Bioinformatics Analysis Pipeline Research Articles

Single-Cell RNA Sequencing of Sickle Cell Reticulocytes to Identify Beta-Globin Genotypes and Associated Gene Expression Differences

Background: Sickle cell disease (SCD) is a genetic condition typically caused by a single point mutation in both copies of the beta-globin (HBB) gene, resulting in sickle (S) hemoglobin (HbS) production instead of adult (A) hemoglobin (HbA). GPH101 (nulabeglogene autogedtemcel) is an investigational gene-edited autologous hematopoietic stem cell-based therapy in clinical development for SCD that is designed to directly correct the underlying mutation, thereby decreasing HbS production and restoring HbA expression. Previous allogeneic hematopoietic stem cell transplant data, mostly in the context of using HLA-matched related donors, has demonstrated a competitive advantage for homozygous (AA) or heterozygous (AS) hemoglobin due to ineffective erythropoiesis of SS erythroid progenitors. At early timepoints following infusion of nulabeglogene autogedtemcel, red blood cells (RBCs) expressing corrected HbA cannot be distinguished from HbA in transfused blood. Because RBCs are enucleated, tracking of gene editing at the genomic level is not possible. However, reticulocytes are immature RBCs that still contain some RNA and could be used to evaluate allelic correction. We sought to develop a single-cell RNA sequencing (scRNAseq) method to enable enumeration of potential gene-editing outcomes in peripheral reticulocytes. Methods: Reticulocytes were sorted from peripheral blood from healthy donors (AA), donors with sickle cell trait (AS), and donors with sickle cell disease (SS) by fluorescence-activated cell sorting (FACS) based on a Live/CD235a+/CD45-/CD71+/TO+ phenotype and mixed at given ratios before application to the 10x genomics workflow for scRNAseq. Individual mRNA molecules were tagged with a unique molecular identifier (UMI) to enable transcript quantification. A custom bioinformatics analysis pipeline was developed to identify HBB variants within the scRNAseq data to identify individual reticulocytes expressing either normal HBB, sickle HBB, or both. Results: Genotypes of individual cells could be clearly distinguished from scRNAseq data using a custom bioinformatics pipeline. Additionally, differential mRNA expression profiling between AA and SS samples was performed and previously known results from bulk RNAseq studies were produced, including upregulation of HbA2 subunit delta and alpha hemoglobin stabilizing protein. An examination of cell populations underlying these expression differences will be presented. Conclusion: Our data demonstrate the utility of scRNAseq for evaluating differential HBB editing outcomes in erythroid progenitors from patients treated with nulabeglogene autogedtemcel to support clinical development. Further evaluation of the clinical utility of the scRNAseq pipeline is planned.

Rapid genome sequencing for pediatrics.

The advancements made in next-generation sequencing (NGS) technology over the past two decades have transformed our understanding of genetic variation in humans and had a profound impact on our ability to diagnose patients with rare genetic diseases. In this review, we discuss the recently developed application of rapid NGS techniques, used to diagnose pediatric patients with suspected rare diseases who are critically ill. We highlight the challenges associated with performing such clinical diagnostics tests in terms of the laboratory infrastructure, bioinformatic analysis pipelines, and the ethical considerations that need to be addressed. We end by looking at what future developments in this field may look like and how they can be used to augment the genetic data to further improve the diagnostic rates for these high-priority patients.

Genomic Characterization of Antimicrobial-Resistant Salmonella enterica in Duck, Chicken, and Pig Farms and Retail Markets in Eastern China.

ABSTRACTAntimicrobial-resistant Salmonella enterica poses a significant public health concern worldwide. However, the dissemination of Salmonella enterica among food animals in eastern China has not been fully addressed. Here, we demonstrated the antimicrobial resistance (AMR) patterns and the whole-genome characterization of 105 S. enterica isolates from 1,480 fecal samples and anal swabs collected from 22 different farms (chickens, ducks, and pigs) and two live animal markets located in Zhejiang and Fujian Provinces in eastern China in 2019. The prevalence of isolates in duck farms (19.17%, 23/120) was statistically significantly higher (P < 0.001) than that in chicken farms (6.61%, 37/523) and pig farms (3.50%, 7/200). Among these isolates, 75.26% (79/105) were multidrug resistant, with the highest rates of resistance to tetracycline (76.20%) and ampicillin (67.62%) and the lowest resistance rate to meropenem (0.00%). The serotypes were consistent with sequence types and were closely related to the sampling animal species and sites. S. enterica serotype Kentucky (20.95%, 22/105) was the most frequent serotype and harbored more AMR patterns and genes than others. Furthermore, IncFII(S) and IncHI2 were the most prevalent replicons. A total of 44 acquired AMR genes were found. Among those genes, aac(6′)-Iaa, blaTEM-1B, floR, dfrA14, fosA7, mph(A), qnrS1, sul1, tet(A), and ARR-3 were the dominant AMR genes mediating the AMR toward aminoglycosides, β-lactams, phenicol, trimethoprim, fosfomycin, macrolide, quinolone, sulfonamides, tetracycline, and rifampin, respectively. The consistency of acquired AMR genes with AMR phenotypes for ampicillin, ceftiofur, ceftazidime, meropenem, sulfamethoxazole-trimethoprim, and tetracycline was >90%. Together, our study highlights the application of whole-genome sequencing to assess veterinary public health threats.IMPORTANCE Public health is a significant concern in China, and the foodborne pathogen Salmonella, which is spread via the animal-borne food chain, plays an important role in the overall disease burden in China annually. The development of advanced sequencing technologies has introduced a new way of understanding emerging pathogens. However, the routine surveillance application of this method in China remains in its infancy. Here, we applied a pool of all isolates from the prevalence data in Zhejiang and Fujian for whole-genome sequencing and combined these data with the cutting-edge bioinformatic analysis pipeline for one-step determination of the complete genetic makeup for all 105 genomes. The illustrated method could provide a cost-effective approach, without labor-intensive laboratory characterization, for predicting serotypes, genotypes, plasmid types, antimicrobial resistance genes, and virulence genes, and thus would provide essential knowledge for emerging pathogens. Our findings and perspectives are essential for delivering updated knowledge on foodborne pathogens in an understudied region in China.

Meta-analysis of larvae of the black soldier fly (Hermetia illucens) microbiota based on 16S rRNA gene amplicon sequencing.

Black soldier fly larvae (BSFL) belong to the most widely reared insects as an alternative protein source at industrial scale. Bacteria in the larval gut can provide benefits for the animal, though some bacteria can also be pathogenic for the insect. Accurate characterization of the BSFL microbiota is important for the production of BSFL in terms of yield and microbiological safety. In this study, 16S ribosomal RNA gene sequence data sets from 11 studies were re-analysed to gain better insights in the BSFL gut microbiota, potential factors that influence their composition, and differences between the gut and the whole larvae microbiota. A core gut microbiota was found consisting of members of Enterococcus, Klebsiella, Morganella, Providencia, and Scrofimicrobium. Further, the factors ‘Study’, ‘Age’ and ‘Feed’ (i.e. rearing substrate of the larvae) significantly affected the microbiota gut composition. When compared to whole larvae, a significantly lower diversity was found for gut samples, suggesting that the larvae harboured additional microbes on their cuticle or in the insect body. Universal choices in insect sample type, primer selection and bio-informatics analysis pipeline can strengthen future meta-analyses and improve our understanding of the BSFL gut microbiota towards the optimization of insect rearing conditions and substrates.

Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method

The World Health Organization (WHO) introduced “Coronavirus disease 19” or “COVID-19” as a novel coronavirus in March 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide crisis. Artificial intelligence and bioinformatics analysis pipelines can assist with finding biomarkers, explanations, and cures. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data. On the other hand, pathway enrichment analysis, as a dominant tool, could help researchers discover potential key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. In this work, we propose a two-stage machine learning approach for pathway analysis. During the first stage, four informative gene sets that can represent important COVID-19 related pathways are selected. These “representative genes” are associated with the COVID-19 pathology. Then, two distinctive networks were constructed for COVID-19 related signaling and disease pathways. In the second stage, the pathways of each network are ranked with respect to some unsupervised scorning method based on our defined informative features. Finally, we present a comprehensive analysis of the top important pathways in both networks. Materials and implementations are available at: https://github.com/MahnazHabibi/Pathway.

Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups.

Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective.

A reappraisal of microbiome dysbiosis during experimental periodontitis.

Periodontitis is a chronic inflammatory disease associated with the presence of dysbiotic microbial communities. Several studies interrogating periodontitis pathogenesis have utilized the murine ligature-induced periodontitis (LIP) model and have further examined the ligature-associated microbiome relying on 16S rRNA-based sequencing techniques. However, it is often very challenging to compare microbial profiles across studies due to important differences in bioinformatic processing and databases used for taxonomic assignment. Thus, our study aim was to reanalyze microbiome sequencing datasets from studies utilizing the LIP model through a standardized bioinformatic analysis pipeline, generating a comprehensive overview of microbial dysbiosis during experimental periodontitis.We conducted a reanalysis of 16S rDNA gene sequencing datasets from nine published studies utilizing the LIP model. Reads were grouped according to the hypervariable region of the 16S rDNA gene amplified (V1-V3 and V4), preprocessed, binned into operational taxonomic units and classified utilizing relevant databases. Alpha- and beta-diversity analyses were conducted, along with relative abundance profiling of microbial communities. Our findings revealed similar microbial richness and diversity across studies and determined shifts in microbial community structure determined by periodontitis induction and study of origin. Clear variations in the relative abundance of bacterial taxa were observed starting on day 5 after ligation and onward, consistent with a distinct microbial composition during health and experimental periodontitis. We also uncovered differentially represented bacterial taxa across studies, dominating periodontal health and LIP-associated communities. Collectively, this reanalysis provides a unified overview of microbial dysbiosis during the LIP model, providing new insights that aim to inform further studies dedicated to unraveling oral host-microbial interactions.

NGS4THAL, a One-Stop Molecular Diagnosis and Carrier Screening Tool for Thalassemia and Other Hemoglobinopathies by Next-Generation Sequencing

Thalassemia is one of the most common genetic diseases and a major health threat worldwide. Accurate, efficient, and scalable analysis of next-generation sequencing (NGS) data is much needed for its molecular diagnosis and carrier screening. We developed NGS4THAL, a bioinformatics analysis pipeline analyzing NGS data to detect pathogenic variants for thalassemia and other hemoglobinopathies. NGS4THAL realigns ambiguously mapped NGS reads derived from the homologous Hb gene clusters for accurate detection of point mutations and small insertions/deletions. It uses a combination of complementary structural variant (SV) detection tools and an in-house database of control data containing specific SVs to achieve accurate detection of the complex SV types. Detected variants are matched with those in HbVar (A Database of Human Hemoglobin Variants and Thalassemia Mutations), allowing recognition of known pathogenic variants, including disease modifiers. Tested on simulation data, NGS4THAL achieved high sensitivity and specificity. For targeted NGS sequencing data from samples with laboratory-confirmed pathogenic Hb variants, it achieved 100% detection accuracy. Application of NGS4THAL on whole genome sequencing data from unrelated studies revealed thalassemia mutation carrier rates for Hong Kong Chinese and Northern Vietnamese that were consistent with previous reports. NGS4THAL is a highly accurate and efficient molecular diagnosis tool for thalassemia and other hemoglobinopathies based on tailored analysis of NGS data and may be scaled for population carrier screening.

Abstract 2801: Detection of biallelic loss of DNA repair genes in formalin-fixed, paraffin embedded (FFPE) tumor samples using a novel tumor-only sequencing panel with error correction

Abstract Background: Loss-of-function (LOF) mutations in DNA damage response (DDR) tumor suppressor genes are compensated for by functional redundancies, exposing synthetic lethal (SL) interactions and opportunities for targeted therapy. Patient selection for SL-based therapy may be improved by assessment of gene-specific loss of heterozygosity (LOH) and biallelic LOF, neither of which is routinely reported by existing targeted sequencing panels. The Synthetic Lethal Interactions for Precision Diagnostics (SNiPDx) targeted sequencing panel features a novel bioinformatic analysis pipeline that enables accurate genome-wide determination of allele-specific copy number, estimation of tumor ploidy and purity, and detection of single nucleotide and indel variants in target genes focused on DDR pathways, all from tumor-only samples. Here we describe the development and accuracy of SNiPDx for detection of LOH and bi-allelic LOF genetic alterations in FFPE samples. Methods: Genomic DNA (&amp;gt;50 ng) was extracted from FFPE samples of multiple solid tumor types (n = 43). Next-generation sequencing was performed on anchored multiplex PCR libraries, constructed using probes that incorporate unique molecular identifiers and span 26 genes and 5,000 genome-wide common germline single-nucleotide polymorphisms (SNPs). Unmatched non-tumor samples (n = 24) were used to generate a reference baseline dataset. The FACETS algorithm, optimized to account for differential DNA fragmentation across samples, was used to assess copy number imbalance in heterozygous SNPs and to quantify tumor purity. Allele fractions at each heterozygous SNP were used to estimate allelic imbalances across chromosomal regions. A reference dataset was derived from matched FFPE tumor samples by whole genome sequencing (WGS) and analysis of sequence data using 3 complementary algorithms. Allele-specific copy number analysis and tumor purity estimation from SNiPDx and WGS data were compared. Results: Copy number was evaluable in 605 genes from 24 matched tumor samples that passed quality control filters. Median sequencing depth across samples by SNiPDx and WGS were 1346x and 18.6x, respectively. LOH detection by SNiPDx was reproducible (100%) across 170 genes from 7 samples sequenced and analyzed in duplicate. A strong correlation was observed between sample purity estimates by WGS and SNiPDx (Pearson’s r = 0.81, p &amp;lt; 0.001). Compared with WGS-derived calls, the sensitivity and specificity of LOH detection by SNiPDx were 95% and 90%, respectively, rising to 97% and 91% in regions with LOH agreement by all 3 WGS algorithms, and to 99% and 97% in diploid regions with no subclonal alterations. b The SNiPDx panel is a novel clinical test for biallelic loss in FFPE tumor-only samples with high accuracy as validated through concordance with a WGS-derived dataset. Citation Format: Dominik Glodzik, Pier Selencia, Ryan Rogge, Ian M. Silverman, Michael Zinda, Maria Koehler, Robert D. Daber, Verity Johnson, Jorge S. Reis-Filho, Victoria Rimkunas. Detection of biallelic loss of DNA repair genes in formalin-fixed, paraffin embedded (FFPE) tumor samples using a novel tumor-only sequencing panel with error correction [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 2801.

An Integrated Mass Spectrometry-Based Glycomics-Driven Glycoproteomics Analytical Platform to Functionally Characterize Glycosylation Inhibitors.

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and mass spectrometry-based glycomics-driven glycoproteomics analysis pipeline was performed to identify an N-glycan inhibitor against lung cancer cells. Combined network pharmacology and in silico screening approaches were used to identify a potential inhibitor, pictilisib, against several glycosylation-related proteins, such as Alpha1-6FucT, GlcNAcT-V, and Alpha2,6-ST-I. A glycomics assay of lung cancer cells treated with pictilisib showed a significant reduction in the fucosylation and sialylation of N-glycans, with an increase in high mannose-type glycans. Proteomics analysis and in vitro assays also showed significant upregulation of the proteins involved in apoptosis and cell adhesion, and the downregulation of proteins involved in cell cycle regulation, mRNA processing, and protein translation. Site-specific glycoproteomics analysis further showed that glycoproteins with reduced fucosylation and sialylation were involved in apoptosis, cell adhesion, DNA damage repair, and chemical response processes. To determine how the alterations in N-glycosylation impact glycoprotein dynamics, modeling of changes in glycan interactions of the ITGA5–ITGB1 (Integrin alpha 5-Integrin beta-1) complex revealed specific glycosites at the interface of these proteins that, when highly fucosylated and sialylated, such as in untreated A549 cells, form greater hydrogen bonding interactions compared to the high mannose-types in pictilisib-treated A549 cells. This study highlights the use of mass spectrometry to identify a potential glycosylation inhibitor and assessment of its impact on cell surface glycoprotein abundance and protein–protein interaction.

A multi-platform reference for somatic structural variation detection

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

Genome‐wide identification of catfish antimicrobial peptides: A new perspective to enhance fish disease resistance

AbstractAntimicrobial peptides (AMPs), also known as host defence peptides, are evolutionarily ancient defensive weapons that can combat infections caused by pathogens. Fish‐derived AMPs have shown microbicidal properties, host immunomodulatory responses, and are induced by a variety of factors, especially pathogenic infection and abiotic stress, which may activate downstream signalling pathways to initiate the expression of antimicrobial peptide genes. Large‐scale genome sequencing has been applied in many aquatic animals, providing databases for the systematic identification of putative AMPs in these species. Recently, the whole genome and the proteome of channel catfish (Ictalurus punctatus) were released; by taking advantage of the antimicrobial peptide database and combining the proteome of catfish, we established a bioinformatic analysis pipeline to identify catfish AMP repertoire. This review introduces an effective method for the identification and development of putative AMPs based on protein datasets, and summarizes the structural properties, immunomodulation and molecular responses in vivo of catfish AMPs. In addition, the evaluation of the antibacterial activity of synthetic or isolated AMPs in vitro, and the applications of AMPs in fish via genetic engineering and clustered regularly interspaced short palindromic repeats associated proteins system (CRISPR/Cas9) were presented, which supports antimicrobial research and aquaculture therapy. The summary and comparison of these AMPs will enhance our understanding and their cross‐species applications. Combined with current genetic engineering and genome editing techniques, it is possible to promote the development of immune processes to protect valuable aquatic animals from infectious diseases.

[Analysis of the methods and quality assurance of metagenomic next-generation sequencing to detect the microbial cfDNA from blood samples in China].

Objective: To investigate the methods and quality assurance of metagenomic next-generation sequencing (mNGS) to detect the microbial cfDNA (mcfDNA) from blood samples in different laboratories across China. Methods: In October 2020, questionnaires about detecting mcfDNA in blood samples with mNGS were distributed to 80 laboratories across the country. The questionnaire included four parts: pre-analysis, during analysis, post-analysis, and carrying out of performance validation for mNGS. (1) Pre-analysis: the requirements for samples quality, such as collection, storage, the transportation conditions of samples; (2) During analysis: the extraction workflows of mcfDNA, the quality requirements of the library, the application of the sequencing platforms and the bioinformatics analysis pipelines; (3) Post-analysis: the standard of interpretation results for mNGS; (4) Carrying out of performance validation: the minimum detection limit for various pathogens. All laboratories are required to fill in the questionnaire according to the actual situation. The feedback data were summarized and analyzed. Results: The 80 laboratories included 20 medical centers and 60 independent medical laboratories. There were 80.0% (64/80) of laboratories indicated that both plasma and serum samples were used to detect mcfDNA in blood, and the rest of the laboratories (16/80, 20.0%) only used plasma samples. The sequencing platforms used by mNGS laboratories involved in the survey included illumina (49), Beijing Genomics Institute (16), Ion Torrent (13) and Nanopore sequencing (2). There were 87.5% (70/80) of laboratories used the integrated analysis tools built by the third-party laboratories, and other laboratories (12.5%, 10/80) independently built the analysis platform by open-source software. The interpretation criteria of mNGS results varied between laboratories, among which the normalized number of pathogen-specific sequences, relative abundance, genome coverage rate, and the detection of the microorganism in the negative control were the main factors considered by laboratories. Most laboratories (76.3%, 61/80) had carried out the performance validation for the mcfDNA mNGS workflows. The limit of detection of the laboratories-developed mNGS workflows for Gram-positive bacteria, Gram-negative bacteria, fungi, parasites, and other pathogens were mainly distributed at 10-100 copies/ml, DNA virus was mainly distributed at 500-1 000 copies/ml. Conclusions: The mNGS workflows of various laboratories are very different. In order to ensure timely and accurate testing results, every laboratory needs to actively optimize the mNGS testing procedures, improve quality assurance measures, and carry out performance validation before mNGS is widely used in clinical settings.

A Rapid and Affordable Screening Tool for Early-Stage Ovarian Cancer Detection Based on MALDI-ToF MS of Blood Serum

Ovarian cancer is a worldwide health issue that grows at a rate of almost 250,000 new cases every year. Its early detection is key for a good prognosis and even curative surgery. However, current medical examination methods and tests have been inefficient in detecting ovarian cancer at the early stage, leading to preventable death. So far, new screening tests based on molecular biomarker analysis techniques have not resulted in any substantial improvement in early-stage diagnosis and increased survival. Thus, whilst there remains clear potential to improve outcomes through early detection, novel approaches are needed. Here, we postulated that MALDI-ToF-mass-spectrometry-based tests can be a solution for effective screening of ovarian cancer. In this retrospective cohort study, we generated and analyzed the mass spectra of 181 serum samples of women with and without ovarian cancer. Using bioinformatics pipelines for analysis, including predictive modeling and machine learning, we found distinct mass spectral patterns composed of 9–20 key combinations of peak intensity or peak enrichment features for each stage of ovarian cancer. Based on a scoring algorithm and obtained patterns, the optimal sensitivity for detecting each stage of cancer was 95–97% with a specificity of 97%. Scoring all algorithms simultaneously could detect all stages of ovarian cancer at 99% sensitivity and 92% specificity. The results further demonstrate that the matrix and mass range analyzed played a key role in improving the mass spectral data quality and diagnostic power. Altogether, with the results reported here and increasing evidence of the MS assay’s diagnostic accuracy and instrument robustness, it has become imminent to consider MS in the clinical application for ovarian cancer screening.

EP376: NGS detection of NUDT15 6-bp insertion and UGT1A1 (TA) repeat polymorphism on a preventative genomics assay

Application of Next-Generation Sequencing (NGS) for pharmacogenetic analysis provides cost-effective and high-throughput alternatives to other genotyping methodologies, making it a method of choice for pharmacogenetic testing. However, the nature of short-read sequencing technologies may carry challenges when it comes to the detection of specific complex structures/variants such as large insertion/deletions (indels), or repeat polymorphisms. These limitations can be overcome by the design of sequencing probes and optimized NGS library preparation process, as well as customized bioinformatic analysis pipelines. UGT1A1 is an important pharmacogene that impacts response to multiple drugs, including the HIV treatment, Atazanavir. The TA repeat polymorphism, rs3064744, is identified across various geographic, ethnic and racial populations and causes reduction in gene activity, resulting in toxicity and dosage effects. NUDT15 plays an important role in the metabolism of thiopurines. The rs746071566 microsatellite polymorphism, c.38GAGTCG[4] (p.13GV[4]), plays a critical role in thiopurine-induced adverse reactions and has been identified as a commonly observed variant in pan-ethnic populations. The current study evaluated the performance of a targeted NGS capture and bioinformatics platform to identify the promoter (TA)7 repeat polymorphism UDP glucuronosyl-transferase UGT1A1, as well as a 6 bp insertion NUDT15 variant. Both variants were analyzed as a part of a 455 gene panel utilized for a preventative genomics assay. The study consisted of 413 validation samples analyzed for UGT1A1 (rs3064744), and 186 analyzed for NUDT15 (rs746071566). The specimens were retrieved from various sources: DNA from Coriell Repository, and DNA isolated from whole blood and saliva by the Maxwell RSC Blood DNA Kit (Promega, Madison, Wisconsin) on the automated Maxwell RSC 48 Instrument (Promega). Sequencing was performed using custom-designed targeted capture and Fast Hybridization Target Enrichment workflow (Twist Bioscience, South San Francisco, CA). The samples were prepared using 50 ng of input DNA and the Twist EF Library Prep Kit 2.0 + UDIs and Fast Hyb reagents (Twist Bioscience), and run in a format of 12 samples per pool and 36 per single run on MiniSeq System (Illumina, San Diego, CA). Selected samples were confirmed using Sanger sequencing. The data was analyzed using the Edico Dragen server (Illumina), and in-house developed proprietary pipeline. For the purpose of this study, the reference ranges were defined for UGT1A1 as *1 AT[7], *28 AT[8], *36 AT[6] and *39 AT[9]. The genotypes frequency detected for UGT1A1 rs3064744 were as follow: AT[7]/AT[6] 0.97%, AT[7]/AT[7] 62%, AT[7]/AT[8] 24%, AT[7]/AT[9] 0.97%, AT[8]/AT[8] 11.8%, AT[9]/AT[9] 0.24%; and for NUDT15 rs746071566 GGAGTC[1]/GGAGTC[1] 99.5%, GGAGTC[1]/GGAGTC[2] 0.5% and GGAGTC[1]/GGAGTC[0] 0%. This data is comparable to the frequency published in the gnomAD database. Additionally, Sanger confirmation analysis performed for selected Coriell samples yielded 100% concordance between NGS and Sanger sequencing data. As previously reported, ∼14% of variants (eg, indels, small CNVs, and complex changes) identified during NGS analysis can impose technical difficulties resulting in decreased detection rates. Both of the variants investigated in this study (UGT1A1 rs3064744 and NUDT15 rs746071566) belong to this group of challenging variants, and thus require specific consideration during the analytical and bioinformatic design and validation. The NGS capture presented in this study was iteratively designed to optimize the ability to sequence across these regions. Performance data for these variants is dependent on wet laboratory procedure and bioinformatic variant assembly because standard VCF calling software packages do not account for repetitive elements. The in-house developed bioinformatic script accurately translated the output of the VCF pipeline into the genotype calls. The comparability of the obtained genotype frequencies with the published data in gnomAD further confirmed the accuracy of the assay performance. Analysis of the samples using Sanger sequencing further confirmed data received on NGS analysis.

ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes

BackgroundThe recent advancements in high-throughput sequencing have resulted in the availability of annotated genomes, as well as of multi-omics data for many living organisms. This has increased the need for graphic tools that allow the concurrent visualization of genomes and feature-associated multi-omics data on single publication-ready plots.ResultsWe present chromoMap, an R package, developed for the construction of interactive visualizations of chromosomes/chromosomal regions, mapping of any chromosomal feature with known coordinates (i.e., protein coding genes, transposable elements, non-coding RNAs, microsatellites, etc.), and chromosomal regional characteristics (i.e. genomic feature density, gene expression, DNA methylation, chromatin modifications, etc.) of organisms with a genome assembly. ChromoMap can also integrate multi-omics data (genomics, transcriptomics and epigenomics) in relation to their occurrence across chromosomes. ChromoMap takes tab-delimited files (BED like) or alternatively R objects to specify the genomic co-ordinates of the chromosomes and elements to annotate. Rendered chromosomes are composed of continuous windows of a given range, which, on hover, display detailed information about the elements annotated within that range. By adjusting parameters of a single function, users can generate a variety of plots that can either be saved as static image or as HTML documents.ConclusionsChromoMap’s flexibility allows for concurrent visualization of genomic data in each strand of a given chromosome, or of more than one homologous chromosome; allowing the comparison of multi-omic data between genotypes (e.g. species, varieties, etc.) or between homologous chromosomes of phased diploid/polyploid genomes. chromoMap is an extensive tool that can be potentially used in various bioinformatics analysis pipelines for genomic visualization of multi-omics data.

Biomonitoring of Fungal and Oomycete Plant Pathogens by Using Metabarcoding.

Fungal and oomycete plant pathogens are responsible for the devastation of various ecosystems such as forest and crop species worldwide. In an effort to protect such natural resources for food, lumber, etc., early detection of non-indigenous phytopathogenic fungi in new areas is a key approach in managing threats at their source of introduction. A workflow was developed using high-throughput sequencing (HTS), more specifically metabarcoding, a method for rapid and higher throughput species screening near high-risk areas, and over larger geographical spaces. Biomonitoring of fungal and oomycete entities of plant pathogens (e.g., airborne spores) regained from environmental samples and their processing by metabarcoding is thoroughly described here. The amplicon-based approach goes from DNA and sequencing library preparation using custom-designed polymerase chain reaction (PCR) fusion primers that target the internal transcribed spacer 1 (ITS1) from fungi and oomycetes and extends to multiplex HTS with the Ion Torrent platform. In addition, a brief and simplified overview of the bioinformatics analysis pipeline and other available tools required to process amplicon sequences is also included. The raw data obtained and processed enable users to select a bioinformatics pipeline in order to directly perform biodiversity, presence/absence, geographical distribution, and abundance analyses through the tools suggested, which allows for accelerated identification of phytopathogens of interest.

Modelling the bioinformatics tertiary analysis research process

BackgroundWith the advancements of Next Generation Techniques, a tremendous amount of genomic information has been made available to be analyzed by means of computational methods. Bioinformatics Tertiary Analysis is a complex multidisciplinary process that represents the final step of the whole bioinformatics analysis pipeline. Despite the popularity of the subject, the Bioinformatics Tertiary Analysis process has not yet been specified in a systematic way. The lack of a reference model results into a plethora of technological tools that are designed mostly on the data and not on the human process involved in Tertiary Analysis, making such systems difficult to use and to integrate.MethodsTo address this problem, we propose a conceptual model that captures the salient characteristics of the research methods and human tasks involved in Bioinformatics Tertiary Analysis. The model is grounded on a user study that involved bioinformatics specialists for the elicitation of a hierarchical task tree representing the Tertiary Analysis process. The outcome was refined and validated using the results of a vast survey of the literature reporting examples of Bioinformatics Tertiary Analysis activities.ResultsThe final hierarchical task tree was then converted into an ontological representation using an ontology standard formalism. The results of our research provides a reference process model for Tertiary Analysis that can be used both to analyze and to compare existing tools, or to design new tools.ConclusionsTo highlight the potential of our approach and to exemplify its concrete applications, we describe a new bioinformatics tool and how the proposed process model informed its design.

Standardized IGH-Based Next-Generation Sequencing for MRD Detection in Follicular Lymphoma

Introduction: MRD monitoring is of great value to determine the risk of relapse in patients with follicular lymphoma (FL).However, MRD assessment is mainly restricted to those 50-65% of patients harbouring a PCR-detectable t(14;18) rearrangement as clonal marker for RQ-PCR. Next generation sequencing (NGS) of immunoglobulin (IG) gene rearrangements might overcome limitations of current PCR-based approaches. Our aim was to test the stability and reproducibility of a standardized amplicon-based NGS approach developed by the EuroClonality-NGS Consortium to a): detect clonally rearranged IG clonotypes and b) to use IG-NGS for MRD quantitation in FL.Methods: Peripheral blood (PB) and bone marrow (BM) samples were collected within randomized trials of the German Low-Grade Lymphoma Study Group (GLSG) and Fondazione Italiana Linfomi treating first-line stage III/IV FL with combined immunotherapy regimen. At first we tested the applicability of NGS for MRD marker screening in randomly selected diagnostic samples from 100 patients (50 PB, 50 BM). Next, NGS-MRD was assessed by sequencing IGH clonotypes in 113 FL patients selected according to the presence of a clonal IGH rearrangement by consensus PCR at diagnosis and the availability of post induction follow-up samples for MRD. Paired-end libraries were prepared and sequenced on Illumina MiSeq (2 x 250bp). FU samples were spiked with predefined copy numbers of internal reference DNA to enable assessment of MRD by normalisation of NGS reads after sequencing. Identification of IGH clonotypes and quantitation of MRD was done with the bioinformatic analysis pipeline ARResT/Interrogate (Bystry, Bioinformatics, 2016). For clonotype identification we used a 5% threshold. For validation NGS results were compared to classical RQ-PCR with either t(14;18) RQ-PCR (n=38 pats.) or IGH ASO RQ-PCR (n= 32 pats.).Results: Marker screening by NGS detected clonal IGH-FR2 sequences with comparable frequency of 26/50 in PB and 24/50 BM samples. Additionally, IGK light chain clonotypes were detected with similar frequencies, thus leading to an IG clonotype being detected in 64% of samples.In the patient cohort analysed for MRD NGS identified an IGH clonotype in 105/113 diagnostic samples. MRD results by RQ-PCR were available in parallel from 70 patients. In 5/8 NGS-neg. diagnostic samples RQ-PCR demonstrated circulating FL cells above the quantitative range underlining the role of somatic hypermutation potentially resulting in false-negativity. At follow-up, 43/123 samples were MRD+ and 42 were MRD- by both methods. 32 were MRD+ by RQ-PCR only, 6 by NGS only. 16/32 NGS-/RQ-PCR+ samples showed low-level MRD below <1E-04 by RQ-PCR. MRD results determined by NGS were well correlated to RQ-PCR values (r2=0.599, p<0.00001).Correlation of NGS-MRD response with outcome in 43 patients from the GLSG cohort demonstrated that achievement of MRD response in PB and/or BM at end of induction is associated with a significantly longer median progression free survival of 11 years compared to 2.2 years for MRD+ patients (median follow-up 6.2 years, Figure 1).Conclusions: The standardized EuroClonality NGS approach shows robust identification of IGH clonotypes in FL patients allowing reliable MRD monitoring. Lymphoma infiltration of diagnostic samples and somatic mutation of IGH are critical points for clonotype identification by NGS. IG-NGS can complement current MRD methods allowing MRD assessment in the majority of FL patients. [Display omitted] DisclosuresPott:Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Boccadoro:Novartis: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; AbbVie: Honoraria; Mundipharma: Research Funding; Sanofi: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Kneba:AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding; Mundipharma: Consultancy, Honoraria, Other: travel support, Research Funding. Hoster:Roche: Other: Travel support, Research Funding.

Germinal Center Program De-Synchronization and Intra-Patient Heterogeneity in Follicular Lymphoma B-Cells Revealed By Integrative Single-Cell Analysis

IntroductionFollicular lymphoma (FL) is an indolent B-cell neoplasm with histological, phenotypical and transcriptional features of germinal center (GC) B-cells, suggesting that FL B-cells are “frozen” in a GC B-cell state. In normal GC B-cells, affinity maturation relies on iterative cycles of somatic hypermutation and proliferation followed by affinity-based selection. Thus GC B-cells are functionally heterogeneous. Although FL sub-clonal genetic diversity is well documented, tumor cell functional heterogeneity in FL has not been studied in detail. Bulk-level transcriptomic analyses fail to capture cellular heterogeneity and are thus insufficient to compare FL B-cells to the heterogeneous GC B-cell subset. Here, we used integrative single-cell analysis of phenotype, gene expression and IGH sequence to assess and compare functional heterogeneity in FL B-cells and GC B-cells.MethodsNormal B-cells were sorted from spleens or tonsil of healthy human donors (n=4), and FL B-cells from diagnostic lymph node biopsies (n=5), all available as frozen viable cell suspensions. Single normal or malignant B-cells were sorted by flow cytometry for parallel analysis of phenotype (9-color panel), gene expression (91-gene panel by microfluidic RT-qPCR), and IGH sequence (RT-PCR followed by Sanger sequencing; FL B-cells only). Custom and existing bioinformatics analysis pipelines were combined for quality control and cell filtering, dimensionality reduction (PCA, t-SNE), clustering, pseudo-time analysis, IGH sequence analysis and integrative data analysis.ResultsIn normal B-cells, single-cell gene expression profiling segregated major cell types (antibody-producing cells, memory B-cells and GC B-cells) and enabled the reconstruction of differentiation trajectories within cell types. Specifically, our data revealed a cyclic continuum of transitional GC B-cell states where synchronized expression of gene modules characterized human GC B-cell functional heterogeneity. Single-cell gene expression profiles defined FL B-cells as an independent cell type, distinct from GC B-cells. In particular, the expression of most characteristic GC-associated genes was de-synchronized in FL B-cells from all patients. Clustering of single FL B-cell 91-gene expression profiles revealed inter-patient and intra-patient heterogeneity, and defined common FL B-cell subsets found in most patients. Intra-patient heterogeneity was not linked to sub-clonal divergence as defined by IGH sequencing.ConclusionsUsing integrative single-cell analyses, we have shown that synchronized expression of characteristic gene modules drives GC B-cells through a cyclic continuum of transitional states. De-synchronization of that program in FL B-cells, and substantial intra-patient gene expression heterogeneity, suggest that FL B-cells are functionally plastic and not blocked to a GC B-cell state. DisclosuresNo relevant conflicts of interest to declare.