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Biotechnological advances in genomics have significantly impacted on molecular diagnosis. As a result, uncovering individual genomic variations has made whole-genome analysis attractive for clinical care of patients suffering from brain diseases. However, to obtain clinically relevant genomic data for successful molecular genetic/genomic diagnosis, interpretation technologies are recognized to be indispensable. Taking into account the predictive power of bioinformatics in basic genetic studies, it has been proposed to use in silico systems biology analysis and data mining for detecting clinically relevant genomic variations by diagnostic healthcare services. Here, we describe an algorithm used as an integral part of molecular diagnosis of clinically relevant genomic pathology (neurogenomic variations) in brain diseases. The bioinformatic technique allows interpreting variations at chromosome and gene levels through systems biology analysis including literature data mining, which enables to modulate the effect of each genomic change at transcriptome, proteome and metabolome levels. Studying neurogenomic variations using this approach, we were able to show that the algorithm can be used as a valuable add-on to whole genome analysis for diagnostic purposes inasmuch as it appreciably increases the efficiency of molecular diagnosis.

Systems Biology Analysis and Literature Data Mining for Unmasking Pathogenic Neurogenomic Variations in Clinical Molecular Diagnosis