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A systems understanding of disease and drug action at the level of cellular biochemical reactions and physiological function would be useful in framing the effect of genomic variations in the context of environmental cues.
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Especially in the context of interrogating whole exomes or even whole genomes, and with increasing knowledge about the effect of genomic variation, identifying the multiple dimensions of a genetic diagnosis becomes urgent, especially if other variants than those related to the clinical phenotype are to be considered as part of comprehensive diagnosis.
This genome information is also essential to identify the transcription regulatory elements for understanding the effects of genomic variations on the bacterial phenotype.
New methods to simultaneously amplify and sequence genomic DNA and full-length mRNA from the same cell may provide powerful tools for assessing the effects of genomic variation on gene expression profiles [96, 97].
The amount of phenotypic information derived from targeted mutations and hypothesis-driven studies is increasing rapidly, and is now being further augmented by high-throughput international efforts to systematically analyse the effects of genomic variation on model organism phenotypes.
Accurate information is required for effective tracking of genomic variations, mapping important quantitative trait loci (QTLs), and discovering novel alleles.
To facilitate further in-depth analysis and experimental validation of effect of variations on lncRNA, we have included a comprehensive mapping of genomic variations in lncRNA loci.
Occurrence of genomic variations in the analyzed regions were checked using the Database of Genomic Variants (http://projects.tcag.ca/variation/) [31].
TO, SW and DG performed the computational mining of genomic variations.
New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.
Databases of genomic variation and phenotypes: existing resources and future needs.
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