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Singlecell variant calling identifies highconfidence sSNVs To assess

Identification of SHH sSNVs in patients with holoprosencephaly A

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of validated truefalse sSNVs detected using the five tools in a

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Clustering Analysis of Clonal and Subclonal SSNVs between Matched BM

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SSNVs in neurotypical control and AD neurons normalized by evenness of

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Concordance of SSNVs Found in Matched BM Clonal PCs and CTCs A Rate

Clonal sSNVs identified in frontal cortex BA9 distribute across

SSNVs in HNSCC tumors A Top panel distribution of SSNVs in each

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Somatic mutation burden in aging cardiomyocytes A sSNVs increase in

Distribution of SSNVs detected by each pipeline and extrapolated after

Identification of SHH sSNVs in patients with holoprosencephaly A

Potential mechanism of formation of sSNVs and their impact on

Distribution of SSNVs detected by each pipeline and extrapolated after

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Singlecell variant calling identifies highconfidence sSNVs To assess

The identified sSNVs affect the translation of the SHH protein A

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Overview of LiRA a Methodology for identifying false positive sSNVs

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Results from application of deTiN to VarScan and Strelka SSNVs ac

Clonal sSNVs identified in frontal cortex BA9 distribute across

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Distribution of sSNVs in relation to gene position comparing AD and

Heat map and bar plot illustrating recovery of SSNVs in the CLL

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Flowchart of GenoClone VarScan2 is used to detect the somatic

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Flowchart of GenoClone VarScan2 is used to detect the somatic

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sSNV and nsSNV frequencies from the polymerase and surface coding

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Burden analysis a Count of somatic singlenucleotide variants sSNVs

SCAN2 mutation signaturebased calling approach for somatic SNVs and

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SCANSNV workflow GATK HaplotypeCaller determines sites with

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sSNV and nsSNV frequencies from the polymerase and surface coding

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sSNV effect measured by region constraint Coding constrained regions

Neuronal somatic mutations are increased in multiple sclerosis lesions

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sSNV and nsSNV frequencies from the polymerase and surface coding

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a Genotype predictions by Conbase in 11 sSNV sites Primers spanning

Tools for detecting somatic single nucleotide variants sSNVs from

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Somatic mutations in single human cardiomyocytes reveal ageassociated

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Flow chart of the eSNVDetect a method to identify expressed SNVs

Counts of mutations detected by four sSNV callers in different patients

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Contrasting somatic mutation patterns in aging human neurons and

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SSNV density in single human cardiomyocytes detected by singlecell

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Shared SNPs and SNVs across datasets A Illustration differentiating

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Analytical workflow for identification of confident SNVs calls

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Overview of analysis pipeline and example results A shows the SNV

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