This document contains a summary of various topics related to bioinformatics and genomics including references to databases, tools, standards, and publications. It discusses databases of gene and protein sequences from as early as the 1960s to the present with trillions of nucleotides currently archived. Formats for sharing quantitative and gene expression data are mentioned as well as the importance of recording experiment details to allow comparisons and replication. Overall, the document provides an overview of the history and current state of genomics data sharing and analysis.

1. Photo: Amy Apprill, Woods Hole Oceanographic Institute

2. pain

3. bioinformatics
oh and btw Elvira works in my lab

4. Custom Perl scripts FTW

5. • Luscombe et al., 2001:
*especially, but definitely not limited to, gene & protein sequence data
**often impressively large datasets. Please do not call it “big data”

6. https://xenabrowser.net/heatmap/

7. http://www.rcsb.org/structure/3V6D

8. Sylvester et al. (2017)
bentzenlab.ca

9. https://www.fda.gov/Food/FoodScienceResearch/WholeGenomeSequencingProgramWGS/ucm363134.htm
Keddy and Beiko (2017)

11. 65 protein sequences

12. 1962

13. Dr. Dayhoff established an on-line computer
database and a sophisticated retrieval system, accessable by
phone to outside users, in September 1980
http://www.dayhoff.cc/MODBiography.html

14. Dec 1982:
680,338 nucleotides Apr 2003:
~40 billion nucleotides
Dec 2017:
~3 trillion nucleotides

17. Kaye et al.
“Data sharing in genomics — re-shaping
scientific practice” Nat Rev Genet 2008

18. Langille et al. (2018) Microbiome
Data-release policies are
only as good as researchers’
willingness to abide by
them, and the will on the
part of journals and funding
bodies to enforce them!

20. • Standard formats
• Efficient representations
quantitative data
• Gene expression
• Metabolite concentrations

21. many ways
protein functional prediction
new results
re-run the analysis yourself

22. ontologies controlled
vocabularies

23. “(i) the recorded information about each experiment should be sufficient to
interpret the experiment and should be detailed enough to enable
comparisons to similar experiments and permit replication of experiments
and (ii) the information should be structured in a way that enables useful
querying as well as automated data analysis and mining.”
Brazma et al. (2001) Nat Genet
g Genes
s Samples (Diffuse large B cell
lymphoma patients of two types,
with different prognoses)
Expression levels:
Low
High
Li et al (2001) Bioinformatics

26. (Also available as XML)

27. Pedro’s Biomolecular ResearchTools, ca. 1997

32. All articles published in Science in 2009 that mention H1N1:
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=science[
journal]+AND+h1n1+AND+2009[pdat]

33. https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=19516283&re
tmode=text&rettype=abstract

36. Evidence codes – was protein function inferred in the lab (and if so, how?), or through
computational predictions?

37. Evidence codes

39. en.wikipedia.org
?

40. GOLD implements MIxS standards

41. Nucleic Acids Research 2018 Database issue
181 databases
Nucleic Acids Research 2017 Web Server issue
86 servers (284 submitted)

44. right
right exact same
exact same

45. 3. Archive the ExactVersions of All External Programs Used
5. Record All Intermediate Results, When Possible in
Standardized Formats
Sandve et al., PLoS Comput Biol 2013

46. Galaxy – customize, execute,
save, and share workflows

47. CSCI6802Tutorial #2 (Michael Hall)

48. G Dudas et al. Nature 1–7 (2017) doi:10.1038/nature22040
Recent paper about Ebola transmission
• 1610 publicly available genomes, 2014-2015
• Data cleaning
• Relaxed molecular clock to infer root
• Markov chains to infer transmission rates
(not location specific, but based on
attributes)
Key Conclusions:
• Median transmission distance = 72 km
• Important factors:
• National vs int’l dispersal
• Distances between regions
• Population at source and destination
• Shared int’l border

49. Dudas et al. (2017) Nature
Jupyter (iPython) Notebook - https://github.com/ebov/space-time

50. must
enforced
modes of access

51. FIN