Analysis of error profiles in deep next-generation sequencing data

Chasing Sequencing Perfection: Marching Toward Higher Accuracy and Lower Costs

Next-generation sequencing (NGS), represented by Illumina platforms, has been an essential cornerstone of basic and applied research. However, the sequencing error rate of 1 per 1000 bp (10-3) represents a serious hurdle for research areas focusing on rare mutations, such as somatic mosaicism or microbe heterogeneity. By examining the high-fidelity sequencing methods developed in the past decade, we summarized three major factors underlying errors and the corresponding 12 strategies mitigating these errors. We then proposed a novel framework to classify 11 preexisting representative methods according to the corresponding combinatory strategies and identified three trends that emerged during methodological developments. We further extended this analysis to eight long-read sequencing methods, emphasizing error reduction strategies. Finally, we suggest two promising future directions that could achieve comparable or even higher accuracy with lower costs in both NGS and long-read sequencing.

Development of a nine-variant reference material panel to standardize cell-free DNA detection

Detection of specific gene mutations in cell-free DNA (cfDNA) serves as a valuable cancer biomarker and is increasingly being explored as an appealing alternative to tissue-based methods. However, the lack of available reference materials poses challenges in accurately evaluating the performance of different assays. In this study, we present the development of a comprehensive reference material panel for cfDNA detection, encompassing nine hotspot mutations in KRAS/BRAF/EGFR/PIK3CA at three variant allele frequencies (VAFs), ranging from 0.33 to 23.9%. To mimic cfDNA, these reference materials were generated by enzymatically digesting cell-line DNA into approximately 154-bp to 173-bp fragments using a laboratory-developed reaction system. The VAFs for each variation were precisely determined through validated digital PCR assays with high accuracy. Furthermore, the reliability and applicability of this panel were confirmed through two independent NGS assays, yielding concordant results. Collectively, our findings suggest that this novel reference material panel holds great potential for validation, evaluation, and quality control processes associated with liquid biopsy assays.

High heteroplasmy is associated with low mitochondrial copy number and selection against non-synonymous mutations in the snail Cepaea nemoralis

Molluscan mitochondrial genomes are unusual because they show wide variation in size, radical genome rearrangements and frequently show high variation (> 10%) within species. As progress in understanding this variation has been limited, we used whole genome sequencing of a six-generation matriline of the terrestrial snail Cepaea nemoralis, as well as whole genome sequences from wild-collected C. nemoralis, the sister species C. hortensis, and multiple other snail species to explore the origins of mitochondrial DNA (mtDNA) variation. The main finding is that a high rate of SNP heteroplasmy in somatic tissue was negatively correlated with mtDNA copy number in both Cepaea species. In individuals with under ten mtDNA copies per nuclear genome, more than 10% of all positions were heteroplasmic, with evidence for transmission of this heteroplasmy through the germline. Further analyses showed evidence for purifying selection acting on non-synonymous mutations, even at low frequency of the rare allele, especially in cytochrome oxidase subunit 1 and cytochrome b. The mtDNA of some individuals of Cepaea nemoralis contained a length heteroplasmy, including up to 12 direct repeat copies of tRNA-Val, with 24 copies in another snail, Candidula rugosiuscula, and repeats of tRNA-Thr in C. hortensis. These repeats likely arise due to error prone replication but are not correlated with mitochondrial copy number in C. nemoralis. Overall, the findings provide key insights into mechanisms of replication, mutation and evolution in molluscan mtDNA, and so will inform wider studies on the biology and evolution of mtDNA across animal phyla.

Techniques, procedures, and applications in host genetic analysis

This chapter overviews genetic techniques' fundamentals and methodological features, including different approaches, analyses, and applications that have contributed to advancing health and disease. The aim is to describe laboratory methodologies and analyses employed to understand the genetic landscape of different biological contexts, from conventional techniques to cutting-edge technologies. Besides describing detailed aspects of the polymerase chain reaction (PCR) and derived types as one of the principles for many novel techniques, we also discuss microarray analysis, next-generation sequencing, and genome editing technologies such as transcription activator-like effector nucleases (TALENs) and the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) systems. These techniques study several phenotypes, ranging from autoimmune disorders to viral diseases. The significance of integrating diverse genetic methodologies and tools to understand host genetics comprehensively and addressing the ethical, legal, and social implications (ELSI) associated with using genetic information is highlighted. Overall, the methods, procedures, and applications in host genetic analysis provided in this chapter furnish researchers and practitioners with a roadmap for navigating the dynamic landscape of host-genome interactions.

Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES)

OBJECTIVES

Tumor mutational burden (TMB) is a significant biomarker for predicting immune checkpoint inhibitor response, but the clinical performance of whole-exome sequencing (WES)-based TMB estimation has received less attention compared to panel-based methods. This study aimed to assess the reliability and comparability of WES-based TMB analysis among laboratories under routine testing conditions.

METHODS

A multicenter study was conducted involving 24 laboratories in China using in silico reference data sets. The accuracy and comparability of TMB estimation were evaluated using matched tumor-normal data sets. Factors such as accuracy of variant calls, limit of detection (LOD) of WES test, size of regions of interest (ROIs) used for TMB calculation, and TMB cutoff points were analyzed.

RESULTS

The laboratories consistently underestimated the expected TMB scores in matched tumor-normal samples, with only 50% falling within the ±30% TMB interval. Samples with low TMB score (<2.5) received the consensus interpretation. Accuracy of variant calls, LOD of the WES test, ROI, and TMB cutoff points were important factors causing interlaboratory deviations.

CONCLUSIONS

This study highlights real-world challenges in WES-based TMB analysis that need to be improved and optimized. This research will aid in the selection of more reasonable analytical procedures to minimize potential methodologic biases in estimating TMB in clinical exome sequencing tests. Harmonizing TMB estimation in clinical testing conditions is crucial for accurately evaluating patients' response to immunotherapy.

Prospects of liquid biopsy in the prognosis and clinical management of gastrointestinal cancers

Gastrointestinal (GI) cancers account for one-fourth of the global cancer incidence and are incriminated to cause one-third of cancer-related deaths. GI cancer includes esophageal, gastric, liver, pancreatic, and colorectal cancers, mostly diagnosed at advanced stages due to a lack of accurate markers for early stages. The invasiveness of diagnostic methods like colonoscopy for solid biopsy reduces patient compliance as it cannot be frequently used to screen patients. Therefore, minimally invasive approaches like liquid biopsy may be explored for screening and early identification of gastrointestinal cancers. Liquid biopsy involves the qualitative and quantitative determination of certain cancer-specific biomarkers in body fluids such as blood, serum, saliva, and urine to predict disease progression, therapeutic tolerance, toxicities, and recurrence by evaluating minimal residual disease and its correlation with other clinical features. In this review, we deliberate upon various tumor-specific cellular and molecular entities such as circulating tumor cells (CTCs), tumor-educated platelets (TEPs), circulating tumor DNA (ctDNA), cell-free DNA (cfDNA), exosomes, and exosome-derived biomolecules and cite recent advances pertaining to their use in predicting disease progression, therapy response, or risk of relapse. We also discuss the technical challenges associated with translating liquid biopsy into clinical settings for various clinical applications in gastrointestinal cancers.

Scuphr: A probabilistic framework for cell lineage tree reconstruction

Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.

Clinical Practice Guideline for Blood-based Circulating Tumor DNA Assays

Circulating tumor DNA (ctDNA) has emerged as a promising tool for various clinical applications, including early diagnosis, therapeutic target identification, treatment response monitoring, prognosis evaluation, and minimal residual disease detection. Consequently, ctDNA assays have been incorporated into clinical practice. In this review, we offer an in-depth exploration of the clinical implementation of ctDNA assays. Notably, we examined existing evidence related to pre-analytical procedures, analytical components in current technologies, and result interpretation and reporting processes. The primary objective of this guidelines is to provide recommendations for the clinical utilization of ctDNA assays.

The Clinical Validity of Urinary Pellet DNA Monitoring for the Diagnosis of Recurrent Bladder Cancer

The aim of this study was to evaluate the clinical validity of monitoring urine pellet DNA (upDNA) of bladder cancer (BC) by digital PCR (dPCR) as a biomarker for early recurrence prediction, treatment efficacy evaluation, and no-recurrence corroboration. Tumor panel sequencing was first performed to select patient-unique somatic mutations to monitor both upDNA and circulating tumor DNA (ctDNA) by dPCR. For longitudinal monitoring using upDNA as well as plasma ctDNA, an average of 7.2 (range, 2 to 12) time points per case were performed with the dPCR assay for 32 previously treated and untreated patients with BC. Clinical recurrence based on imaging and urine cytology was compared using upDNA variant allele frequency (VAF) dynamics. A continuous increasing trend of upDNA VAF ≥1% was considered to indicate molecular recurrence. Most (30/32; 93.8%) cases showed at least one traceable somatic mutation. In 5 of 7 cases (71.4%) with clinical recurrence, upDNA VAF >1% was detected 7 to 15 months earlier than the imaging diagnosis. The upDNA VAF remained high after initial treatment for locally recurrent cases. The clinical validity of upDNA monitoring was confirmed with the observation that 26 of 30 cases (86.7%) were traceable. Local recurrences were not indicated by ctDNA alone. The results support the clinical validity of upDNA monitoring in the management of recurrent BC.

Subclonal Cancer Driver Mutations Are Prevalent in the Unresected Peritumoral Edema of Adult Diffuse Gliomas

Adult diffuse gliomas commonly recur regardless of therapy. As recurrence typically arises from the peritumoral edema adjacent to the resected bulk tumor, the profiling of somatic mutations from infiltrative malignant cells within this critical, unresected region could provide important insights into residual disease. A key obstacle has been the inability to distinguish between next-generation sequencing (NGS) noise and the true but weak signal from tumor cells hidden among the noncancerous brain tissue of the peritumoral edema. Here, we developed and validated True2 sequencing to reduce NGS-associated errors to <1 false positive/100 kb panel positions while detecting 97.6% of somatic mutations with an allele frequency ≥0.1%. True2 was then used to study the tumor and peritumoral edema of 22 adult diffuse gliomas including glioblastoma, astrocytoma, oligodendroglioma, and NF1-related low-grade neuroglioma. The tumor and peritumoral edema displayed a similar mutation burden, indicating that surgery debulks these cancers physically but not molecularly. Moreover, variants in the peritumoral edema included unique cancer driver mutations absent in the bulk tumor. Finally, analysis of multiple samples from each patient revealed multiple subclones with unique mutations in the same gene in 17 of 22 patients, supporting the occurrence of convergent evolution in response to patient-specific selective pressures in the tumor microenvironment that may form the molecular foundation of recurrent disease. Collectively, True2 enables the detection of ultralow frequency mutations during molecular analyses of adult diffuse gliomas, which is necessary to understand cancer evolution, recurrence, and individual response to therapy.

SIGNIFICANCE

True2 is a next-generation sequencing workflow that facilitates unbiased discovery of somatic mutations across the full range of variant allele frequencies, which could help identify residual disease vulnerabilities for targeted adjuvant therapies.

Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome

Singleton or low-frequency driver mutations are challenging to identify. We present a domain driver mutation estimator (DOME) to identify rare candidate driver mutations. DOME analyzes positions analogous to known statistical hotspots and resistant mutations in combination with their functional and biochemical residue context as determined by protein structures and somatic mutation propensity within conserved PFAM domains, integrating the CADD scoring scheme. Benchmarked against seven other tools, DOME exhibited superior or comparable accuracy compared to all evaluated tools in the prediction of functional cancer drivers, with the exception of one tool. DOME identified a unique set of 32 917 high-confidence predicted driver mutations from the analysis of whole proteome missense variants within domain boundaries across 1331 genes, including 1192 noncancer gene census genes, emphasizing its unique place in cancer genome analysis. Additionally, analysis of 8799 TCGA (The Cancer Genome Atlas) and in-house tumor samples revealed 847 potential driver mutations, with mutations in tyrosine kinase members forming the dominant burden, underscoring its higher significance in cancer. Overall, DOME complements current approaches for identifying novel, low-frequency drivers and resistant mutations in personalized therapy.

Characterization and mitigation of artifacts derived from NGS library preparation due to structure-specific sequences in the human genome

BACKGROUND

Hybridization capture-based targeted next generation sequencing (NGS) is gaining importance in routine cancer clinical practice. DNA library preparation is a fundamental step to produce high-quality sequencing data. Numerous unexpected, low variant allele frequency calls were observed in libraries using sonication fragmentation and enzymatic fragmentation. In this study, we investigated the characteristics of the artifact reads induced by sonication and enzymatic fragmentation. We also developed a bioinformatic algorithm to filter these sequencing errors.

RESULTS

We used pairwise comparisons of somatic single nucleotide variants (SNVs) and insertions and deletions (indels) of the same tumor DNA samples prepared using both ultrasonic and enzymatic fragmentation protocols. Our analysis revealed that the number of artifact variants was significantly greater in the samples generated using enzymatic fragmentation than using sonication. Most of the artifacts derived from the sonication-treated libraries were chimeric artifact reads containing both cis- and trans-inverted repeat sequences of the genomic DNA. In contrast, chimeric artifact reads of endonuclease-treated libraries contained palindromic sequences with mismatched bases. Based on these distinctive features, we proposed a mechanistic hypothesis model, PDSM (pairing of partial single strands derived from a similar molecule), by which these sequencing errors derive from ultrasonication and enzymatic fragmentation library preparation. We developed a bioinformatic algorithm to generate a custom mutation "blacklist" in the BED region to reduce errors in downstream analyses.

CONCLUSIONS

We first proposed a mechanistic hypothesis model (PDSM) of sequencing errors caused by specific structures of inverted repeat sequences and palindromic sequences in the natural genome. This new hypothesis predicts the existence of chimeric reads that could not be explained by previous models, and provides a new direction for further improving NGS analysis accuracy. A bioinformatic algorithm, ArtifactsFinder, was developed and used to reduce the sequencing errors in libraries produced using sonication and enzymatic fragmentation.

Liquid Biopsy for Glioma Using Cell-Free DNA in Cerebrospinal Fluid

Glioma is one of the most common primary central nervous system (CNS) tumors, and its molecular diagnosis is crucial. However, surgical resection or biopsy is risky when the tumor is located deep in the brain or brainstem. In such cases, a minimally invasive approach to liquid biopsy is beneficial. Cell-free DNA (cfDNA), which directly reflects tumor-specific genetic changes, has attracted attention as a target for liquid biopsy, and blood-based cfDNA monitoring has been demonstrated for other extra-cranial cancers. However, it is still challenging to fully detect CNS tumors derived from cfDNA in the blood, including gliomas, because of the unique structure of the blood-brain barrier. Alternatively, cerebrospinal fluid (CSF) is an ideal source of cfDNA and is expected to contribute significantly to the liquid biopsy of gliomas. Several successful studies have been conducted to detect tumor-specific genetic alterations in cfDNA from CSF using digital PCR and/or next-generation sequencing. This review summarizes the current status of CSF-based cfDNA-targeted liquid biopsy for gliomas. It highlights how the approaches differ from liquid biopsies of other extra-cranial cancers and discusses the current issues and prospects.

MetaQuad: shared informative variants discovery in metagenomic samples

Motivation

Strain-level analysis of metagenomic data has garnered significant interest in recent years. Microbial single nucleotide polymorphisms (SNPs) are genomic variants that can reflect strain-level differences within a microbial species. The diversity and emergence of SNPs in microbial genomes may reveal evolutionary history and environmental adaptation in microbial populations. However, efficient discovery of shared polymorphic variants in a large collection metagenomic samples remains a computational challenge.

Results

MetaQuad utilizes a density-based clustering technique to effectively distinguish between shared variants and non-polymorphic sites using shotgun metagenomic data. Empirical comparisons with other state-of-the-art methods show that MetaQuad significantly reduces the number of false positive SNPs without greatly affecting the true positive rate. We used MetaQuad to identify antibiotic-associated variants in patients who underwent Helicobacter pylori eradication therapy. MetaQuad detected 7591 variants across 529 antibiotic resistance genes. The nucleotide diversity of some genes is increased 6 weeks after antibiotic treatment, potentially indicating the role of these genes in specific antibiotic treatments.

Availability and implementation

MetaQuad is an open-source Python package available via https://github.com/holab-hku/MetaQuad.

SJPedPanel: A pan-cancer gene panel for childhood malignancies

Background

Large scale genomics projects have identified driver alterations for most childhood cancers that provide reliable biomarkers for clinical diagnosis and disease monitoring using targeted sequencing. However, there is lack of a comprehensive panel that matches the list of known driver genes. Here we fill this gap by developing SJPedPanel for childhood cancers.

Results

SJPedPanel covers 5,275 coding exons of 357 driver genes, 297 introns frequently involved in rearrangements that generate fusion oncoproteins, commonly amplified/deleted regions (e.g., MYCN for neuroblastoma, CDKN2A and PAX5 for B-/T-ALL, and SMARCB1 for AT/RT), and 7,590 polymorphism sites for interrogating tumors with aneuploidy, such as hyperdiploid and hypodiploid B-ALL or 17q gain neuroblastoma. We used driver alterations reported from an established real-time clinical genomics cohort (n=253) to validate this gene panel. Among the 485 pathogenic variants reported, our panel covered 417 variants (86%). For 90 rearrangements responsible for oncogenic fusions, our panel covered 74 events (82%). We re-sequenced 113 previously characterized clinical specimens at an average depth of 2,500X using SJPedPanel and recovered 354 (91%) of the 389 reported pathogenic variants. We then investigated the power of this panel in detecting mutations from specimens with low tumor purity (as low as 0.1%) using cell line-based dilution experiments and discovered that this gene panel enabled us to detect ∼80% variants with allele fraction of 0.2%, while the detection rate decreases to ∼50% when the allele fraction is 0.1%. We finally demonstrate its utility in disease monitoring on clinical specimens collected from AML patients in morphologic remission.

Conclusions

SJPedPanel enables the detection of clinically relevant genetic alterations including rearrangements responsible for subtype-defining fusions for childhood cancers by targeted sequencing of ∼0.15% of human genome. It will enhance the analysis of specimens with low tumor burdens for cancer monitoring and early detection.

High-Yield Lasso Peptide Production in a Burkholderia Bacterial Host by Plasmid Copy Number Engineering

The knotted configuration of lasso peptides confers thermal stability and proteolytic resistance, addressing two shortcomings of peptide-based drugs. However, low isolation yields hinder the discovery and development of lasso peptides. While testing Burkholderia sp. FERM BP-3421 as a bacterial host to produce the lasso peptide capistruin, an overproducer clone was previously identified. In this study, we show that an increase in the plasmid copy number partially contributed to the overproducer phenotype. Further, we modulated the plasmid copy number to recapitulate titers to an average of 160% relative to the overproducer, which is 1000-fold higher than previously reported with E. coli, reaching up to 240 mg/L. To probe the applicability of the developed tools for lasso peptide discovery, we targeted a new lasso peptide biosynthetic gene cluster from endosymbiont Mycetohabitans sp. B13, leading to the isolation of mycetolassin-15 and mycetolassin-18 in combined titers of 11 mg/L. These results validate Burkholderia sp. FERM BP-3421 as a production platform for lasso peptide discovery.

Diversity of antibiotic resistance gene variants at subsequent stages of the wastewater treatment process revealed by a metagenomic analysis of PCR amplicons

Wastewater treatment plants have been recognised as point sources of various antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARG) which are considered recently emerging biological contaminants. So far, culture-based and molecular-based methods have been successfully applied to monitor antimicrobial resistance (AMR) in WWTPs. However, the methods applied do not permit the comprehensive identification of the true diversity of ARGs. In this study we applied next-generation sequencing for a metagenomic analysis of PCR amplicons of ARGs from the subsequent stages of the analysed WWTP. The presence of 14 genes conferring resistance to different antibiotic families was screened by PCR. In the next step, three genes were selected for detailed analysis of changes of the profile of ARG variants along the process. A relative abundance of 79 variants was analysed. The highest diversity was revealed in the ermF gene, with 52 variants. The relative abundance of some variants changed along the purification process, and some ARG variants might be present in novel hosts for which they were currently unassigned. Additionally, we identified a pool of novel ARG variants present in the studied WWTP. Overall, the results obtained indicated that the applied method is sufficient for analysing ARG variant diversity.

Mitochondrial point heteroplasmy: insights from deep-sequencing of human replicate samples

BACKGROUND

Human mitochondrial heteroplasmy is an extensively investigated phenomenon in the context of medical diagnostics, forensic identification and molecular evolution. However, technical limitations of high-throughput sequencing hinder reliable determination of point heteroplasmies (PHPs) with minor allele frequencies (MAFs) within the noise threshold.

RESULTS

To investigate the PHP landscape at an MAF threshold down to 0.1%, we sequenced whole mitochondrial genomes at approximately 7.700x coverage, in multiple technical and biological replicates of longitudinal blood and buccal swab samples from 11 human donors (159 libraries in total). The results obtained by two independent sequencing platforms and bioinformatics pipelines indicate distinctive PHP patterns below and above the 1% MAF cut-off. We found a high inter-individual prevalence of low-level PHPs (MAF < 1%) at polymorphic positions of the mitochondrial DNA control region (CR), their tissue preference, and a tissue-specific minor allele linkage. We also established the position-dependent potential of minor allele expansion in PHPs, and short-term PHP instability in a mitotically active tissue. We demonstrate that the increase in sensitivity of PHP detection to minor allele frequencies below 1% within a robust experimental and analytical pipeline, provides new information with potential applicative value.

CONCLUSIONS

Our findings reliably show different mutational loads between tissues at sub-1% allele frequencies, which may serve as an informative medical biomarker of time-dependent, tissue-specific mutational burden, or help discriminate forensically relevant tissues in a single person, close maternal relatives or unrelated individuals of similar phylogenetic background.

Gut virome in inflammatory bowel disease and beyond

OBJECTIVE

The gut virome is a dense community of viruses inhabiting the gastrointestinal tract and an integral part of the microbiota. The virome coexists with the other components of the microbiota and with the host in a dynamic equilibrium, serving as a key contributor to the maintenance of intestinal homeostasis and functions. However, this equilibrium can be interrupted in certain pathological states, including inflammatory bowel disease, causing dysbiosis that may participate in disease pathogenesis. Nevertheless, whether virome dysbiosis is a causal or bystander event requires further clarification.

DESIGN

This review seeks to summarise the latest advancements in the study of the gut virome, highlighting its cross-talk with the mucosal microenvironment. It explores how cutting-edge technologies may build upon current knowledge to advance research in this field. An overview of virome transplantation in diseased gastrointestinal tracts is provided along with insights into the development of innovative virome-based therapeutics to improve clinical management.

RESULTS

Gut virome dysbiosis, primarily driven by the expansion of Caudovirales, has been shown to impact intestinal immunity and barrier functions, influencing overall intestinal homeostasis. Although emerging innovative technologies still need further implementation, they display the unprecedented potential to better characterise virome composition and delineate its role in intestinal diseases.

CONCLUSIONS

The field of gut virome is progressively expanding, thanks to the advancements of sequencing technologies and bioinformatic pipelines. These have contributed to a better understanding of how virome dysbiosis is linked to intestinal disease pathogenesis and how the modulation of virome composition may help the clinical intervention to ameliorate gut disease management.

Highly Accurate Sequence- and Position-Independent Error Profiling of DNA Synthesis and Sequencing

A comprehensive error analysis of DNA-stored data during processing, such as DNA synthesis and sequencing, is crucial for reliable DNA data storage. Both synthesis and sequencing errors depend on the sequence and the transition of bases of nucleotides; ignoring either one of the error sources leads to technical challenges in minimizing the error rate. Here, we present a methodology and toolkit that utilizes an oligonucleotide library generated from a 10-base-shifted sequence array, which is individually labeled with unique molecular identifiers, to delineate and profile DNA synthesis and sequencing errors simultaneously. This methodology enables position- and sequence-independent error profiling of both DNA synthesis and sequencing. Using this toolkit, we report base transitional errors in both synthesis and sequencing in general DNA data storage as well as degenerate-base-augmented DNA data storage. The methodology and data presented will contribute to the development of DNA sequence designs with minimal error.

Correlation between variant allele frequency and mean tumor molecules with tumor burden in patients with solid tumors

Several studies have demonstrated the prognostic value of circulating tumor DNA (ctDNA); however, the correlation of mean tumor molecules (MTM)/ml of plasma and mean variant allele frequency (mVAF; %) with clinical parameters is yet to be understood. In this study, we analyzed ctDNA data in a pan-cancer cohort of 23 543 patients who had ctDNA testing performed using a personalized, tumor-informed assay (Signatera™, mPCR-NGS assay). For ctDNA-positive patients, the correlation between MTM/ml and mVAF was examined. Two subanalyses were performed: (a) to establish the association of ctDNA with tumor volume and (b) to assess the correlation between ctDNA dynamics and patient outcomes. On a global cohort, a positive correlation between MTM/ml and mVAF was observed. Among 18 426 patients with longitudinal ctDNA measurements, 13.3% had discordant trajectories between MTM/ml and mVAF at subsequent time points. In metastatic patients receiving immunotherapy (N = 51), changes in ctDNA levels expressed both in MTM/ml and mVAF showed a statistically significant association with progression-free survival; however, the correlation with MTM/ml was numerically stronger.

Underreporting of SMARCB1 alteration by clinical sequencing: Integrative patho-genomic analysis captured SMARCB1/INI-1 deficiency in a vulvar yolk sac tumor

•SMARCB1/INI1-deficient gynecologic tumors are rare and clinically aggressive. A subset shows primitive yolk sac tumor features.•Due to technical limitation of next generation sequencing (NGS) and interlaboratory variability in sequencing methodologies and analytical pipelines, SMARCB1 deficiency caused by somatic copy number variations (SCNV) may be underreported by NGS.•To improve identification of SMARCB1/INI1-deficient neoplasm, we propose the following strategy: First, careful pathology slide review and detection of rhabdoid cells should raise the possibility of SMARCB1/INI1 deficiency. Second, INI1 IHC is a useful complementary test to exclude clinical suspicion of SMARCB1 deficiency in the context of negative molecular reporting. Third, knowledge of potential underreporting of SMARCB1 mutation would avoid underdiagnosis.

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.

Merging short and stranded long reads improves transcript assembly

Long-read RNA sequencing has arisen as a counterpart to short-read sequencing, with the potential to capture full-length isoforms, albeit at the cost of lower depth. Yet this potential is not fully realized due to inherent limitations of current long-read assembly methods and underdeveloped approaches to integrate short-read data. Here, we critically compare the existing methods and develop a new integrative approach to characterize a particularly challenging pool of low-abundance long noncoding RNA (lncRNA) transcripts from short- and long-read sequencing in two distinct cell lines. Our analysis reveals severe limitations in each of the sequencing platforms. For short-read assemblies, coverage declines at transcript termini resulting in ambiguous ends, and uneven low coverage results in segmentation of a single transcript into multiple transcripts. Conversely, long-read sequencing libraries lack depth and strand-of-origin information in cDNA-based methods, culminating in erroneous assembly and quantitation of transcripts. We also discover a cDNA synthesis artifact in long-read datasets that markedly impacts the identity and quantitation of assembled transcripts. Towards remediating these problems, we develop a computational pipeline to "strand" long-read cDNA libraries that rectifies inaccurate mapping and assembly of long-read transcripts. Leveraging the strengths of each platform and our computational stranding, we also present and benchmark a hybrid assembly approach that drastically increases the sensitivity and accuracy of full-length transcript assembly on the correct strand and improves detection of biological features of the transcriptome. When applied to a challenging set of under-annotated and cell-type variable lncRNA, our method resolves the segmentation problem of short-read sequencing and the depth problem of long-read sequencing, resulting in the assembly of coherent transcripts with precise 5' and 3' ends. Our workflow can be applied to existing datasets for superior demarcation of transcript ends and refined isoform structure, which can enable better differential gene expression analyses and molecular manipulations of transcripts.

Evaluating the use of non-invasive hair sampling and ddRAD to characterize populations of endangered species: Application to a peripheral population of the European mink

The application of next-generation sequencing (NGS) to non-invasive samples is one of the most promising methods in conservation genomics, but these types of samples present significant challenges for NGS. The European mink (Mustela lutreola) is critically endangered throughout its range. However, important aspects such as census size and inbreeding remain still unknown in many populations, so it is crucial to develop new methods to monitor this species. In this work, we placed hair tubes along riverbanks in a border area of the Iberian population, which allowed the genetic identification of 76 European mink hair samples. We then applied a reduced representation genomic sequencing (ddRAD) technique to a subset of these samples to test whether we could extract sufficient genomic information from them. We show that several problems with the DNA, including contamination, fragmentation, oxidation, and possibly sample mixing, affected the samples. Using various bioinformatic techniques to reduce these problems, we were able to unambiguously genotype 19 hair samples belonging to six individuals. This small number of individuals showed that the demographic status of the species in this peripheral population is worse than expected. The data obtained also allowed us to perform preliminary analyses of relatedness and inbreeding. Although further improvements in sampling and analysis are needed, the application of the ddRAD technique to non-invasively obtained hairs represents a significant advance in the genomic study of endangered species.

Mapinsights: deep exploration of quality issues and error profiles in high-throughput sequence data

High-throughput sequencing (HTS) has revolutionized science by enabling super-fast detection of genomic variants at base-pair resolution. Consequently, it poses the challenging problem of identification of technical artifacts, i.e. hidden non-random error patterns. Understanding the properties of sequencing artifacts holds the key in separating true variants from false positives. Here, we develop Mapinsights, a toolkit that performs quality control (QC) analysis of sequence alignment files, capable of detecting outliers based on sequencing artifacts of HTS data at a deeper resolution compared with existing methods. Mapinsights performs a cluster analysis based on novel and existing QC features derived from the sequence alignment for outlier detection. We applied Mapinsights on community standard open-source datasets and identified various quality issues including technical errors related to sequencing cycles, sequencing chemistry, sequencing libraries and across various orthogonal sequencing platforms. Mapinsights also enables identification of anomalies related to sequencing depth. A logistic regression-based model built on the features of Mapinsights shows high accuracy in detecting 'low-confidence' variant sites. Quantitative estimates and probabilistic arguments provided by Mapinsights can be utilized in identifying errors, bias and outlier samples, and also aid in improving the authenticity of variant calls.

Intronic position +9 and -9 are potentially splicing sites boundary from intronic variants analysis of whole exome sequencing data

Whole exome sequencing (WES) can also detect some intronic variants, which may affect splicing and gene expression, but how to use these intronic variants, and the characteristics about them has not been reported. This study aims to reveal the characteristics of intronic variant in WES data, to further improve the clinical diagnostic value of WES. A total of 269 WES data was analyzed, 688,778 raw variants were called, among these 367,469 intronic variants were in intronic regions flanking exons which was upstream/downstream region of the exon (default is 200 bps). Contrary to expectation, the number of intronic variants with quality control (QC) passed was the lowest at the +2 and -2 positions but not at the +1 and -1 positions. The plausible explanation was that the former had the worst effect on trans-splicing, whereas the latter did not completely abolish splicing. And surprisingly, the number of intronic variants that passed QC was the highest at the +9 and -9 positions, indicating a potential splicing site boundary. The proportion of variants which could not pass QC filtering (false variants) in the intronic regions flanking exons generally accord with "S"-shaped curve. At +5 and -5 positions, the number of variants predicted damaging by software was most. This was also the position at which many pathogenic variants had been reported in recent years. Our study revealed the characteristics of intronic variant in WES data for the first time, we found the +9 and -9 positions might be a potentially splicing sites boundary and +5 and -5 positions were potentially important sites affecting splicing or gene expression, the +2 and -2 positions seem more important splicing site than +1 and -1 positions, and we found variants in intronic regions flanking exons over ± 50 bps may be unreliable. This result can help researchers find more useful variants and demonstrate that WES data is valuable for intronic variants analysis.

Identification of Somatic Mutations in Plasma Cell-Free DNA from Patients with Metastatic Oral Squamous Cell Carcinoma

The accurate diagnosis and treatment of oral squamous cell carcinoma (OSCC) requires an understanding of its genomic alterations. Liquid biopsies, especially cell-free DNA (cfDNA) analysis, are a minimally invasive technique used for genomic profiling. We conducted comprehensive whole-exome sequencing (WES) of 50 paired OSCC cell-free plasma with whole blood samples using multiple mutation calling pipelines and filtering criteria. Integrative Genomics Viewer (IGV) was used to validate somatic mutations. Mutation burden and mutant genes were correlated to clinico-pathological parameters. The plasma mutation burden of cfDNA was significantly associated with clinical staging and distant metastasis status. The genes TTN, PLEC, SYNE1, and USH2A were most frequently mutated in OSCC, and known driver genes, including KMT2D, LRP1B, TRRAP, and FLNA, were also significantly and frequently mutated. Additionally, the novel mutated genes CCDC168, HMCN2, STARD9, and CRAMP1 were significantly and frequently present in patients with OSCC. The mutated genes most frequently found in patients with metastatic OSCC were RORC, SLC49A3, and NUMBL. Further analysis revealed that branched-chain amino acid (BCAA) catabolism, extracellular matrix-receptor interaction, and the hypoxia-related pathway were associated with OSCC prognosis. Choline metabolism in cancer, O-glycan biosynthesis, and protein processing in the endoplasmic reticulum pathway were associated with distant metastatic status. About 20% of tumors carried at least one aberrant event in BCAA catabolism signaling that could possibly be targeted by an approved therapeutic agent. We identified molecular-level OSCC that were correlated with etiology and prognosis while defining the landscape of major altered events of the OSCC plasma genome. These findings will be useful in the design of clinical trials for targeted therapies and the stratification of patients with OSCC according to therapeutic efficacy.

The effect of low-abundance OTU filtering methods on the reliability and variability of microbial composition assessed by 16S rRNA amplicon sequencing

PCR amplicon sequencing may lead to detection of spurious operational taxonomic units (OTUs), inflating estimates of gut microbial diversity. There is no consensus in the analytical approach as to what filtering methods should be applied to remove low-abundance OTUs; moreover, few studies have investigated the reliability of OTU detection within replicates. Here, we investigated the reliability of OTU detection (% agreement in detecting OTU in triplicates) and accuracy of their quantification (assessed by coefficient of variation (CV)) in human stool specimens. Stool samples were collected from 12 participants 22-55 years old. We applied several methods for filtering low-abundance OTUs and determined their impact on alpha-diversity and beta-diversity metrics. The reliability of OTU detection without any filtering was only 44.1% (SE=0.9) but increased after filtering low-abundance OTUs. After filtering OTUs with <0.1% abundance in the dataset, the reliability increased to 87.7% (SE=0.6) but at the expense of removing 6.97% reads from the dataset. When filtering was based on individual sample, the reliability increased to 73.1% after filtering OTUs with <10 copies while removing only 1.12% of reads. High abundance OTUs (>10 copies in sample) had lower CV, indicating better accuracy of quantification than low-abundance OTUs. Excluding very low-abundance OTUs had a significant impact on alpha-diversity metrics sensitive to the presence of rare species (observed OTUs, Chao1) but had little impact on relative abundance of major phyla and families and alpha-diversity metrics accounting for both richness and evenness (Shannon, Inverse Simpson). To increase the reliability of microbial composition, we advise removing OTUs with <10 copies in individual samples, particularly in studies where only one subsample per specimen is available for analysis.

Discordant calls across genotype discovery approaches elucidate variants with systematic errors

Large-scale high-throughput sequencing data sets have been transformative for informing clinical variant interpretation and for use as reference panels for statistical and population genetic efforts. Although such resources are often treated as ground truth, we find that in widely used reference data sets such as the Genome Aggregation Database (gnomAD), some variants pass gold-standard filters, yet are systematically different in their genotype calls across genotype discovery approaches. The inclusion of such discordant sites in study designs involving multiple genotype discovery strategies could bias results and lead to false-positive hits in association studies owing to technological artifacts rather than a true relationship to the phenotype. Here, we describe this phenomenon of discordant genotype calls across genotype discovery approaches, characterize the error mode of wrong calls, provide a list of discordant sites identified in gnomAD that should be treated with caution in analyses, and present a metric and machine learning classifier trained on gnomAD data to identify likely discordant variants in other data sets. We find that different genotype discovery approaches have different sets of variants at which this problem occurs, but there are characteristic variant features that can be used to predict discordant behavior. Discordant sites are largely shared across ancestry groups, although different populations are powered for the discovery of different variants. We find that the most common error mode is that of a variant being heterozygous for one approach and homozygous for the other, with heterozygous in the genomes and homozygous reference in the exomes making up the majority of miscalls.

Population structure and hybridization under contemporary and future climates in a heteroploid foundational shrub species (Artemisia tridentata)

Current and past climatic changes can shift plant climatic niches, which may cause spatial overlap or separation between related taxa. The former often leads to hybridization and introgression, which may generate novel variation and influence the adaptive capacity of plants. An additional mechanism facilitating adaptations to novel environments and an important evolutionary driver in plants is polyploidy as the result of whole genome duplication. Artemisia tridentata (big sagebrush) is a landscape-dominating foundational shrub in the western United States which occupies distinct ecological niches, exhibiting diploid and tetraploid cytotypes. Tetraploids have a large impact on the species' landscape dominance as they occupy a preponderance of the arid spectrum of A. tridentata range. Three distinct subspecies are recognized, which co-occur in ecotones - the transition zone between two or more distinct ecological niches - allowing for hybridization and introgression. Here we assess the genomic distinctiveness and extent of hybridization among subspecies at different ploidies under both contemporary and predicted future climates. We sampled five transects throughout the western United States where a subspecies overlap was predicted using subspecies-specific climate niche models. Along each transect, we sampled multiple plots representing the parental and the potential hybrid habitats. We performed reduced representation sequencing and processed the data using a ploidy-informed genotyping approach. Population genomic analyses revealed distinct diploid subspecies and at least two distinct tetraploid gene pools, indicating independent origins of the tetraploid populations. We detected low levels of hybridization (2.5%) between the diploid subspecies, while we found evidence for increased admixture between ploidy levels (18%), indicating hybridization has an important role in the formation of tetraploids. Our analyses highlight the importance of subspecies co-occurrence within these ecotones to maintain gene exchange and potential formation of tetraploid populations. Genomic confirmations of subspecies in the ecotones support the subspecies overlap predicted by the contemporary climate niche models. However, future mid-century projections of subspecies niches predict a substantial loss in range and subspecies overlap. Thus, reductions in hybridization potential could affect new recruitment of genetically variable tetraploids that are vital to this species' ecological role. Our results underscore the importance of ecotone conservation and restoration.

Benefits of applying molecular barcoding systems are not uniform across different genomic applications

BACKGROUND

Despite the wide variety of Next Generation Sequencing (NGS)-based methods, it remains challenging to detect mutations present at very low frequencies. This problem is particularly relevant in oncology, where the limiting amount of input material, and its low quality, often limit the performance of the assays. Unique Molecular Identifiers (UMIs) are a molecular barcoding system often coupled with computational methods of noise suppression to improve the reliability of detection of rare variants. Although widely adopted, UMI inclusion imposes additional technical complexity and sequencing cost. Currently, there are no guidelines on UMI usage nor a comprehensive evaluation of their advantage across different applications.

METHODS

We used DNA sequencing data generated by molecular barcoding and hybridization-based enrichment, from various types and quantities of input material (fresh frozen, formaldehyde-treated and cell-free DNA), to evaluate the performance of variant calling in different clinically relevant contexts.

RESULTS

Noise suppression achieved by read grouping based on fragment mapping positions ensures reliable variant calling for many experimental designs even without exogenous UMIs. Exogenous barcodes significantly improve performance only when mapping position collisions occur, which is common in cell-free DNA.

CONCLUSIONS

We demonstrate that UMI usage is not universally beneficial across experimental designs and that it is worthwhile to critically consider the comparative advantage of UMI usage for a given NGS application prior to experimental design.

DREAMS: deep read-level error model for sequencing data applied to low-frequency variant calling and circulating tumor DNA detection

Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.

Single-Cell Next-Generation Sequencing to Monitor Hematopoietic Stem-Cell Transplantation: Current Applications and Future Perspectives

Acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) are genetically complex and diverse diseases. Such complexity makes challenging the monitoring of response to treatment. Measurable residual disease (MRD) assessment is a powerful tool for monitoring response and guiding therapeutic interventions. This is accomplished through targeted next-generation sequencing (NGS), as well as polymerase chain reaction and multiparameter flow cytometry, to detect genomic aberrations at a previously challenging leukemic cell concentration. A major shortcoming of NGS techniques is the inability to discriminate nonleukemic clonal hematopoiesis. In addition, risk assessment and prognostication become more complicated after hematopoietic stem-cell transplantation (HSCT) due to genotypic drift. To address this, newer sequencing techniques have been developed, leading to more prospective and randomized clinical trials aiming to demonstrate the prognostic utility of single-cell next-generation sequencing in predicting patient outcomes following HSCT. This review discusses the use of single-cell DNA genomics in MRD assessment for AML/MDS, with an emphasis on the HSCT time period, including the challenges with current technologies. We also touch on the potential benefits of single-cell RNA sequencing and analysis of accessible chromatin, which generate high-dimensional data at the cellular resolution for investigational purposes, but not currently used in the clinical setting.

Next Generation Sequencing (NGS) Target Approach for Undiagnosed Dysglycaemia

Next-generation sequencing (NGS) has revolutionized the field of genomics and created new opportunities for basic research. We described the strategy for the NGS validation of the "dysglycaemia panel" composed by 44 genes related to glucose metabolism disorders (MODY, Wolfram syndrome) and familial renal glycosuria using Ion AmpliSeq technology combined with Ion-PGM. Anonymized DNA of 32 previously genotyped cases with 33 different variants were used to optimize the methodology. Standard protocol was used to generate the primer design, library, template preparation, and sequencing. Ion Reporter tool was used for data analysis. In all the runs, the mean coverage was over 200×. Twenty-nine out of thirty three variants (96.5%) were detected; four frameshift variants were missed. All point mutations were detected with high sensitivity. We identified three further variants of unknown significance in addition to pathogenic mutations previously identified by Sanger sequencing. The NGS panel allowed us to identify pathogenic variants in multiple genes in a short time. This could help to identify several defects in children and young adults that have to receive the genetic diagnosis necessary for optimal treatment. In order not to lose any pathogenic variants, Sanger sequencing is included in our analytical protocol to avoid missing frameshift variants.

satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect

The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.

Etiology of oncogenic fusions in 5,190 childhood cancers and its clinical and therapeutic implication

Oncogenic fusions formed through chromosomal rearrangements are hallmarks of childhood cancer that define cancer subtype, predict outcome, persist through treatment, and can be ideal therapeutic targets. However, mechanistic understanding of the etiology of oncogenic fusions remains elusive. Here we report a comprehensive detection of 272 oncogenic fusion gene pairs by using tumor transcriptome sequencing data from 5190 childhood cancer patients. We identify diverse factors, including translation frame, protein domain, splicing, and gene length, that shape the formation of oncogenic fusions. Our mathematical modeling reveals a strong link between differential selection pressure and clinical outcome in CBFB-MYH11. We discover 4 oncogenic fusions, including RUNX1-RUNX1T1, TCF3-PBX1, CBFA2T3-GLIS2, and KMT2A-AFDN, with promoter-hijacking-like features that may offer alternative strategies for therapeutic targeting. We uncover extensive alternative splicing in oncogenic fusions including KMT2A-MLLT3, KMT2A-MLLT10, C11orf95-RELA, NUP98-NSD1, KMT2A-AFDN and ETV6-RUNX1. We discover neo splice sites in 18 oncogenic fusion gene pairs and demonstrate that such splice sites confer therapeutic vulnerability for etiology-based genome editing. Our study reveals general principles on the etiology of oncogenic fusions in childhood cancer and suggests profound clinical implications including etiology-based risk stratification and genome-editing-based therapeutics.

Benchmarking machine learning robustness in Covid-19 genome sequence classification

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics.

BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis

Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their hash values. However, these hash values can be used only for finding exact-matching seeds as the conventional hashing methods assign distinct hash values for different seeds, including highly similar seeds. Finding only exact-matching seeds causes either (i) increasing the use of the costly sequence alignment or (ii) limited sensitivity. We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches. BLEND (i) utilizes a technique called SimHash, that can generate the same hash value for similar sets, and (ii) provides the proper mechanisms for using seeds as sets with the SimHash technique to find fuzzy seed matches efficiently. We show the benefits of BLEND when used in read overlapping and read mapping. For read overlapping, BLEND is faster by 2.4×-83.9× (on average 19.3×), has a lower memory footprint by 0.9×-14.1× (on average 3.8×), and finds higher quality overlaps leading to accurate de novo assemblies than the state-of-the-art tool, minimap2. For read mapping, BLEND is faster by 0.8×-4.1× (on average 1.7×) than minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND.

Democratizing clinical-genomic data: How federated platforms can promote benefits sharing in genomics

Since the first sequencing of the human genome, associated sequencing costs have dramatically lowered, leading to an explosion of genomic data. This valuable data should in theory be of huge benefit to the global community, although unfortunately the benefits of these advances have not been widely distributed. Much of today's clinical-genomic data is siloed and inaccessible in adherence with strict governance and privacy policies, with more than 97% of hospital data going unused, according to one reference. Despite these challenges, there are promising efforts to make clinical-genomic data accessible and useful without compromising security. Specifically, federated data platforms are emerging as key resources to facilitate secure data sharing without having to physically move the data from outside of its organizational or jurisdictional boundaries. In this perspective, we summarize the overarching progress in establishing federated data platforms, and highlight critical considerations on how they should be managed to ensure patient and public trust. These platforms are enabling global collaboration and improving representation of underrepresented groups, since sequencing efforts have not prioritized diverse population representation until recently. Federated data platforms, when combined with advances in no-code technology, can be accessible to the diverse end-users that make up the genomics workforce, and we discuss potential strategies to develop sustainable business models so that the platforms can continue to enable research long term. Although these platforms must be carefully managed to ensure appropriate and ethical use, they are democratizing access and insights to clinical-genomic data that will progress research and enable impactful therapeutic findings.

NGS-Based Tumor-Informed Analysis of Circulating Tumor DNA

Accurate circulating tumor DNA (ctDNA) detection has an immense biomarker potential in all phases of the cancer disease course. Presence of ctDNA in the blood has been shown to have prognostic value in various cancer types as it may reflect the actual tumor burden. There are two main methods to consider, a tumor-informed and a tumor-agnostic analysis of ctDNA. Both techniques exploit the short half-life of circulating cell-free DNA (cfDNA)/ctDNA for disease monitoring and ultimately future clinical treatment intervention. Urothelial carcinoma is characterized by a high mutation spectrum but very few hotspot mutations. This limits tumor agnostic usability of hotspot mutation or fixed sets of genes for ctDNA detection. Here we focus on a tumor-informed analysis for ultrasensitive patient- and tumor-specific ctDNA detection using personalized mutation panels, probes that bind to specific genomic sequences to enrich for the region of interest. In this chapter, we describe methods for purification of high-quality cfDNA and guidelines for designing tumor-informed customized capture panels for sensitive detection of ctDNA. Furthermore, a detailed protocol for library preparation and panel capture utilizing a double enrichment strategy with low amplification is described.

Measurable Residual Disease Monitoring by Locked Nucleic Acid Quantitative Real-Time PCR Assay for IDH1/2 Mutation in Adult AML

Locked nucleic acid quantitative Real-Time PCR (LNA-qPCR) for IDH1/2 mutations in AML measurable residual disease (MRD) detection is rarely reported. LNA-qPCR was applied to quantify IDH1/2 mutants MRD kinetics in bone marrow from 88 IDH1/2-mutated AML patients, and correlated with NPM1-MRD, clinical characteristics, and outcomes. The median normalized copy number (NCN) of IDH1/2 mutants decreased significantly from 53,228 (range 87−980,686)/ALB × 106 at diagnosis to 773 (range 1.5−103,600)/ALB × 106 at first complete remission (CR). IDH1/2 LNA-qPCR MRD was concordant with remission status or NPM1-MRD in 79.5% (70/88) of patients. Younger patients and patients with FLT3 mutations had higher concordance. The Spearman correlation coefficient (rs) and concordance rate between the log reduction of IDH1/2 LNA-qPCR and NPM1-MRD were 0.68 and 81% (K = 0.63, 95% CI 0.50−0.74), respectively. IDH1/2-MRD > 2 log reduction at first CR predicted significantly better relapse-free survival (3-year RFS rates 52.9% vs. 31.9%, p = 0.007) and cumulative incidence of relapse (3-year CIR rates 44.5% vs. 64.5%, p = 0.012) compared to IDH1/2-MRD ≤ 2 log reduction. IDH1/2-MRD > 2 log reduction during consolidation is also associated with a significantly lower CIR rate than IDH1/2-MRD ≤ 2 log reduction (3-year CIR rates 42.3% vs. 68.8%, p = 0.019). LNA-qPCR for IDH1/2 mutation is a potential MRD technique to predict relapse in IDH1/2-mutated AML patients, especially for those with IDH1/2 MRD > 2 log reduction at first CR or a concurrent FLT3 mutation.

Identifying high-confidence variants in human cytomegalovirus genomes sequenced from clinical samples

Understanding the intrahost evolution of viral populations has implications in pathogenesis, diagnosis, and treatment and has recently made impressive advances from developments in high-throughput sequencing. However, the underlying analyses are very sensitive to sources of bias, error, and artefact in the data, and it is important that these are addressed adequately if robust conclusions are to be drawn. The key factors include (1) determining the number of viral strains present in the sample analysed; (2) monitoring the extent to which the data represent these strains and assessing the quality of these data; (3) dealing with the effects of cross-contamination; and (4) ensuring that the results are reproducible. We investigated these factors by generating sequence datasets, including biological and technical replicates, directly from clinical samples obtained from a small cohort of patients who had been infected congenitally with the herpesvirus human cytomegalovirus, with the aim of developing a strategy for identifying high-confidence intrahost variants. We found that such variants were few in number and typically present in low proportions and concluded that human cytomegalovirus exhibits a very low level of intrahost variability. In addition to clarifying the situation regarding human cytomegalovirus, our strategy has wider applicability to understanding the intrahost variability of other viruses.

The Sum of Two Halves May Be Different from the Whole-Effects of Splitting Sequencing Samples Across Lanes

The advances in high-throughput sequencing (HTS) have enabled the characterisation of biological processes at an unprecedented level of detail; most hypotheses in molecular biology rely on analyses of HTS data. However, achieving increased robustness and reproducibility of results remains a main challenge. Although variability in results may be introduced at various stages, e.g., alignment, summarisation or detection of differential expression, one source of variability was systematically omitted: the sequencing design, which propagates through analyses and may introduce an additional layer of technical variation. We illustrate qualitative and quantitative differences arising from splitting samples across lanes on bulk and single-cell sequencing. For bulk mRNAseq data, we focus on differential expression and enrichment analyses; for bulk ChIPseq data, we investigate the effect on peak calling and the peaks' properties. At the single-cell level, we concentrate on identifying cell subpopulations. We rely on markers used for assigning cell identities; both smartSeq and 10× data are presented. The observed reduction in the number of unique sequenced fragments limits the level of detail on which the different prediction approaches depend. Furthermore, the sequencing stochasticity adds in a weighting bias corroborated with variable sequencing depths and (yet unexplained) sequencing bias. Subsequently, we observe an overall reduction in sequencing complexity and a distortion in the biological signal across technologies, experimental contexts, organisms and tissues.

The Application of Metagenomics to Study Microbial Communities and Develop Desirable Traits in Fermented Foods

The microbial communities present within fermented foods are diverse and dynamic, producing a variety of metabolites responsible for the fermentation processes, imparting characteristic organoleptic qualities and health-promoting traits, and maintaining microbiological safety of fermented foods. In this context, it is crucial to study these microbial communities to characterise fermented foods and the production processes involved. High Throughput Sequencing (HTS)-based methods such as metagenomics enable microbial community studies through amplicon and shotgun sequencing approaches. As the field constantly develops, sequencing technologies are becoming more accessible, affordable and accurate with a further shift from short read to long read sequencing being observed. Metagenomics is enjoying wide-spread application in fermented food studies and in recent years is also being employed in concert with synthetic biology techniques to help tackle problems with the large amounts of waste generated in the food sector. This review presents an introduction to current sequencing technologies and the benefits of their application in fermented foods.

Scalable, ultra-fast, and low-memory construction of compacted de Bruijn graphs with Cuttlefish 2

The de Bruijn graph is a key data structure in modern computational genomics, and construction of its compacted variant resides upstream of many genomic analyses. As the quantity of genomic data grows rapidly, this often forms a computational bottleneck. We present Cuttlefish 2, significantly advancing the state-of-the-art for this problem. On a commodity server, it reduces the graph construction time for 661K bacterial genomes, of size 2.58Tbp, from 4.5 days to 17-23 h; and it constructs the graph for 1.52Tbp white spruce reads in approximately 10 h, while the closest competitor requires 54-58 h, using considerably more memory.

UMIErrorCorrect and UMIAnalyzer: Software for Consensus Read Generation, Error Correction, and Visualization Using Unique Molecular Identifiers

BACKGROUND

Targeted sequencing using unique molecular identifiers (UMIs) enables detection of rare variant alleles in challenging applications, such as cell-free DNA analysis from liquid biopsies. Standard bioinformatics pipelines for data processing and variant calling are not adapted for deep-sequencing data containing UMIs, are inflexible, and require multistep workflows or dedicated computing resources.

METHODS

We developed a bioinformatics pipeline using Python and an R package for data analysis and visualization. To validate our pipeline, we analyzed cell-free DNA reference material with known mutant allele frequencies (0%, 0.125%, 0.25%, and 1%) and public data sets.

RESULTS

We developed UMIErrorCorrect, a bioinformatics pipeline for analyzing sequencing data containing UMIs. UMIErrorCorrect only requires fastq files as inputs and performs alignment, UMI clustering, error correction, and variant calling. We also provide UMIAnalyzer, a graphical user interface, for data mining, visualization, variant interpretation, and report generation. UMIAnalyzer allows the user to adjust analysis parameters and study their effect on variant calling. We demonstrated the flexibility of UMIErrorCorrect by analyzing data from 4 different targeted sequencing protocols. We also show its ability to detect different mutant allele frequencies in standardized cell-free DNA reference material. UMIErrorCorrect outperformed existing pipelines for targeted UMI sequencing data in terms of variant detection sensitivity.

CONCLUSIONS

UMIErrorCorrect and UMIAnalyzer are comprehensive and customizable bioinformatics tools that can be applied to any type of library preparation protocol and enrichment chemistry using UMIs. Access to simple, generic, and open-source bioinformatics tools will facilitate the implementation of UMI-based sequencing approaches in basic research and clinical applications.