mSigSDK -- private, at scale, computation of mutation signatures
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929216946176000 |
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| author | Ge, Aaron Martins, Yasmmin Côrtes Zhang, Tongwu Chen, Kailing Landi, Maria Teresa Park, Brian Balasubramanian, Jeya Almeida, Jonas S |
| author_facet | Ge, Aaron Martins, Yasmmin Côrtes Zhang, Tongwu Chen, Kailing Landi, Maria Teresa Park, Brian Balasubramanian, Jeya Almeida, Jonas S |
| contents | 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 at scale without compromising privacy. Building on this foundation, we developed a Software Development Kit (SDK) called 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 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 its FAIR extensibility as components of other researchers' computational 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_02995 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | mSigSDK -- private, at scale, computation of mutation signatures Ge, Aaron Martins, Yasmmin Côrtes Zhang, Tongwu Chen, Kailing Landi, Maria Teresa Park, Brian Balasubramanian, Jeya Almeida, Jonas S Genomics 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 at scale without compromising privacy. Building on this foundation, we developed a Software Development Kit (SDK) called 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 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 its FAIR extensibility as components of other researchers' computational 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). |
| title | mSigSDK -- private, at scale, computation of mutation signatures |
| topic | Genomics |
| url | https://arxiv.org/abs/2308.02995 |