Detection of generic spaced motifs using submotif pattern mining

MOTIVATION: Identification of motifs is one of the critical stages in studying the regulatory interactions of genes. Motifs can have complicated patterns. In particular, spaced motifs, an important class of motifs, consist of several short segments separated by spacers of different lengths. Locating spaced motifs is not trivial. Existing motif-finding algorithms are either designed for monad motifs (short contiguous patterns with some mismatches) or have assumptions on the spacer lengths or can only handle at most two segments. An effective motif finder for generic spaced motifs is highly desirable. RESULTS: This article proposes a novel approach for identifying spaced motifs with any number of spacers of different lengths. We introduce the notion of submotifs to capture the segments in the spaced motif and formulate the motif-finding problem as a frequent submotif mining problem. We provide an algorithm called SPACE to solve the problem. Based on experiments on real biological datasets, synthetic datasets and the motif assessment benchmarks by Tompa et al., we show that our algorithm performs better than existing tools for spaced motifs with improvements in both sensitivity and specificity and for monads, SPACE performs as good as other tools. AVAILABILITY: The source code is available upon request from the authors.     

A combinatorial optimization approach for diverse motif finding applications

Background  

Discovering approximately repeated patterns, or motifs, in biological sequences is an important and widely-studied problem in computational molecular biology. Most frequently, motif finding applications arise when identifying shared regulatory signals within DNA sequences or shared functional and structural elements within protein sequences. Due to the diversity of contexts in which motif finding is applied, several variations of the problem are commonly studied.     

Subtle motifs: defining the limits of motif finding algorithms

MOTIVATION: What constitutes a subtle motif? Intuitively, it is a motif that is almost indistinguishable, in the statistical sense, from random motifs. This question has important practical consequences: consider, for example, a biologist that is generating a sample of upstream regulatory sequences with the goal of finding a regulatory pattern that is shared by these sequences. If the sequences are too short then one risks losing some of the regulatory patterns that are located further upstream. Conversely, if the sequences are too long, the motif becomes too subtle and one is then likely to encounter random motifs which are at least as significant statistically as the regulatory pattern itself. In practical terms one would like to recognize the sequence length threshold, or the twilight zone, beyond which the motifs are in some sense too subtle. RESULTS: The paper defines the motif twilight zone where every motif finding algorithm would be exposed to random motifs which are as significant as the one which is sought. We also propose an objective tool for evaluating the performance of subtle motif finding algorithms. Finally we apply these tools to evaluate the success of our MULTIPROFILER algorithm to detect subtle motifs.     

Nectar discovery speeds and multimodal displays: assessing nectar search times in bees with radiating and non-radiating guides

Floral displays are often composed of areas of contrasting stimuli which flower visitors use as guides, increasing both foraging efficiency and the likelihood of pollen transfer. Many aspects of how these displays benefit foraging efficiency are still unexplored, particularly those surrounding multimodal signals and the spatial arrangement of the display components. We compare the nectar discovery times of forager bumblebees (Bombus terrestris) when presented with artificial flowers with unimodal or compound displays of visual and/or olfactory stimuli, positioned in either radiating or non-radiating arrangements. We found that the addition of individual display components from either modality reduces nectar discovery time but there was no time benefit to bimodal displays over unimodal displays or any benefit to radiating stimuli arrangements over non-radiating arrangements. However, preference tests revealed a time advantage to radiating unimodal visual patterns over non-radiating unimodal visual patterns when both types were displayed simultaneously. These results suggest that the benefits of multimodal stimuli arrangements to pollinators are unrelated to benefits in nectar discovery time. Our results also suggest that spatial patterns of scent can be used as nectar guides and can reduce nectar discovery times without the aid of visual stimuli.     

Transcription factor binding site identification using the self-organizing map

MOTIVATION: The automatic identification of over-represented motifs present in a collection of sequences continues to be a challenging problem in computational biology. In this paper, we propose a self-organizing map of position weight matrices as an alternative method for motif discovery. The advantage of this approach is that it can be used to simultaneously characterize every feature present in the dataset, thus lessening the chance that weaker signals will be missed. Features identified are ranked in terms of over-representation relative to a background model. RESULTS: We present an implementation of this approach, named SOMBRERO (self-organizing map for biological regulatory element recognition and ordering), which is capable of discovering multiple distinct motifs present in a single dataset. Demonstrated here are the advantages of our approach on various datasets and SOMBRERO's improved performance over two popular motif-finding programs, MEME and AlignACE. AVAILABILITY: SOMBRERO is available free of charge from http://bioinf.nuigalway.ie/sombrero SUPPLEMENTARY INFORMATION: http://bioinf.nuigalway.ie/sombrero/additional.     

Experimental detection of short regulatory motifs in eukaryotic proteins: tips for good practice as well as for bad

It has become clear in outline though not yet in detail how cellular regulatory and signalling systems are constructed. The essential machines are protein complexes that effect regulatory decisions by undergoing internal changes of state. Subcomponents of these cellular complexes are assembled into molecular switches. Many of these switches employ one or more short peptide motifs as toggles that can move between one or more sites within the switch system, the simplest being on-off switches. Paradoxically, these motif modules (termed short linear motifs or SLiMs) are both hugely abundant but difficult to research. So despite the many successes in identifying short regulatory protein motifs, it is thought that only the “tip of the iceberg” has been exposed. Experimental and bioinformatic motif discovery remain challenging and error prone. The advice presented in this article is aimed at helping researchers to uncover genuine protein motifs, whilst avoiding the pitfalls that lead to reports of false discovery.     

DNA motif representation with nucleotide dependency

The problem of discovering novel motifs of binding sites is important to the understanding of gene regulatory networks. Motifs are generally represented by matrices (position weight matrix (PWM) or position specific scoring matrix (PSSM) or strings. However, these representations cannot model biological binding sites well because they fail to capture nucleotide interdependence. It has been pointed out by many researchers that the nucleotides of the DNA binding site cannot be treated independently, e.g. the binding sites of zinc finger in proteins. In this paper, a new representation called Scored Position Specific Pattern (SPSP), which is a generalization of the matrix and string representations, is introduced which takes into consideration the dependent occurrences of neighboring nucleotides. Even though the problem of discovering the optimal motif in SPSP representation is proved to be NP-hard, we introduce a heuristic algorithm called SPSP-Finder, which can effectively find optimal motifs in most simulated cases and some real cases for which existing popular motif finding software, such as Weeder, MEME and AlignACE, fail.     

Discovery genetics: serendipity in basic research

The role of serendipity in science has no better example than the discovery of spontaneous mutations that leads to new mouse models for research. The approach of finding phenotypes and then carrying out genetic analysis is called forward genetics. Serendipity is a key component of discovering and developing mice with spontaneous mutations into animal models of human disease. In this article, the role of serendipity in discovering and developing mouse models is described within a program at The Jackson Laboratory that capitalizes on serendipitous discoveries in large breeding colonies. Also described is how any scientists working with mice can take advantage of serendipitous discoveries as a research strategy to develop new models. Spontaneous mutations cannot be planned but happen in all research mouse colonies and are discovered as unexpected phenotypes. The alert scientist or technician can rationally exploit such chance observations to create new research opportunities.     

The computational challenges of applying comparative-based computational methods to whole genomes

The explosion in genomic sequence available in public databases has resulted in an unprecedented opportunity for computational whole genome analyses. A number of promising comparative-based approaches have been developed for gene finding, regulatory element discovery and other purposes, and it is clear that these tools will play a fundamental role in analysing the enormous amount of new data that is currently being generated. The synthesis of computationally intensive comparative computational approaches with the requirement for whole genome analysis represents both an unprecedented challenge and opportunity for computational scientists. We focus on a few of these challenges, using by way of example the problems of alignment, gene finding and regulatory element discovery, and discuss the issues that have arisen in attempts to solve these problems in the context of whole genome analysis pipelines.