[Paper Review] A data-supported history of bioinformatics tools

Clément Levin, Emeric Dynomant+5 more|arXiv (Cornell University)|Jul 18, 2018

Genomics and Phylogenetic Studies42 references4 citations

TL;DR

This paper presents a data-driven analysis of bioinformatics tool evolution using 23,000 references from the OMICtools database spanning 1992–2018. It maps trends in software development, including country collaborations, programming languages, R&D investments, and technological shifts, offering the most comprehensive systematic overview of the field to date and highlighting increasing analytical complexity in bioinformatics tools.

ABSTRACT

Since the advent of next-generation sequencing in the early 2000s, the volume of bioinformatics software tools and databases has exploded and continues to grow rapidly. Documenting this evolution on a global and time-dependent scale is a challenging task, limited by the scarcity of comprehensive tool repositories. We collected data from over ~23,000 references classified in the OMICtools database, spanning the last 26 years of bioinformatics to present a data-supported snapshot of bioinformatics software tool evolution and the current status, to shed light on future directions and opportunities in this field. The present review explores new aspects of computational biology, including country partnerships, trends in technologies and area of development, research and development (R&D) investments and coding languages. This is the most comprehensive systematic overview of the field to date and provides the community with insights and knowledge on the direction of the development and evolution of bioinformatics software tools, highlighting the increasing complexity of analysis.

Motivation & Objective

• To document the global and time-dependent evolution of bioinformatics software tools and databases since the early 2000s.
• To address the scarcity of comprehensive tool repositories by leveraging a large-scale dataset from the OMICtools database.
• To analyze trends in research and development investments, coding languages, and technological shifts in bioinformatics tool creation.
• To provide insights into the increasing complexity of bioinformatics software and inform future development directions.
• To map international collaborations and country-level contributions to bioinformatics tool development.

Proposed method

• Collected and analyzed 23,000 references from the OMICtools database, covering bioinformatics tools and databases from 1992 to 2018.
• Categorized tools by publication year, country of origin, programming language, and domain of application to track temporal and geographic trends.
• Mapped country partnerships and collaborations using co-authorship and institutional affiliation data from tool publications.
• Tracked shifts in dominant programming languages (e.g., Python, R, C++) over time to assess technological evolution in tool development.
• Analyzed R&D investment trends by correlating tool publication frequency with funding agency reports and institutional outputs.
• Used data visualization and statistical analysis to identify patterns in tool complexity and specialization across biological domains.

Experimental results

Research questions

• RQ1 How has the volume and diversity of bioinformatics tools evolved globally since the early 2000s?
• RQ2 Which programming languages have dominated bioinformatics tool development, and how have their usages changed over time?
• RQ3 What are the major country-level contributions and collaborative partnerships in bioinformatics tool development?
• RQ4 How have R&D investments and technological trends influenced the complexity and specialization of bioinformatics tools?
• RQ5 What patterns in tool development reflect shifts in biological research focus and sequencing technology adoption?

Key findings

• The number of bioinformatics tools and databases has grown exponentially since the early 2000s, with a significant acceleration post-2010.
• Python and R emerged as the dominant programming languages in tool development, with Python showing a sharp rise in popularity after 2010.
• The United States, China, and European countries (especially Germany and the UK) were the leading contributors to tool development, with strong collaborative networks between North America and Europe.
• There was a notable shift toward tools focused on next-generation sequencing data analysis, particularly in genomics and transcriptomics.
• A growing proportion of tools were developed in academic institutions, though industry contributions were increasingly visible in specialized and high-performance applications.
• The complexity of tools has increased, with more tools integrating multiple analysis steps and supporting diverse data types and formats.