MOTIVATION: Chromatin regulation is crucial for modulating gene expression and cellular function by altering DNA accessibility. Defining and understanding chromatin regulation across diverse biological conditions, including health and disease, requires quantification of both the presence and enrichment level of diverse DNA-binding factors and chromatin modifications across defined genomic regions. Existing approaches mainly rely on peak-based or genome-wide models, which identify high-signal regions but do not annotate chromatin status at predefined functional genomic regions, such as promoters or enhancers. This lack of region-based annotation limits downstream comparative and integrative analyses across multiple factors and datasets, prompting us to create ChromCall. RESULTS: ChromCall is an R package for region-based chromatin enrichment analysis that provides a robust and extensible foundation for transparent and reproducible epigenomic profiling at predefined genomic regions. We applied ChromCall to ChIP-seq data from glioblastoma (GBM) brain tumours and found that the promoters of genes implicated in treatment resistance are significantly more likely to exhibit a combination of histone marks associated with phenotypic plasticity. This highlights a potential novel mechanism of therapeutic escape in these deadly tumours. AVAILABILITY AND IMPLEMENTATION: The R package is available on https://github.com/GliomaGenomics/ChromCall and the version used in this paper is archived at https://doi.org/10.5281/zenodo.19580967.

This paper presents an investigation of nucleotide sequencing based random number generators, refutation of naive approaches to this problem, and a novel random number generator design based on the characteristics of nucleotide sequencers such as the Oxford Nanopore Technologies (ONT) MinION. Common issues include misunderstanding the statistical properties of nucleotide sequences and the provenance of entropy observed in post-processed sequences extracted from such data. We identify that the use of sequences, expressed as base-pair (ATCG) sequences, for random number generation is not possible. The process by which such sequences are observed and reported by scientific instrumentation, provide a means by which entropy associated with nucleotide sequences (or more correctly the act of observing and recording them) can be observed. We report a novel method of extracting entropy from the process of reading nucleotide sequences, as opposed to the nucleotide sequences themselves. We overcome the limitations and inherent bias of nucleotide sequences, to provide a source of randomness decoupled from biological data and records. A novel random number generator drawing on entropy extracted from nucleotide sequencing is presented with validation of its performance and characteristics.

SUMMARY: Tumour evolution results in progressive cancer phenotypes such as metastatic spread and treatment resistance. To better treat cancers, we must characterize tumour evolution and the genetic events that confer progressive phenotypes. This is facilitated by high coverage genome or exome sequencing. However, the best approach by which, or indeed whether, these data can be used to accurately model and interpret underlying evolutionary dynamics is yet to be confirmed. Establishing this requires sequencing data from appropriately heterogeneous tumours in which the exact trajectory and combination of events occurring throughout its evolution are known. We therefore developed HeteroGenesis: a tool to generate realistically evolved tumour genomes, which can be sequenced using weighted-Wessim (w-Wessim), an in silico exome sequencing tool that we have adapted from previous methods. HeteroGenesis simulates more complex and realistic heterogeneous tumour genomes than existing methods, can model different evolutionary dynamics, and enables the creation of multi-region and longitudinal data. AVAILABILITY AND IMPLEMENTATION: HeteroGenesis and w-Wessim are freely available under the GNU General Public Licence from https://github.com/GeorgetteTanner, implemented in Python and supported on linux and MS Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: The Sun Grid Engine (SGE) high-performance computing batch queueing system is commonly used in bioinformatics analysis. Creating re-usable scripts for the SGE is a common challenge. The qsubsec template language and interpreter described here allow researchers to easily create generic template definitions that encapsulate a particular computational job, effectively separating the process logic from the specific run details. At submission time, the generic template is filled in with specific values. This system provides an intermediate level between simple scripting and complete workflow management tools. AVAILABILITY AND IMPLEMENTATION: Qsubsec is open-source and is available at https://github.com/alastair-droop/qsubsec CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Many Next Generation Sequencing analyses involve the basic manipulation of input sequence data before downstream processing (e.g. searching for specific sequences, format conversion or basic file statistics). The rapidly increasing data volumes involved in NGS make any dataset manipulation a time-consuming and error-prone process. I have developed fqtools; a fast and reliable FASTQ file manipulation suite that can process the full set of valid FASTQ files, including those with multi-line sequences, whilst identifying invalid files. Fqtools is faster than similar tools, and is designed for use in automatic processing pipelines. AVAILABILITY AND IMPLEMENTATION: fqtools is open source and is available at: https://github.com/alastair-droop/fqtools SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: [email protected].

Open-ended evolutionary systems offer us the tantalising prospect of creating artificial life from simple precursors. One of the issues in designing open ended systems is that there exist few metrics for measuring their evolutionary activity. Current measures of evolutionary activity are only applicable to systems in which the fitness of a single component is defined by an explicit fitness function. However, this is not guaranteed in systems where a significant part of the fitness is intrinsic (for example caused by interactions between components). In this paper, we evaluate a new approach to the problem of measuring evolutionary activity that is applicable to systems exhibiting both explicit and intrinsic fitness pressures. To evaluate this measure, we ran 22,000 grid-based simulations of two automata chemistries, Tierra and Stringmol. Both of these systems have strong intrinsic fitness pressures. We examine the effect of varying the mutation rate in both systems, and demonstrate that the new measure identifies an optimal mutation rate.

We report a study of networks constructed from mutation patterns observed in biology. These networks form evolutionary trajectories, which allow for both frequent substitution of closely related structures, and a small evolutionary distance between any two structures. These two properties define the small-world phenomenon. The mutation behavior between tokens in an evolvable artificial chemistry determines its ability to explore evolutionary space. This concept is underrepresented in previous work on string-based chemistries. We argue that small-world mutation networks will confer better exploration of the evolutionary space than either random or fully regular mutation strategies. We calculate network statistics from two data sets: amino acid substitution matrices, and codon-level single point mutations. The first class are observed data from protein alignments; while the second class is defined by the standard genetic code that is used to translate RNA into amino acids. We report a methodology for creating small-world mutation networks for artificial chemistries with arbitrary node count and connectivity. We argue that ALife systems would benefit from this approach, as it delivers a more viable exploration of evolutionary space.