nbCNV: a multi-constrained optimization model for discovering copy number variants in single-cell sequencing data

Cai, Hongming; Huang, Jingying; Song, Yan

doi:10.1186/s12859-016-1239-7

Cited by 16 publications

(8 citation statements)

References 34 publications

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“…In the second place, extend the applications of AITAC to the analysis of single-cell sequencing data. This will help to discover novel cellto-cell heterogeneity and the corresponding mutations (Vallejos et al, 2015;Zhang et al, 2016). In the last place, expect for the estimation of tumor purity from copy numbers, there many other types of information such as single nucleotide variations and methylation that can support for the tumor purity estimation.…”

Section: Discussionmentioning

confidence: 99%

Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data

Yuan

Zhao

et al. 2020

Front. Genet.

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Inference of absolute copy numbers in tumor genomes is one of the key points in the study of tumor genesis. However, the mixture of tumor and normal cells poses a big challenge to this task. Accurate estimation of tumor purity (i.e., the fraction of tumor cells) is a necessary step to solve this problem. In this paper, we propose a new approach, AITAC, to accurately infer tumor purity and absolute copy numbers in a tumor sample by using high-throughput sequencing (HTS) data. In contrast to many existing algorithms for estimating tumor purity, which usually rely on pre-detected mutation genotypes (heterogeneity and homogeneity), AITAC just requires read depths (RDs) observed at the regions with copy number losses. AITAC creates a non-linear model to correlate tumor purity, observed and expected RDs. It adopts an exhaustive search strategy to scan tumor purity in a wide range, and chooses the tumor purity that minimizes the deviation between observed RDs and expected ones as the optimal solution. We apply the proposed approach to both simulation and real sequencing data sets and demonstrate its performance by comparing with two classical approaches. AITAC is freely available at https://github.com/BDanalysis/aitac and can be expected to become a useful approach for researchers to analyze copy numbers in cancer genome.

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Section: Discussionmentioning

confidence: 99%

Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data

Yuan

Zhao

et al. 2020

Front. Genet.

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“…DNA from the GM12878 human cell line is sequenced on FLO-MIN106 flow cells using the SQK-LSK108 protocol. Benefiting from direct sequencing, epigenetic modifications (Zhang et al, 2016) of DNA (e.g., DNA methylation) are preserved. We only selected some of the sequencing data of chromosome 19 for training.…”

Section: Data Preparationmentioning

confidence: 99%

Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network

et al. 2020

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Nanopore sequencing is promising because of its long read length and high speed. During sequencing, a strand of DNA/RNA passes through a biological nanopore, which causes the current in the pore to fluctuate. During basecalling, context-dependent current measurements are translated into the base sequence of the DNA/RNA strand. Accurate and fast basecalling is vital for downstream analyses such as genome assembly and detecting single-nucleotide polymorphisms and genomic structural variants. However, owing to the various changes in DNA/RNA molecules, noise during sequencing, and limitations of basecalling methods, accurate basecalling remains a challenge. In this paper, we propose Causalcall, which uses an end-to-end temporal convolution-based deep learning model for accurate and fast nanopore basecalling. Developed on a temporal convolutional network (TCN) and a connectionist temporal classification decoder, Causalcall directly identifies base sequences of varying lengths from current measurements in long time series. In contrast to the basecalling models using recurrent neural networks (RNNs), the convolution-based model of Causalcall can speed up basecalling by matrix computation. Experiments on multiple species have demonstrated the great potential of the TCN-based model to improve basecalling accuracy and speed when compared to an RNN-based model. Besides, experiments on genome assembly indicate the utility of Causalcall in reference-based genome assembly.

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“…However, it is incomprehensive to analyze cancer only using gene expression data. The rapid accumulation of omics data can provide disparate, partially independent, and complementary information about the entire genome (Zhang et al, 2016 ). The multi-omic data can lay an important foundation for mining informative biomarkers for cancer (Ruffalo et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset

et al. 2021

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As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to discover potential biomarkers of gastric cancer. That will be very important to detect gastric cancer at earlier stages and thus assist in providing timely treatment. However, most of biological data have the characteristics of high dimension and low sample size. It is hard to process directly without feature selection. Besides, only using some omic data, such as gene expression data, provides limited evidence to investigate gastric cancer associated biomarkers. In this research, gene expression data and DNA methylation data are integrated to analyze gastric cancer, and a feature selection approach is proposed to identify the possible biomarkers of gastric cancer. After the original data are pre-processed, the mutual information (MI) is applied to select some top genes. Then, fold change (FC) and T-test are adopted to identify differentially expressed genes (DEG). In particular, false discover rate (FDR) is introduced to revise p_value to further screen genes. For chosen genes, a deep neural network (DNN) model is utilized as the classifier to measure the quality of classification. The experimental results show that the approach can achieve superior performance in terms of accuracy and other metrics. Biological analysis for chosen genes further validates the effectiveness of the approach.

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nbCNV: a multi-constrained optimization model for discovering copy number variants in single-cell sequencing data

Cited by 16 publications

References 34 publications

Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data

Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data

Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network

A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset

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