Implementation of Cloud based next generation sequencing data analysis in a clinical laboratory

Fluorescent optotracers for bacterial and biofilm detection and diagnostics

Effective treatment of bacterial infections requires methods that accurately and quickly identify which antibiotic should be prescribed. This review describes recent research on the development of optotracing methodologies for bacterial and biofilm detection and diagnostics. Optotracers are small, chemically well-defined, anionic fluorescent tracer molecules that detect peptide- and carbohydrate-based biopolymers. This class of organic molecules (luminescent conjugated oligothiophenes) show unique electronic, electrochemical and optical properties originating from the conjugated structure of the compounds. The photophysical properties are further improved as donor-acceptor-donor (D-A-D)-type motifs are incorporated in the conjugated backbone. Optotracers bind their biopolymeric target molecules via electrostatic interactions. Binding alters the optical properties of these tracer molecules, shown as altered absorption and emission spectra, as well as ON-like switch of fluorescence. As the optotracer provides a defined spectral signature for each binding partner, a fingerprint is generated that can be used for identification of the target biopolymer. Alongside their use for in situ experimentation, optotracers have demonstrated excellent use in studies of a number of clinically relevant microbial pathogens. These methods will find widespread use across a variety of communities engaged in reducing the effect of antibiotic resistance. This includes basic researchers studying molecular resistance mechanisms, academia and pharma developing new antimicrobials targeting biofilm infections and tests to diagnose biofilm infections, as well as those developing antibiotic susceptibility tests for biofilm infections (biofilm-AST). By iterating between the microbial world and that of plants, development of the optotracing technology has become a prime example of successful cross-feeding across the boundaries of disciplines. As optotracers offers a capacity to redefine the way we work with polysaccharides in the microbial world as well as with plant biomass, the technology is providing novel outputs desperately needed for global impact of the threat of antimicrobial resistance as well as our strive for a circular bioeconomy.

Organizational and Technical Security Considerations for Laboratory Cloud Computing

BACKGROUND

Clinical and anatomical pathology services are increasingly utilizing cloud information technology (IT) solutions to meet growing requirements for storage, computation, and other IT services. Cloud IT solutions are often considered on the promise of low cost of entry, durability and reliability, scalability, and features that are typically out of reach for small- or mid-sized IT organizations. However, use of cloud-based IT infrastructure also brings additional security and privacy risks to organizations, as unfamiliarity, public networks, and complex feature sets contribute to an increased surface area for attacks.

CONTENT

In this best-practices guide, we aim to help both managers and IT professionals in healthcare environments understand the requirements and risks when using cloud-based IT infrastructure within the laboratory environment. We will describe how technical, operational, and organizational best practices that can help mitigate security, privacy, and other risks associated with the use of could infrastructure; furthermore, we identify how these best practices fit into healthcare regulatory frameworks.Among organizational best practices, we identify the need for specific hiring requirements, relationships with parent IT groups, mechanisms for reviewing and auditing security practices, and sound practices for onboarding and offboarding employees. Then, we highlight selected specific operational security, account security, and auditing/logging best practices. Finally, we describe how individual cloud technologies have specific resource-level security features.

SUMMARY

We emphasize that laboratory directors, managers, and IT professionals must ensure that the fundamental organizational and process-based requirements are addressed first, to establish the groundwork for technical security solutions and successful implementation of cloud infrastructure.

Metagenomics in diagnosis and improved targeted treatment of UTI

INTRODUCTION

The genomic revolution has transformed our understanding of urinary tract infection. There has been a paradigm shift from the dogmatic statement that urine is sterile in healthy people, as we are becoming forever more familiar with the knowledge that bacterial communities exist within the urinary tracts of healthy people. Metagenomics can investigate the broad populations of microbial communities, analysing all the DNA present within a sample, providing comprehensive data regarding the state of the microenvironment of a patient's urinary tract. This permits medical practitioners to more accurately target organisms that may be responsible for disease-a form of 'precision medicine'.

METHODS AND RESULTS

This paper is derived from an extensive review and analysis of the available literature on the topic of metagenomic sequencing in urological science, using the PubMed search engine. The search yielded a total of 406 results, and manual selection of appropriate papers was subsequently performed. Only one randomised clinical trial comparing metagenomic sequencing to standard culture and sensitivity in the arena of urinary tract infection was found.

CONCLUSION

Out of this process, this paper explores the limitations of traditional methods of culture and sensitivity and delves into the recent studies involving new high-throughput genomic technologies in urological basic and clinical research, demonstrating the advances made in the urinary microbiome in its entire spectrum of pathogens and the first attempts of clinical implementation in several areas of urology. Finally, this paper discusses the challenges that must be overcome for such technology to become widely used in clinical practice.

Next generation sequencing for clinical diagnostics: Five year experience of an academic laboratory

Clinical laboratories have adopted next generation sequencing (NGS) as a gold standard for the diagnosis of hereditary disorders because of its analytic accuracy, high throughput, and potential for cost-effectiveness. We describe the implementation of a single broad-based NGS sequencing assay to meet the genetic testing needs at the University of Minnesota. A single hybrid capture library preparation was used for each test ordered, data was informatically blinded to clinically-ordered genes, and identified variants were reviewed and classified by genetic counselors and molecular pathologists. We performed 2509 sequencing tests from August 2012 till December 2017. The diagnostic yield has remained steady at 25%, but the number of variants of uncertain significance (VUS) included in a patient report decreased over time with 50% of the patient reports including at least one VUS in 2012 and only 22% of the patient reports reporting a VUS in 2017 (p = .002). Among the various clinical specialties, the diagnostic yield was highest in dermatology (60% diagnostic yield) and ophthalmology (42% diagnostic yield) while the diagnostic yield was lowest in gastrointestinal diseases and pulmonary diseases (10% detection yield in both specialties). Deletion/duplication analysis was also implemented in a subset of panels ordered, with 9% of samples having a diagnostic finding using the deletion/duplication analysis. We have demonstrated the feasibility of this broad-based NGS platform to meet the needs of our academic institution by aggregating a sufficient sample volume from many individually rare tests and providing a flexible ordering for custom, patient-specific panels.

Cloud computing applications for biomedical science: A perspective

Biomedical research has become a digital data–intensive endeavor, relying on secure and scalable computing, storage, and network infrastructure, which has traditionally been purchased, supported, and maintained locally. For certain types of biomedical applications, cloud computing has emerged as an alternative to locally maintained traditional computing approaches. Cloud computing offers users pay-as-you-go access to services such as hardware infrastructure, platforms, and software for solving common biomedical computational problems. Cloud computing services offer secure on-demand storage and analysis and are differentiated from traditional high-performance computing by their rapid availability and scalability of services. As such, cloud services are engineered to address big data problems and enhance the likelihood of data and analytics sharing, reproducibility, and reuse. Here, we provide an introductory perspective on cloud computing to help the reader determine its value to their own research.

Review of Clinical Next-Generation Sequencing

CONTEXT

- Next-generation sequencing (NGS) is a technology being used by many laboratories to test for inherited disorders and tumor mutations. This technology is new for many practicing pathologists, who may not be familiar with the uses, methodology, and limitations of NGS.

OBJECTIVE

- To familiarize pathologists with several aspects of NGS, including current and expanding uses; methodology including wet bench aspects, bioinformatics, and interpretation; validation and proficiency; limitations; and issues related to the integration of NGS data into patient care.

DATA SOURCES

- The review is based on peer-reviewed literature and personal experience using NGS in a clinical setting at a major academic center.

CONCLUSIONS

- The clinical applications of NGS will increase as the technology, bioinformatics, and resources evolve to address the limitations and improve quality of results. The challenge for clinical laboratories is to ensure testing is clinically relevant, cost-effective, and can be integrated into clinical care.

A privacy-preserving solution for compressed storage and selective retrieval of genomic data

In clinical genomics, the continuous evolution of bioinformatic algorithms and sequencing platforms makes it beneficial to store patients’ complete aligned genomic data in addition to variant calls relative to a reference sequence. Due to the large size of human genome sequence data files (varying from 30 GB to 200 GB depending on coverage), two major challenges facing genomics laboratories are the costs of storage and the efficiency of the initial data processing. In addition, privacy of genomic data is becoming an increasingly serious concern, yet no standard data storage solutions exist that enable compression, encryption, and selective retrieval. Here we present a privacy-preserving solution named SECRAM (Selective retrieval on Encrypted and Compressed Reference-oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling). Compared with BAM, the de facto standard for storing aligned genomic data, SECRAM uses 18% less storage. Compared with CRAM, one of the most compressed nonencrypted formats (using 34% less storage than BAM), SECRAM maintains efficient compression and downstream data processing, while allowing for unprecedented levels of security in genomic data storage. Compared with previous work, the distinguishing features of SECRAM are that (1) it is position-based instead of read-based, and (2) it allows random querying of a subregion from a BAM-like file in an encrypted form. Our method thus offers a space-saving, privacy-preserving, and effective solution for the storage of clinical genomic data.

CNV-RF Is a Random Forest-Based Copy Number Variation Detection Method Using Next-Generation Sequencing

Simultaneous detection of small copy number variations (CNVs) (<0.5 kb) and single-nucleotide variants in clinically significant genes is of great interest for clinical laboratories. The analytical variability in next-generation sequencing (NGS) and artifacts in coverage data because of issues with mappability along with lack of robust bioinformatics tools for CNV detection have limited the utility of targeted NGS data to identify CNVs. We describe the development and implementation of a bioinformatics algorithm, copy number variation-random forest (CNV-RF), that incorporates a machine learning component to identify CNVs from targeted NGS data. Using CNV-RF, we identified 12 of 13 deletions in samples with known CNVs, two cases with duplications, and identified novel deletions in 22 additional cases. Furthermore, no CNVs were identified among 60 genes in 14 cases with normal copy number and no CNVs were identified in another 104 patients with clinical suspicion of CNVs. All positive deletions and duplications were confirmed using a quantitative PCR method. CNV-RF also detected heterozygous deletions and duplications with a specificity of 50% across 4813 genes. The ability of CNV-RF to detect clinically relevant CNVs with a high degree of sensitivity along with confirmation using a low-cost quantitative PCR method provides a framework for providing comprehensive NGS-based CNV/single-nucleotide variant detection in a clinical molecular diagnostics laboratory.

The long tail and rare disease research: the impact of next-generation sequencing for rare Mendelian disorders

There are an estimated 6000-8000 rare Mendelian diseases that collectively affect 30 million individuals in the United States. The low incidence and prevalence of these diseases present significant challenges to improving diagnostics and treatments. Next-generation sequencing (NGS) technologies have revolutionized research of rare diseases. This article will first comment on the effectiveness of NGS through the lens of long-tailed economics. We then provide an overview of recent developments and challenges of NGS-based research on rare diseases. As the quality of NGS studies improve and the cost of sequencing decreases, NGS will continue to make a significant impact on the study of rare diseases moving forward.

Bioregulatory systems medicine: an innovative approach to integrating the science of molecular networks, inflammation, and systems biology with the patient's autoregulatory capacity?

Bioregulatory systems medicine (BrSM) is a paradigm that aims to advance current medical practices. The basic scientific and clinical tenets of this approach embrace an interconnected picture of human health, supported largely by recent advances in systems biology and genomics, and focus on the implications of multi-scale interconnectivity for improving therapeutic approaches to disease. This article introduces the formal incorporation of these scientific and clinical elements into a cohesive theoretical model of the BrSM approach. The authors review this integrated body of knowledge and discuss how the emergent conceptual model offers the medical field a new avenue for extending the armamentarium of current treatment and healthcare, with the ultimate goal of improving population health.

Benchmarking undedicated cloud computing providers for analysis of genomic datasets

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5-78.2) for E.coli and 53.5% (95% CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5-303.1) and 173.9% (95% CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.