Biology subjects

Tagliaferri, I.

Publications and source records attributed to Tagliaferri, I..

4 recordsLinked to original sources

bioRxiv2024

Biochemical properties of chromatin domains define genome compartmentalization

Chromatin three-dimensional (3D) organization inside the cell nucleus determines the separation of euchromatin and heterochromatin domains. Their segregation results in the definition of active and inactive chromatin compartments, whereby the local concentration of associated proteins, RNA and DNA results in the formation of distinct subnuclear structures. Thus, chromatin domains spatially confined in a specific 3D nuclear compartment are expected to share similar epigenetic features and biochemical properties, in terms of accessibility and solubility. Based on this rationale, we developed the 4f-SAMMY-seq to map euchromatin and heterochromatin based on their accessibility and solubility, starting from as little as 10,000 cells. Adopting a tailored bioinformatic data analysis approach we reconstruct also their 3D segregation in active and inactive chromatin compartments and sub-compartments, thus recapitulating the characteristic properties of distinct chromatin states. A key novelty is the capability to map both the linear segmentation of open and closed chromatin domains, as well as their 3D compartmentalization in one single experiment.

genomics↗

bioRxiv2021

Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer-target gene regulatory network

A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer-target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the most updated knowledge on chromatin three-dimensional (3D) architecture, which is organized in a hierarchy of structural domains. Here we introduce a paradigm shift based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work i) we develop a computational and statistical framework to reconstruct a comprehensive ETG regulatory network leveraging functional genomics data; ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy; and iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.

genomics↗

bioRxiv2020

A ChIC solution for ChIP-seq quality assessment

Despite the widespread adoption of the ChIP-seq technique, there is still no consensus on quality assessment procedures. Quantitative metrics previously proposed in literature are not always effective in discriminating the success or failure of an experiment, thus hampering objectivity and reproducibility of quality control. Here we introduce ChIC, a new framework for ChIP-seq data quality assessment that overcomes the limitations of previous solutions. ChIC is the first method for ChIP-seq quality control directly considering the enrichment profile shape, thus achieving good performances on ChIP targets yielding sharp and broad peaks alike. We integrate a comprehensive set of quality control metrics into one single score reliably summarizing the sample quality. The ChIC score is based on a machine learning classifier trained on a compendium with thousands of ChIP-seq profiles, which can also be used as a reference for easier evaluation of new datasets. ChIC is implemented as a user-friendly R/Bioconductor package.

bioinformatics↗

bioRxiv2020

HPC-REDItools: a Novel HPC-aware Tool for Improved Large Scale RNA-editing Analysis

BackgroundRNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity. The automatic detection of RNA-editing from RNA-seq data is computational intensive and limited to small data sets, thus preventing a reliable genome-wide characterisation of such process. ResultsIn this work we introduce HPC-REDItools, an upgraded tool for accurate RNA-editing events discovery from large dataset repositories. Availability: https://github.com/BioinfoUNIBA/REDItools2. ConclusionsHPC-REDItools is dramatically faster than the previous version, REDItools, enabling big-data analysis by means of a MPI-based implementation and scaling almost linearly with the number of available cores.

bioinformatics↗