How to use omicX statistics like a pro

omicX just launched statistics for tools, a new feature for software developers and users to get a bunch of knowledge about bioinformatics resources.


Let’s see how this will help you become a bigbio data pro!


Why it’s useful


While a few tools are being developed to address very specific needs, most of them aim at helping researchers for mainstream applications in genomics, transcriptomics, or proteomics. This leads to a lot of redundancy, and poses a problem for scientists: How to choose among several seemingly identical solutions?


The usual way of dealing with this problem is a painful, time-consuming review of the specs and literature, to optimize compatibility with other tools, or simply find the solution your peers use the most.


For this, we’ve extended our tools pages to provide meaningful and up-to-date statistics, so you can make better choices.


Tool statistics


Statistics are accessible on a tool’s page, under the protocol section and before its specifications*.


You will find the following graphical charts:


  • Citation per year


A visual representation of a tool’s popularity and usage. This statistic lets you know how many times a tool has been cited in the literature since it’s publication. In this example, DESEQ has been cited 136 times in 2017 and its popularity is increasing.




  • Popular tool citations


Quickly compare a tool’s popularity with other related tools of the same analytical step. This statistic shows you the three most cited tools of the same category. In this example, DESeq2, edgeR and MEGAN are the most popular tools to perform a differential abundant feature detection. Simply move your mouse over the curves to display the number of citation for each year.




  • Related tools


With this chart, you can identify other tools that are often co-used or co-cited with your tool in protocols and publications. The number indicates how many times the pair of tools was co-cited. In this example, SAMtools has been used 241 times with Bowtie in publications.




  • Tool usage and distribution map


With this feature, you can now measure tool usage by country. The color gradient on the map represents the number of publications citing a tool by country. In this example, TopHat was cited in 232 publications originating from a Chinese institution.



  • Associated diseases


Software tools may be used for a wide range of application and diseases. With this word cloud, you can easily identify major diseases associated with the use of your tool in publications. In this example, SAMtools is mainly used for the analysis of cancer or HIV infection data.




*Note that since building statistics requires a substantial amount of data, they may not be displayed on every tool page for now.