Justin M. Zook

Assuring the quality of next-generation sequencing in clinical laboratory practice

Amy S Gargis, Centers for Disease Control and Prevention Lisa Kalman, Centers for Disease Control and Prevention Meredith W Berry, SeqWright Inc David P Bick, Medical College of Wisconsin David P Dimmock, Medical College of Wisconsin Tina Hambuch, Illumina Clinical Services Fei Lu, SeqWright Inc Elaine Lyon, University of Utah Karl V Voelkerding, University of Utah Barbara Zehnbauer, Emory University

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method.

Stable nanoparticle aggregates/agglomerates of different sizes and the effect of their size on hemolytic cytotoxicity

Abstract To study the toxicity of nanoparticles under relevant conditions, it is critical to disperse nanoparticles reproducibly in different agglomeration states in aqueous solutions compatible with cell-based assays. Here, we disperse gold, silver, cerium oxide, and positively-charged polystyrene nanoparticles in cell culture media, using the timing between mixing steps to control agglomerate size in otherwise identical media. These protein-stabilized dispersions are generally stable for at least two days, with mean agglomerate sizes of ∼23 nm silver nanoparticles ranging from 43–1400 nm and average relative standard deviations of less than 10%. Mixing rate, timing between mixing steps and nanoparticle concentration are shown to be critical for achieving reproducible dispersions. We characterize the size distributions of agglomerated nanoparticles by further developing dynamic light scattering theory and diffusion limited colloidal aggregation theory. These theories frequently affect the estimated size by a factor of two or more. Finally, we demonstrate the importance of controlling agglomeration by showing that large agglomerates of silver nanoparticles cause significantly less hemolytic toxicity than small agglomerates.

Extensive sequencing of seven human genomes to characterize benchmark reference materials.

The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCode WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly.

Measuring Agglomerate Size Distribution and Dependence of Localized Surface Plasmon Resonance Absorbance on Gold Nanoparticle Agglomerate Size Using Analytical Ultracentrifugation

Agglomeration of nanoparticles during measurements in relevant biological and environmental media is a frequent problem in nanomaterial property characterization. The primary problem is typically that any changes to the size distribution can dramatically affect the potential nanotoxicity or other size-determined properties, such as the absorbance signal in a biosensor measurement. Herein we demonstrate analytical ultracentrifugation (AUC) as a powerful method for measuring two critical characteristics of nanoparticle (NP) agglomerates in situ in biological media: the NP agglomerate size distribution, and the localized surface plasmon resonance (LSPR) absorbance spectrum of precise sizes of gold NP agglomerates. To characterize the size distribution, we present a theoretical framework for calculating the hydrodynamic diameter distribution of NP agglomerates from their sedimentation coefficient distribution. We measure sedimentation rates for monomers, dimers, and trimers, as well as for larger agglomerates with up to 600 NPs. The AUC size distributions were found generally to be broader than the size distributions estimated from dynamic light scattering and diffusion-limited colloidal aggregation theory, an alternative bulk measurement method that relies on several assumptions. In addition, the measured sedimentation coefficients can be used in nanotoxicity studies to predict how quickly the agglomerates sediment out of solution under normal gravitational forces, such as in the environment. We also calculate the absorbance spectra for monomer, dimer, trimer, and larger gold NP agglomerates up to 600 NPs, to enable a better understanding of LSPR biosensors. Finally, we validate a new method that uses these spectra to deconvolute the net absorbance spectrum of an unknown bulk sample and approximate the proportions of monomers, dimers, and trimers in a polydisperse sample of small agglomerates, so that every sample does not need to be measured by AUC. These results demonstrate the potential utility of AUC to characterize NP agglomeration and sedimentation for nanotoxicity and biosensor studies, as well as to characterize NP agglomerate size and absorbance to improve LSPR and surface-enhanced Raman spectroscopy based biosensors.

Good laboratory practice for clinical next-generation sequencing informatics pipelines

Amy S Gargis, Centers for Disease Control & Prevention Lisa Kalman, Centers for Disease Control & Prevention David P Bick, Medical College of Wisconsin Cristina da Silva, Emory University David P Dimmock, Medical College of Wisconsin Birgit H Funke, Partners Healthcare Personalized Medicine Sivakumar Gowrisankar, Partners Healthcare Personalized Medicine Madhuri Hegde, Emory University Shashikant Kulkarni, Washington University Christopher E Mason, Cornell University

Effects of temperature, acyl chain length, and flow-rate ratio on liposome formation and size in a microfluidic hydrodynamic focusing device

Microfluidic hydrodynamic focusing of an alcohol–lipid mixture into a narrow fluid stream by two oblique buffer streams provides a controlled and reproducible method of forming phospholipid bilayer vesicles (i.e., liposomes) with relatively monodisperse and specific size ranges. Previous work has established that liposome size can be controlled by changing the relative and absolute flow rates of the fluids. In other previous work, a kinetic (non-equilibrium) theoretical description of the detergent dilution liposome formation method was developed, in which planar lipid bilayer discs aggregate until they become sufficiently large to close into spherical liposomes. In this work, we show that an approximation of the kinetic theory can help explain liposome formation for our microfluidic method. This approximation predicts that the liposome radius should be approximately proportional to the ratio of the membrane bending elasticity modulus to the line tension of the hydrophobic edges of the lipid bilayer disc. In combination with very fast microfluidic mixing, this theory enables a new method to measure the ratio of the elasticity modulus to the line tension of membranes. The theory predicts that the temperature should change the liposome size primarily as a result of its effect on the ratio of the membrane bending elasticity modulus to the line tension, in contrast to previous work on microdroplet and microbubble formation, which showed that the effect of temperature on droplet/bubble size was primarily due to viscosity changes. In agreement with theory, most membrane compositions form larger liposomes close to or below the gel-to-liquid crystalline phase transition temperature, where the membrane elasticity modulus is much larger, and they have a much smaller dependence of size on temperature far above the transition temperature, where the membrane elasticity modulus is relatively constant. Other parameters modulated by the temperature (e.g., viscosity, free energy, and diffusion coefficients) appear to have little or no effect on liposome size, because they have counteracting effects on the lipid aggregation rate and the liposome closure time. Experiments are performed using phospholipids with varying hydrophobic acyl chain lengths that have phase transition temperatures ranging from −1 °C to 55 °C, so that the temperature dependence is examined below, above, and around the transition temperature. In addition, the effect of IPA stabilizing the edges of the bilayer discs can be examined by comparing the liposome sizes obtained at different flow-rate ratios. Finally, polydispersity is shown to increase as the median liposome size increases, regardless of whether the change in size is due to changing temperature or flow-rate ratio.

Best Practices for Evaluating Single Nucleotide Variant Calling Methods for Microbial Genomics

Innovations in sequencing technologies have allowed biologists to make incredible advances in understanding biological systems. As experience grows, researchers increasingly recognize that analyzing the wealth of data provided by these new sequencing platforms requires careful attention to detail for robust results. Thus far, much of the scientific Communit’s focus for use in bacterial genomics has been on evaluating genome assembly algorithms and rigorously validating assembly program performance. Missing, however, is a focus on critical evaluation of variant callers for these genomes. Variant calling is essential for comparative genomics as it yields insights into nucleotide-level organismal differences. Variant calling is a multistep process with a host of potential error sources that may lead to incorrect variant calls. Identifying and resolving these incorrect calls is critical for bacterial genomics to advance. The goal of this review is to provide guidance on validating algorithms and pipelines used in variant calling for bacterial genomics. First, we will provide an overview of the variant calling procedures and the potential sources of error associated with the methods. We will then identify appropriate datasets for use in evaluating algorithms and describe statistical methods for evaluating algorithm performance. As variant calling moves from basic research to the applied setting, standardized methods for performance evaluation and reporting are required; it is our hope that this review provides the groundwork for the development of these standards.

Synthetic Spike-in Standards Improve Run-Specific Systematic Error Analysis for DNA and RNA Sequencing

While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a data set used to calculate association of SSEs with various features in the reads and sequence context. This data set is typically either from a part of the data set being “recalibrated” (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 Phred-scaled quality score units, and by as much as 13 units at CpG sites. In addition, since the spike-in data used for recalibration are independent of the genome being sequenced, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration.

A research roadmap for next-generation sequencing informatics

Progress in nine research areas will help generate the knowledge required to advance next-generation sequencing diagnostics to the clinic. Next-generation sequencing technologies are fueling a wave of new diagnostic tests. Progress on a key set of nine research challenge areas will help generate the knowledge required to advance effectively these diagnostics to the clinic.