On Weight Matrix and Free Energy Models for Sequence Motif Detection

The problem of motif detection can be formulated as the construction of a discriminant function to separate sequences of a specific pattern from background. In computational biology, motif detection is used to predict DNA binding sites of a transcription factor (TF), mostly based on the weight matrix (WM) model or the Gibbs free energy (FE) model. However, despite the wide applications, theoretical analysis of these two models and their predictions is still lacking. We derive asymptotic error rates of prediction procedures based on these models under different data generation assumptions. This allows a theoretical comparison between the WM-based and the FE-based predictions in terms of asymptotic efficiency. Applications of the theoretical results are demonstrated with empirical studies on ChIP-seq data and protein binding microarray data. We find that, irrespective of underlying data generation mechanisms, the FE approach shows higher or comparable predictive power relative to the WM approach when the number of observed binding sites used for constructing a discriminant decision is not too small.Comment: 23 pages, 1 figure and 4 table

An Allergen Portrait Gallery: Representative Structures and an Overview of IgE Binding Surfaces

Recent progress in the biochemical classification and structural determination of allergens and allergen–antibody complexes has enhanced our understanding of the molecular determinants of allergenicity. Databases of allergens and their epitopes have facilitated the clustering of allergens according to their sequences and, more recently, their structures. Groups of similar sequences are identified for allergenic proteins from diverse sources, and all allergens are classified into a limited number of protein structural families. A gallery of experimental structures selected from the protein classes with the largest number of allergens demonstrate the structural diversity of the allergen universe. Further comparison of these structures and identification of areas that are different from innocuous proteins within the same protein family can be used to identify features specific to known allergens. Experimental and computational results related to the determination of IgE binding surfaces and methods to define allergen-specific motifs are highlighted

Motif Discovery through Predictive Modeling of Gene Regulation

We present MEDUSA, an integrative method for learning motif models of transcription factor binding sites by incorporating promoter sequence and gene expression data. We use a modern large-margin machine learning approach, based on boosting, to enable feature selection from the high-dimensional search space of candidate binding sequences while avoiding overfitting. At each iteration of the algorithm, MEDUSA builds a motif model whose presence in the promoter region of a gene, coupled with activity of a regulator in an experiment, is predictive of differential expression. In this way, we learn motifs that are functional and predictive of regulatory response rather than motifs that are simply overrepresented in promoter sequences. Moreover, MEDUSA produces a model of the transcriptional control logic that can predict the expression of any gene in the organism, given the sequence of the promoter region of the target gene and the expression state of a set of known or putative transcription factors and signaling molecules. Each motif model is either a $k$-length sequence, a dimer, or a PSSM that is built by agglomerative probabilistic clustering of sequences with similar boosting loss. By applying MEDUSA to a set of environmental stress response expression data in yeast, we learn motifs whose ability to predict differential expression of target genes outperforms motifs from the TRANSFAC dataset and from a previously published candidate set of PSSMs. We also show that MEDUSA retrieves many experimentally confirmed binding sites associated with environmental stress response from the literature.Comment: RECOMB 200

Patterns of predation and meat-eating by chacma baboons in an Afromontane environment

Meat-eating among non-human primates has been well documented but its prevalence among Afromontane baboons is understudied. In this study we report the predatory and meat-eating behaviours of a habituated group of gray-footed chacma baboons (Papio ursinus griseipes) living in an Afromontane environment in South Africa. We calculated a vertebrate-eating rate of 1 every 78.5 hours, increasing to 58.1 hours when unsuccessful predation attempts were included. A key food source was young antelopes, particularly bushbuck (Tragelaphus scriptus), which were consumed once every 115 observation hours. Similar to other baboon research sites, predations seemed mostly opportunistic, adult males regularly scrounged and monopolised prey, there was no evidence they used an active kill bite, and active sharing was absent. This is the first baboon study to report predation of rock python (Python sebae) eggs and likely scavenging of a leopard (Panthera pardus) kill (bushbuck) cached in a tree. We also describe several scramble kleptoparasitism events, tolerating active defence from antelope parents, and the baboons inhibiting public information about predations. In the latter case, baboons with meat often hid beyond the periphery of the group, reducing the likelihood of scrounging by competitors. This often led to prey carcasses being discarded without being fully exploited and potentially providing resources to scavengers. We also highlight the absence of encounters with numerous species, suggesting the baboons are a key component of several species’ landscapes of fear. Given these findings it seems likely that their ecological role in the Soutpansberg has been undervalued, and such conclusions may also hold for other baboon populations

New fossil remains of Homo naledi from the Lesedi Chamber, South Africa

The Rising Star cave system has produced abundant fossil hominin remains within the Dinaledi Chamber, representing a minimum of 15 individuals attributed to Homo naledi. Further exploration led to the discovery of hominin material, now comprising 131 hominin specimens, within a second chamber, the Lesedi Chamber. The Lesedi Chamber is far separated from the Dinaledi Chamber within the Rising Star cave system, and represents a second depositional context for hominin remains. In each of three collection areas within the Lesedi Chamber, diagnostic skeletal material allows a clear attribution to H. naledi. Both adult and immature material is present. The hominin remains represent at least three individuals based upon duplication of elements, but more individuals are likely present based upon the spatial context. The most significant specimen is the near-complete cranium of a large individual, designated LES1, with an endocranial volume of approximately 610 ml and associated postcranial remains. The Lesedi Chamber skeletal sample extends our knowledge of the morphology and variation of H. naledi, and evidence of H. naledi from both recovery localities shows a consistent pattern of differentiation from other hominin species.SP201

Dynamic Bioluminescence Imaging: Development of a Physiological Pharmacokinetic Model of Tumor Metabolism

poster abstractBioluminescence (BLI) is a technology which has been studied extensively across multiple genera for more than 90 years. Over this period, BLI has emerged as a powerful noninvasive tool to study tumor localization, growth, and response to therapy due to the relatively recent technological advancements in instrumentation and molecular biology. This technology takes advantage of molecular transfection of the luciferase (LUC) gene from the North American firefly, Photinus pyralis, into human cancer cells, which are then implanted (ectopic or orthotopic) in mice. Oxidation of the exogenously administered substrate D-luciferin by the LUC gene product results in emission of green-yellow photons which are then evaluated in the context of tumor growth and development. Despite the more than 30 years of characterization, there exists a fundamental gap in our knowledge of the underlying PK/PD processes which are at the heart of nearly all BLI interpretation, and has lead to a dogmatic adherence in the literature to numerical methods which are at best simple corollaries of tumor metabolic rate. In an attempt to fill this void, this paper will present a new PK/PD model which takes advantage of the temporal nature of both substrate transport and light evolution. In addition, we will compare these results to traditional non-model based analyses and show how they differ. Lastly we will present OATS (One at A Time) Parameter Sensitivity and Monte Carlo Noise Analysis to characterize the numerical stability and sensitivity of this new model