Value of Collaboration among Multi-Domain Experts in Analysis of High-Throughput Genomics Data

Abstract

The recent explosion and ease of access to large-scale genomics data is intriguing. However, serious obstacles exist to the optimal management of the entire spectrum from data production in the laboratory through bioinformatic analysis to statistical evaluation and ultimately clinical interpretation. Beyond the multitude of technical issues, what stands out the most is the absence of adequate communication among the specialists in these domains. Successful interdisciplinary collaborations along the genomics pipeline extending from laboratory experiments to bioinformatic analyses to clinical application are notable in large scale, well managed projects such as The Cancer Genome Atlas. However, in certain settings in which the various experts perform their specialized research activities in isolation, the siloed approach to their research contributes to the generation of questionable genomic interpretations. Such situations are particularly concerning when the ultimate endpoint involves genetic/genomic interpretations that are intended for clinical applications. In spite of the fact that clinicians express interest in gaining a better understanding of clinical genomic applications, the lack of communication from upstream experts leaves them with a serious level of discomfort in applying such genomic knowledge to patient care. This discomfort is especially evident among healthcare providers who are not trained as geneticists, in particular primary care physicians. We offer some initiatives that have potential to address this problem, with emphasis on improved and ongoing communication among all the experts in these fields, constituting a comprehensive genomic "pipeline" from laboratory to patient.

Figure 1:. Unidirectional Approach to Transfer of Information among Experts in Relevant Disciplines during Implementation of a Genomic Pipeline: a Linear Approach

The traditional approach to information transfer as depicted in the figure reflects the siloed communication or non-communication among experts in required disciplines contributing to genomic analyses of biological material. Sources of variability - symbols:

1. Technical variability:

   1. Use of fresh tissue versus FFPE (formalin fixed paraffin embedded) tissue; amount of input DNA

   2. Extent of DNA fragmentation

2. Bioinformatics variability-algorithm selection: (see for abbreviation definitions)

   1. Algorithm selection for alignment to reference sequence

   2. Algorithm selection for genetic alteration identification

3. Clinical interpretation of DNA motifs: SNVs (single nucleotide variants), SNPs (single nucleotide polymorphisms), VUS (variants of uncertain/unknown significance), INDELs (insertion/deletions), CNVs (copy number variants), fusion variants

Figure 2:. Multidirectional Approach to Transfer of Information among Experts in Relevant Disciplines during Implementation of a Genomic Pipeline: a Network Approach.

Legend: In contrast to the linear approach (Figure 1), the multidirectional approach releases the constraints of a unidirectional pipeline. This breaking down of the siloes historically have isolated domain experts should enable improved communication and ultimately lead to better quality science. Sources of variability - symbols: see Figure 1 for symbol definitions See Figure 1 Legend for abbreviation definitions