Exploiting structural and topological information to improve prediction of RNA-protein binding sites

The breast and ovarian cancer susceptibility gene BRCA1 encodes a multifunctional tumor suppressor protein BRCA1, which is involved in regulating cellular processes such as cell cycle, transcription, DNA repair, DNA damage response and chromatin remodeling. BRCA1 protein, located primarily in cell nuclei, interacts with multiple proteins and various DNA targets. It has been demonstrated that BRCA1 protein binds to damaged DNA and plays a role in the transcriptional regulation of downstream target genes. As a key protein in the repair of DNA double-strand breaks, the BRCA1-DNA binding properties, however, have not been reported in detail

BioPatML.NET – a bioinformatic pattern search engine

In the present paper, we introduce BioPatML.NET, an application library for the Microsoft Windows .NET framework [2] that implements the BioPatML pattern definition language and sequence search engine. BioPatML.NET is integrated with the Microsoft Biology Foundation (MBF) application library [3], unifying the parsers and annotation services supported or emerging through MBF with the language, search framework and pattern repository of BioPatML. End users who wish to exploit the BioPatML.NET engine and repository without engaging the services of a programmer may do so via the freely accessible web-based BioPatML Editor, which we describe below

Characterizing cancer subtypes as attractors of Hopfield networks

Motivation: Cancer is a heterogeneous progressive disease caused by perturbations of the underlying gene regulatory network that can be described by dynamic models. These dynamics are commonly modeled as Boolean networks or as ordinary differential equations. Their inference from data is computationally challenging, and at least partial knowledge of the regulatory network and its kinetic parameters is usually required to construct predictive models

BioPatML- an XML description language for patterns in biological sequences

Background: A major challenge in computational biology is the description of biological systems in a way that allows their computational evaluation and exchange between institutes and applications. Recent modeling languages that describe various aspects of biological systems such as the genomic composition, spatiotemporal quantities or biochemical reactions predominately rely on XML (eXtensible Markup Language), as a standardized and well supported format. An exception are description languages for patterns in biological sequences that show a great diversity in format and function, which impedes the definition and the exchange of biological patterns. Results: In this paper we introduce BioPatML, an XML-based pattern description language that supports a wide variety of patterns and allows the construction of complex, hierarchically structured patterns and pattern libraries. BioPatML unifies the diversity of current pattern description languages and fills a gap in the set of XML-based description languages for biological systems. The paper discusses the structure and elements of the language, and demonstrates its advantages on three applications. An XML schema, manual and Diana, a command line tool to search BioPatML patterns in nucleotide and amino acid sequences, are available a

RMaNI: Regulatory module network inference framework

Background: Cell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology. In recent years, module-based approaches for GRN inference have been proposed to address this challenge. Despite the demonstrated success of module-based approaches in uncovering biologically meaningful regulatory interactions, their application remains limited a single condition, without supporting the comparison of multiple disease subtypes/conditions. Also, their use remains unnecessarily restricted to computational biologists, as accurate inference of modules and their regulators requires integration of diverse tools and heterogeneous data sources, which in turn requires scripting skills, data infrastructure and powerful computational facilities. New analytical frameworks are required to make module-based GRN inference approach more generally useful to the research community.Results: We present the RMaNI (Regulatory Module Network Inference) framework, which supports cancer subtype-specific or condition specific GRN inference and differential network analysis. It combines both transcriptomic as well as genomic data sources, and integrates heterogeneous knowledge resources and a set of complementary bioinformatic methods for automated inference of modules, their condition specific regulators and facilitates downstream network analyses and data visualization. To demonstrate its utility, we applied RMaNI to a hepatocellular microarray data containing normal and three disease conditions. We demonstrate that how RMaNI can be employed to understand the genetic architecture underlying three disease conditions. RMaNI is freely available at http://inspect.braembl.org.au/bi/inspect/rmani. Conclusion: RMaNI makes available a workflow with comprehensive set of tools that would otherwise be challenging for non-expert users to install and apply. The framework presented in this paper is flexible and can be easily extended to analyse any dataset with multiple disease conditions

Voci e suoni dall'aldilà. L'utopia musicale dell'Elisio in Luciano di Samosata (VH II 5-16)

The aim of this essay is to identify the distinctive elements and motives in the Elysium described by Lucian of Samosata in his True Histories, an extraordinary and paradoxical tale of 'Lucian's Travels'. The music, produced by the Nature and by the voices and the instruments of the Blessed in their hereafter symposium, becomes the main sign of Blissfulness. Lucian builds his Elysium through a literary technique that he calls mixis, consisting in a programmatic contamination of different sources in the poetic tradition. In this research the origins of Lucian's brilliant and complex image of musical Bliss are traced back in earlier Greek literature, passing by ancient Paradises, the Golden Age, Utopia and other fanciful worlds of Happiness

Gene regulatory network inference: Evaluation and application to ovarian cancer allows the prioritization of drug targets

Background: Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality

mCOPA: analysis of heterogeneous features in cancer expression data

Background: Cancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset. Results: We compare our method to other feature-selection approaches, and demonstrate that mCOPA frequently selects more-informative features than do differential expression or variance-based feature selection approaches, and is able to recover observed clinical subtypes more consistently. We demonstrate the application of mCOPA to prostate cancer expression data, and explore the use of outliers in clustering, pathway analysis, and the identification of tumour suppressors. We analyse the under-expressed outliers to identify known and novel prostate cancer tumour suppressor genes, validating these against data in Oncomine and the Cancer Gene Index. We also demonstrate how a combination of outlier analysis and pathway analysis can identify molecular mechanisms disrupted in individual tumours. Conclusions: We demonstrate that mCOPA offers advantages, compared to differential expression or variance, in selecting outlier features, and that the features so selected are better able to assign samples to clinically annotated subtypes. Further, we show that the biology explored by outlier analysis differs from that uncovered in differential expression or variance analysis. mCOPA is an important new tool for the exploration of cancer datasets and the discovery of new cancer subtypes, and can be combined with pathway and functional analysis approaches to discover mechanisms underpinning heterogeneity in cancers