Treffer: mSigSDK - private computation of mutation signatures.

Title:
mSigSDK - private computation of mutation signatures.
Authors:
Ge A; -Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.; -University of Maryland, School of Medicine, Maryland, USA., Zhang T; -Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA., Martins YC; -National Laboratory of Scientific Computing, Petrópolis, Brazil., Landi MT; -Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA., Park B; -Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA., Chen K; -Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA., Balasubramanian J; -Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA., Almeida JS; -Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
Source:
Research square [Res Sq] 2025 Sep 02. Date of Electronic Publication: 2025 Sep 02.
Publication Type:
Journal Article; Preprint
Language:
English
Journal Info:
Country of Publication: United States NLM ID: 101768035 Publication Model: Electronic Cited Medium: Internet ISSN: 2693-5015 (Electronic) Linking ISSN: 26935015 NLM ISO Abbreviation: Res Sq Subsets: PubMed not MEDLINE
References:
PeerJ. 2019 Jan 15;7:e6230. (PMID: 30671301)
Comput Sci Eng. 2016 Sep-Oct;18(5):10-20. (PMID: 29033693)
Nature. 2021 May;593(7857):156-157. (PMID: 33941927)
Am J Epidemiol. 2021 Jun 1;190(6):962-976. (PMID: 33712835)
Nat Genet. 2019 Dec;51(12):1732-1740. (PMID: 31740835)
Bioinformatics. 2021 Aug 4;37(14):2073-2074. (PMID: 33135727)
Entry Date(s):
Date Created: 20250915 Date Completed: 20250919 Latest Revision: 20250919
Update Code:
20250919
PubMed Central ID:
PMC12425081
DOI:
10.21203/rs.3.rs-6536730/v1
PMID:
40951280
Database:
MEDLINE

Weitere Informationen

In our previous work, we demonstrated that it is feasible to perform analysis on mutation signature data without the need for downloads or installations and analyze individual patient data without compromising privacy. Building on this foundation, we developed an in-browser Software Development Kit (a JavaScript SDK), mSigSDK, to facilitate the orchestration of distributed data processing workflows and graphic visualization of mutational signature analysis results. We strictly adhered to modern web computing standards, particularly the modularization standards set by the ECMAScript ES6 framework (JavaScript modules). Our approach allows for the computation to be entirely performed by secure delegation to the computational resources of the user's own machine (in-browser), without any downloads or installations. The mSigSDK was developed primarily as a companion library to the mSig Portal resource of the National Cancer Institute Division of Cancer Epidemiology and Genetics (NIH/NCI/DCEG), with a focus on FAIR extensibility as components of other researchers' own data science constructs. Anticipated extensions include the programmatic operation of other mutation signature API ecosystems such as SIGNAL and COSMIC, advancing towards a data commons for mutational signature research (Grossman et al., 2016).

Competing Interests Statement The authors declare no competing interests. Additional Declarations: No competing interests reported.