The road from next-generation sequencing to personalized medicine

Does Exposure to Burning and Heated Tobacco Affect the Abundance of Perio-Pathogenic Species in the Subgingival Biofilm?

This study investigated the impact of tobacco exposure, specifically through heating and burning, on periodontopathogens in the subgingival microbiome among clinically healthy individuals and those diagnosed with periodontitis. The sample comprised 66 subjects (26–56 years, median 38 yrs; 64% females) classified as non-smokers, classic cigarette smokers, and tobacco heating system (THS) smokers (each N = 22). Full-mouth periodontal examination was performed, and 330 paper-point samples from periodontal pockets were collected. Next-generation sequencing of 16S rRNA genes was conducted to identify the composition of subgingival microbiome. Periodontitis prevalence among the groups was ranked as THS (41%) < non-smokers (44%) < cigarette smokers (68%), without statistically significant differences between the groups. The number of perio-pathogenic species was higher in subjects with periodontitis compared to those without (median 7 vs. 6 species; p = 0.005) but without significant differences between exposure groups: non-smokers (6) = smokers (6) < THS (6.5). When combining exposure and periodontal status, each smoker group had more perio-pathogenic species than non-smokers: non-smokers without periodontitis (5) < smokers without periodontitis (5.5) < THS without periodontitis (6); non-smokers with periodontitis (6.5) < THS with periodontitis (7) = smokers with periodontitis (7). Multiple linear regression indicated periodontitis as the sole predictor of perio-pathogenic species quantity, irrespective of the type of tobacco consumption, sex, age, or oral hygiene (R2 = 0.163; p = 0.005). In conclusion, the quantity of perio-pathogenic species in the subgingival microbiome was more influenced by periodontitis than by exposure to tobacco smoke, regardless of whether it was heated or burned.

Diagnosis of invasive fungal infections: challenges and recent developments

BACKGROUND

The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised patients suffering from various diseases. The role of early and accurate diagnosis in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the changing demands of clinical mycology, the field of fungal diagnostics has evolved and come a long way from traditional methods of microscopy and culturing to more advanced non-culture-based tools. With the advent of more powerful approaches such as novel PCR assays, T2 Candida, microfluidic chip technology, next generation sequencing, new generation biosensors, nanotechnology-based tools, artificial intelligence-based models, the face of fungal diagnostics is constantly changing for the better. All these advances have been reviewed here giving the latest update to our readers in the most orderly flow.

MAIN TEXT

A detailed literature survey was conducted by the team followed by data collection, pertinent data extraction, in-depth analysis, and composing the various sub-sections and the final review. The review is unique in its kind as it discusses the advances in molecular methods; advances in serology-based methods; advances in biosensor technology; and advances in machine learning-based models, all under one roof. To the best of our knowledge, there has been no review covering all of these fields (especially biosensor technology and machine learning using artificial intelligence) with relevance to invasive fungal infections.

CONCLUSION

The review will undoubtedly assist in updating the scientific community's understanding of the most recent advancements that are on the horizon and that may be implemented as adjuncts to the traditional diagnostic algorithms.

Polydimethylsiloxane microstructure-induced acoustic streaming for enhanced ultrasonic DNA fragmentation on a microfluidic chip

Next-generation sequencing (NGS) is an essential technology for DNA identification in genomic research. DNA fragmentation is a critical step for NGS and doing this on-chip is of great interest for future integrated genomic solutions. Here we demonstrate fast acoustofluidic DNA fragmentation via ultrasound-actuated elastic polydimethylsiloxane (PDMS) microstructures that induce acoustic streaming and associated shear forces when placed in the field of an ultrasonic transducer. Indeed, acoustic streaming locally generates high tensile stresses that can mechanically stretch and break DNA molecule chains. The improvement in efficiency of the on-chip DNA fragmentation is due to the synergistic effect of these tensile stresses and ultrasonic cavitation phenomena. We tested these microstructure-induced effects in a DNA-containing microfluidic channel both experimentally and by simulation. The DNA fragmentation process was evaluated by measuring the change in the DNA fragment size over time. The chip works well with both long and short DNA chains; in particular, purified lambda (λ) DNA was cut from 48.5 kbp to 3 kbp in one minute with selected microstructures and further down to 300 bp within two and a half minutes. The fragment size of mouse genomic DNA was reduced from 1.4 kbp to 400 bp in one minute and then to 200 bp in two and a half minutes. The DNA fragmentation efficiency of the chip equipped with the PDMS microstructures was twice that of the chip without the microstructures. Exhaustive comparison shows that the on-chip fragmentation performance reaches the level of high-end professional standards. Recently, DNA fragmentation was shown to be enhanced using vibrating air microbubbles when the chip was placed in an acoustic field. We think the microbubble-free microstructure-based device we present is easier to operate and more reliable, as it avoids microbubble preparation and maintenance, while showing high DNA fragmentation performance.

A Comprehensive Review of Performance of Next-Generation Sequencing Platforms

Background

Next-generation sequencing methods have been developed and proposed to investigate any query in genomics or clinical activity involving DNA. Technical advancement in these sequencing methods has enhanced sequencing volume to several billion nucleotides within a very short time and low cost. During the last few years, the usage of the latest DNA sequencing platforms in a large number of research projects helped to improve the sequencing methods and technologies, thus enabling a wide variety of research/review publications and applications of sequencing technologies.

Objective

The proposed study is aimed at highlighting the most fast and accurate NGS instruments developed by various companies by comparing output per hour, quality of the reads, maximum read length, reads per run, and their applications in various domains. This will help research institutions and biological/clinical laboratories to choose the sequencing instrument best suited to their environment. The end users will have a general overview about the history of the sequencing technologies, latest developments, and improvements made in the sequencing technologies till now.

Results

The proposed study, based on previous studies and manufacturers' descriptions, highlighted that in terms of output per hour, Nanopore PromethION outperformed all sequencers. BGI was on the second position, and Illumina was on the third position.

Conclusion

The proposed study investigated various sequencing instruments and highlighted that, overall, Nanopore PromethION is the fastest sequencing approach. BGI and Nanopore can beat Illumina, which is currently the most popular sequencing company. With respect to quality, Ion Torrent NGS instruments are on the top of the list, Illumina is on the second position, and BGI DNB is on the third position. Secondly, memory- and time-saving algorithms and databases need to be developed to analyze data produced by the 3rd- and 4th-generation sequencing methods. This study will help people to adopt the best suited sequencing platform for their research work, clinical or diagnostic activities.

The enhancement of DNA fragmentation in a bench top ultrasonic water bath with needle-induced air bubbles: Simulation and experimental investigation

Shearing DNA to a certain size is the first step in many medical and biological applications, especially in next-generation gene sequencing technology. In this article, we introduced a highly efficient ultrasonic DNA fragmentation method enhanced by needle-induced air bubbles, which is easy to operate with high throughput. The principle of the bubble-enhanced sonication system is introduced and verified by flow field and acoustic simulations and experiments. Lambda DNA long chains and mouse genomic DNA short chains are used in the experiments for testing the performance of the bubble-enhanced ultrasonic DNA fragmentation system. Air bubbles are an effective enhancement agent for ultrasonic DNA fragmentation; they can obviously improve the sound pressure level in the whole solution, thus, achieving better absorption of ultrasound energy. Growing bubbles also have a stretched function on DNA molecule chains and form a huge pressure gradient in the solution, which is beneficial to DNA fragmentation. Purified λDNA is cut from 48.5 to 2 kbp in 5 min and cut to 300 bp in 30 min. Mouse genomic DNA (≈1400 bp) decreases to 400 bp in 5 min and then reduces to 200 bp in 30 min. This bubble-enhanced ultrasonic method enables widespread access to genomic DNA fragmentation in a standard ultrasonic water bath for many virus sequencing demands even without good medical facilities.

Machine Learning Predictability of Clinical Next Generation Sequencing for Hematologic Malignancies to Guide High-Value Precision Medicine

Advancing diagnostic testing capabilities such as clinical next generation sequencing methods offer the potential to diagnose, risk stratify, and guide specialized treatment, but must be balanced against the escalating costs of healthcare to identify patient cases most likely to benefit from them. Heme-STAMP (Stanford Actionable Mutation Panel for Hematopoietic and Lymphoid Malignancies) is one such next generation sequencing test. Our objective is to assess how well Heme-STAMP pathological variants can be predicted given electronic health records data available at the time of test ordering. The model demonstrated AUROC 0.74 (95% CI: [0.72, 0.76]) with 99% negative predictive value at 6% specificity. A benchmark for comparison is the prevalence of positive results in the dataset at 58.7%. Identifying patients with very low or very high predicted probabilities of finding actionable mutations (positive result) could guide more precise high-value selection of patient cases to test.

Ultrasensitive detection of SARS-CoV-2 RNA and antigen using single-molecule optofluidic chip

Nucleic acids and proteins are the two most important target types used in molecular diagnostics. In many instances, simultaneous sensitive and accurate detection of both biomarkers from the same sample would be desirable, but standard detection methods are highly optimized for one type and not cross-compatible. Here, we report the simultaneous multiplexed detection of SARS-CoV-2 RNAs and antigens with single molecule sensitivity. Both analytes are isolated and labeled using a single bead-based solid-phase extraction protocol, followed by fluorescence detection on a multi-channel optofluidic waveguide chip. Direct amplification-free detection of both biomarkers from nasopharyngeal swab samples is demonstrated with single molecule detection sensitivity, opening the door for ultrasensitive dual-target analysis in infectious disease diagnosis, oncology, and other applications.

SVLR: Genome Structural Variant Detection Using Long-Read Sequencing Data

Genome structural variants (SVs) have great impacts on human phenotype and diversity, and have been linked to numerous diseases. Long-read sequencing technologies arise to make it possible to find SVs of as long as 10,000 nucleotides. Thus, long read-based SV detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect SVs recently. In this article, we present a new method, called SVLR, to detect SVs based on long-read sequencing data. Comparing with existing methods, SVLR can detect three new kinds of SVs: block replacements, block interchanges, and translocations. Although these new SVs are structurally more complicated, SVLR achieves accuracies that are comparable with those of the classic SVs. Moreover, for the classic SVs that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up to 38% without harming the precisions (i.e., >78%). We also point out three directions to further improve SV detection in the future. Source codes: https://github.com/GWYSDU/SVLR.

A weighted empirical Bayes risk prediction model using multiple traits

With rapid advances in high-throughput sequencing technology, millions of single-nucleotide variants (SNVs) can be simultaneously genotyped in a sequencing study. These SNVs residing in functional genomic regions such as exons may play a crucial role in biological process of the body. In particular, non-synonymous SNVs are closely related to the protein sequence and its function, which are important in understanding the biological mechanism of sequence evolution. Although statistically challenging, models incorporating such SNV annotation information can improve the estimation of genetic effects, and multiple responses may further strengthen the signals of these variants on the assessment of disease risk. In this work, we develop a new weighted empirical Bayes method to integrate SNV annotation information in a multi-trait design. The performance of this proposed model is evaluated in simulation as well as a real sequencing data; thus, the proposed method shows improved prediction accuracy compared to other approaches.

Machine learning meets genome assembly

MOTIVATION

With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale.

RESULTS

This paper presents a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers-particularly the ones that use machine learning-to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.

SVIM: structural variant identification using mapped long reads

Motivation

Structural variants are defined as genomic variants larger than 50 bp. They have been shown to affect more bases in any given genome than single-nucleotide polymorphisms or small insertions and deletions. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single-molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long-read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities.

Results

We present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long-read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from Pacific Biosciences and Nanopore sequencing machines.

Availability and implementation

The source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package Index.

Supplementary information

Supplementary data are available at Bioinformatics online.

Prospective Decision Analysis Study of Clinical Genomic Testing in Metastatic Breast Cancer: Impact on Outcomes and Patient Perceptions

PURPOSE

To evaluate the impact of targeted DNA sequencing on selection of cancer therapy for patients with metastatic breast cancer (MBC).

PATIENTS AND METHODS

In this prospective, single-center, single-arm trial, patients with MBC were enrolled within 10 weeks of starting a new therapy. At enrollment, tumor samples underwent next-generation sequencing for any of 315 cancer-related genes to high depth (> 500×) using FoundationOne CDx. Sequencing results were released to providers at the time of disease progression, and physician treatment recommendations were assessed via questionnaire. We evaluated three prespecified questions to assess patients' perceptions of genomic testing.

RESULTS

In all, 100 patients underwent genomic testing, with a median of five mutations (range, 0 to 13 mutations) detected per patient. Genomic testing revealed one or more potential therapies in 98% of patients (98 of 100), and 60% of patients (60 of 100) had one or more recommended treatments with level I/II evidence for actionability. Among the 94 genomic text reports that were released, there was physician questionnaire data for 87 patients (response rate, 92.6%) and 31.0% of patients (27 of 87) had treatment change recommended by their physician. Of these, 37.0% (10 of 27) received the treatment supported by genomic testing. We did not detect a statistically significant difference in time-to-treatment failure (log-rank P = .87) or overall survival (P = .71) among patients who had treatment change supported by genomic testing versus those who had no treatment change. For patients who completed surveys before and after genomic testing, there was a significant decrease in confidence of treatment success, specifically among patients who did not have treatment change supported by genomic testing (McNemar's test of agreement P = .001).

CONCLUSION

In this prospective study, genomic profiling of tumors in patients with MBC frequently identified potential treatments and resulted in treatment change in a minority of patients. Patients whose therapy was not changed on the basis of genomic testing seemed to have a decrease in confidence of treatment success.

Clinical Application of Next-Generation Sequencing as A Liquid Biopsy Technique in Advanced Colorectal Cancer: A Trick or A Treat?

Owing to its advantages over prior relevant technologies, massive parallel or next-generation sequencing (NGS) is rapidly evolving, with growing applications in a wide range of human diseases. The burst in actionable molecular alterations in many cancer types advocates for the practicality of using NGS in the clinical setting, as it permits the parallel characterization of multiple genes in a cost- and time-effective way, starting from low-input DNA. In advanced clinical practice, the oncological management of colorectal cancer requires prior knowledge of KRAS, NRAS, and BRAF status, for the design of appropriate therapeutic strategies, with more gene mutations still surfacing as potential biomarkers. Tumor heterogeneity, as well as the need for serial gene profiling due to tumor evolution and the emergence of novel genetic alterations, have promoted the use of liquid biopsies-especially in the form of circulating tumor DNA (ctDNA)-as a promising alternative to tissue molecular analysis. This review discusses recent studies that have used plasma NGS in advanced colorectal cancer and summarizes the clinical applications, as well as the technical challenges involved in adopting this technique in a clinically beneficial oncological practice.

RNA Analysis as a Tool to Determine Clinically Relevant Gene Fusions and Splice Variants

CONTEXT

- Technologic advances have contributed to the increasing relevance of RNA analysis in clinical oncology practice. The different genetic aberrations that can be screened with RNA include gene fusions and splice variants. Validated methods of identifying these alterations include fluorescence in situ hybridization, immunohistochemistry, reverse transcription-polymerase chain reaction, and next-generation sequencing, which can provide physicians valuable information on disease and treatment of cancer patients.

OBJECTIVE

- To discuss the standard techniques available and new approaches for the identification of gene fusions and splice variants in cancer, focusing on RNA analysis and how analytic methods have evolved in both tissue and liquid biopsies.

DATA SOURCES

- This is a narrative review based on PubMed searches and the authors' own experiences.

CONCLUSIONS

- Reliable RNA-based testing in tissue and liquid biopsies can inform the diagnostic process and guide physicians toward the best treatment options. Next-generation sequencing methodologies permit simultaneous assessment of molecular alterations and increase the number of treatment options available for cancer patients.

Describing the Reportable Range Is Important for Reliable Treatment Decisions: A Multiple Laboratory Study for Molecular Tumor Profiling Using Next-Generation Sequencing

Because interpretation of next-generation sequencing (NGS) data remains challenging, optimization of the NGS process is needed to obtain correct sequencing results. Therefore, extensive validation and continuous monitoring of the quality is essential. NGS performance was compared with traditional detection methods and technical quality of nine NGS technologies was assessed. First, nine formalin-fixed, paraffin-embedded patient samples were analyzed by 114 laboratories by using different detection methods. No significant differences in performance were observed between analyses with NGS and traditional techniques. Second, two DNA control samples were analyzed for a selected number of variants by 26 participants with the use of nine different NGS technologies. Quality control metrics were analyzed from raw data files and a survey about routine procedures. Results showed large differences in coverages, but observed variant allele frequencies in raw data files were in line with predefined variant allele frequencies. Many false negative results were found because of low-quality regions, which were not reported as such. It is recommended to disclose the reportable range, the fraction of targeted genomic regions for which calls of acceptable quality can be generated, to avoid any errors in therapy decisions. NGS can be a reliable technique, only if essential quality control during analysis is applied and reported.

Recent Patterns in Genetic Testing for Breast and Ovarian Cancer Risk in the U.S

INTRODUCTION

Mutations in BRCA genes are strongly associated with increased risk of breast and ovarian cancer, and it is recommended that women at high risk for these mutations be referred for genetic counseling and testing. The Affordable Care Act (ACA) provision implemented in 2010 eliminated cost sharing for BRCA genetic testing for privately insured women with family history of BRCA-related cancers.

METHODS

Using a nationally representative sample from the National Health Interview Survey, this study examined trends in genetic testing for breast and ovarian cancer risk from 2005 to 2015 among women by family history and insurance status. To assess the impact of the ACA provision, a difference-in-differences strategy was used to compare changes in genetic testing after ACA implementation between women with a family history of breast or ovarian cancer and those with a family history of other cancers, stratified by insurance type. Analyses were conducted in 2016.

RESULTS

Genetic testing for breast and ovarian cancer risk increased among women with private or public insurance, but not among uninsured women. Among privately insured women, those with family history of breast or ovarian cancer experienced a net increase of 2.9 percentage points (p=0.001) over those with a family history of other cancers, but no significant difference was observed among women with public insurance, suggesting a positive effect of the ACA provision.

CONCLUSIONS

This study underscores the continued need to improve access to care for all populations. Future work should monitor the impact of policy on genetic testing among the high-risk population.

PARP inhibitors as precision medicine for cancer treatment

Personalized or precision medicine is an emerging treatment approach tailored to individuals or certain groups of patients based on their unique characteristics. These types of therapies guided by biomarkers tend to be more effective than traditional approaches, especially in cancer. The inhibitor against poly (ADP-ribose) polymerase (PARP), olaparib (Lynparza, AstraZeneca), which was approved by the US Food and Drug Administration (FDA) in 2014, demonstrated efficacy specifically for ovarian cancer patients harboring mutations in BRCA genes, which encode proteins in DNA double-strand break repairs. However, the response to PARP inhibitors has been less encouraging in other cancer types that also carry defects in the BRCA genes. Thus, furthering our understanding of the underlying mechanism of PARP inhibitors and resistance is critical to improve their efficacy. In this review, we summarize the results of preclinical studies and the clinical application of PARP inhibitors, and discuss the future direction of PARP inhibitors as a potential marker-guided personalized medicine for cancer treatment.

Utility of Whole Exome Sequencing for Genetic Diagnosis of Previously Undiagnosed Pediatric Neurology Patients

Whole exome sequencing enables scanning a large number of genes for relatively low costs. The authors investigate its use for previously undiagnosed pediatric neurological patients. This retrospective cohort study performed whole exome sequencing on 57 patients of "Magen" neurogenetic clinics, with unknown diagnoses despite previous workup. The authors report on clinical features, causative genes, and treatment modifications and provide an analysis of whole exome sequencing utility per primary clinical feature. A causative gene was identified in 49.1% of patients, of which 17 had an autosomal dominant mutation, 9 autosomal recessive, and 2 X-linked. The highest rate of positive diagnosis was found for patients with developmental delay, ataxia, or suspected neuromuscular disease. Whole exome sequencing warranted a definitive change of treatment for 5 patients. Genetic databases were updated accordingly. In conclusion, whole exome sequencing is useful in obtaining a high detection rate for previously undiagnosed disorders. Use of this technique could affect diagnosis, treatment, and prognostics for both patients and relatives.

Genotype-phenotype correlation in a cohort of Portuguese patients comprising the entire spectrum of VWD types: impact of NGS

The diagnosis of von Willebrand disease (VWD), the most common inherited bleeding disorder, is characterised by a variable bleeding tendency and heterogeneous laboratory phenotype. The sequencing of the entire VWF coding region has not yet become a routine practice in diagnostic laboratories owing to its high costs. Nevertheless, next-generation sequencing (NGS) has emerged as an alternative to overcome this limitation. We aimed to determine the correlation of genotype and phenotype in 92 Portuguese individuals from 60 unrelated families with VWD; therefore, we directly sequenced VWF. We compared the classical Sanger sequencing approach and NGS to assess the value-added effect on the analysis of the mutation distribution in different types of VWD. Sixty-two different VWF mutations were identified, 27 of which had not been previously described. NGS detected 26 additional mutations, contributing to a broad overview of the mutant alleles present in each VWD type. Twenty-nine probands (48.3 %) had two or more mutations; in addition, mutations with pleiotropic effects were detected, and NGS allowed an appropriate classification for seven of them. Furthermore, the differential diagnosis between VWD 2B and platelet type VWD (n = 1), Bernard-Soulier syndrome and VWD 2B (n = 1), and mild haemophilia A and VWD 2N (n = 2) was possible. NGS provided an efficient laboratory workflow for analysing VWF. These findings in our cohort of Portuguese patients support the proposal that improving VWD diagnosis strategies will enhance clinical and laboratory approaches, allowing to establish the most appropriate treatment for each patient.

DGIdb 2.0: mining clinically relevant drug-gene interactions

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that consolidates disparate data sources describing drug-gene interactions and gene druggability. It provides an intuitive graphical user interface and a documented application programming interface (API) for querying these data. DGIdb was assembled through an extensive manual curation effort, reflecting the combined information of twenty-seven sources. For DGIdb 2.0, substantial updates have been made to increase content and improve its usefulness as a resource for mining clinically actionable drug targets. Specifically, nine new sources of drug-gene interactions have been added, including seven resources specifically focused on interactions linked to clinical trials. These additions have more than doubled the overall count of drug-gene interactions. The total number of druggable gene claims has also increased by 30%. Importantly, a majority of the unrestricted, publicly-accessible sources used in DGIdb are now automatically updated on a weekly basis, providing the most current information for these sources. Finally, a new web view and API have been developed to allow searching for interactions by drug identifiers to complement existing gene-based search functionality. With these updates, DGIdb represents a comprehensive and user friendly tool for mining the druggable genome for precision medicine hypothesis generation.

Cavitation Enhancing Nanodroplets Mediate Efficient DNA Fragmentation in a Bench Top Ultrasonic Water Bath

A perfluorocarbon nanodroplet formulation is shown to be an effective cavitation enhancement agent, enabling rapid and consistent fragmentation of genomic DNA in a standard ultrasonic water bath. This nanodroplet-enhanced method produces genomic DNA libraries and next-generation sequencing results indistinguishable from DNA samples fragmented in dedicated commercial acoustic sonication equipment, and with higher throughput. This technique thus enables widespread access to fast bench-top genomic DNA fragmentation.