BACKGROUND: Long non-coding RNAs (lncRNAs) play an important role in regulating gene expression and are thus important for determining phenotypes. Most attempts to measure selection in lncRNAs have focused on the primary sequence. The majority of small RNAs and at least some parts of lncRNAs must fold into specific structures to perform their biological function. Comprehensive assessments of selection acting on RNAs therefore must also encompass structure. Selection pressures acting on the structure of non-coding genes can be detected within multiple sequence alignments. Approaches of this type, however, have so far focused on negative selection. Thus, a computational method for identifying ncRNAs under positive selection is needed. RESULTS: We introduce the SSS-test (test for Selection on Secondary Structure) to identify positive selection and thus adaptive evolution. Benchmarks with biological as well as synthetic controls yield coherent signals for both negative and positive selection, demonstrating the functionality of the test. A survey of a lncRNA collection comprising 15,443 families resulted in 110 candidates that appear to be under positive selection in human. In 26 lncRNAs that have been associated with psychiatric disorders we identified local structures that have signs of positive selection in the human lineage. CONCLUSIONS: It is feasible to assay positive selection acting on RNA secondary structures on a genome-wide scale. The detection of human-specific positive selection in lncRNAs associated with cognitive disorder provides a set of candidate genes for further experimental testing and may provide insights into the evolution of cognitive abilities in humans. AVAILABILITY: The SSS-test and related software is available at: https://github.com/waltercostamb/SSS-test . The databases used in this work are available at: http://www.bioinf.uni-leipzig.de/Software/SSS-test/ .

BACKGROUND: Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM. RESULTS: We bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further. CONCLUSIONS: flowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.

Summary: A standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs; II) Put them in context with the structural sequence alignments they have been created from; III) Investigate results of model comparisons and highlight regions of interest. Availability and implementation: Source code (http://www.github.com/eggzilla/cmv), web-service (http://rna.informatik.uni-freiburg.de/CMVS). Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Dynamic programming algorithms provide exact solutions to many problems in computational biology, such as sequence alignment, RNA folding, hidden Markov models (HMMs), and scoring of phylogenetic trees. Structurally analogous algorithms compute optimal solutions, evaluate score distributions, and perform stochastic sampling. This is explained in the theory of Algebraic Dynamic Programming (ADP) by a strict separation of state space traversal (usually represented by a context free grammar), scoring (encoded as an algebra), and choice rule. A key ingredient in this theory is the use of yield parsers that operate on the ordered input data structure, usually strings or ordered trees. The computation of ensemble properties, such as a posteriori probabilities of HMMs or partition functions in RNA folding, requires the combination of two distinct, but intimately related algorithms, known as the inside and the outside recursion. Only the inside recursions are covered by the classical ADP theory. RESULTS: The ideas of ADP are generalized to a much wider scope of data structures by relaxing the concept of parsing. This allows us to formalize the conceptual complementarity of inside and outside variables in a natural way. We demonstrate that outside recursions are generically derivable from inside decomposition schemes. In addition to rephrasing the well-known algorithms for HMMs, pairwise sequence alignment, and RNA folding we show how the TSP and the shortest Hamiltonian path problem can be implemented efficiently in the extended ADP framework. As a showcase application we investigate the ancient evolution of HOX gene clusters in terms of shortest Hamiltonian paths. CONCLUSIONS: The generalized ADP framework presented here greatly facilitates the development and implementation of dynamic programming algorithms for a wide spectrum of applications.

We develop a theory of algebraic operations over linear and context-free grammars that makes it possible to combine simple "atomic" grammars operating on single sequences into complex, multi-dimensional grammars. We demonstrate the utility of this framework by constructing the search spaces of complex alignment problems on multiple input sequences explicitly as algebraic expressions of very simple one-dimensional grammars. In particular, we provide a fully worked frameshift-aware, semiglobal DNA-protein alignment algorithm whose grammar is composed of products of small, atomic grammars. The compiler accompanying our theory makes it easy to experiment with the combination of multiple grammars and different operations. Composite grammars can be written out in L(A)T(E)X for documentation and as a guide to implementation of dynamic programming algorithms. An embedding in Haskell as a domain-specific language makes the theory directly accessible to writing and using grammar products without the detour of an external compiler. Software and supplemental files available here: http://www.bioinf. uni-leipzig.de/Software/gramprod/.

Stream Fusion, a popular deforestation technique in the Haskell community, cannot fuse the concatMap combinator. This is a serious limitation, as concatMap represents computations on nested streams. The original implementation of Stream Fusion used the Glasgow Haskell Compiler's user-directed rewriting system. A transformation which allows the compiler to fuse many uses of concatMap has previously been proposed, but never implemented, because the host rewrite system was not expressive enough to implement the proposed transformation. In this paper, we develop a custom optimization plugin which implements the proposed concatMap transformation, and study the effectiveness of the transformation in practice. We also provide a new translation scheme for list comprehensions which enables them to be optimized. Within this framework, we extend the transformation to monadic streams. Code featuring uses of concatMap experiences significant speedup when compiled with this optimization. This allows Stream Fusion to outperform its rival, foldr/build, on many list computations, and enables performance-sensitive code to be expressed at a higher level of abstraction.

G-quadruplexes are abundant locally stable structural elements in nucleic acids. The combinatorial theory of RNA structures and the dynamic programming algorithms for RNA secondary structure prediction are extended here to incorporate G-quadruplexes using a simple but plausible energy model. With preliminary energy parameters, we find that the overwhelming majority of putative quadruplex-forming sequences in the human genome are likely to fold into canonical secondary structures instead. Stable G-quadruplexes are strongly enriched, however, in the 5'UTR of protein coding mRNAs.

We present a framework of dynamic programming combinators that provides a high-level environment to describe the recursions typical of dynamic programming over sequence data in a style very similar to algebraic dynamic programming (ADP). Using a combination of type-level programming and stream fusion leads to a substantial increase in performance, without sacrificing much of the convenience and theoretical underpinnings of ADP.

MOTIVATION: RNA secondary structure contains many non-canonical base pairs of different pair families. Successful prediction of these structural features leads to improved secondary structures with applications in tertiary structure prediction and simultaneous folding and alignment. RESULTS: We present a theoretical model capturing both RNA pair families and extended secondary structure motifs with shared nucleotides using 2-diagrams. We accompany this model with a number of programs for parameter optimization and structure prediction. AVAILABILITY: All sources (optimization routines, RNA folding, RNA evaluation, extended secondary structure visualization) are published under the GPLv3 and available at www.tbi.univie.ac.at/software/rnawolf/.

Stochastic models, such as hidden Markov models or stochastic context-free grammars (SCFGs) can fail to return the correct, maximum likelihood solution in the case of semantic ambiguity. This problem arises when the algorithm implementing the model inspects the same solution in different guises. It is a difficult problem in the sense that proving semantic nonambiguity has been shown to be algorithmically undecidable, while compensating for it (by coalescing scores of equivalent solutions) has been shown to be NP-hard. For stochastic context-free grammars modeling RNA secondary structure, it has been shown that the distortion of results can be quite severe. Much less is known about the case when stochastic context-free grammars model the matching of a query sequence to an implicit consensus structure for an RNA family. We find that three different, meaningful semantics can be associated with the matching of a query against the model--a structural, an alignment, and a trace semantics. Rfam models correctly implement the alignment semantics, and are ambiguous with respect to the other two semantics, which are more abstract. We show how provably correct models can be generated for the trace semantics. For approaches, where such a proof is not possible, we present an automated pipeline to check post factum for ambiguity of the generated models. We propose that both the structure and the trace semantics are worth-while concepts for further study, possibly better suited to capture remotely related family members.

MOTIVATION: RNA family models group nucleotide sequences that share a common biological function. These models can be used to find new sequences belonging to the same family. To succeed in this task, a model needs to exhibit high sensitivity as well as high specificity. As model construction is guided by a manual process, a number of problems can occur, such as the introduction of more than one model for the same family or poorly constructed models. We explore the Rfam database to discover such problems. RESULTS: Our main contribution is in the definition of the discriminatory power of RNA family models, together with a first algorithm for its computation. In addition, we present calculations across the whole Rfam database that show several families lacking high specificity when compared to other families. We give a list of these clusters of families and provide a tentative explanation. Our program can be used to: (i) make sure that new models are not equivalent to any model already present in the database; and (ii) new models are not simply submodels of existing families. AVAILABILITY: www.tbi.univie.ac.at/software/cmcompare/. The code is licensed under the GPLv3. Results for the whole Rfam database and supporting scripts are available together with the software.