30 research outputs found
SEMgraph: an R package for causal network inference of high-throughput data with structural equation models
No full textMotivation: With the advent of high-throughput sequencing in molecular biology and medicine, the need for scalable statistical solutions for modeling complex biological systems has become of critical importance. The increasing number of platforms and possible experimental scenarios raised the problem of integrating large amounts of new heterogeneous data and current knowledge, to test novel hypotheses and improve our comprehension of physiological processes and diseases.Results: Combining network analysis and causal inference within the framework of structural equation modeling (SEM), we developed the R package SEMgraph. It provides a fully automated toolkit, managing complex biological systems as multivariate networks, ensuring robustness and reproducibility through data-driven evaluation of model architecture and perturbation, which is readily interpretable in terms of causal effects among system components
Novelty indicator for enhanced prioritization of predicted gene ontology annotations
No full textBiomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new annotations are both effective and efficient, especially if compared with time-consuming trials of biological validation. To limit the possibility that these techniques predict obvious and trivial high-level features, and to help prioritize their results, we introduce a new element that can improve accuracy and relevance of the results of an annotation prediction and prioritization pipeline. We propose a novelty indicator able to state the level of “originality” of the annotations predicted for a specific gene to Gene Ontology (GO) terms. This indicator, joint with our previously introduced prediction steps, helps by prioritizing the most novel interesting annotations predicted. We performed an accurate biological functional analysis of the prioritized annotations predicted with high accuracy by our indicator and previously proposed methods. The relevance of our biological findings proves effectiveness and trustworthiness of our indicator and of its prioritization of predicted annotations
Sem best shortest paths for the characterization of differentially expressed genes
No full textIn the last years, systems and computational biology focused their efforts in uncovering the causal relationships among the observable perturbations of gene regulatory networks and human diseases. This problem becomes even more challenging when network models and algorithms have to take into account slightly significant effects, caused by often peripheral or unknown genes that cooperatively cause the observed diseased phenotype. Many solutions, from community and pathway analysis to information flow simulation, have been proposed, with the aim of reproducing biological regulatory networks and cascades, directly from empirical data as gene expression microarray data. In this contribute, we propose a methodology to evaluate the most important shortest paths between differentially expressed genes in biological interaction networks, with absolutely no need of user-defined parameters or heuristic rules, enabling a free-of-bias discovery and overcoming common issues affecting the most recent network-based algorithms
GenoMetric Query Language: A novel approach to large-scale genomic data management
No full textMotivation: Improvement of sequencing technologies and data processing pipelines is rapidly providing sequencing data, with associated high-level features, of many individual genomes in multiple biological and clinical conditions. They allow for data-driven genomic, transcriptomic and epigenomic characterizations, but require state-of-the-art ‘big data’ computing strategies, with
abstraction levels beyond available tool capabilities.
Results: We propose a high-level, declarative GenoMetric Query Language (GMQL) and a toolkit for its use. GMQL operates downstream of raw data preprocessing pipelines and supports queries over thousands of heterogeneous datasets and samples; as such it is key to genomic ‘big data’ analysis. GMQL leverages a simple data model that provides both abstractions of genomic region data and associated experimental, biological and clinical metadata and interoperability between many data formats. Based on Hadoop framework and Apache Pig platform, GMQL ensures high scalability, expressivity, flexibility and simplicity of use, as demonstrated by several biological query examples on ENCODE and TCGA datasets.
Availability and implementation: The GMQL toolkit is freely available for non-commercial use at http://www.bioinformatics.deib.polimi.it/GMQL/.
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online
GenoMetric Query Language applications to the integrative evaluation of heterogeneous genomic data
No full textMYC up-regulation confers vulnerability to dual inhibition of CDK12 and CDK13 in high-risk Group 3 medulloblastoma
No full textBackgroundMedulloblastoma (MB) is the most common cerebellar malignancy during childhood. Among MB, MYC-amplified Group 3 tumors display the worst prognosis. MYC is an oncogenic transcription factor currently thought to be undruggable. Nevertheless, targeting MYC-dependent processes (i.e. transcription and RNA processing regulation) represents a promising approach.MethodsWe have tested the sensitivity of MYC-driven Group 3 MB cells to a pool of transcription and splicing inhibitors that display a wide spectrum of targets. Among them, we focus on THZ531, an inhibitor of the transcriptional cyclin-dependent kinases (CDK) 12 and 13. High-throughput RNA-sequencing analyses followed by bioinformatics and functional analyses were carried out to elucidate the molecular mechanism(s) underlying the susceptibility of Group 3 MB to CDK12/13 chemical inhibition. Data from International Cancer Genome Consortium (ICGC) and other public databases were mined to evaluate the functional relevance of the cellular pathway/s affected by the treatment with THZ531 in Group 3 MB patients.ResultsWe found that pharmacological inhibition of CDK12/13 is highly selective for MYC-high Group 3 MB cells with respect to MYC-low MB cells. We identified a subset of genes enriched in functional terms related to the DNA damage response (DDR) that are up-regulated in Group 3 MB and repressed by CDK12/13 inhibition. Accordingly, MYC- and CDK12/13-dependent higher expression of DDR genes in Group 3 MB cells limits the toxic effects of endogenous DNA lesions in these cells. More importantly, chemical inhibition of CDK12/13 impaired the DDR and induced irreparable DNA damage exclusively in MYC-high Group 3 MB cells. The augmented sensitivity of MYC-high MB cells to CDK12/13 inhibition relies on the higher elongation rate of the RNA polymerase II in DDR genes. Lastly, combined treatments with THZ531 and DNA damage-inducing agents synergically suppressed viability of MYC-high Group 3 MB cells.ConclusionsOur study demonstrates that CDK12/13 activity represents an exploitable vulnerability in MYC-high Group 3 MB and may pave the ground for new therapeutic approaches for this high-risk brain tumor
Novel genome-scale data models and algorithms for molecular medicine and biomedical research
No full textI genomi sono sistemi complessi, e non semplici entità monolitiche. Il genoma rappresenta l'insieme dell'informazione ereditaria di un organismo o, in termini evoluzionistici, di una specie. Nelle ultime due decadi, le tecnologie di sequenziamento del DNA hanno offerto la concreta possibilità di esplorare tale complessità. Questa rivoluzione scientifica ha portato ad una serie di profonde modificazioni dei paradigmi, ed un collaterale bisogno di sempre più efficienti ed innovativi modelli e soluzioni tecnologiche. Tuttavia, questo processo non è stato graduale, esponendo la ricerca genomica ad un accumulo esplosivo di dati, che sono stati la causa di diverse criticità: tecnologiche, computazionali, e metodologiche. Queste criticità possono essere risolte sviluppando efficienti modelli statistici ed algoritmi di data reduction, capaci di catturare la porzione informativa di un complesso sistema biologico. Negli ultimi anni, ci sono stati numerosi sforzi di produrre standard che unificassero lo scambio e l'integrazione dei dati, attraverso database, ontologie, piattaforme dedicate e metodologie integrative, per fornire una forte base teorica per l'analisi ed il mining dei dati. Lo scopo finale di questa tesi è quello di illustrare tale base teorica, e di come possa essere applicata per la risoluzione di grandi problematiche nel campo della ricerca genomica.Genomes are complex systems, and not just static and monolithic entities. They represent the ensemble of the hereditary information of an organism or, in evolutionary terms, of a species. Sequencing sciences offered through the last two decades the possibility to dive into this complexity. This scientific revolution led to a series of paradigm changes, and the collateral need of efficient technical and methodological innovations. However, this process was not gradual, exposing genome research to an explosive data deluge, thus causing a series of bottlenecks: technological, computational, and methodological, due to the existence of efficient data reduction and analysis algorithm, capable of capturing the informative portion of genomic complexity. In the last years, there have been huge efforts towards the generation of unified standards and databases, ontologies, integrated platforms, integrative methodologies, to provide a strong theoretical background, capable of capturing true biological variation, often masked behind marginal synergic processes. The goal of this thesis was to shed light on this last problem, trying to expose a unifying theoretical foundation needed to approach the study of genome complexity.DIPARTIMENTO DI ELETTRONICA, INFORMAZIONE E BIOINGEGNERIAComputer Science and Engineering28PERNICI, BARBARABONARINI, ANDRE
GMQL Services - generic, easy-to-use infrastructure for genomic data retrieval and processing
No full text
