OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes

Background: The identification of biomarkers for the estimation of cancer patients\u2019 survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants. Methods: In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants. Results: The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data. Conclusions: Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patient

Comparative analysis of nuclear estrogen receptor alpha and beta interactomes in breast cancer cells.

Estrogen Receptor alpha and beta (ER-a and -b) are members of the nuclear receptor family of transcriptional regulators with distinct roles in mediating estrogen dependent breast cancer cell growth and differentiation. Following activation by the hormone, these proteins undergo conformation changes and accumulate in the nucleus, where they bind to chromatin at regulatory sites as homo- and/or heterodimers and assemble in large multiprotein complexes. Although the two ERs share a conserved structure, they exert specific and distinct functional roles in normal and transformed mammary epithelial cells and other cell types. To investigate the molecular bases of such differences, we performed a comparative computational analysis of the nuclear interactomes of the two ER subtypes, exploiting two datasets of receptor interacting proteins identified in breast cancer cell nuclei by Tandem Affinity Purification for their ability to associate in vivo with ligand- activated ER-a and/or ER-b. These datasets comprise 498 proteins, of which only 70 are common to both ERs, suggesting that differences in the nature of the two ER interactomes are likely to sustain the distinct roles of the two receptor subtypes. Functional characterization of the two interactomes and their topological analysis, considering node degree and closeness of the networks, confirmed this possibility. Indeed, clustering and network dissection highlighted the presence of distinct and ER subtype-specific subnetworks endowed with defined functions. Altogether, these data provide new insights on the protein–protein interaction networks controlled by ER-a and -b that mediate their ability to transduce estrogen signaling in breast cancer cells

μ-CS: An extension of the TM4 platform to manage Affymetrix binary data

<p>Abstract</p> <p>Background</p> <p>A main goal in understanding cell mechanisms is to explain the relationship among genes and related molecular processes through the combined use of technological platforms and bioinformatics analysis. High throughput platforms, such as microarrays, enable the investigation of the whole genome in a single experiment. There exist different kind of microarray platforms, that produce different types of binary data (images and raw data). Moreover, also considering a single vendor, different chips are available. The analysis of microarray data requires an initial preprocessing phase (i.e. normalization and summarization) of raw data that makes them suitable for use on existing platforms, such as the TIGR M4 Suite. Nevertheless, the annotations of data with additional information such as gene function, is needed to perform more powerful analysis. Raw data preprocessing and annotation is often performed in a manual and error prone way. Moreover, many available preprocessing tools do not support annotation. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of microarray data are needed.</p> <p>Results</p> <p>The paper presents <it>μ</it>-CS (Microarray Cel file Summarizer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix binary data. <it>μ</it>-CS is based on a client-server architecture. The <it>μ</it>-CS client is provided both as a plug-in of the TIGR M4 platform and as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data, avoiding the manual invocation of external tools (e.g. the Affymetrix Power Tools), the manual loading of preprocessing libraries, and the management of intermediate files. The <it>μ</it>-CS server automatically updates the references to the summarization and annotation libraries that are provided to the <it>μ</it>-CS client before the preprocessing. The <it>μ</it>-CS server is based on the web services technology and can be easily extended to support more microarray vendors (e.g. Illumina).</p> <p>Conclusions</p> <p>Thus <it>μ</it>-CS users can directly manage binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the <it>μ</it>-CS plugin for TM4 can manage Affymetrix binary files without using external tools, such as APT (Affymetrix Power Tools) and related libraries. Consequently, <it>μ</it>-CS offers four main advantages: (i) it avoids to waste time for searching the correct libraries, (ii) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or the use of old libraries, (iii) it implements the annotation of preprocessed data, and finally, (iv) it may enhance the quality of further analysis since it provides the most updated annotation libraries. The <it>μ</it>-CS client is freely available as a plugin of the TM4 platform as well as a standalone application at the project web site (<url>http://bioingegneria.unicz.it/M-CS</url>).</p

Snazer: the simulations and networks analyzer

<p>Abstract</p> <p>Background</p> <p>Networks are widely recognized as key determinants of structure and function in systems that span the biological, physical, and social sciences. They are static pictures of the interactions among the components of complex systems. Often, much effort is required to identify networks as part of particular patterns as well as to visualize and interpret them.</p> <p>From a pure dynamical perspective, simulation represents a relevant <it>way</it>-<it>out</it>. Many simulator tools capitalized on the "noisy" behavior of some systems and used formal models to represent cellular activities as temporal trajectories. Statistical methods have been applied to a fairly large number of replicated trajectories in order to infer knowledge.</p> <p>A tool which both graphically manipulates reactive models and deals with sets of simulation time-course data by aggregation, interpretation and statistical analysis is missing and could add value to simulators.</p> <p>Results</p> <p>We designed and implemented <it>Snazer</it>, the simulations and networks analyzer. Its goal is to aid the processes of visualizing and manipulating reactive models, as well as to share and interpret time-course data produced by stochastic simulators or by any other means.</p> <p>Conclusions</p> <p><it>Snazer </it>is a solid prototype that integrates biological network and simulation time-course data analysis techniques.</p

Finding suitable activity clusters for decomposed process discovery

Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time. Keywords: decomposed process mining, decomposed process discovery, distributed computing, event lo

BioSunMS: a plug-in-based software for the management of patients information and the analysis of peptide profiles from mass spectrometry

<p>Abstract</p> <p>Background</p> <p>With wide applications of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), statistical comparison of serum peptide profiles and management of patients information play an important role in clinical studies, such as early diagnosis, personalized medicine and biomarker discovery. However, current available software tools mainly focused on data analysis rather than providing a flexible platform for both the management of patients information and mass spectrometry (MS) data analysis.</p> <p>Results</p> <p>Here we presented a plug-in-based software, BioSunMS, for both the management of patients information and serum peptide profiles-based statistical analysis. By integrating all functions into a user-friendly desktop application, BioSunMS provided a comprehensive solution for clinical researchers without any knowledge in programming, as well as a plug-in architecture platform with the possibility for developers to add or modify functions without need to recompile the entire application.</p> <p>Conclusion</p> <p>BioSunMS provides a plug-in-based solution for managing, analyzing, and sharing high volumes of MALDI-TOF or SELDI-TOF MS data. The software is freely distributed under GNU General Public License (GPL) and can be downloaded from <url>http://sourceforge.net/projects/biosunms/</url>.</p

Integrated analysis of microRNAs, transcription factors and target genes expression discloses a specific molecular architecture of hyperdiploid multiple myeloma

Multiple Myeloma (MM) is a malignancy characterized by the hyperdiploid (HD-MM) and the non-hyperdiploid (nHD-MM) subtypes. To shed light within the molecular architecture of these subtypes, we used a novel integromics approach. By annotated MM patient mRNA/microRNA (miRNA) datasets, we investigated mRNAs and miRNAs profiles with relation to changes in transcriptional regulators expression. We found that HD-MM displays specific gene and miRNA expression profiles, involving the Signal Transducer and Activator of Transcription (STAT)3 pathway as well as the Transforming Growth Factor-beta (TGF\u3b2) and the transcription regulator Nuclear Protein-1 (NUPR1). Our data define specific molecular features of HD-MM that may translate in the identification of novel relevant druggable targets

Effect of Soy Protein Supplementation on Muscle Adaptations, Metabolic and Antioxidant Status, Hormonal Response, and Exercise Performance of Active Individuals and Athletes: A Systematic Review of Randomised Controlled Trials

This is the final version. Available on open access from Springer via the DOI in this recordAvailability of data and material: Not applicable.Background Protein supplements are important to maintain optimum health and physical performance, particularly in athletes and active individuals to repair and rebuild their skeletal muscles and connective tissues. Soy protein (SP) has gained popularity in recent years as an alternative to animal proteins. Objectives This systematic review evaluates the evidence from randomised controlled clinical trials of the effects of SP supplementation in active individuals and athletes in terms of muscle adaptations, metabolic and antioxidant status, hormonal response and exercise performance. It also explores the differences in SP supplementation effects in comparison to whey protein. Methods A systematic search was conducted in PubMed, Embase and Web of Science, as well as a manual search in Google Scholar and EBSCO, on 27 June 2023. Randomised controlled trials that evaluated the applications of SPs supplementation on sports and athletic-related outcomes that are linked with exercise performance, adaptations and biomarkers in athletes and physically active adolescents and young adults (14 to 39 years old) were included, otherwise, studies were excluded. The risk of bias was assessed according to Cochrane’s revised risk of bias tool. Results A total of 19 eligible original research articles were included that investigated the effect of SP supplementation on muscle adaptations (n = 9), metabolic and antioxidant status (n = 6), hormonal response (n = 6) and exercise performance (n = 6). Some studies investigated more than one effect. SP was found to provide identical increases in lean mass compared to whey in some studies. SP consumption promoted the reduction of exercise-induced metabolic/blood circulating biomarkers such as triglycerides, uric acid and lactate. Better antioxidant capacity against oxidative stress has been seen with respect to whey protein in long-term studies. Some studies reported testosterone and cortisol fluctuations related to SP; however, more research is required. All studies on SP and endurance performance suggested the potential beneficial effects of SP supplementation (10–53.3 g) on exercise performance by improving high-intensity and high-speed running performance, enhancing maximal cardiac output, delaying fatigue and improving isometric muscle strength, improving endurance in recreational cyclists, increasing running velocity and decreasing accumulated lactate levels; however, studies determining the efficacy of soy protein on VO2max provided conflicted results. Conclusion It is possible to recommend SP to athletes and active individuals in place of conventional protein supplements by assessing their dosage and effectiveness in relation to different types of training. SP may enhance lean mass compared with other protein sources, enhance the antioxidant status, and reduce oxidative stress. SP supplementation had an inconsistent effect on testosterone and cortisol levels. SP supplementation may be beneficial, especially after muscle damage, high-intensity/high-speed or repeated bouts of strenuous exercise

A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

<p>Abstract</p> <p>Introduction</p> <p>Raw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma) are used in our study.</p> <p>Method</p> <p>Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification.</p> <p>Results</p> <p>Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively.</p> <p>Conclusion</p> <p>The results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.</p

Distributed generative data mining

A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts.In this paper we propose a technique that we call Distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic.To deploy the proposed technique we extended the Distributed Data Mining system HARVARD and adapted an Inductive Logic Programming system (IndLog) used in a Relational Data Ming task.As a proof-of-concept, the extended system was used to analyse an artificialdataset of a credit scoring problem with eighty million records