Raw Next-generation Sequencing 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.

Ability of the MeltPro MTB/PZA Assay to Detect Susceptibility to Pyrazinamide in Rifampin-Resistant Tuberculosis Patients.

Prediction of susceptibility to pyrazinamide (PZA) directly from sputum has been challenging. The MeltPro MTB/PZA assay, based on melting curve analysis, can simultaneously detect Mycobacterium tuberculosis and the resistance to PZA from sputum. We aimed to evaluate the MeltPro MTB/PZA assay to predict PZA resistance among rifampin-resistant tuberculosis (RR-TB) patients. We prospectively enrolled RR-TB patients in the registered trials, and their baseline sputum samples were obtained to perform the assay and culture. DNA sequencing of culture isolates was analyzed and used as the reference standard. Sanger sequencing was performed for samples with discrepant results between next-generation sequencing (NGS) and the investigational assay. The main analysis was conducted in the population of patients with interpretable results by both NGS and the assay. A total of 239 patients with RR-TB were screened, and 220 underwent the MeltPro MTB/PZA assay. The assay provided no information for 25 of 220 patients (11.4%). Among the remaining 195 patients, 13 had negative culture or insufficient raw NGS sequencing data, and 15 had indeterminate assay results. A total of 167 patients were included in the main analysis. Against DNA sequencing, the sensitivity, specificity, and negative predictive value of the assay for detecting resistance to PZA were 91.4% (95% confidence interval [CI], 87.1% to 95.6%), 89.9% (95% CI, 85.3% to 94.5%), and 95.2% (95% CI, 91.9% to 98.4%), respectively. In conclusion, the MeltPro MTB/PZA assay is a fast semiautomatic molecular platform to rapidly predict resistance to PZA from sputum and holds promise as a screening tool with satisfactory sensitivity. IMPORTANCE This study evaluated the accuracy of the MeltPro MTB/PZA assay at detecting the presence of PZA resistance through registered clinical trials. Compared to DNA sequencing, the assay had high sensitivity and negative predictive value, suggesting its potential utility as a screening tool in clinical practice. The assay could serve as an ideal primary screening tool in low PZA-resistant M. tuberculosis prevalence settings and could be used as an additional test to identify PZA resistance rapidly and initially in the RR-TB population.

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.

Specific T Cell Receptor Gene Repertoire Profiles in Subgroups of CLL Patients with Distinct Genomic Aberrations

Chronic lymphocytic leukemia (CLL) B cells engage in multifaceted bi-directional interactions with bystander cells, including T cells. Immunogenetic studies in CLL revealed clonal expansions of T cells and shared T cell clonotypes between different patients, strongly implying clonal selection by antigens. Although the exact nature of these antigens remains largely elusive, evidence exists that the clonotypic B cell receptor immunoglobulin (BcR IG) may serve as a source of antigenic epitopes for T cells. That said, recurrent genomic aberrations associated with distinct abnormal expression profiles could represent an alternative, non mutually exclusive, source of potent immunogenic onco-antigens that might shape the T cell repertoire in CLL. On these grounds, here we interrogated the T cell receptor (TR) gene repertoire of CLL patients with different genomic aberration profiles with the aim to identify unique signatures that would allude to distinct antigen selection pressures. The study group included 46 patients with CLL, sampled before treatment initiation, who were categorized in 5 subgroups defined by a unique genomic aberration, as follows: +12, n=18; del(11q), n=10; del(13q), n=7; del(17p)/TP53mut, n=6; NOTCH1mut, n=5. Confounding effects of multiple aberrations have been minimized, as we previously established through comprehensive characterization (including FISH, SNP arrays and gene panels) that the analyzed patients carried only one of the above aberrations. Starting material was RNA extracted from blood mononuclear cells. TRBV-TRBD-TRBJ gene rearrangements were RT-PCR amplified and subjected to paired-end next generation sequencing (NGS). Raw NGS reads (n=13,213,563| median: 294,757/sample) were processed through a purpose-built bioinformatics pipeline. Only productive rearrangements (n=9,249,546 | median=199,184/sample) were taken into consideration for the computation of clonotypes i.e. TRB rearrangements with identical TRBV gene usage and amino acid complementarity-determining region 3 (CDR3) sequence. Overall, 513,984 distinct clonotypes (median=10,304 clonotypes/sample) were assessed. The main measure of clonality employed in this study was the median cumulative frequency of the 10 most expanded T cell clonotypes/sample (MCF-10). For comparisons of the clonality profiles, a group of 17 aged-matched healthy individuals were used as controls. All patients displayed oligoclonal T cell expansions with the following MCF-10 values: del(11q): 21.6%, +12: 25%, del(13q): 20.6%, NOTCH1mut: 9.1%, del(17p)/TP53mut: 12.9%; the difference between the del(11q) and +12 groups versus the NOTCH1mut group was statistically significant (p<0.05). The MCF-10 value of the control group was estimated at 17.5%, supporting the notion of age-related decrease in TR repertoire diversity. However, the del(11q), +12 and del(13q) CLL groups displayed elevated clonality, reaching statistical significance (p<0.002) in the case of +12. TRBV gene repertoire analysis revealed that the TRBV12-3 gene predominated in most groups, except for the del(17p)/TP53mut, where the predominant gene was TRBV10-3. Clonotype comparisons disclosed the presence of shared TR clonotypes both within a particular group but also between groups. Overall, 446/513,984 clonotypes were found to be shared by at least two patients across all groups; the vast majority (392/446, 88%) of shared clonotypes appeared to be CLL-biased since they did not match entries in public databases of TR clonotypes from various contexts. Subgroup-specific clonotypes were identified for all aberrations examined; these emerged as unique to the particular subgroups, as revealed by extensive comparisons against both public databases but also a large TR clonotype database from CLL available to us from our previous studies. In conclusion, recurrent genomic aberrations, especially large chromosomal abnormalities, display an oligoclonal TR gene repertoire. The distinct immunogenetic profile of each group examined here and, most importantly, the existence of subgroup-specific clonotypes, suggest that abnormal protein expression and gene dosage effects likely represent a relevant source of CLL-specific selecting antigens. DisclosuresScarfo: Janssen: Honoraria, Other: Travel grants; Astra Zeneca: Honoraria; Abbvie: Honoraria. Anagnostopoulos: Abbvie: Other: clinical trials; Sanofi: Other: clinical trials ; Ocopeptides: Other: clinical trials ; GSK: Other: clinical trials; Incyte: Other: clinical trials ; Takeda: Other: clinical trials ; Amgen: Other: clinical trials ; Janssen: Other: clinical trials; novartis: Other: clinical trials; Celgene: Other: clinical trials; Roche: Other: clinical trials; Astellas: Other: clinical trials . Ghia: AbbVie: Consultancy, Honoraria, Research Funding; Acerta/AstraZeneca: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria, Research Funding; ArQule/MSD: Consultancy, Honoraria; BeiGene: Consultancy, Honoraria; Celgene/Juno/BMS: Consultancy, Honoraria; Gilead: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; Sunesis: Research Funding. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Rosenquist: Roche: Honoraria; Janssen: Honoraria; Illumina: Honoraria; AstraZeneca: Honoraria; Abbvie: Honoraria. Stamatopoulos: Gilead: Honoraria, Research Funding; Abbvie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Baliakas: Janssen: Honoraria; Gilead: Honoraria, Research Funding; Abbvie: Honoraria. Chatzidimitriou: Abbvie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding.

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.

Abstract 244: A computation method for noise reduction based on ultra-deep targeted sequencing data

Abstract Detection of circulating tumor DNA is a promising approach for the diagnosis and monitor of solid tumors. However, sensitive detection of ctDNA remains challenging when minor allele frequency (MAF) is ultra-low. Numerous noise reduction data algorithms have been developed to reduce next-generation sequencing (NGS) errors, but variant callers that can take advantage of ultra-deep sequencing data are lacking. Here we develop a bin-based genotyping (BinGo) method for canceling sequencing noise using ultra-deep (&amp;gt; 100,000 X depth) targeted sequencing data. A mutation reference sample with 44 mutations was diluted to different MAFs at 1% and 0.5%. One blood sample for proficiency test was included. We constructed targeted sequencing libraries by anchored multiplex enrichment for a panel of targets that cover the 44 mutations, and sequenced to a very high depth (Mean X ranged: 71K to 350K).After raw NGS reads were mapped by BWA MEM, BinGo tags unique molecular index (UMI) to each alignment (BAM) to preserve alignment signal of all bases in all raw reads. BinGo then bins all reads with the same UMI into one bin, generating millions of bins. Millions of parallel Samtools mpileup was enabled by the GNU parallel tool. Typical mpileup analyses use the human genome (requires ~4GB RAM) as a reference, making millions of such analyses essentially impossible in a regular computing server. To overcome this hurdle, BinGo uses tailor-made reference sequences comprising the targets only (&amp;lt; 100KB), drastically reduces computation memory consumption and enables millions of mpileup analyses. BinGo recompiles Samtools to increase its default max depth from 8,000 to 8,000,000 to accommodate the ultra-deep data. Variants derived from the same bin are effectively phased, thereby enabling calling of di-nuclei mutations such as BRAF V600K (GT&amp;gt;TT) and V600R (GT&amp;gt;AG). This binning also allows for sensitive calling of insertions and deletions even down to a single read. To profile noise patterns, BinGo uses Samtools tview to acquire every base in every alignment position of the target sequences in all raw reads. Finally, BinGo uses multiple statistical testing to call variants, including calling nucleotide changes over the background noise model that is defined by nucleotide change and sequence context; filtering out strand imbalance likely due to DNA template damage, and a low number of supporting ligation starting sites. With 10 ng DNA as input and at an expected MAF of 0.5%, BinGo demonstrates a 100% (44/44) analytical sensitivity and 100% specificity. When using 1 ng DNA as input and at an expected MAF of 0.5%, only one mutation was missed, leading to a sensitivity of 97.73% (43/44) and 100% specificity. Furthermore, BinGo detected in the blood DNA sample a 15-bp EGFR exon 19 deletion at 0.05%.Bingo integrates UMI-bin derived variants and raw sequencing background noise modeling, enabling analysis of ultra-deep targeted NGS data for rare variant detection. Citation Format: Weiwei Bian, Firaol Kebede, Zongli Zheng. A computation method for noise reduction based on ultra-deep targeted sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 244.

Easymap: A User-Friendly Software Package for Rapid Mapping-by-Sequencing of Point Mutations and Large Insertions.

Mapping-by-sequencing strategies combine next-generation sequencing (NGS) with classical linkage analysis, allowing rapid identification of the causal mutations of the phenotypes exhibited by mutants isolated in a genetic screen. Computer programs that analyze NGS data obtained from a mapping population of individuals derived from a mutant of interest to identify a causal mutation are available; however, the installation and usage of such programs requires bioinformatic skills, modifying or combining pieces of existing software, or purchasing licenses. To ease this process, we developed Easymap, an open-source program that simplifies the data analysis workflows from raw NGS reads to candidate mutations. Easymap can perform bulked segregant mapping of point mutations induced by ethyl methanesulfonate (EMS) with DNA-seq or RNA-seq datasets, as well as tagged-sequence mapping for large insertions, such as transposons or T-DNAs. The mapping analyses implemented in Easymap have been validated with experimental and simulated datasets from different plant and animal model species. Easymap was designed to be accessible to all users regardless of their bioinformatics skills by implementing a user-friendly graphical interface, a simple universal installation script, and detailed mapping reports, including informative images and complementary data for assessment of the mapping results. Easymap is available at http://genetics.edu.umh.es/resources/easymap; its Quickstart Installation Guide details the recommended procedure for installation.

KATK: Fast genotyping of rare variants directly from unmapped sequencing reads.

KATK is a fast and accurate software tool for calling variants directly from raw next-generation sequencing reads. It uses predefined k-mers to retrieve only the reads of interest from the FASTQ file and calls genotypes by aligning retrieved reads locally. KATK does not use data about known polymorphisms and has NC (no call) as the default genotype. The reference or variant allele is called only if there is sufficient evidence for their presence in data. Thusit is not biased against rare variants or de-novo mutations. With simulated datasets, we achieved a false-negative rate of 0.23% (sensitivity 99.77%) and a false discovery rate of 0.19%. Calling all human exonic regions with KATK requires 1-2 h, depending on sequencing coverage.

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.

Using earth mover\u2019s distance for viral outbreak investigations

BackgroundRNA viruses mutate at extremely high rates, forming an intra-host viral population of closely related variants, which allows them to evade the host’s immune system and makes them particularly dangerous. Viral outbreaks pose a significant threat for public health, and, in order to deal with it, it is critical to infer transmission clusters, i.e., decide whether two viral samples belong to the same outbreak. Next-generation sequencing (NGS) can significantly help in tackling outbreak-related problems. While NGS data is first obtained as short reads, existing methods rely on assembled sequences. This requires reconstruction of the entire viral population, which is complicated, error-prone and time-consuming.ResultsThe experimental validation using sequencing data from HCV outbreaks shows that the proposed algorithm can successfully identify genetic relatedness between viral populations, infer transmission direction, transmission clusters and outbreak sources, as well as decide whether the source is present in the sequenced outbreak sample and identify it.ConclusionsIntroduced algorithm allows to cluster genetically related samples, infer transmission directions and predict sources of outbreaks. Validation on experimental data demonstrated that algorithm is able to reconstruct various transmission characteristics. Advantage of the method is the ability to bypass cumbersome read assembly, thus eliminating the chance to introduce new errors, and saving processing time by allowing to use raw NGS reads.

Prostate cancer heterogeneity assessment with multi-regional sampling and alignment-free methods.

Combining alignment-free methods for phylogenetic analysis with multi-regional sampling using next-generation sequencing can provide an assessment of intra-patient tumour heterogeneity. From multi-regional sampling divergent branching, we validated two different lesions within a patient’s prostate. Where multi-regional sampling has not been used, a single sample from one of these areas could misguide as to which drugs or therapies would best benefit this patient, due to the fact these tumours appear to be genetically different. This application has the power to render, in a fraction of the time used by other approaches, intra-patient heterogeneity and decipher aberrant biomarkers. Another alignment-free method for calling single-nucleotide variants from raw next-generation sequencing samples has determined possible variants and genomic locations that may be able to characterize the differences between the two main branching patterns. Alignment-free approaches have been applied to relevant clinical multi-regional samples and may be considered as a valuable option for comparing and determining heterogeneity to help deliver personalized medicine through more robust efforts in identifying targetable pathways and therapeutic strategies. Our study highlights the application these tools could have on patient-aligned treatment indications.

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.