Computational Infrastructure and Basic Data Analysis for High-Throughput Sequencing

The objective of this study was to characterize and compare antigenic and genetic characteristics of Streptococcus pneumoniae strains isolated from patients with invasive and non-invasive pneumococcal infections (PIs) by using the data of high-throughput sequencing.Materials and methods. A total of 158 S. pneumoniae strains were studied. All of them were isolated during different stages of the PEHASus multicenter study performed in 2015-2020. The data analysis was based on the information about whole-genome sequences of 46 strains isolated during the above study. Real-time PCR methods and high-throughput sequencing (the Illumina platform) were used for identification of serotypes. The SeroBA, PneumoCaT software and PubMLST.org website resources were used in the data processing.Results and discussion. The serotypes of all the studied strains were identified. A number of discrepancies among serotypes in serogroup 6 and one discordant result were revealed by the analysis of whole-genome sequences using 2 programs. The PCR methods were effectively used to characterize serotypes in 87% and 69% of the pathogens of invasive and non-invasive PIs, respectively. The serotypes contained in PCV13 accounted for 59% and 37%, while PPV23 serotypes accounted for 78% and 53% of the strains isolated from patients with invasive and non-invasive PIs, respectively. The data analysis was unable to identify either the dominant sequence type (a total of 81 sequence types have been identified) or clonal complexes, except for serotype 3 strains, thus demonstrating consistency with the data from previous studies suggesting the absence of a well-represented clonal structure of S. pneumoniae associated with pneumococcal meningitis in Russia.Conclusion. The obtained data made it possible to identify the distribution of the circulating serotypes and genetic characteristics of the strains isolated from PI patients, thus being instrumental for assessment of the effectiveness of the existing polyvalent vaccines and providing information for improvement of the PCR-based methods of serotyping.

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NASQAR: A web-based platform for high-throughput sequencing data analysis and visualization

10.1101/709980 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ayman Yousif ◽

Nizar Drou ◽

Jillian Rowe ◽

Mohammed Khalfan ◽

Kristin C Gunsalus

Keyword(s):

New York ◽

Data Analysis ◽

Open Source ◽

High Throughput ◽

High Throughput Sequencing ◽

Web Applications ◽

Rna Seq ◽

Sequencing Data ◽

Web Based ◽

Link Type

AbstractBackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource).ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology.ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

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The bacterial community structures in response to the gut passage of earthworm (Eisenia fetida) feeding on cow dung and domestic sludge: Illumina high-throughput sequencing-based data analysis

Ecotoxicology and Environmental Safety ◽

10.1016/j.ecoenv.2019.110149 ◽

2020 ◽

Vol 190 ◽

pp. 110149 ◽

Cited By ~ 2

Author(s):

Jian Hu ◽

Haitao Zhao ◽

Yue Wang ◽

Zhifeng Yin ◽

Yijun Kang

Keyword(s):

Data Analysis ◽

Bacterial Community ◽

High Throughput ◽

Eisenia Fetida ◽

High Throughput Sequencing ◽

Cow Dung ◽

Community Structures ◽

Gut Passage

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Beyond the Ribosome: Extra-translational Functions of tRNA Fragments

Biomarker Insights ◽

10.4137/bmi.s35904 ◽

2016 ◽

Vol 11s1 ◽

pp. BMI.S35904 ◽

Cited By ~ 8

Author(s):

Kevin W. Diebel ◽

Kun Zhou ◽

Aaron B. Clarke ◽

Lynne T. Bemis

Keyword(s):

Data Analysis ◽

High Throughput ◽

Biological Samples ◽

Small Rnas ◽

High Throughput Sequencing ◽

Degradation Products ◽

Noncoding Rnas ◽

Transfer Rnas ◽

Sequencing Studies ◽

Domains Of Life

High-throughput sequencing studies of small RNAs reveal a complex milieu of noncoding RNAs in biological samples. Early data analysis was often limited to microRNAs due to their regulatory nature and potential as biomarkers; however, many more classes of noncoding RNAs are now being recognized. A class of fragments initially excluded from analysis were those derived from transfer RNAs (tRNAs) because they were thought to be degradation products. More recently, critical cellular function has been attributed to tRNA fragments (tRFs), and their conservation across all domains of life has propelled them into an emerging area of scientific study. The biogenesis of tRFs is currently being elucidated, and initial studies show that a diverse array of tRFs are genera ted from all parts of a tRNA molecule. The goal of this review was to present what is currently known about tRFs and their potential as biomarkers for the earlier detection of disease.

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HTSstation: A Web Application and Open-Access Libraries for High-Throughput Sequencing Data Analysis

PLoS ONE ◽

10.1371/journal.pone.0085879 ◽

2014 ◽

Vol 9 (1) ◽

pp. e85879 ◽

Cited By ~ 67

Author(s):

Fabrice P. A. David ◽

Julien Delafontaine ◽

Solenne Carat ◽

Frederick J. Ross ◽

Gregory Lefebvre ◽

...

Keyword(s):

Data Analysis ◽

Open Access ◽

High Throughput ◽

Web Application ◽

High Throughput Sequencing ◽

Sequencing Data ◽

High Throughput Sequencing Data ◽

Sequencing Data Analysis

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Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data

Canadian Journal of Microbiology ◽

10.1139/cjm-2015-0821 ◽

2016 ◽

Vol 62 (8) ◽

pp. 692-703 ◽

Cited By ~ 132

Author(s):

Gregory B. Gloor ◽

Gregor Reid

Keyword(s):

Data Analysis ◽

High Throughput ◽

High Throughput Sequencing ◽

Compositional Data ◽

Critical Role ◽

Compositional Data Analysis ◽

Data Sets ◽

Clear Understanding ◽

Sequencing Data ◽

Microbiome Data

A workshop held at the 2015 annual meeting of the Canadian Society of Microbiologists highlighted compositional data analysis methods and the importance of exploratory data analysis for the analysis of microbiome data sets generated by high-throughput DNA sequencing. A summary of the content of that workshop, a review of new methods of analysis, and information on the importance of careful analyses are presented herein. The workshop focussed on explaining the rationale behind the use of compositional data analysis, and a demonstration of these methods for the examination of 2 microbiome data sets. A clear understanding of bioinformatics methodologies and the type of data being analyzed is essential, given the growing number of studies uncovering the critical role of the microbiome in health and disease and the need to understand alterations to its composition and function following intervention with fecal transplant, probiotics, diet, and pharmaceutical agents.

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