DeepAligner
From HP-SEE Wiki
Contents |
General Information
- Application's name: DeepAligner
- Virtual Research Community: Life Sciences
- Scientific contact: Kozlovszky Miklos, Windisch Gergely; m.kozlovszky at sztaki.hu
- Technical contact: Kozlovszky Miklos, Windisch Gergely; m.kozlovszky at sztaki.hu
- Developers: Obuda University – John von Neumann Faculty of Informatics
- Application website (HP SEE Bioinformatics Portal): http://ls-hpsee.nik.uni-obuda.hu:8080
- Web site: https://biotech.nik.bmf.hu/web/
Short Description
Mapping short fragment reads to open-access eukaryotic genomes is solvable by BLAST and BWA and other sequence alignment tools - BLAST is one of the most frequently used tool in bioinformatics and BWA is a relative new fast light-weighted tool that aligns short sequences. Local installations of these algorithms are typically not able to handle such problem size therefore the procedure runs slowly, while web based implementations cannot accept high number of queries. SEE-HPC infrastructure allows accessing massively parallel architectures and the sequence alignment code is distributed free for academia. Due to the response time and service reliability requirements grid can not be an option for the DeepAligner application.
Problems Solved
The recently used deep sequencing techniques present a new data processing challenge: mapping short fragment reads to open-access eukaryotic (animal: focusing on mouse and rat) genomes at the scale of several hundred thousands.
Scientific and Social Impact
The aim of the task is threefold, the first task is to port the BLAST/BWA algorithms to the massively parallel HP-SEE infrastructure create a BLAST/BWA service, which is capable to serve the short fragment sequence alignment demand of the regional bioinformatics communities, to do sequence analysis with high throughput short fragment sequence alignments against the eukaryotic genomes to search for regulatory mechanisms controlled by short fragments.
Collaborations and Beneficiaries
Serve the short fragment sequence alignment demand of the regional bioinformatics communities. People who are interested in using short fragment alignments will greatly benefit from the availability of this service. The service will be freely available to the LS community. We estimate that a number of 5-15 scientific groups world wide will use our service. Ongoing collaborations so far: Hungarian Bioinformatics Association, Semmelweis University Planned collaboration with the MoSGrid consortium (D-GRID based project, Germany)
Technical Features and HP-SEE Implementation
- Primary programming language: C, perl
- Parallel programming paradigm: Master-slave, MPI, + Multiple serial jobs (data-splitting, parametric studies)
- Main parallel code: WS-PGRADE/gUSE + C/C++
- Pre/post processing code: Perl/BioPerl (in-house development)
- Application tools and libraries: Perl/BioPerl (in-house development)
- Number of cores required: 128-256
- Minimum RAM/core required: 4-8 Gb'
- Storage space during a single run: 2-5 GB
- Long-term data storage: 1-2 TB
Usage Example
IMPLEMENTATION OF THE GENERIC BLAST WORKFLOW Normal applications need to be firstly ported for use with gUSE/WS-PGRADE. Our used porting methodology includes two main steps: workflow development and user specific web interface development based on gUSE’s ASM. gUSE is using a DAG (directed acyclic graph) based workflow concept. In a generic workflow, nodes represent jobs, which are basically batch programs to be executed on one of the DCI’s computing element. Ports represent input/output files the jobs receiving or producing. Arcs between ports represent file transfer operations. gUSE supports Parameter Study type high level parallelization. In the workflow special Generator ports can be used to generate the input files for all parallel jobs automatically while Collector jobs can run after all parallel execution to collect all parallel outputs. During the BLAST porting, we have exploited all the PS capabilities of gUSE.
Parallel job submission into the DCI environment needs to have parameter assignment of the generated parameters. gUSE’s PS workflow components were used to create a DCI-aware parallel BLAST application and realize a complex DCI workflow as a proof of concept. Later on the web-based DCI user interface was created using the Application Specific Module (ASM) of gUSE. On this web GUI, end-users can configure the input parameter like the “e” value or the number of MPI tasks and they can submit the alignment into the DCI environment with arbitrary large parameter fields. During the development of the workflow structure, we have aimed to construct a workflow that will be able to handle the main properties of the parallel BLAST application. To exploit the mechanism of Parameter Study used by gUSE the workflow has developed as a Parameter Study workflow with usage of autogenerator port (second small box around left top box in Fig 5.) and collector job (right bottom box in Fig. 5). The preprocessor job generates a set of input files from some pre-adjusted parameter. Then the second job (middle box in Fig. 5) will be executed as many times as the input files specify. The last job of the workflow is a Collector which is used to collect several files and then process them as a single input. Collectors force delayed job execution until the last file of the input file set to be collected has arrived to the Collector job. The workflow engine computes the expected number of input files at run time. When all the expected inputs arrived to the Collector it starts to process all the incoming inputs files as a single input set. Finally output files will be generated, and will be stored on a Storage Element of the DCI shown as little box around the Collector in.
Due to the strict HPC security constraints, end users should posses valid certificate to utilize the HP-SEE Bioinformatics eScience Gateway. Users can utilize seamlessly the developed workflows on ARC based infrastructure (like the NIIF’s Hungarian supercomputing infrastructure) or on gLite/EMI based infrastructure (Service Grids like SEE-GRID-SCI, or SHIWA). After login, the users should create their own workflow based application instances, which are derived from pre-developed and well-tested workflows.
Publications
- M. Kozlovszky, G. Windisch, Á. Balaskó;Short fragment sequence alignment on the HP-SEE infrastructure;MIPRO 2012, accepted
- M. Kozlovszky, G. Windisch; Supported bioinformatics applications of the HP-SEE project’s infrastructure; Networkshop 2012, accepted
