__EXCLUSIVE__ Download Best Cfg Aim 2013
1.0rc1 (2013-09-24)1.0b2 (2013-05-31)
Introductionplone.app.contenttypes offers default content types for Plone based on Dexterity. This package is a replacement for the types in Products.ATContenttypes.
Download Best Cfg Aim 2013
For this exercise, we need two datasets: a protein structure and a library of compounds. We will download the former directly from the Protein Data Bank; the latter will be created by searching the ChEMBL database (Gaulton et al. 2016).
Multiple databases are available online which provide access to chemical information, e.g. chemical structures, reactions, or literature. In this tutorial, we use a tool which searches the ChEMBL database. There are also Galaxy tools available for downloading data from PubChem.
The fpocket tool generates two different outputs: a Pocket properties log file containing details of all the pockets which fpocket found in the protein. The second output is a collection (a list) containing one PDB file for each of the pockets. Each of the PDB files contains only the atoms in contact with that particular pocket. Note that fpocket assigns a score to each pocket, but you should not assume that the top scoring one is the only one where compounds can bind! For example, the pocket where the ligand in the 2brc PDB file binds is ranked as the second-best according to fpocket.
The output consists of a collection, which contains an SDF output file for each ligand, containing multiple docking poses and scoring files for each of the ligands. We will now perform some processing on these files which extracts scores from the SD-files and selects the best score for each.
In this step, we will group similar molecules together. A key tool in cheminformatics for measuring molecular similarity is fingerprinting, which entails extracting chemical properties of molecules and storing them as a bitstring. These bitstrings can be easily compared computationally, for example with a clustering method. The fingerprinting tools in Galaxy are based on the Chemfp tools (Dalke 2013).
Search-based test data generation methods mostly consider the branch coverage criterion. To the best of our knowledge, only two works exist which propose a fitness function that can support the prime path coverage criterion, while this criterion subsumes the branch coverage criterion. These works are based on the Genetic Algorithm (GA) while scalability of the evolutionary algorithms like GA is questionable. Since there is a general agreement that evolutionary algorithms are inferior to swarm intelligence algorithms, we propose a new approach based on swarm intelligence for covering prime paths. We utilize two prominent swarm intelligence algorithms, i.e., ACO and PSO, along with a new normalized fitness function to provide a better approach for covering prime paths. To make ACO applicable for the test data generation problem, we provide a customization of this algorithm. The experimental results show that PSO and the proposed customization of ACO are both more efficient and more effective than GA when generating test data to cover prime paths. Also, the customized ACO, in comparison to PSO, has better effectiveness while has a worse efficiency.
As shown in Fig. 1, in order to cover prime paths, the test data generation method should be capable to cover those test paths that pass through loops one or more times. Therefore, a search-based test data generation method which regards the prime path coverage criterion needs an appropriate fitness function. To the best of our knowledge, only two works [8, 21] exist proposing fitness functions that can support the prime path coverage criterion. We refer to these fitness functions as NEHD  and BP1 . However, the mentioned works are based on GA, while swarm intelligence algorithms have shown considerable results in the optimization problems .
In PSO , each particle keeps track of a position which is the best solution it has achieved so far as pbx; and the globally optimal solution is stored as gbest. The basic steps of PSO are as follow.
A major challenge for applying ACO to test data generation is the form of pheromone because the search space is continuing and it does not have either node or edge for defining pheromone on it. To tackle this problem, we partition the search space by partitioning every domain of each input variable to b equivalent parts that can be any number dividable by the range of input domain. The best value for b is obtained from sensitivity analysis which is explained more in Sect. 5.
NGSeasy has provided us with the opportunity to start defining and thinking about best practices for building Dockerised modular pipelines. Many of these practices have been adapted in our images. Our (compbio/ngseasy-base) image forms the foundation layer on which each pipeline container application is built.
We include what we think of as some of the best and most useful NGS "power tools" in compbio/ngseasy-base image ( Table 1). These are all tools that allow the user to manipulate BED/SAM/BAM/VCF files in a variety of ways.
Many useful optimisations and recommendations were adapted from bcbio-nextgen ( -nextgen.readthedocs.org/en/latest/ - A python toolkit providing best practice pipelines for fully automated high throughput sequencing analysis - and speedseq ( ) - a flexible and open source framework to rapidly identify genomic variation 33 .
Users should note that deploying the pipeline containers is fairly fast, dependant on network speeds, however, downloading the reference genomes and test datasets for the resources folder can take a while. For example, the install time averages at about 94 min on machines connected to relatively fast networks (i.e. > 500 Mbit/s).
We are presenting these results solely as a "proof of concept". That is, we have successfully Dockerised a full NGS pipeline, that is capable of producing meaningful results, that are comparable with public and "best practice" workflows.
All data is available from the AWS EU region. This may take much longer to download outside of this region. I'm not sure if the authors can do anything about this however as using a CDN may be prohibitive.
For reporting of download/install times, please also list install times from more standard connection speeds. A majority of users will not have 500Mb/s or better download. Is it possible to download subsets of the data? It looks like it currently grabs both hg19, b37 and hs37d5, tripling the download times and space required. Digging into the code it wasn't clear how to get other mentioned genomes like hs38DH.
It's hard to judge the results of your validation without comparing to another best-practice pipeline like bwa + GATK HaplotypeCaller. Having these as a baseline next to your comparisons would strengthen the argument that the current implementation does a comparable job to expected best practice.
What is your experience with larger Docker containers and Docker Hub? Practically I've found a lot of timeout issues trying to download and manage larger images. Do you have workaround/experience with these issues? 350c69d7ab