Raw Next-generation Sequencing Data Research Articles

KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data.

During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.

GeniePool: genomic database with corresponding annotated samples based ona cloud data lake architecture.

In recent years, there are a huge influx of genomic data and a growing need for its phenotypic correlations, yet existing genomic databases do not allow easy storage and accessibility to the combined phenotypic-genotypic information. Freely accessible allele frequency (AF) databases, such as gnomAD, are crucial for evaluating variants but lack correlated phenotype data. The Sequence Read Archive (SRA) accumulates hundreds of thousands of next-generation sequencing (NGS) samples tagged by their submitters and various attributes. However, samples are stored in large raw format files, inaccessible for a common user. To make thousands of NGS samples and their corresponding additional attributes easily available to clinicians and researchers, we generated a pipeline that continuously downloads raw human NGS data uploaded to SRA using SRAtoolkit and preprocesses them using GATK pipeline. Data are then stored efficiently in a cloud data lake and can be accessed via a representational state transfer application programming interface (REST API) and a user-friendly website. We thus generated GeniePool, a simple and intuitive web service and API for querying NGS data from SRA with direct access to information related to each sample and related studies, providing significant advantages over existing databases for both clinical and research usages. Utilizing data lake infrastructure, we were able to generate a multi-purpose tool that can serve many clinical and research use cases. We expect users to explore the meta-data served via GeniePool both in daily clinical practice and in versatile research endeavours. Database URL https://geniepool.link.

Quasispecies Analysis of SARS-CoV-2 of 15 Different Lineages during the First Year of the Pandemic Prompts Scratching under the Surface of Consensus Genome Sequences.

The tremendous majority of SARS-CoV-2 genomic data so far neglected intra-host genetic diversity. Here, we studied SARS-CoV-2 quasispecies based on data generated by next-generation sequencing (NGS) of complete genomes. SARS-CoV-2 raw NGS data had been generated for nasopharyngeal samples collected between March 2020 and February 2021 by the Illumina technology on a MiSeq instrument, without prior PCR amplification. To analyze viral quasispecies, we designed and implemented an in-house Excel file (“QuasiS”) that can characterize intra-sample nucleotide diversity along the genomes using data of the mapping of NGS reads. We compared intra-sample genetic diversity and global genetic diversity available from Nextstrain. Hierarchical clustering of all samples based on the intra-sample genetic diversity was performed and visualized with the Morpheus web application. NGS mapping data from 110 SARS-CoV-2-positive respiratory samples characterized by a mean depth of 169 NGS reads/nucleotide position and for which consensus genomes that had been obtained were classified into 15 viral lineages were analyzed. Mean intra-sample nucleotide diversity was 0.21 ± 0.65%, and 5357 positions (17.9%) exhibited significant (>4%) diversity, in ≥2 genomes for 1730 (5.8%) of them. ORF10, spike, and N genes had the highest number of positions exhibiting diversity (0.56%, 0.34%, and 0.24%, respectively). Nine hot spots of intra-sample diversity were identified in the SARS-CoV-2 NSP6, NSP12, ORF8, and N genes. Hierarchical clustering delineated a set of six genomes of different lineages characterized by 920 positions exhibiting intra-sample diversity. In addition, 118 nucleotide positions (0.4%) exhibited diversity at both intra- and inter-patient levels. Overall, the present study illustrates that the SARS-CoV-2 consensus genome sequences are only an incomplete and imperfect representation of the entire viral population infecting a patient, and that quasispecies analysis may allow deciphering more accurately the viral evolutionary pathways.

Evaluation of EPISEQ SARS-CoV-2 and a Fully Integrated Application to Identify SARS-CoV-2 Variants from Several Next-Generation Sequencing Approaches.

Whole-genome sequencing has become an essential tool for real-time genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide. The handling of raw next-generation sequencing (NGS) data is a major challenge for sequencing laboratories. We developed an easy-to-use web-based application (EPISEQ SARS-CoV-2) to analyse SARS-CoV-2 NGS data generated on common sequencing platforms using a variety of commercially available reagents. This application performs in one click a quality check, a reference-based genome assembly, and the analysis of the generated consensus sequence as to coverage of the reference genome, mutation screening and variant identification according to the up-to-date Nextstrain clade and Pango lineage. In this study, we validated the EPISEQ SARS-CoV-2 pipeline against a reference pipeline and compared the performance of NGS data generated by different sequencing protocols using EPISEQ SARS-CoV-2. We showed a strong agreement in SARS-CoV-2 clade and lineage identification (>99%) and in spike mutation detection (>99%) between EPISEQ SARS-CoV-2 and the reference pipeline. The comparison of several sequencing approaches using EPISEQ SARS-CoV-2 revealed 100% concordance in clade and lineage classification. It also uncovered reagent-related sequencing issues with a potential impact on SARS-CoV-2 mutation reporting. Altogether, EPISEQ SARS-CoV-2 allows an easy, rapid and reliable analysis of raw NGS data to support the sequencing efforts of laboratories with limited bioinformatics capacity and those willing to accelerate genomic surveillance of SARS-CoV-2.

P–530 The use of wide thresholds for detecting intermediate chromosomal CNV up to 80% doesn’t improve PGT-A ability to discriminate true mosaic from uniformly aneuploid embryos

Abstract Study question What is the effect of varying diagnostic thresholds on the accuracy of Next Generation Sequencing (NGS)-based preimplantation genetic testing for aneuploidies (PGT-A)? Summary answer When single trophectoderm biopsies are tested, the employment of 80% upper threshold increases mosaic calls and false negative aneuploidy results compared to more stringent thresholds. What is known already Trophectoderm (TE) biopsy coupled with NGS-based PGT-A technologies are able to accurately predict Inner Cell Mass’ (ICM) constitution when uniform whole chromosome aneuploidies are considered. However, minor technical and biological inconsistencies in NGS procedures and biopsy specimens can result in subtle variability in analytical results. In this context, the stringency of thresholds employed for diagnostic calls can lead to incorrect classification of uniformly aneuploid embryos into the mosaic category, ultimately affecting PGT-A accuracy. In this study, we evaluated the diagnostic predictivity of different aneuploidy classification criteria by employing blinded analysis of chromosome copy number values (CNV) in multifocal blastocyst biopsies. Study design, size, duration The accuracy of different aneuploidy diagnostic cut-offs was assessed comparing chromosomal CNV in intra-blastocysts multifocal biopsies. Enrolled embryos were donated for research between June and September 2020. The Institutional Review Board at the Near East University approved the study (project: YDU/20l9/70–849). Embryos diagnosed with uniform chromosomal alterations (single or multiple) in their clinical TE biopsy (n = 27) were disaggregated into 5 portions: the ICM and 4 TE biopsies. Overall, 135 specimens were collected and analysed. Participants/materials, setting, methods Twenty-seven donated blastocysts were warmed and disaggregated in TE biopsies and ICM (n = 135 biopsies). PGT-A analysis was performed using Ion ReproSeq PGS kit and Ion S5 sequencer (ThermoFisher). Sequencing data were blindly analysed with Ion-Reporter software. Intra-blastocyst comparison of raw NGS data was performed employing different thresholds commonly used for aneuploidy classification. CNV for each chromosome were reported as aneuploid according to 70% or 80% thresholds. Categorical variables were compared using Fisher’s exact test. Main results and the role of chance In this study, a total of 50 aneuploid patterns in 27 disaggregated embryos were explored. Single TE biopsy results were considered as true positive when they displayed the same alteration detected in the ICM at levels above the 70% or 80% thresholds. Alternatively, alterations detected in the euploid or mosaic range were considered as false negative aneuploidy results. When the 70% threshold was applied, aneuploidy findings were confirmed in 94.5% of TE biopsies analyzed (n = 189/200; 95%CI=90.37–37.22), while 5.5% showed a mosaic profile (50–70%) but uniformly abnormal ICM. Positive (PPV) and negative predictive value (NPV) per chromosome were 100.0% (n = 189/189; 95%CI=98.07–100.00) and 99.5% (n = 2192/2203; 95%CI=99.11–99.75) respectively. When the upper cut-off was experimentally placed at 80% of abnormal cells, a significant decrease (p-value=0.0097) in the percentage of confirmed aneuploid calls was observed (86.5%; n = 173/200; 95%CI=80.97–90.91), resulting in mosaicism overcalling, especially in the high range (50–80%). Less stringent thresholds led to extremely high PPV (100.0%; n = 173/173; 95%CI=97.89–100.00), while NPV decreased to 98.8% (n = 2192/2219; 95%CI=98.30–99.23). Furthermore, no additional true mosaic patterns were identified with the use of wide range thresholds for aneuploidy classification. Limitations, reasons for caution This approach involved the analysis of aneuploidy CNV thresholds at the embryo level and lacked from genotyping-based confirmation analysis. Moreover, aneuploid embryos with known meiotic partial deletion/duplication were not included. Wider implications of the findings: The use of wide thresholds for detecting intermediate chromosomal CNV up to 80% doesn’t improve PGT-A ability to discriminate true mosaic from uniformly aneuploid embryos, lowering overall diagnostic accuracy. Hence, a proportion of the embryos diagnosed as mosaic using wide calling thresholds may actually be uniformly aneuploid and inadvertently transferred. Trial registration number N/A

Bioinformatics Pathway Analysis Pipeline for NGS Transcriptome Profile Data on Nasopharyngeal Carcinoma

Next-Generation Sequencing (NGS)-based genomics data have a huge potential to be used in transcriptomic profiling of Nasopharyngeal Carcinoma (NPC) to study the biosynthesis mechanism behind it. The high dimensionality of NGS data is the main challenge in performing the data analysis to extract useful information. In this workflow pipeline, memory-efficient Linux-based software such as HISAT2 and HTSeq are utilized to process the raw NGS data. Furthermore, Differential Expression Gene (DEG) list can be obtained by performing advanced analysis to the aligned Ribonucleic Acid (RNA) sequence using the edgeR protocol. This DEG list is one of the main inputs of biological pathway analysis that can be done in DAVID and PANTHER web-based software. Both tools generate a different pathway result related to inflammation.

DIANA-mAP: Analyzing miRNA from Raw NGS Data to Quantification.

microRNAs (miRNAs) are small non-coding RNAs (~22 nts) that are considered central post-transcriptional regulators of gene expression and key components in many pathological conditions. Next-Generation Sequencing (NGS) technologies have led to inexpensive, massive data production, revolutionizing every research aspect in the fields of biology and medicine. Particularly, small RNA-Seq (sRNA-Seq) enables small non-coding RNA quantification on a high-throughput scale, providing a closer look into the expression profiles of these crucial regulators within the cell. Here, we present DIANA-microRNA-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline that allows the user to perform miRNA NGS data analysis from raw sRNA-Seq libraries to quantification and Differential Expression Analysis in an easy, scalable, efficient, and intuitive way. Emphasis has been given to data pre-processing, an early, critical step in the analysis for the robustness of the final results and conclusions. Through modularity, parallelizability and customization, DIANA-mAP produces high quality expression results, reports and graphs for downstream data mining and statistical analysis. In an extended evaluation, the tool outperforms similar tools providing pre-processing without any adapter knowledge. Closing, DIANA-mAP is a freely available tool. It is available dockerized with no dependency installations or standalone, accompanied by an installation manual through Github.

Jupyter notebook-based tools for building structured datasets from the Sequence Read Archive

The Sequence Read Archive (SRA) is a large public repository that stores raw next-generation sequencing data from thousands of diverse scientific investigations. Despite its promise, reuse and re-analysis of SRA data has been challenged by the heterogeneity and poor quality of the metadata that describe its biological samples. Recently, the MetaSRA project standardized these metadata by annotating each sample with terms from biomedical ontologies. In this work, we present a pair of Jupyter notebook-based tools that utilize the MetaSRA for building structured datasets from the SRA in order to facilitate secondary analyses of the SRA's human RNA-seq data. The first tool, called the Case-Control Finder, finds suitable case and control samples for a given disease or condition where the cases and controls are matched by tissue or cell type. The second tool, called the Series Finder, finds ordered sets of samples for the purpose of addressing biological questions pertaining to changes over a numerical property such as time. These tools were the result of a three-day-long NCBI Codeathon in March 2019 held at the University of North Carolina at Chapel Hill.

Jupyter notebook-based tools for building structured datasets from the Sequence Read Archive

The Sequence Read Archive (SRA) is a large public repository that stores raw next-generation sequencing data from thousands of diverse scientific investigations. Despite its promise, reuse and re-analysis of SRA data has been challenged by the heterogeneity and poor quality of the metadata that describe its biological samples. Recently, the MetaSRA project standardized these metadata by annotating each sample with terms from biomedical ontologies. In this work, we present a pair of Jupyter notebook-based tools that utilize the MetaSRA for building structured datasets from the SRA in order to facilitate secondary analyses of the SRA’s human RNA-seq data. The first tool, called the Case-Control Finder, finds suitable case and control samples for a given disease or condition where the cases and controls are matched by tissue or cell type. The second tool, called the Series Finder, finds ordered sets of samples for the purpose of addressing biological questions pertaining to changes over a numerical property such as time. These tools were the result of a three-day-long NCBI Codeathon in March 2019 held at the University of North Carolina at Chapel Hill.

Dry Panels Supporting External Quality Assessment Programs for Next Generation Sequencing-Based HIV Drug Resistance Testing.

External quality assessment (EQA) is a keystone element in the validation and implementation of next generation sequencing (NGS)-based HIV drug resistance testing (DRT). Software validation and evaluation is a critical element in NGS EQA programs. While the development, sharing, and adoption of wet lab protocols is coupled with the increasing access to NGS technology worldwide, rendering it easy to produce NGS data for HIV-DRT, bioinformatic data analysis remains a bottleneck for most of the diagnostic laboratories. Several computational tools have been made available, via free or commercial sources, to automate the conversion of raw NGS data into an actionable clinical report. Although different software platforms yield equivalent results when identical raw NGS datasets are analyzed for variations at higher abundance, discrepancies arise when variations at lower frequencies are considered. This implies that validation and performance assessment of the bioinformatics tools applied in NGS HIV-DRT is critical, and the origins of the observed discrepancies should be determined. Well-characterized reference NGS datasets with ground truth on the genotype composition at all examined loci and the exact frequencies of HIV variations they may harbor, so-called dry panels, would be essential in such cases. The strategic design and construction of such panels are challenging but imperative tasks in support of EQA programs for NGS-based HIV-DRT and the validation of relevant bioinformatics tools. Here, we present criteria that can guide the design of such dry panels, which were discussed in the Second International Winnipeg Symposium themed for EQA strategies for NGS HIVDR assays.

Jupyter notebook-based tools for building structured datasets from the Sequence Read Archive.

The Sequence Read Archive (SRA) is a large public repository that stores raw next-generation sequencing data from thousands of diverse scientific investigations. Despite its promise, reuse and re-analysis of SRA data has been challenged by the heterogeneity and poor quality of the metadata that describe its biological samples. Recently, the MetaSRA project standardized these metadata by annotating each sample with terms from biomedical ontologies. In this work, we present a pair of Jupyter notebook-based tools that utilize the MetaSRA for building structured datasets from the SRA in order to facilitate secondary analyses of the SRA's human RNA-seq data. The first tool, called the Case-Control Finder, finds suitable case and control samples for a given disease or condition where the cases and controls are matched by tissue or cell type. The second tool, called the Series Finder, finds ordered sets of samples for the purpose of addressing biological questions pertaining to changes over a numerical property such as time. These tools were the result of a three-day-long NCBI Codeathon in March 2019 held at the University of North Carolina at Chapel Hill.

Correction for Li et al., “Discovery of Bat Coronaviruses through Surveillance and Probe Capture-Based Next-Generation Sequencing”

Volume 5, no. 1, e00807-19, 2020, https://doi.org/10.1128/mSphere.00807-19. Page 9: the following sentence should be added to the “Data availability” paragraph: “Raw next-generation sequencing data have been deposited in the NCBI SRA database under accession no. SRR11085733 to SRR11085741.”

Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing

Next generation sequencing (NGS) is a trending new standard for genotypic HIV-1 drug resistance (HIVDR) testing. Many NGS HIVDR data analysis pipelines have been independently developed, each with variable outputs and data management protocols. Standardization of such analytical methods and comparison of available pipelines are lacking, yet may impact subsequent HIVDR interpretation and other downstream applications. Here we compared the performance of five NGS HIVDR pipelines using proficiency panel samples from NIAID Virology Quality Assurance (VQA) program. Ten VQA panel specimens were genotyped by each of six international laboratories using their own in-house NGS assays. Raw NGS data were then processed using each of the five different pipelines including HyDRA, MiCall, PASeq, Hivmmer and DEEPGEN. All pipelines detected amino acid variants (AAVs) at full range of frequencies (1~100%) and demonstrated good linearity as compared to the reference frequency values. While the sensitivity in detecting low abundance AAVs, with frequencies between 1~20%, is less a concern for all pipelines, their specificity dramatically decreased at AAV frequencies <2%, suggesting that 2% threshold may be a more reliable reporting threshold for ensured specificity in AAV calling and reporting. More variations were observed among the pipelines when low abundance AAVs are concerned, likely due to differences in their NGS read quality control strategies. Findings from this study highlight the need for standardized strategies for NGS HIVDR data analysis, especially for the detection of minority HIVDR variants.

Decentralization of Next-Generation RNA Sequencing-Based MammaPrint® and BluePrint® Kit at University Hospitals Leuven and Curie Institute Paris

A previously developed and centrally validated MammaPrint® (MP) and BluePrint® (BP) targeted RNA next-generation sequencing (NGS) kit was implemented and validated in two large academic European hospitals. Additionally, breast cancer molecular subtypes by MP and BP RNA sequencing were compared with immunohistochemistry (IHC). Patients with early breast cancer diagnosed at University Hospitals Leuven and Curie Institute Paris were prospectively included between September 2017 and January 2018. Formalin-fixed paraffin-embedded tissue sections were analyzed with MP and BP NGS technology at the beta sites and with both NGS and microarray technology at Agendia. Raw NGS data generated on Illumina MiSeq instruments at the beta sites were interpreted and compared with NGS and microarray data at Agendia. MP and BP NGS molecular subtypes were compared to surrogate IHC breast cancer subtypes. Equivalence of MP and BP indices was determined by Pearson's correlation coefficient. Acceptable limits were defined a priori, based on microarray data generated at Agendia between 2012 and 2016. The concordance, the Negative Percent Agreement and the Positive Percent Agreement were calculated based on the contingency tables and had to be equal to or higher than 90%. Out of 124 included samples, 48% were MP Low and 52% High Risk with microarray. Molecular subtypes were BP luminal, HER2 or basal in 82%, 8% and 10% respectively. Concordance between MP microarray at Agendia and MP NGS at the beta sites was 91.1%. Concordance of MP High and Low Risk classification between NGS at the beta sites and NGS at Agendia was 93.9%. Concordance of MP and BP molecular subtyping using NGS at the beta sites and microarray at Agendia was 89.5%. Concordance between MP and BP NGS subtyping, and IHC was 71.8% and 76.6%, for two IHC surrogate models. The MP/BP NGS kit was successfully validated in a decentralized setting.

A work lifetime of passion! Is it worth the time, money, stress and relationship changes when you disagree with hierarchy?

A previously developed and centrally validated MammaPrint® (MP) and BluePrint® (BP) targeted RNA next-generation sequencing (NGS) kit was implemented and validated in two large academic European hospitals. Additionally, breast cancer molecular subtypes by MP and BP RNA sequencing were compared with immunohistochemistry (IHC). Patients with early breast cancer diagnosed at University Hospitals Leuven and Curie Institute Paris were prospectively included between September 2017 and January 2018. Formalin-fixed paraffin-embedded tissue sections were analyzed with MP and BP NGS technology at the beta sites and with both NGS and microarray technology at Agendia. Raw NGS data generated on Illumina MiSeq instruments at the beta sites were interpreted and compared with NGS and microarray data at Agendia. MP and BP NGS molecular subtypes were compared to surrogate IHC breast cancer subtypes. Equivalence of MP and BP indices was determined by Pearson's correlation coefficient. Acceptable limits were defined a priori, based on microarray data generated at Agendia between 2012 and 2016. The concordance, the Negative Percent Agreement and the Positive Percent Agreement were calculated based on the contingency tables and had to be equal to or higher than 90%. Out of 124 included samples, 48% were MP Low and 52% High Risk with microarray. Molecular subtypes were BP luminal, HER2 or basal in 82%, 8% and 10% respectively. Concordance between MP microarray at Agendia and MP NGS at the beta sites was 91.1%. Concordance of MP High and Low Risk classification between NGS at the beta sites and NGS at Agendia was 93.9%. Concordance of MP and BP molecular subtyping using NGS at the beta sites and microarray at Agendia was 89.5%. Concordance between MP and BP NGS subtyping, and IHC was 71.8% and 76.6%, for two IHC surrogate models. The MP/BP NGS kit was successfully validated in a decentralized setting.

VONC: A solution for the clinical assessment of somatic genomic alterations.

e13155 Background: Next generation sequencing (NGS) technology is transforming the diagnosis and treatment of cancer. However, the massive scale of data has overwhelmed pathologists who need streamlined tools to process this data, automate report generation and minimize human errors. Methods: We developed the Variant interpretation station for ONCology, VONC, as an end-to-end solution for moving from NGS whole exome and transcriptome data to actionable clinical reports that support cancer diagnosis, prognosis, and personalized treatment strategies for solid and hematologic malignancies. Results: VONC integrates all steps for moving from raw NGS data, variant calling and LIMS, to comprehensive annotation of variants. The main functional feature of VONC is a transparent process that effectively combines automated and expert curation to identify clinically relevant and actionable driver variants. VONC also enables efficient management of multi-group, -role, -system and -site curation processes. In contrast to current tools, VONC handles all somatic and constitutional genomic alterations including SNV, indel, CNV, fusion, splicing, and gene expression. Key data sources include 1) 350,000 variants for 50 tumor types across 57,000 sequenced cancer patients; 2) variant frequencies estimated from 1.5M cancer patients; 3) expert curated literature evidence from 16,818 papers covering 26,496 alterations spanning 2,448 cancer driver genes; and 4) curated database of FDA-approved drugs and recruiting clinical trials. VONC presents a prioritized list of variants in oncogenes and tumor suppressors through functional (literature-based) and structure-based (hotspots) algorithms. This is coupled to all supporting information necessary for clinical decision making. Curators can quickly screen variant type, QC metrics, and frequency in sequencing cohorts of cancer patients as well as healthy subjects. Within minutes, variants can be triaged and annotated with FDA approved, NCCN guidelines reported, or literature supported therapeutics, including resistance and contraindicated. Conclusions: VONC is a clinically-ready tool with an intuitive end-user interface tailored for the rapid assessment of variants in cancer patients, to facilitate personalized cancer medicine in a high-throughput laboratory.

Abstract P4-08-25: Decentralized beta testing of MammaPrint and BluePrint NGS kit at University Hospitals Leuven and Curie Institute Paris

Abstract Background Many countries restrict patient material exchange to central diagnostic laboratories abroad, limiting access to assays like MammaPrint® (MP) and BluePrint® (BP). Both assays are microarray-based, with MP being prognostic for distant recurrence and BP for molecular subtyping of breast cancer (Luminal-, HER2-, and Basal-type). To increase accessibility, decentralization is required with Next Generation Sequencing (NGS) being the preferred testing platform given that most diagnostic laboratories have the technology in place. The aim of this beta testing study is to validate a previously developed and centrally validated MP and BP NGS kit for RNA samples in two large tertiary academic hospitals in Europe. Patients and Methods Patients with early breast cancer diagnosed at the Multidisciplinary Breast Center at University Hospitals Leuven and Curie Institute Paris were prospectively included between September 2017 and January 2018. Patients with bilateral breast cancer or presenting with more than 3 positive lymph nodes were excluded. Only patients with invasive ductal and invasive lobular carcinoma were included. Twenty tissue sections were cut from formalin-fixed, paraffin-embedded (FFPE) blocks; 10 tissue sections were analyzed at the local site using the MP and BP NGS kit, and 10 tissue sections were analyzed at Agendia using the same kit and procedure, as well as with the golden standard method (gene expression microarrays). Targeted RNA sequencing of the 70 MP and 80 BP signature genes was performed on Illumina MiSeq instruments. The raw NGS data generated at the local test sites was sent through a secure file transfer protocol server to Agendia for interpretation and comparison with microarray and NGS performed in the Agendia laboratories. We aimed for a minimum concordance rate between MP and BP outcome of 90% between each local site and Agendia's centralized site. Results In this study, 116 early breast cancer patients were included (73 from University Hospitals Leuven and 43 from Curie Institute). Out of these patients, 52% were MP Low Risk and 48% MP High Risk according to microarray. The patients had a BP luminal, HER2 or basal subtype in respectively 83%, 9% and 8%. Concordance between MP microarray obtained from Agendia and MP NGS obtained from the local sites was 91.4%. Concordance between MP High and Low Risk classification between NGS Leuven versus NGS Agendia was 92.1% and between NGS Curie versus NGS Agendia 95.3%. For BP subtype outcomes, the results from microarray versus NGS for all patients combined from both local sites gave a 98.3% concordance and NGS Agendia versus NGS from each local site gave a 100% concordance. Conclusion The MP and BP NGS kit was successfully validated in a decentralized setting, showing high concordance between results obtained at three different sites. There was a clear benefit of having well-trained NGS experienced diagnostic technical teams. The MP and BP NGS kit the first FFPE targeted RNA sequencing based multigene signature for breast cancer care, will provide a high and equal standard of MP and BP gene expression testing for breast cancer in a decentralized setting. Citation Format: Slembrouck L, Laurent C, Delahaye LJ, Mittempergher L, Vanden Bempt I, Vander Borght S, Darrigues L, Vliegen L, Sintubin P, Raynal V, Bohec M, Reyes C, Rapinat A, Helsmoortel C, Jongen L, Hoste G, Neven P, Wildiers H, Smeets A, Nevelsteen I, Punie K, Van Nieuwenhuysen E, Han S, Salomon AV, Faron EL, Cynober T, Witteveen AT, Neijenhuis S, Glas AM, Reyal F, Floris G. Decentralized beta testing of MammaPrint and BluePrint NGS kit at University Hospitals Leuven and Curie Institute Paris [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-08-25.

Geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data.

Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.

Bartender: a fast and accurate clustering algorithm to count barcode reads.

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data. In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods. Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1. sasha.levy@stonybrook.edu or song.wu@stonybrook.edu. Supplementary data are available at Bioinformatics online.

Secure and robust cloud computing for high-throughput forensic microsatellite sequence analysis and databasing

Next-generation Sequencing (NGS) is a rapidly evolving technology with demonstrated benefits for forensic genetic applications, and the strategies to analyze and manage the massive NGS datasets are currently in development. Here, the computing, data storage, connectivity, and security resources of the Cloud were evaluated as a model for forensic laboratory systems that produce NGS data. A complete front-to-end Cloud system was developed to upload, process, and interpret raw NGS data using a web browser dashboard. The system was extensible, demonstrating analysis capabilities of autosomal and Y-STRs from a variety of NGS instrumentation (Illumina MiniSeq and MiSeq, and Oxford Nanopore MinION). NGS data for STRs were concordant with standard reference materials previously characterized with capillary electrophoresis and Sanger sequencing. The computing power of the Cloud was implemented with on-demand auto-scaling to allow multiple file analysis in tandem. The system was designed to store resulting data in a relational database, amenable to downstream sample interpretations and databasing applications following the most recent guidelines in nomenclature for sequenced alleles. Lastly, a multi-layered Cloud security architecture was tested and showed that industry standards for securing data and computing resources were readily applied to the NGS system without disadvantageous effects for bioinformatic analysis, connectivity or data storage/retrieval. The results of this study demonstrate the feasibility of using Cloud-based systems for secured NGS data analysis, storage, databasing, and multi-user distributed connectivity.