Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Dev #101

Merged
merged 3 commits into from
Feb 9, 2025
Merged

Dev #101

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
cb70140
publication added.
ypriverol Feb 9, 2025
c3daddd
publication added.
ypriverol Feb 9, 2025
73acd40
Merge branch 'master' of https://github.com/bigbio/ibaqpy into dev
ypriverol Feb 9, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
29 changes: 29 additions & 0 deletions CITATION.cff
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,29 @@
cff-version: 1.2.0
message: "If you use ibaqpy in your research, please cite this work."
title: "ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata"
authors:
- family-names: "Zheng"
given-names: "Ping"
- family-names: "Audain"
given-names: "Enrique"
- family-names: "Webel"
given-names: "Henry"
- family-names: "Dai"
given-names: "Chengxin"
- family-names: "Klein"
given-names: "Joshua"
- family-names: "Hitz"
given-names: "Marc-Phillip"
- family-names: "Sachsenberg"
given-names: "Timo"
- family-names: "Bai"
given-names: "Mingze"
- family-names: "Perez-Riverol"
given-names: "Yasset"
abstract: "Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a protein's absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy, a Python package designed to compute iBAQ values efficiently for experiments of any scale."
date-released: "2025-02-08"
doi: "10.1101/2025.02.08.637208"
url: "https://www.biorxiv.org/content/early/2025/02/08/2025.02.08.637208"
journal: "bioRxiv"
publisher: "Cold Spring Harbor Laboratory"
version: "2025.02.08.637208"
8 changes: 6 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -221,9 +221,13 @@ Options:
--help Show this message and exit.
```

### How to cite ibaqpy
### Citation

Wang H, Dai C, Pfeuffer J, Sachsenberg T, Sanchez A, Bai M, Perez-Riverol Y. Tissue-based absolute quantification using large-scale TMT and LFQ experiments. Proteomics. 2023 Oct;23(20):e2300188. doi: [10.1002/pmic.202300188](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202300188). Epub 2023 Jul 24. PMID: 37488995.
> Zheng P, Audain E, Webel H, Dai C, Klein J, Hitz MP, Sachsenberg T, Bai M, Perez-Riverol Y. ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata. bioRxiv 2025.02.08.637208; doi: https://doi.org/10.1101/2025.02.08.637208

Other relevant publications:

> Wang H, Dai C, Pfeuffer J, Sachsenberg T, Sanchez A, Bai M, Perez-Riverol Y. Tissue-based absolute quantification using large-scale TMT and LFQ experiments. Proteomics. 2023 Oct;23(20):e2300188. doi: [10.1002/pmic.202300188](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202300188). Epub 2023 Jul 24. PMID: 37488995.

### Credits

Expand Down
Loading