The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
Open accessAbstract
Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We develop MuSE ( http://bioinformatics.mdanderson.org/main/MuSE ), Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base. MuSE adopts a sample-specific error model that reflects the underlying tumor heterogeneity to greatly improve the overall accuracy. We demonstrate the accuracy of MuSE in calling subclonal mutations in the context of large-scale tumor sequencing projects using whole exome and whole genome sequencing.
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Cites
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The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Aaltonen, L. A., Abascal, F., Abeshouse, A., Aburatani, H., Adams, D. J., Agrawal, N., Ahn, K. S., Ahn, S.-M., Aikata, H., et al. (2020) · Nature · doi:10.1038/s41586-020-1969-6 · Cited by 3,064
Self-citationThe Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma
Ricketts, C. J., De Cubas, A. A., Fan, H., Smith, C. C., Lang, M., Reznik, E., Bowlby, R., Gibb, E. A., Akbani, R., Beroukhim, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.06.032 · Cited by 734
Self-citationPerspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics
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Self-citationImmuno-genomic landscape of osteosarcoma
Wu, C.-C., Beird, H. C., Andrew Livingston, J., Advani, S., Mitra, A., Cao, S., Reuben, A., Ingram, D., Wang, W.-L., Ju, Z., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14646-w · Cited by 251
Self-citationA practical guide for mutational signature analysis in hematological malignancies
Maura, F., Degasperi, A., Nadeu, F., Leongamornlert, D., Davies, H., Moore, L., Royo, R., Ziccheddu, B., Puente, X. S., Avet-Loiseau, H., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11037-8 · Cited by 184
Metadata
- Authors
- Fan, Y., Xi, L., Hughes, D. S. T., Zhang, J., Zhang, J., Futreal, P. A., Wheeler, D. A., & Wang, W.
- Venue
- Genome Biology
- Published
- 2016-08-24
- Volume
- 17
- Issue
- 1
- Pages
- 178-178
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- OpenAlex
- W2513185582
- Cited by
- 286
- References
- 6
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. https://app.cochat.ai/papers/10.1186/s13059-016-1029-6
- The paper
- Fan, Y., Xi, L., Hughes, D. S. T., Zhang, J., Zhang, J., Futreal, P. A., Wheeler, D. A., & Wang, W. (2016). MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data. Genome Biology. https://doi.org/10.1186/s13059-016-1029-6
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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