Recent years have seen a revolution in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq). As the number, size and complexity of scRNA-seq datasets continue to increase, so does the number of computational methods and software tools for extracting meaning from them. Since 2016 the scRNA-tools database has catalogued software tools for analysing scRNA-seq data. With the number of tools in the database passing 1000, researchers at Helmholtz Zentrum München take this opportunity to provide an update on the state of the project and the field. Analysis of five years of analysis tool tracking data clearly shows the evolution of the field, and that the focus of developers has moved from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. The researchers also find evidence that open science practices reward developers with increased recognition and help accelerate the field.
Overview of the scRNA-tools database
A) Line plot of the number of tools in the scRNA-tools database over time. The development of tools for analysing scRNA-seq data has continued to accelerate with more than 1000 tools currently recorded. Dotted line shows a quadratic fit (y = 0.0002x2 + 0.1x +60.7). B) Publication status of tools in the scRNA-tools database. Around 70 percent of tools have at least one peer-reviewed publication while more than 20 percent have an associated preprint. C) Bar charts showing the distribution of platforms. software licenses and software repositories for tools in the scRNA-tools database. Colors indicate proportions of tools using R or Python. D) Bar chart showing the proportion of tools in the database assigned to each analysis category. Categories are grouped by broad phases of a standard scRNA-seq analysis workflow.
Availability – https://www.scrna-tools.org/