@inproceedings{14:ff:nuchart:cibb,
  month = jun,
  author = {Fabio Tordini and Maurizio Drocco and Ivan Merelli and Luciano
            Milanesi and Pietro Li{\`o} and Marco Aldinucci},
  series = {{LNBI}},
  keywords = {fastflow},
  booktitle = {Proc. of the 11th Intl. meeting on Computational Intelligence
               methods for Bioinformatics and Biostatistics (CIBB)},
  url = {http://calvados.di.unipi.it/storage/paper_files/2014_nuchart_cibb.pdf},
  address = {Cambridge, UK},
  title = {NuChart-II: a graph-based approach for the analysis and
           interpretation of Hi-C data},
  abstract = {Long-range chromosomal associations between genomic re- gions, and
              their repositioning in the 3D space of the nucleus, are now
              considered to be key contributors to the regulation of gene
              expressions, and important links have been highlighted with other
              genomic features involved in DNA rearrangements. Recent Chromosome
              Conformation Capture (3C) measurements performed with high
              throughput sequenc- ing (Hi-C) and molecular dynamics studies show
              that there is a large correlation between co-localization and
              co-regulation of genes, but these important researches are
              hampered by the lack of biologists-friendly anal- ysis and
              visualisation software. In this work we present NuChart-II, a
              software that allows the user to annotate and visualize a list of
              input genes with information relying on Hi-C data, integrating
              knowledge data about genomic features that are involved in the
              chromosome spatial or- ganization. This software works directly
              with sequenced reads to identify related Hi-C fragments, with the
              aim of creating gene-centric neighbour- hood graphs on which
              multi-omics features can be mapped. NuChart-II is a highly
              optimized implementation of a previous prototype package de-
              veloped in R, in which the graph-based representation of Hi-C data
              was tested. The prototype showed inevitable problems of
              scalability while working genome-wide on large datasets:
              particular attention has been paid in optimizing the data
              structures employed while constructing the neighbourhood graph, so
              as to foster an efficient parallel implementation of the software.
              The normalization of Hi-C data has been modified and improved, in
              order to provide a reliable estimation of proximity likelihood for
              the genes.},
  publisher = {Springer},
  year = {2014},
}

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