scholarly journals Practices and views of neurologists regarding the use of whole-genome sequencing in clinical settings: a web-based survey

2017 ◽  
Vol 25 (7) ◽  
pp. 801-808 ◽  
Author(s):  
Iris Jaitovich Groisman ◽  
Thierry Hurlimann ◽  
Amir Shoham ◽  
Béatrice Godard
Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 201
Author(s):  
Sang Mee Hwang ◽  
Hee Won Cho ◽  
Tae Yeul Kim ◽  
Jeong Su Park ◽  
Jongtak Jung ◽  
...  

Carbapenem-resistant Acinetobacter baumannii (CRAB) outbreaks in hospital settings challenge the treatment of patients and infection control. Understanding the relatedness of clinical isolates is important in distinguishing outbreak isolates from sporadic cases. This study investigated 11 CRAB isolates from a hospital outbreak by whole-genome sequencing (WGS), utilizing various bioinformatics tools for outbreak analysis. The results of multilocus sequence typing (MLST), single nucleotide polymorphism (SNP) analysis, and phylogenetic tree analysis by WGS through web-based tools were compared, and repetitive element polymerase chain reaction (rep-PCR) typing was performed. Through the WGS of 11 A. baumannii isolates, three clonal lineages were identified from the outbreak. The coexistence of blaOXA-23, blaOXA-66, blaADC-25, and armA with additional aminoglycoside-inactivating enzymes, predicted to confer multidrug resistance, was identified in all isolates. The MLST Oxford scheme identified three types (ST191, ST369, and ST451), and, through whole-genome MLST and whole-genome SNP analyses, different clones were found to exist within the MLST types. wgSNP showed the highest discriminatory power with the lowest similarities among the isolates. Using the various bioinformatics tools for WGS, CRAB outbreak analysis was applicable and identified three discrete clusters differentiating the separate epidemiologic relationships among the isolates.


2021 ◽  
pp. 153537022110400
Author(s):  
Haseeb Nisar ◽  
Bilal Wajid ◽  
Samiah Shahid ◽  
Faria Anwar ◽  
Imran Wajid ◽  
...  

Rare diseases affect nearly 300 million people globally with most patients aged five or less. Traditional diagnostic approaches have provided much of the diagnosis; however, there are limitations. For instance, simply inadequate and untimely diagnosis adversely affects both the patient and their families. This review advocates the use of whole genome sequencing in clinical settings for diagnosis of rare genetic diseases by showcasing five case studies. These examples specifically describe the utilization of whole genome sequencing, which helped in providing relief to patients via correct diagnosis followed by use of precision medicine.


2021 ◽  
Vol 132 ◽  
pp. S300
Author(s):  
Julia Gerow ◽  
Karryn Crisamore ◽  
Solomon Adams ◽  
Lisa Parker ◽  
Mylynda Massart ◽  
...  

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Vítor Borges ◽  
Miguel Pinheiro ◽  
Pedro Pechirra ◽  
Raquel Guiomar ◽  
João Paulo Gomes

2018 ◽  
Author(s):  
Vítor Borges ◽  
Miguel Pinheiro ◽  
Pedro Pechirra ◽  
Raquel Guiomar ◽  
João Paulo Gomes

AbstractA new era of flu surveillance has already started based on the genetic characterization and exploration of influenza virus evolution at whole-genome scale. Although this has been prioritized by national and international health authorities, the demanded technological transition to whole-genome sequencing (WGS)-based flu surveillance has been particularly delayed by the lack of bioinformatics infrastructures and/or expertise to deal with primary next-generation sequencing (NGS) data. Here, we launch INSaFLU (“INSide the FLU”), which, to the best of our knowledge, is the first influenza-specific bioinformatics free web-based suite that deals with primary data (reads) towards the automatic generation of the output data that are actually the core first-line “genetic requests” for effective and timely influenza laboratory surveillance (e.g., type and sub-type, gene and whole-genome consensus sequences, variants’ annotation, alignments and phylogenetic trees). By handling NGS data collected from any amplicon-based schema, the implemented pipeline enables any laboratory to perform advanced, multi-step software intensive analyses in a user-friendly manner without previous training in bioinformatics. INSaFLU gives access to user-restricted sample databases and projects’ management, being a transparent and highly flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus completely cumulative and scalable, fitting the need for a continuous epidemiological surveillance during the flu epidemics. Multiple outputs are provided in nomenclature-stable and standardized formats that can be explored in situ or through multiple compatible downstream applications for fine-tune data analysis. This platform additionally flags samples as “putative mixed infections” if the population admixture enrolls influenza viruses with clearly distinct genetic backgrounds, and enriches the traditional “consensus-based” influenza genetic characterization with relevant data on influenza sub-population diversification through a depth analysis of intra-patient minor variants. This dual approach is expected to strengthen our ability not only to detect the emergence of antigenic and drug resistance variants, but also to decode alternative pathways of influenza evolution and to unveil intricate routes of transmission. In summary, INSaFLU supplies public health laboratories and influenza researchers with an open “one size fits all” framework, potentiating the operationalization of a harmonized multi-country WGS-based surveillance for influenza virus.INSaFLU can be accessed through https://insaflu.insa.pt (see homepage view in Figure 1).


2018 ◽  
Author(s):  
Mark Stevenson ◽  
Alistair T Pagnamenta ◽  
Heather G Mack ◽  
Judith A Savige ◽  
Kate E Lines ◽  
...  

2016 ◽  
Vol 94 (suppl_5) ◽  
pp. 146-146
Author(s):  
D. M. Bickhart ◽  
L. Xu ◽  
J. L. Hutchison ◽  
J. B. Cole ◽  
D. J. Null ◽  
...  

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