shinyChromosome shinyChromosome
Interactive create non-circular plots of Whole Genomes
Interactive create non-circular plots of Whole Genomes
binQTL binQTL
Composite interval mapping (CIM) is the dominating algorithmused...
Composite interval mapping (CIM) is the dominating algorithmused in QTL analysis of various phenotypes in experimental populations. With the development of next-generation sequening, genotyping of SNPs became much easier, which leads to the development of
shinyCircos shinyCircos
Creation of Circos plot is one of the most efficient approaches t...
Creation of Circos plot is one of the most efficient approaches to visualize genomic data. However, the installation and use of existing tools to make Circos plot are challenging for users lacking of coding experiences. To address this issue, we developed an R/Shiny application shinyCircos, a graphical user interface for interactive creation of Circos plot. shinyCircos can be easily installed either on computers for personal use or on local or public servers to provide online use to the community. Furthermore, various types of Circos plots could be easily generated and decorated with simple mouse-click. shinyCircos and its manual are freely available at https://github.com/venyao/shinyCircos. shinyCircos is deployed at https://yimingyu.shinyapps.io/shinycircos/ and http://shinycircos.ncpgr.cn/ for online use.
binQTL binQTL
Composite interval mapping (CIM) is the dominating algorithmused...
Composite interval mapping (CIM) is the dominating algorithmused in QTL analysis of various phenotypes in experimental populations. With the development of next-generation sequening, genotyping of SNPs became much easier, which leads to the development of
funRiceGenes funRiceGenes
We built a comprehensive and accurate dataset of ∼2800 functional...
We built a comprehensive and accurate dataset of ∼2800 functionally characterized rice genes and ∼5000 members of different gene families by integrating data from available databases and reviewing every publication on rice functional genomic studies. The dataset accounts for 19.2% of the 39 045 annotated protein-coding rice genes, which provides the most exhaustive archive for investigating the functions of rice genes. We also constructed 214 gene interaction networks based on 1841 connections between 1310 genes. The largest network with 762 genes indicated that pleiotropic genes linked different biological pathways. Increasing degree of conservation of the flowering pathway was observed among more closely related plants, implying substantial value of rice genes for future dissection of flowering regulation in other crops. All data are deposited in the funRiceGenes database (https://funricegenes.github.io/). Functionality for advanced search and continuous updating of the database are provided by a Shiny application (http://funricegenes.ncpgr.cn/).
intansv intansv
Identification of structural variations between individuals is...
Identification of structural variations between individuals is very important for the understanding of phenotype variations and diseases. Despite the existence of dozens of programs for prediction of structural variations, none of them is the golden standard in this field and the results of multiple programs were usually integrated to get more reliable predictions. Annotation and visualization of structural variations are important for the understanding of their functions. However, no program provides these functions currently as far as we are concerned. We report an R package, intansv, which can integrate the predictions of multiple programs as well as annotate and visualize structural variations. The source code and the help manual of intansv is freely available at https://github.com/venyao/intansv and http://www.bioconductor.org/packages/devel/bioc/html/intansv.html.
MaizeSNPDB MaizeSNPDB
With the rapid decreasing of sequencing cost, large volume of gen...
With the rapid decreasing of sequencing cost, large volume of genotype data has been generated in many organisms based on high-throughput sequencing, which was utilized in various fields of biological studies in the post-genome era. The raw sequencing data were usually deposited in the NCBI SRA database. Construction of the database to store and analyze the processed genotype data is an essential step for the utilization of the genotype data by the community. Up to now, a comprehensive genotype database is still missing from maize, which is an important crop of the world. We report the construction of the MaizeSNPDB database using genotype data of 1210 maize line across 35,370,939 SNP sites refined from a large set of genomic variations reported by the maize HapMap 3 project. We further implemented several genetic analysis programs as graphical interfaces in the MaizeSNPDB database. SNPs in user-specified genomic regions could be easily extracted and analyzed in MaizeSNPDB. The whole dataset and code of MaizeSNPDB is available at https://github.com/venyao/MaizeSNPDB. MaizeSNPDB is deployed at http://venyao.xyz/MaizeSNPDB/ for online use. The MaizeSNPDB database is of great value to future maize functional genomic studies, which can also facilitate marker-assisted breeding in maize.
ECOGEMS ECOGEMS
We proposed to store large-scale genotype data as integer sparse...
We proposed to store large-scale genotype data as integer sparse matrices, which consumed much fewer computing resources for storage and analysis than traditional approaches. In addition, the raw genotype data could be readily recovered from integer sparse matrices. Utilizing this approach, we stored the genotype data of 1612 Asian cultivated rice accessions and 446 Asian wild rice accessions across 8 584 244 SNP sites in the ECOGEMS database with 310 MB of disk usage. Graphical interface for visualization, analysis and download of SNP data were implemented in ECOGEMS, which made it a valuable resource for rice functional genomic studies. The code and data of ECOGEMS are freely available at https://github.com/venyao/ECOGEMS. ECOGEMS is deployed at http://ecogems.ncpgr.cn and http://venyao.xyz/ECOGEMS/ for online use.
Currently, Wen Yao is not followed by anyone.
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