ID mappings

36. ID Challenges • Avoid errors: map IDs correctly • Beware of 1-to-many mappings • Gene name ambiguity – not a good ID • e.g. FLJ92943, LFS1, TRP53, p53 • Better to use the standard gene symbol: TP53 • Excel error-introduction • OCT4 is changed to October-4 (paste as text) • Problems reaching 100% coverage • E.g. due to version issues • Use multiple sources toincrease coverage Zeeberg BR et al. Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics BMC Bioinformatics. 2004 Jun 23;5:80

38. Recommendations • For proteins and genes • (doesn’t consider splice forms) • Map everything to Entrez Gene IDs or Official Gene Symbols using a spreadsheet • If 100% coverage desired, manually curate missing mappings using multiple resources • Be careful of Excel auto conversions – especially when pasting large gene lists! • Remember to format cells as ‘text’ before pasting

39. What Have We Learned? • Genes and their products and attributes have many identifiers (IDs) • Genomics often requires conversion of IDs from one type to another • ID mapping services are available • Use standard, commonly used IDs to reduce ID mapping challenges

40. Pathway Enrichment Analysis • Gene identifiers • Pathways and other gene annotation • Gene Ontology • Ontology Structure • Annotation • BioMart + other sources

41. Pathways and other gene function attributes • Available in databases • Pathways • Gene Ontology biological process, pathway databases e.g. Reactome • Other annotations • Gene Ontology molecular function, cell location • Chromosome position • Disease association • DNA properties • TF binding sites, gene structure (intron/exon), SNPs • Transcript properties • Splicing, 3’ UTR, microRNA binding sites • Protein properties • Domains, secondary and tertiary structure, PTM sites • Interactions with other genes

42. Pathways and other gene function attributes • Available in databases • Pathways • Gene Ontology biological process, pathway databases e.g. Reactome • Other annotations • Gene Ontology molecular function, cell location • Chromosome position • Disease association • DNA properties • TF binding sites, gene structure (intron/exon), SNPs • Transcript properties • Splicing, 3’ UTR, microRNA binding sites • Protein properties • Domains, secondary and tertiary structure, PTM sites • Interactions with other genes

43. What is the Gene Ontology (GO)? • Set of biological phrases (terms) which are applied to genes: • protein kinase • apoptosis • membrane • Dictionary: term definitions • Ontology: A formal system for describing knowledge • www.geneontology.org www.geneontology.org

44. GO Structure • Terms are related within a hierarchy • is-a • part-of • Describes multiple levels of detail of gene function • Terms can have more than one parent or child

45. What GO Covers? • GO terms divided into three aspects: • cellular component • molecular function • biological process glucose-6-phosphate isomerase activity Cell division

46. Part 1/2: Terms • Where do GO terms come from? • GO terms are added by editors at EBI and gene annotation database groups • Terms added by request • Experts help with major development

47. Part 2/2: Annotations • Genes are linked, or associated, with GO terms by trained curators at genome databases • Known as ‘gene associations’ or GO annotations • Multiple annotations per gene • Some GO annotations created automatically (without human review)

48. Hierarchicalannotation • Genes annotated to specific term in GO automatically added to all parents of that term AURKB

49. Annotation Sources • Manual annotation • Curated by scientists • High quality • Small number (time-consuming to create) • Reviewed computational analysis • Electronic annotation • Annotation derived without human validation • Computational predictions (accuracy varies) • Lower ‘quality’ than manual codes • Key point: be aware of annotation origin

50. For your information Evidence Types • Experimental Evidence Codes • EXP: Inferred from Experiment • IDA: Inferred from Direct Assay • IPI: Inferred from Physical Interaction • IMP: Inferred from Mutant Phenotype • IGI: Inferred from Genetic Interaction • IEP: Inferred from Expression Pattern • Author Statement Evidence Codes • TAS: Traceable Author Statement • NAS: Non-traceable Author Statement • Curator Statement Evidence Codes • IC: Inferred by Curator • ND: No biological Data available • Computational Analysis Evidence Codes • ISS: Inferred from Sequence or Structural Similarity • ISO: Inferred from Sequence Orthology • ISA: Inferred from Sequence Alignment • ISM: Inferred from Sequence Model • IGC: Inferred from Genomic Context • RCA: inferred from Reviewed Computational Analysis • IEA: Inferred from electronic annotation http://www.geneontology.org/GO.evidence.shtml