IntSIM: An Integrated Simulator of Next-Generation Sequencing Data

Yuan, Xiguo; Zhang, Junying; Yang, Liying

doi:10.1109/tbme.2016.2560939

Cited by 47 publications

(33 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the future work, we intend to make improvements to the current version of MFCNV from the following aspects: In the first place, the contamination of normal genomes in the sequenced genomes is an important factor to influence the power of CNV detection (Yu et al, 2011;Yuan et al, 2017). Thus, it is meaningful to integrate the module of tumor purity prediction into the pipeline of CNV detection.…”

Section: Discussionmentioning

confidence: 99%

“…The performance evaluation is crucial to decide whether the proposed method is valid or not. Simulation studies are usually regarded as an appropriate and feasible way to evaluate performance for existing and newly developed methods (Yuan et al, 2012a(Yuan et al, , 2017(Yuan et al, , 2018b. This is because the ground truth underlying the simulation samples could be used for an exact calculation of true-positive and false-positive rates.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…The design of the simulation experiments is described as follows: we adopt our previously developed simulation tool IntSIM (Yuan et al, 2017) to produce various datasets with varying tumor purity from 0.2 to 0.4 and varying sequencing coverage depth from 4× to 6× (Yuan et al, 2019b). In each simulation configuration, 50 replicated samples are generated for a sufficient test of our proposed method and the peer methods.…”

Section: Simulation Studiesmentioning

confidence: 99%

See 2 more Smart Citations

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Zhao

Huang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Copy number variation (CNV) is a very important phenomenon in tumor genomes and plays a significant role in tumor genesis. Accurate detection of CNVs has become a routine and necessary procedure for a deep investigation of tumor cells and diagnosis of tumor patients. Next-generation sequencing (NGS) technique has provided a wealth of data for the detection of CNVs at base-pair resolution. However, such task is usually influenced by a number of factors, including GC-content bias, sequencing errors, and correlations among adjacent positions within CNVs. Although many existing methods have dealt with some of these artifacts by designing their own strategies, there is still a lack of comprehensive consideration of all the factors. In this paper, we propose a new method, MFCNV, for an accurate detection of CNVs from NGS data. Compared with existing methods, the characteristics of the proposed method include the following: (1) it makes a full consideration of the intrinsic correlations among adjacent positions in the genome to be analyzed, (2) it calculates read depth, GC-content bias, base quality, and correlation value for each genome bin and combines them as multiple features for the evaluation of genome bins, and (3) it addresses the joint effect among the factors via training a neural network algorithm for the prediction of CNVs. We test the performance of the MFCNV method by using simulation and real sequencing data and make comparisons with several peer methods. The results demonstrate that our method is superior to other methods in terms of sensitivity, precision, and F1-score and can detect many CNVs that other methods have not discovered. MFCNV is expected to be a complementary tool in the analysis of mutations in tumor genomes and can be extended to be applied to the analysis of single-cell sequencing data.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Simulation Studiesmentioning

confidence: 99%

Section: Simulation Studiesmentioning

confidence: 99%

See 1 more Smart Citation

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

Zhao

Huang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…Simulation study is a reasonable way to evaluate the performance of computational algorithms in CNV detection (Yuan et al, 2017). Here, we used one of the popular simulators, ART (Huang et al, 2012), to generate synthetic datasets by considering tumor purity ranging from 0.2 to 0.8.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…A lot of computational approaches have been developed for CNV detection by using sequencing data (Yu et al, 2014;Zhang et al, 2015;Chen et al, 2017;Yuan et al, 2018b). However, contamination of normal cells in tumor tissues makes the observed magnitudes of signals in CNV regions diminished, which will lead to a decreased power in the detection of genomic mutations if the fraction is unknown (Yuan et al, 2012(Yuan et al, , 2017(Yuan et al, , 2018a. In addition, the contamination of normal cells in tumor tissues can also lead to adverse effects on subsequent genomic analysis, and further poses effects on patient's condition analysis in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Zhao

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

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.

show abstract

SM-RCNV: a statistical method to detect recurrent copy number variations in sequenced samples

Yuan

Zhang

et al. 2019

Genes Genom

View full text Add to dashboard Cite

IntSIM: An Integrated Simulator of Next-Generation Sequencing Data

Abstract: Simulation with IntSIM can be expected to evaluate performance of methods in detecting various types of variants, analyzing tumor samples, and especially providing a realistic assessment of effect of tumor purity on identification of somatic mutations.

Cited by 47 publications

References 61 publications

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data

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

SM-RCNV: a statistical method to detect recurrent copy number variations in sequenced samples

Contact Info

Product

Resources

About