Bringing Hadoop into Bioinformatics with Cloudgene and CloudMan
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1 Bringing Hadoop into Bioinformatics with Cloudgene and CloudMan Sebastian Schönherr, Lukas Forer, Davor Davidovic, Hansi Weissensteiner, Florian Kronenberg, Enis Afgan Dublin, BOSC 2015
2 All started at BOSC 2012
3 BOSC 2012
4 BOSC CloudMan Cluster on the Cloud for everyone Configures Galaxy automatically Features Private/public cloud support, Instance sharing, dynamic cluster scaling, Persistent storage, re-launch your cluster Enis Afgan, Johns Hopkins University & RBI
5 CloudMan 2015 Cloud manager in several cloud infrastructures Amazon AWS: Since 2010 Nectar: Since 2012 Jetstream: Coming late 2015 EGI ENGAGE H2020 project Deploy your own version of Galaxy on the Cloud Using Ansible playbook + Packer
6 BOSC 2012
7 BOSC Cloudgene Improve usability of Hadoop in Bioinformatics A graphical execution platform for Hadoop programs Interface to integrate programs (YAML) Combine several programs into a workflow Setting up a Hadoop cluster on the cloud Lukas Forer Sebastian Schönherr - Medical University of Innsbruck
8 Cloudgene 2015 From a general workflow system to a Software-as- A-Service platform Dedicated service for a given workflow Already 2 services up and running Supports Hadoop YARN Stack MRv2, Apache Spark Combine Hadoop + Pig + Command Line Programs + R (RMarkdown) programs into one workflow Automatic file staging
9 BOSC Cloudgene + CloudMan Similar ideas, different context Galaxy Workflowsystem Per job parallelization using SGE Cluster in the cloud Cloudgene Workflowsystem Per task parallelization using Hadoop
10 BOSC Cloudgene + CloudMan
11 Project started in 2014 Platform for Big Data Bioinformatics Analysis Combine the projects CloudMan for Hadoop cluster provisioning Cloudgene for Hadoop execution Find a suitable use case
12 MapReduce in Bioinformatics S. Schoenherr VO NoSQL 14
13 A Real World Use case Michigan Imputation Server Cloudgene as the underlying framework Our workflow includes QC + Phasing + Imputation Cooperation with Center of Statistical Genetics, University of Michigan Gonçalo Abecasis Michael Boehnke Christian Fuchsberger
14 Overall Workflow Reference Panels: 1000 Genomes / Hapmap / HRC
15
16 Benefits Why CloudMan? Provide our services on private & public clouds Data sensitivity Provide best practices pipeline to everyone Reach a wide user community (Nectar, Jetstream)
17 Benefits Why Cloudgene? Well-tested platform for running (Hadoop) services Provides user management, admin dashboards,... Focus on the service implementation itself, not on the infrastructure Service 1: Michigan Imputation Server Service 2: mtdna-server Detecting heteroplasmies and contamination in mtdna NGS data Service 3:? (Maybe after this meeting)
18 Software Stack Bioinformatics Workflows Cloudgene MapReduce Platform
19 Software Stack Bioinformatics Imputation Workflows Server Cloudgene MapReduce Platform CloudMan Infrastructure Manager
20 Current Project Status Hadoop + Cloudgene running on CloudMan Fully distributed mode Run a WordCount YARN example with Cloudgene Current work Install services as apps (Cloudgene), scaling of cluster (CloudMan) Updates / Screenshots
21 Codefest 2015 Build a Docker Image for Hadoop + Cloudgene We integrated mtdna-server docker pull seppinho/cdh5-pseudo-mtdnaserver Hadoop Galaxy Adapter (CRS4) Perfect fit Export our workflow and integrate it into Galaxy (tbd)
22 Acknowledgement CloudMan Enis Afgan and Davor Davidovic wiki.galaxyproject.org/cloudman Cloudgene Lukas Forer and Sebastian Schönherr cloudgene.uibk.ac.at Michigan Imputation Server Gonçalo Abecasis; Michael Boehnke; Christian Fuchsberger imputationserver.sph.umich.edu
23 Thanks to BOSC!
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