1,721,013 research outputs found
Hi-C analysis: from data generation to integration
Full text linkIn the epigenetics field, large-scale functional genomics datasets of ever-increasing size and complexity have been produced using experimental techniques based on high-throughput sequencing. In particular, the study of the 3D organization of chromatin has raised increasing interest, thanks to the development of advanced experimental techniques. In this context, Hi-C has been widely adopted as a high-throughput method to measure pairwise contacts between virtually any pair of genomic loci, thus yielding unprecedented challenges for analyzing and handling the resulting complex datasets. In this review, we focus on the increasing complexity of available Hi-C datasets, which parallels the adoption of novel protocol variants. We also review the complexity of the multiple data analysis steps required to preprocess Hi-C sequencing reads and extract biologically meaningful information. Finally, we discuss solutions for handling and visualizing such large genomics datasets
Computational methods for analyzing genome-wide chromosome conformation capture data
No full textIn all organisms, chromatin is packed to fulfil structural constraints and functional requirements. The hierarchical model of chromatin organization in the 3D nuclear space encompasses different topologies at diverse scale lengths, with chromosomes occupying distinct volumes, further organized in compartments, inside which the chromatin fibers fold into large domains and short-range loops. In the recent years, the combination of chromosome conformation capture (3C) techniques and high-throughput sequencing allowed probing chromatin spatial organization at the whole genome-scale. 3C-based methods produce enormous amounts of genomic data that are analyzed using ad-hoc computational procedures. Here, we review the common pipelines and methods for the analysis of genome-wide chromosome conformation capture data, highlighting recent developments in key steps for the identification of chromatin structures
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
Circr, a Computational Tool to Identify miRNA:circRNA Associations
Full text linkCircular RNAs (circRNAs) are known to act as important regulators of the microRNA (miRNA) activity. Yet, computational resources to identify miRNA:circRNA interactions are mostly limited to already annotated circRNAs or affected by high rates of false positive predictions. To overcome these limitations, we developed Circr, a computational tool for the prediction of associations between circRNAs and miRNAs. Circr combines three publicly available algorithms for de novo prediction of miRNA binding sites on target sequences (miRanda, RNAhybrid, and TargetScan) and annotates each identified miRNA:target pairs with experimentally validated miRNA:RNA interactions and binding sites for Argonaute proteins derived from either ChIPseq or CLIPseq data. The combination of multiple tools for the identification of a single miRNA recognition site with experimental data allows to efficiently prioritize candidate miRNA:circRNA interactions for functional studies in different organisms. Circr can use its internal annotation database or custom annotation tables to enhance the identification of novel and not previously annotated miRNA:circRNA sites in virtually any species. Circr is written in Python 3.6 and is released under the GNU GPL3.0 License at https://github.com/bicciatolab/Circr
popsicleR: a R Package for pre-processing and quality control analysis of single cell RNA-seq data
Full text linkThe advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Preprocessing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of preprocessing parameters. Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available https://github.com/biccialolab/popsicleR . (C) 2022 The Authors. Published by Elsevier Ltd
A multifactorial ‘Consensus Signature’ by in silico analysis to predict response to neoadjuvant anthracycline-based chemotherapy in triple-negative breast cancer
Full text linkBACKGROUND: Owing to the complex processes required for anthracycline-induced cytotoxicity, a prospectively defined multifactorial Consensus Signature (ConSig) might improve prediction of anthracycline response in triple-negative breast cancer (TNBC) patients, whose only standard systemic treatment option is chemotherapy.
AIMS: We aimed to construct and evaluate a multifactorial signature, comprising measures of each function required for anthracycline sensitivity in TNBC.
METHODS: ConSigs were constructed based on five steps required for anthracycline function: drug penetration, nuclear topoisomerase IIα (topoIIα) protein location, increased topoIIα messenger RNA (mRNA) expression, apoptosis induction, and immune activation measured by, respectively, HIF1α or SHARP1 signature, LAPTM4B mRNA, topoIIα mRNA, Minimal Gene signature
or YWHAZ mRNA, and STAT1 signature. TNBC patients treated with neoadjuvant anthracycline-based chemotherapy without taxane were identified from publicly available gene expression data derived with Affymetrix HG-U133 arrays (training set). In silico analyses of correlation between gene expression data and pathological complete response (pCR) were performed using receiver-operating characteristic curves. To determine anthracycline specificity, ConSigs were assessed in patients treated with anthracycline plus taxane. Specificity, sensitivity, positive and negative predictive value, and odds ratio (OR) were calculated for ConSigs. Analyses were repeated in two validation gene expression data sets derived using different microarray platforms.
RESULTS: In the training set, 29 of 147 patients had pCR after anthracycline-based chemotherapy. Various combinations of components were evaluated, with the most powerful anthracycline response predictors being ConSig1: (STAT1+topoIIα mRNA +LAPTM4B) and ConSig2: (STAT1+topoIIα mRNA+HIF1α). ConSig1 demonstrated high negative predictive value (85%) and high OR
for no pCR (3.18) and outperformed ConSig2 in validation sets for anthracycline specificity. CONCLUSIONS: With further validation, ConSig1 may help refine selection of TNBC patients for anthracycline chemotherapy
Comparison of computational methods for Hi-C data analysis
Full text linkHi-C is a genome-wide sequencing technique used to investigate 3D chromatin conformation inside the nucleus. Computational methods are required to analyze Hi-C data and identify chromatin interactions and topologically associating domains (TADs) from genome-wide contact probability maps. We quantitatively compared the performance of 13 algorithms in their analyses of Hi-C data from six landmark studies and simulations. This comparison revealed differences in the performance of methods for chromatin interaction identification, but more comparable results for TAD detection between algorithms
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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