Paul Pavlidis at #ICG13: Monitoring changes in the Gene Ontology and their impact on genomic data analysis
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This document discusses how changes over time to the Gene Ontology (GO) and GO annotations can impact genomic data analysis and enrichment results. The author analyzed over 2,500 gene lists from past studies and found that enrichment results become less semantically similar over time, with 47% having less similar results after 11 years on average compared to the initial time of publication. While objective changes may occur, subjective impressions of results can remain the same. Researchers are encouraged to use the GOtrack database to evaluate how changes may affect their own data and results.
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Paul Pavlidis at #ICG13: Monitoring changes in the Gene Ontology and their impact on genomic data analysis
- 1. Monitoring changes in the Gene Ontology and their impact on genomic data analysis Paul Pavlidis, PhD University of British Columbia, Vancouver, BC Canada https://pavlab.msl.ubc.ca October 25, 2018 GigaScience Prize Track
- 2. 2 Matthew Jacobson Adriana Estela Sedeño-Cortés
- 3. The Gene Ontology in 60 seconds 3 GO = Hierarchy of >45000 terms describing gene function Applied by annotators to genes with evidence codes (“GO annotations” = GOA) Used in tens of thousands of papers • Gene description • Algorithm evaluation • Enrichment analysis GRIN1
- 4. Both GO and GOA change over time Does it matter? • Are old enrichment results and other interpretations based on GO still valid? • Will new results be valid in the future? No easy way for researchers to easily evaluate the effects on their own data. 4
- 5. 5 GOtrack database • Data for 9 model organisms • Dating back to 2001 • Over 200,000,000 data points • Updated monthly Web app functionality Track genes and terms Track enrichment results
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- 7. Annotation churn 7 Term present Term absent
- 8. 8
- 9. Ingredients for an enrichment analysis 9 Statistica l test
- 10. Evaluating the effect of GO/GOA changes Inputs: Gene lists from MSigDB • >2500 Chemical and genetic perturbations (CGP) – “hit lists” • 0.5-16 years old (median 11) 10
- 11. Evaluating the effect on enrichment analysis• Perform enrichment analysis using GO/GOA for the time of publication (t0) to a recent time point (tnow) • Compare the lists of enriched terms at t0 and tnow using semantic similarity measures (Jaccard and others) 11 Define a null distribution: t0-tnow comparisons for randomly selected pairs of hit lists Random pair
- 12. New results tend to have more sig. terms 12 Mean t0 = 21; tnow = 110. One point = one hit list
- 13. 13 Null (random hit list pairs) All t0-tnow comparisons Semantic similarity drops over time • Overall 47% have results less similar than the 95%ile of the null • Correlation between similarity and age is - 0.34
- 14. Objective changes may conflict with subjective impressions 14 DNA replication mitosis M phase of mitotic cell cycle DNA modification biopolymer methylation methylation pattern specification process regulation of gene expression, epigenetic somatic stem cell population maintenance stem cell population maintenance maintenance of cell number DNA replication initiation G1/S transition of mitotic cell cycle cell cycle G1/S phase transition mitotic nuclear division gene silencing cell fate specification endoderm development t0 tnow Example of one hit list as an extreme case: Jaccard similarity = 0.0
- 15. 15
- 16. Conclusions Enrichment results change over time, but how much this matters is difficult to predict •Use GOtrack to judge for yourself 16
- 17. Sanja Rogic Shreejoy Tripathy Lilah Toker Ogan Mancarci Marjan Farahbod Manuel Belmadani Alex Morin Margot Gunning Eric Chu Nivi Thatra Nathaniel Lim Shams Bhuiyan Simran Rai Stepan Tesar Dima Vavilov Aman Sharma Calvin Chang John Phan Jimmy Liu Former members Min Feng Ellie Hogan Sophia Ly Cindy-Lee Crichlow Brandon Huntington Ben Callaghan Matthew Jacobson Dmitry Tebaykin James Liu Patrick Savage Brenna Li Justin Leong Nikolaus Fortelny Nathan Holmes Patrick Tan Kris Anderson Rachel Edgar Elodie Portales-Casamar Adri Sedeño Jesse Gillis Leon French Carolyn Ch’ng Meeta Mistry Raymond Lim Eloi Mercier Anton Zoubarev Cameron McDonald Thea Van Rossum Nicolas St. George Frances Lui Artemis Lai Gayathiri Charathsandran Luchia Tseng John Choi Fangwen Zhao Jenni Hantula Tianna Koreman Olivia Marais Hugh Brown Celia Siu Cathy Kwok Willie Kwok Nathan Eveleigh Collaborators Kurt Haas Doug Allen Tim O’Connor Cathy Rankin Chris Loewen Chris Overall Shernaz Bamji Michael Kobor Geoff Hicks Suzanne Lewis Etienne Sibille Gustavo Turecki
Notes de l'éditeur
- Previous work
- Previous work only tested small numbers of gene sets and usually over shorter periods of time. Allows us to also look at the effect of time
- Stability analysis of 2,573 published hit lists. (A) Change in number of significant GO terms. Each point is one CGP hit list. Points are jittered to reduce overplotting. (B) Similarity of enrichment results, using the complete Jaccard index. The CGP hit lists are binned into most recent (orange), old (green), and oldest (blue). The distribution for the CPs is in black. The blue vertical line indicates the 95%ile of the null.
- This is a worst-case scenario for overlap, but completely typical for how the results look. “The other side of this situation is whether objectively low scores (compared to the null) match subjective impressions of “instability”, as well. The answer is yes, but arguably less convincingly. For example, the hit list BENPORATH_ES_2 (Ben-Porath et al., 2008, 40 genes) has a complete Jaccard similarity between t0 and tnow of 0.0. At t0 , the enriched terms included “DNA replication”, “mitosis”, “methylation”, and “epigenetic regulation of gene expression”. While none of these terms are enriched at tnow , highly related terms such as “DNA replication initiation”, “mitotic nuclear division” and “gene silencing” are enriched (Supplementary File 2).” Hit list from “An embryonic stem cell–like gene expression signature in poorly differentiated aggressive human tumors”