Cpg prediction software

Connect data using our outbound connectors to feed the spreadsheet, business intelligence, CRM, and planning tools of your choice. Our API integration lets you choose from hundreds of data sources. Raw data you need is programmatically converted using our API key and data tables to query the Crisp database. The result? A dynamic, unified way to easily connect all your data sources and tools in one place.

You have lots of data. But what do you do with all of it? And how can you harness it into insights you can use? Crisp gives you the data you need, with the ability to leverage it for a true competitive advantage. You can analyze and organize multiple sources of data, and then collect and transform it into unified, actionable insights for a supply chain you can rely on. Feeding a growing population sustainably and efficiently is a global challenge.

Using the Crisp platform collectively reduces global food waste. RxSugar keeps up with demand using real-time sales and inventory data across distribution channels.

CpGProD calculates these parameters to predict the strand of each potential promoter and the probability to be over this strand. You may also use the following table to directly access the files:. The CGIs separated by less than nucleotides are grouped together. Second step: CpGProD identifies the potential promoters and their orientation. The accuracies ACC of the supported algorithms were also higher [ 24 ]. CpGPAP has several advantages over other analysis platforms.

The platform also provides a graphical overview of the putative islands. A stand-alone version with no limitations on the input sequence length is also available. This stand-alone version includes a visual display function. CpGPAP integrates relevant approaches from the literature to provide the user with more options. The CpG island prediction parameters can be selected freely. We believe that the proposed predictor platform can be of assistance to biologists involved in the study of CpG islands.

The development of the analysis platform is a continuation of our previous CpG island study [ 24 ]. PSO is a population-based stochastic optimization algorithm, which was developed by simulating the social behavior of organisms [ 12 ]. In PSO, each particle in the search space can be considered to be an individual bird in a flock, which changes its position based on its memory and its knowledge of its neighbors. Each particle from a swarm represents a candidate solution.

The individual best value pbest i is the position of the i -th particle with the highest fitness at a given iteration; the best position of all pbest is called gbest. Particles use their individual memory pbest and the swarm's knowledge gbest as a whole to move around a multidimensional search space until the termination condition is reached. PSO has been successfully applied in many fields, including operon [ 25 ] and CpG island prediction [ 24 ], amongst others. GA is a stochastic search algorithm modeled after the process of natural selection that underlies biological evolution [ 13 ].

The standard GA procedure applies the following genetic operators: chromosome encoding and initialization, selection, crossover and mutation, which is the process by which a whole generation of new offsprings is computed. By applying genetic operators on strings in the mating pool, a new population of strings is formed in the next generation.

The implementation of the genetic operators is repeated in each subsequent generation until a termination condition is reached. GAs have been successfully applied in many fields, e. If the PSO and GA search processes fall into a local optimum for five consecutive generations, the complementary concept is used to leave this local region and re-enter the global search.

First, users select the optimization algorithms used to predict the CpG islands Figure 1A. Then, the optimization algorithm's parameters and CpG island related parameters are set and the input sequence is uploaded Figure 1B. Users can choose whether to display a visualization of the prediction results Figure 3 , and CpGPAP parameters can be freely modified. In addition, while the other algorithms were initially designed based on the GGF criteria i. License: none for academic users.

For any restrictions regarding the use by non-academics please contact the corresponding author. Feil R, Berger F: Convergent evolution of genomic imprinting in plants and mammals. Trend Genet. Nucleic Acids Res. J Mol Biol. Trends Genet. In Silico Biol. BMC Bioinforma. Article Google Scholar. PloS Comput Biol.

Phys Rev E. Kennedy J, Eberhart R: Particle swarm optimization. Google Scholar. Kienesberger PC, Oberer M, Lass A, Zechner R: Mammalian patatin domain containing proteins: a family with diverse lipolytic activities involved in multiple biological functions.

J Lipid Res. Nat Biotechnol. PLoS Genet. Br J Cancer. Biochem Biophys Res Commun. BMC Genomics. J Med Biol Eng. CAS Google Scholar. FEBS Lett. Cancer Res. PLoS One. Download references. You can also search for this author in PubMed Google Scholar. Correspondence to Cheng-Hong Yang. LYC conceived of the study and drafted the manuscript.