The road from next-generation sequencing to personalized medicine

Abstract

Moving from a traditional medical model of treating pathologies to an individualized predictive and preventive model of personalized medicine promises to reduce the healthcare cost on an overburdened and overwhelmed system. Next-generation sequencing (NGS) has the potential to accelerate the early detection of disorders and the identification of pharmacogenetics markers to customize treatments. This review explains the historical facts that led to the development of NGS along with the strengths and weakness of NGS, with a special emphasis on the analytical aspects used to process NGS data. There are solutions to all the steps necessary for performing NGS in the clinical context where the majority of them are very efficient, but there are some crucial steps in the process that need immediate attention.

Keywords: CADD; GWAVA; NGS; functional prediction program; genomics; personalized medicine; workflow management system.

Figure 1. Timeline: the major events in next-generation sequencing. On the left is the year of the event

EST: Expressed sequence tag; IHGSC: International human genome sequencing consortium; ENCODE: Encyclopedia of DNA elements; NCBI: National Center for Biotechnology Information; WGS: Whole-exome sequencing.

Figure 2. Generic pipeline for the analysis of next-generation sequencing

Multiple steps involved in the analysis of data from the next-generation sequencing. The paired-end short reads, from the sequencing machine, are submitted to a quality control process. The adaptors are removed from the reads, and then the reads are mapped to the human reference by using short-read sequencing mapping tools. The alignments in the sequence alignment/map format are cleaned with tools like Pickard and transformed into a binary version of the sequence alignment/map format BAM. The BAM file is processed with tools like the Genome Analysis Toolkit to clean up the alignments. Quality control reports are generated, and variants are extracted by the use of variant callers. The document containing the variants or variant call format is annotated and filtered. Low-frequency variants that are known or predicted to be damaging are validated and used to generate a final report to the physicians or genetic counselors. BAM: Binary Sequence Alignment/Map format; dbNSFP: Lightweight database of human nonsynonymous SNPs and their functional predictions; GATK: Genome Analysis Toolkit; HGMD: Human gene mutation database; HPG: High performance genomics; MAF: Minor allele frequency; QC: Quality control; SAM: Sequence Alignment/Map format; SRSMT: Short read sequencing mapping tools; VEF: Variant effect predictor; VCF: Variant call format.

Figure 3. The road from next-generation sequencing to personalized medicine

An overall view of how next-generation sequencing will be incorporated into the medical healthcare system. At the time of birth, a small sample of blood is taken from the patient and submitted to whole genome sequencing. The physicians and genetic counselors will provide a detailed family and medical history to an entity that will store and analyze the next-generation sequencing data. This entity will receive additional information such as metabolomics, proteomics and transcriptomes, among others, as well as new bioinformatics interpretation will be performed in collaboration with molecular biologist, physicians and genetic counselors. The physicians will review the reports and formulate recommendations and treatments for the patient. The process will be interactive with constant communication between the doctor, patient and entity in charge of the data interpretation.