VPsero: Rapid Serotyping of Vibrio parahaemolyticus Using Serogroup-Specific Genes Based on Whole-Genome Sequencing Data

Vibrio parahaemolyticus has emerged as a significant enteropathogen in human and marine habitats worldwide, notably in regions where aquaculture products constitute a major nutritional source. It is a growing cause of diseases including gastroenteritis, wound infections, and septicemia. Serotyping assays use commercially available antisera to identify V. parahaemolyticus strains, but this approach is limited by high costs, complicated procedures, cross-immunoreactivity, and often subjective interpretation. By leveraging high-throughput sequencing technologies, we developed an in silico method based on comparison of gene clusters for lipopolysaccharide (LPSgc) and capsular polysaccharide (CPSgc) by firstly using the unique-gene strategy. The algorithm, VPsero, which exploits serogroup-specific genes as markers, covers 43 K and all 12 O serogroups in serotyping assays. VPsero is capable of predicting serotypes from assembled draft genomes, outputting LPSgc/CPSgc sequences, and recognizing possible novel serogroups or populations. Our tool displays high specificity and sensitivity in prediction toward V. parahaemolyticus strains, with an average sensitivity in serogroup prediction of 0.910 for O and 0.961 for K serogroups and a corresponding average specificity of 0.990 for O and 0.998 for K serogroups.

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Multi-platform discovery of haplotype-resolved structural variation in human genomes

10.1101/193144 ◽

2017 ◽

Cited By ~ 32

Author(s):

Mark J.P. Chaisson ◽

Ashley D. Sanders ◽

Xuefang Zhao ◽

Ankit Malhotra ◽

David Porubsky ◽

...

Keyword(s):

Genome Sequencing ◽

Large Scale ◽

Structural Variation ◽

High Throughput Sequencing ◽

Whole Genome Sequencing Data ◽

Sequencing Data ◽

Full Spectrum ◽

Variant Discovery ◽

Sequencing Technologies ◽

Sequencing Studies

ABSTRACTThe incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent–child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per human genome. We also discover 156 inversions per genome—most of which previously escaped detection. Fifty-eight of the inversions we discovered intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The method and the dataset serve as a gold standard for the scientific community and we make specific recommendations for maximizing structural variation sensitivity for future large-scale genome sequencing studies.

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Comprehensive identification of transposable element insertions using multiple sequencing technologies

Nature Communications ◽

10.1038/s41467-021-24041-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Chong Chu ◽

Rebeca Borges-Monroy ◽

Vinayak V. Viswanadham ◽

Soohyun Lee ◽

Heng Li ◽

...

Keyword(s):

Transposable Element ◽

Structure And Function ◽

Endogenous Retroviruses ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Short Read ◽

Sequencing Technologies ◽

Long Read ◽

And Function

AbstractTransposable elements (TEs) help shape the structure and function of the human genome. When inserted into some locations, TEs may disrupt gene regulation and cause diseases. Here, we present xTea (x-Transposable element analyzer), a tool for identifying TE insertions in whole-genome sequencing data. Whereas existing methods are mostly designed for short-read data, xTea can be applied to both short-read and long-read data. Our analysis shows that xTea outperforms other short read-based methods for both germline and somatic TE insertion discovery. With long-read data, we created a catalogue of polymorphic insertions with full assembly and annotation of insertional sequences for various types of retroelements, including pseudogenes and endogenous retroviruses. Notably, we find that individual genomes have an average of nine groups of full-length L1s in centromeres, suggesting that centromeres and other highly repetitive regions such as telomeres are a significant yet unexplored source of active L1s. xTea is available at https://github.com/parklab/xTea.

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Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples

Viruses ◽

10.3390/v13102006 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2006

Author(s):

Anna Y Budkina ◽

Elena V Korneenko ◽

Ivan A Kotov ◽

Daniil A Kiselev ◽

Ilya V Artyushin ◽

...

Keyword(s):

Large Scale ◽

High Throughput Sequencing ◽

Metagenomic Data ◽

Sequencing Data ◽

Viral Pathogens ◽

Genomic Databases ◽

Bioinformatic Pipeline ◽

Viral Genomes ◽

Sequencing Technologies ◽

Viral Screening

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

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JuLI: accurate detection of DNA fusions in clinical sequencing for precision oncology

10.1101/521039 ◽

2019 ◽

Author(s):

Hyun-Tae Shin ◽

Nayoung K. D. Kim ◽

Jae Won Yun ◽

Boram Lee ◽

Sungkyu Kyung ◽

...

Keyword(s):

High Throughput Sequencing ◽

False Negative ◽

Detection Algorithm ◽

Clinical Samples ◽

Whole Genome Sequencing Data ◽

Precision Oncology ◽

Sequencing Data ◽

Clinical Sequencing ◽

Accurate Detection ◽

High Depth

ABSTRACTAccurate detection of genomic fusions by high-throughput sequencing in clinical samples with inadequate tumor purity and formalin-fixed paraffin embedded (FFPE) tissue is an essential task in precise oncology. We developed the fusion detection algorithm Junction Location Identifier (JuLI) for optimization of high-depth clinical sequencing. We implemented novel filtering steps to minimize false positives and a joint calling function to increase sensitivity in clinical setting. We comprehensively validated the algorithm using high-depth sequencing data from cancer cell lines and clinical samples and whole genome sequencing data from NA12878. We showed that JuLI outperformed state-of-the-art fusion callers in cases with high-depth clinical sequencing and rescued a driver fusion from false negative in plasma cell-free DNA. JuLI is freely available via GitHub (https://github.com/sgilab/JuLI).

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Natrix: A Snakemake-based workflow for processing, clustering, and taxonomically assigning amplicon sequencing reads

10.1101/2020.09.23.309864 ◽

2020 ◽

Author(s):

Marius Welzel ◽

Anja Lange ◽

Dominik Heider ◽

Michael Schwarz ◽

Bernd Freisleben ◽

...

Keyword(s):

High Throughput Sequencing ◽

Workflow Management ◽

Amplicon Sequencing ◽

Version Control ◽

Marker Genes ◽

Sequencing Data ◽

Taxonomic Assignment ◽

Ecological Processes ◽

Sequencing Technologies ◽

User Friendly

AbstractSequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires effcient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an effcient workflow management system. We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub (https://github.com/MW55/Natrix).

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GeneHummus: A pipeline to define gene families and their expression in legumes and beyond

10.1101/436659 ◽

2018 ◽

Author(s):

Jose V. Die ◽

Moamen Mahmoud Elmassry ◽

Kimberly Hathaway LeBlanc ◽

Olaitan I. Awe ◽

Allissa Dillman ◽

...

Keyword(s):

Large Scale ◽

Rapid Development ◽

High Specificity ◽

Gene Families ◽

Model Systems ◽

Auxin Receptor ◽

Plant Gene ◽

Sequencing Technologies ◽

Specificity And Sensitivity ◽

The Impact

AbstractDuring the last decade, plant biotechnological laboratories have sparked a monumental revolution with the rapid development of next sequencing technologies at affordable prices. Soon, these sequencing technologies and assembling of whole genomes will extend beyond the plant computational biologists and become commonplace within the plant biology disciplines. The current availability of large-scale genomic resources for non-traditional plant model systems (the so-called ‘orphan crops’) is enabling the construction of high-density integrated physical and genetic linkage maps with potential applications in plant breeding. The newly available fully sequenced plant genomes represent an incredible opportunity for comparative analyses that may reveal new aspects of genome biology and evolution. Analysis of the expansion and evolution of gene families across species is a common approach to infer biological functions. To date, the extent and role of gene families in plants has only been partially addressed and many gene families remain to be investigated. Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family, typically combining numerous BLAST searches and manually cleaning data. Due to the increasing abundance of genome sequences and the agronomical interest in plant gene families, the field needs a clear, automated annotation tool. Here, we present the GeneHummus pipeline, a step-by-step R-based pipeline for the identification, characterization and expression analysis of plant gene families. The impact of this pipeline comes from a reduction in hands-on annotation time combined with high specificity and sensitivity in extracting only proteins from the RefSeq database and providing the conserved domain architectures based on SPARCLE. As a case study we focused on the auxin receptor factors gene (ARF) family in Cicer arietinum (chickpea) and other legumes. We anticipate that our pipeline should be suitable for any plant gene family, and likely other gene families, vastly improving the speed and ease of genomic data processing.

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Comprehensive identification of somatic nucleotide variants in human brain tissue

Genome Biology ◽

10.1186/s13059-021-02285-3 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 1

Author(s):

Yifan Wang ◽

◽

Taejeong Bae ◽

Jeremy Thorpe ◽

Maxwell A. Sherman ◽

...

Keyword(s):

Best Practices ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Somatic Mosaicism ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Single Individual ◽

Sequencing Data ◽

Specificity And Sensitivity ◽

Neuropsychiatric Diseases

Abstract Background Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. Results Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. Conclusions This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

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PhageTerm: a Fast and User-friendly Software to Determine Bacteriophage Termini and Packaging Mode using randomly fragmented NGS data

10.1101/108100 ◽

2017 ◽

Cited By ~ 2

Author(s):

Julian Garneau ◽

Florence Depardieu ◽

Louis-Charles Fortier ◽

David Bikard ◽

Marc Monot

Keyword(s):

High Throughput Sequencing ◽

Next Generation Sequencing Data ◽

Sequencing Data ◽

Link Type ◽

Sequencing Technologies ◽

Statistical Framework ◽

Fastq Format ◽

Viral Particles ◽

User Friendly ◽

Ngs Data

ABSTRACTBacteriophages are the most abundant viruses on earth and display an impressive genetic as well as morphologic diversity. Among those, the most common order of phages is the Caudovirales, whose viral particles packages linear double stranded DNA (dsDNA). In this study we investigated how the information gathered by high throughput sequencing technologies can be used to determine the DNA termini and packaging mechanisms of dsDNA phages. The wet-lab procedures traditionally used for this purpose rely on the identification and cloning of restriction fragment which can be delicate and cumbersome. Here, we developed a theoretical and statistical framework to analyze DNA termini and phage packaging mechanisms using next-generation sequencing data. Our methods, implemented in the PhageTerm software, work with sequencing reads in fastq format and the corresponding assembled phage genome.PhageTerm was validated on a set of phages with well-established packaging mechanisms representative of the termini diversity: 5’cos (lambda), 3’cos (HK97), pac (P1), headful without a pac site (T4), DTR (T7) and host fragment (Mu). In addition, we determined the termini of 9Clostridium difficilephages and 6 phages whose sequences where retrieved from the sequence read archive (SRA).A direct graphical interface is available as a Galaxy wrapper version athttps://galaxy.pasteur.frand a standalone version is accessible athttps://sourceforge.net/projects/phageterm/.

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ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab112 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Rajesh Detroja ◽

Alessandro Gorohovski ◽

Olawumi Giwa ◽

Gideon Baum ◽

Milana Frenkel-Morgenstern

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Complex Disease ◽

Single Cells ◽

Reference Database ◽

Sequencing Data ◽

Sequencing Technologies ◽

High Throughput Sequencing Data ◽

Chimeric Rnas ◽

Sensitivity Specificity

Abstract Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.

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Genome-wide profiling of heritable and de novo STR variations

10.1101/077727 ◽

2016 ◽

Cited By ~ 7

Author(s):

Thomas Willems ◽

Dina Zielinski ◽

Assaf Gordon ◽

Melissa Gymrek ◽

Yaniv Erlich

Keyword(s):

Tandem Repeats ◽

High Throughput Sequencing ◽

De Novo ◽

Genetic Diseases ◽

Whole Genome Sequencing Data ◽

Sequencing Data ◽

High Throughput Sequencing Data ◽

Genome Wide ◽

A Genome ◽

Short Tandem

AbstractShort tandem repeats (STRs) are highly variable elements that play a pivotal role in multiple genetic diseases, population genetics applications, and forensic casework. However, STRs have proven problematic to genotype from high-throughput sequencing data. Here, we describe HipSTR, a novel haplotype-based method for robustly genotyping, haplotyping, and phasing STRs from whole genome sequencing data and report a genome-wide analysis and validation of de novo STR mutations.

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