scholarly journals Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Oliver Stirrup ◽  
Joseph Hughes ◽  
Matthew Parker ◽  
David G Partridge ◽  
James G Shepherd ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Sequence Data ◽  
Rapid Assessment ◽  
Epidemiological Data ◽  
Rapid Identification ◽  
Infection Prevention And Control ◽  
Sequencing Data ◽  
Additional Information ◽  
Research And Innovation ◽  
Close Sequence

Background: Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult. Methods: We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hours following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020. Results: We analysed data from 326 HOCIs. Among HOCIs with time-from-admission >8 days the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time-from-admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%). Conclusions: The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period. Funding: COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute.

scholarly journals Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data

2020 ◽  
Author(s):  
Oliver T Stirrup ◽  
Joseph Hughes ◽  
Matthew Parker ◽  
David G Partridge ◽  
James G Shepherd ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Sequence Data ◽  
Rapid Assessment ◽  
Epidemiological Data ◽  
Rapid Identification ◽  
Infection Prevention And Control ◽  
Sequencing Data ◽  
Additional Information ◽  
Close Sequence ◽  
Healthcare Associated

AbstractBackgroundRapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult.MethodsWe developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hours following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020.ResultsWe analysed data from 326 HOCIs. Among HOCIs with time-from-admission ≥8 days the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time-from-admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%).ConclusionsThe methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period.

scholarly journals Simultaneous estimation of per cell division mutation rate and turnover rate from bulk tumour sequence data

2021 ◽  
Author(s):  
Gergely Tibély ◽  
Dominik Schrempf ◽  
Imre Derényi ◽  
Gergely J. Szöllősi
Keyword(s):  
Cell Death ◽  
Cell Division ◽  
Tumor Growth ◽  
Mutation Rate ◽  
Turnover Rate ◽  
Sequence Data ◽  
Simultaneous Estimation ◽  
Cell Turnover ◽  
Sequencing Data ◽  
Cell Divisions

AbstractTumors often harbor orders of magnitude more mutations than heal thy tissues. The increased number of mutations may be due to an elevated mutation rate or frequent cell death and correspondingly rapid cell turnover leading to an increased number of cell divisions and more mutations, or some combination of both these mechanisms. It is difficult to disentangle the two based on widely available bulk sequencing data where mutations from individual cells are intermixed. As a result, the cell linage tree of the tumor cannot be resolved. Here we present a method that can simultaneously estimate the cell turnover rate and the rate of mutations from bulk sequencing data by averaging over ensembles of cell lineage trees parameterized by cell turnover rate. Our method works by simulating tumor growth and matching the observed data to these simulations by choosing the best fitting set of parameters according to an explicit likelihood-based model. Applying it to a real tumor sample, we find that both the mutation rate and the intensity of death is high.Author SummaryTumors frequently harbor an elevated number of mutations, compared to healthy tissue. These extra mutations may be generated either by an increased mutation rate or the presence of cell death resulting in increased cellular turn over and additional cell divisions for tumor growth. Separating the effects of these two factors is a nontrivial problem. Here we present a method which can simultaneously estimate cell turnover rate and genomic mutation rate from bulk sequencing data. Our method is based on the maximum likelihood estimation of the parameters of a generative model of tumor growth and mutations. Applying our method to a human hepatocellular carcinoma sample reveals an elevated per cell division mutation rate and high cell turnover.

scholarly journals In Silico Probe-Based Detection of Citrus Viruses in NGS Data

2017 ◽  
Vol 107 (8) ◽  
pp. 988-993 ◽  
Author(s):  
T. L. Jooste ◽  
M. Visser ◽  
G. Cook ◽  
J. T. Burger ◽  
H. J. Maree
Keyword(s):  
Sequence Data ◽  
Simultaneous Detection ◽  
Rapid Identification ◽  
Sequencing Data ◽  
Metagenomic Sample ◽  
Field Samples ◽  
Input Dataset ◽  
Time Required ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

The conservation of plant biosecurity relies on the rapid identification of pathogenic organisms, including viruses. With next-generation sequencing (NGS), it is possible to identify multiple viruses within a metagenomic sample. In this study, we explored the use of electronic probes (e-probes) for the simultaneous detection of 11 recognized citrus viruses. E-probes were designed and screened against raw sequencing data to minimize the bioinformatic processing time required. The e-probes were able to accurately detect their cognate viruses in simulated datasets, without any false negatives or positives. The efficiency of the e-probe-based approach was validated with NGS datasets generated from different RNA preparations: double-stranded RNA (dsRNA) from ‘Mexican’ lime infected with different Citrus tristeza virus (CTV) genotypes, dsRNA from field samples, and small RNA and total RNA from grapefruit infected with the CTV T3 genotype. A set of probes was made available that is able to accurately detect CTV in sequence data regardless of the input dataset or the genotype that plants are infected with.

scholarly journals Detection of Candida auris Antifungal Drug Resistance Markers Directly from Clinical Skin Swabs

2019 ◽  
Vol 63 (12) ◽  
Author(s):  
Milena Kordalewska ◽  
Annie Lee ◽  
Yanan Zhao ◽  
David S. Perlin
Keyword(s):  
Drug Resistance ◽  
Antifungal Susceptibility ◽  
Rapid Assessment ◽  
Rapid Identification ◽  
Antifungal Drug ◽  
Infection Prevention And Control ◽  
Molecular Diagnostic ◽  
Candida Auris ◽  
Content Type ◽  
Antifungal Drug Resistance

ABSTRACT Accurate and rapid assessment of Candida auris antifungal drug resistance is crucial for effective infection prevention and control actions, as well as for patient management. Here, performance of a molecular diagnostic platform, enabling rapid identification of FKS1 and ERG11 mutations conferring echinocandin and azole resistance, respectively, was evaluated on a panel of clinical skin swabs. Gene sequencing and antifungal susceptibility testing were used as the gold standard. All swabs were correctly categorized as harboring wild-type or mutant C. auris.

scholarly journals REscan: inferring repeat expansions and structural variation in paired-end short read sequencing data

2020 ◽  
Author(s):  
Russell Lewis McLaughlin
Keyword(s):  
Structural Variation ◽  
Sequence Data ◽  
Neurological Diseases ◽  
Repeat Expansion ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Repeat Expansions ◽  
Paired End Sequencing

Abstract Motivation Repeat expansions are an important class of genetic variation in neurological diseases. However, the identification of novel repeat expansions using conventional sequencing methods is a challenge due to their typical lengths relative to short sequence reads and difficulty in producing accurate and unique alignments for repetitive sequence. However, this latter property can be harnessed in paired-end sequencing data to infer the possible locations of repeat expansions and other structural variation. Results This article presents REscan, a command-line utility that infers repeat expansion loci from paired-end short read sequencing data by reporting the proportion of reads orientated towards a locus that do not have an adequately mapped mate. A high REscan statistic relative to a population of data suggests a repeat expansion locus for experimental follow-up. This approach is validated using genome sequence data for 259 cases of amyotrophic lateral sclerosis, of which 24 are positive for a large repeat expansion in C9orf72, showing that REscan statistics readily discriminate repeat expansion carriers from non-carriers. Availabilityand implementation C source code at https://github.com/rlmcl/rescan (GNU General Public Licence v3).

scholarly journals Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities

2009 ◽  
Vol 75 (23) ◽  
pp. 7537-7541 ◽  
Author(s):  
Patrick D. Schloss ◽  
Sarah L. Westcott ◽  
Thomas Ryabin ◽  
Justine R. Hall ◽  
Martin Hartmann ◽  
...  
Keyword(s):  
16S Rrna ◽  
Software Package ◽  
Sequence Data ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Laptop Computer ◽  
Operational Taxonomic Units ◽  
Β Diversity ◽  
Single Piece

ABSTRACT mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.

scholarly journals Base-substitution mutation rate across the nuclear genome of Alpheus snapping shrimp and the timing of isolation by the Isthmus of Panama

2020 ◽  
Author(s):  
Katherine Silliman ◽  
Jane L. Indorf ◽  
Nancy Knowlton ◽  
William E. Browne ◽  
Carla Hurt
Keyword(s):  
Mutation Rate ◽  
Sequence Data ◽  
Nuclear Genome ◽  
Base Substitution ◽  
Snapping Shrimp ◽  
Isthmus Of Panama ◽  
Species Pairs ◽  
Genotype By Sequencing ◽  
Independent Calibration ◽  
Taxonomic Groups

AbstractThe formation of the Isthmus of Panama and final closure of the Central American Seaway (CAS) provides an independent calibration point for examining the rate of DNA substitutions. This vicariant event has been widely used to estimate the substitution rate across mitochondrial genomes and to date evolutionary events in other taxonomic groups. Nuclear sequence data is increasingly being used to complement mitochondrial datasets for phylogenetic and evolutionary investigations; these studies would benefit from information regarding the rate and pattern of DNA substitutions derived from the nuclear genome. To estimate this genomewide neutral mutation rate (μ), genotype-by-sequencing (GBS) datasets were generated for three transisthmian species pairs in Alpheus snapping shrimp. Using a Bayesian coalescent approach (G-PhoCS) applied to 44,960 GBS loci, we estimated μ to be 2.64E-9 substitutions/site/year, when calibrated with the closure of the CAS at 3 Ma. This estimate is remarkably similar to experimentally derived mutation rates in model arthropod systems, strengthening the argument for a recent closure of the CAS. To our knowledge this is the first use of transisthmian species pairs to calibrate the rate of molecular evolution from GBS data.

scholarly journals Inferring species compositions of complex fungal communities from long- and short-read sequence data

2021 ◽  
Author(s):  
Yiheng Hu ◽  
Laszlo Irinyi ◽  
Minh Thuy Vi Hoang ◽  
Tavish Eenjes ◽  
Abigail Graetz ◽  
...  
Keyword(s):  
Community Composition ◽  
Pathogen Detection ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Composition Analysis ◽  
Sequencing Data ◽  
Species Classification ◽  
Shotgun Metagenomics ◽  
Query Coverage

Background: The kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. Results: We compared different metagenomics sequencing and analysis strategies for the identification of fungal species. Using two fungal mock communities of 44 phylogenetically diverse species, we compared species classification and community composition analysis pipelines using shotgun metagenomics and amplicon sequencing data generated from both short and long read sequencing technologies. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungi-specific database. During the assessment of classification algorithms, we found that applying cut-offs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. Conclusion: Overall, our study expands the toolkit for identifying fungi by improving sequence-based fungal classification, and provides a practical guide for the design of metagenomics analyses.

scholarly journals Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

2020 ◽  
Author(s):  
Andrew J. Page ◽  
Nabil-Fareed Alikhan ◽  
Michael Strinden ◽  
Thanh Le Viet ◽  
Timofey Skvortsov
Keyword(s):  
Mycobacterium Tuberculosis ◽  
State Of The Art ◽  
Sequence Data ◽  
Human Pathogen ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Long Reads ◽  
Long Read

AbstractSpoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

scholarly journals ALSgeneScanner: a pipeline for the analysis and interpretation of DNA NGS data of ALS patients

2018 ◽  
Author(s):  
Alfredo Iacoangeli ◽  
Ahmad Al Khleifat ◽  
William Sproviero ◽  
Aleksey Shatunov ◽  
Ashley R Jones ◽  
...  
Keyword(s):  
Motor Neurons ◽  
Health Care Professionals ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Whole Exome ◽  
Exome Sequence Data ◽  
Als Patients ◽  
Ngs Data

AbstractAmyotrophic lateral sclerosis (ALS, MND) is a neurodegenerative disease of upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two years of first symptoms. Genetic factors are an important cause of ALS, with variants in more than 25 genes having strong evidence, and weaker evidence available for variants in more than 120 genes. With the increasing availability of Next-Generation sequencing data, non-specialists, including health care professionals and patients, are obtaining their genomic information without a corresponding ability to analyse and interpret it. Furthermore, the relevance of novel or existing variants in ALS genes is not always apparent. Here we present ALSgeneScanner, a tool that is easy to install and use, able to provide an automatic, detailed, annotated report, on a list of ALS genes from whole genome sequence data in a few hours and whole exome sequence data in about one hour on a readily available mid-range computer. This will be of value to non-specialists and aid in the interpretation of the relevance of novel and existing variants identified in DNA sequencing data.

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