scholarly journals Progress in Understanding and Sequencing the Genome of Brassica rapa

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Chang Pyo Hong ◽  
Soo-Jin Kwon ◽  
Jung Sun Kim ◽  
Tae-Jin Yang ◽  
Beom-Seok Park ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Brassica Rapa ◽  
Genome Structure ◽  
Fold Increase ◽  
Whole Genome ◽  
Sequencing Project ◽  
Physical Maps ◽  
Basic Genome ◽  
New Perspective

Brassica rapa, which is closely related to Arabidopsis thaliana, is an important crop and a model plant for studying genome evolution via polyploidization. We report the current understanding of the genome structure of B. rapa and efforts for the whole-genome sequencing of the species. The tribe Brassicaceae, which comprises ca. 240 species, descended from a common hexaploid ancestor with a basic genome similar to that of Arabidopsis. Chromosome rearrangements, including fusions and/or fissions, resulted in the present-day “diploid” Brassica species with variation in chromosome number and phenotype. Triplicated genomic segments of B. rapa are collinear to those of A. thaliana with InDels. The genome triplication has led to an approximately 1.7-fold increase in the B. rapa gene number compared to that of A. thaliana. Repetitive DNA of B. rapa has also been extensively amplified and has diverged from that of A. thaliana. For its whole-genome sequencing, the Brassica rapa Genome Sequencing Project (BrGSP) consortium has developed suitable genomic resources and constructed genetic and physical maps. Ten chromosomes of B. rapa are being allocated to BrGSP consortium participants, and each chromosome will be sequenced by a BAC-by-BAC approach. Genome sequencing of B. rapa will offer a new perspective for plant biology and evolution in the context of polyploidization.

scholarly journals Allele-Specific Quantification of Structural Variations in Cancer Genomes

2016 ◽  
Author(s):  
Yang Li ◽  
Shiguo Zhou ◽  
David C. Schwartz ◽  
Jian Ma
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Graphical Model ◽  
Genome Structure ◽  
Cancer Genome ◽  
Whole Genome ◽  
Structural Variations ◽  
Cancer Genomes ◽  
Allele Specific

AbstractOne of the hallmarks of cancer genome is aneuploidy, resulting in abnormal copy numbers of alleles. Structural variations (SVs) can further modify the aneuploid cancer genomes into a mixture of rearranged genomic segments with extensive range of somatic copy number alterations (CNAs). Indeed, aneuploid cancer genomes have significantly higher rate of CNAs and SVs. However, although methods have been developed to identify SVs and allele-specific copy number of genome (ASCNG) separately, no existing algorithm can simultaneously analyze SVs and ASCNG. Such integrated approach is particularly important to fully understand the complexity of cancer genomes. Here we introduce a new algorithm called Weaver to provide allele-specific quantification of SVs and CNAs in aneuploid cancer genomes. Weaver uses a probabilistic graphical model by utilizing cancer whole genome sequencing data to simultaneously estimate the digital copy number and inter-connectivity of SVs. Our simulation evaluation, comparison with single-molecule Optical Mapping analysis, and real data applications (including MCF-7, HeLa, and TCGA whole genome sequencing samples) demonstrated that Weaver is highly accurate and can greatly refine the analysis of complex cancer genome structure.

scholarly journals JAX-CNV: A whole genome sequencing-based algorithm for copy number detection at clinical grade level

2021 ◽  
Author(s):  
Wan-Ping Lee ◽  
Qihui Zhu ◽  
Xiaofei Yang ◽  
Silvia Liu ◽  
Eliza Cerveira ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Copy Number Variant ◽  
Fold Increase ◽  
Chromosomal Microarray ◽  
Whole Genome ◽  
False Discovery ◽  
Calling Algorithm

We aimed to develop a whole genome sequencing (WGS)-based copy number variant (CNV) calling algorithm with the potential of replacing chromosomal microarray assay (CMA) for clinical diagnosis. JAX-CNV is thus developed for CNV detection from WGS. The performance of this CNV calling algorithm was evaluated in a blinded manner on 31 samples and compared to the results of clinically-validated CMAs. Comparing to 112 CNVs reported by clinically-validated CMAs of the 31 samples, JAX-CNV is 100% recalling them. Besides, JAX-CNV identified an average of 30 CNVs per individual that is an approximately seven-fold increase compared to calls of clinically-validated CMAs. Experimental validation of 24 randomly selected CNVs, showed one false positive (i.e., a false discovery rate of 4.17%). A robustness test on lower-coverage data revealed a 100% sensitivity for CNVs greater than 300 kb (the current threshold for College of American Pathologists) down to 10x coverage. For CNVs greater than 50 kb, sensitivities were 100% for coverages deeper than 20x, 97% for 15x, and 95% for 10x. We developed a WGS-based CNV pipeline, including this newly developed CNV caller JAX-CNV, and found it capable of detecting CMA reported CNVs at 100% sensitivity with about 4% false discovery rate. We propose that JAX-CNV could be further examined in a multi-institutional study to justify the transition of first-tier genetic testing from CMAs to WGS. JAX-CNV is available on https://github.com/TheJacksonLaboratory/JAX-CNV.

scholarly journals Exploring the Diversity of Bacillus whole genome sequencing projects using Peasant, the Prokaryotic Assembly and Annotation Tool

2017 ◽  
Author(s):  
Jonathon Brenner ◽  
Laurynas Kalesinskas ◽  
Catherine Putonti
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Annotation Tool ◽  
Illumina Platform ◽  
Desktop Computer ◽  
High Quality ◽  
Sequencing Project ◽  
Computational Resources

ABSTRACTBackgroundThe persistent decrease in cost and difficulty of whole genome sequencing of microbial organisms has led to a dramatic increase in the number of species and strains characterized from a wide variety of environments. Microbial genome sequencing can now be conducted by small laboratories and as part of undergraduate curriculum. While sequencing is routine in microbiology, assembly, annotation and downstream analyses still require computational resources and expertise, often necessitating familiarity with programming languages. To address this problem, we have created a light-weight, user-friendly tool for the assembly and annotation of microbial sequencing projects.ResultsThe Prokaryotic Assembly and Annotation Tool, Peasant, automates the processes of read quality control, genome assembly, and annotation for microbial sequencing projects. High-quality assemblies and annotations can be generated by Peasant without the need of programming expertise or high-performance computing resources. Furthermore, statistics are calculated so that users can evaluate their sequencing project. To illustrate the computational speed and accuracy of Peasant, the SRA records of 322 Illumina platform whole genome sequencing assays for Bacillus species were retrieved from NCBI, assembled and annotated on a single desktop computer. From the assemblies and annotations produced, a comprehensive analysis of the diversity of over 200 high-quality samples was conducted, looking at both the 16S rRNA phylogenetic marker as well as the Bacillus core genome.ConclusionsPeasant provides an intuitive solution for high-quality whole genome sequence assembly and annotation for users with limited programing experience and/or computational resources. The analysis of the Bacillus whole genome sequencing projects exemplifies the utility of this tool. Furthermore, the study conducted here provides insight into the diversity of the species, the largest such comparison conducted to date.

scholarly journals Assessing whole genome sequencing variation for Alzheimer’s disease in 4707 individuals from the Alzheimer’s Disease Sequencing Project (ADSP)

2020 ◽  
Vol 16 (S3) ◽  
Author(s):  
Gina M. Peloso ◽  
Yanbing Wang ◽  
Honghuang Lin ◽  
Chloé Sarnowski ◽  
Achilleas N. Pitsillides ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
Sequencing Project

scholarly journals Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zihuai He ◽  
Linxi Liu ◽  
Chen Wang ◽  
Yann Le Guen ◽  
Justin Lee ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variants ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Association Tests ◽  
Sequencing Project ◽  
Risk Variants ◽  
Sequencing Studies

AbstractThe analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.

scholarly journals Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

2021 ◽  
Author(s):  
Zihuai He ◽  
Linxi Liu ◽  
Chen Wang ◽  
Yann Le Guen ◽  
Justin Lee ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variants ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Association Tests ◽  
Sequencing Project ◽  
Risk Variants ◽  
Sequencing Studies

AbstractThe analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.

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