Bioinformatics tools for analysing viral genomic data

The field of viral genomics and bioinformatics is experiencing a strong resurgence due to high-throughput sequencing (HTS) technology, which enables the rapid and cost-effective sequencing and subsequent assembly of large numbers of viral genomes. In addition, the unprecedented power of HTS technologies has enabled the analysis of intra-host viral diversity and quasispecies dynamics in relation to important biological questions on viral transmission, vaccine resistance and host jumping. HTS also enables the rapid identification of both known and potentially new viruses from field and clinical samples, thus adding new tools to the fields of viral discovery and metagenomics. Bioinformatics has been central to the rise of HTS applications because new algorithms and software tools are continually needed to process and analyse the large, complex datasets generated in this rapidly evolving area. In this paper, the authors give a brief overview of the main bioinformatics tools available for viral genomic research, with a particular emphasis on HTS technologies and their main applications. They summarise the major steps in various HTS analyses, starting with quality control of raw reads and encompassing activities ranging from consensus and de novo genome assembly to variant calling and metagenomics, as well as RNA sequencing

Gibbs Sampling with Low-Power Spiking Digital Neurons

Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inference and learning in these algorithms uses a Markov Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms the kernel of this sampler which can be realized from the firing statistics of noisy integrate-and-fire neurons on a neuromorphic VLSI substrate. This paper demonstrates such an implementation on an array of digital spiking neurons with stochastic leak and threshold properties for inference tasks and presents some key performance metrics for such a hardware-based sampler in both the generative and discriminative contexts.Comment: Accepted at ISCAS 201

Analysis of Paramyxovirus Transcription and Replication by High-Throughput Sequencing.

We have developed a high-throughput sequencing (HTS) workflow for investigating paramyxovirus transcription and replication. We show that sequencing of oligo(dT)-selected polyadenylated mRNAs, without considering the orientation of the RNAs from which they had been generated, cannot accurately be used to analyze the abundance of viral mRNAs because genomic RNA copurifies with the viral mRNAs. The best method is directional sequencing of infected cell RNA that has physically been depleted of ribosomal and mitochondrial RNA followed by bioinformatic steps to differentiate data originating from genomes from viral mRNAs and antigenomes. This approach has the advantage that the abundance of viral mRNA (and antigenomes) and genomes can be analyzed and quantified from the same data. We investigated the kinetics of viral transcription and replication during infection of A549 cells with parainfluenza virus type 2 (PIV2), PIV3, PIV5, or mumps virus and determined the abundances of individual viral mRNAs and readthrough mRNAs. We found that the mRNA abundance gradients differed significantly between all four viruses but that for each virus the pattern remained relatively stable throughout infection. We suggest that rapid degradation of non-poly(A) mRNAs may be primarily responsible for the shape of the mRNA abundance gradient in parainfluenza virus 3, whereas a combination of this factor and disengagement of RNA polymerase at intergenic sequences, particularly those at the NP:P and P:M gene boundaries, may be responsible in the other viruses.IMPORTANCE High-throughput sequencing (HTS) of virus-infected cells can be used to study in great detail the patterns of virus transcription and replication. For paramyxoviruses, and by analogy for all other negative-strand RNA viruses, we show that directional sequencing must be used to distinguish between genomic RNA and mRNA/antigenomic RNA because significant amounts of genomic RNA copurify with poly(A)-selected mRNA. We found that the best method is directional sequencing of total cell RNA, after the physical removal of rRNA (and mitochondrial RNA), because quantitative information on the abundance of both genomic RNA and mRNA/antigenomes can be simultaneously derived. Using this approach, we revealed new details of the kinetics of virus transcription and replication for parainfluenza virus (PIV) type 2, PIV3, PIV5, and mumps virus, as well as on the relative abundance of the individual viral mRNAs

Anatomy of a cortical simulator

Insights into brain’s high-level computational principles will lead to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. An important step towards this end is the study of large networks of cortical spiking neurons. We have built a cortical simulator, C2, incorporating several algorithmic enhancements to optimize the simulation scale and time, through: computationally efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion; memory efficient representation of simulation state; and communication efficient message exchanges. Using phenomenological, single-compartment models of spiking neurons and synapses with spike-timing dependent plasticity, we represented a rat-scale cortical model (55 million neurons, 442 billion synapses) in 8TB memory of a 32,768processor BlueGene/L. With 1 millisecond resolution for neuronal dynamics and 1-20 milliseconds axonal delays, C2 can simulate 1 second of model time in 9 seconds per Hertz of average neuronal firing rate. In summary, by combining state-of-the-art hardware with innovative algorithms and software design, we simultaneously achieved unprecedented time-to-solution on an unprecedented problem size. 1

Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core

We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including its capacity to decorrelate similar odor representations without foreknowledge of the statistical distributions of analyte features. Our system is based on a digital neuromorphic chip consisting of 256 leaky-integrate-and-fire neurons, 1024 × 256 crossbar synapses, and address-event representation communication circuits. The neural circuits configured in the chip reflect established connections among mitral cells, periglomerular cells, external tufted cells, and superficial short-axon cells within the olfactory bulb, and accept input from convergent sets of sensors configured as olfactory sensory neurons. This configuration generates functional transformations comparable to those observed in the glomerular layer of the mammalian olfactory bulb. Our circuits, consuming only 45 pJ of active power per spike with a power supply of 0.85 V, can be used as the first stage of processing in low-power artificial chemical sensing devices inspired by natural olfactory systems

ViCTree: an automated framework for taxonomic classification from protein sequences

Motivation: The increasing rate of submission of genetic sequences into public databases is providing a growing resource for classifying the organisms that these sequences represent. To aid viral classification, we have developed ViCTree, which automatically integrates the relevant sets of sequences in NCBI GenBank and transforms them into an interactive maximum likelihood phylogenetic tree that can be updated automatically. ViCTree incorporates ViCTreeView, which is a JavaScript-based visualisation tool that enables the tree to be explored interactively in the context of pairwise distance data. Results: To demonstrate utility, ViCTree was applied to subfamily Densovirinae of family Parvoviridae. This led to the identification of six new species of insect virus. Availability: ViCTree is open-source and can be run on any Linux- or Unix-based computer or cluster. A tutorial, the documentation and the source code are available under a GPL3 license, and can be accessed at http://bioinformatics.cvr.ac.uk/victree_web/