Implementierung von Unternehmensleitbildern

Kretzschmar, Kirsten

doi:10.1007/978-3-658-32662-3

Cited by 3 publications

(3 citation statements)

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“…3 ). To assess the consistency between SCReadCounts and the variant callers across all cells, we split the alignments based on cell barcodes [ 33 ] and performed variant call on all the individual cell alignments using GATK in samples SRR10156295, SRR10156296, SRR10156299 and SRR10156300. GATK called all the SNVs for which 2 or more reads bearing the variant nucleotide were tabulated by scReadCounts (relaxed filtering, See 1 ) in the corresponding cells (See Fig.…”

Section: Resultsmentioning

confidence: 99%

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

et al. 2021

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Background Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. Results To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available (https://github.com/HorvathLab/NGS) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. Conclusions SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

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Section: Resultsmentioning

confidence: 99%

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

et al. 2021

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“…STARsolo integrates read mapping, read-to-gene assignment, cell barcode demultiplexing and unique molecular identifier (UMI) collapsing [12]. To generate individual cell alignments we adopted a publicly available python script which splits the pooled scRNA-seq alignments based on cellular barcode [21].…”

Section: Data Processingmentioning

confidence: 99%

“…Next, using the cellular barcodes and an in-house optimized script [21], we split the pooled scRNAseq alignments into individual cell alignments each containing reads from a single cell. On each of these alignments we then applied GATK and Strelka2 in parallel, filtered the variant lists to retain only highquality calls (Methods), and generated the intersection between GATK and Strelka2 to be used for our downstream analyses.…”

Section: Analytical Pipelinementioning

confidence: 99%

Improved SNV discovery in barcode-stratified scRNA-seq alignments

Prashant

Liu

Dillard

et al. 2021

Preprint

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Single cell SNV analysis is an emerging and promising strategy to connect cell-level genetic variation to cell phenotypes. At the present, SNV detection from 10x Genomics scRNA-seq data is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gain of information of SNV assessments from individual cell scRNA-seq data, where the alignments are split by barcode prior to the variant call. For our analyses we use publicly available sequencing data on the human breast cancer cell line MCF7 cell line generated at consequent time-points during anti-cancer treatment. We analyzed SNV calls by three popular variant callers – GATK, Strelka2 and Mu-tect2, in combination with a method for cell-level tabulation of the sequencing read counts bearing SNV alleles – SCReadCounts. Our analysis shows that variant calls on individual cell alignments identify at least two-fold higher number of SNVs as compared to the pooled scRNA-seq. We demonstrate that scSNVs exclusively called in the single cell alignments (scSNVs) are substantially enriched in novel genetic variants and in coding functional annotations, in particular, stop-codon and missense substitutions. Furthermore, we find that the expression of some scSNVs correlates with the expression of their harbouring gene (cis-scReQTLs).Overall, our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes on the need of cell-level variant detection approaches and tools. Given the growing accumulation of scRNA-seq datasets, cell-level variant assessments are likely to significantly contribute to the understanding of the cellular heterogeneity and the relationship between genetics variants and functional phenotypes. In addition, cell-level variant assessments from scRNA-seq can be highly informative in cancer where they can help elucidate somatic mutations evolution and functionality.

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Strategie

Schmid

2022

Electronic Governance

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Implementierung von Unternehmensleitbildern

Cited by 3 publications

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SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

Improved SNV discovery in barcode-stratified scRNA-seq alignments

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