Evidence map›Paper›PMID 36932333›Full record

ArticleBMC bioinformatics2023

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution.

Gianluca Ascolani, Fabrizio Angaroni, Davide Maspero, Francesco Craighero, Narra Lakshmi Sai Bhavesh, Rocco Piazza, Chiara Damiani, Daniele Ramazzotti, Marco Antoniotti, Alex Graudenzi

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Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Gianluca AscolaniDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy. gianluca.ascolani@unimib.it.
Fabrizio AngaroniDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.
Davide MasperoDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.
Francesco CraigheroDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.
Narra Lakshmi Sai BhaveshDepartment of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani, Rajasthan, India.
Rocco PiazzaDepartment of Medicine and Surgery, University of Milan-Bicocca, University of Milan-Bicocca, Milan, Italy.
Chiara DamianiDepartment of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy.
Daniele RamazzottiDepartment of Medicine and Surgery, University of Milan-Bicocca, University of Milan-Bicocca, Milan, Italy.
Marco AntoniottiDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.
Alex GraudenziDepartment of Informatics, Systems and Communication, University of Milan-Bicocca, Milan, Italy.

Funding

AIRC-IG 22082CRUK/AIRC Accelerator Award 22790Google Summer of Code GSoC 2020
6 · The paper itself

Abstract

backgroundLongitudinal single-cell sequencing experiments of patient-derived models are increasingly employed to investigate cancer evolution. In this context, robust computational methods are needed to properly exploit the mutational profiles of single cells generated via variant calling, in order to reconstruct the evolutionary history of a tumor and characterize the impact of therapeutic strategies, such as the administration of drugs. To this end, we have recently developed the LACE framework for the Longitudinal Analysis of Cancer Evolution.

resultsThe LACE 2.0 release aimed at inferring longitudinal clonal trees enhances the original framework with new key functionalities: an improved data management for preprocessing of standard variant calling data, a reworked inference engine, and direct connection to public databases.

conclusionsAll of this is accessible through a new and interactive Shiny R graphical interface offering the possibility to apply filters helpful in discriminating relevant or potential driver mutations, set up inferential parameters, and visualize the results. The software is available at: github.com/BIMIB-DISCo/LACE.

Indexed as

NeoplasmsSoftwareClone CellsHumansClonal analysisInterfaceLongitudinalShinySingle cell

Identifiers

PMID36932333
PMCPMC10022199

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.