Indel Calling Research Articles

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration’s (FDA’s) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

Pacybara: accurate long-read sequencing for barcoded mutagenized allelic libraries.

Long-read sequencing technologies, an attractive solution for many applications, often suffer from higher error rates. Alignment of multiple reads can improve base-calling accuracy, but some applications, e.g. sequencing mutagenized libraries where multiple distinct clones differ by one or few variants, require the use of barcodes or unique molecular identifiers. Unfortunately, sequencing errors can interfere with correct barcode identification, and a given barcode sequence may be linked to multiple independent clones within a given library. Here we focus on the target application of sequencing mutagenized libraries in the context of multiplexed assays of variant effects (MAVEs). MAVEs are increasingly used to create comprehensive genotype-phenotype maps that can aid clinical variant interpretation. Many MAVE methods use long-read sequencing of barcoded mutant libraries for accurate association of barcode with genotype. Existing long-read sequencing pipelines do not account for inaccurate sequencing or nonunique barcodes. Here, we describe Pacybara, which handles these issues by clustering long reads based on the similarities of (error-prone) barcodes while also detecting barcodes that have been associated with multiple genotypes. Pacybara also detects recombinant (chimeric) clones and reduces false positive indel calls. In three example applications, we show that Pacybara identifies and correctly resolves these issues. Pacybara, freely available at https://github.com/rothlab/pacybara, is implemented using R, Python, and bash for Linux. It runs on GNU/Linux HPC clusters via Slurm, PBS, or GridEngine schedulers. A single-machine simplex version is also available.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Abstract 5036: Analytical and clinical validity of Northstar Select, a quantitatively enhanced liquid biopsy assay for comprehensive genomic profiling of solid tumors

Abstract Background: Liquid biopsy assays have emerged as vital tools in precision oncology. Among these assays, Comprehensive Genomic Profiling (CGP) tests in particular have improved treatment selection for late-stage solid tumor cancer patients by allowing for the detection via circulating tumor DNA (ctDNA) of somatic mutations in the tumor, many of which have corresponding targeted therapies. However, technical restrictions in the performance of such tests have limited their utility when ctDNA signal is low. BillionToOne’s Northstar Select assay is a new CGP test that achieves increased performance by addressing these limitations. Innovation: By using Quantitative Counting Template (QCT) technology, Northstar Select is able to track the yield of multiple processing steps, contributing to the higher sensitivity of the assay by maximizing the number of decoded molecules after sequencing. In addition, it employs chemistry and panel design optimizations, as well as novel algorithms for SNV, indel, CNV, fusion, and MSI calling to increase sensitivity and specificity. Analytical Performance: The performance of Northstar Select surpasses that of other commercially available tests for multiple mutation types, detecting more pathogenic and actionable mutations at low tumor DNA fraction while maintaining similarly high specificity. In analytical validation (N=103), limit of detection was determined to be 0.13-0.16% variant allele fraction (VAF) for SNVs and indels, 2.11 copies for copy number amplifications, 1.8 copies for copy number losses, 0.35% VAF for fusions, and 0.07% tumor fraction for MSI. Clinical Performance: Northstar Select was determined to have high clinical utility as well, with a study of clinical samples (N=481) from a variety of tumor types yielding <2% of reports with 0 mutations detected. A head-to-head comparison with other commercial liquid biopsy assays on a subset of the clinical samples (N=192) showed that the assay detected 115 additional pathogenic mutations in total, leading to a higher proportion of patients with at least one pathogenic mutation detected (90%) compared to comparators (79%). Conclusion: These results demonstrate that the Northstar Select assay is an innovative and high-performing diagnostic for treatment selection in oncology, leveraging quantitative approaches in process design, chemistry, and bioinformatics to offer superior analytical and clinical performance compared to the current landscape of liquid biopsy CGP tests. Citation Format: Xavier Bower, Jan Wignall, Joyce Zhu, Naomi Searle, Matthew Varga, Emilio Rosas-Linhard, Jason Luong, Esther Lin, Marie Simon, John ten Bosch, Wen Zhou. Analytical and clinical validity of Northstar Select, a quantitatively enhanced liquid biopsy assay for comprehensive genomic profiling of solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5036.

Utility of long-read sequencing for All of Us

The All of Us (AoU) initiative aims to sequence the genomes of over one million Americans from diverse ethnic backgrounds to improve personalized medical care. In a recent technical pilot, we compare the performance of traditional short-read sequencing with long-read sequencing in a small cohort of samples from the HapMap project and two AoU control samples representing eight datasets. Our analysis reveals substantial differences in the ability of these technologies to accurately sequence complex medically relevant genes, particularly in terms of gene coverage and pathogenic variant identification. We also consider the advantages and challenges of using low coverage sequencing to increase sample numbers in large cohort analysis. Our results show that HiFi reads produce the most accurate results for both small and large variants. Further, we present a cloud-based pipeline to optimize SNV, indel and SV calling at scale for long-reads analysis. These results lead to widespread improvements across AoU.

Düşük Kapsamlı WGS Verileri Kullanılarak Hızlı ve Yüksek Doğruluklu Guppy Baz Çağırma Modellerinin Varyant Çağırma Üzerine Etkisinin İncelenmesi

Long-read sequencing technologies such as Oxford Nanopore Technologies (ONT) enabled researchers to sequence long reads fast and cost-effectively. ONT sequencing uses nanopores integrated into semiconductor surfaces and sequences the genomic materials using changes in current across the surface as each nucleotide passes through the nanopore. The default output of ONT sequencers is in FAST5 format. The first and one of the most important steps of ONT data analysis is the conversion of FAST5 files to FASTQ files using “base caller” tools. Generally, base caller tools pre-trained deep learning models to transform electrical signals into reads. Guppy, the most commonly used base caller, uses 2 main model types, fast and high accuracy. Since the computation duration is significantly different between these two models, the effect of models on the variant calling process has not been fully understood. This study aims to evaluate the effect of different models on performance on variant calling.
 In this study, 15 low-coverage long-read sequencing results coming from different flow cells of NA12878 (gold standard data) were used to compare the variant calling results of Guppy. 
 Obtained results indicated that the number of output FASTQ files, read counts and average read lengths between fast and high accuracy models are not statistically significant while pass/fail ratios of the base called datasets are significantly higher in high accuracy models. Results also indicated that the difference in pass/fail ratios arises in a significant difference in the number of called Single Nucleotide Polymorphisms (SNPs), insertions and deletions (InDels). Interestingly the true positive rates of SNPs are not significantly different. These results show that using fast models for SNP calling does not affect the true positive rates statistically. The primary observation in this study, using fast models does not decrease the true positive rate but decreases the called variants that arise due to altered pass/fail ratios. Also, it is not advised to use fast models for InDel calling while both the number of InDels and true positive rates are significantly lower in fast models.
 This study, to the best of our knowledge, is the first study that evaluates the effect of different base calling models of Guppy, one of the most common and ONT-supported base callers, on variant calling.

Scalable Nanopore sequencing of human genomes provides a comprehensive view of haplotype-resolved variation and methylation.

Long-read sequencing technologies substantially overcome the limitations of short-reads but have not been considered as a feasible replacement for population-scale projects, being a combination of too expensive, not scalable enough or too error-prone. Here we develop an efficient and scalable wet lab and computational protocol, Napu, for Oxford Nanopore Technologies long-read sequencing that seeks to address those limitations. We applied our protocol to cell lines and brain tissue samples as part of a pilot project for the National Institutes of Health Center for Alzheimer's and Related Dementias. Using a single PromethION flow cell, we can detect single nucleotide polymorphisms with F1-score comparable to Illumina short-read sequencing. Small indel calling remains difficult within homopolymers and tandem repeats, but achieves good concordance to Illumina indel calls elsewhere. Further, we can discover structural variants with F1-score on par with state-of-the-art de novo assembly methods. Our protocol phases small and structural variants at megabase scales and produces highly accurate, haplotype-specific methylation calls.

A Bos taurus sequencing methods benchmark for assembly, haplotyping, and variant calling

Inspired by the production of reference data sets in the Genome in a Bottle project, we sequenced one Charolais heifer with different technologies: Illumina paired-end, Oxford Nanopore, Pacific Biosciences (HiFi and CLR), 10X Genomics linked-reads, and Hi-C. In order to generate haplotypic assemblies, we also sequenced both parents with short reads. From these data, we built two haplotyped trio high quality reference genomes and a consensus assembly, using up-to-date software packages. The assemblies obtained using PacBio HiFi reaches a size of 3.2 Gb, which is significantly larger than the 2.7 Gb ARS-UCD1.2 reference. The BUSCO score of the consensus assembly reaches a completeness of 95.8%, among highly conserved mammal genes. We also identified 35,866 structural variants larger than 50 base pairs. This assembly is a contribution to the bovine pangenome for the “Charolais” breed. These datasets will prove to be useful resources enabling the community to gain additional insight on sequencing technologies for applications such as SNP, indel or structural variant calling, and de novo assembly.

Comprehensive de novo mutation discovery with HiFi long-read sequencing

BackgroundLong-read sequencing (LRS) techniques have been very successful in identifying structural variants (SVs). However, the high error rate of LRS made the detection of small variants (substitutions and short indels < 20 bp) more challenging. The introduction of PacBio HiFi sequencing makes LRS also suited for detecting small variation. Here we evaluate the ability of HiFi reads to detect de novo mutations (DNMs) of all types, which are technically challenging variant types and a major cause of sporadic, severe, early-onset disease.MethodsWe sequenced the genomes of eight parent–child trios using high coverage PacBio HiFi LRS (~ 30-fold coverage) and Illumina short-read sequencing (SRS) (~ 50-fold coverage). De novo substitutions, small indels, short tandem repeats (STRs) and SVs were called in both datasets and compared to each other to assess the accuracy of HiFi LRS. In addition, we determined the parent-of-origin of the small DNMs using phasing.ResultsWe identified a total of 672 and 859 de novo substitutions/indels, 28 and 126 de novo STRs, and 24 and 1 de novo SVs in LRS and SRS respectively. For the small variants, there was a 92 and 85% concordance between the platforms. For the STRs and SVs, the concordance was 3.6 and 0.8%, and 4 and 100% respectively. We successfully validated 27/54 LRS-unique small variants, of which 11 (41%) were confirmed as true de novo events. For the SRS-unique small variants, we validated 42/133 DNMs and 8 (19%) were confirmed as true de novo event. Validation of 18 LRS-unique de novo STR calls confirmed none of the repeat expansions as true DNM. Confirmation of the 23 LRS-unique SVs was possible for 19 candidate SVs of which 10 (52.6%) were true de novo events. Furthermore, we were able to assign 96% of DNMs to their parental allele with LRS data, as opposed to just 20% with SRS data.ConclusionsHiFi LRS can now produce the most comprehensive variant dataset obtainable by a single technology in a single laboratory, allowing accurate calling of substitutions, indels, STRs and SVs. The accuracy even allows sensitive calling of DNMs on all variant levels, and also allows for phasing, which helps to distinguish true positive from false positive DNMs.

Revisiting chloroplast genomic landscape and annotation towards comparative chloroplast genomes of Rhamnaceae

BackgroundMassive parallel sequencing technologies have enabled the elucidation of plant phylogenetic relationships from chloroplast genomes at a high pace. These include members of the family Rhamnaceae. The current Rhamnaceae phylogenetic tree is from 13 out of 24 Rhamnaceae chloroplast genomes, and only one chloroplast genome of the genus Ventilago is available. Hence, the phylogenetic relationships in Rhamnaceae remain incomplete, and more representative species are needed.ResultsThe complete chloroplast genome of Ventilago harmandiana Pierre was outlined using a hybrid assembly of long- and short-read technologies. The accuracy and validity of the final genome were confirmed with PCR amplifications and investigation of coverage depth. Sanger sequencing was used to correct for differences in lengths and nucleotide bases between inverted repeats because of the homopolymers. The phylogenetic trees reconstructed using prevalent methods for phylogenetic inference were topologically similar. The clustering based on codon usage was congruent with the molecular phylogenetic tree. The groups of genera in each tribe were in accordance with tribal classification based on molecular markers. We resolved the phylogenetic relationships among six Hovenia species, three Rhamnus species, and two Ventilago species. Our reconstructed tree provides the most complete and reliable low-level taxonomy to date for the family Rhamnaceae. Similar to other higher plants, the RNA editing mostly resulted in converting serine to leucine. Besides, most genes were subjected to purifying selection. Annotation anomalies, including indel calling errors, unaligned open reading frames of the same gene, inconsistent prediction of intergenic regions, and misannotated genes, were identified in the published chloroplast genomes used in this study. These could be a result of the usual imperfections in computational tools, and/or existing errors in reference genomes. Importantly, these are points of concern with regards to utilizing published chloroplast genomes for comparative genomic analysis.ConclusionsIn summary, we successfully demonstrated the use of comprehensive genomic data, including DNA and amino acid sequences, to build a reliable and high-resolution phylogenetic tree for the family Rhamnaceae. Additionally, our study indicates that the revision of genome annotation before comparative genomic analyses is necessary to prevent the propagation of errors and complications in downstream analysis and interpretation.

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

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. 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. 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.

Decoding the diversity of killer immunoglobulin-like receptors by deep sequencing and a high-resolution imputation method

The killer cell immunoglobulin-like receptor (KIR) recognizes human leukocyte antigen (HLA) class I molecules and modulates the function of natural killer cells. Despite its role in immunity, the complex genomic structure has limited a deep understanding of the KIR genomic landscape. Here we conduct deep sequencing of 16 KIR genes in 1,173 individuals. We devise a bioinformatics pipeline incorporating copy number estimation and insertion or deletion (indel) calling for high-resolution KIR genotyping. We define 118 alleles in 13 genes and demonstrate a linkage disequilibrium structure within and across KIR centromeric and telomeric regions. We construct a KIR imputation reference panel (nreference= 689, imputation accuracy= 99.7%), apply it to biobank genotype (ntotal= 169,907), and perform phenome-wide association studies of 85 traits. We observe a dearth of genome-wide significant associations, even in immune traits implicated previously to be associated with KIR (the smallest p= 1.5× 10-4). Our pipeline presents a broadly applicable framework to evaluate innate immunity in large-scale datasets.

Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data.

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.

HELLO: improved neural network architectures and methodologies for small variant calling

BackgroundModern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of Deep Neural Networks to variant calling, surpassing the accuracy of classical approaches in many settings. DeepVariant, arguably the most popular among such methods, transforms the problem of variant calling into one of image recognition where a Deep Neural Network analyzes sequencing data that is formatted as images, achieving high accuracy. In this paper, we explore an alternative approach to designing Deep Neural Networks for variant calling, where we use meticulously designed Deep Neural Network architectures and customized variant inference functions that account for the underlying nature of sequencing data instead of converting the problem to one of image recognition.ResultsResults from 27 whole-genome variant calling experiments spanning Illumina, PacBio and hybrid Illumina-PacBio settings suggest that our method allows vastly smaller Deep Neural Networks to outperform the Inception-v3 architecture used in DeepVariant for indel and substitution-type variant calls. For example, our method reduces the number of indel call errors by up to 18%, 55% and 65% for Illumina, PacBio and hybrid Illumina-PacBio variant calling respectively, compared to a similarly trained DeepVariant pipeline. In these cases, our models are between 7 and 14 times smaller.ConclusionsWe believe that the improved accuracy and problem-specific customization of our models will enable more accurate pipelines and further method development in the field. HELLO is available at https://github.com/anands-repo/hello

Generalizable characteristics of false-positive bacterial variant calls.

Minimizing false positives is a critical issue when variant calling as no method is without error. It is common practice to post-process a variant-call file (VCF) using hard filter criteria intended to discriminate true-positive (TP) from false-positive (FP) calls. These are applied on the simple principle that certain characteristics are disproportionately represented among the set of FP calls and that a user-chosen threshold can maximize the number detected. To provide guidance on this issue, this study empirically characterized all false SNP and indel calls made using real Illumina sequencing data from six disparate species and 166 variant-calling pipelines (the combination of 14 read aligners with up to 13 different variant callers, plus four ‘all-in-one’ pipelines). We did not seek to optimize filter thresholds but instead to draw attention to those filters of greatest efficacy and the pipelines to which they may most usefully be applied. In this respect, this study acts as a coda to our previous benchmarking evaluation of bacterial variant callers, and provides general recommendations for effective practice. The results suggest that, of the pipelines analysed in this study, the most straightforward way of minimizing false positives would simply be to use Snippy. We also find that a disproportionate number of false calls, irrespective of the variant-calling pipeline, are located in the vicinity of indels, and highlight this as an issue for future development.

Evaluation of a hybridization capture-based hereditary cancer panel for the ion semiconductor-based next-generation sequencing system

BackgroundThere is an unmet need to use the ThermoFisher Ion sequencing platform with a hybridization capture-based next-generation sequencing(NGS) panel. However, the analytical performance of combining an ion semiconductor-based sequencing system and a hybridization capture-based NGS panel has not been thoroughly evaluated. Therefore, we compared the analytic performance of Illumina's NextSeq and Ion S5 XL using a hybridization capture-based target enrichment method. MethodsWe included 31 clinical samples and NA12878 reference material for comparing two different NGS instruments using a hybridization capture-based panel. The hybridization-based capture hereditary cancer predisposition (HCP) panel was designed for Ion S5 XL Sequencer and Illumina platforms, respectively. ResultsWe obtained comparable data involving sequencing run metrics and analytical performance of two different NGS systems using a hybridization-based capture panel designed for each NGS platform. The sensitivity, specificity, and accuracy of single-nucleotide variant and indel calling were 97.06%, 100%, and 100% for the Ion S5™ XL system and 98.53%, 100%, and 100% for NextSeq 550Dx platform, respectively. ConclusionsThis study demonstrated that a hybrid capture panel kit can be successfully implemented using the ThermoFisher Scientific Ion S5 XL instrument and offers the opportunity to select a variety of hybridization-based capture panels from various manufacturers.

Improved Clinical Assessment of Myeloid Malignancies By Transcriptome Profiling

Clinical genetic testing for myeloid malignancies including acute myeloid leukemia (AML) and myelodysplatic syndromes (MDS) currently relies on cytogenetic assays and targeted sequencing of clinically relevant genes. As more genes with diagnostic and prognostic relevance to these diseases are discovered, traditional testing methods will not be able to scale to effectively meet clinical needs. Next-generation sequencing platforms offer a potential way around this bottle-neck, but the analytic and clinical validity of these methods must be firmly established. To determine whether RNA-Seq, whole-genome sequencing (WGS), or whole-exome sequencing (WES) could meet or exceed the current clinical standard of care, we used these platforms to study retrospective and prospective cohorts of patients with myeloid malignancies.From a retrospective cohort of 176 patients, we generated RNA-Seq, WES, WGS libraries (176, 92, and 18, respectively). The patient cohort spanned a broad range of myeloid malignancies, including AML, MDS, and therapy-related AML and MDS. We observed that, for similar sequencing efforts, RNA-Seq libraries provided the highest coverage depth over clinically relevant genes, while WGS and WES platforms suffered from recurrent coverage dropouts, particularly in the GC-rich CEBPA gene (median coverage depths of 406x, 115x, and 33x for RNA-Seq, WES, and WGS libraries). We used de novo assembly to assess gene fusions from the RNA-Seq libraries, and demonstrated that all gene fusions previously detected by karyotyping were detectable (41 distinct patients, including 13 inv(16), 9 t(15;17), 8 t(8;21), 3 t(9;11), and 2 t(9;11)). Additionally, we identified five patients with novel rearrangements in the MLL gene family, with potential therapy-altering relevance. We observed complete sensitivity (30 cases) for FLT3 -ITD detection, and detected nine novel low-frequency events undetectable by the previous assay, which were validated using a targeted sequencing panel. We generated SNV and short indel calls for all platforms, and observed that most variants were concordant between platforms (2,079 matched variants aross 92 patients for the RNA-Seq/WES comparison). Missed variant calls (102) in WES libraries were generally due to poor sequencing coverage, while missing calls (44) in RNA-Seq libraries were generally due to allele-specific expression. We generated HLA genotypes for all patients using the RNA-Seq patients, and observed near-total concordance with previous clinical results, increasing the clinical utility of RNA-Seq based testing (of 621 called alleles, only two mismatches were observed at two-digit resolution).To determine whether gene expression information could be used to identify distinct risk groups within normal-karyotype AMLs, we performed unsupervised clustering analysis and network-based clustering analysis, identifying distinct clusters using both approaches. We demonstrate distinct co-occurrence of two expression clusters with NPM1 - and FLT3 alterations, and distinct survival outcomes for these novel clusters. Additionally, we observed distinct pathway enrichments associated with these clusters, linking molecular subtypes with gene expression signatures.Altogether, we observed that RNA-Seq offered substantial clinical benefits, due to the ability to recover expressed gene fusions and whole-transcriptome expression information, SNV and short indel variants, and HLA genotype information. Additionally, we observed gene expression heterogeneity which can be used to better stratify intermediate-risk patients. WGS and WES platforms showed recurrent weaknesses in coverage for clinically-relevant genes, and provided less actionable information overall. This work provides crucial analytic validation for the incorporation of RNA-Seq based testing for patients with myeloid malignancies, which may lead to substantial clinical benefits for those patients. DisclosuresNo relevant conflicts of interest to declare.

Ratatosk: hybrid error correction of long reads enables accurate variant calling and assembly

A major challenge to long read sequencing data is their high error rate of up to 15%. We present Ratatosk, a method to correct long reads with short read data. We demonstrate on 5 human genome trios that Ratatosk reduces the error rate of long reads 6-fold on average with a median error rate as low as 0.22 %. SNP calls in Ratatosk corrected reads are nearly 99 % accurate and indel calls accuracy is increased by up to 37 %. An assembly of Ratatosk corrected reads from an Ashkenazi individual yields a contig N50 of 45 Mbp and less misassemblies than a PacBio HiFi reads assembly.

Accuracy and efficiency of germline variant calling pipelines for human genome data

Advances in next-generation sequencing technology have enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data. However, there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using the Genome in a Bottle Consortium, “synthetic-diploid” and simulated WGS datasets. DRAGEN and DeepVariant show better accuracy in SNP and indel calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient execution speed, and therefore superior performance in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also suggests a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical applications.

Comparative assessments of indel annotations in healthy and cancer genomes with next-generation sequencing data

BackgroundInsertion and deletion (indel) is one of the major variation types in human genomes. Accurate annotation of indels is of paramount importance in genetic variation analysis and investigation of their roles in human diseases. Previous studies revealed a high number of false positives from existing indel calling methods, which limits downstream analyses of the effects of indels on both healthy and disease genomes. In this study, we evaluated seven commonly used general indel calling programs for germline indels and four somatic indel calling programs through comparative analysis to investigate their common features and differences and to explore ways to improve indel annotation accuracy.MethodsIn our comparative analysis, we adopted a more stringent evaluation approach by considering both the indel positions and the indel types (insertion or deletion sequences) between the samples and the reference set. In addition, we applied an efficient way to use a benchmark for improved performance comparisons for the general indel calling programsResultsWe found that germline indels in healthy genomes derived by combining several indel calling tools could help remove a large number of false positive indels from individual programs without compromising the number of true positives. The performance comparisons of somatic indel calling programs are more complicated due to the lack of a reliable and comprehensive benchmark. Nevertheless our results revealed large variations among the programs and among cancer types.ConclusionsWhile more accurate indel calling programs are needed, we found that the performance for germline indel annotations can be improved by combining the results from several programs. In addition, well-designed benchmarks for both germline and somatic indels are key in program development and evaluations.