Next-generation Sequencing Datasets Research Articles

Benchmarking variant callers in next-generation and third-generation sequencing analysis.

DNA variants represent an important source of genetic variations among individuals. Next- generation sequencing (NGS) is the most popular technology for genome-wide variant calling. Third-generation sequencing (TGS) has also recently been used in genetic studies. Although many variant callers are available, no single caller can call both types of variants on NGS or TGS data with high sensitivity and specificity. In this study, we systematically evaluated 11 variant callers on 12 NGS and TGS datasets. For germline variant calling, we tested DNAseq and DNAscope modes from Sentieon, HaplotypeCaller mode from GATK and WGS mode from DeepVariant. All the four callers had comparable performance on NGS data and 30× coverage of WGS data was recommended. For germline variant calling on TGS data, we tested DNAseq mode from Sentieon, HaplotypeCaller mode from GATK and PACBIO mode from DeepVariant. All the three callers had similar performance in SNP calling, while DeepVariant outperformed the others in InDel calling. TGS detected more variants than NGS, particularly in complex and repetitive regions. For somatic variant calling on NGS, we tested TNscope and TNseq modes from Sentieon, MuTect2 mode from GATK, NeuSomatic, VarScan2, and Strelka2. TNscope and Mutect2 outperformed the other callers. A higher proportion of tumor sample purity (from 10 to 20%) significantly increased the recall value of calling. Finally, computational costs of the callers were compared and Sentieon required the least computational cost. These results suggest that careful selection of a tool and parameters is needed for accurate SNP or InDel calling under different scenarios.

TP53 Mutations Predict Sensitivity to Adjuvant Gemcitabine in Patients with Pancreatic Ductal Adenocarcinoma: Next-Generation Sequencing Results from the CONKO-001 Trial.

We performed next-generation sequencing (NGS) in the CONKO-001 phase III trial to identify clinically relevant prognostic and predictive mutations and conducted a functional validation in The Cancer Genome Atlas (TCGA) sequencing data. Patients of the CONKO-001 trial received curatively intended surgery for pancreatic adenocarcinoma (PDAC) followed by adjuvant chemotherapy with gemcitabine (Gem) or observation only (Obs). Tissue samples of 101 patients were evaluated by NGS of 37 genes. Cox proportional hazard models were applied for survival analysis. In addition, functional genomic analyses were performed in an NGS and RNA-sequencing dataset of 146 pancreatic tumors from TCGA. The most common mutations in the CONKO cohort were KRAS (75%), TP53 (60%), SMAD4 (10%), CDKNA2 (9%), as well as SWI/SNF (12%) complex alterations. In untreated patients, TP53 mutations were a negative prognostic factor for disease-free survival (DFS; HR mut vs. WT 2.434, P = 0.005). With respect to gemcitabine treatment, TP53 mutations were a positive predictive factor for gemcitabine efficacy [TP53mut: HR for DFS Gem vs. Obs, 0.235 (0.130 - 0.423; P < 0.001); TP53wt: HR for DFS Gem vs. Obs, 0.794 (0.417 - 1.513; P = 0.483)] with a significant test for interaction (P = 0.003). In the TCGA dataset, TP53 mutations were associated with shortened DFS. In CONKO-001, the benefit from adjuvant gemcitabine was confined to the TP53mut patient group. This potentially clinical relevant observation needs to be confirmed in independent prospective studies. The sensitivity of TP53mut PDAC to gemcitabine in CONKO-001 provides a lead for further mechanistic investigations.

Ionizing Radiation Alters the Transition/Transversion Ratio in the Exome of Human Gingiva Fibroblasts.

Little is known about the mutational impact of ionizing radiation (IR) exposure on a genome-wide level in mammalian tissues. Recent advancements in sequencing technology have provided powerful tools to perform exome-wide analyses of genetic variation. This also opened up new avenues for studying and characterizing global genomic IR-induced effects. However, genotypes generated by next generation sequencing (NGS) studies can contain errors, which may significantly impact the power to detect signals in common and rare variant analyses. These genotyping errors are not explicitly detected by the standard Genotype Analysis ToolKit (GATK) and Variant Quality Score Recalibration (VQSR) tool and thus remain a potential source of false-positive variants in whole exome sequencing (WES) datasets. In this context, the transition-transversion ratio (Ti/Tv) is commonly used as an additional quality check. In case of IR experiments, this is problematic when Ti/Tv itself might be influenced by IR treatment. It was the aim of this study to determine a suitable threshold for variant filters for NGS datasets from irradiated cells in order to achieve high data quality using Ti/Tv, while at the same time being able to investigate radiation-specific effects on the Ti/Tv ratio for different radiation doses. By testing a variety of filter settings and comparing the obtained results with publicly available datasets, we observe that a coverage filter setting of depth (DP) 3 and genotype quality (GQ) 20 is sufficient for high quality single nucleotide variants (SNVs) calling in an analysis combining GATK and VSQR and that Ti/Tv values are a consistent and useful indicator for data quality assessment for all tested NGS platforms. Furthermore, we report a reduction in Ti/Tv in IR-induced mutations in primary human gingiva fibroblasts (HGFs), which points to an elevated proportion of transversions among IR-induced SNVs and thus might imply that mismatch repair (MMR) plays a role in the cellular damage response to IR-induced DNA lesions.

Comparison of microbiome samples: methods and computational challenges.

The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed in recent years. Here, we review three key challenges in the comparison of such data sets: species identification and quantification, the efficient computation of distances between metagenomic samples and the identification of metagenomic features associated with a phenotype such as disease status. We present current solutions for such challenges, considering both reference-based methods relying on a database of reference genomes and reference-free methods working directly on all sequencing reads from the samples.

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era.

The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.

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.

Landscape of Non-canonical Cysteines in Human VH Repertoire Revealed by Immunogenetic Analysis

SummaryHuman antibody repertoire data captured through next-generation sequencing (NGS) has enabled deeper insights into B cell immunogenetics and paratope diversity. By analyzing large public NGS datasets, we map the landscape of non-canonical cysteines in human variable heavy-chain domains (VHs) at the repertoire level. We identify remarkable usage of non-canonical cysteines within the heavy-chain complementarity-determining region 3 (CDR-H3) and other CDRs and framework regions. Furthermore, our study reveals the diversity and location of non-canonical cysteines and their associated motifs in human VHs, which are reminiscent of and more complex than those found in other non-human species such as chicken, camel, llama, shark, and cow. These results explain how non-canonical cysteines strategically occur in the human antibodyome to expand its paratope space. This study will guide the design of human antibodies harboring disulfide-stabilized long CDR-H3s to access difficult-to-target epitopes and influence a paradigm shift in developability involving non-canonical cysteines.

Abstract B054: Analysis of a cancer gene expression and next-generation sequencing dataset representing hispanic predominant South Florida population

Abstract People of different races and ethnicities have different likelihoods of being diagnosed with certain types of cancer and respond differently to therapeutic agents. However, genome-wide association studies of cancer have typically assumed fixed genetic effects across ethnicities, and rarely compared and contrasted findings across ethnic groups, especially the Hispanic population. Baptist Health South Florida enrolled more than 3,000 patients into the Total Cancer Care Protocol, a prospective longitudinal outcomes study designed to bank tissues and clinical information in order to allow scientists and clinicians to bring cancer treatment to a new level. Tissue samples were snap-frozen within 15 minutes of surgical removal, macrodissected to ≥85% tumor purity, and quantified for the percent of malignancy, cellularity, stroma, normalcy, and necrosis. Longitudinal clinical and pathologic data were extracted from patients' electronic medical records to annotate the biospecimens. We analyzed gene expression and sequencing data from 443 cancer patients enrolled in this study whose tumors were molecularly profiled. All the cases had gene expression data, and 32% (n=140) also had next-generation sequencing data. Most common primary cancers were breast cancer (44.2%, n=196), followed by large bowel (18.9%, n=84), lung (15.8%, n=70), and uterine (9.5%, n=42) cancer. The uniqueness of this data is the distinctive racial and ethnic distribution of the patients; the majority of them were Hispanic (51.7%, n=229) specifically from Cuban, Mexican, Puerto Rican, Central American, and South American populations, followed by whites (36.3%, n=161), blacks (6.1%, n=27), other racial/ethnic groups (5.0%, n=22) and unknown (1.0%, n=4). Microarrays, across all sites of origin, were normalized using IRON against the median sample. An RNA quality-related technical artifact was observed and corrected by subtracting the 1st principal component of a partial least squares (PLS) model trained against the RNA integrity number (RIN), a measure of RNA quality. All samples were then extracted from this master normalized quality-corrected dataset and gene expression analysis was performed. For sequencing data, reads were aligned to the human reference genome (hs37d5) using the Burrows-Wheeler Aligner (BWA). Duplicate reads were marked with Picard-Tools. Indel realignment and base quality score recalibration were performed with GATK. Variations observed among ethnic and racial groups for each primary cancer are described in this representative South Florida population. Citation Format: Zuanel Diaz, Arpit Mehta, Zasha Pou, Muni B. Rubens, Don Parris, Miguel Villalona-Calero. Analysis of a cancer gene expression and next-generation sequencing dataset representing hispanic predominant South Florida population [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B054.

SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data

BackgroundRecently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested sampling arrangement with single-cell and bulk samples so that developers of analysis methods can assess accuracy and precision.ResultsWe have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated) samples where each sample is designated bulk or single-cell. Our tool uses a simple configuration file to define the experimental arrangement and can be integrated into software pipelines for testing of variant callers or other genomic tools.ConclusionsThe DNA sequencing data generated by our simulator is representative of real data and integrates seamlessly with standard downstream analysis tools.

Detection of low-frequency resistance-mediating SNPs in next-generation sequencing data of Mycobacterium tuberculosis complex strains with binoSNP

Accurate drug resistance detection is key for guiding effective tuberculosis treatment. While genotypic resistance can be rapidly detected by molecular methods, their application is challenged by mixed mycobacterial populations comprising both susceptible and resistant cells (heteroresistance). For this, next-generation sequencing (NGS) based approaches promise the determination of variants even at low frequencies. However, accurate methods for a valid detection of low-frequency variants in NGS data are currently lacking. To tackle this problem, we developed the variant detection tool binoSNP which allows the determination of low-frequency single nucleotide polymorphisms (SNPs) in NGS datasets from Mycobacterium tuberculosis complex (MTBC) strains. By taking a reference-mapped file as input, binoSNP evaluates each genomic position of interest using a binomial test procedure. binoSNP was validated using in-silico, in-vitro, and serial patient isolates datasets comprising varying genomic coverage depths (100-500×) and SNP allele frequencies (1-30%). Overall, the detection limit for low-frequency SNPs depends on the combination of coverage depth and allele frequency of the resistance-associated mutation. binoSNP allows for valid detection of resistance associated SNPs at a 1% frequency with a coverage ≥400×. In conclusion, binoSNP provides a valid approach to detect low-frequency resistance-mediating SNPs in NGS data from clinical MTBC strains. It can be implemented in automated, end-user friendly analysis tools for NGS data and is a step forward towards individualized TB therapy.

CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets.

Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can't be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively.

Comparing Phylogenetic Approaches to Reconstructing Cell Lineage From Microsatellites With Missing Data.

Due to the imperfect fidelity of DNA replication, somatic cells acquire DNA mutations at each division which record their lineage history. Microsatellites, tandem repeats of DNA nucleotide motifs, mutate more frequently than other genomic regions and by observing microsatellite lengths in single cells and implementing suitable inference procedures, the cell lineage tree of an organism can be reconstructed. Due to recent advances in single cell Next Generation Sequencing (NGS) and the phylogenetic methods used to infer lineage trees, this work investigates which computational approaches best exploit the lineage information found in single cell NGS data. We simulated trees representing cell division with mutating microsatellites, and tested a range of available phylogenetic algorithms to reconstruct cell lineage. We found that distance-based approaches are fast and accurate with fully observed data. However, Maximum Parsimony and the computationally intensive probabilistic methods are more robust to missing data and therefore better suited to reconstructing cell lineage from NGS datasets. We also investigated how robust reconstruction algorithms are to different tree topologies and mutation generation models. Our results show that the flexibility of Maximum Parsimony and the probabilistic approaches mean they can be adapted to allow good reconstruction across a range of biologically relevant scenarios.

Population Genomics Training for the Next Generation of Conservation Geneticists: ConGen 2018 Workshop.

The increasing availability and complexity of next-generation sequencing (NGS) data sets make ongoing training an essential component of conservation and population genetics research. A workshop entitled “ConGen 2018” was recently held to train researchers in conceptual and practical aspects of NGS data production and analysis for conservation and ecological applications. Sixteen instructors provided helpful lectures, discussions, and hands-on exercises regarding how to plan, produce, and analyze data for many important research questions. Lecture topics ranged from understanding probabilistic (e.g., Bayesian) genotype calling to the detection of local adaptation signatures from genomic, transcriptomic, and epigenomic data. We report on progress in addressing central questions of conservation genomics, advances in NGS data analysis, the potential for genomic tools to assess adaptive capacity, and strategies for training the next generation of conservation genomicists.

Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes.

SummaryGenome detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next-generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present the Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS)-related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2000 sequences per submission and the analysis of a new whole-genome sequence will take approximately 1 min. The tool has been tested and validated with hundreds of whole genomes from 10 coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease.Availability and implementation https://www.genomedetective.com/app/typingtool/cov Contact koen@emweb.be or deoliveira@ukzn.ac.zaSupplementary information Supplementary data are available at Bioinformatics online.

Abstract PD8-03: Clinico-pathological and molecular features in young women with metastatic breast cancer

Abstract Introduction: Advanced breast cancer can be more aggressive and portend a worse prognosis in the younger population (age &amp;lt; 50). While several studies have outlined clinico-pathologic features and molecular features of primary disease in young women with breast cancer, data in the metastatic setting remain scarce. Thus, the goal of this study was to explore the underlying clinic-pathologic and molecular profile of young women with metastatic breast cancer. Methods: We conducted a retrospective analysis of 138 females with metastatic breast cancer treated at Northwestern Medicine who provided consent for serial evaluation of circulating biomarkers. Patient were divided into two cohorts based on age at the time of metastasis, namely premenopausal (defined as age &amp;lt; 50) and postmenopausal (age ≥ 50). CellSearch™ immunomagnetic kit (Menarini Silicon Biosystems) was utilized to enumerate circulating tumor cells (CTCs), and the CellSearch™ CXC Kit (in 7.5 cc whole blood) characterized CTC HER2 expression. Circulating tumor DNA (ctDNA) was sequenced using the Guardant360 next-generation sequencing (NGS) assay (Guardant Health). When available, tissue samples from the primary and metastatic site(s) were sequenced using FoundationOne and/or Tempus xT NGS assays. Associations were drawn using Pearson’s χ2 test, independent samples T-tests, and multivariate logistic regression. Results: Of the 138 women, 54 (39%) were premenopausal with median age of 42 (range: 28-49), and 84 (61%) were postmenopausal with median age of 57 (range: 50-81). For the premenopausal cohort, 96% had invasive ductal carcinoma, 2% invasive lobular carcinoma and 2% mixed/unknown, compared to postmenopausal with 74%, 19% and 7% respectively, p=0.003. No statistically significant association was observed based on disease subtype (HR+/HER2-, HR+/HER2+, HR-/HER2+, TNBC), correspondingly stratified as 46%, 17%, 9%, 28% (premenopausal) and 57%, 10%, 13% 20% (postmenopausal). In total, 39% of the premenopausal and 40% of the postmenopausal patients had inflammatory breast cancer (IBC). Among patients initially diagnosed with non-metastatic disease, time (years) to metastasis was 2.76 (95% CI 2.11 to 3.41) for the premenopausal and 6.08 (95% CI 4.89 to 7.27) for the postmenopausal cohort, p=0.0001. Statistically significant associations were found when comparing the NGS datasets, derived from serial collection of ctDNA +/- tissue samples. Specifically, the premenopausal group had a higher incidence of GATA3 (11 vs 6 cases; p=0.017) alterations, but a lower incidence of NF1 (1 vs 18; p&amp;lt;0.001) and RB1 (1 vs 9; p=0.049) alterations. Most common gene alterations included TP53 (67%), PIK3CA (45%), ERBB2 (26%), GATA3 (26%), MYC (24%), FGFR1 (19%) and ESR1 (17%) for the younger cohort, versus TP53(67%), PIK3CA (39%), ESR1 (27%), NF1 (27%), MYC (23%), ERBB2 (23%) and FGFR1 (20%) for the postmenopausal cohort. There were no statistically significant differences between the cohorts in terms of total number of ctDNA alterations at baseline draw (median[IQR]: 3[1-7] premenopausal group; 5[2-7] postmenopausal group), presence of ≥5 CTCs (54%; 46% of total cases, respectively), occurrence of CTC clusters (26%; 24%), or HER2+ CTC expression (44%; 45%). Conclusion: Our data reveal that premenopausal women diagnosed with metastatic breast cancer had a more rapid progression to metastasis and differ from their postmenopausal counterparts in both their pathologic profile (almost exclusively invasive ductal carcinoma) and molecular profile (notably, gene alteration frequencies of NF1, RB1 and GATA3), which could have significant implications in developing targeted treatment paradigms for younger women. Additionally, CTC prevalence in the metastatic setting differs from earlier stage breast cancer data showing a higher proportion of ≥5 CTCs in postmenopausal patients. Citation Format: Kristen Carroll, Ami M Shah, Lorenzo Gerratana, Chenyu Lin, Andrew A Davis, Qiang Zhang, Youbin Zhang, Lisa Flaum, Amir Behdad, Leonidas C Platanias, William J Gradishar, Massimo Cristofanilli. Clinico-pathological and molecular features in young women with metastatic breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD8-03.

Tomato RNA-seq Data Mining Reveals the Taxonomic and Functional Diversity of Root-Associated Microbiota.

Next-generation approaches have enabled researchers to deeply study the plant microbiota and to reveal how microbiota associated with plant roots has key effects on plant nutrition, disease resistance, and plant development. Although early “omics” experiments focused mainly on the species composition of microbial communities, new “meta-omics” approaches such as meta-transcriptomics provide hints about the functions of the microbes when interacting with their plant host. Here, we used an RNA-seq dataset previously generated for tomato (Solanum lycopersicum) plants growing on different native soils to test the hypothesis that host-targeted transcriptomics can detect the taxonomic and functional diversity of root microbiota. Even though the sequencing throughput for the microbial populations was limited, we were able to reconstruct the microbial communities and obtain an overview of their functional diversity. Comparisons of the host transcriptome and the meta-transcriptome suggested that the composition and the metabolic activities of the microbiota shape plant responses at the molecular level. Despite the limitations, mining available next-generation sequencing datasets can provide unexpected results and potential benefits for microbiota research.

An Online Database for Exploring Over 2,000 Arabidopsis Small RNA Libraries.

Small RNAs (sRNAs) play a wide range of important roles in plants, from maintaining genome stability and enhancing disease resistance to regulating developmental processes. Over the past decade, next-generation sequencing technologies have allowed us to explore the sRNA populations with unprecedented depth and accuracy. The community has accumulated a tremendous amount of sRNA sequencing (sRNA-seq) data from various genotypes, tissues, and treatments. However, it has become increasingly challenging to access these "big data" and extract useful information, particularly for researchers lacking sophisticated bioinformatics tools and expensive computational resources. Here, we constructed an online website, Arabidopsis Small RNA Database (ASRD, http://ipf.sustech.edu.cn/pub/asrd), that allows users to easily explore the information from publicly available Arabidopsis (Arabidopsis thaliana) sRNA libraries. Our database contains ∼2.3 billion sRNA reads, representing ∼250 million unique sequences from 2,024 sRNA-seq libraries. We downloaded the raw data for all libraries and reprocessed them with a unified pipeline so that the normalized abundance of any particular sRNA or the sum of abundances of sRNAs from a genic or transposable element region can be compared across all libraries. We also integrated an online Integrative Genomics Viewer browser into our Web site for convenient visualization. ASRD is a free, web-accessible, and user-friendly database that supports the direct query of over 2,000 Arabidopsis sRNA-seq libraries. We believe this resource will help plant researchers take advantage of the vast next-generation sequencing datasets available in the public domain.

SMusket: Spark-based DNA error correction on distributed-memory systems

Next-Generation Sequencing (NGS) technologies have revolutionized genomics research over the last decade, bringing new opportunities for scientists to perform groundbreaking biological studies. Error correction in NGS datasets is considered an important preprocessing step in many workflows as sequencing errors can severely affect the quality of downstream analysis. Although current error correction approaches provide reasonably high accuracies, their computational cost can be still unacceptable when processing large datasets. In this paper we propose SparkMusket (SMusket), a Big Data tool built upon the open-source Apache Spark cluster computing framework to boost the performance of Musket, one of the most widely adopted and top-performing multithreaded correctors. Our tool efficiently exploits Spark features to implement a scalable error correction algorithm intended for distributed-memory systems built using commodity hardware. The experimental evaluation on a 16-node cluster using four publicly available datasets has shown that SMusket is up to 15.3 times faster than previous state-of-the-art MPI-based tools, also providing a maximum speedup of 29.8 over its multithreaded counterpart. SMusket is publicly available under an open-source license at https://github.com/rreye/smusket.

Improved HLA typing of Class I and Class II alleles from next-generation sequencing data.

Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever-increasing availability of next-generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.

Draft genome assembly and transcriptome sequencing of the golden algae Hydrurus foetidus (Chrysophyceae).

Hydrurusfoetidus is a freshwater chrysophyte alga. It thrives in cold rivers in polar and high alpine regions. It has several morphological traits reminiscent of single-celled eukaryotes, but can also form macroscopic thalli. Despite its ability to produce polyunsaturated fatty acids, its life under cold conditions and its variable morphology, very little is known about its genome and transcriptome. Here, we present an extensive set of next-generation sequencing data, including genomic short reads from Illumina sequencing and long reads from Nanopore sequencing, as well as full length cDNAs from PacBio IsoSeq sequencing and a small RNA dataset (smaller than 200 bp) sequenced with Illumina. The genome sequences were combined to produce an assembly consisting of 5069 contigs, with a total assembly size of 171 Mb and a 77% BUSCO completeness. The new data generated here may contribute to a better understanding of the evolution and ecological roles of chrysophyte algae, as well as to resolve the branching patterns at a larger phylogenetic scale.