scholarly journals The advancements of genomics and data integration in sarcoma research

2017 ◽  
Vol 3 (4) ◽  
Author(s):  
Ricardo J. Flores ◽  
Aaron J. Kelly ◽  
Manjula Nakka ◽  
Xiang Chen ◽  
Jiayi Sun ◽  
...  
Keyword(s):  
Big Data ◽  
Next Generation Sequencing ◽  
Data Integration ◽  
High Throughput ◽  
Biomedical Research ◽  
High Dimensionality ◽  
Systems Medicine ◽  
Biological Models ◽  
Complex Relationships ◽  
Generation Sequencing

<p>In the age of big data, genomics and clinical research have reached a crossroads. A wealth of data is being generated, but it is becoming increasingly complicated to analyze these data to extract meaningful results. The ability to understand biological systems holistically has unprecedented potential to transform how cancers are treated. Recent major advances leading biomedical research towards “systems medicine” have been fueled by high-throughput platforms, such as microarrays and next-generation sequencing, which can capture vast amounts of data in different genomic spaces. Unfortunately, because of high dimensionality and complex relationships among these data, inferring comprehensive and useful biological models has proven computationally and statistically challenging. However, novel bioinformatic methods for data integration of cancer genomic datasets have been developed. In this review, we will describe the applications of various genomic approaches in sarcoma research and introduce bioinformatic methods for data integration. With the continuing evolution of technological and bioinformatic methodologies, the application of big data within clinics and hospitals will ultimately result in significant improvements on how cancers are detected and treated.</p>

scholarly journals Translational Biomedical Informatics in the Cloud: Present and Future

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jiajia Chen ◽  
Fuliang Qian ◽  
Wenying Yan ◽  
Bairong Shen
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Next Generation Sequencing ◽  
Clinical Data ◽  
High Throughput ◽  
Exponential Growth ◽  
Translational Bioinformatics ◽  
Translational Biomedical Informatics ◽  
Generation Sequencing

Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics. The scale and dimensionality of data, however, pose obvious challenges in data mining, storage, and integration. In this paper we demonstrated the utility and promise of cloud computing for tackling the big data problems. We also outline our vision that cloud computing could be an enabling tool to facilitate translational bioinformatics research.

Next-Generation Sequencing: An Emerging Tool for Drug Designing

2019 ◽  
Vol 25 (31) ◽  
pp. 3350-3357 ◽  
Author(s):  
Pooja Tripathi ◽  
Jyotsna Singh ◽  
Jonathan A. Lal ◽  
Vijay Tripathi
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput ◽  
Large Scale ◽  
Massively Parallel Sequencing ◽  
Genomic Research ◽  
Biological Research ◽  
Next Generation ◽  
Human Welfare ◽  
Drug Designing ◽  
Generation Sequencing

Background: With the outbreak of high throughput next-generation sequencing (NGS), the biological research of drug discovery has been directed towards the oncology and infectious disease therapeutic areas, with extensive use in biopharmaceutical development and vaccine production. Method: In this review, an effort was made to address the basic background of NGS technologies, potential applications of NGS in drug designing. Our purpose is also to provide a brief introduction of various Nextgeneration sequencing techniques. Discussions: The high-throughput methods execute Large-scale Unbiased Sequencing (LUS) which comprises of Massively Parallel Sequencing (MPS) or NGS technologies. The Next geneinvolved necessarily executes Largescale Unbiased Sequencing (LUS) which comprises of MPS or NGS technologies. These are related terms that describe a DNA sequencing technology which has revolutionized genomic research. Using NGS, an entire human genome can be sequenced within a single day. Conclusion: Analysis of NGS data unravels important clues in the quest for the treatment of various lifethreatening diseases and other related scientific problems related to human welfare.

scholarly journals Advances in genetics and molecular breeding of three legume crops of semi-arid tropics using next-generation sequencing and high-throughput genotyping technologies

2012 ◽  
Vol 37 (5) ◽  
pp. 811-820 ◽  
Author(s):  
Rajeev K Varshney ◽  
Himabindu Kudapa ◽  
Manish Roorkiwal ◽  
Mahendar Thudi ◽  
Manish K Pandey ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput ◽  
Molecular Breeding ◽  
Next Generation ◽  
Legume Crops ◽  
Generation Sequencing ◽  
Semi Arid

scholarly journals Robust Sub-nanomolar Library Preparation for High Throughput Next Generation Sequencing

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Wells W. Wu ◽  
Je-Nie Phue ◽  
Chun-Ting Lee ◽  
Changyi Lin ◽  
Lai Xu ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput ◽  
Library Preparation ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals Big data from small samples: Informatics of next-generation sequencing in cytopathology

2016 ◽  
Vol 125 (4) ◽  
pp. 236-244 ◽  
Author(s):  
Sinchita Roy-Chowdhuri ◽  
Somak Roy ◽  
Sara E. Monaco ◽  
Mark J. Routbort ◽  
Liron Pantanowitz
Keyword(s):  
Big Data ◽  
Next Generation Sequencing ◽  
Small Samples ◽  
Next Generation ◽  
Generation Sequencing

Big Data Analysis Techniques for Visualization of Genomics in Medicinal Plants

Biotechnology ◽  
2019 ◽  
pp. 804-837
Author(s):  
Hithesh Kumar ◽  
Vivek Chandramohan ◽  
Smrithy M. Simon ◽  
Rahul Yadav ◽  
Shashi Kumar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Next Generation Sequencing ◽  
Medicinal Plants ◽  
Big Data Analysis ◽  
Next Generation ◽  
Genome Data ◽  
Huge Data ◽  
Transcriptomics Data ◽  
Generation Sequencing

In this chapter, the complete overview and application of Big Data analysis in the field of health care industries, Clinical Informatics, Personalized Medicine and Bioinformatics is provided. The major tools and databases used for the Big Data analysis are discussed in this chapter. The development of sequencing machines has led to the fast and effective ways of generating DNA, RNA, Whole Genome data, Transcriptomics data, etc. available in our hands in just a matter of hours. The complete Next Generation Sequencing (NGS) huge data analysis work flow for the medicinal plants are discussed in the chapter. This chapter serves as an introduction to the big data analysis in Next Generation Sequencing and concludes with a summary of the topics of the remaining chapters of this book.

scholarly journals Evaluation of TypeSeq, a Novel High-Throughput, Low-Cost, Next-Generation Sequencing-Based Assay for Detection of 51 Human Papillomavirus Genotypes

2019 ◽  
Vol 220 (10) ◽  
pp. 1609-1619 ◽  
Author(s):  
Sarah Wagner ◽  
David Roberson ◽  
Joseph Boland ◽  
Aimée R Kreimer ◽  
Meredith Yeager ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
Next Generation Sequencing ◽  
High Throughput ◽  
Vaccine Efficacy ◽  
Linear Array ◽  
Human Papillomaviruses ◽  
Next Generation ◽  
Hpv Genotyping ◽  
Positive Agreement ◽  
Generation Sequencing

AbstractBackgroundHuman papillomaviruses (HPV) cause over 500 000 cervical cancers each year, most of which occur in low-resource settings. Human papillomavirus genotyping is important to study natural history and vaccine efficacy. We evaluated TypeSeq, a novel, next-generation, sequencing-based assay that detects 51 HPV genotypes, in 2 large international epidemiologic studies.MethodsTypeSeq was evaluated in 2804 cervical specimens from the Study to Understand Cervical Cancer Endpoints and Early Determinants (SUCCEED) and in 2357 specimens from the Costa Rica Vaccine Trial (CVT). Positive agreement and risks of precancer for individual genotypes were calculated for TypeSeq in comparison to Linear Array (SUCCEED). In CVT, positive agreement and vaccine efficacy were calculated for TypeSeq and SPF10-LiPA.ResultsWe observed high overall and positive agreement for most genotypes between TypeSeq and Linear Array in SUCCEED and SPF10-LiPA in CVT. There was no significant difference in risk of precancer between TypeSeq and Linear Array in SUCCEED or in estimates of vaccine efficacy between TypeSeq and SPF10-LiPA in CVT.ConclusionsThe agreement of TypeSeq with Linear Array and SPF10-LiPA, 2 well established standards for HPV genotyping, demonstrates its high accuracy. TypeSeq provides high-throughput, affordable HPV genotyping for world-wide studies of cervical precancer risk and of HPV vaccine efficacy.

scholarly journals Performance of a high-throughput next-generation sequencing method for analysis of HIV drug resistance and viral load

2020 ◽  
Vol 75 (12) ◽  
pp. 3510-3516 ◽  
Author(s):  
Jessica M Fogel ◽  
David Bonsall ◽  
Vanessa Cummings ◽  
Rory Bowden ◽  
Tanya Golubchik ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Viral Load ◽  
Next Generation Sequencing ◽  
High Throughput ◽  
Whole Genome ◽  
Hiv Viral Load ◽  
Viral Loads ◽  
Viral Load Assay ◽  
Generation Sequencing ◽  
Hiv 1

Abstract Objectives To evaluate the performance of a high-throughput research assay for HIV drug resistance testing based on whole genome next-generation sequencing (NGS) that also quantifies HIV viral load. Methods Plasma samples (n = 145) were obtained from HIV-positive MSM (HPTN 078). Samples were analysed using clinical assays (the ViroSeq HIV-1 Genotyping System and the Abbott RealTime HIV-1 Viral Load assay) and a research assay based on whole-genome NGS (veSEQ-HIV). Results HIV protease and reverse transcriptase sequences (n = 142) and integrase sequences (n = 138) were obtained using ViroSeq. Sequences from all three regions were obtained for 100 (70.4%) of the 142 samples using veSEQ-HIV; results were obtained more frequently for samples with higher viral loads (93.5% for 93 samples with &gt;5000 copies/mL; 50.0% for 26 samples with 1000–5000 copies/mL; 0% for 23 samples with &lt;1000 copies/mL). For samples with results from both methods, drug resistance mutations (DRMs) were detected in 33 samples using ViroSeq and 42 samples using veSEQ-HIV (detection threshold: 5.0%). Overall, 146 major DRMs were detected; 107 were detected by both methods, 37 were detected by veSEQ-HIV only (frequency range: 5.0%–30.6%) and two were detected by ViroSeq only. HIV viral loads estimated by veSEQ-HIV strongly correlated with results from the Abbott RealTime Viral Load assay (R2 = 0.85; n = 142). Conclusions The NGS-based veSEQ-HIV method provided results for most samples with higher viral loads, was accurate for detecting major DRMs, and detected mutations at lower levels compared with a method based on population sequencing. The veSEQ-HIV method also provided HIV viral load data.

Sign in / Sign up

Export Citation Format

Share Document