TY - JOUR
T1 - Nextcast
T2 - A software suite to analyse and model toxicogenomics data
AU - Serra, Angela
AU - Saarimäki, Laura Aliisa
AU - Pavel, Alisa
AU - del Giudice, Giusy
AU - Fratello, Michele
AU - Cattelani, Luca
AU - Federico, Antonio
AU - Laurino, Omar
AU - Marwah, Veer Singh
AU - Fortino, Vittorio
AU - Scala, Giovanni
AU - Kinaret, Pia Anneli Sofia
AU - Greco, Dario
N1 - Funding Information:
This research was funded by the EU H2020 projects NanoSolveIT (Grant No. 814572) and NanoinformaTIX (grant agreement No 814426), Academy of Finland (Grant No. 322761), and Novo Nordisk Foundation.
Publisher Copyright:
© 2022 The Authors
PY - 2022/1
Y1 - 2022/1
N2 - The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).
AB - The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).
KW - Computational toxicology
KW - Nextcast
KW - Pipeline
KW - Predictive toxicology
KW - Software suite
KW - Toxicogenomics
U2 - 10.1016/j.csbj.2022.03.014
DO - 10.1016/j.csbj.2022.03.014
M3 - Article
AN - SCOPUS:85126894113
SN - 2001-0370
VL - 20
SP - 1413
EP - 1426
JO - Computational and structural biotechnology journal
JF - Computational and structural biotechnology journal
ER -