This document summarizes Ben Busby's presentation on leveraging commercial and open source bioinformatics tools for clinical and research discoveries from large datasets. The presentation covers analyzing human genomic variants related to chronic disease and cancer, viruses like Zika, and bacteria responsible for foodborne pathogens. It also discusses data transfer, storage, popular metagenomics tools, genome analysis using graphs, and matching expressed variants in memory. The document lists Busby's contact information and links to slides and provides information on NCBI training workshops on bioinformatics.

1. Ben Busby, Ph.D.
Lead, Bioinformatics Training, NCBI
Chair, Department of Bioinformatics and Data Science, FAES
ben.busby@nih.gov
Efficiently Leveraging Commercial and Open Source Bioinformatics Tools for
Clinical Interventions and Research Discoveries from Very Large Datasets

2. Please note that all views are my own and not necessarily those
of any Federal agency. No mention of any commercial or non-
profit entity should be considered an endorsement.
Slides available at:
https://www.slideshare.net/benbusby/genomic-futures-v2
• Human Genomic Variants – Chronic Disease and Cancer
• Viruses – Zika
• Bacteria – Food Borne Pathogens
• Data Transfer and Storage

4. Graphic Credit:
Spencer Martin, UBC

5. © Martine Zilversmit 2013

6. http://1.usa.gov/1J1xmYs
NCBI NGS Online Workshop – Available on the
NCBI YouTube Channel!

9. 14,201
53,216
139,311
374,464
485,727
566,181
660,665
876,849
1,002,935
2007 2008 2009 2010 2011 2012 2013 2014 2015
Subjects

22. Combined score is
the average of SVs,
mappability, GC..
NCBI region list
Encode blacklist

23. Genome in a Bottle

24. Combined score is
the average of SVs,
mappability, GC..
NCBI region list
Encode blacklist

26. Matching Expressed Variants in-memory;
Analyzing with Graphs
https://f1000research.com/articles/5-674/v1

27. phenvar.colorado.edu

28. Variants are Often Pleiotropic

31. Carpentries, MOOCs, Semi-Traditional
Coursework and Mentoring

32. NCBI Webinars

41. • Escherichia and Shigella
• Campylobacter
• Acinetobacter
• Salmonella
• Klebsiella
• Listeria

44. 44
• Qiime
• Mothur
• Nepthele
• MetAMOS
• MetaViz
• mash
Popular Metagenomics Tools!

45. Another example – Cas9

48. • March 20-22, NIH Campus
• May 22-24, UC BioFrontiers Institute Boulder CO
• June 19-21, NYGC
• August 17-19, NIH Campus
• September 25-27, Pittsburgh, PA
• October 11-13, Microbial and Metagenomics, NCBI
(with ASM and CDC)

49. Come work at NCBI for 4-6 weeks!
Email bioinformatics-training@ncbi.nlm.nih.gov
for more information!

50. My View of Data Transfer Principles
• Metadata Search
• Rapid NoSQL (for now)
• Integration
• Non-ambiguous identifiers
• Transferring Small amounts of Data
• Data still gets transferred in the cloud
• Underlying structure
• Finding specific data from validated formats
• Democratization of Data
• Rapid comparison by domain experts
• Reporting
• Metrics to report data upload and [unique IP] download of datasets
• Post-publication User Review

52. 52
EDirect (Search API) Cookbook

53. 53
New APIs!