238 research outputs found
Non-parametric Bayesian modelling of digital gene expression data
Next-generation sequencing technologies provide a revolutionary tool for
generating gene expression data. Starting with a fixed RNA sample, they
construct a library of millions of differentially abundant short sequence tags
or "reads", which constitute a fundamentally discrete measure of the level of
gene expression. A common limitation in experiments using these technologies is
the low number or even absence of biological replicates, which complicates the
statistical analysis of digital gene expression data. Analysis of this type of
data has often been based on modified tests originally devised for analysing
microarrays; both these and even de novo methods for the analysis of RNA-seq
data are plagued by the common problem of low replication. We propose a novel,
non-parametric Bayesian approach for the analysis of digital gene expression
data. We begin with a hierarchical model for modelling over-dispersed count
data and a blocked Gibbs sampling algorithm for inferring the posterior
distribution of model parameters conditional on these counts. The algorithm
compensates for the problem of low numbers of biological replicates by
clustering together genes with tag counts that are likely sampled from a common
distribution and using this augmented sample for estimating the parameters of
this distribution. The number of clusters is not decided a priori, but it is
inferred along with the remaining model parameters. We demonstrate the ability
of this approach to model biological data with high fidelity by applying the
algorithm on a public dataset obtained from cancerous and non-cancerous neural
tissues
Genomic scale sub-family assignment of protein domains
Many classification schemes for proteins and domains are either hierarchical or semi-hierarchical yet most databases, especially those offering genome-wide analysis, only provide assignments to sequences at one level of their hierarchy. Given an established hierarchy, the problem of assigning new sequences to lower levels of that existing hierarchy is less hard (but no less important) than the initial top level assignment which requires the detection of the most distant relationships. A solution to this problem is described here in the form of a new procedure which can be thought of as a hybrid between pairwise and profile methods. The hybrid method is a general procedure that can be applied to any pre-defined hierarchy, at any level, including in principle multiple sub-levels. It has been tested on the SCOP classification via the SUPERFAMILY database and performs significantly better than either pairwise or profile methods alone. Perhaps the greatest advantage of the hybrid method over other possible approaches to the problem is that within the framework of an existing profile library, the assignments are fully automatic and come at almost no additional computational cost. Hence it has already been applied at the SCOP family level to all genomes in the SUPERFAMILY database, providing a wealth of new data to the biological and bioinformatics communities
Improving protein secondary structure prediction using a simple k-mer model
Motivation: Some first order methods for protein sequence analysis inherently treat each position as independent. We develop a general framework for introducing longer range interactions. We then demonstrate the power of our approach by applying it to secondary structure prediction; under the independence assumption, sequences produced by existing methods can produce features that are not protein like, an extreme example being a helix of length 1. Our goal was to make the predictions from state of the art methods more realistic, without loss of performance by other measures
The SUPERFAMILY database in 2007: families and functions
The SUPERFAMILY database provides protein domain assignments, at the SCOP ‘superfamily’ level, for the predicted protein sequences in over 400 completed genomes. A superfamily groups together domains of different families which have a common evolutionary ancestor based on structural, functional and sequence data. SUPERFAMILY domain assignments are generated using an expert curated set of profile hidden Markov models. All models and structural assignments are available for browsing and download from . The web interface includes services such as domain architectures and alignment details for all protein assignments, searchable domain combinations, domain occurrence network visualization, detection of over- or under-represented superfamilies for a given genome by comparison with other genomes, assignment of manually submitted sequences and keyword searches. In this update we describe the SUPERFAMILY database and outline two major developments: (i) incorporation of family level assignments and (ii) a superfamily-level functional annotation. The SUPERFAMILY database can be used for general protein evolution and superfamily-specific studies, genomic annotation, and structural genomics target suggestion and assessment
TreeVector: Scalable, Interactive, Phylogenetic Trees for the Web
Background: Phylogenetic trees are complex data forms that need to be graphically displayed to be human-readable. Traditional techniques of plotting phylogenetic trees focus on rendering a single static image, but increases in the production of biological data and large-scale analyses demand scalable, browsable, and interactive trees. Methodology/Principal Findings: We introduce TreeVector, a Scalable Vector Graphics–and Java-based method that allows trees to be integrated and viewed seamlessly in standard web browsers with no extra software required, and can be modified and linked using standard web technologies. There are now many bioinformatics servers and databases with a range of dynamic processes and updates to cope with the increasing volume of data. TreeVector is designed as a framework to integrate with these processes and produce user-customized phylogenies automatically. We also address the strengths of phylogenetic trees as part of a linked-in browsing process rather than an end graphic for print. Conclusions/Significance: TreeVector is fast and easy to use and is available to download precompiled, but is also open source. It can also be run from the web server listed below or the user’s own web server. It has already been deployed o
Evolution of the calcium-based intracellular signalling system
To progress our understanding of molecular evolution from a collection of well-studied genes toward the level of the cell, we must consider whole systems. Here, we reveal the evolution of an important intracellular signaling system. The calcium-signaling toolkit is made up of different multidomain proteins that have undergone duplication, recombination, sequence divergence, and selection. The picture of evolution, considering the repertoire of proteins in the toolkit of both extant organisms and ancestors, is radically different from that of other systems. In eukaryotes, the repertoire increased in both abundance and diversity at a far greater rate than general genomic expansion. We describe how calcium-based intracellular signaling evolution differs not only in rate but in nature, and how this correlates with the disparity of plants and animals
Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer
Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer
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