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This work was supported by the Italian Ministry for University.

Analysis of institutional authors

Di Stefano, MAuthor

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Article

essHi-C: essential component analysis of Hi-C matrices

Publicated to:Bioinformatics. 37 (15): 2088-2094 - 2021-08-01 37(15), DOI: 10.1093/bioinformatics/btab062

Authors: Franzini, S; Di Stefano, M; Micheletti, C

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Abstract

Motivation: Hi-C matrices are cornerstones for qualitative and quantitative studies of genome folding, from its territorial organization to compartments and topological domains. The high dynamic range of genomic distances probed in Hi-C assays reflects in an inherent stochastic background of the interactions matrices, which inevitably convolve the features of interest with largely non-specific ones. Results: Here, we introduce and discuss essHi-C, a method to isolate the specific or essential component of Hi-C matrices from the non-specific portion of the spectrum compatible with random matrices. Systematic comparisons show that essHi-C improves the clarity of the interaction patterns, enhances the robustness against sequencing depth of topologically associating domains identification, allows the unsupervised clustering of experiments in different cell lines and recovers the cell-cycle phasing of single-cells based on Hi-C data. Thus, essHi-C provides means for isolating significant biological and physical features from Hi-C matrices.

Keywords

ArticleBioinformaticsCell cycle phaseChromosome conformationControlled studyDomainsDynamicsGene-regulationGenomeHuman cellLong-range interactionsOrganizationPrinciplesSoftwareTopology

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Bioinformatics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 5/57, thus managing to position itself as a Q1 (Primer Cuartil), in the category Mathematical & Computational Biology. Notably, the journal is positioned above the 90th percentile.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-03:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 19.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 19 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 2.75.
  • The number of mentions on the social network X (formerly Twitter): 6 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Italy.