Variant Calling Research Articles

Detection of circulating tumor DNA by tumor-informed whole-genome sequencing enables prediction of recurrence in stage III colorectal cancer patients

IntroductionCirculating tumor (ctDNA) can be used to detect residual disease after cancer treatment. Detecting low-level ctDNA is challenging, due to the limited number of recoverable ctDNA fragments at any target loci. In response, we applied tumor-informed whole-genome sequencing (WGS), leveraging thousands of mutations for ctDNA detection. MethodsPerformance was evaluated in serial plasma samples (n = 1283) from 144 stage III colorectal cancer patients. Tumor/normal WGS was used to establish a patient-specific mutational fingerprint, which was searched for in 20x WGS plasma profiles. For reproducibility, paired aliquots of 172 plasma samples were analyzed in two independent laboratories. De novo variant calling was performed for serial plasma samples with a ctDNA level > 10 % (n = 17) to explore genomic evolution. ResultsWGS-based ctDNA detection was prognostic of recurrence: post-operation (Hazard ratio [HR] 6.75, 95 %CI 3.18–14.3, p < 0.001), post-adjuvant chemotherapy (HR 28.9, 95 %CI 10.1–82.8; p < 0.001), and during surveillance (HR 22.8, 95 %CI 13.7–37.9, p < 0.0001). The 3-year cumulative incidence of ctDNA detection in recurrence patients was 95 %. ctDNA was detected a median of 8.7 months before radiological recurrence. The independently analyzed plasma aliquots showed excellent agreement (Cohens Kappa=0.9, r = 0.99). Genomic characterization of serial plasma revealed significant evolution in mutations and copy number alterations, and the timing of mutational processes, such as 5-fluorouracil-induced mutations. ConclusionOur study supports the use of WGS for sensitive ctDNA detection and demonstrates that post-treatment ctDNA detection is highly prognostic of recurrence. Furthermore, plasma WGS can identify genomic differences distinguishing the primary tumor and relapsing metastasis, and monitor treatment-induced genomic changes.

Benchmarking whole exome sequencing in the German network for personalized medicine

IntroductionWhole Exome Sequencing (WES) has emerged as an efficient tool in clinical cancer diagnostics to broaden the scope from panel-based diagnostics to screening of all genes and enabling robust determination of complex biomarkers in a single analysis. MethodsTo assess concordance, six formalin-fixed paraffin-embedded (FFPE) tissue specimens and four commercial reference standards were analyzed by WES as matched tumor-normal DNA at 21 NGS centers in Germany, each employing local wet-lab and bioinformatics. Somatic and germline variants, copy-number alterations (CNAs), and complex biomarkers were investigated. Somatic variant calling was performed in 494 diagnostically relevant cancer genes. The raw data were collected and re-analyzed with a central bioinformatic pipeline to separate wet- and dry-lab variability. ResultsThe mean positive percentage agreement (PPA) of somatic variant calling was 76 % while the positive predictive value (PPV) was 89 % in relation to a consensus list of variants found by at least five centers. Variant filtering was identified as the main cause for divergent variant calls. Adjusting filter criteria and re-analysis increased the PPA to 88 % for all and 97 % for the clinically relevant variants. CNA calls were concordant for 82 % of genomic regions. Homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) status were concordant for 94 %, 93 %, and 93 % of calls, respectively. Variability of CNAs and complex biomarkers did not decrease considerably after harmonization of the bioinformatic processing and was hence attributed mainly to wet-lab differences. ConclusionContinuous optimization of bioinformatic workflows and participating in round robin tests are recommended.

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline.

DNA sequencing is becoming more affordable and faster through advances in high-throughput technologies. This rise in data availability has contributed to the development of novel algorithms to elucidate previously obscure features and led to an increased reliance on complex workflows to integrate such tools into analyses pipelines. To facilitate the analysis of DNA sequencing data, we created metapipeline-DNA, a highly configurable and extensible pipeline. It encompasses a broad range of processing including raw sequencing read alignment and recalibration, variant calling, quality control and subclonal reconstruction. Metapipeline-DNA also contains configuration options to select and tune analyses while being robust to failures. This standardizes and simplifies the ability to analyze large DNA sequencing in both clinical and research settings. Metapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is freely available at https://github.com/uclahs-cds/metapipeline-DNA.

An ultra-dense linkage map identified quantitative trait loci corresponding to fruit quality- and size-related traits in red goji berry.

Goji berries are a small-fruited shrub with industrial importance whose fruit considered beneficial in both fresh and dried forms. Current germplasms of goji berries include small fruits with a short shelf life, less sweet and bitter taste, and a lack of appropriate genetic information. This study aimed to employ whole genome resequencing to generate an ultra-dense bin linkage map and to elucidate the genetic basis of goji fruit quality and size using quantitative trait loci (QTL) mapping analysis in a cross-pollinated hybrid population. To achieve this goal, human sensory tests were carried out to determine the bitter taste (BT) and sweet taste (ST), and to quantify the soluble solid content (SSC), fruit firmness (FF), and fruit size-related traits of fresh goji fruits over three or four years. The results revealed that the goji bin linkage map based on resequencing spanned a total length of 966.42 cM and an average bin interval of 0.03 cM. Subsequent variant calling and ordering resulted in 3,058 bins containing 35,331 polymorphic markers across 12 chromosomes. A total of 99 QTLs, with individual loci in different environments explaining a phenotypic variance of 1.21-16.95% were identified for the studied traits. Ten major effects, including colocalized QTLs corresponding to different traits, were identified on chromosomes 1, 3, 5, 6, 7, and 8, with a maximum Logarithm of Odds (LOD) of 29.25 and 16.95% of explained phenotypic variance (PVE). In addition, four stable loci, one for FF, one for fruit weight (FW), and two for fruit shape index (FSI), were mainly mapped on chromosomes 5, 6, and 7, elucidating 2.10-16.95% PVE. These findings offer valuable insights into the genetic architecture of goji fruit traits along with identified specific loci and markers to further improve and develop sweeter, less bitter and larger fruited goji berry cultivars with extended shelf life.

Benchmarking UMI-aware and standard variant callers for low frequency ctDNA variant detection

BackgroundCirculating tumour DNA (ctDNA) is a subset of cell free DNA (cfDNA) released by tumour cells into the bloodstream. Circulating tumour DNA has shown great potential as a biomarker to inform treatment in cancer patients. Collecting ctDNA is minimally invasive and reflects the entire genetic makeup of a patient’s cancer. ctDNA variants in NGS data can be difficult to distinguish from sequencing and PCR artefacts due to low abundance, particularly in the early stages of cancer. Unique Molecular Identifiers (UMIs) are short sequences ligated to the sequencing library before amplification. These sequences are useful for filtering out low frequency artefacts. The utility of ctDNA as a cancer biomarker depends on accurate detection of cancer variants.ResultsIn this study, we benchmarked six variant calling tools, including two UMI-aware callers for their ability to call ctDNA variants. The standard variant callers tested included Mutect2, bcftools, LoFreq and FreeBayes. The UMI-aware variant callers benchmarked were UMI-VarCal and UMIErrorCorrect. We used both datasets with known variants spiked in at low frequencies, and datasets containing ctDNA, and generated synthetic UMI sequences for these datasets. Variant callers displayed different preferences for sensitivity and specificity. Mutect2 showed high sensitivity, while returning more privately called variants than any other caller in data without synthetic UMIs – an indicator of false positive variant discovery. In data encoded with synthetic UMIs, UMI-VarCal detected fewer putative false positive variants than all other callers in synthetic datasets. Mutect2 showed a balance between high sensitivity and specificity in data encoded with synthetic UMIs.ConclusionsOur results indicate UMI-aware variant callers have potential to improve sensitivity and specificity in calling low frequency ctDNA variants over standard variant calling tools. There is a growing need for further development of UMI-aware variant calling tools if effective early detection methods for cancer using ctDNA samples are to be realised.

Exome Sequencing Starting from Single Cells.

Single-cell genomic analysis enables researchers to gain novel insights across diverse research areas, including developmental biology, tumor heterogeneity, and disease pathogenesis. Conducting single-cell genomic analysis using next-generation sequencing (NGS) methods has traditionally been challenging as the amount of genomic DNA present in a single cell is limited. Advancements in multiple displacement amplification (MDA) technologies allow the unbiased amplification of limited quantities of DNA under conditions that maintain its integrity. This method of amplification results in high yield and facilitates the generation of high-complexity NGS libraries that ensure the highest coverage to effectively allow variant calling. With the introduction of new sequencing platforms and chemistry, whole genome sequencing became a more cost-effective application, but enrichment of specific regions of interest further reduces the amount of required sequencing output and associated costs. There are two enrichment methods, polymerase chain reaction (PCR)-based and hybrid-capture-based methods. PCR-based methods are very flexible and highly effective but focus on specific loci, typically known to be associated with disease. Inherited diseases of unknown genetic origin require a more comprehensive approach to capture the genetic variation that is not yet associated with a specific disease. Hybrid capture enrichment methods require considerable amounts of DNA such that exome enrichment from single cells is only possible after preamplification of this limited material. This article describes the complete workflow from single cells and small quantities of DNA to exome-NGS libraries for Illumina sequencing instruments and includes the following protocols: © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Whole genome amplification from single cells or small amounts of gDNA Basic Protocol 2: NGS library generation of MDA-amplified material Basic Protocol 3: Exome enrichment.

O51 EXPLORING GENETIC BASIS OF RESISTANT HYPERTENSION IN EUROPEAN AND AFRICAN POPULATIONS

Background: Approximately 15% of hypertensive (HTN) patients experience resistant hypertension (RHTN) despite the use of ≥ 3 antihypertensive agents (diuretic and blockers of calcium channels and the renin-angiotensin system) at maximum tolerated doses. Intriguingly, genetic variation and alterations in gene content may contribute to RHTN developing. Aim: To comprehend the genetic determinants of RHTN across diverse ethnicities, this study has compared the gene content and presence of single nucleotide variants (SNVs) in RHTN versus controlled-HTN and normotensive participants within European (EU) and South African (SA) populations. Methods: Blood-isolated DNA was collected from 6 participants from EU and 15 from SA. Patients were categorized into 3 groups: RHTN (SA; n=5 and EU; n=6), controlled-HTN (n=5), and normotensive (n=5). DNA was used for Whole Exome Sequencing (WES) analysis. Library preparation and exome capture were done using the MGIEasy Universal DNA library prep and exome capture v5 probe set. Mapping was done using GATK, and the variant calling and annotation were performed using with the SIFT tool and R software. Results: PCA analysis and minimum spanning network showed significant genetic diversity between EU and SA populations. WES identified 16 common genes, with 207 and 220 genes associated to RHTN in EU and SA, respectively. These genes were involved in cardiac electrical conduction (K+/Ca++ channels) and lipid storage (HDL deficiency). In addition, SNV within pharmacogenes in SA (CYP2D6, CYP3A5, APOE) and EU (ADD1) cohorts alongside genes linked to disease susceptibility (RyR1 and PPP1CA) were also identified. RyR1 and PPP1CA are both implicated in HTN development through their roles in calcium signaling and vascular smooth muscle cell function. Noteworthy, two SNVs, rs121918006 and rs61732908, within RyR1 were previously linked to HTN. Conclusions: The large genetic differences observed between EU and SA subjects highlight the diverse genetic landscape surrounding RHTN. Notably, specific genes and their related SNV that associated to cardiac calcium channels and lipid storage could show potential targets for personalized therapeutic intervention. Scientific Session 15: Exploring Novel Therapeutic Approaches to Kidney Disease

Genetic Markers of Spina Bifida in an Indian Cohort.

To identify the genetic markers of spina bifida through a systematic survey of the exome in an Indian cohort. Three consecutive patients (P1: 1 year, male; P2: 2.8 years, male; and P3: 10 years, female) with spina bifida (lumbosacral meningomyelocele) underwent whole-exome sequencing (libraries: SureSelect Human All Exon V8; sequencing: 2 * 150 bp paired-end run, 100×) with NovaSeq 6000. Data analysis was performed using SMART-One™ (secondary analysis) and SMARTer™ (tertiary analysis) for automated quality check, alignment (GRCh38/hg38), variant calling, annotation (ClinVar, OMIM, avsnp150, 1000 Genomes v5b, ExAC v0.3, gnomAD v4.0, and esp6500vi2all v0.0.25), v0.0.25), interpretation. The pathogenic and likely pathogenic (ClinVar/ InterVar), non-synonymous, exonic markers (read depth ≥ 5) were matched with the Familial Neural Tube Defects (Version 1.10) panel (FNTD panel). Pathogenic variants overlapping with the FNTD panel were MTRR, CC2D2A, and ZIC2 in P1 and P2, TGIF1 in P1 only, and none in P3. Novel pathogenic/likely pathogenic variants common to all three patients were PRUNE1, PKD1, PDZD2, and DAB2 in the homozygous state as well as in the heterozygous state, PLK1 and NLGN2. The possible role of such markers in etiopathogenesis was explored through a literatur search. The genetic landscape of the spina bifida in an Indian cohort is diverse compared to that reported from other parts of the world. A comprehensive catalog of single-nucleotide variants in the etiopathogenesis of the spina bifida on a background of the Familial Neural Tube Defects Panel has been generated.

433. UNRAVELLING THE MOLECULAR MECHANISMS BEHIND TUMOUR DIFFERENTIATION IN ESOPHAGEAL ADENOCARCINOMA

Abstract Background Esophageal adenocarcinoma tumors are divided into three grades based on the tumour’s histological differentiation: well, moderate and poor. Poorly differentiated tumours have a worse survival rate than moderate and well tumours. Understanding the molecular programs of this differentiation may lead to the identification of novel therapeutic interventions specific to tumour differentiation. We have utilized laser-capture microdissection to enrich tumour cells followed by gene expression profiling (RNA-seq) to identify gene expression programs and whole genome sequencing for differentiating specific mutations and copy number changes. Collectively, these results will enable us to unravel the molecular drivers of tumour differentiation. Methods Laser capture microdissection was applied to N=127 RNA-seq samples from N=74 patients and N=103 from N=81 patients from a mix of primary tumour biopsies, resections, and metastatic biopsies. Most samples have a matching RNA-seq and WGS sample. We used a standard pipeline to analyze the WGS data and produce somatic mutation, structural variant, and copy number calls. The gene expression data was segregated into two sets a test set consisting of N=74 samples and a test set of N=53 samples. Non-negative matrix factorization was used to identify eleven gene expression programs. Results Our testing RNA-seq cohort consisted of N=74 samples from N=74 patients with N=4 G1, N=26 G2, N=35 G3, and N=9 missing differentiation data. Our initial goal was to unravel the gene expression programs that correlate with tumour differentiation. Our non-negative matrix factorization analysis yielded 11 gene signatures, N=3 programs enriched in glandular gene expression, N=3 enriched in EMT pathways, N=2 with fibroblasts, and N=3 associated with immune / inflammation genes (not shown) (Figure 1). Moreover, the glandular signatures were associated with G1/G2 and the EMT and fibroblast signatures with G3. Moreover, the glandular 2 signature was associated with HER2 amplifications. Conclusion In this work we have begun to unravel the gene expression and genomic changes associated with tumour differentiation. We have found signatures enriched for both G1/G2 and G3 tumours and from these signatures we have observed gene expression heterogeneity within the different tumour differentiation categories. Moreover, the G3 tumours are enriched in fibroblasts despite our laser-capture microdissection. We are currently working on a classification model to predict tumor differentiation from these gene expression programs and are looking to further integrate our whole genome data to find additional genomic drivers.

Comparison of Results from Two Commercially Available In-House Tissue-Based Comprehensive Genomic Profiling Solutions: Research Use Only AVENIO Tumor Tissue Comprehensive Genomic Profiling Kit and TruSight Oncology 500 Assay

Increased adoption of personalized medicine has brought comprehensive genomic profiling (CGP) to the forefront. However, differences in assay, bioinformatics, and reporting systems and lack of understanding of their complex interplay are a challenge for implementation and achieving uniformity in CGP testing. Two commercially available, tissue-based, in-house CGP assays were compared, in combination with a tertiary analysis solution in a research use only (RUO) context: the AVENIO Tumor Tissue CGP RUO Kit paired with navify Mutation Profiler (RUO) software and the TruSight Oncology 500 RUO assay paired with PierianDx Clinical Genomics Workspace software. Agreements and differences between the assays were assessed for short variants (SVs), copy number alterations (CNAs), rearrangements, tumor mutational burden (TMB), and microsatellite instability (MSI), including variant categorization and clinical trial-matching (CTM) recommendations. Results showed good overall agreement for SV, known gene fusion, and MSI detection. Important differences were obtained in TMB scoring, CNA detection, and CTM. Differences in variant and biomarker detection could be explained by bioinformatic approaches to variant calling, filtering, tiering, and normalization; differences in CTM, by underlying reported variants and conceptual differences in system parameters. Thus, distinctions between different approaches may lead to inconsistent results. Complexities in calling, filtering, and interpreting variants illustrate key considerations for implementation of any high-quality CGP in the laboratory and bringing uniformity to genomic insight results.

ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers

The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

De Novo Genome Assemblies From Two Indigenous Americans from Arizona Identify New Polymorphisms in Non-Reference Sequences.

There is a collective push to diversify human genetic studies by including underrepresented populations. However, analyzing DNA sequence reads involves the initial step of aligning the reads to the GRCh38/hg38 reference genome which is inadequate for non-European ancestries. In this study, using long-read sequencing technology, we constructed de novo genome assemblies from two indigenous Americans from Arizona (IAZ). Each assembly included ∼17 Mb of DNA sequence not present [nonreference sequence (NRS)] in hg38, which consists mostly of repeat elements. Forty NRSs totaling 240 kb were uniquely anchored to the hg38 primary assembly generating a modified hg38-NRS reference genome. DNA sequence alignment and variant calling were then conducted with whole-genome sequencing (WGS) sequencing data from 387 IAZ using both the hg38 and modified hg38-NRS reference maps. Variant calling with the hg38-NRS map identified ∼50,000 single-nucleotide variants present in at least 5% of the WGS samples which were not detected with the hg38 reference map. We also directly assessed the NRSs positioned within genes. Seventeen NRSs anchored to regions including an identical 187 bp NRS found in both de novo assemblies. The NRS is located in HCN2 79 bp downstream of Exon 3 and contains several putative transcriptional regulatory elements. Genotyping of the HCN2-NRS revealed that the insertion is enriched in IAZ (minor allele frequency = 0.45) compared to other reference populations tested. This study shows that inclusion of population-specific NRSs can dramatically change the variant profile in an underrepresented ethnic groups and thereby lead to the discovery of previously missed common variations.

Expanded carrier screening for inherited genetic disease using exome and genome sequencing.

The goal of this study was to assess the feasibility of using exome (ES) and genome sequencing (GS) in guiding preconception genetic screening (PCGS) for couples who are planning to conceive by creating a workflow for identifying risk alleles for autosomal recessive (AR) and X-linked (XL) disorders without the constraints of a predetermined, targeted gene panel. There were several limitations and challenges related to reporting and the technical aspects of ES and GS, which are listed in the discussion. We selected 150 couples from a cohort of families (trios) enrolled in a research protocol where the goal was to define the genetic etiology of disease in an affected child. Pre-existing, de-identified parental sequencing data were analyzed to define variants that would place the couple at risk of having a child affected by an AR or XL disorder. We identified 17 families who would be selected for counseling about risk alleles. We noted that only 3 of these at-risk couples would be identified if we limited ourselves to the current ACMG-recommended expanded carrier screening gene panel. ES and GS successfully identified couples who are at risk of having a child with a rare AR or XL disorder that would have been missed by the current recommended guidelines. Current limitations of this approach include ethical concerns, difficulties in reporting results including variant calling due to the rare nature of some of the variants, determining which disorders to report, as well as technical difficulties in detecting certain variants such as repeat expansions.

Lynch syndrome-associated and sporadic microsatellite unstable colorectal cancers: different patterns of clonal evolution yield highly similar tumours.

Microsatellite unstable colorectal cancer (MSI-CRC) can arise through germline mutations in mismatch repair (MMR) genes in individuals with Lynch syndrome (LS), or sporadically through promoter methylation of the MMR gene MLH1. Despite the different origins of hereditary and sporadic MSI tumours, their genomic features have not been extensively compared. A prominent feature of MMR-deficient genomes is the occurrence of many indels in short repeat sequences, an understudied mutation type due to the technical challenges of variant calling in these regions. In this study, we performed whole genome sequencing and RNA-sequencing on 29 sporadic and 14 hereditary MSI-CRCs. We compared the tumour groups by analysing genome-wide mutation densities, microsatellite repeat indels, recurrent protein-coding variants, signatures of single base, doublet base, and indel mutations, and changes in gene expression. We show that the mutational landscapes of hereditary and sporadic MSI-CRCs, including mutational signatures and mutation densities genome-wide and in microsatellites, are highly similar. Only a low number of differentially expressed genes were found, enriched to interferon-γ regulated immune response pathways. Analysis of the variance in allelic fractions of somatic variants in each tumour group revealed higher clonal heterogeneity in sporadic MSI-CRCs. Our results suggest that the differing molecular origins of MMR deficiency in hereditary and sporadic MSI-CRCs do not result in substantial differences in the mutational landscapes of these tumours. The divergent patterns of clonal evolution between the tumour groups may have clinical implications, as high clonal heterogeneity has been associated with decreased tumour immunosurveillance and reduced responsiveness to immunotherapy.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study

IntroductionMolecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability. MethodsThis retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort. ResultsModels trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %. DiscussionModel explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy. ConclusionAlthough deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.

Biologically-informed Killer cell immunoglobulin-like receptor (KIR) gene annotation tool.

Natural killer (NK) cells are essential components of the innate immune system, with their activity significantly regulated by Killer cell Immunoglobulin-like Receptors (KIRs). The diversity and structural complexity of KIR genes present significant challenges for accurate genotyping, essential for understanding NK cell functions and their implications in health and disease. Traditional genotyping methods struggle with the variable nature of KIR genes, leading to inaccuracies that can impede immunogenetic research. These challenges extend to high-quality phased assemblies, which have been recently popularized by the Human Pangenome Consortium. This paper introduces BAKIR (Biologically-informed Annotator for KIR locus), a tailored computational tool designed to overcome the challenges of KIR genotyping and annotation on high-quality, phased genome assemblies. BAKIR aims to enhance the accuracy of KIR gene annotations by structuring its annotation pipeline around identifying key functional mutations, thereby improving the identification and subsequent relevance of gene and allele calls. It uses a multi-stage mapping, alignment, and variant calling process to ensure high-precision gene and allele identification, while also maintaining high recall for sequences that are significantly mutated or truncated relative to the known allele database. BAKIR has been evaluated on a subset of the HPRC assemblies, where BAKIR was able to improve many of the associated annotations and call novel variants. BAKIR is freely available on GitHub, offering ease of access and use through multiple installation methods, including pip, conda, and singularity container, and is equipped with a user-friendly command-line interface, thereby promoting its adoption in the scientific community.

Advancing long-read nanopore genome assembly and accurate variant calling for rare disease detection.

More than 50% of families with suspected rare monogenic diseases remain unsolved after whole genome analysis by short read sequencing (SRS). Long-read sequencing (LRS) could help bridge this diagnostic gap by capturing variants inaccessible to SRS, facilitating long-range mapping and phasing, and providing haplotype-resolved methylation profiling. To evaluate LRS's additional diagnostic yield, we sequenced a rare disease cohort of 98 samples, including 41 probands and some family members, using nanopore sequencing, achieving per sample ∼36x average coverage and 32 kilobase (kb) read N50 from a single flow cell. Our Napu pipeline generated assemblies, phased variants, and methylation calls. LRS covered, on average, coding exons in ∼280 genes and ∼5 known Mendelian disease genes that were not covered by SRS. In comparison to SRS, LRS detected additional rare, functionally annotated variants, including SVs and tandem repeats, and completely phased 87% of protein-coding genes. LRS detected additional de novo variants, and could be used to distinguish postzygotic mosaic variants from prezygotic de novos . Eleven probands were solved, with diverse underlying genetic causes including de novo and compound heterozygous variants, large-scale SVs, and epigenetic modifications. Our study demonstrates LRS's potential to enhance diagnostic yield for rare monogenic diseases, implying utility in future clinical genomics workflows.

Genotype Representation Graphs: Enabling Efficient Analysis of Biobank-Scale Data.

Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. Current practice is to store large-scale genetic polymorphism data using tabular data structures and file formats, where rows and columns represent samples and genetic variants. However, encoding genetic data in such formats has become unsustainable. For example, the UK Biobank polymorphism data of 200,000 phased whole genomes has exceeded 350 terabytes (TB) in Variant Call Format (VCF), cumbersome and inefficient to work with. To mitigate the computational burden, we introduce the Genotype Representation Graph (GRG), an extremely compact data structure to losslessly present phased whole-genome polymorphisms. A GRG is a fully connected hierarchical graph that exploits variant-sharing across samples, leveraging ideas inspired by Ancestral Recombination Graphs. Capturing variant-sharing in a multitree structure compresses biobank-scale human data to the point where it can fit in a typical server's RAM (5-26 gigabytes (GB) per chromosome), and enables graph-traversal algorithms to trivially reuse computed values, both of which can significantly reduce computation time. We have developed a command-line tool and a library usable via both C++ and Python for constructing and processing GRG files which scales to a million whole genomes. It takes 160GB disk space to encode the information in 200,000 UK Biobank phased whole genomes as a GRG, more than 13 times smaller than the size of compressed VCF. We show that summaries of genetic variants such as allele frequency and association effect can be computed on GRG via graph traversal that runs significantly faster than all tested alternatives, including vcf.gz, PLINK BED, tree sequence, XSI, and Savvy. Furthermore, GRG is particularly suitable for doing repeated calculations and interactive data analysis. We anticipate that GRG-based algorithms will improve the scalability of various types of computation and generally lower the cost of analyzing large genomic datasets.

Genomic landscape of gallbladder cancer: insights from whole exome sequencing.

Gallbladder carcinoma (GBC) is a common gastrointestinal malignancy noted for its aggressive characteristics and poor prognosis, which is mostly caused by delayed detection. However, the scarcity of information regarding somatic mutations in Indian patients with GBC has hampered the development of efficient therapeutic options. In the present study, we attempted to bridge this gap by revealing the mutational profile of GBC. To evaluate the somatic mutation profile, whole exome sequencing (WES) was performed on 66 matched tumor and blood samples from individuals with GBC. Somatic variant calling was performed using GATK pipeline. Variants were annotated at pathogenic and oncogenic levels, using ANNOVAR, VEP tools and the OncoKB database. Mutational signature analysis, oncogenic pathway analysis and cancer driver genes identification were performed at the functional level by using the maftools package. Our findings focused on the eight most altered genes with pathogenic and oncogenic mutations: TP53, SMAD4, ERBB3, KRAS, ARID1A, PIK3CA, RB1, and AXIN1. Genes with pathogenic single nucleotide variations (SNVs) were enriched in oncogenic signaling pathways, particularly RTK-RAS, WNT, and TP53 pathways. Furthermore, our research related certain mutational signatures, such as cosmic 1, cosmic 6, and cosmic 18, 29, to known characteristics including patient age and tobacco smoking, providing important insights into disease etiology. Given the scarcity of exome-based sequencing studies focusing on the Indian population, this study represents a significant step forward in providing a framework for additional in-depth mutational analysis. Genes with substantial oncogenic and pathogenic mutations are promising candidates for developing targeted mutation panels, particularly for GBC detection.

VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing

Structural variants (SVs) significantly contribute to human genome diversity and play a crucial role in precision medicine. Although advancements in single-molecule long-read sequencing offer a groundbreaking resource for SV detection, identifying SV breakpoints and sequences accurately and robustly remains challenging. We introduce VolcanoSV, an innovative hybrid SV detection pipeline that utilizes both a reference genome and local de novo assembly to generate a phased diploid assembly. VolcanoSV uses phased SNPs and unique k-mer similarity analysis, enabling precise haplotype-resolved SV discovery. VolcanoSV is adept at constructing comprehensive genetic maps encompassing SNPs, small indels, and all types of SVs, making it well-suited for human genomics studies. Our extensive experiments demonstrate that VolcanoSV surpasses state-of-the-art assembly-based tools in the detection of insertion and deletion SVs, exhibiting superior recall, precision, F1 scores, and genotype accuracy across a diverse range of datasets, including low-coverage (10x) datasets. VolcanoSV outperforms assembly-based tools in the identification of complex SVs, including translocations, duplications, and inversions, in both simulated and real cancer data. Moreover, VolcanoSV is robust to various evaluation parameters and accurately identifies breakpoints and SV sequences.