A short review of variants calling for single-cell-sequencing data with applications

Wei, Zhuo-Hui; Shu, Chang; Huang, Jingying; Cai, Hongming

doi:10.1016/j.biocel.2017.09.018

Cited by 5 publications

(3 citation statements)

References 86 publications

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“…The rates of identity, deletion, insertion, and mismatch are determined from the mapping results using japsa. The homopolymer and methylation of DNA are two common factors that may cause base deletion and base substitution (Wei et al, 2017. Taking into account their effects, mapping errors of the assembled genomes are further classified into six types in the context of the reference genome.…”

Section: Genome Assembly and Error Analysismentioning

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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Section: Genome Assembly and Error Analysismentioning

confidence: 99%

Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network

et al. 2020

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“…False positive error rate at about 10 −5 per base (Esteban et al, 1993). Multiple annealing and looping based amplification cycles (MALBAC) has high coverage with low allelic dropout rate; however, it has high error rates because of the polymerase used in the process (Wei et al, 2017). Degenerate oligonucleotide primer polymerase chain reaction (DOP-PCR) has low coverage across the genome and has high error rates due to the polymerases (Gawad et al, 2016).…”

Section: Problem Settingmentioning

confidence: 99%

Scuphr: A probabilistic framework for cell lineage tree reconstruction

Koptagel

Jun

Lagergren

2018

Preprint

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Reconstruction of cell lineage trees from single-cell DNA sequencing data, has the potential to become a fundamental tool in study of development of disease, in particular cancer. For cells without copy number alterations that has not been exposed to specific marking techniques, that is normal cells, lineage tracing is naturally based on somatic point mutations. Current single cell sequencing techniques applicable to such cells require an amplification step, which introduces errors, and still often suffer from so-called allelic dropout. We present a detailed model of current technologies for the purpose of estimating the distance between cells without copy number changes, based on single-cell DNA sequencing data. The model is well suited for full Bayesian analysis by introducing prior probabilities for key parameters as well as maximum a posteriori estimation using expectation maximization algorithm. Our model outputs distance between two cells, simultaneously taking all the other cells into account. In particular, the model contains variables associated with pairs of loci, of which one is homozygous and the other heterozygous, and has the capacity to perform Bayesian probabilistic read phasing. By applying a fast distance based method, such as FNJ, to the estimated distance, a cell lineage tree can be obtained. In contrast to MCMC based methods, FNJ can easily handle data sets with tens of thousands of taxa. The high accuracy of the so obtained method, called SCuPhr, is shown in studies of several synthetic data set.

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“…A real help to overcome the underestimation of mosaic variants will possibly arrive by the development of single-cell sequencing technologies that, actually, are under development as a liquid biopsy tool to investigate cancer patients noninvasively. In particular, limits, such as elevated costs and obtaining enough genetic material for library preparation, make these methods still far from the adoption by the health care system, but, in the next future, they could represent a promising tool for somatic variants analysis (Wei et al, 2017; Zhu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Improved Molecular Diagnosis of McCune–Albright Syndrome and Bone Fibrous Dysplasia by Digital PCR

et al. 2019

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McCune–Albright syndrome (MAS) is a rare congenital disorder characterized by the association of endocrine and nonendocrine anomalies caused by somatic activating variants of GNAS. The mosaic state of variants makes the clinical presentation extremely heterogeneous depending on involved tissues. Biological samples bearing a low level of mosaicism frequently lead to false-negative results with an underestimation of causative molecular alterations, and the analysis of biopsies is often needed to obtain a molecular diagnosis. To date, no reliable analytical method for the noninvasive testing of blood is available. This study was aimed at validating a novel and highly sensitive technique, the digital PCR (dPCR), to increase the detection rate of GNAS alterations in patients with a clinical suspicion of MAS and, in particular, in blood. We screened different tissues (blood, bone, cutis, ovary, and ovarian cyst) collected from 54 MAS patients by different technical approaches. Considering blood, Sanger was unable to detect mutations, the allele-specific PCR and the co-amplification at lower denaturation temperature had a 9.1% and 18.1% detection rate, respectively, whereas the dPCR reached a 37.8% detection rate. In conclusion, the dPCR resulted in a cost-effective, reliable, and rapid method allowing the selective amplification of low-frequency variants and able to improve GNAS mutant allele detection, especially in the blood.

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A short review of variants calling for single-cell-sequencing data with applications

Cited by 5 publications

References 86 publications

Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network

Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network

Scuphr: A probabilistic framework for cell lineage tree reconstruction

Improved Molecular Diagnosis of McCune–Albright Syndrome and Bone Fibrous Dysplasia by Digital PCR

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