Login / Signup

Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework.

Lev I LevitskyJoshua A KleinMark V IvanovMikhail V Gorshkov
Published in: Journal of proteome research (2019)
Many of the novel ideas that drive today's proteomic technologies are focused essentially on experimental or data-processing workflows. The latter are implemented and published in a number of ways, from custom scripts and programs, to projects built using general-purpose or specialized workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished. Facilitating the development of reproducible data-processing workflows becomes essential for increasing the efficiency of proteomic research. To assist in overcoming the bioinformatics challenges in the daily practice of proteomic laboratories, 5 years ago we developed and announced Pyteomics, a freely available open-source library providing Python interfaces to proteomic data. We summarize the new functionality of Pyteomics developed during the time since its introduction.
Keyphrases
  • electronic health record
  • label free
  • big data
  • healthcare
  • primary care
  • mass spectrometry
  • physical activity
  • public health
  • palliative care
  • machine learning
  • clinical practice
  • artificial intelligence