scholarly journals ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

2020 ◽  
Author(s):  
David Lähnemann ◽  
Johannes Köster ◽  
Ute Fischer ◽  
Arndt Borkhardt ◽  
Alice C. McHardy ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Single Cells ◽  
Variant Calling ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Biologically Relevant ◽  
Amplification Bias ◽  
Missing Genotypes

ABSTRACTObtaining accurate mutational profiles from single cell DNA is essential for the analysis of genomic cell-to-cell heterogeneity at the finest level of resolution. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. As a result, single cell DNA sequencing data violates the assumptions of variant callers developed for bulk sequencing, which when applied to single cells generate significant numbers of false positives and false negatives. Only dedicated models accounting for amplification bias and errors will be able to provide more accurate calls.We present ProSolo, a probabilistic model for calling single nucleotide variants from multiple displacement amplified single cell DNA sequencing data. It introduces a mechanistically motivated empirical model of amplification bias that improves the quantification of genotyping uncertainty. To account for amplification errors, it jointly models the single cell sample with a bulk sequencing sample from the same cell population—also enabling a biologically relevant imputation of missing genotypes for the single cell. Through these innovations, ProSolo achieves substantially higher performance in calling and genotyping single nucleotide variants in single cells in comparison to all state-of-the-art tools. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly; not only for single nucleotide variant calls, but also for artefacts of single cell methodology that one may wish to identify, such as allele dropout.ProSolo’s model is implemented into a flexible framework, encouraging extensions. The source code and usage instructions are available at: https://github.com/prosolo/prosolo

scholarly journals Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David Lähnemann ◽  
Johannes Köster ◽  
Ute Fischer ◽  
Arndt Borkhardt ◽  
Alice C. McHardy ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Single Cells ◽  
Variant Calling ◽  
Sequencing Data ◽  
Computationally Efficient ◽  
Single Nucleotide Variants ◽  
Efficient Manner ◽  
Single Nucleotide ◽  
Amplification Bias

AbstractAccurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrates all relevant MDA biases in a site-specific and scalable—because computationally efficient—manner. This achieves a higher accuracy in calling and genotyping single nucleotide variants in single cells in comparison to state-of-the-art tools and supports imputation of insufficiently covered genotypes, when downstream tools cannot handle missing data. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly. ProSolo is implemented in an extendable framework, with code and usage at: https://github.com/prosolo/prosolo

scholarly journals MQuad enables clonal substructure discovery using single cell mitochondrial variants

2021 ◽  
Author(s):  
Aaron Wing Cheung Kwok ◽  
Chen Qiao ◽  
Rongting Huang ◽  
Mai-Har Sham ◽  
Joshua W. K. Ho ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Copy Number Variations ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Single Cell Sequencing ◽  
Mtdna Variants ◽  
Python Package

AbstractMitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via https://github.com/single-cell-genetics/MQuad.

scholarly journals SNV identification from single-cell RNA sequencing data

2019 ◽  
Vol 28 (21) ◽  
pp. 3569-3583 ◽  
Author(s):  
Patricia M Schnepp ◽  
Mengjie Chen ◽  
Evan T Keller ◽  
Xiang Zhou
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Specific Gene ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Cell Rna Sequencing ◽  
Sequencing Studies ◽  
Genomic Regions

Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

scholarly journals Linked-read analysis identifies mutations in single cell DNA sequencing data

2017 ◽  
Author(s):  
Craig L. Bohrson ◽  
Allison R. Barton ◽  
Michael A. Lodato ◽  
Rachel E. Rodin ◽  
Vinay Viswanadham ◽  
...  
Keyword(s):  
Single Cell ◽  
Dna Isolation ◽  
Single Cells ◽  
Mutation Rates ◽  
Accurate Estimation ◽  
Single Cell Level ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Cell Level ◽  
Single Nucleotide

AbstractWhole-genome sequencing of DNA from single cells has the potential to reshape our understanding of the mutational heterogeneity in normal and disease tissues. A major difficulty, however, is distinguishing artifactual mutations that arise from DNA isolation and amplification from true mutations. Here, we describe linked-read analysis (LiRA), a method that utilizes phasing of somatic single nucleotide variants with nearby germline variants to identify true mutations, thereby allowing accurate estimation of somatic mutation rates at the single cell level.

scholarly journals SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing

2021 ◽  
Author(s):  
Hana Rozhoñová ◽  
Daniel Danciu ◽  
Stefan Stark ◽  
Gunnar Rätsch ◽  
Andr&eacute Kahles ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Variant Calling ◽  
Bayesian Filtering ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Sequencing Technologies ◽  
The Cost ◽  
Low Coverage ◽  
Clonal Composition

Recently developed single-cell DNA sequencing technologies enable whole-genome, amplifi-cation-free sequencing of thousands of cells at the cost of ultra-low coverage of the sequenced data(<0.05x per cell), which mostly limits their usage to the identification of copy number alterations(CNAs) in multi-megabase segments. Aside from CNA-based subclone detection, single-nucleotide vari-ant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumorheterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible whensuperimposing the sequenced genomes of hundreds of genetically similar cells. Here we present SingleCell Data Tumor Clusterer (SECEDO, lat. 'to separate'), a new method to cluster tumor cells basedsolely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. The core aspects ofthe method are an efficient Bayesian filtering of relevant loci and the exploitation of read overlapsand phasing information. We applied SECEDO to a synthetic dataset simulating 7,250 cells and eighttumor subclones from a single patient, and were able to accurately reconstruct the clonal composition,detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the totalpopulation. When applied to four real single-cell sequencing datasets from a breast cancer patient,SECEDO was able to recover the major clonal composition in each dataset at the original sequencingdepth of 0.03x per cell, an 8-fold improvement relative to the state of the art. Variant calling on theresulting clusters recovered more than twice as many SNVs with double the allelic ratio compared tocalling on all cells together, demonstrating the utility of SECEDO. SECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo.

scholarly journals Accurate genotyping of single cells with Octopus

2021 ◽  
Author(s):  
Daniel Cooke ◽  
Gerton Lunter ◽  
David Wedge
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Bayesian Model ◽  
Single Cells ◽  
Variant Calling ◽  
Sequencing Error ◽  
Sequencing Data

Abstract We describe an extension to our variant calling tool, Octopus (https://github.com/luntergroup/octopus), for single-cell DNA sequencing data. Octopus jointly genotypes cells from a lineage, accounting for amplification stochasticity and sequencing error with a haplotype-based Bayesian model. Octopus is considerably more accurate at genotyping single cells than existing methods.

scholarly journals Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Andrew Currin ◽  
Neil Swainston ◽  
Mark S Dunstan ◽  
Adrian J Jervis ◽  
Paul Mulherin ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Dna Sequencing ◽  
Cost Effective ◽  
Polymorphism Analysis ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Synthetic Dna ◽  
Design Build ◽  
Hardware Costs

Abstract Synthetic biology utilizes the Design–Build–Test–Learn pipeline for the engineering of biological systems. Typically, this requires the construction of specifically designed, large and complex DNA assemblies. The availability of cheap DNA synthesis and automation enables high-throughput assembly approaches, which generates a heavy demand for DNA sequencing to verify correctly assembled constructs. Next-generation sequencing is ideally positioned to perform this task, however with expensive hardware costs and bespoke data analysis requirements few laboratories utilize this technology in-house. Here a workflow for highly multiplexed sequencing is presented, capable of fast and accurate sequence verification of DNA assemblies using nanopore technology. A novel sample barcoding system using polymerase chain reaction is introduced, and sequencing data are analyzed through a bespoke analysis algorithm. Crucially, this algorithm overcomes the problem of high-error rate nanopore data (which typically prevents identification of single nucleotide variants) through statistical analysis of strand bias, permitting accurate sequence analysis with single-base resolution. As an example, 576 constructs (6 × 96 well plates) were processed in a single workflow in 72 h (from Escherichia coli colonies to analyzed data). Given our procedure’s low hardware costs and highly multiplexed capability, this provides cost-effective access to powerful DNA sequencing for any laboratory, with applications beyond synthetic biology including directed evolution, single nucleotide polymorphism analysis and gene synthesis.

scholarly journals SCELLECTOR: ranking amplification bias in single cells using shallow sequencing

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Vivekananda Sarangi ◽  
Alexandre Jourdon ◽  
Taejeong Bae ◽  
Arijit Panda ◽  
Flora Vaccarino ◽  
...  
Keyword(s):  
Single Cell ◽  
Multiple Displacement Amplification ◽  
Single Cells ◽  
Sequencing Data ◽  
High Coverage ◽  
Amplification Bias ◽  
Single Cell Profiling ◽  
High Concordance ◽  
Human Neuronal Cells

Abstract Background The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA. However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. Results We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. Conclusion Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.

scholarly journals Estimating the Allele-Specific Expression of SNVs From 10× Genomics Single-Cell RNA-Sequencing Data

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 240 ◽  
Author(s):  
Prashant N. M. ◽  
Hongyu Liu ◽  
Pavlos Bousounis ◽  
Liam Spurr ◽  
Nawaf Alomran ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Sequencing Data ◽  
Specific Expression ◽  
Single Nucleotide ◽  
Healthy Donors ◽  
Allele Expression ◽  
Single Cell Rna Sequencing ◽  
Allele Specific

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.

scholarly journals SomatoSim: precision simulation of somatic single nucleotide variants

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Marwan A. Hawari ◽  
Celine S. Hong ◽  
Leslie G. Biesecker
Keyword(s):  
High Throughput Sequencing ◽  
Variant Calling ◽  
Simulated Data ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Somatic Variant ◽  
Simulation Tools ◽  
Gold Standard Dataset ◽  
High Level

Abstract Background Somatic single nucleotide variants have gained increased attention because of their role in cancer development and the widespread use of high-throughput sequencing techniques. The necessity to accurately identify these variants in sequencing data has led to a proliferation of somatic variant calling tools. Additionally, the use of simulated data to assess the performance of these tools has become common practice, as there is no gold standard dataset for benchmarking performance. However, many existing somatic variant simulation tools are limited because they rely on generating entirely synthetic reads derived from a reference genome or because they do not allow for the precise customizability that would enable a more focused understanding of single nucleotide variant calling performance. Results SomatoSim is a tool that lets users simulate somatic single nucleotide variants in sequence alignment map (SAM/BAM) files with full control of the specific variant positions, number of variants, variant allele fractions, depth of coverage, read quality, and base quality, among other parameters. SomatoSim accomplishes this through a three-stage process: variant selection, where candidate positions are selected for simulation, variant simulation, where reads are selected and mutated, and variant evaluation, where SomatoSim summarizes the simulation results. Conclusions SomatoSim is a user-friendly tool that offers a high level of customizability for simulating somatic single nucleotide variants. SomatoSim is available at https://github.com/BieseckerLab/SomatoSim.

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