scholarly journals Automated genotyping of microsatellite loci from feces with high throughput sequences

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258906
Author(s):  
Isabel Salado ◽  
Alberto Fernández-Gil ◽  
Carles Vilà ◽  
Jennifer A. Leonard
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput ◽  
Microsatellite Loci ◽  
High Throughput Sequencing ◽  
Individual Identification ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Software Packages ◽  
Generation Sequencing

Ecological and conservation genetic studies often use noninvasive sampling, especially with elusive or endangered species. Because microsatellites are generally short in length, they can be amplified from low quality samples such as feces. Microsatellites are highly polymorphic so few markers are enough for reliable individual identification, kinship determination, or population characterization. However, the genotyping process from feces is expensive and time consuming. Given next-generation sequencing (NGS) and recent software developments, automated microsatellite genotyping from NGS data may now be possible. These software packages infer the genotypes directly from sequence reads, increasing throughput. Here we evaluate the performance of four software packages to genotype microsatellite loci from Iberian wolf (Canis lupus) feces using NGS. We initially combined 46 markers in a single multiplex reaction for the first time, of which 19 were included in the final analyses. Megasat was the software that provided genotypes with fewer errors. Coverage over 100X provided little additional information, but a relatively high number of PCR replicates were necessary to obtain a high quality genotype from highly unoptimized, multiplexed reactions (10 replicates for 18 of the 19 loci analyzed here). This could be reduced through optimization. The use of new bioinformatic tools and next-generation sequencing data to genotype these highly informative markers may increase throughput at a reasonable cost and with a smaller amount of laboratory work. Thus, high throughput sequencing approaches could facilitate the use of microsatellites with fecal DNA to address ecological and conservation questions.

scholarly journals Microsatellite loci discovery from next-generation sequencing data and loci characterization in the epizoic barnacleChelonibia testudinaria(Linnaeus, 1758)

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2019 ◽  
Author(s):  
Christine Ewers-Saucedo ◽  
John D. Zardus ◽  
John P. Wares
Keyword(s):  
Next Generation Sequencing ◽  
Microsatellite Markers ◽  
Microsatellite Loci ◽  
Next Generation Sequencing Data ◽  
Model Organisms ◽  
Next Generation ◽  
Sequencing Data ◽  
Genetic Studies ◽  
Evolutionary Features ◽  
Generation Sequencing

Microsatellite markers remain an important tool for ecological and evolutionary research, but are unavailable for many non-model organisms. One such organism with rare ecological and evolutionary features is the epizoic barnacleChelonibia testudinaria(Linnaeus, 1758).Chelonibia testudinariaappears to be a host generalist, and has an unusual sexual system, androdioecy. Genetic studies on host specificity and mating behavior are impeded by the lack of fine-scale, highly variable markers, such as microsatellite markers. In the present study, we discovered thousands of new microsatellite loci from next-generation sequencing data, and characterized 12 loci thoroughly. We conclude that 11 of these loci will be useful markers in future ecological and evolutionary studies onC. testudinaria.

scholarly journals Benchmarking Variant Identification Tools for Plant Diversity Discovery

2019 ◽  
Author(s):  
Xing Wu ◽  
Christopher Heffelfinger ◽  
Hongyu Zhao ◽  
Stephen L. Dellaporta
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput Sequencing ◽  
Crop Improvement ◽  
Variant Calling ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Variant Discovery ◽  
Variant Filtering ◽  
Generation Sequencing

Abstract Background The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets. Results A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. A 2-step imputation method, which utilized a set of high-confidence SNPs as the reference panel, showed up to 60% higher accuracy than direct LD-based imputation. Conclusions Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.

MVSC: A Multi-variation Simulator of Cancer Genome

2020 ◽  
Vol 23 (4) ◽  
pp. 326-333
Author(s):  
Ning Li ◽  
Jialiang Yang ◽  
Wen Zhu ◽  
Ying Liang
Keyword(s):  
Next Generation Sequencing ◽  
Genome Structure ◽  
Cancer Genome ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Genomic Variants ◽  
Software Packages ◽  
Tumor Genome ◽  
Generation Sequencing

Background: Many forms of variations exist in the genome, which are the main causes of individual phenotypic differences. The detection of variants, especially those located in the tumor genome, still faces many challenges due to the complexity of the genome structure. Thus, the performance assessment of variation detection tools using next-generation sequencing platforms is urgently needed. Method: We have created a software package called the Multi-Variation Simulator of Cancer genomes (MVSC) to simulate common genomic variants, including single nucleotide polymorphisms, small insertion and deletion polymorphisms, and structural variations (SVs), which are analogous to human somatically acquired variations. Three sets of variations embedded in genomic sequences in different periods were dynamically and sequentially simulated one by one. Results: In cancer genome simulation, complex SVs are important because this type of variation is characteristic of the tumor genome structure. Overlapping variations of different sizes can also coexist in the same genome regions, adding to the complexity of cancer genome architecture. Our results show that MVSC can efficiently simulate a variety of genomic variants that cannot be simulated by existing software packages. Conclusion: The MVSC-simulated variants can be used to assess the performance of existing tools designed to detect SVs in next-generation sequencing data, and we also find that MVSC is memory and time-efficient compared with similar software packages.

scholarly journals Microsatellite loci discovery from next-generation sequencing data and marker characterization in the epizoic barnacle Chelonibia testudinaria (Linnaeus, 1798)

2016 ◽  
Author(s):  
Christine Ewers-Saucedo ◽  
John D Zardus ◽  
John P Wares
Keyword(s):  
Next Generation Sequencing ◽  
Microsatellite Loci ◽  
Next Generation Sequencing Data ◽  
Model Organisms ◽  
Next Generation ◽  
Sequencing Data ◽  
Genetic Studies ◽  
Evolutionary Features ◽  
Evolutionary Studies ◽  
Generation Sequencing

Microsatellite markers remain an important tool for ecological and evolutionary research, but are unavailable for many non-model organisms. One such organism with rare ecological and evolutionary features is the epizoic barnacle Chelonibia testudinaria (Linnaeus, 1758). Chelonibia testudinaria appears to be a host generalist, and has a unusual sexual system, androdioecy. Genetic studies on host specificity and mating behavior are impeded by the lack of fine-scale, highly variable markers. In the present study, we discovered thousands of new microsatellite loci from next-generation sequencing data, and characterized 12 loci thoroughly. We conclude that 11 of these loci will be useful markers in future ecological and evolutionary studies on C. testudinaria.

scholarly journals Benchmarking variant identification tools for plant diversity discovery

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Xing Wu ◽  
Christopher Heffelfinger ◽  
Hongyu Zhao ◽  
Stephen L. Dellaporta
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput Sequencing ◽  
Crop Improvement ◽  
Variant Calling ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Variant Discovery ◽  
Variant Filtering ◽  
Generation Sequencing

Abstract Background The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets. Results A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. A 2-step imputation method, which utilized a set of high-confidence SNPs as the reference panel, showed up to 60% higher accuracy than direct LD-based imputation. Conclusions Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.

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