Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking.

BACKGROUND: High-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling. RESULTS: Our approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants. CONCLUSIONS: Our findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.

Disregarding multimappers leads to biases in the functional assessment of NGS data.

BACKGROUND: Standard ChIP-seq and RNA-seq processing pipelines typically disregard sequencing reads whose origin is ambiguous ("multimappers"). This usual practice has potentially important consequences for the functional interpretation of the data: genomic elements belonging to clusters composed of highly similar members are left unexplored. RESULTS: In particular, disregarding multimappers leads to the underrepresentation in epigenetic studies of recently active transposable elements, such as AluYa5, L1HS and SVAs. Furthermore, this common strategy also has implications for transcriptomic analysis: members of repetitive gene families, such the ones including major histocompatibility complex (MHC) class I and II genes, are under-quantified. CONCLUSION: Revealing inherent biases that permeate routine tasks such as functional enrichment analysis, our results underscore the urgency of broadly adopting multimapper-aware bioinformatic pipelines -currently restricted to specific contexts or communities- to ensure the reliability of genomic and transcriptomic studies.

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.

Application value of metagenomic next-generation sequencing in hematological patients with high-risk febrile neutropenia.

Background: Metagenomic next-generation sequencing (mNGS) is a novel non-invasive and comprehensive technique for etiological diagnosis of infectious diseases. However, its practical significance has been seldom reported in the context of hematological patients with high-risk febrile neutropenia, a unique patient group characterized by neutropenia and compromised immune responses. Methods: This retrospective study evaluated the results of plasma cfDNA sequencing in 164 hematological patients with high-risk febrile neutropenia. We assessed the diagnostic efficacy and clinical impact of mNGS, comparing it with conventional microbiological tests. Results: mNGS identified 68 different pathogens in 111 patients, whereas conventional methods detected only 17 pathogen types in 36 patients. mNGS exhibited a significantly higher positive detection rate than conventional methods (67.7% vs. 22.0%, P < 0.001). This improvement was consistent across bacterial (30.5% vs. 9.1%), fungal (19.5% vs. 4.3%), and viral (37.2% vs. 9.1%) infections (P < 0.001 for all comparisons). The anti-infective treatment strategies were adjusted for 51.2% (84/164) of the patients based on the mNGS results. Conclusions: mNGS of plasma cfDNA offers substantial promise for the early detection of pathogens and the timely optimization of anti-infective therapies in hematological patients with high-risk febrile neutropenia.

Pragmatic nationwide master observational trial based on genomic alterations in advanced solid tumors: KOrean Precision Medicine Networking Group Study of MOlecular profiling guided therapy based on genomic alterations in advanced Solid tumors (KOSMOS)-II study protocol KCSG AL-22-09.

BACKGROUND: Next-generation sequencing (NGS) has been introduced to many Korean institutions to support molecular diagnostics in cancer since 2017, when it became eligible for reimbursement by the National Health Insurance Service. However, the uptake of molecularly guided treatment (MGT) based on NGS results has been limited because of stringent regulations regarding prescriptions outside of approved indications, a lack of clinical trial opportunities, and limited access to molecular tumor boards (MTB) at most institutions. The KOSMOS-II study was designed to demonstrate the feasibility and effectiveness of MGT, informed by MTBs, using a nationwide precision medicine platform. METHODS: The KOSMOS-II trial is a large-scale nationwide master observational study. It involves a framework for screening patients with metastatic solid tumors for actionable genetic alterations based on local NGS testing. It recommends MGT through a remote and centralized MTB meeting held biweekly. MGT can include one of the following options: Tier 1, the therapeutic use of investigational drugs targeting genetic alterations such as ALK, EGFR, ERBB2, BRAF, FH, ROS1, and RET, or those with high tumor mutational burden; Tier 2, comprising drugs with approved indications or those permitted for treatment outside of the indications approved by the Health Insurance Review and Assessment Service of Korea; Tier 3, involving clinical trials matching the genetic alterations recommended by the MTB. Given the anticipated proportion of patients receiving MGT in the range of 50% ± 3.25%, this study aims to enroll 1,000 patients. Patients must have progressed to one or more lines of therapy and undergone NGS before enrollment. DISCUSSION: This pragmatic master protocol provides a mass-screening platform for rare genetic alterations and high-quality real-world data. Collateral clinical trials, translational studies, and clinico-genomic databases will contribute to generating evidence for drug repositioning and the development of new biomarkers. TRIAL REGISTRATION: NCT05525858.

Identification of the novel HLA-DQA1*02:32 allele by next-generation sequencing.

HLA-DQA1*02:32 differs from DQA1*02:01:01 by one nucleotide substitution in codon 210 in exon 4.

Characterisation of the novel HLA-C*15:274 allele using short and long-read sequencing technologies.

The novel HLA-C*15:274 allele, first described in a potential bone marrow donor from Brazil.

Multi-centre analytical performance verification of an IVD assay to quantify donor-derived cell-free DNA in solid organ transplant recipients.

Donor-derived cell-free DNA (dd-cfDNA) has been widely studied as biomarker for non-invasive allograft rejection monitoring. Earlier rejection detection enables more prompt diagnosis and intervention, ultimately improving patient treatment and outcomes. This multi-centre study aims to verify analytical performance of a next-generation sequencing-based dd-cfDNA assay at end-user environments. Three independent laboratories received the same experimental design and 16 blinded samples to perform cfDNA extraction and the dd-cfDNA assay workflow. dd-cfDNA results were compared between sites and against manufacturer validation to evaluate concordance, reproducibility, repeatability and verify analytical performance. A total of 247 sample libraries were generated across 18 runs, with completion time of <24 h. A 96.0% first pass rate highlighted minimal failures. Overall observed versus expected dd-cfDNA results demonstrated good concordance and a strong positive correlation with linear least squares regression r2 = 0.9989, and high repeatability and reproducibility within and between sites, respectively (p > 0.05). Manufacturer validation established limit of blank 0.18%, limit of detection 0.23% and limit of quantification 0.23%, and results from independent sites verified those limits. Parallel analyses illustrated no significant difference (p = 0.951) between dd-cfDNA results with or without recipient genotype. The dd-cfDNA assay evaluated here has been verified as a reliable method for efficient, reproducible dd-cfDNA quantification in plasma from solid organ transplant recipients without requiring genotyping. Implementation of onsite dd-cfDNA testing at clinical laboratories could facilitate earlier detection of allograft injury, bearing great potential for patient care.

Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data.

The advancement of Long-Read Sequencing (LRS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing events and isoform expressions. Recently, numerous computational tools for isoform detection using long-read sequencing data have been developed. Nevertheless, there remains a deficiency in comparative studies that systemically evaluate the performance of these tools, which are implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmark analysis of thirteen methods implemented in nine tools capable of identifying isoform structures from long-read RNA-seq data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, RNA sequins (sequencing spike-ins) data, as well as experimental data. Our findings demonstrate IsoQuant as a highly effective tool for isoform detection with LRS, with Bambu and StringTie2 also exhibiting strong performance. These results offer valuable guidance for future research on alternative splicing analysis and the ongoing improvement of tools for isoform detection using LRS data.

CAREx: context-aware read extension of paired-end sequencing data.

BACKGROUND: Commonly used next generation sequencing machines typically produce large amounts of short reads of a few hundred base-pairs in length. However, many downstream applications would generally benefit from longer reads. RESULTS: We present CAREx-an algorithm for the generation of pseudo-long reads from paired-end short-read Illumina data based on the concept of repeatedly computing multiple-sequence-alignments to extend a read until its partner is found. Our performance evaluation on both simulated data and real data shows that CAREx is able to connect significantly more read pairs (up to 99 % for simulated data) and to produce more error-free pseudo-long reads than previous approaches. When used prior to assembly it can achieve superior de novo assembly results. Furthermore, the GPU-accelerated version of CAREx exhibits the fastest execution times among all tested tools. CONCLUSION: CAREx is a new MSA-based algorithm and software for producing pseudo-long reads from paired-end short read data. It outperforms other state-of-the-art programs in terms of (i) percentage of connected read pairs, (ii) reduction of error rates of filled gaps, (iii) runtime, and (iv) downstream analysis using de novo assembly. CAREx is open-source software written in C++ (CPU version) and in CUDA/C++ (GPU version). It is licensed under GPLv3 and can be downloaded at ( https://github.com/fkallen/CAREx ).

Development, validation and application of single molecule molecular inversion probe based novel integrated genetic screening method for 29 common lysosomal storage disorders in India.

BACKGROUND: Current clinical diagnosis pathway for lysosomal storage disorders (LSDs) involves sequential biochemical enzymatic tests followed by DNA sequencing, which is iterative, has low diagnostic yield and is costly due to overlapping clinical presentations. Here, we describe a novel low-cost and high-throughput sequencing assay using single-molecule molecular inversion probes (smMIPs) to screen for causative single nucleotide variants (SNVs) and copy number variants (CNVs) in genes associated with 29 common LSDs in India. RESULTS: 903 smMIPs were designed to target exon and exon-intron boundaries of targeted genes (n = 23; 53.7 kb of the human genome) and were equimolarly pooled to create a sequencing library. After extensive validation in a cohort of 50 patients, we screened 300 patients with either biochemical diagnosis (n = 187) or clinical suspicion (n = 113) of LSDs. A diagnostic yield of 83.4% was observed in patients with prior biochemical diagnosis of LSD. Furthermore, diagnostic yield of 73.9% (n = 54/73) was observed in patients with high clinical suspicion of LSD in contrast with 2.4% (n = 1/40) in patients with low clinical suspicion of LSD. In addition to detecting SNVs, the assay could detect single and multi-exon copy number variants with high confidence. Critically, Niemann-Pick disease type C and neuronal ceroid lipofuscinosis-6 diseases for which biochemical testing is unavailable, could be diagnosed using our assay. Lastly, we observed a non-inferior performance of the assay in DNA extracted from dried blood spots in comparison with whole blood. CONCLUSION: We developed a flexible and scalable assay to reliably detect genetic causes of 29 common LSDs in India. The assay consolidates the detection of multiple variant types in multiple sample types while having improved diagnostic yield at same or lower cost compared to current clinical paradigm.

Distinct Driver Pathway Enrichments and a High Prevalence of TSC2 Mutations in Right Colon Cancer in Chile: A Preliminary Comparative Analysis.

Colorectal cancer (CRC) is the second leading cause of cancer deaths globally. While ethnic differences in driver gene mutations have been documented, the South American population remains understudied at the genomic level, despite facing a rising burden of CRC. We analyzed tumors of 40 Chilean CRC patients (Chp) using next-generation sequencing and compared them to data from mainly Caucasian cohorts (TCGA and MSK-IMPACT). We identified 388 mutations in 96 out of 135 genes, with TP53 (45%), KRAS (30%), PIK3CA (22.5%), ATM (20%), and POLE (20%) being the most frequently mutated. TSC2 mutations were associated with right colon cancer (44.44% in RCRC vs. 6.45% in LCRC, p-value = 0.016), and overall frequency was higher compared to TCGA (p-value = 1.847 × 10-5) and MSK-IMPACT cohorts (p-value = 3.062 × 10-2). Limited sample size restricts definitive conclusions, but our data suggest potential differences in driver mutations for Chilean patients, being that the RTK-RAS oncogenic pathway is less affected and the PI3K pathway is more altered in Chp compared to TCGA (45% vs. 25.56%, respectively). The prevalence of actionable pathways and driver mutations can guide therapeutic choices, but can also impact treatment effectiveness. Thus, these findings warrant further investigation in larger Chilean cohorts to confirm these initial observations. Understanding population-specific driver mutations can guide the development of precision medicine programs for CRC patients.

Development, High-Throughput Profiling, and Biopanning of a Large Phage Display Single-Domain Antibody Library.

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

Case report: Chronic Candida albicans meningitis: a rare entity diagnosed by metagenomic next-generation sequencing.

The aetiology of chronic aseptic meningitis is difficult to establish. Candida meningitis in particular is often diagnosed late, as cerebrospinal fluid (CSF) work-up and imaging findings are nonspecific. A 35-year-old patient with chronic aseptic meningitis, for which repeated microbiological testing of CSF was unrevealing, was finally diagnosed with Candida albicans (C. albicans) meningitis with cauda equina involvement using metagenomic next-generation sequencing (mNGS). This report highlights the diagnostic challenges and the difficulties of treating shunt-associated fungal meningitis.

Metagenomic next-generation sequencing confirmed a case of sporadic human infection with Streptococcus suis in an urban area.

INTRODUCTION: Streptococcus suis (S. suis) disease is a zoonotic infection caused by invasive S. suis and can lead to meningitis, septic shock, arthritis, and endocarditis. Early treatment is the key to reducing mortality. However, clinical manifestations of most cases are atypical, severely limiting rapid diagnosis and treatment. CASE REPORT: Here, we report a 74-year-old female patient diagnosed with S. suis infection. The main symptoms were hearing loss, lumbago, and scattered ecchymosis of the lower extremities and trunk. Blood non-specific infection indexes were significantly increased and platelets were significantly decreased; however, no pathogens were obtained from routine blood culture. Finally, the S. suis infection was confirmed by metagenomic next-generation sequencing (mNGS) of blood and cerebrospinal fluid. After antibiotic treatment, the limb and trunk scattered ecchymosis and lumbago symptoms were significantly relieved, but the hearing did not recover. CONCLUSIONS: Human infection with S. suis is rare in central cities, and it is easy to misdiagnose, especially in cases with atypical early symptoms. mNGS technology, combined with clinical observation, is helpful to clarify the direction of diagnosis and treatment, which is conducive to patient recovery.

Successful diagnosis of Mycobacterium marinum infection by mNGS in a patient with Human Immunodeficiency Virus: a case report.

INTRODUCTION: Mycobacterium marinum infection rarely occurs and has atypical symptoms. It is challenging to distinguish disseminated M. marinum infection from multifocal dermatosis caused by other factors clinically. CASE PRESENTATION: Herein, we reported a 68-year-old male patient with Human Immunodeficiency Virus (HIV) who presented redness and swelling in his left hand after being stabbed by marine fish for over 2 months. Mycobacterium tuberculosis infection was considered according to biochemical and pathological examinations, while empirical anti-infection treatment was ineffective. RESULTS: The metagenomic next-generation sequencing (mNGS) detected a large amount of M. marinum sequences, and the patient was finally diagnosed with M. marinum infection. After one month of combination therapy with ethambutol, rifabutin, moxifloxacin, and linezolid, the swelling disappeared significantly. In this case, the successful application of mNGS in diagnosing and treating M. marinum infection has improved the understanding of the microbe both in the laboratory and clinically, especially in patients with HIV. CONCLUSIONS: For diseases with atypical symptoms or difficulty in determining the pathogens, mNGS is suggested in clinical procedures for rapid and accurate diagnosis and treatment.

Dataset including whole blood gene expression profiles and matched leukocyte counts with utility for benchmarking cellular deconvolution pipelines.

OBJECTIVES: Cellular deconvolution is a valuable computational process that can infer the cellular composition of heterogeneous tissue samples from bulk RNA-sequencing data. Benchmark testing is a crucial step in the development and evaluation of new cellular deconvolution algorithms, and also plays a key role in the process of building and optimizing deconvolution pipelines for specific experimental applications. However, few in vivo benchmarking datasets exist, particularly for whole blood, which is the single most profiled human tissue. Here, we describe a unique dataset containing whole blood gene expression profiles and matched circulating leukocyte counts from a large cohort of human donors with utility for benchmarking cellular deconvolution pipelines. DATA DESCRIPTION: To produce this dataset, venous whole blood was sampled from 138 total donors recruited at an academic medical center. Genome-wide expression profiling was subsequently performed via next-generation RNA sequencing, and white blood cell differentials were collected in parallel using flow cytometry. The resultant final dataset contains donor-level expression data for over 45,000 protein coding and non-protein coding genes, as well as matched neutrophil, lymphocyte, monocyte, and eosinophil counts.

High-throughput DNA sequence analysis elucidates novel insight into the genetic basis of adaptation in local sheep.

Understanding how evolutionary factors related to climate adaptation and human selection have influenced the genetic architecture of domesticated animals is of great interest in biology. In the current study, by using 304 whole genomes from different geographical regions (including Europe, north Africa, Southwest Asia, east Asia, west Africa, south Asia, east Africa, Australia and Turkey), We evaluate global sheep population dynamics in terms of genetic variation and population structure. We further conducted comparative population analysis to study the genetic underpinnings of climate adaption to local environments and also morphological traits. In order to identify genomic signals under selection, we applied fixation index (FST) and also nucleotide diversity (θπ) statistical measurements. Our results revealed several candidate genes on different chromosomes under selection for local climate adaptation (e.g. HOXC12, HOXC13, IRF1, FGD2 and GNAQ), body size (PDGFA, HMGA2, PDE3A) and also morphological related traits (RXFP2). The discovered candidate genes may offer newel insights into genetic underpinning of regional adaptation and commercially significant features in local sheep.

Next-generation sequencing identified that RET variation associates with lymph node metastasis and the immune microenvironment in thyroid papillary carcinoma.

BACKGROUND: To date, although most thyroid carcinoma (THCA) achieves an excellent prognosis, some patients experience a rapid progression episode, even with differentiated THCA. Nodal metastasis is an unfavorable predictor. Exploring the underlying mechanism may bring a deep insight into THCA. METHODS: A total of 108 THCA from Chinese patients with next-generation sequencing (NGS) were recruited. It was used to explore the gene alteration spectrum of THCA and identify gene alterations related to nodal metastasis in papillary thyroid carcinoma (PTC). The Cancer Genome Atlas THCA cohort was further studied to elucidate the relationship between specific gene alterations and tumor microenvironment. A pathway enrichment analysis was used to explore the underlying mechanism. RESULTS: Gene alteration was frequent in THCA. BRAF, RET, POLE, ATM, and BRCA1 were the five most common altered genes. RET variation was positively related to nodal metastasis in PTC. RET variation is associated with immune cell infiltration levels, including CD8 naïve, CD4 T and CD8 T cells, etc. Moreover, Step 3 and Step 4 of the cancer immunity cycle (CIC) were activated, whereas Step 6 was suppressed in PTC with RET variation. A pathway enrichment analysis showed that RET variation was associated with several immune-related pathways. CONCLUSION: RET variation is positively related to nodal metastasis in Chinese PTC, and anti-tumor immune response may play a role in nodal metastasis triggered by RET variation.

Direct Analysis of HIV mRNA m6A Methylation by Nanopore Sequencing.

The post-transcriptional processing and chemical modification of HIV RNA are understudied aspects of HIV virology, primarily due to the limited ability to accurately map and quantify RNA modifications. Modification-specific antibodies or modification-sensitive endonucleases coupled with short-read RNA sequencing technologies have allowed for low-resolution or limited mapping of important regulatory modifications of HIV RNA such as N6-methyladenosine (m6A). However, a high-resolution map of where these sites occur on HIV transcripts is needed for detailed mechanistic understanding. This has recently become possible with new sequencing technologies. Here, we describe the direct RNA sequencing of HIV transcripts using an Oxford Nanopore Technologies sequencer and the use of this technique to map m6A at near single nucleotide resolution. This technology also provides the ability to identify splice variants with long RNA reads and thus, can provide high-resolution RNA modification maps that distinguish between overlapping splice variants. The protocols outlined here for m6A also provide a powerful paradigm for studying any other RNA modifications that can be detected on the nanopore platform.