In a recent high profile publication (Liu and Kraja et al. Nature Genetics. (2016) 48(10): 1162-70), the authors successfully utilized the Literature Lab™ platform to perform disease, anatomy, and pathway enrichment analysis on significant genes identified in the study. The 31 gene set used in this analysis was derived from meta-analysis of association results for blood pressure among a total of 327,288 individuals.
The results indicate that Literature Lab™ gene set enrichment analysis can provide important validation of known disease and pathway associations in large scale meta-analysis studies. Beyond this, Literature Lab™ can produce novel insights that open interpretations to new and advanced areas. In this case, nutritional and metabolic disease associations that point to potential roles for associated genes in metabolic syndrome.
A recent comment in Genome Biology (Ziemann et al. Genome Biology (2016) 17:177) revisited the issue of the Excel software under default settings mistakenly converting gene symbols to dates and floating - point numbers, as originally described in 2004.
The results of the authors' analysis of 18 prominent journals and 7,467 gene lists demonstrated that the proportion of published articles with Excel files containing gene lists affected by gene name errors is 19.6 %
Literature Lab™ subverts this dilemma by use of the Gene Thesaurus, a repository of human gene and protein nomenclature, containing symbols, names and aliases gathered from major genomic data repositories combined with extensive human and machine-assisted curation.
The Scientist magazine, July 2016
"Literature Lab is an easy to use automated functional analysis and data mining tool uniquely suited to uncover associations and co-occurrences among diseases, pathways, chemical actions, etc. in the literature. It was of great use to us in a recent study to uncover shared genetic etiology among allergic diseases” - Tesfaye Mersha, Assistant Professor, University of Cincinnati, Department of Pediatrics.
“We believe that these features are uniquely powerful in our automated approach to database interrogation and statistical analysis on the literature”, explains Paul Martinez, President and CEO. “Other tools have their databases periodically and cumulatively updated, and there is seldom time or resources to edit mistakes of the past. With quarterly releases of the database for statistical analysis on gene lists, and automated "as of this second" searches on associations of interest, Literature Lab™ sets a benchmark for timeliness that is far ahead of other functional analysis tools.”
The Scientist magazine, February 2016
“One of the most powerful strengths of the Literature Lab™ platform is that it addresses two big data issues", says Damon Anderson, Vice President of Business Development. "First, modern technologies are producing genetic data at unprecedented rates often resulting in a backlog of experimental questions with no real actionable answers. Second, the PubMed literature record is expanding at an ever increasing rate, resulting in a huge resource of critical information that remains relatively untapped”, explains Dr. Anderson.
The number of abstracts in PubMed in the period mined by Literature Lab™ is 16,674,480 (update - 17,553,429 - 9/30/2016). A record 1,081,927 were added to PubMed in 2015, a 29% increase over 2010 and a 118% increase over 2000 “This rate will only continue to climb in the coming years, making it increasingly more difficult to identify actionable associations from genetic data. Finding that needle in the haystack is a virtual impossibility using tradition Boolean search methods. A Literature Lab™ analysis of a 25 gene set is the equivalent of 1 billion manual queries of PubMed”, says Paul Martinez, President and CEO. “That’s a truly powerful advantage of the Literature Lab™ technology.”