Searching journal content for articles similar to Zhang et al. 33 (11): 2002.

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  1. Method: Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models
    • Yuelin Liu,
    • Xuan Cindy Li,
    • Farid Rashidi Mehrabadi,
    • Alejandro A. Schäffer,
    • Drew Pratt,
    • David R. Crawford,
    • Salem Malikić,
    • Erin K. Molloy,
    • Vishaka Gopalan,
    • Stephen M. Mount,
    • Eytan Ruppin,
    • Kenneth D. Aldape,
    • and S. Cenk Sahinalp
    Genome Res. July 2023 33: 1089-1100; Published in Advance June 14, 2023, doi:10.1101/gr.277608.122
    ...in the data sets, which calls for data imputation or aggregation; on the other hand, tumor lineage inference using CNAs called from single-cell sequencing data often involves ambiguities in the determination of chromosomal breakpoints that may mislead the conclusion. DNA methylation, the addition of a methyl...
  2. Research: Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer
    • Marco L. Leung,
    • Alexander Davis,
    • Ruli Gao,
    • Anna Casasent,
    • Yong Wang,
    • Emi Sei,
    • Eduardo Vilar,
    • Dipen Maru,
    • Scott Kopetz,
    • and Nicholas E. Navin
    Genome Res. August 2017 27: 1287-1299; Published in Advance May 25, 2017, doi:10.1101/gr.209973.116
    ....56% and a false positive error rate of 1.74%. These error rates are low for single-cell DNA sequencing data and suggest that the technical noise does not greatly confound the inference of the tree topologies.Integrated phylogenetic treesTo better understand the timing of the CNA events relative to the mutational...
  3. Method: PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data
    • Salem Malikic,
    • Farid Rashidi Mehrabadi,
    • Simone Ciccolella,
    • Md. Khaledur Rahman,
    • Camir Ricketts,
    • Ehsan Haghshenas,
    • Daniel Seidman,
    • Faraz Hach,
    • Iman Hajirasouliha,
    • and S. Cenk Sahinalp
    Genome Res. November 2019 29: 1860-1877; Published in Advance October 18, 2019, doi:10.1101/gr.234435.118
    ...of copies of a variant allele, this can even be potentially beneficial and reduce allelic dropout (since such an event would provide more starting material for the PCR amplification step that is typically performed prior to DNA sequencing).We ran PhISCS using single-cell data, first without considering any...
    OPEN ACCESS ARTICLE
  4. Method: SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data
    • Hamim Zafar,
    • Nicholas Navin,
    • Ken Chen,
    • and Luay Nakhleh
    Genome Res. November 2019 29: 1847-1859; Published in Advance October 18, 2019, doi:10.1101/gr.243121.118
    .... Recently developed single-cell DNA sequencing (SCS) technologies promise to resolve ITH to a single-cell level. However, technical errors in SCS data sets, including false-positives (FP) and false-negatives (FN) due to allelic dropout, and cell doublets, significantly complicate these tasks. Here, we...
  5. Research: Single-cell sequencing deciphers a convergent evolution of copy number alterations from primary to circulating tumor cells
    • Yan Gao,
    • Xiaohui Ni,
    • Hua Guo,
    • Zhe Su,
    • Yi Ba,
    • Zhongsheng Tong,
    • Zhi Guo,
    • Xin Yao,
    • Xixi Chen,
    • Jian Yin,
    • Zhao Yan,
    • Lin Guo,
    • Ying Liu,
    • Fan Bai,
    • X. Sunney Xie,
    • and Ning Zhang
    Genome Res. August 2017 27: 1312-1322; Published in Advance May 9, 2017, doi:10.1101/gr.216788.116
    ...cellularity in approaches using cell-free DNA (cfDNA) or tissue, CNAs can be detected in a single CTCwith high sensitivity by single-cell sequencing. By interrogating the CTC CNA patterns from 23 patients, we were able to identify 11 focal regions that are frequently amplified or deleted, including well...
  6. Research: Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia
    • Lili Wang,
    • Jean Fan,
    • Joshua M. Francis,
    • George Georghiou,
    • Sarah Hergert,
    • Shuqiang Li,
    • Rutendo Gambe,
    • Chensheng W. Zhou,
    • Chunxiao Yang,
    • Sheng Xiao,
    • Paola Dal Cin,
    • Michaela Bowden,
    • Dylan Kotliar,
    • Sachet A. Shukla,
    • Jennifer R. Brown,
    • Donna Neuberg,
    • Dario R. Alessi,
    • Cheng-Zhong Zhang,
    • Peter V. Kharchenko,
    • Kenneth J. Livak,
    • and Catherine J. Wu
    Genome Res. August 2017 27: 1300-1311; Published in Advance July 5, 2017, doi:10.1101/gr.217331.116
    ...by bulk genomic sequencing using three experimental approaches: targeted DNA, whole transcriptome, and targeted RNA (Fig. 1A). Our targeted RNA-based approach reliably detects subclonalmutations and enables recapitulation of single-cell DNA information, including phylogenetic structure. Integrative...
  7. Method: Modeling and predicting cancer clonal evolution with reinforcement learning
    • Stefan Ivanovic and
    • Mohammed El-Kebir
    Genome Res. July 2023 33: 1078-1088; Published in Advance June 21, 2023, doi:10.1101/gr.277672.123
    ...methods.CloMu identifies orthogonally validated fitness values in an acute myeloid leukemia cohortWe analyzed a cohort of 123 acute myeloid leukemia (AML) patients that underwent high-throughput single-cell DNA panel sequencing (Morita et al. 2020). Because of the relatively small number of patients, we...
  8. Method: TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data
    • Gavin Ha,
    • Andrew Roth,
    • Jaswinder Khattra,
    • Julie Ho,
    • Damian Yap,
    • Leah M. Prentice,
    • Nataliya Melnyk,
    • Andrew McPherson,
    • Ali Bashashati,
    • Emma Laks,
    • Justina Biele,
    • Jiarui Ding,
    • Alan Le,
    • Jamie Rosner,
    • Karey Shumansky,
    • Marco A. Marra,
    • C. Blake Gilks,
    • David G. Huntsman,
    • Jessica N. McAlpine,
    • Samuel Aparicio,
    • and Sohrab P. Shah
    Genome Res. November 2014 24: 1881-1893; Published in Advance July 24, 2014, doi:10.1101/gr.180281.114
    ...allele read counts for segmentation of subFigure 6. Single-cell validation of subclonal deletions in DG1136g using deep DNA sequencing of individual nuclei. (A,B) The 28 nuclei for Set1 and 18 nuclei for Set2 were designated as tumor and normal cell type using the status of mutations. Themutant allele...
    OPEN ACCESS ARTICLE
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