DNAMA
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| - | + | The working resource that is possible to use trough network computer clustering will allow us to put in analysis as much samples as we wish and that those analysis will be finished in one to few hours. Moreover, we will tray to modified the algorithms in order to have multi-loci analysis to get a consensus three that will suggest the most possible pathways of phylogeny with much higher level of confidence | |
== Scientific and Social Impact == | == Scientific and Social Impact == | ||
Revision as of 09:54, 7 July 2011
Contents |
General Information
- Application's name: DNA Multicore Analysis
- Virtual Research Community: Life Sciences
- Scientific contact: Danilo Mrdak, danilomrdak@gmail.com
- Technical contact: Luka Filipovic, lukaf@ac.me
- Developers: Center of Informatin System - University of Montenegro, School of Computer & Communication Sciences Laboratory for Computational Biology and Bioinformatics,
- Web site: -
Short Description
Using of Network Cluster Web with potential of super-computer performances for DNA sequences analyzing will give us unlimited potential for DNA research. This will give us unlimited potential in term of analyzed sequence number and time consumption for analysis to be carried out. As many of DNA comparing and analyzing software use Monte Carlo and Markov chain algorithms that are time consuming regarding to sequence numbers, super-computer resource will faster our job and make the robust and overall analysis possible.Using of all published sequences for one group (e.g. for all salmonid species: salmons, trout, grayling, river huchon) from the same DNA region (mitochondrial D-loop DNA, Cytochrom b gene…) will give us more detailed insight in their relationships and phylogeny relationships.
Problems Solved
The working resource that is possible to use trough network computer clustering will allow us to put in analysis as much samples as we wish and that those analysis will be finished in one to few hours. Moreover, we will tray to modified the algorithms in order to have multi-loci analysis to get a consensus three that will suggest the most possible pathways of phylogeny with much higher level of confidence
Scientific and Social Impact
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Collaborations and Beneficiaries
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Technical Features and HP-SEE Implementation
- Primary programming language: C
- Parallel programming paradigm: MPI, OpenMPI
- Main parallel code: MPI, OpenMPI
- Pre/post processing code: C
- Application tools and libraries: RAxML
- Number of cores required: up to 1024
- Minimum RAM/core required: 1 GB
- Storage space during a single run: 256 MB
- Long-term data storage: 1 GB
Usage Example
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Publications
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