Transcriptomic databases

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Rapidly evolving omics produce information vital for shedding light on many problems across different biological fields and demand effective storage, maintenance and access solutions. Transcriptomic databases store information necessary for identifying differentially expressed sets of genes or responses to different treatment conditions and, as such, are being developed and improved with a particular focus. The aim of this review is to provide an overview of currently available transcriptomic databases as broadly divided into the categories of human, animal and plant transcriptome data. Human transcriptome databases are most numerous and could be further broadly divided into two categories, with most sources mainly dedicated to cancer and other disease pathologies and the other category containing a broader scope of data on development and gene interactions with other helpful sources such as raw read and non-regulatory RNA repositories. Animal databases focus particularly on model organisms and important parasitic and arthropod species as well as a commercially important fish species. Plant databases discussed would be of particular relevance to agronomically important plants such as crops, tree and vegetable species. The importance of transcriptome databases for researchers, clinicians and industry scientists could not be overemphasized: As the technology develops further, the information accessible worldwide would be vital for better diagnostics, more accurate candidate gene studies for elucidating the genetic nature of disorders across organisms and faster results achieved in plant or animal breeding programs. As the database interfaces become more user-friendly and the data analysis technology develops, major impacts are expected to be made across all areas of biological science and industry and the roots of these impacts would be in the extensive data provided by omics databases, transcriptome databases in particular.




Molkenov, A., Zhelambayeva, A., Yermekov, A., Mussurova, S., Sarkytbayeva, A., Kalykhbergenov, Y., … Kairov, U. (2018). Transcriptomic databases. In Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (Vol. 1–3, pp. 341–351). Elsevier.

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