Difference between revisions of "Category:Multiple sequence alignment"

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[[Category:Bioinformatics]]
 
[[Category:Bioinformatics]]
 
The purpose of [http://en.wikipedia.org/wiki/Multiple_sequence_alignment multiple sequence alignment] is to match up a number of related biological sequences against one another as well as possible, so that in each position in the resulting alignment, all residues have the same biological role in the original sequences. This is done by moving and stretching the sequences with respect to one another, eventually finding the optimal fit. This is essentially a multidimensional optimisation problem with a large solution space, so clever heuristics are needed in order to make the problem tractable. Many algorithms have been proposed for solving this with maximal efficiency, and there is often a tradeoff between quality on one hand, and speed and slim resource requirements on the other.
 
The purpose of [http://en.wikipedia.org/wiki/Multiple_sequence_alignment multiple sequence alignment] is to match up a number of related biological sequences against one another as well as possible, so that in each position in the resulting alignment, all residues have the same biological role in the original sequences. This is done by moving and stretching the sequences with respect to one another, eventually finding the optimal fit. This is essentially a multidimensional optimisation problem with a large solution space, so clever heuristics are needed in order to make the problem tractable. Many algorithms have been proposed for solving this with maximal efficiency, and there is often a tradeoff between quality on one hand, and speed and slim resource requirements on the other.
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== Experts ==
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{{list experts}}

Latest revision as of 09:28, 4 June 2012

The purpose of multiple sequence alignment is to match up a number of related biological sequences against one another as well as possible, so that in each position in the resulting alignment, all residues have the same biological role in the original sequences. This is done by moving and stretching the sequences with respect to one another, eventually finding the optimal fit. This is essentially a multidimensional optimisation problem with a large solution space, so clever heuristics are needed in order to make the problem tractable. Many algorithms have been proposed for solving this with maximal efficiency, and there is often a tradeoff between quality on one hand, and speed and slim resource requirements on the other.

Experts

No experts have currently registered expertise on this specific subject. List of registered field experts:

  FieldAE FTEGeneral activities
Anders Hast (UPPMAX)UPPMAXVisualisation, Digital Humanities30Software and usability for projects in digital humanities
Anders Sjölander (UPPMAX)UPPMAXBioinformatics100Bioinformatics support and training, job efficiency monitoring, project management
Anders Sjöström (LUNARC)LUNARCGPU computing
MATLAB
General programming
Technical acoustics
50Helps users with MATLAB, General programming, Image processing, Usage of clusters
Birgitte Brydsö (HPC2N)HPC2NParallel programming
HPC
Training, general support
Björn Claremar (UPPMAX)UPPMAXMeteorology, Geoscience100Support for geosciences, Matlab
Björn Viklund (UPPMAX)UPPMAXBioinformatics
Containers
100Bioinformatics, containers, software installs at UPPMAX
Chandan Basu (NSC)NSCComputational science100EU projects IS-ENES and PRACE.
Working on climate and weather codes
Diana Iusan (UPPMAX)UPPMAXComputational materials science
Performance tuning
50Compilation, performance optimization, and best practice usage of electronic structure codes.
Frank Bramkamp (NSC)NSCComputational fluid dynamics100Installation and support of computational fluid dynamics software.
Hamish Struthers (NSC)NSCClimate research80Users support focused on weather and climate codes.
Henric Zazzi (PDC)PDCBioinformatics100Bioinformatics Application support
Jens Larsson (NSC)NSCSwestore
Jerry Eriksson (HPC2N)HPC2NParallel programming
HPC
HPC, Parallel programming
Joachim Hein (LUNARC)LUNARCParallel programming
Performance optimisation
85HPC training
Parallel programming support
Performance optimisation
Johan HellsvikPDCMaterialvetenskap30materials theory, modeling of organic magnetic materials,
Johan Raber (NSC)NSCComputational chemistry50
Jonas Lindemann (LUNARC)LUNARCGrid computing
Desktop environments
20Coordinating SNIC Emerging Technologies
Developer of ARC Job Submission Tool
Grid user documentation
Leading the development of ARC Storage UI
Lunarc Box
Lunarc HPC Desktop
Krishnaveni Chitrapu (NSC)NSCSoftware development
Lars Eklund (UPPMAX)UPPMAXChemistry
Data management
FAIR
Sensitive data
100Chemistry codes, databases at UPPMAX, sensitive data, PUBA agreements
Lars Viklund (HPC2N)HPC2NGeneral programming
HPC
HPC, General programming, installation of software, support, containers
Lilit Axner (PDC)PDCComputational fluid dynamics50
Marcus Lundberg (UPPMAX)UPPMAXComputational science
Parallel programming
Performance tuning
Sensitive data
100I help users with productivity, program performance, and parallelisation. I also work with allocations and with sensitive data questions
Martin Dahlö (UPPMAX)UPPMAXBioinformatics10Bioinformatic support
Matias Piqueras (UPPMAX)UPPMAXHumanities, Social sciences70Support for humanities and social sciences, machine learning
Mikael Djurfeldt (PDC)PDCNeuroinformatics100
Mirko Myllykoski (HPC2N)HPC2NParallel programming
GPU computing
Parallel programming, HPC, GPU programming, advanced support
Pavlin Mitev (UPPMAX)UPPMAXComputational materials science100
Pedro Ojeda-May (HPC2N)HPC2NMolecular dynamics
Machine learning
Quantum Chemistry
Training, HPC, Quantum Chemistry, Molecular dynamics, R, advanced support
Peter Kjellström (NSC)NSCComputational science100All types of HPC Support.
Peter Münger (NSC)NSCComputational science60Installation and support of MATLAB, Comsol, and Julia.
Rickard Armiento (NSC)NSCComputational materials science40Maintainer of the scientific software environment at NSC.
Szilard PallPDCMolecular dynamics55Algorithms & methods for accelerating molecular dynamics, Parallelization and acceleration of molecular dynamics on modern high performance computing architectures, High performance computing, manycore and heterogeneous architectures, GPU computing
Thomas Svedberg (C3SE)C3SESolid mechanics
Torben Rasmussen (NSC)NSCComputational chemistry100Installation and support of computational chemistry software.
Wei Zhang (NSC)NSCComputational science
Parallel programming
Performance optimisation
code optimization, parallelization.
Weine Olovsson (NSC)NSCComputational materials science90Application support, installation and help
Åke Sandgren (HPC2N)HPC2NComputational science50SGUSI

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