This document provides a summary and review of notable publications in translational bioinformatics from approximately 2014 to early 2015. It begins with an introduction and overview of the goals and process for selecting publications. Several key topics and publications are then highlighted, including precision medicine and clinical prediction models, variation analysis, cancer genomics, clinical applications of genomics, pharmacogenomics, systems biology approaches, and natural language processing. The document concludes with thanks and acknowledges limitations in scope.

1. Translational
Bioinformatics 2015:
TheYear in Review
Russ B. Altman, MD, PhD
Stanford University

2. Disclosures
•Founder & Consultant, Personalis Inc (genome
sequencing for clinical applications). Consultant,
Pfizer (pharmaceuticals).
•Funding support: NIH, NSF, Pfizer, Oracle,
Microsoft, LightspeedVentures, PARSA Foundation.
•I am a fan of informatics, genomics, medicine &
clinical pharmacology.

3. Goals
•Provide an overview of the scientific trends and
publications in translational bioinformatics
•Create a “snapshot” of what seems to be
important in Spring, 2015 for the amusement of
future generations.
•Marvel at the progress made and the
opportunities ahead.

4. Process
1. Follow literature through the year
2. Solicit nominations from colleagues
3. Search key journals and key topics on PubMed
4. Evaluate & ponder
5. Select papers to highlight in ~1-3 slides

5. Caveats
•Translational bioinformatics = informatics methods
that link biological entities (genes, proteins, small
molecules) to clinical entities (diseases, symptoms,
drugs)--or vice versa.
•Considered last ~14 months
•Focused on human biology and clinical implications:
molecules, clinical data, informatics.
•NOTE: Amazing biological papers with
straightforward informatics generally not included.
•NOTE: Amazing informatics papers which don’t link
clinical to molecular generally not included.

6. Final list
•215 Semi-Finalists, 101 Finalists
•22 Presented here + 29 “shout outs” = 51
•Apologies to those I misjudged. Mistakes are mine.
•7 TOPICS: TBI & Society, Variation Triage, Cancer,
Clinical Genomics, Drugs, Systems & Networks, NLP
Applications, Odds & Ends
•Slides and bibliography will be posted at
rbaltman.wordpress.com

7. Thanks!
Conversations and recommendations
Phil Bourne
Atul Butte
Andrea Califano
Josh Denny
Michel Dumontier
Peter Elkin
Emily Flynn
Lewis Frey
Mark Gerstein
George Hripcsak
John Hogenesch
Enoch Huang
Larry Hunter
Rachel Karchin
Natalia Khuri
Alan Laederach
Yong Li
Tianyun Liu
Yves Lussier
Hua Fan-Minogue
Lucila Ohno-Machado
Chirag Patel
Beth Percha
Raul Rabadan
Dan Roden
Neil Sarkar
Nigam Shah
Jost Stuart
Peter Tarczy-Hornoch
Nick Tatonetti
Jessie Tenenbaum
Olga Troyanskaya
Piet van der Graaf
Scott Waldman
Dennis Wall
Rong Xu

8. TBI & Society

9. “A new initiative on precision medicine.” (Collins &
Varmus, NEJM)
• Goal: Define & advance precision medicine—
treating disease considering individual variability.
• Method: Follow up on President Obama’s
announcement in State of Union address.
• Result: Major funding effort by NIH focused on
cancer initially, all diseases eventually. Create cohort
of 1x 106 individuals to support this.
• Conclusion:Translational Informatics is central to
discovery and implementation of precision medicine.
This conference will grow.
25635347

11. “Transparent Reporting of a multivariable prediction
model for Individual Prognosis or Diagnosis (TRIPOD):
the TRIPOD statement.” (Collins et al,Ann Intern Med)
• Goal: Improve the ways that prediction models (of all
types) are reported in the literature.
• Method: Develop a set of recommendations for
reporting studies that develop, validate, or update a
prediction model—both diagnostic & prognostic
• Result: Checklist of 22 items, copublished in multiple
journals.
• Conclusion: Prediction models will be key to precision
medicine, and should be communicated clearly.
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13. 24487276

14. “Why we should care about what you get for ‘only $99’
from a personal genomic service.” (Murray, Ann Intern
Med)
• Conclusion: DTC testing challenges traditional role of
physicians
“Misinterpretation of TPMT by a DTC Genetic Testing
Company” (Brownstein et al, Clin Pharm & Ther)
• Conclusion: Rare variants were misinterpreted and
could have caused harm.
“Regulatory changes raise troubling questions for
genomic testing.” (Evans et al, Genet Med)
• Conclusion:There are logical inconsistencies between
CLIA and HIPPA 25255365
24514942
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15. CLIA v. HIPAA…kind of like King Kong v. Godzilla…

16. or Ninja v Samurai…
CLIA v. HIPAA…kind of like King Kong v. Godzilla…

17. “Meaningful use of pharmacogenetics.” (Ratain &
Johnson, Clin Pharm & Ther)
• Conclusion: PGx is ready for implementation
“Useless until proven effective: the clinical utility of
preemptive pharmacogenetic testing.” (Janssens &
Deverka, Clin Pharm & Ther)
• Conclusion: PGx is not ready for implementation
“Pharmacogenomic knowledge gaps and educational
resource needs among physicians in selected
specialties.” (Johansen Taber & Dickinson et al, PGx
Pers Med)
• Conclusion: MDs are unsure how to use PGx.
25399712
25399713
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18. King Kong favors PGx
Ninjas favor PGx…but
EVERYONE needs more
education
about the issue

19. Variation Triage
(warning: huge topic this year)

20. “Guidelines for investigating causality of sequence
variants in human disease.” (MacArthur et al, Nature)
• Goal: Clear guidelines for reporting disease-causing
variants in genome
• Method: Discuss key challenges in establishing and
documenting evidence of causality
• Result: Propose guidelines for summarizing
confidence in variant pathogenicity.
• Conclusion: Harmonization of reporting
expectations will assist in dissemination of good
genetic annotation information.
24759409

21. 24487276

22. 24487276

23. “GRASP: analysis of genotype-phenotype results from
1390 genome-wide association studies and
corresponding open access database” (Leslie et al,
Bioinformatics)
• Goal: Create a publicly available database of GWAS
results.
• Method: Annotation of 1390 GWAS studies with
search + manual annotation.
• Result: > 6.2 Million SNPs associated with
phenotypes.
• Conclusion:A useful resource for integration with
other data sets in support of precision medicine.
24931982

25. “Effective diagnosis of genetic disease by
computational phenotype analysis of the disease-
associated genome.” (Zemojtel et al, Sci Trans Med)
• Goal: Create an automated platform for diagnosis of
rare Mendelian disease, including enriched “disease-
associated” NGS panel.
• Method: Assess semantic similarity of phenotype to
known diseases, and assess variant pathogenicity.
• Result: Mean rank of 2.1 on 50 retrospective cases,
and 2.4 on 11/40 prospective cases.
• Conclusion: Methods for automated diagnosis of
novel genetic diseases are within reach.
25186178

28. “Disease Risk Factors Identified Through Shared Genetic
Architecture and Electronic Medical Records ” (Li et al,
Sci Trans Med)
• Goal: Evaluate relationships between risk factors and
diseases based on shared genetic architecture.
• Method: Using statistical similarity measure between
diseases and traits, found 120 similar pairs and
evaluated EMR for 5 of them.
• Result: Several traits appear before their associated
disease, offering a potential early warning system.
• Conclusion: Shared genetic architecture can provide
early clues to disease risk and prognosis.
24786325

31. “A network based method for analysis of lncRNA-
disease associations and prediction of lncRNAs
implicated in diseases.” (Yang et al, PLoS ONE)
• Goal: Understand role of long noncoding RNAs in
disease
• Method: Build lncRNA-gene network and use
propagation algorithm to infer lncRNA-disease arcs.
• Result: 768 potential lncRNA-disease associations,
with validation on known cases.
• Conclusion: lncRNA are important modulators of
epigenetic and genetic signals relevant to disease.
24498199

33. “Pathogenic variants for Mendelian and complex traits
in exomes of 6,517 European and African Americans:
implications for the return of incidental
results.” (Tabor et al,A J Hum Genet)
Result: Risk alleles of potential utility for both
Mendelian and complex are in every individual.
Implications for return of results.
“Joint Analysis of Functional Genomic Data and
Genome-wide Association Studies of 18 Human
Traits.” (Pickrell,A J Hum Genet)
Result:Assessed which (of 450) genetic/epigenetic
features are most associated with GWAS hits.
Shout Outs for Variant Triage
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34. “Adjusting for heritable covariates can bias effect
estimates in genome-wide association
studies.” (Aschard et al,A J Hum Genet)
“Meta-analysis of Correlated Traits via Summary
Statistics from GWASs with an Application in
Hypertension.” (Zhu et al,A J Hum Genet)
“Clinical phenotype-based gene prioritization: an initial
study using semantic similarity and the human
phenotype ontology” (Masino et al, BMC Bioinf)
Shout Outs for Variant Triage
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35. “SNPsea: an algorithm to identify cell types, tissues
and pathways affected by risk loci.” (Slowikowski et al,
Bioinformatics)
Result: Tool for assessing SNP-related gene
enrichment in cell types, tissues, pathways.
Shout Outs for Variant Triage
24813542

36. Cancer

37. “Cancer etiology.Variation in cancer risk among
tissues can be explained by the number of stem cell
divisions.” (Tomasetti &Vogelstein, Science)
• Goal: Assess the contribution of cell division
frequency to cancer risk
• Method: Assess for each tissue of origin the
expected number of cell divisions
• Result: Risk of cancer is strongly associated with the
normal number of divisions for self-renewal. Less
than 1/3 due to inherited mutations/environment.
• Conclusion: Cancer is mostly due to bad luck.
25554788

39. “Pan-cancer network analysis identifies combinations
of rare somatic mutations across pathways and
protein complexes.” (Leiserson et al, Nat Gen)
• Goal: Integrate many cancer data sets to find
mutated subnetworks that recur
• Method: HotNet2 algorithm uses diffusion
algorithm over protein-protein interaction network
• Result: 16 significantly mutated subnetworks mixing
known and unknown pathways, including rare
mutations.
• Conclusion: New diagnostic and therapeutic
opportunities associated with accumulating mass of
cancer genomic information. 25501392

40. 24487276

41. 24487276

42. “Genetic basis for clinical response to CTLA-4
blockade in melanoma.” (Snyder et al, NEJM)
• Goal: Understand basis for differential response to
therapeutic immunomodulatory antibodies in
melanoma
• Method: Sequence genome of responders/
nonresponders. Analyze.
• Result: Creation of certain mutated versions of host
proteins on cell surface (neo-antigens) correlates
with efficacy.
• Conclusion: Immune attack may be mediated by
neo-antigens (potentially similar previously
presented antigens from infections) 25409260

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44. 25409260

45. “Predicting cancer-specific vulnerability via data-
driven detection of synthetic lethality.” (Jerby-
Arnon et al, Cell)
Result: Developed a pipeline to characterize
synthetic lethal genes in cancer. Captures known
partners and suggests new ones, particularly those
that are gain-of-function and thus amenable to
potential druggability.
Shout Outs for Cancer Genomics
25171417

46. Clinical Genomics

47. “Genomic surveillance elucidates Ebola virus origin
and transmission during the 2014 outbreak.” (Gire et
al, Science)
• Goal: Understand genomes of Ebola outbreak in
Africa
• Method: Sequence 99 Ebola genomes from 78
patients to 2000x coverage.
• Result: West African variant diverged from central in
2004, crossed 2014, no new sources. Lots of
mutations for therapeutic opportunity.
• Conclusion: Genomics can be applied very rapidly
and effectively in public health emergencies.
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48. 25214632

50. “Clinical Interpretation and Implications of Whole-
Genome Sequencing” (Dewey et al, JAMA)
• Goal: Examine coverage of current NGS data on
clinically relevant genome.
• Method: 12 individual genomes deeply analyzed
manually.
• Result: 10-20% of key variants not interrogated
adequately, 100 SNPs/genome took humans 54
hours to annotate, 2-6 disease causing variants per
subject.
• Conclusion: Accuracy is still an issue, and manual
annotation is still necessary for best genome
interpretations. 24618965

51. 24487276

52. 24487276

53. “A probabilistic model to predict clinical phenotypic
traits from genome sequencing.” (Chen et al, PLoS
Comp Bio)
• Goal: Assess our ability to predict binary
phenotypes from genome data.
• Method: Bayesian model based on Personal
Genome Project data, applied to 146 phenotypes.
• Result: 16% of phenotypes robustly predictable, best
performer in CAGI assessment.
• Conclusion: Although not diagnostic, we are starting
to use genetics to adjust disease probabilities.
25188385

56. “Personalized pharmacogenomics profiling using
whole-genome sequencing.” (Mizzi et al,
Pharmacogenomics)
Result: Analyzed 482 genomes, found 1012 novel
pharmacogene variations. Conclude: sequencing is
necessary, genotyping not sufficient
“Missense variants in CFTR nucleotide-binding
domains predict quantitative phenotypes associated
with cystic fibrosis disease severity.” (Masica et al,
Hum Mol Gen)
Result: Classifier can predict disease severity from
genotypes in three prognostic classes.
Shout Outs for Genomic Applications
25141897
25489051

57. Drugs

58. “A community computational challenge to predict the
activity of pairs of compounds.” (Bansal et al, Nat
Biotech)
• Goal: Community assessment of ability to predict
synergism/antagonism of cancer drugs.
• Method: Blinding prediction, based on individual
drug response ‘omic profiles
• Result: 4/32 methods better than random. Best
algorithm assumed serial drug use, modeled
“residual” contribution. Ensemble of methods =
best.
• Conclusion: Extrapolation is harder than
interpolation. Encouraging results on hard problem.
25419740

61. “Systems pharmacology augments drug safety
surveillance.” (Lorberbaum et al, Clin Pharm & Ther)
• Goal: Improve pharmacovigilance with integration of
systems biology, chemical genomics data
• Method: Modular assembly of drug safety
subnetworks (MADSS) algorithm.
• Result: Improved ability to predict drug associations
to side effects for MI, GI, Liver, Kidney systems.
• Conclusion: System biology network inference can
assist in prediction and understanding of side effects.
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64. “Validating drug repurposing signals using electronic
health records: a case study of metformin associated
with reduced cancer mortality.” (Xu et al, JAMIA)
Result: EHR analysis suggests Metformin protective
of cancer.
“3D Pharmacophoric Similarity improves Multi
Adverse Drug Event Identification in
Pharmacovigilance.” (Vilar et al, Sci Rep)
Result: Structural similarity of drugs allows them to
‘borrow” information for improved
pharmacovigilance
Shout Outs for Drugs
25053577
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65. Systems & Networks

66. “Human symptoms-disease network.” (Zhou et al, Nat
Comms)
• Goal: Mine PubMED to build a symptom-based
network of human diseases, relate to underlying
molecular interactions.
• Method: Combine disease-symptom network with
disease-gene network to evaluate overlap of both.
• Result: Symptom-based similarity correlates with
shared genetic structure. Diversity of symptoms
correlates to disease genetic complexity.
• Conclusion: Similarity of diseases, symptoms, genetic
architecture are all highly linked and can lead to
useful hypotheses about diagnosis & treatment.
24967666

69. “Obesity accelerates epigenetic aging of human
liver.” (Horvath et al, PNAS)
• Goal: Understand the relationship between
epigenetic and obesity.
• Method: Use novel epigenetic biomarker of aging
(measure of DNA methylation) to associate BMI and
‘effective’ age.
• Result: Epigenetic age increases 3.3 yrs for each 10
BMI units. Not clearly reversible with weight loss.
279 genes under-expressed in old livers.
• Conclusion: Epigenetic changes associated with
disease may be useful for understanding disease
onset, natural history and comorbidities. 25313081

71. “A circadian gene expression atlas in mammals:
implications for biology and medicine.” (Zhang et al,
PNAS)
• Goal: Characterize the role of circadian clock in
‘mammal’ gene expression.
• Method: Measure tissue-specific gene expression
over 24 hours.
• Result: 43% of proteins show circadian expression
variation, often tissue specific. noncoding RNAs may
be involved in control. Most drugs target genes that
are rhythmic.
• Conclusion: The clock may have important
implications for biological variability and drug 25349387

74. “Robust clinical outcome prediction based on Bayesian analysis of
transcriptional profiles and prior causal networks” (Zarringhalam et al,
Bioinformatics)
Result: Generate differential expression profile for individual patients,
and infer specific regulation model.
“A multiscale statistical mechanical framework integrates biophysical
and genomic data to assemble cancer networks.” (AlQuraishi et al, Nat
Gen)
Result: Combine genomic, structural, biochemical data to infer detailed
impact of mutations in proteins involved in cancer signaling networks.
“Cross-species regulatory network analysis identifies a synergistic
interaction between FOXM1 and CENPF that drives prostate cancer
malignancy.” (Aytes et al, Cancer Cell)
Result: Compare regulatory networks for mouse/human to find
conserved master regulators promoting tumor growth.
22995991
25362484
Shout Outs for Systems & Networks
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75. NLP Applications

76. “dRiskKB: a large-scale disease-disease risk
relationship knowledge base constructed from
biomedical text” (Xu et al, BMC Bioinformatics)
• Goal: Systematically (re)characterize phenotype
relationships among diseases.
• Method: Use text mining to extract disease risk
pairs, analyzed correlations with underlying genetics.
• Result: 34,448 unique pairs among 12,981 diseases.
• Conclusion:
24725842

80. “Integrated text mining and chemoinformatics analysis
associates diet to health benefit at molecular
level” (Jensen et al, PLoS Comp Bio)
• Goal:Assemble knowledge of food-phytochemical
and food-disease associations.
• Method: Data mining of text, classification to
assemble associations, including both positive/
negative associations.
• Result: 20,654 phytochemicals associated to 1,592
human disease phenotypes
• Conclusion: Systematic approach to nutrition can be
incorporated into precision medicine
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84. “NCBI disease corpus: a resource for disease name
recognition and concept normalization” (Dogan et al, J
Biomed Inf)
Result: Fully and carefully annotated corpus of 793
papers.
“Literome: PubMed-scale genomic knowledge base in
the cloud” (Poon et al, Bioinformatics)
“A literature search tool for intelligent extraction of
disease-associated genes” (Jung et al, JAMIA)
Results: Publicly available gene-gene & gene-
phenotype interactions mined from PubMED.
Shout Outs for NLP Applications
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85. Odds & Ends

86. “Modeling 3D facial shape from DNA.” (Claes et al,
PLoS Genet)
• Goal:Assess the impact of genetic variations on
facial shape.
• Method: Parameterize “face space” and associate
features with Ancestry Informative Markers
• Result: 20 genes show significant effects on facial
features.
• Conclusion:These allow approximation of
appearance based on SNPs in genome.
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89. SLC35D1
SNPs —
extreme
effect
LRP6 SNPs
FGFR1 SNPs

90. SLC35D1
SNPs —
extreme
effect
LRP6 SNPs
FGFR1 SNPs

91. “Proteomics.Tissue-based map of the human
proteome.” (Uhlen et al, Science)
• Goal: Survey human proteome variation in human
tissues.
• Method: Quantitative transcriptomics +
immunohistochemistry for localization in 32 tissues.
• Result: Detected > 90% of putative protein coding
genes. Characterized secretome, membraneome,
druggome.
• Conclusion: Major resource for integrative analysis
of human biology.
25613900

93. “A field guide to genomics research.” (Bild et al, PLoS
Biol)
• Goal: Characterize common pitfalls in genomics
research
• Method: Reflection on personality types
• Result: 6 genome researcher phenotypes…
• Conclusion:You can figure out which phenotype you
match, and you don’t need your SNPs…
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1. Farmer—storehouse of data, tools—no design
2. Gold Miner—keeps digging until finds something
significant
3. Cowboy—wrangles data without analyzing it properly
4. Hermit—always isolates themselves, no collaboration
5. Master(with Servant)—unreasonable expectations
about time and complexity of appropriate analysis
6. Jailer—keeps own data locked up, never shares

95. “Temporal disease trajectories condensed from
population-wide registry data covering 6.2 million
patients.” (Jensen et al, Natt Comms)
“The top 100 papers.” (Van Noorden, Nature)
“Bibliometrics: Is your most cited work your
best?” (Ioannidis et al, Nature)
“Humans can discriminate more than 1 trillion
olfactory stimuli.” (Bushdid et al, Science)
“Fossilized nuclei and chromosomes reveal 180 million
years of genomic stasis in royal ferns.” (Bomfleur,
Science)
Shout Outs for Odds & Ends
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97. 2014 Crystal ball...
Emphasis on non European-descent populations for
discovery of disease associations
Crowd-based discovery in translational bioinformatics
Methods to recommend treatment for cancer based on
genome/transcriptome
Increase in “trained systems” (ala Watson) applications
in translational bioinformatics
Repurposing with combinations of drugs (vs. one)
More cost-effectiveness evidence for genomics
Linking essential genes, drug targets, and drug response

98. 2015 Crystal ball...
Increase in “trained systems” (a la IBM’s Watson)
applications in translational bioinformatics
Increased attention to genetic x environment analyses
Mega-cohort studies start to report out findings
Immuno-informatics and systems immunology explode
Increased integration of EMR, genomics, imaging
The term “precision medicine” will be mentioned more
frequently in PubMED abstracts.
IF invited, I will give this talk in person.

99. Thanks.
russ.altman@stanford.edu