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Cited by 2,369

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Next-Generation DNA Sequencing Methods

Mardis, Elaine R.·2008·Annual Review of Genomics and Human Genetics

Abstract

Recent scientific discoveries that resulted from the application of next-generation DNA sequencing technologies highlight the striking impact of these massively parallel platforms on genetics. These new methods have expanded previously focused readouts from a variety of DNA preparation protocols to a genome-wide scale and have fine-tuned their resolution to single base precision. The sequencing of RNA also has transitioned and now includes full-length cDNA analyses, serial analysis of gene expression (SAGE)-based methods, and noncoding RNA discovery. Next-generation sequencing has also enabled novel applications such as the sequencing of ancient DNA samples, and has substantially widened the scope of metagenomic analysis of environmentally derived samples. Taken together, an astounding potential exists for these technologies to bring enormous change in genetic and biological research and to enhance our fundamental biological knowledge.

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Crossref · 10.1146/annurev.genom.9.081307.164359

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Metadata

Authors
Mardis, E. R.
Venue
Annual Review of Genomics and Human Genetics
Published
2008-09-01
Volume
9
Issue
1
Pages
387-402
ISSN
  • 1527-8204 (ISSN-L)
  • 1545-293X
OpenAlex
W2103903744
Cited by
2,369
References
14
FWCI
8.51
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Next-Generation DNA Sequencing Methods. https://app.cochat.ai/papers/10.1146/annurev.genom.9.081307.164359
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Mardis, E. R. (2008). Next-Generation DNA Sequencing Methods. Annual Review of Genomics and Human Genetics. https://doi.org/10.1146/annurev.genom.9.081307.164359

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.