A unified STR profiling system across multiple species with whole genome sequencing data

Abstract Background Short tandem repeats (STRs) serve as genetic markers in forensic scenes due to their high polymorphism in eukaryotic genomes. A variety of STRs profiling systems have been developed for species including human, dog, cat, cattle, etc. Maintaining these systems simultaneously can be costly. These mammals share many high similar regions along their genomes. With the availability of the massive amount of the whole genomics data of these species, it is possible to develop a unified STR profiling system. In this study, our objective is to propose and develop a unified set of STR loci that could be simultaneously applied to multiple species. Result To find a unified STR set, we collected the whole genome sequence data of the concerned species and mapped them to the human genome reference. Then we extracted the STR loci across the species. From these loci, we proposed an algorithm which selected a subset of loci by incorporating the optimized combined power of discrimination. Our results show that the unified set of loci have high combined power of discrimination, >1−10−9, for both individual species and the mixed population, as well as the random-match probability, <10−7 for all the involved species, indicating that the identified set of STR loci could be applied to multiple species. Conclusions We identified a set of STR loci which shared by multiple species. It implies that a unified STR profiling system is possible for these species under the forensic scenes. The system can be applied to the individual identification or paternal test of each of the ten common species which are Sus scrofa (pig), Bos taurus (cattle), Capra hircus (goat), Equus caballus (horse), Canis lupus familiaris (dog), Felis catus (cat), Ovis aries (sheep), Oryctolagus cuniculus (rabbit), and Bos grunniens (yak), and Homo sapiens (human). Our loci selection algorithm employed a greedy approach. The algorithm can generate the loci under different forensic parameters and for a specific combination of species.

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MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates

PeerJ ◽

10.7717/peerj.5895 ◽

2018 ◽

Vol 6 ◽

pp. e5895 ◽

Cited By ~ 35

Author(s):

Thomas Andreas Kohl ◽

Christian Utpatel ◽

Viola Schleusener ◽

Maria Rosaria De Filippo ◽

Patrick Beckert ◽

...

Keyword(s):

Antibiotic Resistance ◽

Mycobacterium Tuberculosis ◽

Genome Sequence ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Phylogenomic Analysis ◽

Whole Genome ◽

Sequencing Data ◽

Desktop Computer

Analyzing whole-genome sequencing data of Mycobacterium tuberculosis complex (MTBC) isolates in a standardized workflow enables both comprehensive antibiotic resistance profiling and outbreak surveillance with highest resolution up to the identification of recent transmission chains. Here, we present MTBseq, a bioinformatics pipeline for next-generation genome sequence data analysis of MTBC isolates. Employing a reference mapping based workflow, MTBseq reports detected variant positions annotated with known association to antibiotic resistance and performs a lineage classification based on phylogenetic single nucleotide polymorphisms (SNPs). When comparing multiple datasets, MTBseq provides a joint list of variants and a FASTA alignment of SNP positions for use in phylogenomic analysis, and identifies groups of related isolates. The pipeline is customizable, expandable and can be used on a desktop computer or laptop without any internet connection, ensuring mobile usage and data security. MTBseq and accompanying documentation is available from https://github.com/ngs-fzb/MTBseq_source.

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Population-level genome-wide STR typing in Plasmodium species reveals higher resolution population structure and genetic diversity relative to SNP typing

10.1101/2021.05.19.444768 ◽

2021 ◽

Author(s):

Jiru Han ◽

Jacob E Munro ◽

Anthony Kocoski ◽

Alyssa E Barry ◽

Melanie Bahlo

Keyword(s):

Genetic Diversity ◽

Large Scale ◽

Tandem Repeats ◽

Plasmodium Species ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Genome Wide ◽

Field Samples

Short tandem repeats (STRs) are highly informative genetic markers that have been used extensively in population genetics analysis. They are an important source of genetic diversity and can also have functional impact. Despite the availability of bioinformatic methods that permit large-scale genome-wide genotyping of STRs from whole genome sequencing data, they have not previously been applied to sequencing data from large collections of malaria parasite field samples. Here, we have genotyped STRs using HipSTR in more than 3,000 Plasmodium falciparum and 174 Plasmodium vivax published whole-genome sequence data from samples collected across the globe. High levels of noise and variability in the resultant callset necessitated the development of a novel method for quality control of STR genotype calls. A set of high-quality STR loci (6,768 from P. falciparum and 3,496 from P. vivax) were used to study Plasmodium genetic diversity, population structures and genomic signatures of selection and these were compared to genome-wide single nucleotide polymorphism (SNP) genotyping data. In addition, the genome-wide information about genetic variation and other characteristics of STRs in P. falciparum and P. vivax have been made available in an interactive web-based R Shiny application PlasmoSTR (https://github.com/bahlolab/PlasmoSTR).

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Candida auris Whole-Genome Sequence Benchmark Dataset for Phylogenomic Pipelines

Journal of Fungi ◽

10.3390/jof7030214 ◽

2021 ◽

Vol 7 (3) ◽

pp. 214

Author(s):

Rory M. Welsh ◽

Elizabeth Misas ◽

Kaitlin Forsberg ◽

Meghan Lyman ◽

Nancy A. Chow

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Epidemiological Data ◽

Benchmark Dataset ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Candida Auris ◽

Surveillance Networks

Candida auris is a multidrug-resistant pathogen that represents a serious public health threat due to its rapid global emergence, increasing incidence of healthcare-associated outbreaks, and high rates of antifungal resistance. Whole-genome sequencing and genomic surveillance have the potential to bolster C. auris surveillance networks moving forward. Laboratories conducting genomic surveillance need to be able to compare analyses from various national and international surveillance partners to ensure that results are mutually trusted and understood. Therefore, we established an empirical outbreak benchmark dataset consisting of 23 C. auris genomes to help validate comparisons of genomic analyses and facilitate communication among surveillance networks. Our outbreak benchmark dataset represents a polyclonal phylogeny with three subclades. The genomes in this dataset are from well-vetted studies that are supported by multiple lines of evidence, which demonstrate that the whole-genome sequencing data, phylogenetic tree, and epidemiological data are all in agreement. This C. auris benchmark set allows for standardized comparisons of phylogenomic pipelines, ultimately promoting effective C. auris collaborations.

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Genomes of Symbiodiniaceae reveal extensive sequence divergence but conserved functions at family and genus levels

10.1101/800482 ◽

2019 ◽

Cited By ~ 1

Author(s):

Raúl A. González-Pech ◽

Yibi Chen ◽

Timothy G. Stephens ◽

Sarah Shah ◽

Amin R. Mohamed ◽

...

Keyword(s):

Genome Sequence ◽

De Novo ◽

Sequence Divergence ◽

Whole Genome Sequence ◽

Individual Species ◽

Ecological Niches ◽

Whole Genome ◽

Structural Rearrangements ◽

Extensive Sequence ◽

Gene Functions

AbstractDinoflagellates of the family Symbiodiniaceae (Order Suessiales) are predominantly symbiotic, and many are known for their association with corals. The genetic and functional diversity among Symbiodiniaceae is well acknowledged, but the genome-wide sequence divergence among these lineages remains little known. Here, we present de novo genome assemblies of five isolates from the basal genus Symbiodinium, encompassing distinct ecological niches. Incorporating existing data from Symbiodiniaceae and other Suessiales (15 genome datasets in total), we investigated genome features that are common or unique to these Symbiodiniaceae, to genus Symbiodinium, and to the individual species S. microadriaticum and S. tridacnidorum. Our whole-genome comparisons reveal extensive sequence divergence, with no sequence regions common to all 15. Based on similarity of k-mers from whole-genome sequences, the distances among Symbiodinium isolates are similar to those between isolates of distinct genera. We observed extensive structural rearrangements among symbiodiniacean genomes; those from two distinct Symbiodinium species share the most (853) syntenic gene blocks. Functions enriched in genes core to Symbiodiniaceae are also enriched in those core to Symbiodinium. Gene functions related to symbiosis and stress response exhibit similar relative abundance in all analysed genomes. Our results suggest that structural rearrangements contribute to genome sequence divergence in Symbiodiniaceae even within a same species, but the gene functions have remained largely conserved in Suessiales. This is the first comprehensive comparison of Symbiodiniaceae based on whole-genome sequence data, including comparisons at the intra-genus and intra-species levels.

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KoVariome: Korean National Standard Reference Variome database of whole genomes with comprehensive SNV, indel, CNV, and SV analyses

10.1101/187096 ◽

2017 ◽

Author(s):

Jungeun Kim ◽

Jessica A. Weber ◽

Sungwoong Jho ◽

Jinho Jang ◽

JeHoon Jun ◽

...

Keyword(s):

Sequence Data ◽

Copy Number Variations ◽

Genetic Variations ◽

Korean Population ◽

National Standard ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Personal Genome ◽

Whole Genome ◽

Sequencing Data

AbstractHigh-coverage whole-genome sequencing data of a single ethnicity can provide a useful catalogue of population-specific genetic variations. Herein, we report a comprehensive analysis of the Korean population, and present the Korean National Standard Reference Variome (KoVariome). As a part of the Korean Personal Genome Project (KPGP), we constructed the KoVariome database using 5.5 terabases of whole genome sequence data from 50 healthy Korean individuals with an average coverage depth of 31×. In total, KoVariome includes 12.7M single-nucleotide variants (SNVs), 1.7M short insertions and deletions (indels), 4K structural variations (SVs), and 3.6K copy number variations (CNVs). Among them, 2.4M (19%) SNVs and 0.4M (24%) indels were identified as novel. We also discovered selective enrichment of 3.8M SNVs and 0.5M indels in Korean individuals, which were used to filter out 1,271 coding-SNVs not originally removed from the 1,000 Genomes Project data when prioritizing disease-causing variants. CNV analyses revealed gene losses related to bone mineral densities and duplicated genes involved in brain development and fat reduction. Finally, KoVariome health records were used to identify novel disease-causing variants in the Korean population, demonstrating the value of high-quality ethnic variation databases for the accurate interpretation of individual genomes and the precise characterization of genetic variations.

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Autosomal recessive loci contribute significantly to quantitative variation of male fertility in a dairy cattle population

BMC Genomics ◽

10.1186/s12864-021-07523-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Maya Hiltpold ◽

Naveen Kumar Kadri ◽

Fredi Janett ◽

Ulrich Witschi ◽

Fritz Schmitz-Hsu ◽

...

Keyword(s):

Dairy Cattle ◽

Male Fertility ◽

Semen Quality ◽

Quantitative Variation ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Cattle Population ◽

Brown Swiss ◽

Causal Variants

Abstract Background Cattle are ideally suited to investigate the genetics of male fertility. Semen from individual bulls is used for thousands of artificial inseminations for which the fertilization success is monitored. Results from the breeding soundness examination and repeated observations of semen quality complement the fertility evaluation for each bull. Results In a cohort of 3881 Brown Swiss bulls that had genotypes at 683,609 SNPs, we reveal four novel recessive QTL for male fertility on BTA1, 18, 25, and 26 using haplotype-based association testing. A QTL for bull fertility on BTA1 is also associated with sperm head shape anomalies. All other QTL are not associated with any of the semen quality traits investigated. We perform complementary fine-mapping approaches using publicly available transcriptomes as well as whole-genome sequencing data of 125 Brown Swiss bulls to reveal candidate causal variants. We show that missense or nonsense variants in SPATA16, VWA3A, ENSBTAG00000006717 and ENSBTAG00000019919 are in linkage disequilibrium with the QTL. Using whole-genome sequence data, we detect strong association (P = 4.83 × 10− 12) of a missense variant (p.Ile193Met) in SPATA16 with male fertility. However, non-coding variants exhibit stronger association at all QTL suggesting that variants in regulatory regions contribute to variation in bull fertility. Conclusion Our findings in a dairy cattle population provide evidence that recessive variants may contribute substantially to quantitative variation in male fertility in mammals. Detecting causal variants that underpin variation in male fertility remains difficult because the most strongly associated variants reside in poorly annotated non-coding regions.

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Accurate Phasing of Pedigree Genotypes Using Whole Genome Sequence Data

10.1101/148510 ◽

2017 ◽

Author(s):

A.N. Blackburn ◽

M.Z. Kos ◽

N.B. Blackburn ◽

J.M. Peralta ◽

P. Stevens ◽

...

Keyword(s):

Error Rate ◽

Sequence Data ◽

Software Implementation ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Genotype Data ◽

Whole Genome ◽

Genotyping Error ◽

Sequencing Data ◽

Missing Genotypes

AbstractPhasing, the process of predicting haplotypes from genotype data, is an important undertaking in genetics and an ongoing area of research. Phasing methods, and associated software, designed specifically for pedigrees are urgently needed. Here we present a new method for phasing genotypes from whole genome sequencing data in pedigrees: PULSAR (Phasing Using Lineage Specific Alleles / Rare variants). The method is built upon the idea that alleles that are specific to a single founding chromosome within a pedigree, which we refer to as lineage-specific alleles, are highly informative for identifying haplotypes that are identical-by-decent between individuals within a pedigree. Through extensive simulation we assess the performance of PULSAR in a variety of pedigree sizes and structures, and we explore the effects of genotyping errors and presence of non-sequenced individuals on its performance. If the genotyping error rate is sufficiently low PULSAR can phase > 99.9% of heterozygous genotypes with a switch error rate below 1 x 10-4 in pedigrees where all individuals are sequenced. We demonstrate that the method is highly accurate and consistently outperforms the long-range phasing approach used for comparison in our benchmarking. The method also holds promise for fixing genotype errors or imputing missing genotypes. The software implementation of this method is freely available.

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GenomeGraphR: A user-friendly open-source web application for foodborne pathogen Whole Genome Sequencing data integration, analysis, and visualization.

10.1101/495309 ◽

2018 ◽

Author(s):

Moez Sanaa ◽

Régis Pouillot ◽

Francisco J Garces-Vega ◽

Errol Strain ◽

Jane M Van Doren

Keyword(s):

Genome Sequencing ◽

Pathogen Detection ◽

Foodborne Pathogen ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

High Quality ◽

User Friendly ◽

Integration Analysis

Food safety risk assessments and large-scale epidemiological investigations have the potential to provide better and new types of information when whole genome sequence (WGS) data are effectively integrated. Today, the NCBI Pathogen Detection database WGS collections have grown significantly through improvements in technology, coordination, and collaboration, such as the GenomeTrakr and PulseNet networks. However, high-quality genomic data is not often coupled with high-quality epidemiological or food chain metadata. We have created a set of tools for cleaning, curation, integration, analysis and visualization of microbial genome sequencing data. It has been tested using Salmonella enterica and Listeria monocytogenes data sets provided by NCBI Pathogen Detection (160,000 sequenced isolates). GenomeGraphR presents foodborne pathogen WGS data and associated curated metadata in a user-friendly interface that allows a user to query a variety of research questions such as, transmission sources and dynamics, global reach, and persistence of genotypes associated with contamination in the food supply and foodborne illness across time or space. The application is freely available (https://fda-riskmodels.foodrisk.org/genomegraphr/).

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Copy Number Variation Identification on 3,800 Alzheimer’s Disease Whole Genome Sequencing Data from the Alzheimer’s Disease Sequencing Project

Frontiers in Genetics ◽

10.3389/fgene.2021.752390 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wan-Ping Lee ◽

Albert A. Tucci ◽

Mitchell Conery ◽

Yuk Yee Leung ◽

Amanda B. Kuzma ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Copy Number Variation ◽

Copy Number ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Sequencing Project ◽

Number Variation

Alzheimer’s Disease (AD) is a progressive neurologic disease and the most common form of dementia. While the causes of AD are not completely understood, genetics plays a key role in the etiology of AD, and thus finding genetic factors holds the potential to uncover novel AD mechanisms. For this study, we focus on copy number variation (CNV) detection and burden analysis. Leveraging whole-genome sequence (WGS) data released by Alzheimer’s Disease Sequencing Project (ADSP), we developed a scalable bioinformatics pipeline to identify CNVs. This pipeline was applied to 1,737 AD cases and 2,063 cognitively normal controls. As a result, we observed 237,306 and 42,767 deletions and duplications, respectively, with an average of 2,255 deletions and 1,820 duplications per subject. The burden tests show that Non-Hispanic-White cases on average have 16 more duplications than controls do (p-value 2e-6), and Hispanic cases have larger deletions than controls do (p-value 6.8e-5).

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Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

10.1101/607127 ◽

2019 ◽

Author(s):

Allison L. Hicks ◽

Nicole Wheeler ◽

Leonor Sánchez-Busó ◽

Jennifer L. Rakeman ◽

Simon R. Harris ◽

...

Keyword(s):

Machine Learning ◽

Antibiotic Resistance ◽

Antibiotic Susceptibility ◽

Sequence Data ◽

Model Performance ◽

Outcome Data ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data

AbstractPrediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.Author SummaryMachine learning-based prediction of antibiotic resistance from bacterial genome sequences represents a promising tool to rapidly determine the antibiotic susceptibility profile of clinical isolates and reduce the morbidity and mortality resulting from inappropriate and ineffective treatment. However, while there has been much focus on demonstrating the diagnostic potential of these modeling approaches, there has been little assessment of potential caveats and prerequisites associated with implementing predictive models of drug resistance in the clinical setting. Our results highlight significant biological and technical challenges facing the application of machine learning-based prediction of antibiotic resistance as a diagnostic tool. By outlining specific factors affecting model performance, our findings provide a framework for future work on modeling drug resistance and underscore the necessity of continued comprehensive sampling and reporting of treatment outcome data for building reliable and sustainable diagnostics.

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