1. gene-to-gene relationships & networks
GenoMesh: A genome-wide MeSH-based literature mining system predicts implicit
gene-to-gene relationships & networks
Yongqun “Oliver” He Unit for Laboratory Animal Medicine Department of Microbiology and Immunology Center for Computational Medicine and Bioinformatics
Comprehensive Cancer Center
University of Michigan Medical School Ann Arbor, MI 48109

2. Outline Background Development & evaluation of GenoMesh algorithm
GenoMesh web system and features
Usages of GenoMesh
Reference: Zuoshuang Xiang, Tingting Qin, Zhaohui S. Qin, and Yongqun He. A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks.
BMC Systems Biology. 2013, 7(Suppl 3):S9. (InCoB 2013)

3. MEDLINE/PubMed and MeSH
MEDLINE: citations and abstracts from biomedical literature
PubMed: free access to MEDLINE
new articles daily
Currently > 20 mill. Articles
MeSH: Medical Subject Headings
Controlled vocabulary for indexing articles for PubMed.
16 top-level Hierarchies
2013: 26,853 MeSH descriptors
Growth of Medline
Example of MeSH tree

4. Gene-gene Interaction Literature Mining
Two general strategies:
Gene co-occurrence
Two genes are related if in the same article
Particularly in titles, abstracts, or sentences
Example program: PubGene
Limitation: unable to predict unknown relations
Infer gene relatedness based on common linkage to keywords (e.g. GO, MeSH)
Advantage: predict new gene-gene interactions.
Example programs: ARROWSMITH, MeSHmap
No genome-wide MeSH-based approach reported before
Different methods, often not optimized
GenoMesh: genome-wide MeSH-based
E. coli (well studied) and Brucella (less studied)

5. MeSH-based Prediction of Gene-gene Interaction
Here is an example
2 E. coli genes hfq and sodB
hfq – 137 papers
sodB – 97 papers
Each paper associated with a list of MeSH terms
Some MeSH terms are shared by two groups
hfq and sodB predicted to be associated
Figure generated by GenoMesh
Red line: co-occurrence
grey line: no co-occurrence

6. GenoMesh algorithm Pipeline Gene-gene dissimilarity matrix
MeSH term weighted
each term is highly or rarely used
TF-IDF: term frequency-inverse document frequency
E. coli, Brucella
Preprocessing
Gene-article matrix
Gene-MeSH matrix
Six scores tested to measure gene-gene dissimilarity
Cosine coefficient
Euclidean distance ….
Gene-gene dissimilarity matrix
Clustering, network

7. Receiver operating characteristic
What’s the best combination: MeSH term weighting and dissimilarity score calculation?
Gold standard data for evaluation:
RegulonDB
– E. coli gene regulation database
The winners are:
Square root weighting
Cosine coefficient similarity calculation
Receiver operating characteristic
(ROC) curve analysis

8. Normal Distribution observed using dissimilarity scores of random networks
Bill Gates pledged $10 billion for vaccines for the “decade of the vaccine.”
The distribution of the gene-gene dissimilarities from randomly selected groups of E. coli genes approximates a normal distribution with the peak in the range of

9. GenoMesh able to predict implicit gene-gene interactions
Top E. coli 10 gene pairs predicated using literature data
before 2004 and verified by literature data afterwards
 All proven valid

10. GenoMesh clusters genes of E
GenoMesh clusters genes of E. coli flagella biogenesis & Brucella Type VI secretion system
A: 32 E. coli flagellar genes clustered
Figure 3. Clusters of E. coli genes involving E. coli flagella biogenesis. (A) Thirty-two E. coli flagellar genes were clustered together; (B) Six E. coli flagellar genes were clustered together. The neighbour branch of the six-gene branch includes five E. coli genes.
A cluster of Brucella genes that includes 8 virB genes
B: 6 E. coli flagellar genes clustered
8 Brucella virB genes clustered

11. GenoMesh analysis of 31 E. coli pathways containing at least 10 genes from EcoCyc
All have significant
Z-value and
p-value
So GenoMesh can be used to study gene interaction networks

12. GenoMesh Web Site http://genomesh.hegroup.org
Bill Gates pledged $10 billion for vaccines for the “decade of the vaccine.”

13. Analysis of the term “Neutrophil Activation” from the GenoMesh MeSHBrowse website
Bill Gates pledged $10 billion for vaccines for the “decade of the vaccine.”
This is a
GenoMesh
MeSHBrowse
example

14. homologous E. coli results
GenoMesh predicts new Brucella gene-gene interactions by comparing with
homologous E. coli results
Homologous E. coli and Brucella genes & associated genes

15. Summary
The GenoMesh genome-wide MeSH-based literature mining algorithm and web system is generated and evaluated
GenoMesh:
predicts implicit gene-gene interactions
clusters genes based on associations
generates gene interaction networks
Discussion
More pathogens will be included
Also applicable for human and other eukaryotes
Host-pathogen interactions

16. Acknowledgements
He Lab at the University of Michigan (UM) Ann Arbor, MI, USA
Zuoshuang Xiang
Tingting Qin
Emory School of Medicine, Atlanta, GA, USA
Zhaohui Steve Qin
Funding:
NIH-NIAID Grant 1R01AI081062
A pilot grant at the UM Center for Computational Medicine and Biology (CCMB)