Tomi Suomi

ROTS 2.0 improves statistical modeling in omics studies by optimizing reproducibility across various experimental designs and provides open-source implementations in R and Python.

This study benchmarks eight de novo peptide sequencing models on proteomics datasets to evaluate their performance and identify limitations in resolving sequence ambiguity.

The paper introduces a new pathway analysis method for longitudinal data, demonstrating its application in early type 1 diabetes studies with subtle biological signals.

PhosPiR automates phospho-proteomic analysis by integrating multiple R packages to provide data cleaning, statistical testing, enrichment analysis, and visualization for high-dimensional mass spectrometry data.

The paper introduces RolDE, a new differential expression method for longitudinal proteomics data, which outperforms existing methods in handling missing values and reproducibility.

The paper introduces a DIA-only metaproteomics approach that improves peptide yields and reduces data requirements compared to DDA-assisted methods.

This study introduces data-independent acquisition mass spectrometry for metaproteomics, showing improved reproducibility in characterizing microbial function.