De Novo Assembly of Nucleotide Sequences in a Compressed Feature Space

Sequencing technologies allow for an in-depth analysis of biological species but the size of the generated datasets introduce a number of analytical challenges. Recently, we demonstrated the application of numerical sequence representations and data transformations for the alignment of short reads to a reference genome. Here, we expand out approach for de novo assembly of short reads. Our results demonstrate that highly compressed data can encapsulate the signal suffi- ciently to accurately assemble reads to big contigs or complete genomes

On the origins of Mendelian disease genes in man: the impact of gene duplication

Over 3,000 human diseases are known to be linked to heritable genetic variation, mapping to over 1,700 unique genes. Dating of the evolutionary age of these disease-associated genes has suggested that they have a tendency to be ancient, specifically coming into existence with early metazoa. The approach taken by past studies, however, assumes that the age of a disease is the same as the age of its common ancestor, ignoring the fundamental contribution of duplication events in the evolution of new genes and function. Here, we date both the common ancestor and the duplication history of known human disease-associated genes. We find that the majority of disease genes (80%) are genes that have been duplicated in their evolutionary history. Periods for which there are more disease-associated genes, for example, at the origins of bony vertebrates, are explained by the emergence of more genes at that time, and the majority of these are duplicates inferred to have arisen by whole-genome duplication. These relationships are similar for different disease types and the disease-associated gene's cellular function. This indicates that the emergence of duplication-associated diseases has been ongoing and approximately constant (relative to the retention of duplicate genes) throughout the evolution of life. This continued until approximately 390 Ma from which time relatively fewer novel genes came into existence on the human lineage, let alone disease genes. For single-copy genes associated with disease, we find that the numbers of disease genes decreases with recency. For the majority of duplicates, the disease-associated mutation is associated with just one of the duplicate copies. A universal explanation for heritable disease is, thus, that it is merely a by-product of the evolutionary process; the evolution of new genes (de novo or by duplication) results in the potential for new diseases to emerge

Innovation processes and industrial districts

In this survey, we examine the operations of innovation processes within industrial districts by exploring the ways in which differentiation, specialization, and integration affect the generation, diffusion, and use of new knowledge in such districts. We begin with an analysis of the importance of the division of labour and then investigate the effects of social embeddedness on innovation. We also consider the effect of forms of organization within industrial districts at various stages of product and process life, and we examine the negative aspects of embeddedness for innovation. We conclude with a discussion of the possible consequences of new information and communications technologies on innovation in industrial districts

Local Binary Patterns as a Feature Descriptor in Alignment-free Visualisation of Metagenomic Data

Shotgun sequencing has facilitated the analysis of complex microbial communities. However, clustering and visualising these communities without prior taxonomic information is a major challenge. Feature descriptor methods can be utilised to extract these taxonomic relations from the data. Here, we present a novel approach consisting of local binary patterns (LBP) coupled with randomised singular value decomposition (RSVD) and Barnes-Hut t-stochastic neighbor embedding (BH-tSNE) to highlight the underlying taxonomic structure of the metagenomic data. The effectiveness of our approach is demonstrated using several simulated and a real metagenomic datasets

Ebolavirus is evolving but not changing: No evidence for functional change in EBOV from 1976 to the 2014 outbreak

The 2014 epidemic of Ebola virus disease (EVD) has had a devastating impact in West Africa. Sequencing of ebolavirus (EBOV) from infected individuals has revealed extensive genetic variation, leading to speculation that the virus may be adapting to humans, accounting for the scale of the 2014 outbreak. We computationally analyze the variation associated with all EVD outbreaks, and find none of the amino acid replacements lead to identifiable functional changes. These changes have minimal effect on protein structure, being neither stabilizing nor destabilizing, are not found in regions of the proteins associated with known functions and tend to cluster in poorly constrained regions of proteins, specifically intrinsically disordered regions. We find no evidence that the difference between the current and previous outbreaks is due to evolutionary changes associated with transmission to humans. Instead, epidemiological factors are likely to be responsible for the unprecedented spread of EVD

Adaptive HIV-1 evolutionary trajectories are constrained by protein stability

Despite the use of combination antiretroviral drugs for the treatment of HIV-1 infection, the emergence of drug resistance remains a problem. Resistance may be conferred either by a single mutation or a concerted set of mutations. The involvement of multiple mutations can arise due to interactions between sites in the amino acid sequence as a consequence of the need to maintain protein structure. To better understand the nature of such epistatic interactions, we reconstructed the ancestral sequences of HIV-1's Pol protein, and traced the evolutionary trajectories leading to mutations associated with drug resistance. Using contemporary and ancestral sequences we modelled the effects of mutations (i.e. amino acid replacements) on protein structure to understand the functional effects of residue changes. Although the majority of resistance-associated sequences tend to destabilise the protein structure, we find there is a general tendency for protein stability to decrease across HIV-1's evolutionary history. That a similar pattern is observed in the non-drug resistance lineages indicates that non-resistant mutations, for example, associated with escape from the immune response, also impacts on protein stability. Maintenance of optimal protein structure therefore represents a major constraining factor to the evolution of HIV-1

Gender and school-level differences in students' moderate and vigorous physical activity levels when taught basketball through the tactical games model

The Tactical Games Model (TGM) prefaces the cognitive components of physical education (PE), which has implications for physical activity (PA) accumulation. PA recommendations suggest students reach 50% moderate-vigorous physical activity (MVPA). However, this criterion does not indicate the contribution from vigorous physical activity (VPA). Consequently, this study investigated: a) the effects of TGM delivery on MVPA/VPA and, b) gender/school level differences. Participants were 78 seventh and 96 fourth/fifth grade coeducational PE students from two different schools. Two teachers taught 24 (middle) and 30 (elementary) level one TGM basketball lessons. Students wore Actigraph GT3× triaxial accelerometers. Data were analyzed using four one-way ANOVAs. Middle school boys had significantly higher MVPA/VPA (34.04/22.37%) than girls (25.14/15.47%). Elementary school boys had significantly higher MVPA/VPA (29.73/18.33%) than girls (23.03/14.33%). While TGM lessons provide a context where students can accumulate VPA consistent with national PA recommendations, teachers need to modify lesson activities to enable equitable PA participation

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualisation toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery