scholarly journals High-Throughput Sequencing and Metagenomic Data Analysis

2020 ◽  
Author(s):  
Ahmed Shuikan ◽  
Sulaiman Ali Alharbi ◽  
Dalal Hussien M. Alkhalifah ◽  
Wael N. Hozzein
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Metagenomic Data

scholarly journals Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis

Genomics ◽  
2017 ◽  
Vol 109 (2) ◽  
pp. 83-90 ◽  
Author(s):  
Yan Guo ◽  
Yulin Dai ◽  
Hui Yu ◽  
Shilin Zhao ◽  
David C. Samuels ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Sequencing Data Analysis

scholarly journals Antigenic and genetic characterization of Streptococcus pneumoniae strains isolated from patients with invasive and non-invasive pneumococcal infections by using high-throughput sequencing

2021 ◽  
Vol 98 (5) ◽  
pp. 512-518
Author(s):  
K. O. Mironov ◽  
I. I. Gaponova ◽  
V. I. Korchagin ◽  
Yu. V. Mikhailova ◽  
A. A. Shelenkov ◽  
...  
Keyword(s):  
Data Analysis ◽  
Streptococcus Pneumoniae ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Pneumococcal Infections ◽  
Genome Sequences ◽  
Illumina Platform ◽  
Genetic Characteristics ◽  
Non Invasive

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.

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

2019 ◽  
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.

scholarly journals Beyond the Ribosome: Extra-translational Functions of tRNA Fragments

2016 ◽  
Vol 11s1 ◽  
pp. BMI.S35904 ◽  
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.

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

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e85879 ◽  
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

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

2016 ◽  
Vol 62 (8) ◽  
pp. 692-703 ◽  
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.

scholarly journals MetaGenSense : A web application for analysis and visualization of high throughput sequencing metagenomic data

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 86 ◽  
Author(s):  
Damien Correia ◽  
Olivia Doppelt-Azeroual ◽  
Jean-Baptiste Denis ◽  
Mathias Vandenbogaert ◽  
Valérie Caro
Keyword(s):  
High Throughput ◽  
Web Application ◽  
Input Data ◽  
High Throughput Sequencing ◽  
Information Management System ◽  
Health Concern ◽  
Metagenomic Data ◽  
Team Members ◽  
Related Organism ◽  
Scientific Results

The detection and characterization of emerging infectious agents has been a continuing public health concern. High Throughput Sequencing (HTS) or Next-Generation Sequencing (NGS) technologies have proven to be promising approaches for efficient and unbiased detection of pathogens in complex biological samples, providing access to comprehensive analyses. As NGS approaches typically yield millions of putatively representative reads per sample, efficient data management and visualization resources have become mandatory. Most usually, those resources are implemented through a dedicated Laboratory Information Management System (LIMS), solely to provide perspective regarding the available information. We developed an easily deployable web-interface, facilitating management and bioinformatics analysis of metagenomics data-samples. It was engineered to run associated and dedicated Galaxy workflows for the detection and eventually classification of pathogens. The web application allows easy interaction with existing Galaxy metagenomic workflows, facilitates the organization, exploration and aggregation of the most relevant sample-specific sequences among millions of genomic sequences, allowing them to determine their relative abundance, and associate them to the most closely related organism or pathogen. The user-friendly Django-Based interface, associates the users’ input data and its metadata through a bio-IT provided set of resources (a Galaxy instance, and both sufficient storage and grid computing power). Galaxy is used to handle and analyze the user’s input data from loading, indexing, mapping, assembly and DB-searches. Interaction between our application and Galaxy is ensured by the BioBlend library, which gives API-based access to Galaxy’s main features. Metadata about samples, runs, as well as the workflow results are stored in the LIMS. For metagenomic classification and exploration purposes, we show, as a proof of concept, that integration of intuitive exploratory tools, like Krona for representation of taxonomic classification, can be achieved very easily. In the trend of Galaxy, the interface enables the sharing of scientific results to fellow team members.

Sign in / Sign up

Export Citation Format

Share Document