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Summary: High-throughput sequencing can enhance the analysis of aptamer libraries generated by the Systematic Evolution of Ligands by EXponential enrichment. Robust analysis of the resulting sequenced rounds is best implemented by determining a ranked consensus of reads following the processing by multiple aptamer detection algorithms. While several such approaches have been developed to this end, their installation and implementation is problematic. We developed AptCompare, a cross-platform program that combines six of the most widely used analytical approaches for the identification of RNA aptamer motifs and uses a simple weighted ranking to order the candidate aptamers, all driven within the same GUI-enabled environment. We demonstrate AptCompare's performance by identifying the top-ranked candidate aptamers from a previously published selection experiment in our laboratory, with follow-up bench assays demonstrating good correspondence between the sequences' rankings and their binding affinities., Availability and Implementation: The source code and pre-built virtual machine images are freely available at https://bitbucket.org/shiehk/aptcompare., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Motivation: DNA methylation is an epigenetic mark that can stably repress gene expression. Because of its biological and clinical significance, several methods have been developed to compare genome-wide patterns of methylation between groups of samples. The application of gene set analysis to identify relevant groups of genes that are enriched for differentially methylated genes is often a major component of the analysis of these data. This can be used, for example, to identify processes or pathways that are perturbed in disease development. We show that gene-set analysis, as it is typically applied to genome-wide methylation assays, is severely biased as a result of differences in the numbers of CpG sites associated with different classes of genes and gene promoters., Results: We demonstrate this bias using published data from a study of differential CpG island methylation in lung cancer and a dataset we generated to study methylation changes in patients with long-standing ulcerative colitis. We show that several of the gene sets that seem enriched would also be identified with randomized data. We suggest two existing approaches that can be adapted to correct the bias. Accounting for the bias in the lung cancer and ulcerative colitis datasets provides novel biological insights into the role of methylation in cancer development and chronic inflammation, respectively. Our results have significant implications for many previous genome-wide methylation studies that have drawn conclusions on the basis of such strongly biased analysis., Contact: cathal.seoighe@nuigalway.ie, Supplementary Information: Supplementary data are available at Bioinformatics online.

Summary: BioconductorBuntu is a custom distribution of Ubuntu Linux that automatically installs a server-side microarray processing environment, providing a user-friendly web-based GUI to many of the tools developed by the Bioconductor Project, accessible locally or across a network. System installation is via booting off a CD image or by using a Debian package provided to upgrade an existing Ubuntu installation. In its current version, several microarray analysis pipelines are supported including oligonucleotide, dual-or single-dye experiments, including post-processing with Gene Set Enrichment Analysis. BioconductorBuntu is designed to be extensible, by server-side integration of further relevant Bioconductor modules as required, facilitated by its straightforward underlying Python-based infrastructure. BioconductorBuntu offers an ideal environment for the development of processing procedures to facilitate the analysis of next-generation sequencing datasets., Availability: BioconductorBuntu is available for download under a creative commons license along with additional documentation and a tutorial from (http://bioinf.nuigalway.ie).

Cytosines at cytosine-guanine (CG) dinucleotides are the near-exclusive target of DNA methyltransferases in mammalian genomes. Spontaneous deamination of methylcytosine to thymine makes methylated cytosines unusually susceptible to mutation and consequent depletion. The loci where CG dinucleotides remain relatively enriched, presumably due to their unmethylated status during the germ cell cycle, have been referred to as CpG islands. Currently, CpG islands are solely defined by base compositional criteria, allowing annotation of any sequenced genome. Using a novel bioinformatic approach, we show that CG clusters can be identified as an inherent property of genomic sequence without imposing a base compositional a priori assumption. We also show that the CG clusters co-localize in the human genome with hypomethylated loci and annotated transcription start sites to a greater extent than annotations produced by prior CpG island definitions. Moreover, this new approach allows CG clusters to be identified in a species-specific manner, revealing a degree of orthologous conservation that is not revealed by current base compositional approaches. Finally, our approach is able to identify methylating genomes (such as Takifugu rubripes) that lack CG clustering entirely, in which it is inappropriate to annotate CpG islands or CG clusters.

Motivation: One of the limiting factors in deciphering transcriptional regulatory networks is the effectiveness of motif-finding software. An emerging avenue for improving motif-finding accuracy aims to incorporate generalized binding constraints of related transcription factors (TFs), named familial binding profiles (FBPs), as priors in motif identification methods. A motif-finder can thus be 'biased' towards finding motifs from a particular TF family. However, current motif-finders allow only a single FBP to be used as a prior in a given motif-finding run. In addition, current FBP construction methods are based on manual clustering of position specific scoring matrices (PSSMs) according to the known structural properties of the TF proteins. Manual clustering assumes that the binding preferences of structurally similar TFs will also be similar. This assumption is not true, at least not for some TF families. Automatic PSSM clustering methods are thus required for augmenting the usefulness of FBPs., Results: A novel method is developed for automatic clustering of PSSM models. The resulting FBPs are incorporated into the SOMBRERO motif-finder, significantly improving its performance when finding motifs related to those that have been incorporated. SOMBRERO is thus the only existing de novo motif-finder that can incorporate knowledge of all known PSSMs in a given motif-finding run., Availability: The methods outlined will be incorporated into the next release of SOMBRERO, which is available from http://bioinf.nuigalway.ie/sombrero

Motivation: The automatic identification of over-represented motifs present in a collection of sequences continues to be a challenging problem in computational biology. In this paper, we propose a self-organizing map of position weight matrices as an alternative method for motif discovery. The advantage of this approach is that it can be used to simultaneously characterize every feature present in the dataset, thus lessening the chance that weaker signals will be missed. Features identified are ranked in terms of over-representation relative to a background model., Results: We present an implementation of this approach, named SOMBRERO (self-organizing map for biological regulatory element recognition and ordering), which is capable of discovering multiple distinct motifs present in a single dataset. Demonstrated here are the advantages of our approach on various datasets and SOMBRERO's improved performance over two popular motif-finding programs, MEME and AlignACE., Availability: SOMBRERO is available free of charge from http://bioinf.nuigalway.ie/sombrero, Supplementary Information: http://bioinf.nuigalway.ie/sombrero/additional.