Genomic futures v_pitt_kent_osu

Learning and Development Freelancer

Medical Genetics, Virology, Food Borne Pathogens, and Data Transfer and Storage

Software

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

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

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

14,201
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2007 2008 2009 2010 2011 2012 2013 2014 2015
Subjects

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

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

Carpentries, MOOCs, Semi-Traditional
Coursework and Mentoring

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

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

• 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)

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

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

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The detection and analysis of rare genomic events requires integrative analysis across large cohorts with terabytes to petabytes of genomic data. Contemporary genomic analysis tools have not been designed for this scale of data-intensive computing. This talk presents ADAM, an Apache 2 licensed library built on top of the popular Apache Spark distributed computing framework. ADAM is designed to allow genomic analyses to be seamlessly distributed across large clusters, and presents a clean API for writing parallel genomic analysis algorithms. In this talk, we’ll look at how we’ve used ADAM to achieve a 3.5× improvement in end-to-end variant calling latency and a 66% cost improvement over current toolkits, without sacrificing accuracy. We will talk about a recent recompute effort where we have used ADAM to recall the Simons Genome Diversity Dataset against GRCh38. We will also talk about using ADAM alongside Apache Hbase to interactively explore large variant datasets.

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Whole genome based metagenomics analyses hold the key to discover novel species from microbial communities, reveal their full metabolic potentials, and understand their interactions with each other. Metagenomics projects based on next generation sequencing typically produce 100GB to 1000GB unstructured data. Unlike many other big data problems, analysis of metagenomics data often generates temporary files with 100 to 1000 times of the original size, posing a significant challenge in both hardware infrastructure and software algorithms. Here we report our experience with evaluating Apache Spark in metagenomics data analysis for its speed, scalability, robustness, and most importantly, ease of programming. We developed a Spark-based scalable metagenomics application to deconvolute individual genomes from a complex microbial community with thousands of species. We then systematically tested its performance on synthetic and real world datasets using the Elastic MapReduce framework provided by Amazon Web Services. Our preliminary results suggest Spark provides a cost-effective solution with rapid development/deployment cycles for metagenomics data analysis. These experience likely extends to other big genomics data analyses, in both research and production settings.

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