scholarly journals Detection of Respiratory Pathogens in Parapneumonic Effusions by Hypothesis-free, Next-Generation Sequencing (NGS)

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S17-S17
Author(s):  
Krow Ampofo ◽  
Andrew Pavia ◽  
Anne J Blaschke ◽  
Robert Schlaberg
Keyword(s):  
Next Generation Sequencing ◽  
Pathogen Detection ◽  
Respiratory Pathogens ◽  
Bacterial Identification ◽  
Next Generation ◽  
University Of Utah ◽  
Species Specific ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Background Species-specific polymerase chain reaction (PCR) testing of pleural fluid (PF) from children with parapneumonic effusion (PPE) has increased pathogen identification in pediatric PPE. However, a pathogen is not detected in 25–35% of cases. Hypothesis-free, next-generation sequencing (NGS) provides a more comprehensive alternative and has led to pathogen detection in PCR-negative samples. However, the utility of NGS in the evaluation of PF from children with PPE is unknown. Methods Archived PF (n = 20) from children younger than 18 years with PPE and hospitalized at Primary Children’s Hospital, Utah, in 2015 and previously tested by PCR were evaluated. Ten PCR-negative and 10 PCR-positive PF specimens were tested using RNA-seq at an average depth of 7.7×106 sequencing reads per sample. NGS data were analyzed with Taxonomer. We compared pathogens detected by blood and PF culture, PCR, and NGS. Results Overall, compared with blood/PF culture, PF PCR and PF NGS testing of PF increased bacterial identification from 15% to 50% (P < 0.05) and 65% (P = 0.003), respectively. Pathogen detection in PF by PCR and NGS were comparable (50 vs. 65%, p = NS) (Table). However, compared with PF PCR, NGS significantly increased detection of S. pyogenes (20% vs. 55%; P < 0.05), with 100% concordance when detected by PCR and culture. Detection of Fusobacterium spp. (10 vs. 10%) by PF NGS and PF PCR were comparable. In contrast, there was no detection of S. pneumoniae (15 vs. 0%) by PF NGS compared with PF PCR. Conclusion PF NGS testing significantly improves bacterial identification and comparable to PF PCR testing, which can help inform antimicrobial selection. However there were differences in detection of S. pneumoniae and S. pyogenes. Further studies of NGS testing of PF of children with PPE are needed to assess its potential in the evaluation of PPE in children. Disclosures A. J. Blaschke, BioFire Diagnostics LLC: Collaborator, Have intellectual property in BioFire Diagnostics through the University of Utah and Investigator, Licensing agreement or royalty and Research support; R. Schlaberg, IDbyDNA: Co-founder, Consultant and Shareholder, Stock

scholarly journals Mining and Development of Novel SSR Markers Using Next Generation Sequencing (NGS) Data in Plants

Molecules ◽  
2018 ◽  
Vol 23 (2) ◽  
pp. 399 ◽  
Author(s):  
Sima Taheri ◽  
Thohirah Lee Abdullah ◽  
Mohd Yusop ◽  
Mohamed Hanafi ◽  
Mahbod Sahebi ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Ssr Markers ◽  
Next Generation ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

scholarly journals ViennaNGS: A toolbox for building efficient next- generation sequencing analysis pipelines

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 50 ◽  
Author(s):  
Michael T. Wolfinger ◽  
Jörg Fallmann ◽  
Florian Eggenhofer ◽  
Fabian Amman
Keyword(s):  
Next Generation Sequencing ◽  
Sequence Motif ◽  
Software Components ◽  
Sequencing Analysis ◽  
Next Generation ◽  
File Formats ◽  
Next Generation Sequencing Analysis ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

Recent achievements in next-generation sequencing (NGS) technologies lead to a high demand for reuseable software components to easily compile customized analysis workflows for big genomics data. We present ViennaNGS, an integrated collection of Perl modules focused on building efficient pipelines for NGS data processing. It comes with functionality for extracting and converting features from common NGS file formats, computation and evaluation of read mapping statistics, as well as normalization of RNA abundance. Moreover, ViennaNGS provides software components for identification and characterization of splice junctions from RNA-seq data, parsing and condensing sequence motif data, automated construction of Assembly and Track Hubs for the UCSC genome browser, as well as wrapper routines for a set of commonly used NGS command line tools.

scholarly journals Whole genome sequencing suggests transmission of Corynebacterium diphtheriae-caused cutaneous diphtheria in two siblings, Germany, 2018

2019 ◽  
Vol 24 (2) ◽  
Author(s):  
Anja Berger ◽  
Alexandra Dangel ◽  
Tilmann Schober ◽  
Birgit Schmidbauer ◽  
Regina Konrad ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Corynebacterium Diphtheriae ◽  
Whole Genome ◽  
Next Generation ◽  
Insect Bites ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

In September 2018, a child who had returned from Somalia to Germany presented with cutaneous diphtheria by toxigenic Corynebacterium diphtheriae biovar mitis. The child’s sibling had superinfected insect bites harbouring also toxigenic C. diphtheriae. Next generation sequencing (NGS) revealed the same strain in both patients suggesting very recent human-to-human transmission. Epidemiological and NGS data suggest that the two cutaneous diphtheria cases constitute the first outbreak by toxigenic C. diphtheriae in Germany since the 1980s.

scholarly journals ViennaNGS: A toolbox for building efficient next- generation sequencing analysis pipelines

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 50 ◽  
Author(s):  
Michael T. Wolfinger ◽  
Jörg Fallmann ◽  
Florian Eggenhofer ◽  
Fabian Amman
Keyword(s):  
Next Generation Sequencing ◽  
Sequence Motif ◽  
Software Components ◽  
Sequencing Analysis ◽  
Next Generation ◽  
File Formats ◽  
Next Generation Sequencing Analysis ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

Recent achievements in next-generation sequencing (NGS) technologies lead to a high demand for reuseable software components to easily compile customized analysis workflows for big genomics data. We present ViennaNGS, an integrated collection of Perl modules focused on building efficient pipelines for NGS data processing. It comes with functionality for extracting and converting features from common NGS file formats, computation and evaluation of read mapping statistics, as well as normalization of RNA abundance. Moreover, ViennaNGS provides software components for identification and characterization of splice junctions from RNA-seq data, parsing and condensing sequence motif data, automated construction of Assembly and Track Hubs for the UCSC genome browser, as well as wrapper routines for a set of commonly used NGS command line tools.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer

2020 ◽  
Vol 20 (22) ◽  
pp. 1968-1980
Author(s):  
Nidhi Shukla ◽  
Narmadhaa Siva ◽  
Babita Malik ◽  
Prashanth Suravajhala
Keyword(s):  
Prostate Cancer ◽  
Next Generation Sequencing ◽  
Candidate Genes ◽  
Next Generation ◽  
Recent Past ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Omic Data ◽  
Generation Sequencing

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

The impact of next generation sequencing on sarcoma diagnosis.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e23528-e23528
Author(s):  
Gang Zhao ◽  
Lu Xie ◽  
Wei Guo ◽  
Yanfeng Xi ◽  
Yanzhi Cui ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Spindle Cell ◽  
Cell Tumor ◽  
Low Grade ◽  
Round Cell ◽  
Next Generation ◽  
Small Round Cell ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

e23528 Background: The rarity and heterogeneity of sarcoma has been complicating the diagnosis of sarcoma for years. Even expert pathologists of sarcoma could make mistakes in the diagnosis of this disease. The availability of Next Generation Sequencing (NGS) data enabled more accurate diagnosis of sarcoma. In this study, we systematically described the application of NGS on the diagnosis of sarcoma and the contribution of NGS to the diagnostic accuracy of sarcoma. Methods: A multi-center, retrospective study included 235 sarcoma patients’ tumor and paired normal samples that were sent from 56 hospitals to a College of American Pathologists (CAP) accredited and Clinical Laboratory Improvement Amendments (CLIA) certified laboratory, at Shanghai, China for Next Generation Sequencing (NGS) was performed. Using next generation sequencing based YS panel consisting 450 genes, these 235 sarcoma patients’ sample were sequenced and the NGS data was analyzed. The initial diagnosis without NGS information was reconsidered by expert pathologists. Results: Taking into consideration both the initial diagnosis and the NGS results, the final diagnosis of these 235 sarcoma cases included 8 low grade malignant fibromyxoid tumors, 11 dermatofibrosarcoma protuberans (DFSP), 38 myxoliposarcomas, 22 alveolar rhabdomyosarcomas, 11 alveolar soft tissue sarcoma, 2 desmoplastic small round cell tumors, 37 NTRK rearrangement spindle cell tumors, 40 Ewing’s sarcoma and 66 synoviosarcomas. In total, 29% initial diagnoses were changed according to NGS identified fusions, including 13% low grade malignant fibromyxoid tumors (1 FUS- CREB3L2 fusion), 27% DFSPs (3 COL1A1- PDGFB fusions), 11% myxoliposarcomas (3 FUS- DDIT3 fusions and 1 EWSR1- DDIT3 fusion), 14% alveolar rhabdomyosarcomas (2 PAX7- FOXO1 fusions and 1 FOXO1- LINC00598 fusion), 18% alveolar soft tissue sarcomas (2 ASPSCR1- TFE3 fusions), 50% desmoplastic small round cell tumor (1 EWSR1- WT1 fusion), 95% NTRK rearrangement spindle cell tumors, 13% Ewing’s sarcomas (3 EWSR1- FLI1 fusions and 2 EWSR1- ERG fusions) and 21% synoviosarcomas (9 SS18- SSX1 fusions and 5 SS18- SSX2 fusions). Conclusions: NGS would be highly recommended for accurate diagnosis of sarcoma, especially for NTRK rearrangement spindle cell tumor, the majority of which were confirmed according to NGS identified fusions.

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