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  1. Article ; Online: Methods for copy number aberration detection from single-cell DNA-sequencing data

    Xian F. Mallory / Mohammadamin Edrisi / Nicholas Navin / Luay Nakhleh

    Genome Biology, Vol 21, Iss 1, Pp 1-

    2020  Volume 22

    Abstract: Abstract Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring ... ...

    Abstract Abstract Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.
    Keywords Tumor evolution ; Intra-tumor heterogeneity ; Single-cell DNA sequencing ; Copy number aberrations ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
    Language English
    Publishing date 2020-08-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data.

    Xian F Mallory / Mohammadamin Edrisi / Nicholas Navin / Luay Nakhleh

    PLoS Computational Biology, Vol 16, Iss 7, p e

    2020  Volume 1008012

    Abstract: Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide ... ...

    Abstract Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods-Ginkgo, HMMcopy, and CopyNumber-on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.
    Keywords Biology (General) ; QH301-705.5
    Subject code 310
    Language English
    Publishing date 2020-07-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Comments on the model parameters in “SiFit

    Hamim Zafar / Anthony Tzen / Nicholas Navin / Ken Chen / Luay Nakhleh

    Genome Biology, Vol 20, Iss 1, Pp 1-

    inferring tumor trees from single-cell sequencing data under finite-sites models”

    2019  Volume 2

    Keywords Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
    Language English
    Publishing date 2019-05-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: SCMarker

    Fang Wang / Shaoheng Liang / Tapsi Kumar / Nicholas Navin / Ken Chen

    PLoS Computational Biology, Vol 15, Iss 10, p e

    Ab initio marker selection for single cell transcriptome profiling.

    2019  Volume 1007445

    Abstract: Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes ... ...

    Abstract Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.
    Keywords Biology (General) ; QH301-705.5
    Subject code 612
    Language English
    Publishing date 2019-10-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: SiFit

    Hamim Zafar / Anthony Tzen / Nicholas Navin / Ken Chen / Luay Nakhleh

    Genome Biology, Vol 18, Iss 1, Pp 1-

    inferring tumor trees from single-cell sequencing data under finite-sites models

    2017  Volume 20

    Abstract: Abstract Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of ... ...

    Abstract Abstract Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.
    Keywords Tumor evolution ; Intra-tumor heterogeneity ; Single-cell sequencing ; Finite-sites model ; Phylogenetic tree ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
    Language English
    Publishing date 2017-09-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Anaplastic transformation in thyroid cancer revealed by single-cell transcriptomics

    Lina Lu / Jennifer Rui Wang / Ying C. Henderson / Shanshan Bai / Jie Yang / Min Hu / Cheng-Kai Shiau / Timothy Pan / Yuanqing Yan / Tuan M. Tran / Jianzhuo Li / Rachel Kieser / Xiao Zhao / Jiping Wang / Roza Nurieva / Michelle D. Williams / Maria E. Cabanillas / Ramona Dadu / Naifa Lamki Busaidy /
    Mark Zafereo / Nicholas Navin / Stephen Y. Lai / Ruli Gao

    The Journal of Clinical Investigation, Vol 133, Iss

    2023  Volume 11

    Abstract: The deadliest anaplastic thyroid cancer (ATC) often transforms from indolent differentiated thyroid cancer (DTC); however, the complex intratumor transformation process is poorly understood. We investigated an anaplastic transformation model by ... ...

    Abstract The deadliest anaplastic thyroid cancer (ATC) often transforms from indolent differentiated thyroid cancer (DTC); however, the complex intratumor transformation process is poorly understood. We investigated an anaplastic transformation model by dissecting both cell lineage and cell fate transitions using single-cell transcriptomic and genetic alteration data from patients with different subtypes of thyroid cancer. The resulting spectrum of ATC transformation included stress-responsive DTC cells, inflammatory ATC cells (iATCs), and mitotic-defective ATC cells and extended all the way to mesenchymal ATC cells (mATCs). Furthermore, our analysis identified 2 important milestones: (a) a diploid stage, in which iATC cells were diploids with inflammatory phenotypes and (b) an aneuploid stage, in which mATCs gained aneuploid genomes and mesenchymal phenotypes, producing excessive amounts of collagen and collagen-interacting receptors. In parallel, cancer-associated fibroblasts showed strong interactions among mesenchymal cell types, macrophages shifted from M1 to M2 states, and T cells reprogrammed from cytotoxic to exhausted states, highlighting new therapeutic opportunities for the treatment of ATC.
    Keywords Genetics ; Oncology ; Medicine ; R
    Subject code 610
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher American Society for Clinical Investigation
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: MEDALT

    Fang Wang / Qihan Wang / Vakul Mohanty / Shaoheng Liang / Jinzhuang Dou / Jincheng Han / Darlan Conterno Minussi / Ruli Gao / Li Ding / Nicholas Navin / Ken Chen

    Genome Biology, Vol 22, Iss 1, Pp 1-

    single-cell copy number lineage tracing enabling gene discovery

    2021  Volume 22

    Abstract: Abstract We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis ( ... ...

    Abstract Abstract We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution. The source code of our study is available at https://github.com/KChen-lab/MEDALT .
    Keywords Single-cell ; scDNA-seq ; scRNA-seq ; Copy number alteration ; Tumor evolution ; Lineage tracing ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
    Language English
    Publishing date 2021-02-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Mesenchymal and stem-like prostate cancer linked to therapy-induced lineage plasticity and metastasis

    Hyunho Han / Yan Wang / Josue Curto / Sreeharsha Gurrapu / Sara Laudato / Alekya Rumandla / Goutam Chakraborty / Xiaobo Wang / Hong Chen / Yan Jiang / Dhiraj Kumar / Emily G. Caggiano / Monica Capogiri / Boyu Zhang / Yan Ji / Sankar N. Maity / Min Hu / Shanshan Bai / Ana M. Aparicio /
    Eleni Efstathiou / Christopher J. Logothetis / Nicholas Navin / Nora M. Navone / Yu Chen / Filippo G. Giancotti

    Cell Reports, Vol 39, Iss 1, Pp 110595- (2022)

    2022  

    Abstract: Summary: Bioinformatic analysis of 94 patient-derived xenografts (PDXs), cell lines, and organoids (PCOs) identifies three intrinsic transcriptional subtypes of metastatic castration-resistant prostate cancer: androgen receptor (AR) pathway + prostate ... ...

    Abstract Summary: Bioinformatic analysis of 94 patient-derived xenografts (PDXs), cell lines, and organoids (PCOs) identifies three intrinsic transcriptional subtypes of metastatic castration-resistant prostate cancer: androgen receptor (AR) pathway + prostate cancer (PC) (ARPC), mesenchymal and stem-like PC (MSPC), and neuroendocrine PC (NEPC). A sizable proportion of castration-resistant and metastatic stage PC (M-CRPC) cases are admixtures of ARPC and MSPC. Analysis of clinical datasets and mechanistic studies indicates that MSPC arises from ARPC as a consequence of therapy-induced lineage plasticity. AR blockade with enzalutamide induces (1) transcriptional silencing of TP53 and hence dedifferentiation to a hybrid epithelial and mesenchymal and stem-like state and (2) inhibition of BMP signaling, which promotes resistance to AR inhibition. Enzalutamide-tolerant LNCaP cells re-enter the cell cycle in response to neuregulin and generate metastasis in mice. Combined inhibition of HER2/3 and AR or mTORC1 exhibits efficacy in models of ARPC and MSPC or MSPC, respectively. These results define MSPC, trace its origin to therapy-induced lineage plasticity, and reveal its sensitivity to HER2/3 inhibition.
    Keywords CP: Cancer ; Biology (General) ; QH301-705.5
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer

    Ruli Gao / Charissa Kim / Emi Sei / Theodoros Foukakis / Nicola Crosetto / Leong-Keat Chan / Maithreyan Srinivasan / Hong Zhang / Funda Meric-Bernstam / Nicholas Navin

    Nature Communications, Vol 8, Iss 1, Pp 1-

    2017  Volume 12

    Abstract: Single cell RNA sequencing is a powerful tool for understanding cellular diversity but is limited by cost, throughput and sample preparation. Here the authors use nanogrid technology with integrated imaging to sequence thousands of cancer nuclei in ... ...

    Abstract Single cell RNA sequencing is a powerful tool for understanding cellular diversity but is limited by cost, throughput and sample preparation. Here the authors use nanogrid technology with integrated imaging to sequence thousands of cancer nuclei in parallel from fresh or frozen tissue.
    Keywords Science ; Q
    Language English
    Publishing date 2017-08-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Author Correction

    Kiyomi Morita / Feng Wang / Katharina Jahn / Tianyuan Hu / Tomoyuki Tanaka / Yuya Sasaki / Jack Kuipers / Sanam Loghavi / Sa A. Wang / Yuanqing Yan / Ken Furudate / Jairo Matthews / Latasha Little / Curtis Gumbs / Jianhua Zhang / Xingzhi Song / Erika Thompson / Keyur P. Patel / Carlos E. Bueso-Ramos /
    Courtney D. DiNardo / Farhad Ravandi / Elias Jabbour / Michael Andreeff / Jorge Cortes / Kapil Bhalla / Guillermo Garcia-Manero / Hagop Kantarjian / Marina Konopleva / Daisuke Nakada / Nicholas Navin / Niko Beerenwinkel / P. Andrew Futreal / Koichi Takahashi

    Nature Communications, Vol 12, Iss 1, Pp 1-

    Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics

    2021  Volume 1

    Keywords Science ; Q
    Language English
    Publishing date 2021-05-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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