MCMC-ODPR : primer design optimization using Markov Chain Monte Carlo sampling

Background Next generation sequencing technologies often require numerous primer designs that require good target coverage that can be financially costly. We aimed to develop a system that would implement primer reuse to design degenerate primers that could be designed around SNPs, thus find the fewest necessary primers and the lowest cost whilst maintaining an acceptable coverage and provide a cost effective solution. We have implemented Metropolis-Hastings Markov Chain Monte Carlo for optimizing primer reuse. We call it the Markov Chain Monte Carlo Optimized Degenerate Primer Reuse (MCMC-ODPR) algorithm. Results After repeating the program 1020 times to assess the variance, an average of 17.14% fewer primers were found to be necessary using MCMC-ODPR for an equivalent coverage without implementing primer reuse. The algorithm was able to reuse primers up to five times. We compared MCMC-ODPR with single sequence primer design programs Primer3 and Primer-BLAST and achieved a lower primer cost per amplicon base covered of 0.21 and 0.19 and 0.18 primer nucleotides on three separate gene sequences, respectively. With multiple sequences, MCMC-ODPR achieved a lower cost per base covered of 0.19 than programs BatchPrimer3 and PAMPS, which achieved 0.25 and 0.64 primer nucleotides, respectively. Conclusions MCMC-ODPR is a useful tool for designing primers at various melting temperatures at good target coverage. By combining degeneracy with optimal primer reuse the user may increase coverage of sequences amplified by the designed primers at significantly lower costs. Our analyses showed that overall MCMC-ODPR outperformed the other primer-design programs in our study in terms of cost per covered base

Using Probability to Reason about Soft Deadlines

Soft deadlines are significant in systems in which a bound on the response time is important, but the failure to meet the response time is not a disaster. Soft deadlines occur, for example, in telephony and switching networks. We investigate how to put probabilistic bounds on the time-complexity of a concurrent logic program by combining (on-line) profiling with an (off-line) probabilistic complexity analysis. The profiling collects information on the likelihood of case selection and the analysis uses this information to infer the probability of an agent terminating within k steps. Although the approach does not reason about synchronization, we believe that its simplicity and good (essentially quadratic) complexity mean that it is a promising first step in reasoning about soft deadlines

Systematic evaluation of design choices for software development tools

[Abstract]: Most design and evaluation of software tools is based on the intuition and experience of the designers. Software tool designers consider themselves typical users of the tools that they build and tend to subjectively evaluate their products rather than objectively evaluate them using established usability methods. This subjective approach is inadequate if the quality of software tools is to improve and the use of more systematic methods is advocated. This paper summarises a sequence of studies that show how user interface design choices for software development tools can be evaluated using established usability engineering techniques. The techniques used included guideline review, predictive modelling and experimental studies with users

Calibration of optimal execution of financial transactions in the presence of transient market impact

Trading large volumes of a financial asset in order driven markets requires the use of algorithmic execution dividing the volume in many transactions in order to minimize costs due to market impact. A proper design of an optimal execution strategy strongly depends on a careful modeling of market impact, i.e. how the price reacts to trades. In this paper we consider a recently introduced market impact model (Bouchaud et al., 2004), which has the property of describing both the volume and the temporal dependence of price change due to trading. We show how this model can be used to describe price impact also in aggregated trade time or in real time. We then solve analytically and calibrate with real data the optimal execution problem both for risk neutral and for risk averse investors and we derive an efficient frontier of optimal execution. When we include spread costs the problem must be solved numerically and we show that the introduction of such costs regularizes the solution.Comment: 31 pages, 8 figure