Generation Sequencing Datasets Research Articles

Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data

BackgroundThe Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool.ResultsWe evaluated 13 MHC class I and/or class II HLA callers that are currently available for free academic use and run on either Whole Exome Sequencing (WES) or RNA sequencing data. Computational resource requirements were highly variable between these tools. Three orthogonal approaches were used to evaluate the accuracy on several large publicly available datasets: a direct benchmark using PCR-derived gold standard HLA calls, a correlation analysis with population-based allele frequencies and an analysis of the concordance between the different tools. The highest MHC-I calling accuracies were found for Optitype (98.0%) and arcasHLA (99.4%) on WES and RNA sequencing data respectively, while for MHC-II HLA-HD was the most accurate tool for both data types (96.2% and 99.4% on WES and RNA data respectively).ConclusionThe optimal strategy for HLA genotyping from NGS data depends on the availability of either WES or RNA data, the size of the dataset and the available computational resources. If sufficient resources are available, we recommend Optitype and HLA-HD for MHC-I and MHC-II genotype calling respectively.

Targeted Enrichment of Low-Abundance and Uncharacterized Taxon Members in Complex Microbial Community with Primer-Free FISH ProbesDesigned from Next Generation Sequencing Dataset.

Methods to obtain high-quality assembled genomic information of rare and unclassified member species in complex microbial communities remain a high priority in microbial ecology. Additionally, the supplementation of three-dimensional spatial information that highlights the morphology and spatial interaction would provide additional insights to its ecological role in the community. Fluorescent in-situ hybridization (FISH) coupling with fluorescence-activated cell sorting (FACS) is a powerful tool that enables the detection, visualization, and separation of low-abundance microbial members in samples containing complex microbial compositions. Here, we have described the workflow from designing the appropriate FISH probes from metagenomics or metatranscriptomics datasets to the preparation and treatment of samples to be used in FISH-FACS procedures.

Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of the present investigation was to identify gene signature specific to T2DM. The next generation sequencing (NGS) dataset GSE81608 was retrieved from the gene expression omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) between T2DM and normal controls. Then, Gene Ontology (GO) and pathway enrichment analysis, protein-protein interaction (PPI) network, modules, miRNA (micro RNA)-hub gene regulatory network construction and TF (transcription factor)-hub gene regulatory network construction, and topological analysis were performed. Receiver operating characteristic curve (ROC) analysis was also performed to verify the prognostic value of hub genes. A total of 927 DEGs (461 were up regulated and 466 down regulated genes) were identified in T2DM. GO and REACTOME results showed that DEGs mainly enriched in protein metabolic process, establishment of localization, metabolism of proteins, and metabolism. The top centrality hub genes APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1 were screened out as the critical genes. ROC analysis provides prognostic value of hub genes. The potential crucial genes, especially APP, MYH9, TCTN2, USP7, SYNPO, GRB2, HSP90AB1, UBC, HSPA5, and SQSTM1, might be linked with risk of T2DM. Our study provided novel insights of T2DM into genetics, molecular pathogenesis, and novel therapeutic targets.

MA14.05 Clinicopathologic and Genomic Significances of the Amount of High-Grade Histologic Components in Lung Adenocarcinoma

Recent proposal of grading system for lung adenocarcinoma (LUAD) from the pathology committee of International Association for the Study of Lung Cancer emphasizes the amount of high-grade components (HGC) in the prognostic stratification of LUAD. Underlying pathobiologic features supporting the biologic significance of HGC in LUAD has not been fully evaluated yet. A total of 175 cases of surgically resected LUAD from January to December 2015 in Asan Medical Center were histologically evaluated. Proportions of HGCs, including solid, micropapillary, and cribriform patterns, were individually quantified, and cases were classified by the cutoff of 1, 5, and 20% according to the summated proportion of HGCs. Genomic features including the mutational profile, fraction of genome altered (FGA), and tumor mutation burden (TMB), were analyzed from the correspondent targeted next generation sequencing dataset and correlated with the HGC class. About 14% (25/175), 27% (47/175) and 30% of the cases (52/175) had 1-<5%, 5-<20% and ≥20% of HGCs, respectively (Figure 1A). Frequencies of lymphovascular invasion was proportionally elevated when the HGCs components were elevated (P<0.0001). Remarkably, >90% of the cases harboring ≥1% of HGCs had spread-through alveolar space (STAS) (P<0.0001). The pathological stage was also elevated in all three groups with HGCs (P<0.0001) (Figure 1B). Comparison of the mutational profile revealed that the rates of EGFR mutation were significantly diminished in HGC ≥20% group only, but TP53 mutation rates were proportionally elevated when the HGCs were elevated (P = 0.001) (Figure 1C). Furthermore, FGA and TMB rates were elevated in all three groups with HGC ≥1% (FGA, P = 0.001; TMB, P<0.0001) (Figure 1D). On univariate survival analysis, both groups with HGC 5-<20% and ≥20% showed significantly inferior overall and recurrence-free survivals with similar survival lengths; HGC 1-<5% groups showed intermediate prognosis (Figure 1E). On multivariate survival analyses, HGC 5-<20% and ≥20% groups were independently associated with poor OS, but only HGC 5-<20% group was statistically significant in comparison with RFS. The clinicopathological characteristics and genomic alterations were significantly different even when ≥1% of the HGCs were accompanied in LUAD. Furthermore, both groups with 5-<20% and ≥20% of HGCs correlated with inferior survival outcomes with similar level, which implicates the clinicopathologic and genomic significance of minor HGCs in LUAD.

Data quality-aware genomic data integration

Genomic data are growing at unprecedented pace, along with new protocols, update polices, formats and guidelines, terminologies and ontologies, which are made available every day by data providers. In this continuously evolving universe, enforcing quality on data and metadata is increasingly critical. While many aspects of data quality are addressed at each individual source, we focus on the need for a systematic approach when data from several sources are integrated, as such integration is an essential aspect for modern genomic data analysis. Data quality must be assessed from many perspectives, including accessibility, currency, representational consistency, specificity, and reliability.In this article we review relevant literature and, based on the analysis of many datasets and platforms, we report on methods used for guaranteeing data quality while integrating heterogeneous data sources. We explore several real-world cases that are exemplary of more general underlying data quality problems and we illustrate how they can be resolved with a structured method, sensibly applicable also to other biomedical domains. The overviewed methods are implemented in a large framework for the integration of processed genomic data, which is made available to the research community for supporting tertiary data analysis over Next Generation Sequencing datasets, continuously loaded from many open data sources, bringing considerable added value to biological knowledge discovery.

Unraveling the Regulation of Cancer/Testis Antigens in Tumorigenesis Through an Analysis of Normal Germ Cell Development in Rodents.

Cancer/testis (CT) antigens are proteins aberrantly overexpressed in various tumorigenic cells, but they can also be normally expressed in the mammalian germline. Most CT antigens are highly immunogenic and known to be involved in cancer cell proliferation and tumor metastasis. A recent genome-wide analysis systematically identified CT antigen expression in 19 cancer types, significantly expanding the repertoire of CT antigens by 5-fold,from over 200 to approximately 1000. However, their function and regulation in tumorigenesis remain poorly understood. The shared functional characteristics between germ cells and cancer cells, if methodically defined, offer a unique gateway to understanding the regulation of CT antigens in cancers by studying gametogenesis. Nonetheless, such studies also provide insightful information on the role of CT antigens in spermatogenesis. Herein, we analyzed publicly available next generation sequencing datasets generated from normal adult testes in rodents, primordial germ cells and cancer samples across a series of published studies and databases. Based on these analyses, we report that a subset of CT antigens belonged to the core fitness gene family. Furthermore, super-enhancers both in normal testes and various cancers controlled specific CT antigens. We found that DNA methylation of CT antigens, such as TEX101 and TAF7L, was inversely correlated with their expression in both normal primordial germ cells and various cancers, which was mediated at least partly by DNA methyltransferase1 (DNMT1). By analyzing data from a testis knockout model, we showed that TAF7L could further influence the expression of additional CT antigens, which also held true in tumors. These findings not only confirmed the previous notion that CT antigens regulate cancer dynamics, but also showed that understanding the regulation of CT antigens during gametogenesis can offer new insights for cancer research.

Abstract LB-316: Innovative multimodal single-cell workflow to interrogate cellular responses to cancer therapy

Abstract Elucidation of biological processes that drive cellular migration, proliferation, stemness and differentiation have powered our view of tumor initiation and promotion. These avenues of research are reliant on engaging key biomarkers to measure and quantify cell systems. Widely practiced methodologies include metabolomic assays, immunoassays, imaging platforms and genomic tools. Understanding the genomic landscape of cancer has proven valuable for the characterization of molecular events that drive evolution of tumorigenesis and fostering progress in identifying druggable regimens for patient treatment scenarios. For sampling convenience, genomic measurements are typically applied to bulk cell samples. Bulk samples often obscure important rare cell types that define disease progression. Single-cell genomic analysis can highlight rare cells. Such efforts commonly generate one “modal” of information, most often next generation sequencing datasets containing RNA transcriptomic expression profiles. While transcriptomic information has great utility for plotting global patterns of response, correlating RNA dynamics to genomic variation and protein levels has proven difficult, especially at the single-cell level. We report a new approach to measure multiple modalities simultaneously from up to 10,000 individual cells using high-throughput droplet microfluidics paired with next generation sequencing readouts. Our procedure determines targeted protein levels, mRNA transcript levels and somatic gDNA sequence variations including copy number variants. This approach can resolve over 20 proteins, 100s of targeted transcripts and DNA amplicons. We employ oligo-conjugated antibody panels to probe cell surface markers and targeted RNA and DNA amplification to resolve gene expression levels and genomic variants. Cell suspensions are first stained with cocktails of oligonucleotide-antibodies. Cells are loaded onto the Mission Bio Tapestri microfluidic cartridge for generation of a droplet. This droplet biochemistry allows for concurrent cell lysis and release of gDNA/mRNA. A second droplet formation event brings together a barcoded bead and multiplex PCR amplification reagents. After amplification, the combined libraries are sequenced yielding a multimodal readout. We applied this technique to several dynamic immunology and oncology in vitro models. We explored the relationship between genotype-to-phenotype in breast carcinoma cell lines cultured under different drug regimen conditions, PBMC cell population responses to mitogenic perturbations and imatinib impact on retinoblastoma and BCR-ABL driven leukemic cells. We note CNV influences (such as ERRB) on gene and protein expression as well as RNA-protein concordances. Our pilot studies demonstrate the utility of multimodal resolution in further illuminating cellular states and biological responses in cancer cells. Citation Format: David Ruff, Dalia Dhingra, Pedro Mendez, Aik Ooi, Adam Sciambi, Shu Wang. Innovative multimodal single-cell workflow to interrogate cellular responses to cancer therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-316.

Therapeutic vulnerabilities among KRAS G12C mutant (mut) advanced cancers based on co-alteration (co-alt) patterns.

3625 Background: Oncogenic KRAS mut drive cancers and confer therapeutic resistance by activating MAPK signaling. Inhibiting KRAS has been elusive until the recent promising phase I trials with KRAS G12C inhibitors (i). We characterized frequencies of KRAS G12C mut and gene co-alt among advanced cancer patients (pts) to identify therapeutic vulnerabilities for combination development. Methods: We analyzed next generation sequencing datasets from MD Anderson Cancer Center (MDACC, n = 42,316) and AACR GENIE (n = 56,970). Genes and individual alterations were annotated for potential actionability with approved or investigational drugs and grouped into 12 oncogenic pathways. Frequencies of potential drug combinations with KRAS G12Ci were estimated per tumor type based on co-occurrence of potentially actionable alterations. Results: KRAS G12C was present in 850/34,801 (2.4%) advanced solid tumor and 22/7698 (0.3%) hematologic malignancy pts in MDACC dataset; and 1422 (2.5%) pts in AACR GENIE. Among solid tumor pts, 798 had histology data and 640 had ≥46 gene profiling. Most common cancers were non-small cell lung (NSCLC, 67%), colorectal (CRC, 24%), other gastrointestinal (oGI, 4%) and gynecologic (gyn, 2%). KRAS G12C prevalence was 19.5% (441/2265) in NSCLC and 4.2% (146/3469) in CRC. Genes most commonly co-altered were TP53 (42%), STK11 (17%) and MET (11%) in NSCLC; TP53 (58%), APC (54%) and PIK3CA (24%) in CRC; TP53 (42%), APC (21%) and ATM (21%) in oGI; TP53 (56%), PIK3CA (25%), and PTEN (19%) in gyn cancers. These co-alt did not impact overall survival. In both datasets, as compared to KRAS wild, KRAS G12C was significantly co-altered with STK11 in NSCLC; PIK3CA and SMAD4 in CRC (P &lt; 0.05 for all). EGFR mut in NSCLC and BRAF mut in CRC rarely co-occurred with KRAS G12C (P &lt; 0.01). Most frequently co-altered oncogenic pathways in NSCLC, CRC, oGI and gyn cancers respectively included PI3K (27, 32, 33, 44 %), receptor tyrosine kinases (13, 16, 42, 13 %) and DNA damage repair (12, 10, 38, 19 %). Potentially actionable co-alt frequencies suggest that combining KRAS G12Ci with mTORi or PI3Ki would be indicated most frequently, in 24% and 13% of all pts respectively. Conclusions: KRAS G12Ci development is most relevant for NSCLC, gastrointestinal and gyn cancers. The co-alt patterns highlight relevant oncogenic pathways and candidate drugs for future combination therapies. Co-inhibition of PI3K-mTOR and MAPK pathways has shown synergism in prior pre-clinical studies but had poor tolerance in pts. There is opportunity to revisit this approach with the new KRAS G12Ci.

Virus-induced transposable element expression up-regulation in human and mouse host cells.

Virus-host cell interactions initiate a host cell-defensive response during virus infection. How transposable elements in the host cell respond to viral stress at the molecular level remains largely unclear. By reanalyzing next generation sequencing data sets from dozens of virus infection studies from the Gene Expression Omnibus database, we found that genome-wide transposon expression up-regulation in host cells occurs near antiviral response genes and exists in all studies regardless of virus, species, and host cell tissue types. Some transposons were found to be up-regulated almost immediately upon infection and before increases in virus replication and significant increases in interferon β expression. These findings indicate that transposon up-regulation is a common phenomenon during virus infection in human and mouse and that early up-regulated transposons are part of the first wave response during virus infection.

SMaSH: Sample matching using SNPs in humans

BackgroundInadvertent sample swaps are a real threat to data quality in any medium to large scale omics studies. While matches between samples from the same individual can in principle be identified from a few well characterized single nucleotide polymorphisms (SNPs), omics data types often only provide low to moderate coverage, thus requiring integration of evidence from a large number of SNPs to determine if two samples derive from the same individual or not.MethodsWe select about six thousand SNPs in the human genome and develop a Bayesian framework that is able to robustly identify sample matches between next generation sequencing data sets.ResultsWe validate our approach on a variety of data sets. Most importantly, we show that our approach can establish identity between different omics data types such as Exome, RNA-Seq, and MethylCap-Seq. We demonstrate how identity detection degrades with sample quality and read coverage, but show that twenty million reads of a fairly low quality RNA-Seq sample are still sufficient for reliable sample identification.ConclusionOur tool, SMASH, is able to identify sample mismatches in next generation sequencing data sets between different sequencing modalities and for low quality sequencing data.

Identification of muscle-specific candidate genes in Simmental beef cattle using imputed next generation sequencing.

Genome-wide association studies (GWAS) have commonly been used to identify candidate genes that control economically important traits in livestock. Our objective was to detect potential candidate genes associated mainly with muscle development traits related to dimension of hindquarter in cattle. A next generation sequencing (NGS) dataset to imputed to 12 million single nucleotide polymorphisms (SNPs) (from 1252 Simmental beef cattle) were used to search for genes affecting hindquarter traits using a linear, mixed model approach. We also used haplotype and linkage disequilibrium blocks to further support our identifications. We identified 202 significant SNPs in the bovine BTA4 chromosome region associated with width of hind leg, based on a stringent statistical threshold (p = 0.05/ effective number of SNPs identified). After exploring the region around these SNPs, we found candidate genes that were potentially related to the associated markers. More importantly, we identified a region of approximately 280 Kb on the BTA4 chromosome that harbored several muscle specific candidate genes, genes to be in a potential region for muscle development. However, we also found candidate gene SLC13A1 on BTA4, which seems to be associated with bone disorders (such as chondrodysplasia) in Simmental beef cattle.

The lonely driver or the orchestra of mutations? How next generation sequencing datasets contradict the concept of single driver checkpoint mutations in solid tumours - NSCLC as a scholarly example.

Driver mutations are considered to be responsible for the majority of cancers and several of those mutations provide targets in order to set up personalized therapies. So far the generally accepted opinion had been that driver mutations occur as stand-alone factors, but novel sequencing technologies induced an essential rethink. Next generation sequencing approaches have shown that double, triple or multiple concurrent mutations could occur within the same tumour and may by interaction influence sensitivity to anticancer drugs and therapy success. This review focusses on this novel concept and discusses the challenges for molecular pathology and laboratory diagnostics while providing putative solutions to overcome the present pitfalls, thereby taking NSCLC as an example.

Breast cancer brain metastases show increased levels of genomic aberration-based homologous recombination deficiency scores relative to their corresponding primary tumors

BackgroundBased on its mechanism of action, PARP inhibitor therapy is expected to benefit mainly tumor cases with homologous recombination deficiency (HRD). Therefore, identification of tumor types with increased HRD is important for the optimal use of this class of therapeutic agents. HRD levels can be estimated using various mutational signatures from next generation sequencing data and we used this approach to determine whether breast cancer brain metastases show altered levels of HRD scores relative to their corresponding primary tumor. Patients and methodsWe used a previously published next generation sequencing dataset of 21 matched primary breast cancer/brain metastasis pairs to derive the various mutational signatures/HRD scores strongly associated with HRD. We also carried out the myChoice HRD analysis on an independent cohort of 17 breast cancer patients with matched primary/brain metastasis pairs. ResultsAll of the mutational signatures indicative of HRD showed a significant increase in the brain metastases relative to their matched primary tumor in the previously published whole exome sequencing dataset. In the independent validation cohort, the myChoice HRD assay showed an increased level in 87.5% of the brain metastases relative to the primary tumor, with 56% of brain metastases being HRD positive according to the myChoice criteria. ConclusionsThe consistent observation that brain metastases of breast cancer tend to have higher HRD measures may raise the possibility that brain metastases may be more sensitive to PARP inhibitor treatment. This observation warrants further investigation to assess whether this increase is common to other metastatic sites as well, and whether clinical trials should adjust their strategy in the application of HRD measures for the prioritization of patients for PARP inhibitor therapy.

Processing of big heterogeneous genomic datasets for tertiary analysis of Next Generation Sequencing data.

We previously proposed a paradigm shift in genomic data management, based on the Genomic Data Model (GDM) for mediating existing data formats and on the GenoMetric Query Language (GMQL) for supporting, at a high level of abstraction, data extraction and the most common data-driven computations required by tertiary data analysis of Next Generation Sequencing datasets. Here, we present a new GMQL-based system with enhanced accessibility, portability, scalability and performance. The new system has a well-designed modular architecture featuring: (i) an intermediate representation supporting many different implementations (including Spark, Flink and SciDB); (ii) a high-level technology-independent repository abstraction, supporting different repository technologies (e.g., local file system, Hadoop File System, database or others); (iii) several system interfaces, including a user-friendly Web-based interface, a Web Service interface, and a programmatic interface for Python language. Biological use case examples, using public ENCODE, Roadmap Epigenomics and TCGA datasets, demonstrate the relevance of our work. The GMQL system is freely available for non-commercial use as open source project at: http://www.bioinformatics.deib.polimi.it/GMQLsystem/. Supplementary data are available at Bioinformatics online.

Bacterial and Fungal Next Generation Sequencing Datasets and Metadata from Citrus Infected with ‘Candidatus Liberibacter asiaticus’

Citrus production throughout the world is being severely threatened by Huanglongbing (HLB), which is a disease associated with the bacteria ‘Candidatus Liberibacter asiaticus’ (CLas), africanus, and americanus. This Resource Announcement provides amplicon-based next generation sequencing (NGS) datasets of the bacterial and fungal rRNA internal transcribed spacer (ITS) region from CLas-infected citrus budwood, leaves, and roots from five orchards located in different geographical regions in Florida (USA). To our knowledge, this is the first amplicon-based NGS study (i) that describes the fungal taxa associated with citrus and (ii) that provides comparative analyses of the bacterial and fungal taxa associated with budwood, leaves, and roots from the same citrus trees. This report also provides the sample metadata linked to these sequence datasets including HLB severity rating, tissue type, citrus rootstock, citrus scion, geographical region, and year trees were planted. When analyzed with other similar datasets, we anticipate that researchers will be able to obtain a greater understanding of the factors that shape the citrus microbiome as well as identify individual microorganisms or consortia of microorganisms that play a role in HLB suppression or exacerbation.

ENCoRE: an efficient software for CRISPR screens identifies new players in extrinsic apoptosis

BackgroundAs CRISPR/Cas9 mediated screens with pooled guide libraries in somatic cells become increasingly established, an unmet need for rapid and accurate companion informatics tools has emerged. We have developed a lightweight and efficient software to easily manipulate large raw next generation sequencing datasets derived from such screens into informative relational context with graphical support. The advantages of the software entitled ENCoRE (Easy NGS-to-Gene CRISPR REsults) include a simple graphical workflow, platform independence, local and fast multithreaded processing, data pre-processing and gene mapping with custom library import.ResultsWe demonstrate the capabilities of ENCoRE to interrogate results from a pooled CRISPR cellular viability screen following Tumor Necrosis Factor-alpha challenge. The results not only identified stereotypical players in extrinsic apoptotic signaling but two as yet uncharacterized members of the extrinsic apoptotic cascade, Smg7 and Ces2a. We further validated and characterized cell lines containing mutations in these genes against a panel of cell death stimuli and involvement in p53 signaling.ConclusionsIn summary, this software enables bench scientists with sensitive data or without access to informatic cores to rapidly interpret results from large scale experiments resulting from pooled CRISPR/Cas9 library screens.

Dynamic transcriptional control of macrophage miRNA signature via inflammation responsive enhancers revealed using a combination of next generation sequencing-based approaches

MicroRNAs are important components of the post-transcriptional fine-tuning of macrophage gene expression in physiological and pathological conditions. However, the mechanistic underpinnings and the cis-acting genomic factors of how macrophage polarizing signals induce miRNA expression changes are not well characterized. Therefore, we systematically evaluated the transcriptional basis underlying the inflammation-mediated regulation of macrophage microRNome using the combination of different next generation sequencing datasets. We investigated the LPS-induced expression changes at mature miRNA and pri-miRNA levels in mouse macrophages utilizing a small RNA-seq method and publicly available GRO-seq dataset, respectively. Next, we identified an enhancer set associated with LPS-responsive pri-miRNAs based on publicly available H3K4 mono-methylation-specific ChIP-seq and GRO-seq datasets. This enhancer set was further characterized by the combination of publicly available ChIP and ATAC-seq datasets. Finally, direct interactions between the miR-155-coding genomic region and its distal regulatory elements were identified using a 3C–seq approach. Our analysis revealed 15 robustly LPS-regulated miRNAs at the transcriptional level. In addition, we found that these miRNA genes are associated with an inflammation-responsive enhancer network. Based on NFκB-p65 and JunB transcription factor binding, we showed two distinct enhancer subsets associated with LPS-activated miRNAs that possess distinct epigenetic characteristics and LPS-responsiveness. Finally, our 3C–seq analysis revealed the LPS-induced extensive reorganization of the pri-miR-155-associated functional chromatin domain as well as chromatin loop formation between LPS-responsive enhancers and the promoter region. Our genomic approach successfully combines various genome-wide datasets and allows the identification of the putative regulatory elements controlling miRNA expression in classically activated macrophages.

FastQ_brew: module for analysis, preprocessing, and reformatting of FASTQ sequence data

BackgroundNext generation sequencing datasets are stored as FASTQ formatted files. In order to avoid downstream artefacts, it is critical to implement a robust preprocessing protocol of the FASTQ sequence in order to determine the integrity and quality of the data.ResultsHere I describe fastQ_brew which is a package that provides a suite of methods to evaluate sequence data in FASTQ format and efficiently implements a variety of manipulations to filter sequence data by size, quality and/or sequence. fastQ_brew allows for mismatch searches to adapter sequences, left and right end trimming, removal of duplicate reads, as well as reads containing non-designated bases. fastQ_brew also returns summary statistics on the unfiltered and filtered FASTQ data, and offers FASTQ to FASTA conversion as well as FASTQ reverse complement and DNA to RNA manipulations.ConclusionsfastQ_brew is open source and freely available to all users at the following webpage: https://github.com/dohalloran/fastQ_brew.

A viral metagenomic approach on a non-metagenomic experiment: Mining next generation sequencing datasets from pig DNA identified several porcine parvoviruses for a retrospective evaluation of viral infections.

Shot-gun next generation sequencing (NGS) on whole DNA extracted from specimens collected from mammals often produces reads that are not mapped (i.e. unmapped reads) on the host reference genome and that are usually discarded as by-products of the experiments. In this study, we mined Ion Torrent reads obtained by sequencing DNA isolated from archived blood samples collected from 100 performance tested Italian Large White pigs. Two reduced representation libraries were prepared from two DNA pools constructed each from 50 equimolar DNA samples. Bioinformatic analyses were carried out to mine unmapped reads on the reference pig genome that were obtained from the two NGS datasets. In silico analyses included read mapping and sequence assembly approaches for a viral metagenomic analysis using the NCBI Viral Genome Resource. Our approach identified sequences matching several viruses of the Parvoviridae family: porcine parvovirus 2 (PPV2), PPV4, PPV5 and PPV6 and porcine bocavirus 1-H18 isolate (PBoV1-H18). The presence of these viruses was confirmed by PCR and Sanger sequencing of individual DNA samples. PPV2, PPV4, PPV5, PPV6 and PBoV1-H18 were all identified in samples collected in 1998–2007, 1998–2000, 1997–2000, 1998–2004 and 2003, respectively. For most of these viruses (PPV4, PPV5, PPV6 and PBoV1-H18) previous studies reported their first occurrence much later (from 5 to more than 10 years) than our identification period and in different geographic areas. Our study provided a retrospective evaluation of apparently asymptomatic parvovirus infected pigs providing information that could be important to define occurrence and prevalence of different parvoviruses in South Europe. This study demonstrated the potential of mining NGS datasets non-originally derived by metagenomics experiments for viral metagenomics analyses in a livestock species.

BATVI: Fast, sensitive and accurate detection of virus integrations

BackgroundThe study of virus integrations in human genome is important since virus integrations were shown to be associated with diseases. In the literature, few methods have been proposed that predict virus integrations using next generation sequencing datasets. Although they work, they are slow and are not very sensitive.Results and discussionThis paper introduces a new method BatVI to predict viral integrations. Our method uses a fast screening method to filter out chimeric reads containing possible viral integrations. Next, sensitive alignments of these candidate chimeric reads are called by BLAST. Chimeric reads that are co-localized in the human genome are clustered. Finally, by assembling the chimeric reads in each cluster, high confident virus integration sites are extracted.ConclusionWe compared the performance of BatVI with existing methods VirusFinder and VirusSeq using both simulated and real-life datasets of liver cancer patients. BatVI ran an order of magnitude faster and was able to predict almost twice the number of true positives compared to other methods while maintaining a false positive rate less than 1%. For the liver cancer datasets, BatVI uncovered novel integrations to two important genes TERT and MLL4, which were missed by previous studies. Through gene expression data, we verified the correctness of these additional integrations.BatVI can be downloaded from http://biogpu.ddns.comp.nus.edu.sg/~ksung/batvi/index.html.