Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers

Investigation into the genotyping performance of a unique molecular identifier based microhaplotypes MPS panel in complex DNA mixture

In forensic science, genotyping mixed DNA is a critical and complex task. Sequencing errors and allele sharing complicate the analysis, particularly in cases involving unbalanced mixtures, multiple contributors, and kinship relationships. Massively parallel sequencing (MPS) panels comprising highly polymorphic microhaplotypes (MHs) offer a promising approach for detecting unique alleles in mixtures with a mixture ratio greater than 10:1, involving more than two contributors or contributors with kinship. However, sequencing errors such as base substitution and InDels on the MPS platform remain a significant challenge in genotyping complex mixed DNA. The barcoding approach has been introduced to MPS to distinguish true alleles from sequencing errors. This method employs unique molecular identifiers (UMIs) to tag individual DNA molecules, allowing for the identification and correction of random sequencing errors. By generating consensus sequences from read replicates associated with the same UMI, this approach enhances the accuracy of allele detection. In this study, UMIs were incorporated into developing a highly polymorphic panel consisting of 105 MHs, with an average effective number of alleles (Ae) of 6.9. Various types of mixed DNA samples were prepared, including unbalanced mixtures with ratios ranging from 1:1-160:1, multi-contributor mixtures with 2-6 contributors, and kinship-involved mixtures with parent-offspring to fourth-degree relatives contributors. Unique alleles were quantified, and mixture proportions (Mx) were calculated separately using sequencing reads and the number of UMI families with more than 10 members. The results demonstrated that UMI played a critical role in identifying sequencing errors and enhancing the accuracy of allele genotyping in unbalanced mixtures. A strong correlation (R² = 0.96) between UMI count and DNA template amount demonstrated that DNA template amount could be inferred from UMI count. Mx values derived from the number of UMIs were consistent across loci and showed a high correlation with mixture ratios (R2 = 0.85). Additionally, the panel efficiently detected unique alleles across all three types of complex DNA mixtures. Overall, this study underscores the importance of UMIs in mitigating PCR and sequencing biases, thereby improving the performance of the MH-MPS panel for genotyping complex DNA mixtures. UMIs represent a valuable tool for mixed DNA genotyping and hold potential for boarder applications in probabilistic genotyping.

Somatic DNA Variants in Epilepsy Surgery Brain Samples from Patients with Lesional Epilepsy

Epilepsy affects 50 million people worldwide and is drug-resistant in approximately one-third of cases. Even when a structural lesion is identified as the epileptogenic focus, understanding the underlying genetic causes is crucial to guide both counseling and treatment decisions. Both somatic and germline DNA variants may contribute to the lesion itself and/or influence the severity of symptoms. We therefore used whole exome sequencing (WES) to search for potentially pathogenic somatic DNA variants in brain samples from children with lesional epilepsy who underwent epilepsy surgery. WES was performed on 20 paired DNA samples extracted from both lesional brain tissue and reference tissue from the same patient, such as leukocytes or fibroblasts. The paired WES data were jointly analyzed using GATK Mutect2 to identify somatic single nucleotide variants (SNVs) or insertions/deletions (InDels), which were subsequently evaluated in silico for their disease-causing potential using MutationTaster2021. We identified known pathogenic somatic variants in five patients (25%) with variant allele frequencies (VAF) ranging from 3-35% in the genes MTOR, TSC2, PIK3CA, FGFR1, and PIK3R1 as potential causes of cortical malformations or central nervous system (CNS) tumors. Depending on the VAF, we used different methods such as Sanger sequencing, allele-specific qPCR, or targeted ultra-deep sequencing (amplicon sequencing) to confirm the variant. In contrast to the usually straightforward confirmation of germline variants, the validation of somatic variants is more challenging because current methods have limitations in sensitivity, specificity, and cost-effectiveness. In our study, WES identified additional somatic variant candidates in additional genes with VAFs ranging from 0.7-7.0% that could not be validated by an orthogonal method. This highlights the importance of variant validation, especially for those with very low allele frequencies.

Enhancing testing efficacy of high-density SNP microarrays to distinguish pedigrees belonging to the same kinship class

Kinship testing, which involves genotyping genetic markers and comparing their profiles between individuals, holds significant applications in forensic science. However, the prevalent use of independent markers often lacks the discriminatory power to distinguish pedigrees belong to the same kinship class. While numerous studies have attempted to address this challenge through diverse approaches, the testing efficacy of high-density SNP microarrays in combination with the likelihood approach remains unclear. In this study, we further explored the utilization of linked autosomal SNPs derived from microarrays with the likelihood approach. Several SNP panels with differing numbers of loci were developed and putative pedigrees were constructed to evaluated to test their efficacy in distinguishing second-degree relationships, including grandparent-grandchild, half-siblings, and avuncular. Our findings indicate that the use of high-density SNP microarrays is theoretically feasible for discriminating second-degree relationships, with balanced classification rates ranging from 0.444 to 0.853. Moreover, to optimize the practical effectiveness of discriminating pedigrees belonging to the same kinship class, several other aspects such as adding additional SNPs or an additional relative and examining the effects of genotype errors and population selection were discussed. Our results revealed that the employment of denser marker sets with more accurate genotyping methods may be beneficial. Additionally, the inclusion of additional relatives and the selection of an appropriate reference population also appear to be crucial factors for enhancing the accuracy of kinship testing. In conclusion, our study provides insights into the potential of high-density SNPs in kinship testing and highlights the need for further optimization and examination into various factors that may contribute to enhancing testing efficacy.

Principles of digital sequencing using unique molecular identifiers

Massively parallel sequencing technologies have long been used in both basic research and clinical routine. The recent introduction of digital sequencing has made previously challenging applications possible by significantly improving sensitivity and specificity to now allow detection of rare sequence variants, even at single molecule level. Digital sequencing utilizes unique molecular identifiers (UMIs) to minimize sequencing-induced errors and quantification biases. Here, we discuss the principles of UMIs and how they are used in digital sequencing. We outline the properties of different UMI types and the consequences of various UMI approaches in relation to experimental protocols and bioinformatics. Finally, we describe how digital sequencing can be applied in specific research fields, focusing on cancer management where it can be used in screening of asymptomatic individuals, diagnosis, treatment prediction, prognostication, monitoring treatment efficacy and early detection of treatment resistance as well as relapse.

Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules

Unique molecular identifiers are random oligonucleotide sequences that remove PCR amplification biases. However, the impact that PCR associated sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We show that PCR errors are a source of inaccuracy in both bulk and single-cell sequencing data, and synthesizing unique molecular identifiers using homotrimeric nucleotide blocks provides an error-correcting solution that allows absolute counting of sequenced molecules.

Deep mutational scanning of proteins in mammalian cells

Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.