1,721,171 research outputs found
Computational analysis of hi-c data
No full textThe chromatin organization in the 3D nuclear space is essential for genome functionality. This spatial organization encompasses different topologies at diverse scale lengths with chromosomes occupying distinct volumes and individual chromosomes folding into compartments, inside which the chromatin fiber is packed in large domains (as the topologically associating domains, TADs) and forms short-range interactions (as enhancer-promoter loops). The widespread adoption of high-throughput techniques derived from chromosome conformation capture (3C) has been instrumental in investigating the nuclear organization of chromatin. In particular, Hi-C has the potential to achieve the most comprehensive characterization of chromatin 3D structures, as in principle it can detect any pair of restriction fragments connected as a result of ligation by proximity. However, the analysis of the enormous amount of genomic data produced by Hi-C techniques requires the application of complex, multistep computational procedures that may constitute a difficult task also for expert computational biologists. In this chapter, we describe the computational analysis of Hi-C data obtained from the lymphoblastoid cell line GM12878, detailing the processing of raw data, the generation and normalization of the Hi-C contact map, the detection of TADs and chromatin interactions, and their visualization and annotation
Integration of bioinformatic predictions and experimental data to identify circRNA-miRNA associations
Full text linkCircular RNAs (circRNAs) have recently emerged as a novel class of transcripts, characterized by covalently linked 3′–5′ ends that result in the so-called backsplice junction. During the last few years, thousands of circRNAs have been identified in different organisms. Yet, despite their role as disease biomarker started to emerge, depicting their function remains challenging. Different studies have shown that certain circRNAs act as miRNA sponges, but any attempt to generalize from the single case to the “circ-ome” has failed so far. In this review, we explore the potential to define miRNA “sponging” as a more general function of circRNAs and describe the different approaches to predict miRNA response elements (MREs) in known or novel circRNA sequences. Moreover, we discuss how experiments based on Ago2-IP and experimentally validated miRNA:target duplexes can be used to either prioritize or validate putative miRNA-circRNA associations
PREDA: An R-package to identify regional variations in genomic data
No full textChromosomal patterns of genomic signals represent molecular ngerprints that may reveal how the local structural organization of a genome impacts the functional control mechanisms. Thus, the integrative analysis of multiple sources of genomic data and information deepens the resolution and enhances the interpretation of stand-alone high-throughput data. In this note, we present PREDA (Position RElated Data Analysis), an R package for detecting regional variations in genomics data. PREDA identies relevant chromosomal patterns in high-throughput data using a smoothing approach that accounts for distance and density variability of genomics features. Custom-designed data structures allow efciently managing diverse signals in different genomes. A variety of smoothing functions and statistics empower exible and robust workows. The modularity of package design allows an easy deployment of custom analytical pipelines. Tabular and graphical representations facilitate downstream biological interpretation of results. © The Author 2011. Published by Oxford University Press. All rights reserved
Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: A comparative review
Full text linkA variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org. © The Author 2012. Published by Oxford University Press
APTANI2: Update of aptamer selection through sequence-structure analysis
Full text linkSummary: Here we present APTANI2, an expanded and optimized version of APTANI, a computational tool for selecting target-specific aptamers from high-throughput-Systematic Evolution of Ligands by Exponential Enrichment data through sequence-structure analysis. As compared to its original implementation, APTANI2 ranks aptamers and identifies relevant structural motifs through the calculation of a score that combines frequency and structural stability of each secondary structure predicted in any aptamer sequence. In addition, APTANI2 comprises modules for a deeper investigation of sequence motifs and secondary structures, a graphical user interface that enhances its usability, and coding solutions that improve performances
Computational methods for the integrative analysis of single-cell data
Full text linkRecent advances in single-cell technologies are providing exciting opportunities for dissecting tissue heterogeneity and investigating cell identity, fate and function. This is a pristine, exploding field that is flooding biologists with a new wave of data, each with its own specificities in terms of complexity and information content. The integrative analysis of genomic data, collected at different molecular layers from diverse cell populations, holds promise to address the full-scale complexity of biological systems. However, the combination of different single-cell genomic signals is computationally challenging, as these data are intrinsically heterogeneous for experimental, technical and biological reasons. Here, we describe the computational methods for the integrative analysis of single-cell genomic data, with a focus on the integration of single-cell RNA sequencing datasets and on the joint analysis of multimodal signals from individual cells
Microarray data mining using Bioconductor packages
Full text linkBackground - This paper describes the results of a Gene Ontology (GO) term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure (LAP) was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge. Results - GO enrichment analysis identified significant (raw p-value <0.05) GO terms for all three contrasts included in the analysis. Some of the GO terms linked to, generally, primary immune responses or secondary immune responses indicating the GO enrichment analysis is a useful approach to analyze microarray data. The comparisons of GO enrichment results using chicken gene information and chicken-human orthologous gene information showed more refined GO terms related to immune responses when using chicken-human orthologous gene information, this suggests that using chicken-human orthologous gene information has higher power to detect significant GO terms with more refined functionality. Furthermore, three chromosome regions were identified to be significantly up-regulated in contrast MM8-PM8 (q-value <0.01). Conclusion - Overall, this paper describes a practical approach to analyze microarray data in farm animals where the genome information is still incomplete. For farm animals, such as chicken, with currently limited gene annotation, borrowing gene annotation information from orthologous genes in well-annotated species, such as human, will help improve the pathway analysis results substantially. Furthermore, LAP analysis approach is a relatively new and very useful way to be applied in microarray analysi
Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data
Full text linkSince the pioneering NCI-60 panel of the late'80's, several major screenings of genetic profiling and drug testing in cancer cell lines have been conducted to investigate how genetic backgrounds and transcriptional patterns shape cancer's response to therapy and to identify disease-specific genes associated with drug response. Historically, pharmacogenomics screenings have been largely heterogeneous in terms of investigated cell lines, assay technologies, number of compounds, type and quality of genomic data, and methods for their computational analysis. The analysis of this enormous and heterogeneous amount of data required the development of computational methods for the integration of genomic profiles with drug responses across multiple screenings. Here, we will review the computational tools that have been developed to integrate cancer cell lines' genomic profiles and sensitivity to small molecule perturbations obtained from different screenings
COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach
Full text linkThe COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources
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