Preclinical safety and biodistribution of CRISPR targeting SIV in non-human primates.

In this study, we demonstrate the safety and utility of CRISPR-Cas9 gene editing technology for in vivo editing of proviral DNA in ART-treated, virally controlled simian immunodeficiency virus (SIV) infected rhesus macaques, an established model for HIV infection. EBT-001 is an AAV9-based vector delivering SaCas9 and dual guide RNAs designed to target multiple regions of the SIV genome: the viral LTRs, and the Gag gene. The results presented here demonstrate that a single IV inoculation of EBT-001 at each of 3 dose levels (1.4 × 1012, 1.4 × 1013 and 1.4 × 1014 genome copies/kg) resulted in broad and functional biodistribution of AAV9-EBT-001 to known tissue reservoirs of SIV. No off-target effects or abnormal pathology were observed, and animals returned to their normal body weight after receiving EBT-001. Importantly, the macaques that received the 2 highest doses of EBT-001 showed improved absolute lymphocyte counts as compared to antiretroviral-treated controls. Taken together, these results demonstrate safety, biodistribution, and in vivo proviral DNA editing following IV administration of EBT-001, supporting the further development of CRISPR-based gene editing as a potential therapeutic approach for HIV in humans.

Building the foundation for a modern patient-partnered infrastructure to study temporomandibular disorders.

Background: Conflicting reports from varying stakeholders related to prognosis and outcomes following placement of temporomandibular joint (TMJ) implants gave rise to the development of the TMJ Patient-Led RoundTable initiative. Following an assessment of the current availability of data, the RoundTable concluded that a strategically Coordinated Registry Network (CRN) is needed to collect and generate accessible data on temporomandibular disorder (TMD) and its care. The aim of this study was therefore to advance the clinical understanding, usage, and adoption of a core minimum dataset for TMD patients as the first foundational step toward building the CRN. Methods: Candidate data elements were extracted from existing data sources and included in a Delphi survey administered to 92 participants. Data elements receiving less than 75% consensus were dropped. A purposive multi-stakeholder sub-group triangulated the items across patient and clinician-based experience to remove redundancies or duplicate items and reduce the response burden for both patients and clinicians. To reliably collect the identified data elements, the identified core minimum data elements were defined in the context of technical implementation within High-performance Integrated Virtual Environment (HIVE) web-application framework. HIVE was integrated with CHIOS™, an innovative permissioned blockchain platform, to strengthen the provenance of data captured in the registry and drive metadata to record all registry transaction and create a robust consent network. Results: A total of 59 multi-stakeholder participants responded to the Delphi survey. The completion of the Delphi surveys followed by the application of the required group consensus threshold resulted in the selection of 397 data elements (254 for patient-generated data elements and 143 for clinician generated data elements). The infrastructure development and integration of HIVE and CHIOS™ was completed showing the maintenance of all data transaction information in blockchain, flexible recording of patient consent, data cataloging, and consent validation through smart contracts. Conclusion: The identified data elements and development of the technological platform establishes a data infrastructure that facilitates the standardization and harmonization of data as well as perform high performance analytics needed to fully leverage the captured patient-generated data, clinical evidence, and other healthcare ecosystem data within the TMJ/TMD-CRN.

Where Is Research in the Era of Electronic Health Records?

IntroductionData extraction from electronic health records (EHRs) for use in clinical research continues to be labor-intensive and to offer little benefit over traditional paper chart reviews. This is largely due to poor integration of EHR systems with hospital process flow, which still relies heavily on traditional paperwork as a means of documentation. DiscussionNew methods in data collection through mobile applications have streamlined data entry through better data standardization and improved overall data quality. However, mobile applications address only a portion of the problem. Data entry errors and legacy integration will continue to be an issue when there are changes between practitioners with different EHR systems. The combination of a mobile application with the cloud platform has been applied in multiple specialties to monitor recovery and patient-reported outcomes. ConclusionMobile applications along with a virtual cloud environment to host data provide a reasonable solution for consolidating patient data and can accelerate population research.

Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside.

Artificial intelligence (AI)-based technologies are the most rapidly growing field of innovation in healthcare with the promise to achieve substantial improvements in delivery of patient care across all disciplines of medicine. Recent advances in imaging technology along with marked expansion of readily available advanced health information, data offer a unique opportunity for interventional radiology (IR) to reinvent itself as a data-driven specialty. Additionally, the growth of AI-based applications in diagnostic imaging is expected to have downstream effects on all image-guidance modalities. Therefore, the Society of Interventional Radiology Foundation has called upon 13 key opinion leaders in the field of IR to develop research priorities for clinical applications of AI in IR. The objectives of the assembled research consensus panel were to assess the availability and understand the applicability of AI for IR, estimate current needs and clinical use cases, and assemble a list of research priorities for the development of AI in IR. Individual panel members proposed and all participants voted upon consensus statements to rank them according to their overall impact for IR. The results identified the top priorities for the IR research community and provide organizing principles for innovative academic-industrial research collaborations that will leverage both clinical expertise and cutting-edge technology to benefit patient care in IR.

Development and Usability Testing of a Mobile Application to Monitor Patient-Reported Outcomes after Stress Urinary Incontinence Surgery.

OBJECTIVE: To develop and perform a usability testing of a mobile application (app) with representative users of surgeons and female patients undergoing stress urinary incontinence (SUI) surgery. METHODS: A mobile app was developed with the Medical Device Epidemiology Network (MDEpiNet) High-Performance Integrated Virtual Environment (HIVE) to streamline the collection of patient-reported outcomes following SUI surgery using validated questionnaires. It was designed as a collaborative effort with the Women's Health Initiative (WHI), including patient and surgeon involvement. The app evaluation questions addressed the user's rating on the clarity and length of the questions and the comfort level in using the interface for patients to report post-operative outcomes and surgeons to review them. RESULTS: A total of twenty patients and 5 surgeons tested the mobile app and reported their experience and level of satisfaction. The average patient experience score was 9 out of ten, with ten being the best. Eleven patients (55%) were interested in replacing in-person follow-up visits with the app. The surgeons reported an average user experience score of 8.6 out of ten. Four surgeons (80%) were interested in replacing in-person visits with the app. The combined experience score between all users was 8.9 out of ten. Fifteen out of twenty-five users (60%) showed interest in replacing in-person visits with the app. CONCLUSION: The mobile app for SUI captured important patient-reported outcomes with a high satisfaction reporting from patients and surgeons.

Can a brief training intervention on schizophrenia and depression improve knowledge, attitudes and practices of primary healthcare workers? The experience in Armenia.

BACKGROUND: Although the World Health Organization has called for mental health services to be integrated into primary care, mental health remains in most countries, and especially in low- and middle-income countries, one of the most neglected topics in the training curriculum of frontline health workers. As a result, primary healthcare professionals leave medical and nursing schools with insufficient knowledge, and often with negative attitudes towards mental disorders. AIM: We investigated the effect of a brief training intervention on schizophrenia and depression conducted among general practitioners and nurses in Armenia. METHODS: Training interventions were one-day, face-to-face, interactive workshops, including didactic presentations and discussions of case studies. We used a quasi-experimental design of the before/after type, to compare data on knowledge, attitudes and practices collected before and after the training sessions. RESULTS: Mean scores for knowledge, attitudes and practices increased significantly (p < 0.001) among both nurses and GPs for both schizophrenia (111 GPs and 167 nurses) and depression (459 GPs and 197 nurses). CONCLUSIONS: Our experience suggests that a brief training intervention can result in significant improvements in knowledge, attitudes and practices among primary healthcare workers and could help improve mental health services.

Streamlined Subpopulation, Subtype, and Recombination Analysis of HIV-1 Half-Genome Sequences Generated by High-Throughput Sequencing.

High-throughput sequencing (HTS) has been widely used to characterize HIV-1 genome sequences. There are no algorithms currently that can directly determine genotype and quasispecies population using short HTS reads generated from long genome sequences without additional software. To establish a robust subpopulation, subtype, and recombination analysis workflow, we amplified the HIV-1 3'-half genome from plasma samples of 65 HIV-1-infected individuals and sequenced the entire amplicon (∼4,500 bp) by HTS. With direct analysis of raw reads using HIVE-hexahedron, we showed that 48% of samples harbored 2 to 13 subpopulations. We identified various subtypes (17 A1s, 4 Bs, 27 Cs, 6 CRF02_AGs, and 11 unique recombinant forms) and defined recombinant breakpoints of 10 recombinants. These results were validated with viral genome sequences generated by single genome sequencing (SGS) or the analysis of consensus sequence of the HTS reads. The HIVE-hexahedron workflow is more sensitive and accurate than just evaluating the consensus sequence and also more cost-effective than SGS.IMPORTANCE The highly recombinogenic nature of human immunodeficiency virus type 1 (HIV-1) leads to recombination and emergence of quasispecies. It is important to reliably identify subpopulations to understand the complexity of a viral population for drug resistance surveillance and vaccine development. High-throughput sequencing (HTS) provides improved resolution over Sanger sequencing for the analysis of heterogeneous viral subpopulations. However, current methods of analysis of HTS reads are unable to fully address accurate population reconstruction. Hence, there is a dire need for a more sensitive, accurate, user-friendly, and cost-effective method to analyze viral quasispecies. For this purpose, we have improved the HIVE-hexahedron algorithm that we previously developed with in silico short sequences to analyze raw HTS short reads. The significance of this study is that our standalone algorithm enables a streamlined analysis of quasispecies, subtype, and recombination patterns from long HIV-1 genome regions without the need of additional sequence analysis tools. Distinct viral populations and recombination patterns identified by HIVE-hexahedron are further validated by comparison with sequences obtained by single genome sequencing (SGS).

A second generation leishmanization vaccine with a markerless attenuated Leishmania major strain using CRISPR gene editing.

Leishmaniasis is a neglected tropical disease caused by Leishmania protozoa transmitted by infected sand flies. Vaccination through leishmanization with live Leishmania major has been used successfully but is no longer practiced because it resulted in occasional skin lesions. A second generation leishmanization is described here using a CRISPR genome edited L. major strain (LmCen-/-). Notably, LmCen-/- is a genetically engineered centrin gene knock-out mutant strain that is antibiotic resistant marker free and does not have detectable off-target mutations. Mice immunized with LmCen-/- have no visible lesions following challenge with L. major-infected sand flies, while non-immunized animals develop large and progressive lesions with a 2-log fold higher parasite burden. LmCen-/- immunization results in protection and an immune response comparable to leishmanization. LmCen-/- is safe since it is unable to cause disease in immunocompromised mice, induces robust host protection against vector sand fly challenge and because it is marker free, can be advanced to human vaccine trials.

Development of an Integrated Platform Using Multidisciplinary Real-World Data to Facilitate Biomarker Discovery for Medical Products.

Translational multidisciplinary research is important for the Center for Devices and Radiological Health's efforts for utilizing real-world data (RWD) to enhance predictive evaluation of medical device performance in patient subpopulations. As part of our efforts for developing new RWD-based evidentiary approaches, including in silico discovery of device-related risk predictors and biomarkers, this study aims to characterize the sex/race-related trends in hip replacement outcomes and identify corresponding candidate single nucleotide polymorphisms (SNPs). Adverse outcomes were assessed by deriving RWD from a retrospective analysis of hip replacement hospital discharge data from the National Inpatient Sample (NIS). Candidate SNPs were explored using pre-existing data from the Personalized Medicine Research Project (PMRP). High-Performance Integrated Virtual Environment was used for analyzing and visualizing putative associations between SNPs and adverse outcomes. Ingenuity Pathway Analysis (IPA) was used for exploring plausibility of the sex-related candidate SNPs and characterizing gene networks associated with the variants of interest. The NIS-based epidemiologic evidence showed that periprosthetic osteolysis (PO) was most prevalent among white men. The PMRP-based genetic evidence associated the PO-related male predominance with rs7121 (odds ratio = 4.89; 95% confidence interval = 1.41-17.05) and other candidate SNPs. SNP-based IPA analysis of the expected gene expression alterations and corresponding signaling pathways suggested possible role of sex-related metabolic factors in development of PO, which was substantiated by ad hoc epidemiologic analysis identifying the sex-related differences in metabolic comorbidities in men vs. women with hip replacement-related PO. Thus, our in silico study illustrates RWD-based evidentiary approaches that may facilitate cost/time-efficient discovery of biomarkers for informing use of medical products.

Baseline human gut microbiota profile in healthy people and standard reporting template.

A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. This list can be expanded to 863 organisms if closely related proteomes are considered. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individual's microbiome to the growing knowledgebase of "normal" microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and their abundances, this study also generated a collection of assembled contiguous sequences (contigs) of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBI's Short Read Archive.

Microbiota of MR1 deficient mice confer resistance against Clostridium difficile infection.

Clostridium difficile (Cd) infection (CDI) typically occurs after antibiotic usage perturbs the gut microbiota. Mucosa-associated invariant T cells (MAIT) are found in the gut and their development is dependent on Major histocompatibility complex-related protein 1 (MR1) and the host microbiome. Here we were interested in determining whether the absence of MR1 impacts resistance to CDI. To this end, wild-type (WT) and MR1-/- mice were treated with antibiotics and then infected with Cd spores. Surprisingly, MR1-/- mice exhibited resistance to Cd colonization. 16S rRNA gene sequencing of feces revealed inherent differences in microbial composition. This colonization resistance was transferred from MR1-/- to WT mice via fecal microbiota transplantation, suggesting that MR1-dependent factors influence the microbiota, leading to CDI susceptibility.

Novel reference genes in colorectal cancer identify a distinct subset of high stage tumors and their associated histologically normal colonic tissues.

BACKGROUND: Reference genes are often interchangeably called housekeeping genes due to 1) the essential cellular functions their proteins provide and 2) their constitutive expression across a range of normal and pathophysiological conditions. However, given the proliferative drive of malignant cells, many reference genes such as beta-actin (ACTB) and glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) which play critical roles in cell membrane organization and glycolysis, may be dysregulated in tumors versus their corresponding normal controls METHODS: Because Next Generation Sequencing (NGS) technology has several advantages over hybridization-based technologies, such as independent detection and quantitation of transcription levels, greater sensitivity, and increased dynamic range, we evaluated colorectal cancers (CRC) and their histologically normal tissue counterparts by NGS to evaluate the expression of 21 "classical" reference genes used as normalization standards for PCR based methods. Seventy-nine paired tissue samples of CRC and their patient matched healthy colonic tissues were subjected to NGS analysis of their mRNAs. RESULTS: We affirmed that 17 out of 21 classical reference genes had upregulated expression in tumors compared to normal colonic epithelial tissue and dramatically so in some cases. Indeed, tumors were distinguished from normal controls in both unsupervised hierarchical clustering analyses (HCA) and principal component analyses (PCA). We then identified 42 novel potential reference genes with minimal coefficients of variation (CV) across 79 CRC tumor pairs. Though largely consistently expressed across tumors and normal control tissues, a subset of high stage tumors (HSTs) as well as some normal tissue samples (HSNs) located adjacent to these HSTs demonstrated dysregulated expression, thus identifying a subset of tumors with a potentially distinct and aggressive biological profile. CONCLUSION: While classical CRC reference genes were found to be differentially expressed between tumors and normal controls, novel reference genes, identified via NGS, were more consistently expressed across malignant and normal colonic tissues. Nonetheless, a subset of HST had profound dysregulation of such genes as did many of the histologically normal tissues adjacent to such HSTs, indicating that the HSTs so distinguished may have unique biological properties and that their histologically normal tissues likely harbor a small population of microscopically undetected but metabolically active tumors.

Identification and quantification of defective virus genomes in high throughput sequencing data using DVG-profiler, a novel post-sequence alignment processing algorithm.

Most viruses are known to spontaneously generate defective viral genomes (DVG) due to errors during replication. These DVGs are subgenomic and contain deletions that render them unable to complete a full replication cycle in the absence of a co-infecting, non-defective helper virus. DVGs, especially of the copyback type, frequently observed with paramyxoviruses, have been recognized to be important triggers of the antiviral innate immune response. DVGs have therefore gained interest for their potential to alter the attenuation and immunogenicity of vaccines. To investigate this potential, accurate identification and quantification of DVGs is essential. Conventional methods, such as RT-PCR, are labor intensive and will only detect primer sequence-specific species. High throughput sequencing (HTS) is much better suited for this undertaking. Here, we present an HTS-based algorithm called DVG-profiler to identify and quantify all DVG sequences in an HTS data set generated from a virus preparation. DVG-profiler identifies DVG breakpoints relative to a reference genome and reports the directionality of each segment from within the same read. The specificity and sensitivity of the algorithm was assessed using both in silico data sets as well as HTS data obtained from parainfluenza virus 5, Sendai virus and mumps virus preparations. HTS data from the latter were also compared with conventional RT-PCR data and with data obtained using an alternative algorithm. The data presented here demonstrate the high specificity, sensitivity, and robustness of DVG-profiler. This algorithm was implemented within an open source cloud-based computing environment for analyzing HTS data. DVG-profiler might prove valuable not only in basic virus research but also in monitoring live attenuated vaccines for DVG content and to assure vaccine lot to lot consistency.

Codon and Codon-Pair Usage Tables (CoCoPUTs): Facilitating Genetic Variation Analyses and Recombinant Gene Design.

Usage of sequential codon-pairs is non-random and unique to each species. Codon-pair bias is related to but clearly distinct from individual codon usage bias. Codon-pair bias is thought to affect translational fidelity and efficiency and is presumed to be under the selective pressure. It was suggested that changes in codon-pair utilization may affect human disease more significantly than changes in single codons. Although recombinant gene technologies often take codon-pair usage bias into account, codon-pair usage data/tables are not readily available, thus potentially impeding research efforts. The present computational resource (https://hive.biochemistry.gwu.edu/review/codon2) systematically addresses this issue. Building on our recent HIVE-Codon Usage Tables, we constructed a new database to include genomic codon-pair and dinucleotide statistics of all organisms with sequenced genome, available in the GenBank. We believe that the growing understanding of the importance of codon-pair usage will make this resource an invaluable tool to many researchers in academia and pharmaceutical industry.

A complete Leishmania donovani reference genome identifies novel genetic variations associated with virulence.

Leishmania donovani is responsible for visceral leishmaniasis, a neglected and lethal parasitic disease with limited treatment options and no vaccine. The study of L. donovani has been hindered by the lack of a high-quality reference genome and this can impact experimental outcomes including the identification of virulence genes, drug targets and vaccine development. We therefore generated a complete genome assembly by deep sequencing using a combination of second generation (Illumina) and third generation (PacBio) sequencing technologies. Compared to the current L. donovani assembly, the genome assembly reported within resulted in the closure over 2,000 gaps, the extension of several chromosomes up to telomeric repeats and the re-annotation of close to 15% of protein coding genes and the annotation of hundreds of non-coding RNA genes. It was possible to correctly assemble the highly repetitive A2 and Amastin virulence gene clusters. A comparative sequence analysis using the improved reference genome confirmed 70 published and identified 15 novel genomic differences between closely related visceral and atypical cutaneous disease-causing L. donovani strains providing a more complete map of genes associated with virulence and visceral organ tropism. Bioinformatic tools including protein variation effect analyzer and basic local alignment search tool were used to prioritize a list of potential virulence genes based on mutation severity, gene conservation and function. This complete genome assembly and novel information on virulence factors will support the identification of new drug targets and the development of a vaccine for L. donovani.

Sequence comparison of Francisella tularensis LVS, LVS-G and LVS-R.

Francisella tularensis is a gram-negative organism found in many regions of the world. F. tularensis can cause a fatal, febrile illness, although these natural tularemia infections are rare in the United States. However, the development of F. tularensis as a potential weapon of bioterrorism during the Cold War spurred the development of a live attenuated vaccine, LVS, from F. tularensis subsp. holarctica in the 1960s. Two colony morphology variants, LVS-G and LVS-R, were generated from parental LVS by plate passage and by acridine orange mutagenesis, respectively. In vaccinated mice, LVS-G and LVS-R exhibit altered immunogenicity and protective capacities. While the exact nature of the mutations in these strains was unknown, previous studies indicated that both had altered lipopolysaccharide structures. To better understand the impact of these mutations on LVS' immunogenicity, we sequenced the genomes of LVS-G and LVS-R as well as our parental laboratory stock of LVS, originally obtained from ATCC, and compared these to the F. tularensis subsp. holarctica LVS genome currently deposited in GenBank. The results indicate that the genomic sequence of ATCC LVS is nearly identical to that of the human LVS vaccine. Furthermore, a limited number of genomic mutations likely account for the phenotypes of LVS-G and LVS-R.

Evolution of echovirus 11 in a chronically infected immunodeficient patient.

Deep sequencing was used to determine complete nucleotide sequences of echovirus 11 (EV11) strains isolated from a chronically infected patient with CVID as well as from cases of acute enterovirus infection. Phylogenetic analysis showed that EV11 strains that circulated in Israel in 1980-90s could be divided into four clades. EV11 strains isolated from a chronically infected individual belonged to one of the four clades and over a period of 4 years accumulated mutations at a relatively constant rate. Extrapolation of mutations accumulation curve into the past suggested that the individual was infected with circulating EV11 in the first half of 1990s. Genomic regions coding for individual viral proteins did not appear to be under strong selective pressure except for protease 3C that was remarkably conserved. This may suggest its important role in maintaining persistent infection.

Enabling precision medicine via standard communication of HTS provenance, analysis, and results.

A personalized approach based on a patient's or pathogen's unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to findable, accessible, interoperable, and reusable (FAIR) guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet-lab procedures to computational methods. The BioCompute framework (https://w3id.org/biocompute/1.3.0) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed, and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCOs) offer that standard and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the "Open-Stand.org principles for collaborative open standards development." With high-throughput sequencing (HTS) studies communicated using a BCO, regulatory agencies (e.g., Food and Drug Administration [FDA]), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next-generation sequencing workflow exchange, reporting, and regulatory reviews.

Separation and assembly of deep sequencing data into discrete sub-population genomes.

Sequence heterogeneity is a common characteristic of RNA viruses that is often referred to as sub-populations or quasispecies. Traditional techniques used for assembly of short sequence reads produced by deep sequencing, such as de-novo assemblers, ignore the underlying diversity. Here, we introduce a novel algorithm that simultaneously assembles discrete sequences of multiple genomes present in populations. Using in silico data we were able to detect populations at as low as 0.1% frequency with complete global genome reconstruction and in a single sample detected 16 resolved sequences with no mismatches. We also applied the algorithm to high throughput sequencing data obtained for viruses present in sewage samples and successfully detected multiple sub-populations and recombination events in these diverse mixtures. High sensitivity of the algorithm also enables genomic analysis of heterogeneous pathogen genomes from patient samples and accurate detection of intra-host diversity, enabling not just basic research in personalized medicine but also accurate diagnostics and monitoring drug therapies, which are critical in clinical and regulatory decision-making process.