Euan Ashley | Search bioRxiv

bioRxiv2016

The Next Generation Precision Medical Record - A Framework for Integrating Genomes and Wearable Sensors with Medical Records

Current medical records are rigid with regards to emerging big biomedical data. Examples of poorly integrated big data that already exist in clinical practice include whole genome sequencing and wearable sensors for real time monitoring. Genome sequencing enables conventional diagnostic interrogation and forms the fundamental baseline for precision health throughout a patients lifetime. Mobile sensors enable tailored monitoring regimes for both reducing risk through precision health interventions and acute condition surveillance. In order to address the absence of these data in the Electronic Medical Record (EMR), we worked with the SAP Personalized Medicine team to re-envision a modern medical record with these components. The pilot project used 37 patient families with complex medical records, whole genome sequencing and some level of wearable monitoring. Core functionality included patient timelines with integrated text analytics, personalized genomic curation and wearable alerts. The current phase is being rolled out to over 1500 patients in clinics across the hospital system. While fundamentally research, we believe this proof of principle platform is the first of its kind and represents the future of data driven clinical medicine.

Genomics↗

bioRxiv2015

Effect of lossy compression of quality scores on variant calling

Recent advancements in sequencing technology have led to a drastic reduction in the cost of genome sequencing. This development has generated an unprecedented amount of genomic data that must be stored, processed, and communicated. To facilitate this effort, compression of genomic files has been proposed. Specifically, lossy compression of quality scores is emerging as a natural candidate for reducing the growing costs of storage. A main goal of performing DNA sequencing in population studies and clinical settings is to identify genetic variation. Though the field agrees that smaller files are advantageous, the cost of lossy compression, in terms of variant discovery, is unclear.\n\nBioinformatic algorithms to identify SNPs and INDELs from next-generation DNA sequencing data use base quality score information; here, we evaluate the effect of lossy compression of quality scores on SNP and INDEL detection. We analyze several lossy compressors introduced recently in the literature. Specifically, we investigate how the output of the variant caller when using the original data (uncompressed) differs from that obtained when quality scores are replaced by those generated by a lossy compressor. Using gold standard genomic datasets such as the GIAB (Genome In A Bottle) consensus sequence for NA12878 and simulated data, we are able to analyze how accurate the output of the variant calling is, both for the original data and that previously lossily compressed. We show that lossy compression can significantly alleviate the storage while maintaining variant calling performance comparable to that with the uncompressed data. Further, in some cases lossy compression can lead to variant calling performance which is superior to that using the uncompressed file. We envisage our findings and framework serving as a benchmark in future development and analyses of lossy genomic data compressors.\n\nThe Supplementary Data can be found at http://web.stanford.edu/~iochoa/supplementEffectLossy.zip.

Bioinformatics↗