During the last four years, Mjølner Informatics has developed mobile banking apps for 11 Danish banks on the two major platforms iOS and Android, in total 22 apps. We make two contributions with this industrial practice paper. In the first place, we describe the development of these apps with emphasis on issues regarding variability handling and maximizing reuse. Secondly, we discuss a number of lessons learned from this process. This includes consideration of how ready we are to possible future challenges regarding variability and observation of significant differences between the two platforms.

Bankdata and Mjølner have cooperated in the development of a new feature for 12 Danish mobile banking apps. Bankdata is the main system provider and Mjølner is subcontractor. Different teams from Bankdata have collected requirements, developed the necessary backend and middleware software, and designed the user interface. One team from Mjølner has implemented the app feature. The cooperation between the teams was centered around design-level requirements. Our contribution is to describe and discuss a number of lessons learned regarding requirements representations, requirements tools, and cooperation process; we have faced challenges, which were amplified by our distributed teams set-up. We also briefly describe a number of initiatives we have launched recently to alleviate the problems and improve the handling of design-level requirements in our future cooperation.

Comparative methods for RNA secondary structure prediction use evolutionary information from RNA alignments to increase prediction accuracy. The model is often described in terms of stochastic context-free grammars (SCFGs), which generate a probability distribution over secondary structures. It is, however, unclear how this probability distribution changes as a function of the input alignment. As prediction programs typically only return a single secondary structure, better characterisation of the underlying probability space of RNA secondary structures is of great interest. In this work, we show how to efficiently compute the information entropy of the probability distribution over RNA secondary structures produced for RNA alignments by a phylo-SCFG, and implement it for the PPfold model. We also discuss interpretations and applications of this quantity, including how it can clarify reasons for low prediction reliability scores. PPfold and its source code are available from http://birc.au.dk/software/ppfold/.

UNLABELLED: PPfold is a multi-threaded implementation of the Pfold algorithm for RNA secondary structure prediction. Here we present a new version of PPfold, which extends the evolutionary analysis with a flexible probabilistic model for incorporating auxiliary data, such as data from structure probing experiments. Our tests show that the accuracy of single-sequence secondary structure prediction using experimental data in PPfold 3.0 is comparable to RNAstructure. Furthermore, alignment structure prediction quality is improved even further by the addition of experimental data. PPfold 3.0 therefore has the potential of producing more accurate predictions than it was previously possible. AVAILABILITY AND IMPLEMENTATION: PPfold 3.0 is available as a platform-independent Java application and can be downloaded from http://birc.au.dk/software/ppfold.

BACKGROUND: The prediction of the structure of large RNAs remains a particular challenge in bioinformatics, due to the computational complexity and low levels of accuracy of state-of-the-art algorithms. The pfold model couples a stochastic context-free grammar to phylogenetic analysis for a high accuracy in predictions, but the time complexity of the algorithm and underflow errors have prevented its use for long alignments. Here we present PPfold, a multithreaded version of pfold, which is capable of predicting the structure of large RNA alignments accurately on practical timescales. RESULTS: We have distributed both the phylogenetic calculations and the inside-outside algorithm in PPfold, resulting in a significant reduction of runtime on multicore machines. We have addressed the floating-point underflow problems of pfold by implementing an extended-exponent datatype, enabling PPfold to be used for large-scale RNA structure predictions. We have also improved the user interface and portability: alongside standalone executable and Java source code of the program, PPfold is also available as a free plugin to the CLC Workbenches. We have evaluated the accuracy of PPfold using BRaliBase I tests, and demonstrated its practical use by predicting the secondary structure of an alignment of 24 complete HIV-1 genomes in 65 minutes on an 8-core machine and identifying several known structural elements in the prediction. CONCLUSIONS: PPfold is the first parallelized comparative RNA structure prediction algorithm to date. Based on the pfold model, PPfold is capable of fast, high-quality predictions of large RNA secondary structures, such as the genomes of RNA viruses or long genomic transcripts. The techniques used in the parallelization of this algorithm may be of general applicability to other bioinformatics algorithms.

BACKGROUND: The population mutation rate (theta) remains one of the most fundamental parameters in genetics, ecology, and evolutionary biology. However, its accurate estimation can be seriously compromised when working with error prone data such as expressed sequence tags, low coverage draft sequences, and other such unfinished products. This study is premised on the simple idea that a random sequence error due to a chance accident during data collection or recording will be distributed within a population dataset as a singleton (i.e., as a polymorphic site where one sampled sequence exhibits a unique base relative to the common nucleotide of the others). Thus, one can avoid these random errors by ignoring the singletons within a dataset. RESULTS: This strategy is implemented under an infinite sites model that focuses on only the internal branches of the sample genealogy where a shared polymorphism can arise (i.e., a variable site where each alternative base is represented by at least two sequences). This approach is first used to derive independently the same new Watterson and Tajima estimators of theta, as recently reported by Achaz 1 for error prone sequences. It is then used to modify the recent, full, maximum-likelihood model of Knudsen and Miyamoto 2, which incorporates various factors for experimental error and design with those for coalescence and mutation. These new methods are all accurate and fast according to evolutionary simulations and analyses of a real complex population dataset for the California seahare. CONCLUSION: In light of these results, we recommend the use of these three new methods for the determination of theta from error prone sequences. In particular, we advocate the new maximum likelihood model as a starting point for the further development of more complex coalescent/mutation models that also account for experimental error and design.

UNLABELLED: We have developed a series of programs which assist in maintenance of structural RNA databases. A main program BLASTs the RNA database against GenBank and automatically extends and realigns the sequences to include the entire range of the RNA query sequences. After manual update of the database, other programs can examine base pair consistency and phylogenetic support. The output can be applied iteratively to refine the structural alignment of the RNA database. Using these tools, the number of potential misannotations per sequence was reduced from 20 to 3 in the Signal Recognition Particle RNA database. AVAILABILITY: A quick-server and programs are available at http://www.bioinf.au.dk/rnadbtool/

MOTIVATION: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic context-free grammars (SCFGs) to give a prior probability distribution of structures. RESULTS: The phylogenetic tree relating the sequences can be found by maximum likelihood (ML) estimation from the model introduced here. The tree is shown to reveal information about the structure, due to mutation patterns. The inclusion of a prior distribution of RNA structures ensures good structure predictions even for a small number of related sequences. Prediction is carried out using maximum a posteriori estimation (MAP) estimation in a Bayesian approach. For small sequence sets, the method performs very well compared to current automated methods.