Endo

Repository for the TMEM184B Endoylysosomal Acidification paper

Adapted from source code used to analyze data in Dr. Martha Bhattacharya's lab at the University of Arizona

• The lab studies transmembrane protein 184b (TMEM184B), a protein involved in axon degeneration and cancer, across multiple neuroscience model systems

The Journal of Cell Science publication associated with this repository is the third to utilize function(s) from the TMEM package. The first publication has its own dedicated repository, eriklarsen4/Itch, and the second publication's repository, eriklarsen4/Hippo, is under development. Please see those repositories for each paper's respective URL. Among other datasets and analyses, they contain multiple, functionally validated bulk RNAseq datasets.

Please see the FIREpHly microscopy data vignette and the immunoprecipitation- label-free mass spec (IPMS) data vignette for more detail on each dataset's data cleaning and analysis.

Additional computational biology work on the published data is outlined in broad strokes here, with other vignettes in progress. This work expands on the "gold standard" mixture modeling approach in affinity-precipitation mass spectrometry (APMS or IPMS) to infer proteins that truly interact with a target protein from a mixture of proteins.

Three packages are associated with this repository:

1. A custom R package, TMEM, used for downstream gene set analyses and visualizations
2. This repository's R package, Endo, which gathers the data used for the publication
3. A python package, saint (name change is forthcoming), which enables "plug-and-play" analysis of protein-protein interactions (interactomes)
   • this package contains two protein-interaction inference pipelines-- the SAINT algorithm, and a more complex iteration of it-- enabling benchmarking relative to the original gold-standard in interactomics