GeTallele: A Method for Analysis of DNA and RNA Allele Frequency Distributions

PN, Gimenes N, Reis MX, Matsas S, Ferreira CG. Next-generation sequencing impact on cancer care: applications, challenges, and future directions

Fundamentally precision oncology illustrates the path in which molecular profiling of tumors can illuminate their biological behavior, diversity, and likely outcomes by identifying distinct genetic mutations, protein levels, and other biomarkers that underpin cancer progression. Next-generation sequencing became an indispensable diagnostic tool for diagnosis and treatment guidance in current clinical practice. Nowadays, tissue analysis benefits from further support through methods like comprehensive genomic profiling and liquid biopsies. However, precision medicine in the field of oncology presents specific hurdles, such as the cost-benefit balance and widespread accessibility, particularly in countries with low- and middle-income. A key issue is how to effectively extend next-generation sequencing to all cancer patients, thus empowering treatment decision-making. Concerns also extend to the quality and preservation of tissue samples, as well as the evaluation of health technologies. Moreover, as technology advances, novel next-generation sequencing assessments are being developed, including the study of Fragmentomics. Therefore, our objective was to delineate the primary uses of next-generation sequencing, discussing its' applications, limitations, and prospective paths forward in Oncology.

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

BACKGROUND

Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAFRNA) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell.

RESULTS

Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci.

CONCLUSION

ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset.

AVAILABILITY

https://github.com/HorvathLab/NGS/tree/master/scReQTL.