HMMER

HMMER is a software package for working with profile hidden Markov models (HMM) of known regions in proteins.

Availability

Resource	Centre	Description
Akka	HPC2N	capability cluster resource of 54 TFLOPS with infiniband interconnect
Kalkyl	UPPMAX	cluster resource of about 21 TFLOPS
Kappa	NSC	throughput cluster resource of 26 TFLOPS
Matter	NSC	cluster resource of 37 TFLOPS dedicated to materials science
Triolith	NSC	Capability cluster with 338 TFLOPS peak and 1:2 Infiniband fat-tree

General info

An HMM is a statistical model that describes the known sequence variations within a specific group of proteins that may be of special interest; for example a protein family with known function, or a domain containing a well studied interaction surface or an active site. HMM is a machine learning technique [1] where the models are built from training examples that are known good members, and where the finished models can be used to reliably classify and annotate new or poorly understood protein sequences in an automated fashion. Large libraries of trusted HMMs (such as Pfam) are of course immensely beneficial, as they can be used to automatically classify large portions of newly sequenced genomes, directly as they become available.

The HMMER package contains applications for working with HMMs, for example for:

• Building and calibrating HMMs.
• Matching an HMM against a sequence database (for finding new members).
• Matching a sequence against an HMM database (for finding new sequence features).

Versions

There are two verions of HMMER that can conceivably be useful:

• HMMER-2.3.2: Old stable version.
• HMMER-3.0: Fast, but backwards incompatible and non-feature-complete.

Their implementations and output (and potentially also the actual results) are vastly different, so ongoing projects are not recommended to switch between them. For new projects, it is highly recommended to spend some time to deduce which version is the most suitable.

HMMER-3 may seem like an obvious choice; it is much faster than its predecessor and it is currently used in large scale production (e.g. by Pfam), and it is also promoted as the official main HMMER version. However, HMMER-3.0 is not feature complete. Especially, the old default alignment behavior (glocal, hmm_ls) is missing, so if this feature is necessary: choose HMMER-2.3.2.

Computational considerations

Work locally

Many of the features in HMMER require access to database flatfiles, and standard practice when running a compute cluster is to copy all necessary files to a node local directory before any work is done with them. This behaviour is highly encouraged on most resources, since multiple simultaneous accesses to the same large files on a shared disk is likely to cause problems for all computations currently running on the resource, and not only for the owner of the badly behaving jobs. For this reason, most SNIC resources have amenities in place to aid you in running your HMMER jobs in an optimal manner (for example prepare_db and $HMMER_DB_DIR, described for example here).

Do not run out of memory

If possible, you should ensure that you have enough RAM to hold the database as well as the results and still have some headroom. This ensures that HMMER will not need to read data from disk unnecessarily, which otherwise would cause significant slowdown. This is less important with HMMER-2.3.2, since the HMM-sequence alignment implementation is so CPU intensive that memory and disk considerations are less likely to have an impact on runtime. Nevertheless, ensuring that the database files remain cached can be done for example by:

• Choose a system with enough RAM
  Multiprocessor systems generally have more memory than single processor systems, and the database will also require proportionally less memory, since only one copy is needed in the OS file cache regardless of the number of processors using it.
• Partition the search space
  For huge databases or very restricted amounts available memory it may be required to split the database into manageable chunks and process them as separate jobs.

Use your processors wisely

Users should not have to worry about this since the HMMER default behaviour is to run on all the CPU cores it can detect on the compute node, which is nearly always the most desirable. However, should you need to control this for any reason, the behaviour can be controlled by the --ncpus command line option, or the $HMMER_NCPUS environment variable, which should already be set to the correct value if you are using a preinstalled HMMER version on a SNIC resource.

Compilation

Benchmarks at NSC have shown that Intel compilers seem to produce more efficient HMMER binaries than does gcc.

HMMER-2 is also extremely sensitive to hardware vectorisation optimisations, even to the extent that -xSSE4.2 produces significantly faster binaries than does -xSSE4.1, so it makes sense to set this as aggressively as possible. NSC uses icc 11.1.069 and CFLAGS="-O3 -ip -xSSE4.2" for the SNIC systems Kappa and Matter, and CFLAGS="-O3 -ip -xSSSE3" for Neolith.

The official binary distribution of HMMER-3.0 is compiled with icc, and is fully adequate for threaded operation. However, some of the HMMER-3 binaries (e.g. hmmscan) have been shown to perform upward of 50% better with MPI parallelisation, so this may constitute reason to recompile locally. It may also be profitable to carefully select MPI library, as intelmpi in standard configuration can give fluctuations in runtime upwards of +~100% with hmmsearch, while openmpi seemingly performs optimally and consistently. For HMMER-3, NSC uses icc 11.1.069 and openmpi 1.4.1, and uses the compiler flags automatically detected by the configure script.

License

License: Free.

Experts

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

		Field	AE FTE	General activities
Anders Hast (UPPMAX)	UPPMAX	Visualisation, Digital Humanities	30	Software and usability for projects in digital humanities
Anders Sjölander (UPPMAX)	UPPMAX	Bioinformatics	100	Bioinformatics support and training, job efficiency monitoring, project management
Anders Sjöström (LUNARC)	LUNARC	GPU computing MATLAB General programming Technical acoustics	50	Helps users with MATLAB, General programming, Image processing, Usage of clusters
Birgitte Brydsö (HPC2N)	HPC2N	Parallel programming HPC		Training, general support
Björn Claremar (UPPMAX)	UPPMAX	Meteorology, Geoscience	100	Support for geosciences, Matlab
Björn Viklund (UPPMAX)	UPPMAX	Bioinformatics Containers	100	Bioinformatics, containers, software installs at UPPMAX
Chandan Basu (NSC)	NSC	Computational science	100	EU projects IS-ENES and PRACE. Working on climate and weather codes
Diana Iusan (UPPMAX)	UPPMAX	Computational materials science Performance tuning	50	Compilation, performance optimization, and best practice usage of electronic structure codes.
Frank Bramkamp (NSC)	NSC	Computational fluid dynamics	100	Installation and support of computational fluid dynamics software.
Hamish Struthers (NSC)	NSC	Climate research	80	Users support focused on weather and climate codes.
Henric Zazzi (PDC)	PDC	Bioinformatics	100	Bioinformatics Application support
Jens Larsson (NSC)	NSC	Swestore
Jerry Eriksson (HPC2N)	HPC2N	Parallel programming HPC		HPC, Parallel programming
Joachim Hein (LUNARC)	LUNARC	Parallel programming Performance optimisation	85	HPC training Parallel programming support Performance optimisation
Johan Hellsvik	PDC	Materialvetenskap	30	materials theory, modeling of organic magnetic materials,
Johan Raber (NSC)	NSC	Computational chemistry	50
Jonas Lindemann (LUNARC)	LUNARC	Grid computing Desktop environments	20	Coordinating 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)	NSC	Software development
Lars Eklund (UPPMAX)	UPPMAX	Chemistry Data management FAIR Sensitive data	100	Chemistry codes, databases at UPPMAX, sensitive data, PUBA agreements
Lars Viklund (HPC2N)	HPC2N	General programming HPC		HPC, General programming, installation of software, support, containers
Lilit Axner (PDC)	PDC	Computational fluid dynamics	50
Marcus Lundberg (UPPMAX)	UPPMAX	Computational science Parallel programming Performance tuning Sensitive data	100	I help users with productivity, program performance, and parallelisation. I also work with allocations and with sensitive data questions
Martin Dahlö (UPPMAX)	UPPMAX	Bioinformatics	10	Bioinformatic support
Matias Piqueras (UPPMAX)	UPPMAX	Humanities, Social sciences	70	Support for humanities and social sciences, machine learning
Mikael Djurfeldt (PDC)	PDC	Neuroinformatics	100
Mirko Myllykoski (HPC2N)	HPC2N	Parallel programming GPU computing		Parallel programming, HPC, GPU programming, advanced support
Pavlin Mitev (UPPMAX)	UPPMAX	Computational materials science	100
Pedro Ojeda-May (HPC2N)	HPC2N	Molecular dynamics Machine learning Quantum Chemistry		Training, HPC, Quantum Chemistry, Molecular dynamics, R, advanced support
Peter Kjellström (NSC)	NSC	Computational science	100	All types of HPC Support.
Peter Münger (NSC)	NSC	Computational science	60	Installation and support of MATLAB, Comsol, and Julia.
Rickard Armiento (NSC)	NSC	Computational materials science	40	Maintainer of the scientific software environment at NSC.
Szilard Pall	PDC	Molecular dynamics	55	Algorithms & 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)	C3SE	Solid mechanics
Torben Rasmussen (NSC)	NSC	Computational chemistry	100	Installation and support of computational chemistry software.
Wei Zhang (NSC)	NSC	Computational science Parallel programming Performance optimisation		code optimization, parallelization.
Weine Olovsson (NSC)	NSC	Computational materials science	90	Application support, installation and help
Åke Sandgren (HPC2N)	HPC2N	Computational science	50	SGUSI

Links

• Official website
• HMMER-3 documentation
• HMMER-2.3.2 release (contains pdf documentation)