Open-source bioinformatics tools for proteomics

Empowering your data analysis

Learn about and install open-source software for your proteomic research

As the field of proteomics rapidly advances, researchers require powerful and accessible tools for data analysis. At SomaLogic, we are committed to fostering collaboration within the research community, believing it fuels progress and innovation.

This dedication extends to our development of open-source bioinformatics tools available on Github, a platform for sharing and collaborating on software projects.

These open-source bioinformatics tools are free and available on Github. Explore the list below to find tools that meet your specific needs.

SomaPlotr icon

SomaPlotr

A highly specialized suite of standardized plotting routines based on the “Grammar of Graphics” framework of mapping variables to aesthetics used in ‘ggplot2’. Graphics types are biased towards visualizing SomaScan (proteomic) data.

Install package

Code icon

SomaDataIO

The SomaDataIO package loads and exports ‘SomaScan’ data via the ‘SomaLogic Operating Co., Inc.’ proprietary data file, called an ADAT (‘*.adat’). The package also exports auxiliary functions for manipulating, wrangling, and extracting relevant information from an ADAT object once in memory.

Install package

Canopy graph icon

Canopy

Python package canopy loads the SomaLogic, Inc. proprietary data file called an *.adat. The package provides auxiliary functions for extracting relevant information from the ADAT object once in the Python environment.

Install package

Michael Hinterberg, PhD, featured on Technology Networks

Simple and Straightforward High-Throughput Proteomics Analysis With Michael Hinterberg Video | Technology Networks

Teach me in 10 with Michael Hinterberg

Simple and Straightforward
High-Throughput Proteomics Analysis

Michael Hinterberg, PhD, provides an overview and inspiration for obtaining and analyzing high-dimensional proteomic data, showing how proteomic data are readily accessible for exploratory data analysis and hypothesis testing to those who are already familiar with genomic analysis or traditional statistics in other scientific fields. Watch Dr Hinterberg on this episode of Teach Me in 10.

Watch video