scholarly journals Construction of high-density genetic map and QTL mapping in Nicotiana tabacum backcrossing BC4F3 population using whole-genome sequencing

2021 ◽  
Author(s):  
Zhijun Tong ◽  
Sanjie Jiang ◽  
Weiming He ◽  
Xuejun Chen ◽  
Lixin Yin ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Snp Markers ◽  
High Density ◽  
Genetic Maps ◽  
Recurrent Parent ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
A Genome ◽  
Bc4f3 Population ◽  
Marker Assisted Backcrossing

Backcrossing is a powerful tool for plant breeding. The improved marker-assisted backcrossing intends to transfer targeted genes or quantitative trait loci (QTLs) of interest from a donor parent into a recurrent parent. In this study, a tobacco BC4F3 population was generated using Y3 and K326 as hybrid parents and YF1-1 as F<sub>1</sub> parents. High-throughput sequencing data of 381 pedigree populations were used to construct high-density genetic maps containing 24 142 high-quality single nucleotide polymorphism (SNP) markers with an average genetic distance of 0.59 cM. A genome module analysis was then performed for all the offspring. A total of forty-three candidate QTLs for six agronomics traits were identified. This study provides original biomarkers for tobacco breeding and offers clues for prospective backcrossing applications in other plants.

scholarly journals Development of an SNP Marker Set for Marker-Assisted Backcrossing Using Genotyping-By-Sequencing in Tetraploid Perilla

2021 ◽  
Author(s):  
Yun-Joo Kang ◽  
Bo-Mi Lee ◽  
Jangmi Kim ◽  
Moon Nam ◽  
Myoung-Hee Lee ◽  
...  
Keyword(s):  
Recurrent Selection ◽  
Diploid Species ◽  
Genotyping By Sequencing ◽  
Snp Markers ◽  
Genetic Maps ◽  
Linkage Maps ◽  
Linkage Groups ◽  
Genome Wide ◽  
A Genome ◽  
Marker Assisted Backcrossing

Abstract High-quality molecular markers are essential for marker-assisted selection to accelerate breeding progress. Compared with diploid species, recently diverged polyploid crop species tend to have highly similar homeologous subgenomes, which is expected to limit the development of broadly applicable locus-specific single-nucleotide polymorphism (SNP) assays. Furthermore, it is particularly challenging to make genome-wide marker sets for species that lack a reference genome. Here, we report the development of a genome-wide set of kompetitive allele specific PCR (KASP) markers for marker-assisted recurrent selection (MARS) in the tetraploid minor crop perilla. To find locus-specific SNP markers across the perilla genome, we used genotyping-by-sequencing (GBS) to construct linkage maps of two F2 populations. The two resulting high-resolution linkage maps comprised 2,326 and 2,454 SNP markers that spanned a total genetic distance of 2,133 cM across 16 linkage groups and 2,169 cM across 21 linkage groups, respectively. We then obtained a final genetic map consisting of 22 linkage groups with 1,123 common markers from the two genetic maps. We selected 96 genome-wide markers for MARS and confirmed the accuracy of markers in the two F2 populations using a high-throughput Fluidigm system. We confirmed that 91.8% of the SNP genotyping results from the Fluidigm assay were the same as the results obtained through GBS. These results provide a foundation for marker-assisted backcrossing and the development of new varieties of perilla.

scholarly journals Construction and Analysis of High-Density Linkage Map Using High-Throughput Sequencing Data

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e98855 ◽  
Author(s):  
Dongyuan Liu ◽  
Chouxian Ma ◽  
Weiguo Hong ◽  
Long Huang ◽  
Min Liu ◽  
...  
Keyword(s):  
Linkage Map ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
High Density ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
High Density Linkage Map

scholarly journals Genome-wide profiling of heritable and de novo STR variations

2016 ◽  
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.

scholarly journals Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations

Genetics ◽  
2018 ◽  
Vol 209 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Timothy P. Bilton ◽  
Matthew R. Schofield ◽  
Michael A. Black ◽  
David Chagné ◽  
Phillip L. Wilcox ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Genetic Maps ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Low Coverage

scholarly journals MaGuS: a tool for map-guided scaffolding and quality assessment of genome assemblies

2015 ◽  
Author(s):  
Mohammed-Amin Madoui ◽  
Carole Dossat ◽  
Leo d'Agata ◽  
Edwin van der Vossen ◽  
Jan van Oeveren ◽  
...  
Keyword(s):  
High Throughput ◽  
Genome Assembly ◽  
High Throughput Sequencing ◽  
Draft Genome ◽  
Genetic Maps ◽  
Sequencing Data ◽  
A Genome ◽  
Genome Map ◽  
Genome Assemblies ◽  
Complex Genome

Background Scaffolding is a crucial step in the genome assembly process. Current methods based on large fragment paired-end reads or long reads allow an increase in continuity but often lack consistency in repetitive regions, resulting in fragmented assemblies. Here, we describe a novel tool to link assemblies to a genome map to aid complex genome reconstruction by detecting assembly errors and allowing scaffold ordering and anchoring. Results We present MaGuS (map-guided scaffolding), a modular tool that uses a draft genome assembly, a genome map, and high-throughput paired-end sequencing data to estimate the quality and to enhance the continuity of an assembly. We generated several assemblies of the Arabidopsis genome using different scaffolding programs and applied MaGuS to select the best assembly using quality metrics. Then, we used MaGuS to perform map-guided scaffolding to increase continuity by creating new scaffold links in low-covered and highly repetitive regions where other commonly used scaffolding methods lack consistency. Conclusions MaGuS is a powerful reference-free evaluator of assembly quality and a map-guided scaffolder that is freely available at https://github.com/institut-de-genomique/MaGuS. Its use can be extended to other high-throughput sequencing data (e.g., long-read data) and also to other map data (e.g., genetic maps) to improve the quality and the continuity of large and complex genome assemblies.

scholarly journals Training Genotype Callers with Neural Networks

2016 ◽  
Author(s):  
Remi Torracinta ◽  
Fabien Campagne
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Genotype Calling ◽  
High Throughput Sequencing Data ◽  
Source Contributions ◽  
A Genome ◽  
Deep Learning Model ◽  
Genotype Concordance

ABSTRACTWe present an open source software toolkit for training deep learning models to call genotypes in high-throughput sequencing data. The software supports SAM, BAM, CRAM and Goby alignments and the training of models for a variety of experimental assays and analysis protocols. We evaluate this software in the Illumina Platinum whole genome datasets and find that a deep learning model trained on 80% of the genome achieves a 0.986% accuracy on variants (genotype concordance) when trained with 10% of the data from a genome. The software is distributed at https://github.com/CampagneLaboratory/variationanalysis. The software makes it possible to train genotype calling models on consumer hardware with CPUs or GPU(s). It will enable individual investigators and small laboratories to train and evaluate their own models and to make open source contributions. We welcome contributions to extend this early prototype or evaluate its performance on other gold standard datasets.

scholarly journals Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yuhua Fu ◽  
Pengyu Fan ◽  
Lu Wang ◽  
Ziqiang Shu ◽  
Shilin Zhu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Target Genes ◽  
Target Prediction ◽  
Large Data ◽  
Sequencing Data ◽  
Regulate Gene Expression ◽  
High Throughput Sequencing Data ◽  
Annotation Information ◽  
Public Data ◽  
Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

scholarly journals deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

2020 ◽  
Vol 49 (D1) ◽  
pp. D877-D883
Author(s):  
Fangzhou Xie ◽  
Shurong Liu ◽  
Junhao Wang ◽  
Jiajia Xuan ◽  
Xiaoqin Zhang ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Clinical Information ◽  
Sequencing Data ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
High Throughput Sequencing Data ◽  
Expression Atlas ◽  
Expression Evolution ◽  
Noninvasive Biomarkers ◽  
Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing &gt;45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

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