Literature DB >> 29474526

IWTomics: testing high-resolution sequence-based 'Omics' data at multiple locations and scales.

Marzia A Cremona1, Alessia Pini2, Fabio Cumbo3,4, Kateryna D Makova5,6, Francesca Chiaromonte1,5,7, Simone Vantini2.   

Abstract

Summary: With increased generation of high-resolution sequence-based 'Omics' data, detecting statistically significant effects at different genomic locations and scales has become key to addressing several scientific questions. IWTomics is an R/Bioconductor package (integrated in Galaxy) that, exploiting sophisticated Functional Data Analysis techniques (i.e. statistical techniques that deal with the analysis of curves), allows users to pre-process, visualize and test these data at multiple locations and scales. The package provides a friendly, flexible and complete workflow that can be employed in many genomic and epigenomic applications. Availability and implementation: IWTomics is freely available at the Bioconductor website (http://bioconductor.org/packages/IWTomics) and on the main Galaxy instance (https://usegalaxy.org/). Supplementary information: Supplementary data are available at Bioinformatics online.

Mesh:

Year:  2018        PMID: 29474526      PMCID: PMC6022652          DOI: 10.1093/bioinformatics/bty090

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  4 in total

1.  The interval testing procedure: A general framework for inference in functional data analysis.

Authors:  Alessia Pini; Simone Vantini
Journal:  Biometrics       Date:  2016-01-26       Impact factor: 2.571

2.  ChIP-seq analysis reveals distinct H3K27me3 profiles that correlate with transcriptional activity.

Authors:  Matthew D Young; Tracy A Willson; Matthew J Wakefield; Evelyn Trounson; Douglas J Hilton; Marnie E Blewitt; Alicia Oshlack; Ian J Majewski
Journal:  Nucleic Acids Res       Date:  2011-06-07       Impact factor: 16.971

3.  Integration and Fixation Preferences of Human and Mouse Endogenous Retroviruses Uncovered with Functional Data Analysis.

Authors:  Rebeca Campos-Sánchez; Marzia A Cremona; Alessia Pini; Francesca Chiaromonte; Kateryna D Makova
Journal:  PLoS Comput Biol       Date:  2016-06-16       Impact factor: 4.475

4.  The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update.

Authors:  Enis Afgan; Dannon Baker; Marius van den Beek; Daniel Blankenberg; Dave Bouvier; Martin Čech; John Chilton; Dave Clements; Nate Coraor; Carl Eberhard; Björn Grüning; Aysam Guerler; Jennifer Hillman-Jackson; Greg Von Kuster; Eric Rasche; Nicola Soranzo; Nitesh Turaga; James Taylor; Anton Nekrutenko; Jeremy Goecks
Journal:  Nucleic Acids Res       Date:  2016-05-02       Impact factor: 16.971

  4 in total
  4 in total

1.  Functional data analysis for computational biology.

Authors:  Marzia A Cremona; Hongyan Xu; Kateryna D Makova; Matthew Reimherr; Francesca Chiaromonte; Pedro Madrigal
Journal:  Bioinformatics       Date:  2019-09-01       Impact factor: 6.937

2.  Human L1 Transposition Dynamics Unraveled with Functional Data Analysis.

Authors:  Di Chen; Marzia A Cremona; Zongtai Qi; Robi D Mitra; Francesca Chiaromonte; Kateryna D Makova
Journal:  Mol Biol Evol       Date:  2020-12-16       Impact factor: 16.240

3.  Non-B DNA: a major contributor to small- and large-scale variation in nucleotide substitution frequencies across the genome.

Authors:  Wilfried M Guiblet; Marzia A Cremona; Robert S Harris; Di Chen; Kristin A Eckert; Francesca Chiaromonte; Yi-Fei Huang; Kateryna D Makova
Journal:  Nucleic Acids Res       Date:  2021-02-22       Impact factor: 16.971

4.  Functional data analysis characterizes the shapes of the first COVID-19 epidemic wave in Italy.

Authors:  Tobia Boschi; Jacopo Di Iorio; Lorenzo Testa; Marzia A Cremona; Francesca Chiaromonte
Journal:  Sci Rep       Date:  2021-08-30       Impact factor: 4.379

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.