xAtlas: scalable small variant calling across heterogeneous next-generation sequencing experiments

Single cell RNA-seq: a novel tool to unravel virus-host interplay

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

HMZDupFinder: a robust computational approach for detecting intragenic homozygous duplications from exome sequencing data

Homozygous duplications contribute to genetic disease by altering gene dosage or disrupting gene regulation and can be more deleterious to organismal biology than heterozygous duplications. Intragenic exonic duplications can result in loss-of-function (LoF) or gain-of-function (GoF) alleles that when homozygosed, i.e. brought to homozygous state at a locus by identity by descent or state, could potentially result in autosomal recessive (AR) rare disease traits. However, the detection and functional interpretation of homozygous duplications from exome sequencing data remains a challenge. We developed a framework algorithm, HMZDupFinder, that is designed to detect exonic homozygous duplications from exome sequencing (ES) data. The HMZDupFinder algorithm can efficiently process large datasets and accurately identifies small intragenic duplications, including those associated with rare disease traits. HMZDupFinder called 965 homozygous duplications with three or less exons from 8,707 ES with a recall rate of 70.9% and a precision of 16.1%. We experimentally confirmed 8/10 rare homozygous duplications. Pathogenicity assessment of these copy number variant alleles allowed clinical genomics contextualization for three homozygous duplications alleles, including two affecting known OMIM disease genes EDAR (MIM# 224900), TNNT1(MIM# 605355), and one variant in a novel candidate disease gene: PAAF1.

Combined Bioinformatic and Splicing Analysis of Likely Benign Intronic and Synonymous Variants Reveals Evidence for Pathogenicity

Background

Current clinical variant analysis pipelines focus on coding variants and intronic variants within 10-20 bases of an exon-intron boundary that may affect splicing. The impact of newer splicing prediction algorithms combined with in vitro splicing assays on rare variants currently considered Benign/Likely Benign (B/LB) is unknown.

Methods

Exome sequencing data from 576 pediatric cancer patients enrolled in the Texas KidsCanSeq study were filtered for intronic or synonymous variants absent from population databases, predicted to alter splicing via SpliceAI (>0.20), and scored as potentially deleterious by CADD (>10.0). Total cellular RNA was extracted from monocytes and RT-PCR products analyzed. Subsequently, rare synonymous or intronic B/LB variants in a subset of genes submitted to ClinVar were similarly evaluated. Variants predicted to lead to a frameshifted splicing product were functionally assessed using an in vitro splicing reporter assay in HEK-293T cells.

Results

KidsCanSeq exome data analysis revealed a rare, heterozygous, intronic variant (NM_177438.3(DICER1):c.574-26A>G) predicted by SpliceAI to result in gain of a secondary splice acceptor site. The proband had a personal and family history of pleuropulmonary blastoma consistent with DICER1 syndrome but negative clinical sequencing reports. Proband RNA analysis revealed alternative DICER1 transcripts including the SpliceAI-predicted transcript.Similar bioinformatic analysis of synonymous or intronic B/LB variants (n=31,715) in ClinVar from 61 Mendelian disease genes yielded 18 variants, none of which could be scored by MaxEntScan. Eight of these variants were assessed (DICER1 n=4, CDH1 n=2, PALB2 n=2) using in vitro splice reporter assay and demonstrated abnormal splice products (mean 66%; range 6% to 100%). Available phenotypic information from submitting laboratories demonstrated DICER1 phenotypes in 2 families (1 variant) and breast cancer phenotypes for PALB2 in 3 families (2 variants).

Conclusions

Our results demonstrate the power of newer predictive splicing algorithms to highlight rare variants previously considered B/LB in patients with features of hereditary conditions. Incorporation of SpliceAI annotation of existing variant data combined with either direct RNA analysis or in vitro assays has the potential to identify disease-associated variants in patients without a molecular diagnosis.

Whole genome sequencing for USH2A-associated disease reveals several pathogenic deep-intronic variants that are amenable to splice correction

A significant number of individuals with a rare disorder such as Usher syndrome (USH) and (non-)syndromic autosomal recessive retinitis pigmentosa (arRP) remain genetically unexplained. Therefore, we assessed subjects suspected of USH2A-associated disease and no or mono-allelic USH2A variants using whole genome sequencing (WGS) followed by an improved pipeline for variant interpretation to provide a conclusive diagnosis. One hundred subjects were screened using WGS to identify causative variants in USH2A or other USH/arRP-associated genes. In addition to the existing variant interpretation pipeline, a particular focus was put on assessing splice-affecting properties of variants, both in silico and in vitro. Also structural variants were extensively addressed. For variants resulting in pseudoexon inclusion, we designed and evaluated antisense oligonucleotides (AONs) using minigene splice assays and patient-derived photoreceptor precursor cells. Biallelic variants were identified in 49 of 100 subjects, including novel splice-affecting variants and structural variants, in USH2A or arRP/USH-associated genes. Thirteen variants were shown to affect USH2A pre-mRNA splicing, including four deep-intronic USH2A variants resulting in pseudoexon inclusion, which could be corrected upon AON treatment. We have shown that WGS, combined with a thorough variant interpretation pipeline focused on assessing pre-mRNA splicing defects and structural variants, is a powerful method to provide subjects with a rare genetic condition, a (likely) conclusive genetic diagnosis. This is essential for the development of future personalized treatments and for patients to be eligible for such treatments.

xAtlas: scalable small variant calling across heterogeneous next-generation sequencing experiments

BACKGROUND

The growing volume and heterogeneity of next-generation sequencing (NGS) data complicate the further optimization of identifying DNA variation, especially considering that curated high-confidence variant call sets frequently used to validate these methods are generally developed from the analysis of comparatively small and homogeneous sample sets.

FINDINGS

We have developed xAtlas, a single-sample variant caller for single-nucleotide variants (SNVs) and small insertions and deletions (indels) in NGS data. xAtlas features rapid runtimes, support for CRAM and gVCF file formats, and retraining capabilities. xAtlas reports SNVs with 99.11% recall and 98.43% precision across a reference HG002 sample at 60× whole-genome coverage in less than 2 CPU hours. Applying xAtlas to 3,202 samples at 30× whole-genome coverage from the 1000 Genomes Project achieves an average runtime of 1.7 hours per sample and a clear separation of the individual populations in principal component analysis across called SNVs.

CONCLUSIONS

xAtlas is a fast, lightweight, and accurate SNV and small indel calling method. Source code for xAtlas is available under a BSD 3-clause license at https://github.com/jfarek/xatlas.