Motif discovery in sequence data is fundamental to many biological problems such as antibody biomarker identification. Recent advances in instrumental techniques make it possible to generate thousands of protein sequences at once, which raises a big data issue for the existing motif finding algorithms: They either work only in a small scale of several hundred sequences or have to trade accuracy for efficiency. In this work, we demonstrate that by intelligently clustering sequences, it is possible to significantly improve the scalability of all the existing motif finding algorithms without losing accuracy at all. An anchor based sequence clustering algorithm (ASC) is thus proposed to divide a sequence dataset into multiple smaller clusters so that sequences sharing the same motif will be located into the same cluster. Then an existing motif finding algorithm can be applied to each individual cluster to generate motifs. In the end, the results from multiple clusters are merged together as final output. Experimental results show that our approach is generic and orders of magnitude faster than traditional motif finding algorithms. It can discover motifs from protein sequences in the scale that no existing algorithm can handle. In particular, ASC reduces the running time of a very popular motif finding algorithm, MEME, from weeks to a few minutes with even better accuracy.

BACKGROUND: Dendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons. We fit a log-linear model to the contingency table of spine features such as spine type and distance from the soma to first determine which features were important in modeling the spines, as well as the relationships between such features. A multinomial logistic regression was then used to predict the spine types using the features suggested by the log-linear model, along with neighboring spine information. Finally, an important variant of Ripley's K-function applicable to linear networks was used to study the spatial distribution of spines along dendrites. RESULTS: Our study indicated that in the culture system, (i) dendritic spine densities were "completely spatially random", (ii) spine type and distance from the soma were independent quantities, and most importantly, (iii) spines had a tendency to cluster with other spines of the same type. CONCLUSIONS: Although these results may vary with other systems, our primary contribution is the set of statistical tools for morphological modeling of spines which can be used to assess neuronal cultures following gene manipulation such as RNAi, and to study induced pluripotent stem cells differentiated to neurons.

The multifactorial nature of disease motivates the use of systems-level analyses to understand their pathology. We used a systems biology approach to study tau aggregation, one of the hallmark features of Alzheimer's disease. A mathematical model was constructed to capture the current state of knowledge concerning tau's behavior and interactions in cells. The model was implemented in silico in the form of ordinary differential equations. The identifiability of the model was assessed and parameters were estimated to generate two cellular states: a population of solutions that corresponds to normal tau homeostasis and a population of solutions that displays aggregation-prone behavior. The model of normal tau homeostasis was robust to perturbations, and disturbances in multiple processes were required to achieve an aggregation-prone state. The aggregation-prone state was ultrasensitive to perturbations in diverse subsets of networks. Tau aggregation requires that multiple cellular parameters are set coordinately to a set of values that drive pathological assembly of tau. This model provides a foundation on which to build and increase our understanding of the series of events that lead to tau aggregation and may ultimately be used to identify critical intervention points that can direct the cell away from tau aggregation to aid in the treatment of tau-mediated (or related) aggregation diseases including Alzheimer's.

MOTIVATION: Gene duplications and losses (GDLs) are important events in genome evolution. They result in expansion or contraction of gene families, with a likely role in phenotypic evolution. As more genomes become available and their annotations are improved, software programs capable of rapidly and accurately identifying the content of ancestral genomes and the timings of GDLs become necessary to understand the unique evolution of each lineage. RESULTS: We report EvolMAP, a new algorithm and software that utilizes a species tree-based gene clustering method to join all-to-all symmetrical similarity comparisons of multiple gene sets in order to infer the gene composition of multiple ancestral genomes. The algorithm further uses Dollo parsimony-based comparison of the inferred ancestral genes to pinpoint the timings of GDLs onto evolutionary intervals marked by speciation events. Using EvolMAP, first we analyzed the expansion of four families of G-protein coupled receptors (GPCRs) within animal lineages. Additional to demonstrating the unique expansion tree for each family, results also show that the ancestral eumetazoan genome contained many fewer GPCRs than modern animals, and these families expanded through concurrent lineage-specific duplications. Second, we analyzed the history of GDLs in mammalian genomes by comparing seven proteomes. In agreement with previous studies, we report that the mammalian gene family sizes have changed drastically through their evolution. Interestingly, although we identified a potential source of duplication for 75% of the gained genes, remaining 25% did not have clear-cut sources, revealing thousands of genes that have likely gained their distinct sequence identities within the descent of mammals. AVAILABILITY: Query server, source code and executable are available at http://kosik-web.mcdb.ucsb.edu/evolmap/index.htm .