GEOmAtik<sup>TM</sup>: automated platform for mining and classification of individual datasets of NCBI GEO
Creators: name: Ritvik Vipra
name: Devaki Kelkar
name: Madhura Vipra
Publisher: name: F1000Research
Year: 2017

description: NCBI Gene Expression Omnibus (GEO) is the largest public repository (holding 82702 series &amp; 2128073 samples) of genomic data. The repository grows by 300,000 new data-sets per year and is increasingly used to reanalyze the available data with newer set of questions to discover new relationships among diseases, drugs and pathways leading to smarter diagnostic and treatment options. In 2015, over 600 papers were published which cited re-analysis of data from GEO. Though (re)analyzing GEO data for new hypotheses is proving to be a crucial tool for researchers, GEO stays relatively untapped as GEO metadata mining and analysis requires both computational skills and manual efforts due to its complexity and heterogeneity. This is because GEO relies on submitter for meta-data completeness and accuracy. Data in GEO database is diverse in terms of technology, source, quality, quantity and granularity. There could be several times disparity between the experiments described in the summary and the actual data submitted thus reading just Summary/overall design/titles of GSM IDS may not divulge all details, requiring reading of sample accession pages individually. Further manual intervention is needed to choose right data-sets for comparisons of gene expression e.g. disease vs. control, mutant vs. wild type, drug treated vs. untreated/vehicle treated etc. for analyzing relationships. Available automation options fetch data, but subsequent tasks like data filtering, massaging, annotation, and analysis demand huge manual biocuration efforts which are not only time and resource intensive but are prone to accuracy limitations. This is rate limiting in cases of large scale bioinformatics efforts. This complexity is specially challenging to novice users, for those who wish to carry out large scale studies, and when researchers have limited computing knowledge. Valuable time is lost in finding, preparing, and annotating the data, which hampers the speed of the research project.<br><br>Athena Consultancy has developed an innovative platform GEOmAtik<sup>TM</sup>, that integrates text mining and machine learning to provide a one-step solution for mining and functional  annotations of GEO data-sets.  The platform provides capability to select, edit and save GSE IDs, GSM IDs, and associated metadata after classifying GSM samples into 5 distinct sub classes such as test and associated control. The entire process occurs in a single go without any manual intervention saving several weeks of tedious tagging work that is usually required to annotate large number of data-sets for correct comparisons. This helps expedite the studies to a great extent.  <br><br>Salient Features of GEOmAtik<sup>TM  </sup> 


	Integrated mining and annotation of GEO datasets
	Inbuilt, tested, and verified disease and drug library
	Targeted search
	Ease of applying filters
	Functional annotation of GEO datasets
	Data classification to 5 distinct classes for right comparisons
	High speed and precision
	Flat, machine accessible, human readable output
	Manual provision to fine-tune selection
	Option to download data at all steps