Next-generation Sequencing Data Analysis Research Articles

Diversity of RNA viruses in the cosmopolitan monoxenous trypanosomatid Leptomonas pyrrhocoris

BackgroundTrypanosomatids are parasitic flagellates well known because of some representatives infecting humans, domestic animals, and cultural plants. Many trypanosomatid species bear RNA viruses, which, in the case of human pathogens Leishmania spp., influence the course of the disease. One of the close relatives of leishmaniae, Leptomonas pyrrhocoris, has been previously shown to harbor viruses of the groups not documented in other trypanosomatids. At the same time, this species has a worldwide distribution and high prevalence in the natural populations of its cosmopolitan firebug host. It therefore represents an attractive model to study the diversity of RNA viruses.ResultsWe surveyed 106 axenic cultures of L. pyrrhocoris and found that 64 (60%) of these displayed 2–12 double-stranded RNA fragments. The analysis of next-generation sequencing data revealed four viral groups with seven species, of which up to five were simultaneously detected in a single trypanosomatid isolate. Only two of these species, a tombus-like virus and an Ostravirus, were earlier documented in L. pyrrhocoris. In addition, there were four new species of Leishbuviridae, the family encompassing trypanosomatid-specific viruses, and a new species of Qinviridae, the family previously known only from metatranscriptomes of invertebrates. Currently, this is the only qinvirus with an unambiguously determined host. Our phylogenetic inferences suggest reassortment in the tombus-like virus owing to the interaction of different trypanosomatid strains. Two of the new Leishbuviridae members branch early on the phylogenetic tree of this family and display intermediate stages of genomic segment reduction between insect Phenuiviridae and crown Leishbuviridae.ConclusionsThe unprecedented wide range of viruses in one protist species and the simultaneous presence of up to five viral species in a single Leptomonas pyrrhocoris isolate indicate the uniqueness of this flagellate. This is likely determined by the peculiarity of its firebug host, a highly abundant cosmopolitan species with several habits ensuring wide distribution and profuseness of L. pyrrhocoris, as well as its exposure to a wider spectrum of viruses compared to other trypanosomatids combined with a limited ability to transmit these viruses to its relatives. Thus, L. pyrrhocoris represents a suitable model to study the adoption of new viruses and their relationships with a protist host.

Impact of Concurrent Genomic Alterations on Clinical Outcomes in Patients With ALK-Rearranged NSCLC

IntroductionALK tyrosine kinase inhibitors have exhibited promising activity against advanced ALK-rearranged NSCLC. However, co-occurring genetic alterations, such as CDKN2A/B or TP53, may negatively affect the efficacy of targeted therapies. MethodsFrom December 2017 to December 2022, this study cohort analyzed next-generation sequencing data of 116 patients with metastatic ALK-rearranged NSCLC from five Latin American cancer centers. Clinicopathologic and molecular features were associated with clinical outcomes and risk of brain metastasis (BrM) in patients with and without concurrent somatic alterations. ResultsAll patients (N = 116) received a second-generation ALK tyrosine kinase inhibitor, and alectinib was selected in 87.2% of cases. Coalterations occurred in 62% of the cases; the most frequent were TP53 mutations (27%) and CDKN2A/B loss (18%). The loss of CDKN2A/B was associated with an increased risk of BrM, with a cumulative incidence of 33.3% versus 7.4% in the non-coaltered subgroup. Compared with patients without coalterations, patients with concurrent CDKN2A/B loss (n = 21) had a shorter median progression-free survival (10.2 versus 34.2 mo, p < 0.001) and overall survival (26.2 versus 80.7 mo, p < 0.001). In the multivariate analysis, co-occurring CDKN2A/B loss was associated with poorer progression-free survival and OS despite the presence of other somatic coalterations, TP53 mutations, BrM, and Eastern Cooperative Oncology Group Performance Status. ConclusionsThis study confirmed the worse prognostic value, which depicted co-occurring alterations in patients with ALK rearrangement. CDKN2A/B loss was substantially associated with worse outcomes and a higher risk of brain metastases. The evidence presented in our study may help select patients with ALK-positive tumors suitable for treatment escalation and closer brain follow-up.

Evaluation of an optimized germline exomes pipeline using BWA-MEM2 and Dragen-GATK tools.

The next-generation sequencing (NGS) technology represents a significant advance in genomics and medical diagnosis. Nevertheless, the time it takes to perform sequencing, data analysis, and variant interpretation is a bottleneck in using next-generation sequencing in precision medicine. For accurate and efficient performance in clinical diagnostic lab practice, a consistent data analysis pipeline is necessary to avoid false variant calls and achieve optimum accuracy. This study aims to compare the performance of two NGS data analysis pipeline compartments, including short-read mapping (BWA-MEM and BWA-MEM2) and variant calling (GATK-HaplotypeCaller and DRAGEN-GATK). On Whole Exome Sequencing (WES) data, computational performance was assessed using several criteria, including mapping efficiency, variant calling performance, false positive calls rate, and time. We examined four gold-standard WES data sets: Ashkenazim father (NA24149), Ashkenazim mother (NA24143), Ashkenazim son (NA24385), and Asian son (NA25631). In addition, eighteen exome samples were analyzed based on different read counts, and coverage was used precisely in the run-time assessment. By using BWA-MEM 2 and Dragen-GATK, this study achieved faster and more accurate detection for SNVs and indels than the standard GATK Best Practices workflow. This systematic comparison will enable the bioinformatics community to develop a more efficient and faster solution for analyzing NGS data.

STRategy: A support system for collecting and analyzing next-generation sequencing data of short tandem repeats for forensic science.

Short tandem repeats (STRs) are short repeated sequences commonly found in the human genome and valuable in forensic science, used for human identity and relatedness markers. Next-generation sequencing (NGS) technologies, e.g., ForenSeq Signature Prep, can sequence STRs, inferring length-based alleles and single nucleotide polymorphisms (SNPs) and providing valuable insights into population and sub-population structures. Despite the potential benefits of NGS for STRs, no open-source software platform integrates the collection, management, and analysis of STR data from NGS into one place. Users must use multiple programs to process their STR data and then collect the results into a separate database or a file system folder. Moreover, analyzing repeat structures (STR repeat motifs) may require learning multiple software tools, making the process inefficient and cumbersome. To address this gap, we introduce the STRategy, a standalone web-based application supporting essential STR data management and analysis capabilities. The STRategy allows users to collect their data into its database, automatically calculates forensic parameters, and visualizes the analyzed data in various forms. Users can search the database using different options, such as by profile, loci, and genotypes, with and without a specific test kit. Moreover, users can also find the nucleotide variants of a locus among the samples. We designed the STRategy for internal use in a laboratory or an organization. Hence, our system includes role-based access control that allows users to search for or access specific data based on their responsibilities. The administrator role can customize the system, for example, configure maps according to the samples' geographic data, and manage reference STR repeat motifs. A laboratory or an organization can download and install a copy of STRategy on their local system using Docker, as described in https://github.com/cucpbioinfo/STRategy. In summary, the STRategy is an end-to-end system that provides users with a database to collect the analyzed STR data from NGS, the dynamic analyses of forensic parameters, and the variants of STR patterns according to the newly added samples, which are then explorable via various search options and visualizations. The system is helpful for both forensic investigations and forensic genetics.

NgsJulia: population genetic analysis of next-generation DNA sequencing data with Julia language

A sound analysis of DNA sequencing data is important to extract meaningful information and infer quantities of interest. Sequencing and mapping errors coupled with low and variable coverage hamper the identification of genotypes and variants and the estimation of population genetic parameters. Methods and implementations to estimate population genetic parameters from sequencing data available nowadays either are suitable for the analysis of genomes from model organisms only, require moderate sequencing coverage, or are not easily adaptable to specific applications. To address these issues, we introduce ngsJulia, a collection of templates and functions in Julia language to process short-read sequencing data for population genetic analysis. We further describe two implementations, ngsPool and ngsPloidy, for the analysis of pooled sequencing data and polyploid genomes, respectively. Through simulations, we illustrate the performance of estimating various population genetic parameters using these implementations, using both established and novel statistical methods. These results inform on optimal experimental design and demonstrate the applicability of methods in ngsJulia to estimate parameters of interest even from low coverage sequencing data. ngsJulia provide users with a flexible and efficient framework for ad hoc analysis of sequencing data.ngsJulia is available from: https://github.com/mfumagalli/ngsJulia.

BacSeq: A User-Friendly Automated Pipeline for Whole-Genome Sequence Analysis of Bacterial Genomes.

Whole-genome sequencing (WGS) of bacterial pathogens is widely conducted in microbiological, medical, and clinical research to explore genetic insights that could impact clinical treatment and molecular epidemiology. However, analyzing WGS data of bacteria can pose challenges for microbiologists, clinicians, and researchers, as it requires the application of several bioinformatics pipelines to extract genetic information from raw data. In this paper, we present BacSeq, an automated bioinformatic pipeline for the analysis of next-generation sequencing data of bacterial genomes. BacSeq enables the assembly, annotation, and identification of crucial genes responsible for multidrug resistance, virulence factors, and plasmids. Additionally, the pipeline integrates comparative analysis among isolates, offering phylogenetic tree analysis and identification of single-nucleotide polymorphisms (SNPs). To facilitate easy analysis in a single step and support the processing of multiple isolates, BacSeq provides a graphical user interface (GUI) based on the JAVA platform. It is designed to cater to users without extensive bioinformatics skills.

MIRACUM-Pipe: An Adaptable Pipeline for Next-Generation Sequencing Analysis, Reporting, and Visualization for Clinical Decision Making.

(1) Background: Next-generation sequencing (NGS) of patients with advanced tumors is becoming an established method in Molecular Tumor Boards. However, somatic variant detection, interpretation, and report generation, require in-depth knowledge of both bioinformatics and oncology. (2) Methods: MIRACUM-Pipe combines many individual tools into a seamless workflow for comprehensive analyses and annotation of NGS data including quality control, alignment, variant calling, copy number variation estimation, evaluation of complex biomarkers, and RNA fusion detection. (3) Results: MIRACUM-Pipe offers an easy-to-use, one-prompt standardized solution to analyze NGS data, including quality control, variant calling, copy number estimation, annotation, visualization, and report generation. (4) Conclusions: MIRACUM-Pipe, a versatile pipeline for NGS, can be customized according to bioinformatics and clinical needs and to support clinical decision-making with visual processing and interactive reporting.

Extreme Gradient Boosting Tuned with Metaheuristic Algorithms for Predicting Myeloid NGS Onco-Somatic Variant Pathogenicity.

The advent of next-generation sequencing (NGS) technologies has revolutionized the field of bioinformatics and genomics, particularly in the area of onco-somatic genetics. NGS has provided a wealth of information about the genetic changes that underlie cancer and has considerably improved our ability to diagnose and treat cancer. However, the large amount of data generated by NGS makes it difficult to interpret the variants. To address this, machine learning algorithms such as Extreme Gradient Boosting (XGBoost) have become increasingly important tools in the analysis of NGS data. In this paper, we present a machine learning tool that uses XGBoost to predict the pathogenicity of a mutation in the myeloid panel. We optimized the performance of XGBoost using metaheuristic algorithms and compared our predictions with the decisions of biologists and other prediction tools. The myeloid panel is a critical component in the diagnosis and treatment of myeloid neoplasms, and the sequencing of this panel allows for the identification of specific genetic mutations, enabling more accurate diagnoses and tailored treatment plans. We used datasets collected from our myeloid panel NGS analysis to train the XGBoost algorithm. It represents a data collection of 15,977 mutations variants composed of a collection of 13,221 Single Nucleotide Variants (SNVs), 73 Multiple Nucleoid Variants (MNVs), and 2683 insertion deletions (INDELs). The optimal XGBoost hyperparameters were found with Differential Evolution (DE), with an accuracy of 99.35%, precision of 98.70%, specificity of 98.71%, and sensitivity of 1.

Molecular profiling as a predictor of treatment response and survival in unresectable pancreatic ductal adenocarcinoma.

e16279 Background: To date, few targetable mutations have been found to enhance PDAC treatment. Next-generation sequencing (NGS) of somatic mutation profiles may help tailor cytotoxic chemotherapy selection and guide translational research. Methods: We analyzed NGS data from a retrospective cohort of 142 patients with unresectable biopsy-proven PDAC from August 2009 to December 2022. Individual mutations were grouped into 14 distinct signaling pathways: cell cycle, DNA damage repair, KIT, ErbB2, TP53, MAPK-RAS, PIK3, TGF-beta, NOTCH, ALK, FLT3, FGFR, and WNT. Treatment selection, duration of response, and survival were recorded. We stratified patients based on their first-line therapy, either FOLFIRINOX (FFX) or gemcitabine/nab-paclitaxel (GA). We present median time-to-first progression (PFS1) and overall survival (OS) based on the presence or absence of mutations in these pathways. The time-to-event endpoint was estimated by the Kaplan-Meier method. Results: Of 142 samples, 131 were analyzed for PFS1; 57 received FFX, and 60 received GA. In the FFX group, no pathway predicted PFS1 with statistical significance. In the GA group, KIT pathway mutations (n = 10) showed improved PFS1 vs. non-KIT mutated tumors (10.3 mos vs. 6.2 mos, p = 0.02). Similar improvement in PFS1 was seen for mutations in the PIK3 pathway (n = 25, 6.6 vs. 5.7, p = 0.02) and FLT3 pathway (n = 4, 13.1 vs. 6.2, p = 0.02). 125 samples were analyzed for OS. KIT pathway mutations (n = 21) showed a better OS vs. non-KIT mutated samples (21.3 mos vs. 12.2 mos, p = 0.04), as did NOTCH mutated samples (n = 44, 15 vs. 12.3, p &lt; 0.01). FLT3 pathway mutations showed a trend toward improved OS (23.6 vs. 13, p = 0.3). PIK3 mutated tumors had no difference in OS. 8 samples were KRAS WT; removing these from the analysis did not change the results. Conclusions: In this sample, KIT, FLT3, and PIK3 pathway mutated tumors had improved PFS1 with GA (but not FFX) treatment. Tumors with KIT and NOTCH pathway mutations had an improved OS. This and similar analyses could help identify potential drivers of treatment response for unresectable pancreatic tumors. [Table: see text]

Racial differences in prognostic role of white blood cell ratios in gastrointestinal stromal tumors.

e23507 Background: Gastrointestinal stromal tumors (GIST) are rare mesenchymal tumors with an estimated 3,300 to 6,000 new cases per year in the United States. Per SEER database, significant racial disparity exists with an incidence rate twice as high for African Americans as compared to Whites. Currently, parameters used in stratification of disease risk can only be determined by postoperative histopathological evaluation. Cancer-related inflammation is linked to different stages of tumor development so markers found in peripheral blood have been studied as surrogates. We present a study comparing the predictive prognostic ability of preoperative serum inflammatory markers in primary untreated GIST and highlight the differences between Black and White patients. Methods: This is a retrospective analysis of 149 patients who underwent operative resection of primary untreated GIST. Preoperative blood samples were used for the analysis. Patients were excluded if they showed clinical signs of infection during time of pre-operative blood sampling or had non-GIST synchronous malignancy. Clinicopathological data was obtained from patient chart review and logistic regression models were used to evaluate data. There is ongoing next-generation sequencing data analysis. Results: 149 patients were included: 80 (53.7%) Black, 43 (28.9%) White, 26 (17.5%) were other. The mean values for neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to lymphocyte ratio (MLR) were 3.62 (SD 3.07), 191.55 (SD 119.15), and 0.47 (SD 0.49) respectively. On univariate analysis NLR and PLR were significant prognostic indicators of high and intermediate risk GISTs per the National Institute of Health (NIH) categories [NLR p = 0.008, PLR p = 0.007]. Upon subgroup analysis, NLR and PLR showed the highest statistical significance for predictors of high and intermediate risk GISTs among the Black patient population [NLR odds ratio (OR) 2.685; 95% confidence interval (CI) 0.976-7.381; p = 0.05 and PLR OR 1.792; 95% CI 1.054-3.049; p = 0.031]. The same was not shown to be true for the White patient population. 11 out 149 patients progressed since diagnosis of which 8 were Black and 3 White. 7 were in the high risk category, 3 in low risk and 1 in intermediate risk with an overall mean NLR and PLR 5.25 and 244 respectively. The average time to progression was noted to 28 months. Conclusions: The NLR and PLR are easily obtainable and cost-effective indexes which have the prognostic ability to serve as surrogates for high and intermediate risk GISTs. They appear to be more predictive in the Black patient population than in White. Further study is warranted for incorporation into risk stratification systems.

Detection of Potential Viral Sequence from Next Generation Sequencing Data Using Convolutional Neural Network

Next Generation Sequencing (NGS) is a modern sequencing technology that can determine the sequences of RNA and DNA faster and at lower cost. The availability of NGS data has sparked numerous efforts in bioinformatics, especially in the study of genetic variation and viral sequence detection. Viral sequence detection has been one of the important processes in studying virus-induced diseases. Common methods in detecting viral sequences involve alignment of the sequence with existing databases, which remains limited as these databases might be incomplete and difficult to detect highly divergent viruses. Thus, machine learning and deep learning have been used in this regard, to unveil the patterns that distinguish viral sequences through learning from the NGS data. This study focuses on viral sequence detection using convolutional neural network (CNN). This study intended to investigate how CNN model can be used for analysis of NGS data and develop a CNN model for detecting potential viral sequences from NGS data. The CNN architecture used for this study is based on an existing design that divided into two branches namely pattern and frequency branch that cater for extracting different aspects of information from the data and lastly combined into a full model. This study further implemented slightly modified architecture that includes additional convolution layer and pooling layer. Then, parameter tuning is implemented to identify near optimal parameters for the CNN to elucidate the performance impact. The evaluation of the optimized CNN model is done using a dataset with 18,445 DNA sequences. The results show that the CNN model in this study achieved a better performance compared with existing in terms of area under receiver operating characteristics curve (AUROC) for full model (+0.1434).

Identification of Macrocyclic Peptide Families from Combinatorial Libraries Containing Noncanonical Amino Acids Using Cheminformatics and Bioinformatics Inspired Clustering.

In the past decade, macrocyclic peptides gained increasing interest as a new therapeutic modality to tackle intracellular and extracellular therapeutic targets that had been previously classified as "undruggable". Several technological advances have made discovering macrocyclic peptides against these targets possible: 1) the inclusion of noncanonical amino acids (NCAAs) into mRNA display, 2) increased availability of next generation sequencing (NGS), and 3) improvements in rapid peptide synthesis platforms. This type of directed-evolution based screening can produce large numbers of potential hit sequences given that DNA sequencing is the functional output of this platform. The current standard for selecting hit peptides from these selections for downstream follow-up relies on the frequency counting and sorting of unique peptide sequences which can result in the generation of false negatives due to technical reasons including low translation efficiency or other experimental factors. To overcome our inability to detect weakly enriched peptide sequences among our large data sets, we wanted to develop a clustering method that would enable the identification of peptide families. Unfortunately, utilizing traditional clustering algorithms, such as ClustalW, is not possible for this technology due to the incorporation of NCAAs in these libraries. Therefore, we developed a new atomistic clustering method with a Pairwise Aligned Peptide (PAP) chemical similarity metric to perform sequence alignments and identify macrocyclic peptide families. With this method, low enriched peptides, including isolated sequences (singletons), can now be clustered into families providing a comprehensive analysis of NGS data resulting from macrocycle discovery selections. Additionally, upon identification of a hit peptide with the desired activity, this clustering algorithm can be used to identify derivatives from the initial data set for structure-activity relationship (SAR) analysis without requiring additional selection experiments.

MSdb: An integrated expression atlas of human musculoskeletal system

The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.

Unraveling microbial community by next-generation sequencing in living membrane bioreactors for wastewater treatment

This study delves into the microbial community complexity and its role in self-forming dynamic membrane (SFDM) systems, designed to remove nutrients and pollutants from wastewater, by means of the analysis of Next-Generation Sequencing (NGS) data. In these systems, microorganisms are naturally incorporated into the SFDM layer, which acts as a biological and physical filter. The microorganisms present in an innovative and highly efficient aerobic, electrochemically enhanced, encapsulated SFDM bioreactor were studied to elucidate the nature of the dominant microbial communities present in sludge and in encapsulated SFDM, patented as living membrane® (LM) of the experimental setup. The results were compared to those obtained from the microbial communities found in similar experimental reactors without an applied electric field. The data gathered from the NGS microbiome profiling showed that the microbial consortia found in the experimental systems are comprised of archaeal, bacterial, and fungal communities. However, the distribution of the microbial communities found in e-LMBR and LMBR had significant differences. The results showed that the presence of an intermittently applied electric field in e-LMBR promotes the growth of some types of microorganisms (mainly electroactive microorganisms) responsible for the highly efficient treatment of the wastewater and for the mitigation of the membrane fouling found for those bioreactors.

Genetic investigation of 124 SNPs in a Myanmar population using the Precision ID Identity Panel and the Illumina MiSeq

Single nucleotide polymorphisms (SNPs) have become popular in forensic genetics as an alternative to short tandem repeats (STRs). The Precision ID Identity Panel (Thermo Fisher Scientific), consisting of 90 autosomal SNPs and 34 Y-chromosomal SNPs, enabled human identification studies on global populations through next-generation sequencing (NGS). However, most previous studies on the panel have used the Ion Torrent platform, and there are few reports on the Southeast Asian population. Here, a total of 96 unrelated males from Myanmar (Yangon) were analyzed with the Precision ID Identity Panel on a MiSeq (Illumina) using an in-house TruSeq compatible universal adapter and a custom variant caller, Visual SNP. The sequencing performance evaluated by locus balance and heterozygote balance was comparable to that of the Ion Torrent platform. For 90 autosomal SNPs, the combined match probability (CMP) was 6.994 × 10−34, lower than that of 22 PowerPlex Fusion autosomal STRs (3.130 × 10−26). For 34 Y-SNPs, 14 Y-haplogroups (mostly O2 and O1b) were observed. We found 51 cryptic variations (42 haplotypes) around target SNPs, of which haplotypes corresponding to 33 autosomal SNPs decreased CMP. Interpopulation analysis revealed that the Myanmar population is genetically closer to the East and Southeast Asian populations. In conclusion, the Precision ID Identity Panel can be successfully analyzed on the Illumina MiSeq and provides high discrimination power for human identification in the Myanmar population. This study broadened the accessibility of the NGS-based SNP panel by expanding the available NGS platforms and adopting a robust NGS data analysis tool.

Abstract 2081: Nano2NGS: A framework for converting nanopore sequencing data to NGS-liked sequencing data for data analysis

Abstract Background: Nanopore sequencing, also known as single molecule real-time sequencing, enables deciphering single DNA/RNA molecules without the polymerase chain reaction (PCR). Although nanopore sequencing has made significant progress in some areas, its application has been limited compared with next-generation sequencing (NGS) due to specific design principle and data characteristics, especially in hotspot mutation detection, metagenomic analysis, short tandem repeat (STR) detection and human leukocyte antigen (HLA) typing. Methods: The Nano2NGS framework included 4 modules, namely Nano2NGS-Muta, -Meta, -STR and -HLA. For hotspot mutation detection, we tested Nano2NGS-Muta + Freebayes using simulated data including 12 hotspot mutations (SNVs and InDels), and standard sample including 7 hotspot mutations with ~5% mutation frequency. For metagenomic analysis, we tested Nano2NGS-Meta + Kraken2 using simulated data including 20 bacterial species from the NCBI database, and standard sample including 7 bacteria with ~14% theoretical microbial composition. For STR detection, we tested Nano2NGS-STR using simulated data including 4 STR markers (DYS392, DYS438, DYS448, and DYS635), and standard sample including 44 STR markers from the intersection of two commercial products. For HLA typing, we tested Nano2NGS-HLA + Athlates using 1 simulated data and 1 standard sample. The GM12878 gDNA sample was used as negative control. All samples have 3 experimental repetitive. The nanopore sequencing was performed on the QNome-9604/3841 instrument according to the manufacturer’s instructions. Results: The performance of all 4 modules in simulated datasets was perfect. In standard sample datasets, for hotspot mutation detection, we found the repeated experiments detection sensitivity was 95.24%, 100.00% and 85.71%, respectively. The limit of detection (LoD) of Nano2NGS-Muta + Freebayes may be 2% or even 5% according to the negative dataset. For metagenomic analysis, we found the overall performance of Nano2NGS-Meta + Kraken2 was better, and R-square value compared with expected relative abundance was 0.9357. For STR detection, we found Nano2NGS-STR achieved the best performance compared with Tandem-Genotypes and TriCoLOR, with 71.97% (9948) and 53.03% (2800M) concordance, respectively. For HLA typing, we found the result of Nano2NGS-HLA + Athlates was HLA-A*02/HLA-A*11, HLA-B*35/HLA-B*39 and HLA-C*07/HLA-C*12, which were consistent with the expected genotypes of allele group (field 1). Conclusions: Nano2NGS framework converts nanopore sequencing data into NGS-liked short-read data and can be compatible with the NGS data analysis software. Importantly, Nano2NGS breaks the barriers of data analysis methods between short-read sequencing and long-read sequencing, and accelerates the application of nanopore sequencing technology in scientific research and clinical practice. Citation Format: Jidong Lang. Nano2NGS: A framework for converting nanopore sequencing data to NGS-liked sequencing data for data analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2081.

Methods for analyzing next-generation sequencing data 20. Properties and statistical models of RNA-seq count data.

Methods for analyzing next-generation sequencing data 20. Properties and statistical models of RNA-seq count data.

Analysis of well-annotated next-generation sequencing data reveals increasing cases of SARS-CoV-2 reinfection with Omicron

SARS-CoV-2 has extensively mutated creating variants of concern (VOC) resulting in global infection surges. The Omicron VOC reinfects individuals exposed to earlier variants of SARS-CoV-2 at a higher frequency than previously seen for non-Omicron VOC. An analysis of the sub-lineages associated with an Omicron primary infection and Omicron reinfection reveals that the incidence of Omicron-Omicron reinfections is occurring over a shorter time interval than seen after a primary infection with a non-Omicron VOC. Our analysis suggests that a single infection from SARS-CoV-2 may not generate the protective immunity required to defend against reinfections from emerging Omicron lineages. This analysis was made possible by Next-generation sequencing (NGS) of a Danish cohort with clinical metadata on both infections occurring in the same individual. We suggest that the continuation of COVID-19 NGS and inclusion of clinical metadata is necessary to ensure effective surveillance of SARS-CoV-2 genomics, assist in treatment and vaccine development, and guide public health recommendations.

On potential limitations of differential expression analysis with non-linear machine learning models

Recently, there has been a growing interest in bioinformatics toward the adoption of increasingly complex machine learning models for the analysis of next-generation sequencing data with the goal of disease subtyping (i.e., patient stratification based on molecular features) or risk-based classification for specific endpoints, such as survival. With gene-expression data, a common approach consists in characterising the emerging groups by exploiting a differential expression analysis, which selects relevant gene sets coupled with pathway enrichment analysis, providing an insight into the underlying biological processes. However, when non-linear machine learning models are involved, differential expression analysis could be limiting since patient groupings identified by the model could be based on a set of genes that are hidden to differential expression due to its linear nature, affecting subsequent biological characterisation and validation. The aim of this study is to provide a proof-of-concept example demonstrating such a limitation. Moreover, we suggest that this issue could be overcome by the adoption of the innovative paradigm of eXplainable Artificial Intelligence, which consists in building an additional explainer to get a trustworthy interpretation of the model outputs and building a reliable set of genes characterising each group, preserving also non-linear relations, to be used for downstream analysis and validation.

Adaptation to the Progress in Cancer Genomic Medicine by a Japanese Community Hospital

Background: Remarkable progress in cancer genomic medicine (CGM) has been made with the advent of next-generation sequencing and advanced computational data analysis approaches. In Japan gene panel testing has been covered by the National Health Insurance System since June 2019. Although Nagoya Memorial Hospital has been designated as a regional medical support hospital, their medical staff are unfamiliar with CGM and generally experience difficulty in explaining the genetic testing to cancer patients.&#x0D; Methods: A multi-disciplinary CGM team was created in July 2019 to adapt to the clinical application of gene panel testing. Hospital functions were then maintained focusing on the following three aspects: a pathology system for handling genetic information, human resource development related to CGM, and a patient support system, including genetic counseling.&#x0D; Results: Third party ISO15189 (International Organization for Standardization) certification was acquired for the Department of Pathology to establish a quality-assured laboratory. Here, we report on 21 cancer patients who consulted and received information from the CGM outpatient department of our hospital. Among them 14 patients were introduced into a group of certified hospitals by the Japanese Ministry of Health, Labour, and Welfare and 10 patients underwent gene panel tests.&#x0D; Conclusions: As a regional medical support hospital dealing with many cancer patients, we will further improve hospital functions to match the progress in CGM.