The role of bioinformatics in Proteomics

Ricardo Jorge Paid, PhD - 18 October 2021

Bioinformatics is a multiple disciplinary area that integrates biology with informatics, computer science and mathematics.

Bioinformatics is a multiple disciplinary area that integrates biology with informatics, computer science and mathematics. Historically, bioinformatics has its origins in the early 60s to help to decode DNA and protein structure. When sequencing become possible, bioinformatics started to play a key role in the development of multiple algorithms and software tools that enabled the reconstruction and analysis of gene and protein sequences.

As the result of its initial success and the evolution of computer power, bioinformatics has become very popular and necessary among biological sciences. Thus, many bioinformatics tools and methodologies have been developed for:

  • Comparing gene and protein sequences to establish evolutionary relationships (e.g. phylogenetic algorithms) and disease associations.
    Information storage is associated with genes and proteins through bioinformatics databases such as NCBI and Uniprot.
    Predicting protein structure, function and disease association based on sequence analysis.
    Multiple gene/protein/metabolites quantification and analysis of entire genomes, proteomes and metabolomes.

In proteomics, the quantification and analysis of all proteins from an individual and its comparison within a population of individuals is the central focus of this field. During the last decades, the evolution of mass spectrometry towards an accurate high-throughput technology was crucial to make possible the application of proteomics to biological sciences, drug discovery and precision medicine. However, the analysis of proteomics has become too complex and time-consuming to extract biologically relevant insights. One reason is due to a large amount of data to analyse. Another is the complex interplay of its components as numerous regulatory interactions of signalling pathways components, metabolism and gene expression are involved during the regulation of cellular processes. Further, complexity can also be attributed to distinct temporal-dependent behaviours that should be analysed in an integrative manner to fully grasp the regulatory behaviours.

In the last decade, bioinformatics development of state-of-the-art tools has become a solution to analyse complex data from proteomics and extract biological insights. In part, this was motivated by the lack of available generic and suitable solutions in the field of “pure” informatics and classical statistical analysis that account for the biological complexity. For these reasons, bioinformatics has become a frequent partner of proteomics and many bioinformatics solutions have been developed for proteomics.

In brief, modern bioinformatics solutions such as the ones developed by Kinomica (KScan®) can provide the following advantages for extracting relevant insights in biological sciences and precision medicine:

  • Faster and accurate proteomics quantification
  • Processing large amounts of data at reasonable timing.
  • Automated and unbiased systematic data analysis.
  • Integration of multiple data processing and statistical methodologies into pipelines of analysis.
  • Faster screening of Biomarkers for cellular processes and drug responses.
  • Identification of biomarkers with predictive potential.
  • Systematic gene ontology mapping and functional inference.
  • Faster and automated reporting with dynamic visualization plots.
  • Application of machine-learning for facilitating predictive modelling.
  • Application of artificial intelligence for improved and automated decisions.
  • Efficient management of biological data into tailor-made databases.
  • Implementation of tailor-made user-friendly interfaces for proteomics specialists.


  • European Molecular Biology Laboratory,
  • The National Center for Biotechnology Information,
  • UniProt,

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