Evidence map›Paper›PMID 40951280›Full record

ArticleResearch square2025

mSigSDK - private computation of mutation signatures.

Aaron Ge, Tongwu Zhang, Yasmmin Côrtes Martins, Maria Teresa Landi, Brian Park, Kailing Chen, Jeya Balasubramanian, Jonas S Almeida

Abstract readPreprint
In one paragraph

Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Aaron Ge-Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
Tongwu Zhang-Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
Yasmmin Côrtes Martins-National Laboratory of Scientific Computing, Petrópolis, Brazil.
Maria Teresa Landi-Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
Brian Park-Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
Kailing Chen-Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
Jeya Balasubramanian-Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
Jonas S Almeida-Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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).

Identifiers

PMID40951280
PMCPMC12425081

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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.