Of course, I would like to thank all of the members of FreyreLab for their cooperation and continuous support!
And thank you for reading! 😄

freyrelab.org/es/team

Finally, we conclude with a series of guidelines for improving the accomplishment of standards (such as the FAIR principles), as well as the availability of data and metadata.

On the other hand, we assess the availability of bacterial microarray data. Although this technology is being progressively displaced by RNAseq, it accounts for about half of the high-throughput gene expression records of bacteria, highlighting the importance of reevaluating all kinds of datasets.

Regarding biological metadata, we analyse the adherence of the 'characteristics' fields to a <tag>:<value> structured format, as reported in the GEO documentation. Nevertheless, we found poor to mid adherence to this format, again limiting our possibilities for programmatic access to metadata.

In addition to those concerns, we report several inconsistencies in the GEO documentation that, we argue, strongly limit our potential to automatically extract metadata from GEO records.

However, this raises several concerns about data availability, reusability, and processing. In this perspective, we evaluate the severity of these issues in the GEO database, focusing on bacterial datasets.

The explosion in the amount of transcriptomics data (DNA microarrays and RNAseq) opens the possibility of harnessing publicly available datasets for big data applications to better understand gene regulation.

How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO Abstract. The Gene Expression Omnibus (GEO) is the largest functional genomics repository, including ~5 million entries related to the main transcriptomic

I am excited to share that Briefing in Bioinformatics has published our perspective, in which we evaluate the challenges posed by massive transcriptomics data reutilization in GEO.

academic.oup.com/bib/article/...