Development of a Smart Sleep Mask with Multiple Sensors.

In this work, we demonstrated a Smart Sleep Mask with several integrated physiological sensors such as 3-axis accelerometers, respiratory acoustic sensor, and an eye movement sensor. In particular, using infrared optical sensors, eye movement frequency, direction, and amplitude can be directly monitored and recorded during sleep sessions. We also developed a mobile app for data storage, signal processing and data analytics.

Targeted Single Primer Enrichment Sequencing with Single End Duplex-UMI.

For specific detection of somatic variants at very low levels, artifacts from the NGS workflow have to be eliminated. Various approaches using unique molecular identifiers (UMI) to analytically remove NGS artifacts have been described. Among them, Duplex-seq was shown to be highly effective, by leveraging the sequence complementarity of two DNA strands.

smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers.

Motivation: Low-frequency DNA mutations are often confounded with technical artifacts from sample preparation and sequencing. With unique molecular identifiers (UMIs), most of the sequencing errors can be corrected. However, errors before UMI tagging, such as DNA polymerase errors during end repair and the first PCR cycle, cannot be corrected with single-strand UMIs and impose fundamental limits to UMI-based variant calling.

Association Between Inherited Germline Mutations in Cancer Predisposition Genes and Risk of Pancreatic Cancer.

Importance: Individuals genetically predisposed to pancreatic cancer may benefit from early detection. Genes that predispose to pancreatic cancer and the risks of pancreatic cancer associated with mutations in these genes are not well defined.

Objective: To determine whether inherited germline mutations in cancer predisposition genes are associated with increased risks of pancreatic cancer.

Detecting very low allele fraction variants using targeted DNA sequencing and a novel molecular barcode-aware variant caller.

Background: Detection of DNA mutations at very low allele fractions with high accuracy will significantly improve the effectiveness of precision medicine for cancer patients. To achieve this goal through next generation sequencing, researchers need a detection method that 1) captures rare mutation-containing DNA fragments efficiently in the mix of abundant wild-type DNA; 2) sequences the DNA library extensively to deep coverage; and 3) distinguishes low level true variants from amplification and sequencing errors with high accuracy. Targeted enrichment using PCR primers provides researchers with a convenient way to achieve deep sequencing for a small, yet most relevant region using benchtop sequencers.

Quantitative analysis of differences in copy numbers using read depth obtained from PCR-enriched samples and controls.

Background: Next-generation sequencing (NGS) is rapidly becoming common practice in clinical diagnostics and cancer research. In addition to the detection of single nucleotide variants (SNVs), information on copy number variants (CNVs) is of great interest. Several algorithms exist to detect CNVs by analyzing whole genome sequencing data or data from samples enriched by hybridization-capture.

Edge effects in calling variants from targeted amplicon sequencing.

Background: Analysis of targeted amplicon sequencing data presents some unique challenges in comparison to the analysis of random fragment sequencing data. Whereas reads from randomly fragmented DNA have arbitrary start positions, the reads from amplicon sequencing have fixed start positions that coincide with the amplicon boundaries. As a result, any variants near the amplicon boundaries can cause misalignments of multiple reads that can ultimately lead to false-positive or false-negative variant calls.

Comparison of somatic mutation calling methods in amplicon and whole exome sequence data.

Background: High-throughput sequencing is rapidly becoming common practice in clinical diagnosis and cancer research. Many algorithms have been developed for somatic single nucleotide variant (SNV) detection in matched tumor-normal DNA sequencing. Although numerous studies have compared the performance of various algorithms on exome data, there has not yet been a systematic evaluation using PCR-enriched amplicon data with a range of variant allele fractions.