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Cited by 896

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Whole genome amplification from a single cell: implications for genetic analysis.

Open access
Zhang, L, Cui, X, Schmitt, K, Hubert, R, Navidi, W, Arnheim, N·1992·Proceedings of the National Academy of Sciences

Abstract

We have developed an in vitro method for amplifying a large fraction of the DNA sequences present in a single haploid cell by repeated primer extensions using a mixture of 15-base random oligonucleotides. We studied 12 genetic loci and estimate that the probability of amplifying any sequence in the genome to a minimum of 30 copies is not less than 0.78 (95% confidence). Whole genome amplification beginning with a single cell, or other samples with very small amounts of DNA, has significant implications for multipoint mapping by sperm or oocyte typing and possibly for genetic disease diagnosis, forensics, and the analysis of ancient DNA samples.

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Crossref · 10.1073/pnas.89.13.5847

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Metadata

Authors
Zhang, L., Cui, X., Schmitt, K., Hubert, R., Navidi, W., & Arnheim, N.
Venue
Proceedings of the National Academy of Sciences
Published
1992-07
Volume
89
Issue
13
Pages
5847-5851
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2145903120
Cited by
896
References
2
FWCI
8.78
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Whole genome amplification from a single cell: implications for genetic analysis. https://app.cochat.ai/papers/10.1073/pnas.89.13.5847
The paper
Zhang, L., Cui, X., Schmitt, K., Hubert, R., Navidi, W., & Arnheim, N. (1992). Whole genome amplification from a single cell: implications for genetic analysis. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.89.13.5847

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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.