NGS Data Analysis Research Articles

Internal Overview of Prostatic Cancer Cases and Quality of BRCA1 and BRCA2 NGS Data from the FFPE Tissue.

Background: Comprehensive genomic profiling (CGP) has gained an important role in patients with advanced prostate cancer following the introduction of PARP inhibitors in daily clinical practice. Here, we report an overview of CGP results, specifically of BRCA1 and BRCA2 HRD-repair system genes, from patients with prostate cancer analyzed in our institution, and we compare our results with those available from more recent scientific literature. Methods: The study cohort consisted of 70 patients. Somatic DNA was extracted from Formalin-Fixed Paraffin-Embedded (FFPE) tissue using a MagCore Genomic DNA FFPE One-Step Kit for MagCore System. The DNA was quantified by EasyPGX® Real-Time qPCR and EasyPGX® Analysis Software (version 4.0.13). Tissue somatic DNA libraries were prepared with Myriapod® NGS BRCA1-2 panel-NG035 and sequenced in a Mi-Seq® System. The sequence alignment in hg19 and the variant calling were performed using Myriapod® NGS Data Analysis Software version 5.0.8 NG900-SW 5.0.8 with a software detection limit (LoD) of 95%. Variants with a coverage of 500 and VAF% ≥ 5 were evaluated. Results: Tumor tissue NGS was unsuccessful in 46/70 patients (66%). Mutations of the BRCA2 gene were detected in 4 of the samples: (1) BRCA2 ex10 c.1244A>G p.His415Arg VAF = 51.03%; (2) BRCA2 ex11 c.5946delT p.Ser1982fs VAF = 72.1%; (3) BRCA2 ex11 c.3302A>G p.His1101Arg VAF = 52.9%; and (4) BRCA2 ex11 c.3195_3198delTAAT p.Asn1066fs VAF = 51.1%. Conclusions: The results from our internal overview seem to support the data and to confirm the performance of the technical issues reported in the literature. Considering the advanced age of our patients, with 84% of men over the age of 65, the application of alternative and less invasive procedures such as liquid biopsy, could be a more suitable solution for some cases.

Next generation sequencing: Forensic applications and policy considerations

AbstractNext generation sequencing (NGS) or massively parallel sequencing (MPS) is a high‐throughput technology that can be used to analyze DNA and RNA molecules with greater sensitivity, scalability, and speed than traditional Sanger sequencing. NGS systems are slowly gaining popularity in forensic science and may eventually become the future of forensic DNA analysis. The data generated can be utilized to conduct an array of forensic biology analyses such as short tandem repeats (STR) profiling, forensic genetic genealogy, predictive DNA phenotyping, and more. However, the adoption of NGS for forensic casework is associated with a lot of ethical, social, and legal concerns. This article provides a comprehensive review of NGS systems, data analysis, and forensic applications. It also provides policy considerations that aim to reduce harm and bias, while promoting informed consent, standardization, transparency, and accountability.This article is categorized under: Forensic Biology > Interpretation of Biological Evidence Forensic Biology > Ethical and Social Implications Forensic Biology > Forensic DNA Technologies

Whole-Exome Analysis for Polish Caucasian Patients with Retinal Dystrophies and the Creation of a Reference Genomic Database for the Polish Population.

We present the results of the first study of a large cohort of patients with inherited retinal dystrophies (IRD) performed for the Polish population using whole-exome sequencing (WES) in the years 2016-2019. Moreover, to facilitate such diagnostic analyses and enable future application of gene therapy and genome editing for IRD patients, a Polish genomic reference database (POLGENOM) was created based on whole-genome sequences of healthy Polish Caucasian nonagenarians and centenarians. The newly constructed database served as a control, providing a comparison for variant frequencies in the Polish population. The diagnostic yield for the selected group of IRD patients reached 64.9%. The study uncovered the most common pathogenic variants in ABCA4 and USH2A in the European population, along with several novel causative variants. A significant frequency of the ABCA4 complex haplotype p.(Leu541Pro; Ala1038Val) was observed, as well as that of the p.Gly1961Glu variant. The first VCAN causative variant NM_004385.5:c.4004-2A>G in Poland was found and described. Moreover, one of the first patients with the RPE65 causative variants was identified, and, in consequence, could receive the dedicated gene therapy. The availability of the reference POLGENOM database enabled comprehensive variant characterisation during the NGS data analysis, confirming the utility of a population-specific genomic database for enhancing diagnostic accuracy. Study findings suggest the significance of genetic testing in elder patients with unclear aetiology of eye diseases. The combined approach of NGS and the reference genomic database can improve the diagnosis, management, and future treatment of IRDs.

An Unusual PDGFRA Mutation in Gastrointestinal Stromal Tumor and Its NGS Data Analysis

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract [1]. About 75% of GISTs harbor activating mutations of KIT and About 10% of GISTs harbor PDGFRA activating mutations [2]. PDGFRA is the second most mutated oncogene in GIST and are particularly associated with gastric tumors [3,4]. The introduction of tyrosine kinase inhibitors (TKIs) has revolutionized the treatment of GISTs, with specific efficacy seen in tumors with PDGFRA and KIT mutations. However, different PDGFRA mutations can impact the response to these therapies differently [5,6]. For example, the PDGFRA D842V mutation is resistant to imatinib but may respond to newer drugs like avapritinib. Therefore, it is critically important to identify any specific PDGFRA mutations in tumor molecular testing. Here, we report a case with an unusual PDGFRA mutation with a discussion about its NGS data analysis.

JSI-124 Induces Cell Cycle Arrest and Regulates the Apoptosis in Glioblastoma Cells.

Cucurbitacin I (JSI-124), derived from Cucurbitaceae, has shown the potential to induce apoptosis and cell cycle arrest in some cancer cells. However, the effect of JSI-124 on glioblastoma multiforme (GBM) cell cycle and apoptosis is still unclear. Our investigation revealed that JSI-124 effectively reduced cell viability in GBM cells, leading to apoptosis and increased caspase-3 activity. Intriguingly, JSI-124 caused the accumulation of G2/M phase to regulate cell cycle, confirmed by MPM-2 staining and increased protein synthesis during mitosis by mitotic index analysis. Western blot analysis found that JSI-124 affected the progression of G2/M arrest by downregulating the CDK1 and upregulating the cyclinB1, suggesting that JSI-124 disrupted the formation and function of the cyclin B1/CDK1 complex in GBM8401 and U87MG cells. However, we found the JSI-124-regulated cell cycle G2/M and apoptosis-relative gene in GBM8401 and U87MG cells by NGS data analysis. Notably, we found that the GBM8401 and U87MG cells observed regulation of apoptosis and cell-cycle-related signaling pathways. Taken together, JSI-124 exhibited the ability to induce G2/M arrest, effectively arresting the cell cycle at critical stages. This arrest is accompanied by the initiation of apoptosis, highlighting the dual mechanism of action of JSI-124. Collectively, our findings emphasize that JSI-124 holds potential as a therapeutic agent for GBM by impeding cell cycle progression, inhibiting cell proliferation, and promoting apoptosis. As demonstrated by our in vitro experiments, these effects are mediated through modulation of key molecular targets.

Alternative allele frequency recallibration after NGS data analysis.

3077 Background: With the spread of NGS in routine clinical practice data analysis methods actively evolve to increase and refine information yield. Not much attention is given to variant allele fraction (VAF) estimation though it impacts clinical insignificance of subclonal somatic variants and identification of variant origin. Methods: Real-world sequencing data of 2379 samples (including 781 tumor-only and 1598 blood-only sequencing datasets) obtained via amplicon-based sequencing was used for the retrospective analysis of variant allele frequency estimation of identified genetic variants employing different methods including ITVC, SiNVICT, Mutect2 and SGA. Original method (AODvAF) utilizing alignment-free algorithm based on wild-type and alternative genome subsequence abundance estimation was developed and compared with the original output from NGS data analysis software. Anticipated VAF estimation was based on analyte type and database annotation. Results: Across 3447 identified potentially germline variations (PGV) VAF estimations provided by ITVC, SiNVICT and SGA were highly correlated (Pearson correlation coefficient [PCC] 1.00), while Mutect2 and AODvAF demonstrated discordant results (Mutect2/AODvAF vs ITVC PCC 0.87/0.82). VAF miscalculation defined by absolute difference between estimated and anticipated VAF of 10% and more was noted for 25%, 26%, 22%, 15% and 7% for ITVC, SiNVICT, SGA, Mutect2 and AODvAF respectively. Across 308 pathogenic BRCA1, BRCA2, PALB2 mutations identified in blood-only sequencing 25, 15, 16, 10 and 5 were characterized by VAF of 30% and lower based on ITVC, SiNVICT, SGA, Mutect2 and AODvAF estimations respectively (54, 49, 33, 51 and 9 for VAF 40% and lower), which may lead to misinterpretation of these variants as post-zygotic. Automatic in-silico annotation of variant origin (germline vs somatic) based on ISOWN was correct for 89%, 91%, 94%, 93% and 98% of these variants. Variant origin for 55 pathogenic variants of genes associated with hereditary cancer syndromes identified in tumor-only sequencing was verified by Sanger sequencing. Cohen's kappa coefficient for variant origin discrimination (Sanger vs in-silico prediction) was 0.91, 0.90, 0.91, 0.94 and 0.97 based on VAF provided by ITVC, SiNVICT, SGA, Mutect2 and AODvAF respectively. Conclusions: Accurate VAF calculation allows precise variant origin identification and impacts interpretation of variants. Developed method allows correction of VAF after NGS data core analysis.

A microRNA Arising from the Negative Strand of SARS-CoV-2 Genome Targets FOS to Reduce AP-1 Activity.

Virus-encoded microRNAs were first reported in the Epstein-Barr virus in 2004. Subsequently, a few hundred viral miRNAs have been identified, mainly in DNA viruses belonging to the herpesviridae family. To date, only 30 viral miRNAs encoded by RNA viruses are reported by miRBase. Since the outbreak of the SARS-CoV-2 pandemic, several studies have predicted and, in some cases, experimentally validated miRNAs originating from the positive strand of the SARS-CoV-2 genome. By integrating NGS data analysis and qRT-PCR approaches, we found that SARS-CoV-2 also encodes for a viral miRNA arising from the minus (antisense) strand of the viral genome, in the region encoding for ORF1ab, herein referred to as SARS-CoV-2-miR-AS1. Our data show that the expression of this microRNA increases in a time course analysis of SARS-CoV-2 infected cells. Furthermore, enoxacin treatment enhances the accumulation of the mature SARS-CoV-2-miR-AS1 in SARS-CoV-2 infected cells, arguing for a Dicer-dependent processing of this small RNA. In silico analysis suggests that SARS-CoV-2-miR-AS1 targets a set of genes which are translationally repressed during SARS-CoV-2 infection. We experimentally validated that SARS-CoV-2-miR-AS1 targets FOS, thus repressing the AP-1 transcription factor activity in human cells.

Case Report of a Glioma Patient with Homozygous Missense Amino Acid Substitution in KDR Gene

AbstractGliomas are the most commonly seen cancers of the central nervous system with a variable genetic predisposition. Here, we report a homozygous missense variant in the KDR gene in a patient with recurrent glioma. The 35-year-old male patient was diagnosed with stage IV glioma with a recurrence after 10 years from a low-grade stage two glioma. The patient underwent a repeat right craniotomy and ventriculoperitoneal shunt placement. Biopsy of the lesion showed areas of necrosis with microvascular proliferation and multinucleated tumor cells. An in-depth analysis of NGS data comprising a multigene panel of 351 genes (Agilent Cancer Core Panel) found a homozygous missense variant in exon 25 of the KDR gene that resulted in a substitution of an amino acid glutamine for arginine at codon 1118. The KDR gene or VEGF2 receptor is a type III receptor tyrosine kinase of the VEGF gene involved in angiogenesis. We hypothesize that the variation in the KDR gene may have a role in the patient's transition from grade II to grade IV glioma. While the clinical relevance of this mutation is not clear, screening mutations in the protein tyrosine and serine/threonine kinase domain of the KDR will provide critical insights into the development and progression of glioma in the pediatric and adult populations.

A Transcriptomic Analysis to Identify Prevalent lncRNAs in Gingivobuccal Oral Cancer

Objective: Gingivobuccal cancer is a subtype of oral cancer, prevalent in developing countries where the use of tobacco and Areca (betel) nut is rampant. This study aims to search through the publically available NCBI data to find comparisons between the normal and cancer-affected transcripts of RNA, which are molecules that copied the genetic information from DNA and that will be “read” as instructions to produce other molecules in the body. Using bioinformatic tools, we analyzed the variations in the transcripts and examined how they were correlated with other molecular processes in the body. We specifically investigated the role of “long noncoding RNAs (lncRNAs)” because it is speculated that they play an active role in the causality of cancer. Our analysis found a correlation of CTA-384D8.35 lncRNA with gingivobuccal cancer after analyzing the Hazard Ratio using the Cox Proportional-Hazard Model and is expected to pave the way for future studies. Methods: In this study, transcriptomic analysis was performed on a publicly available gingivobuccal cancer dataset obtained from NCBI. We developed a novel method for identification of annotated lncRNAs, which helped us select and correlate the lncRNA transcripts with the gingivobuccal cancer disease. Furthermore, the results were validated using the GEPIA2 tool for integrated analysis of co-lncRNA and GEPIA databases. Findings: From 24 RNA-Seq samples (12 normal samples and 12 tumor samples), a total of 16,065 potential lncRNA transcripts were recognized. From these 16,065 lncRNA transcripts, 1201 lncRNA transcripts were further filtered out based on a threshold adjusted p value of <0.01. We were able to for the first time identify these 1201 lncRNA transcripts, which were not annotated for gingivobuccal squamous cell carcinoma previously. Integrated analysis of co-lncRNA and GEPIA databases revealed that a total of 11 lncRNAs were upregulated, whereas 29 lncRNAs were downregulated. Furthermore, CTA-384D8.35 showed a significant correlation with disease-free survival time in HNSC. CTA-384D8.35 which is downregulated in gingivobuccal cancer has been correlated with tumor stage, metastasis, cancer status, clinical stage, and grade. Novelty: In this report we are for the first time correlating the CTA-384D8.35 lncRNA with the gingivobuccal cancer disease with the help of statistical and survival analysis. Furthermore,1201 lncRNA transcripts, which have previously not been annotated for gingivobuccal squamous cell carcinoma, were identified in this study based on a threshold adjusted p value of <0.01. Finally we have also introduced a novel method of identifying annotated lncRNAs in a disease as well as improved upon the NGS data analysis pipeline by determining the strandness of the raw RNASeq data. Differential expression of lncRNAs could possibly play a significant role in carcinogenesis. Therefore the identification of lncRNAs gains priority, which are speculated to play a role in the mechanism and formation of various forms of cancer. For identification and analysis of lncRNAs, a comparison of their expression in normal and affected tissues is likely to confirm their involvement in the disease or the lack thereof. The introduction of a novel methodology along with the identification of CTA-384D8.35 lncRNA paves the way for further analysis pertaining to lncRNAs as prognostic or diagnostic biomarkers. Since CTA-384D8.35 lncRNA has previously been correlated with a number of cancer hallmarks, it gains significant traction to be considered as an important aspect in the biomarker identification pertaining to gingivobuccal cancer. This study has reported for the first time the lncRNAs that are prevalent in gingivobuccal squamous cell carcinoma. Keywords: Gingivobuccal cancer; lncRNA; oral cancer; epigenetics; NGS data analysis

Web-Based Computational Tools for Base Editors.

CRISPR-based base editors are efficient genome editing tools for use in base correction. Currently, there are various versions and types of base editors with different substitution patterns, editing windows, and protospacer adjacent motif (PAM) sequences. For the design of target sequences, consideration of off-target sequences is required. In addition, for assessment of base editing outcomes in bulk populations, the analysis of high-throughput sequencing data is required. Several web browser-based computation programs have been developed for the purpose of target design and NGS data analysis, especially for users with less computational knowledge. In this manuscript, depending on the purpose of each program, we provide an explanation of useful tools including BE-Designer for design of targets and BE-Analyzer for analysis of NGS data that were developed by our group, CRISPResso2 for analysis of NGS data developed by Luca Pinello group, DeepBaseEditor for prediction of target efficiency developed by Hyongbum Henry Kim group, and BE-Hive for prediction of target outcome developed by David Liu group.

ESTABLISHMENT OF A BIOINFORMATICS PIPELINE FOR THE DETECTION OF PATHOGENIC BACTERIA

Next-generation sequencing-based methods using partial 16S rRNA gene amplicons are extensively applied today in studies of the plant metagenomes. NGS sequencing creates huge sets of raw data making analysis a challenging task. Lack of computational and bioinformatics knowledge and tools for analyzing high throughput data to interpret correct biological variations is a major problem. In addition, downstream analysis of NGS data with the available bioinformatics platforms create various challenges in inferring microbial composition. The available commercial software are expensive and individual opensource tools are usually operate stand alone as they are not combined for a user-friendly workflow. Therefore, beginners in bioinformatics might find analysis procedures are complicated, expensive, and timeconsuming with the associated learning. In the present study, a bioinformatics pipeline is developed to analyze the 16S rDNA amplicons of plant metagenome. Microbial DNA was extracted from imported seed potato tubers. Extracted DNA was sequenced using Ion Torrent Next Generation Sequencing technology by amplifying 400 bp V1-V2 region of 16S rRNA gene for the detection of bacterial pathogens. The pipeline was built by stringing together many command line tools; Quality checking of raw fastq data using FastQC, trimming of low-quality data with Trimmomatric, alignment of trimmed data using the BWA-MEM algorithm, removal of duplicate reads with Picard Mark Duplicates tool and finally generation of the phylogenetic tree and taxonomic profile with MEGAN 5. The developed pipeline was used to analyze the 16S rRNA next generation sequences and the reliability of the results has been checked with the use of mock communities for validation. The pipeline often can be executed on laptop sized machines to obtain the output in a couple of hours enabling easy access for the researchers.

Natural GMOs: a history of research

Agrobacterium mediated transformation underlies the production of most modern lines of transgenic plants. At the same time, in nature, plants are described that have been transformed by Agrobacterium without human intervention. They are called natural GMOs. Such plants were first described by White in 1983 [1] within the genus Nicotiana L., and the phenomenon of horizontal gene transfer from Agrobacterium to plants was considered unique for representatives of this genus for a long time. Only in 2012, another genus of natural GMOs was found. It was Linaria Mill. In 2019, the list of species of natural GMOs was increased by an order of magnitude and is constantly updated until now [2]. Several stages can be identified in the history of natural GMO research: 1. Description of individual examples of natural GMOs. 2. Estimation of the frequency of the horizontal gene transfer from Agrobacterium to plants based on the analysis of NGS data. 3. Description of the diversity of cT-DNA in terms of composition and copy number [3]. 4. Studies of the functions of individual pGMO genes [4]. 5. Phylogenetic studies of natural GMOs [5]. During the analysis of cT-DNA of natural GMOs, it became clear that they differ in the composition and intactness of transgenes, which can be interpreted from the point of view of the lack of a common function in all cT-DNAs. Genes that were not previously found in known Agrobacterium strains, are identifiedIn in cT-DNAs, indicating a greater biodiversity of Agrobacterium than previously thought [2]. Many cT-DNA genes are intact and expressed. For some it was possible to identify products [4]. All these discoveries lead us to understanding of the role of horizontal gene transfer from Agrobacterium to plants during their evolution. The work was performed using the equipment of the RC of St. Petersburg State University Development of Molecular and Cellular Technologies and Biobank with the support of the Ministry of Science and Higher Education of the RF in accordance with agreement No. 075-15-2022-322 dated 04/22/2022 on the provision of a grant in the form of a subsidy from the Federal budget of the Russian Federation for the creation and development of the world-class Scientific Center Agrotechnologies for the Future.

A Review on Bioinformatics Tools for Transcriptomics NGS Data Analysis

With the advancement in technologies, there has been a great expansion of data generation in various fields and disciples, including genomics, pharmacogenomics, epigenomics, transcriptomics, metabolomics, and proteomics. Regarding sequencing technologies, the advent of next-generation sequencing methods has enabled researchers to analyze the entire genome or multiple genes simultaneously for mutation detection or gene expression studies. For this reason, the demand for appropriate bioinformatics tools and pipelines for NGS Data Analysis to conduct precision analysis with a high level of accuracy is very high. The field of transcriptomics requires analysis of the entire RNA transcripts that define the transcriptional state of the cells. The analysis of the entire transcriptome is challenging and is supplemented with the utilization of various bioinformatic tools. This review provides an overview of the - generation sequencing and analysis in various fields like genomics and epigenomics, with a special focus on transcriptome analysis.

Abundance of Poleroviruses within Tasmanian Pea Crops and Surrounding Weeds, and the Genetic Diversity of TuYV Isolates Found.

The genus Polerovirus contains positive-sense, single-stranded RNA plant viruses that cause significant disease in many agricultural crops, including vegetable legumes. This study aimed to identify and determine the abundance of Polerovirus species present within Tasmanian pea crops and surrounding weeds that may act as virus reservoirs. We further sought to examine the genetic diversity of TuYV, the most commonly occurring polerovirus identified. Pea and weed samples were collected during 2019–2020 between October and January from thirty-four sites across three different regions (far northwest, north, and midlands) of Tasmania and tested by RT-PCR assay, with selected samples subject to next-generation sequencing. Results revealed that the presence of polerovirus infection and the prevalence of TuYV in both weeds and pea crops varied across the three Tasmanian cropping regions, with TuYV infection levels in pea crops ranging between 0 and 27.5% of tested plants. Overall, two species members from each genus, Polerovirus and Potyvirus, one member from each of Luteovirus, Potexvirus, and Carlavirus, and an unclassified virus from the family Partitiviridae were also found as a result of NGS data analysis. Analysis of gene sequences of the P0 and P3 genes of Tasmanian TuYV isolates revealed substantial genetic diversity within the collection, with a few isolates appearing more closely aligned with BrYV isolates. Questions remain around the differentiation of TuYV and BrYV species. Phylogenetic inconsistency in the P0 and P3 ORFs supports the concept that recombination may have played a role in TuYV evolution in Tasmania. Results of the evolutionary analysis showed that the selection pressure was higher in the P0 gene than in the P3 gene, and the majority of the codons for each gene are evolving under purifying selection. Future full genome-based analyses of the genetic variations will expand our understanding of the evolutionary patterns existing among TuYV populations in Tasmania.

Analyzing Multifactorial RNA-Seq Experiments with DicoExpress.

The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using generalized linear models for differential analysis of RNA-Seq data and the advantage of mixture models to perform co-expression analysis. To offer a managed setting to use these modeling approaches, we developed DiCoExpress that provides a standardized R pipeline to perform an RNA-Seq analysis. Without any particular knowledge in statistics or R programming, beginners can perform a complete RNA-Seq analysis from quality controls to co-expression through differential analysis based on contrasts inside a generalized linear model. An enrichment analysis is proposed both on the lists of differentially expressed genes, and the co-expressed gene clusters. This video tutorial is conceived as a step-by-step protocol to help users take full advantage of DiCoExpress and its potential in empowering the biological interpretation of an RNA-Seq experiment.

Abstract 625: Immunophenotyping of TCR and BCR clonotypes

Abstract TCR and BCR repertoire profiling holds great potential for understanding disease mechanisms and for development of new therapeutics in infectious diseases, autoimmunity and in immuno-oncology. However, this potential could be greatly improved by combining information about receptor clonotypes with immunophenotypes of T and B cells. To facilitate these studies, we developed a novel technology for combined profiling of all human TCR and BCR variable regions and phenotypic characterization of immune cells. The developed TCR/BCR immunophenotyping method involves multiplex RT-PCR amplification and sequencing of CDR3 regions of TCR and BCR genes and a set of the most informative T- and B-cell phenotyping genes. Bioinformatic analysis of NGS data allows profiling of TCR/BCR clonotypes, and identification of major immune cell subtypes and their activation status. Data will be presented showing how combined TCR/BCR clonotype analysis combined with targeted expression profiling of immune cells can be applied for large-scale discovery of novel cell typing and activation biomarkers in several immune-responsive model systems. Preliminary studies indicate the assay has unparalleled throughput, sensitivity, and improved cost-effectiveness for high-throughput immunity biomarker discovery applications. Citation Format: Alex Chenchik, Mikhail Makhanov, Tianbing Liu, Lester Kobzik. Immunophenotyping of TCR and BCR clonotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 625.

REVERSE: a user-friendly web server for analyzing next-generation sequencing data from in vitro selection/evolution experiments.

Next-generation sequencing (NGS) enables the identification of functional nucleic acid sequences from in vitro selection/evolution experiments and illuminates the evolutionary process at single-nucleotide resolution. However, analyzing the vast output from NGS can be daunting, especially with limited programming skills. We developed REVERSE (Rapid EValuation of Experimental RNA Selection/Evolution) (https://www.reverseserver.org/), a web server that implements an integrated computational pipeline through a graphical user interface, which performs both pre-processing and detailed sequence level analyses within minutes. Raw FASTQ files are quality-filtered, dereplicated, and trimmed before being analyzed by either of two pipelines. The first pipeline counts, sorts, and tracks enrichment of unique sequences and user-defined sequence motifs. It also identifies mutational intermediates present in the sequence data that connect two input sequences. The second pipeline sorts similar sequences into clusters and tracks enrichment of peak sequences. It also performs nucleotide conservation analysis on the cluster of choice and generates a consensus sequence. Both pipelines generate downloadable spreadsheets and high-resolution figures. Collectively, REVERSE is a one-stop solution for the rapid analysis of NGS data obtained from in vitro selection/evolution experiments that obviates the need for computational expertise.

Germline predisposition in oncologic and dermatologic melanoma cohorts.

10523 Background: Melanoma has recently been suggested to be a highly heritable cancer with high twin-twin concordance. In contrast, prior studies on patients with familial atypical multiple mole melanoma (FAMMM) syndrome, multiple primary melanomas and early age of onset, all known to be associated with germline CDKN2A/CDK4 pathogenic variants, suggest that individuals with melanoma who carry germline alterations are rare. We studied the overall prevalence of germline cancer predisposition in a large prospective oncologic cohort with comparison to other defined cohorts of individuals with melanoma with available germline data. Methods: Individuals who presented to medical oncology clinic with a diagnosis of melanoma and personal or family history of multiple cancers were offered germline testing with a commercially available next generation sequencing panel (Invitae, San Francisco, CA). Eligibility criteria required ≥ 2 melanomas in an individual or family; melanoma and other cancer(s) in an individual; melanoma and at least 2 other cancers in 1st- or 2nd-degree relatives; age ≤35 at diagnosis; or limited family structure. Comparative analysis of germline NGS data from 3 additional selected and non-selected melanoma datasets was performed. Results: In a cohort of 400 oncology patients with melanoma who consented to commercial germline testing of 85 cancer-associated genes, a germline pathogenic/likely pathogenic (gP/LP) positive rate of 15.3% (n = 61) was observed. Genes previously associated with inherited melanoma ( BAP1, BRCA1/2, CDKN2A, MITF, TP53) comprised less than one-third of gP/LP variants (20, 32.7%); the majority of germline variants were in cancer predisposition genes not traditionally associated with melanoma (e.g. BRIP1, CHEK2, MSH2, PMS2, MLH1, RAD51C, BLM). Family history of non-cutaneous cancer (42, 69%) and personal history of melanoma with ≥ 1 non-cutaneous cancer (22, 36%) were the most common eligibility criteria met in gP/LP variant carriers. Analysis of germline data from other large oncologic and dermatologic melanoma datasets yielded gP/LP variant positive rates of 10.6% in an unselected oncologic melanoma cohort (TCGA, n = 470), 15.8% in a selected commercial testing cohort (Invitae, n = 12,571), and 14.5% in a highly selected, primarily dermatologic subset (Boston-Athens, n = 289). Conclusions: In oncologic and dermatologic cohorts, germline testing of selected individuals with melanoma yields rates of clinically impactful P/LP variant detection which exceed consensus standards for pretest probability. Most P/LP variants were found in genes associated with non-cutaneous cancers. Obtaining a family and personal history of cancer, particularly non-cutaneous cancers, and referring for broad panel-based germline testing in all individuals with melanoma are recommended.

Identification of missed viruses by metagenomic sequencing of clinical respiratory samples from Kenya

Pneumonia remains a major cause of mortality and morbidity. Most molecular diagnoses of viruses rely on polymerase chain reaction (PCR) assays that however can fail due to primer mismatch. We investigated the performance of routine virus diagnostics in Kilifi, Kenya, using random-primed viral next generation sequencing (viral NGS) on respiratory samples which tested negative for the common viral respiratory pathogens by a local standard diagnostic panel. Among 95 hospitalised pneumonia patients and 95 household-cohort individuals, analysis of viral NGS identified at least one respiratory-associated virus in 35 (37%) and 23 (24%) samples, respectively. The majority (66%; 42/64) belonged to the Picornaviridae family. The NGS data analysis identified a number of viruses that were missed by the diagnostic panel (rhinovirus, human metapneumovirus, respiratory syncytial virus and parainfluenza virus), and these failures could be attributed to PCR primer/probe binding site mismatches. Unexpected viruses identified included parvovirus B19, enterovirus D68, coxsackievirus A16 and A24 and rubella virus. The regular application of such viral NGS could help evaluate assay performance, identify molecular causes of missed diagnoses and reveal gaps in the respiratory virus set used for local screening assays. The results can provide actionable information to improve the local pneumonia diagnostics and reveal locally important viral pathogens.

ImmunoDataAnalyzer: a bioinformatics pipeline for processing barcoded and UMI tagged immunological NGS data

BackgroundNext-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior.ResultsImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples.ConclusionsIMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/.