Notes: Bioinformatics Open Source Conference 2016 day 2 morning: Open research and data science

I’m at the 2016 Bioinformatics Open Source Conference (BOSC) in Orlando and these are the notes from the morning session on the second day. This includes a keynote from Steven Salzberg on openness in science and a session on data science.

Other notes:


Steven Salzberg – Open source, open access, and open data: why science moves faster in an open world

Steven talking about power of open science in doinng better work. Tells a story about Glimmer – developed at TIGR – initial open source release from Steven. Lawyers preferred to try and sell Glimmer, but ended up making executive decision to release open source. Next open source package was MUMmer 3, which did super fast large scale alignments (moving to GitHub soon). Also very popular and well cited, thanks to being open. Citations source of credit in academia – currency we need. Bowtie/TopHat/Cufflinks – open software for NGS with over 20k citations. HISTAT2 is the successor to TopHat and StringTie is the successor to Cufflinks. If you develop useful open software, people will use it. Mentions GATK as example of not following this model – initially open source, supported by public grants, now has free-only-to-academics license.

Open Data is a bigger issue and talks through the human genome sequence race (1998-2001): Celera versus public project. ABI3700 was the technology enabling this. Questions about how Celera would make money: one idea was patenting human genes. Now invalidated thanks to BRCA Supreme Court decision. At the time NHGRI released data immediately to the public domain so it couldn’t be patented. A personal example from Steven – sequencing of 12 Drosophila species. Wolbachia bacteria co-evolve with most fruit flies. Q from Michael Eisen – did any of the 11 new Drosophila have Wolbachia? Yes, found 3 new species. Possible due to sharing of the data. Another open project – rapid data release from Influenza Genome Project to develop flu: 19,000 genomes and counting. At start asked Influenza researchers for samples – paid for it and only restriction is that it would be publicly available. Most declined because did not want to release data publicly. Some examples of major genomics projects that failed at data sharing. ENCODE was a pure data generation project without any hypothesis. Data released but embargoed and in 2014 changed the policy due to outside pressure. So 2003-2014 data harder to use than it should be. GTEx – massive RNA-seq analysis project. V3: could not publish until consortium does. V4: 9 month restriction, and finally in V5 lifted restrictions. Human data can be harder to share because of restrictions. However there are loopholes that allow producers to sit on data for 5 years or more until publication. Some positive developments: Biden launch of major open access database to advance cancer research. Good examples: ADNI Alzheimer’s project + Sage with great access policies. Releasing data helps correct bad science: example of tardigrade with initial paper claiming lots of horizontal gene transfer. A second group did not have transfer – looked at new data plus theirs and found everything is contamination in first paper. There are downsides to sharing data. If you publish a dataset, you lose control over authorship. Current problem where we need credit. How can we include better credit/co-authorship to help the people who produced the data?

Open access papers: everyone has access. Journal distribution artifact of 1800’s journal service to scientists. Now we have the internet. Patients forced changed to make NIH data be open access after 12 months. Why 12 months? Lobbying from journal publishers. Science kept secret is no different from science that was never done. What is the goal of science? Solve problems, communicate to others so they can help. Progress is slower without working together. Great reminder about working on hard problems together to help the world become a better place.

Data Science

Alyssa Morrow – Mango: Data Exploration on Large Genomic Datasets

Why do we need distributed visualization? Data explosion – human genome (10Gb) to TCGA (3Pb). Current browsers don’t scale – single node: 0.00125% of entire genome. Ideally you’d be multicore and able to explore thousands of whole genome samples. Mango browser uses Parquet and GA4GH Schemas, runs on Apache Spark managed with Toil, using ADAM for data management. avacado – scalable variant calling plus gnocchi – parallel variant analysis. Architecture – ADAM, Access optimization on top of it, Scalatra and pileup.js on frontend side. Goal is to have interactive latencies on top of batch-oriented platform. Provides persistent store optimizations with a front end cache. Provided memory optimizations for data already in memory – interval trees for region selection. Implemented discovery mode to do intelligent pre-fetching of feature/variant density. Scales nicely over lots of nodes.

Frank Nothaft – ADAM Enables Distributed Analyses Across Large Scale Genomic Datasets

ADAM genome analysis platform is a great project to build analyses on top of Apache Spark. Frank show examples of GATK best practice and run times – slow because designed for flat file formats on cluster systems. Formats optimize for specific use cases. Flat architectures expose bad programming interfaces that are not productive and hard to do. In ADAM start with a schema data model and build from that. Successes in other fields (networking) thanks to data formats (IP). You can optimize whatever you want on both sides as long as you have the same schema. ADAM/Spark has horizontal scalability – nice graph of utiliziation over 1024 cores. Working on validation project of ADAM framework against GATK best practices using Toil for parallelization. Uses 260 individuals from Simons Genome Diversity project. Toil pipeline system designed for cloud and takes advantage of spot pricing and failures. To run on Toil, they adapted to start up service job that manages Spark. Then workflow speaks to Spark cluster. Produces statistically similar results to GATK, 30x faster anad 3x cheaper than GATK. Working pipeline using hg19 and hg38.

Hannes Hettling – SUPERSMART – A Self-Updating platform for Estimating Rates of Speciation and Migration, Ages, and Relationships of Taxa

SUPERSMART works to understand global biodiversity and biogeography by handling large scale phylogenies. Typical processing workflow – data retrieval, filtering/quality control, multiple sequence alignment then tree inference. SUPERSMART integrates all of these components with scaling for very large phylogenies. Uses cleaned data from PhyLoTA as baseline in SUPERSMART. As an integrated tool, it has a lot of dependencies. To make it easier provide virtualized installs with all version – uses Vagrant/VirtualBox so only two dependencies. Host a box at Naturalis/supersmart. Also Docker container and integrates with Galaxy.

Lorena Pantano Rubino – Characterization of the small RNA transcriptome using the bcbio-nextgen python framework

Lorena talking about her great work integrating small RNA analysis into bcbio. microRNA have important functionality and lots of diversity with isomiRs. Small, complex and biologically important. bcbio has variant calling, RNA-seq, small RNA-seq and contains over 200 peer reviewed tools installed via Bioconda. small RNA pipeline does everything you can imagine wanting to process with short RNAs – lots of custom tools also built by Lorena. seqcluster to deal with multi-mapped reads on the same small RNA. Visualization interface built in to select. Fits into MultiQC for interactive summarization of results. Used miRQC for validation and quality control of pipeline. Shows plots of resource usage. Starting an open project for small RNA annotation and analysis: MIRTop.

Fabien Campagne – MetaR: simple, high-level languages for data analysis with the R ecosystem

MetaR aims at providing an interactive data analysis environment as part of R. IDEs like Rstudio are primarily useful for experienced programmers. Provide some nice features, along with interactive notebooks like IPython. Notebooks can be challenging because of breaking up into fragmented chunks. MetaR tries to put advantages of UI with ability to program using language workbench technolgy. Provides composable R – can combine a Biomart statement with R and then it generates it into pure R. End up with seamless interaction trying to remove disconnect between different interactive languages. Works with source control and provides better reproducibility by combining MetaR with Docker.

Jorge Duitama – Development of NGSEP as an open-source comprehensive solution for analysis of high throughput sequencing data

NGSEP is yet another tool, in Jorge’s words, to help with sequencing analysis. Does alignment, variant calling including custom algorithms. Contains implementation of Indel realignment, structural variant detection with CNVnator + custom algorithms. Integrates with Galaxy, iPlant and DNANexus. Uses for WGS of 100 genomes in rice, now part of Rice 3000 genomes project. Cool examples of structural variation in important rice genes detected.

Kam Dahlquist – GRNmap and GRNsight: open source software for dynamical systems modeling and visualization of medium-scale gene regulatory networks

GRNmap and GRNsight used as platform for talking about the open science ecosystem. How can open science facilitate teaching and connections. Students benefit from Open Source and Open Data – helps them become involved with the community and analysis. Software development done for teaching and as student projects. Cool way of integrating research in wet lab into teaching undergraduates. GRNmap collaboration with math students, GNRsight with computer science students. Math collaborators not used to working with open source repositories. GRNsight with computer science folks and used GitHub and open collaboration from the beginning. We need to teach software best practices while developing useful code. Great opportunities for undergraduates.


One thought on “Notes: Bioinformatics Open Source Conference 2016 day 2 morning: Open research and data science

  1. Pingback: Notes: Bioinformatics Open Source Conference 2016 day 2 afternoon: developer tools and reproducible analyses | Small Change Bioinformatics

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