Journal:Bioinformatics workflow for clinical whole genome sequencing at Partners HealthCare Personalized Medicine

Full article title	Bioinformatics workflow for clinical whole genome sequencing at Partners HealthCare Personalized Medicine
Journal	Journal of Personalized Medicine
Author(s)	Tsai, E.A.; Shakbatyan, R.; Evan, J.; Rossetti, P.; Graham, C.; Sharma, H.; Lin, C.-F., Lebo, M.S.
Author affiliation(s)	Partners HealthCare, Brigham and Women’s Hospital, Harvard Medical School
Primary contact	Tel.: +1-617-768-8292
Editors	Weiss, S.T.; Liggett, S.B.
Year published	2016
Volume and issue	6(1)
Page(s)	12
DOI	10.3390/jpm6010012
ISSN	2075-4426
Distribution license	Creative Commons Attribution 4.0 International
Website	http://www.mdpi.com/2075-4426/6/1/12/htm
Download	http://www.mdpi.com/2075-4426/6/1/12/pdf (PDF)

Abstract

Effective implementation of precision medicine will be enhanced by a thorough understanding of each patient’s genetic composition to better treat his or her presenting symptoms or mitigate the onset of disease. This ideally includes the sequence information of a complete genome for each individual. At Partners HealthCare Personalized Medicine, we have developed a clinical process for whole genome sequencing (WGS) with application in both healthy individuals and those with disease. In this manuscript, we will describe our bioinformatics strategy to efficiently process and deliver genomic data to geneticists for clinical interpretation. We describe the handling of data from FASTQ to the final variant list for clinical review for the final report. We will also discuss our methodology for validating this workflow and the cost implications of running WGS.

Keywords: clinical sequencing, WGS, NGS, next generation sequencing, bioinformatics, validation, precision medicine

Introduction

Precision medicine is becoming an increasing focus in medical research. To achieve the resolution necessary to personalize clinical care, greater attention has been drawn towards higher resolution of the patient genome. Next generation sequencing (NGS) provided a cost-effective method for targeted sequencing of known disease genes at base pair resolution. Moreover, the advent of exome sequencing enabled rapid discovery of genes causing Mendelian disorders. While gene panels and exome sequencing have proved fast and cost-effective for delivering genomic results back to the patient, these technologies are limited by our current knowledge of the exome, which changes over time. Additionally, the use of targeted capture may introduce biases to the data, including PCR duplicates, depth of coverage disparities, and failures at difficult to amplify target regions.

Practical considerations such as sequencing costs, data processing and maintenance, and data analysis complexities are important considerations when a laboratory is considering a new NGS program. These issues are amplified in whole genome sequencing (WGS) due to the volume of the data and have long been barriers to entry for clinical laboratories looking to adopt WGS. Despite the ability of WGS to interrogate the entirety of the genome, clinical interpretation still often focuses on only 3% of the genome (i.e., exome data, pharmacogenomics risk variants, and single nucleotide variants associated with complex disease risk). Therefore, WGS services may be overlooked for clinical applications as they trend towards increased costs and longer turnaround times due to a heavier computational load, increased number of variants for analysis, and larger data archives. However, the steadily decreasing cost of sequencing and storage now allow laboratories to consider genome sequencing. WGS, and more specifically PCR-free WGS, also decreases the need to re-sequence each time the coding sequences of targeted regions change, novel genes are discovered, or a new reference genome is released. The balance between cost, turnaround time, accuracy, and completeness has to be addressed when launching a WGS program. Here, we describe the workflow we adopted and the challenges we met supporting the bioinformatics of WGS in a clinical setting.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.