The impact of next-generation sequencing on genomics

By Jun Zhang, R. Chiodini, A. Badr, Genfa Zhang. Journal of genetics and genomics = Yi chuan xue bao, 2011

578 citations recorded by the source corpus.

This article reviews basic concepts, general applications, and the potential impact of next-generation sequencing (NGS) technologies on genomics, with particular reference to currently available and possible future platforms and bioinformatics. NGS technologies have demonstrated the capacity to sequence DNA at unprecedented speed, thereby enabling previously unimaginable scientific achievements and novel biological applications. But, the massive data produced by NGS also presents a significant challenge for data storage, analyses, and management solutions. Advanced bioinformatic tools are essential for the successful application of NGS technology. As evidenced throughout this review, NGS technologies will have a striking impact on genomic research and the entire biological field. With its ability to tackle the unsolved challenges unconquered by previous genomic technologies, NGS is likely to unravel the complexity of the human genome in terms of genetic variations, some of which may be confined to susceptible loci for some common human conditions. The impact of NGS technologies on genomics will be far reaching and likely change the field for years to come.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1b9c146da87d0f487b0e91ccc8b01ab955ff6476

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Citations in the corpus

Next‐generation sequencing technologies for environmental DNA research (2012, background): “Although available NGS technologies utilize quite diverse chemistry and base incorporation ⁄ detection tools, they share two main steps: library fragmentation ⁄ amplicon library preparation and detection of the incorporated nucleotides (Glenn 2011; Zhang et al. 2011).”
Big Data Application in Biomedical Research and Health Care: A Literature Review (2016, background): “The agent is able to (1) provide an interface for DICOM, HL7, HTTP, and XDS standard and nonstandardized data; (2) synchronize metadata on local PACS and RIS; (3) cache images and data received from local PACS, RIS, any input tool, or another agent; (4) archive image data with compression and multit”
DeePromoter: Robust Promoter Predictor Using Deep Learning (2019, background): “Recently, with the development of the next-generation sequencing (NGS) (Behjati and Tarpey, 2013) more genes of diﬀerent organisms have been sequenced and their gene elements have been computationally explored (Zhang et al., 2011).”
Molecular genetic tools for environmental monitoring of New Zealand's aquatic habitats, past, present and the future (2013, background): “Although available platforms utilise quite diverse chemistry and base incorporation/detection tools, they all share two main steps: library preparation (fragmentation or amplicon preparation); and detection of the incorporated nucleotide (Zhang et al. 2011).”
Reactome pathway analysis: a high-performance in-memory approach (2017, background): “Since the size of the data samples is constantly increasing [2, 3], Reactome offers a set of pathway analysis tools which aim to deal with this scenario and yet provide reliable and accurate results with interactive (seconds) response time for genome-wide datasets.”
Microbial Biodiversity and Bioremediation Assessment Through Omics Approaches (2020, background): “It produces a massively parallel analysis from multiple samples where small sections of DNA are ligated with adaptors for random reads during DNA amplification which provides enormous data within a short duration (Zhang et al., 2011).”
A SPECTRAL APPROACH INTEGRATING FUNCTIONAL GENOMIC ANNOTATIONS FOR CODING AND NONCODING VARIANTS (2016, background): “The tremendous progress in massively parallel sequencing technologies enables investigators to efficiently obtain genetic information down to single base resolution on a genome-wide scale [1, 2, 3].”
The Parasitic Plant Genome Project: New Tools for Understanding the Biology of Orobanche and Striga (2012, methodology): “Transcriptome sequencing was conducted using a combination of 454 and Illumina technologies (Zhang et al. 2011).”