SNES: single nucleus exome sequencing
Open accessAbstract
Single-cell genome sequencing methods are challenged by poor physical coverage and high error rates, making it difficult to distinguish real biological variants from technical artifacts. To address this problem, we developed a method called SNES that combines flow-sorting of single G1/0 or G2/M nuclei, time-limited multiple-displacement-amplification, exome capture, and next-generation sequencing to generate high coverage (96%) data from single human cells. We validated our method in a fibroblast cell line, and show low allelic dropout and false-positive error rates, resulting in high detection efficiencies for single nucleotide variants (92%) and indels (85%) in single cells.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Rapid technological advances have enabled genome-wide profiling of RNA, DNA, protein, epigenetic modifications, chromatin accessibility, and other molecular events in single cells.
“rapid technological advances have enabled genome-wide profiling of RNA7–10, DNA11–17, protein18–21, epigenetic modifications22–26, chromatin accessibility27,28, and other molecular events29 in single cells.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [16]
Experimental noise can strongly affect estimates of heterogeneity due to false positives, false negatives (including allelic dropouts), and data sparsity.
“Some of the challenges highlighted here, including experimental noise16,204, can strongly affect estimates of heterogeneity due to false positives, false negatives (including allelic dropouts77), and data sparsity; these will require new methods and adaptation of existing ones (e.g., in phylogenetic reconstruction205).”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [16]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 33 · Cited by 112
- Authors
- Leung, M. L., Wang, Y., Waters, J., & Navin, N. E.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Corresponding author
- Navin, Nicholas E
- Venue
- Genome Biology
- Published
- 2015-03-25
- Volume
- 16
- Issue
- 1
- Pages
- 55-55
- ISSN
- 1465-6906 (ISSN-L)
- PMC
- PMC4373516
- OpenAlex
- W2080309308
- Cited by
- 112
- References
- 33
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 28 · 2021: 6 · 2020: 3 · 2019: 10 · 2018: 13 · 2017: 21 · 2016: 16 · 2015: 7
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Damon Runyon Cancer Research Foundation
- Cancer Prevention and Research Institute of Texas (RP140106, CPRIT RP140106, CA016672)
- Robert J. Kleberg, Jr. and Helen C. Kleberg Foundation
- National Institutes of Health (CA- 016672)
- University of Texas MD Anderson Cancer Center ((CA016672))
- National Cancer Institute (CA 016672, R01CA169244, 1RO1CA169244‐01, R21CA174397, P30CA016672)
- Moonshot Research and Development Program
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- single-cell DNA sequencing, flow cytometric cell sorting, multiple displacement amplification, allele dropout, variant calling, single nucleotide variants, indels, human fibroblasts, technical artifacts
- MeSH terms
- Alleles, Cell Line, Fibroblasts, Humans, Genome, Human, Polymorphism, Single Nucleotide, INDEL Mutation, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Exome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
