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Continous integration with PHP and Jenkins

Overview Continuous integration (CI) is a development practice which allows testing, building and checking the quality of the application in automated way without manual developer intervention. It requires developers to integrate code into a shared repository several times a day or every commit/merge to the given branch of the repository. Each check-in is then verified by an automated build, allowing teams to detect problems early.

While PHP Continuous Integration (PHPCI) does not require the the code compilation as
build” one can understand the set of task like testing and quality checks which are performed in timely manner or when the certain conditions are met.
PHPCI can be performed on Jenkins Server. The detailed description of jenkins server configuration in is presented here. PHPCI requires PHing package which can be provided into application by Composer.

Recent posts

Application and implementation of probabilistic profile-profile comparison methods for protein fold recognition

Fold recognition is a method of fold detecting and protein tertiary structure prediction applied for proteins lacking homologues sequences of known fold and structure deposited in the Protein Data Bank. They are based on assumption that there is strictly limited number of different protein folds in nature, mostly as a result of evolution and due to basic physical and chemical constraints of polypeptide chains. Fold recognition methods are useful for protein structure prediction, evolutionary analysis, metabolic pathways and enzymatic efficiency prediction, molecular docking and drug design. Currently there are about 1300 discovered and characterized protein folds in SCOP and CATH databases. Every newly discovered protein sequence has significant chances to be classified into one of those folds. Many different approaches have been proposed for finding the correct fold for a new sequence and it is often useful to include evolutionary information for query as well as for target proteins.…

GRDB – Gene Relational DataBase

We have developed a fully automated web service available for the academic community which purpose is to increase the sensitivity of the detection of distantly related protein families. Predicted secondary structure information was added to the information about sequence conservation and variability, a technique known from hybrid threading approaches. The accuracy of the meta profiles created this way is compared with profiles containing only sequence information and with the standard approach of aligning a single sequence with a profile. Additionally, the alignment of meta profiles is more sensitive in detecting remote homology between protein families and more effective than aligning two sequence-only profiles or profile to sequence. The specificity of the alignment score is improved in it’s lower range compared to the robust sequence-only profiles. (More)

NaRCiSuS

Structural genomics is the wide term which describes process of determination of structure representation of information in human genome and at present is limited almost exclusively on proteins. Although in common understanding genetic information means “genes and their encoded protein products”, thousands of human genes produce transcripts which are important in biological point of view but they do not necessarily produce proteins. Furthermore, even though the sequence of the human DNA is known by now, the meaning of the most of the sequences still remains unknown. It is very likely that a large amount of genes has been highly underestimated, mainly because the actual gene finders only work well for large, highly expressed, evolutionary conserved protein-coding genes. Most of those genome elements encode for RNA from which transfer and ribosomal RNAs are the classical examples. But beside these well-known molecules there is a vast unknown world of tiny RNAs that might play a crucial …

ELM: the status of the 2010 eukaryotic linear motif resource

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a 'Bar…

International Supercomputing Conference, Hamburg, Germany 23th June 2009

The International Supercomputing Conference is the Europe’s premier HPC event. The attendance allows observing trends science and technology of High Performance Computing for whole next year. 2009 edition achieved record numbers of attendees and exhibitors, a level of success even more impressive given the international economic crisis. With its move to Hamburg, ISC’09 reached a significantly higher level of attendance, bringing 1,670 HPC industry leaders. Research labs demonstrated their scientific applications of supercomputing in most recent fields such us GPGPU accelerators, clouds and green computing. Furthermore, this, ISC’09 also welcomed several first-time exhibitors. The exhibition brought countless highlights such as the demo of both the IS5000 40 Gb/s infniband switches and low-latency 10 Gigabit Ethernet. The attendance on ISC’09 allows to anticipate future development of ATLAS system and to present current achievements’ of ToK4nEDA team.








GPGPU Accelerated Sparse Linear Solver for Fast Simulation of On-Chip Coupled Problems

Continued device scaling into the nanometer region has given rise to new effects that previously had negligible impact but now present greater challenges to designing successful mixed-signal silicon. Design efforts are further exacebated by unprecedented computational resource requirements for accurate design simulation and verification. This paper presents a GPGPU accelerated sparse linear solver for fast simulation of on-chip coupled problems using nVIDIA and ATI GPGPU accelerators on a multi-core computational cluster and evaluate parallelization strategies from a computational perspective.