scholarly journals INE: a rice genome database with an integrated map view

2000 ◽  
Vol 28 (1) ◽  
pp. 97-101 ◽  
Author(s):  
K. Sakata
Keyword(s):  
Rice Genome ◽  
Genome Database ◽  
Integrated Map

scholarly journals Sketch distance-based clustering of chromosomes for large genome database compression

BMC Genomics ◽  
2019 ◽  
Vol 20 (S10) ◽  
Author(s):  
Tao Tang ◽  
Yuansheng Liu ◽  
Buzhong Zhang ◽  
Benyue Su ◽  
Jinyan Li
Keyword(s):  
Compression Ratio ◽  
Large Scale ◽  
Rapid Development ◽  
Rice Genome ◽  
Sequencing Data ◽  
Genome Database ◽  
Compression Algorithms ◽  
Large Genome ◽  
Reference Selection ◽  
Reference Genomes

Abstract Background The rapid development of Next-Generation Sequencing technologies enables sequencing genomes with low cost. The dramatically increasing amount of sequencing data raised crucial needs for efficient compression algorithms. Reference-based compression algorithms have exhibited outstanding performance on compressing single genomes. However, for the more challenging and more useful problem of compressing a large collection of n genomes, straightforward application of these reference-based algorithms suffers a series of issues such as difficult reference selection and remarkable performance variation. Results We propose an efficient clustering-based reference selection algorithm for reference-based compression within separate clusters of the n genomes. This method clusters the genomes into subsets of highly similar genomes using MinHash sketch distance, and uses the centroid sequence of each cluster as the reference genome for an outstanding reference-based compression of the remaining genomes in each cluster. A final reference is then selected from these reference genomes for the compression of the remaining reference genomes. Our method significantly improved the performance of the-state-of-art compression algorithms on large-scale human and rice genome databases containing thousands of genome sequences. The compression ratio gain can reach up to 20-30% in most cases for the datasets from NCBI, the 1000 Human Genomes Project and the 3000 Rice Genomes Project. The best improvement boosts the performance from 351.74 compression folds to 443.51 folds. Conclusions The compression ratio of reference-based compression on large scale genome datasets can be improved via reference selection by applying appropriate data preprocessing and clustering methods. Our algorithm provides an efficient way to compress large genome database.

scholarly journals Characterization of a Novel Flavone O-Methyltransferase Gene in Rice

2006 ◽  
Vol 1 (11) ◽  
pp. 1934578X0600101 ◽  
Author(s):  
Jian-Min Zhou ◽  
Yukiharu Fukushi ◽  
Xiao-Feng Wang ◽  
Ragai K. Ibrahim
Keyword(s):  
Recombinant Protein ◽  
Rice Genome ◽  
Health Benefits ◽  
Major Product ◽  
Genome Database ◽  
Potential Health ◽  
Grain Crops ◽  
Methyltransferase Gene ◽  
Trimethyl Ether

We report the characterization of a novel flavone O-methyltransferase cDNA (OsOMT1) from the rice genome database. Its recombinant protein utilizes tricetin as the preferred substrate, catalyzing its stepwise methylation to the monomethyl- (selgin), dimethyl- (tricin), as the major product, and trimethyl ether derivatives. The use of this gene to increase tricin content in grain crops is discussed in relation to its potential health benefits.

scholarly journals Quantitative Trait Loci from Two Genotypes of Oat (Avena sativa) Conditioning Resistance to Puccinia coronata

2015 ◽  
Vol 105 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Ebrahiem M. Babiker ◽  
Tyler C. Gordon ◽  
Eric W. Jackson ◽  
Shiaoman Chao ◽  
Stephen A. Harrison ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Partial Resistance ◽  
Rice Genome ◽  
Cost Effective ◽  
Crown Rust ◽  
Nucleotide Polymorphisms ◽  
Genome Database ◽  
Louisiana State University ◽  
Resistant Sources ◽  
Trait Locus

Developing oat cultivars with partial resistance to crown rust would be beneficial and cost-effective for disease management. Two recombinant inbred-line populations were generated by crossing the susceptible cultivar Provena with two partially resistant sources, CDC Boyer and breeding line 94197A1-9-2-2-2-5. A third mapping population was generated by crossing the partially resistant sources to validate the quantitative trait locus (QTL) results. The three populations were evaluated for crown rust severity in the field at Louisiana State University (LSU) in 2009 and 2010 and at the Cereal Disease Laboratory (CDL) in St. Paul, MN, in 2009, 2010, and 2011. An iSelect platform assay containing 5,744 oat single nucleotide polymorphisms was used to genotype the populations. From the 2009 CDL test, linkage analyses revealed two QTLs for partial resistance in the Provena/CDC Boyer population on chromosome 19A. One of the 19A QTLs was also detected in the 2009 LSU test. Another QTL was detected on chromosome 12D in the CDL 2009 test. In the Provena/94197A1-9-2-2-2-5 population, only one QTL was detected, on chromosome 13A, in the CDL 2011 test. The 13A QTL from the Provena/94197A1-9-2-2-2-5 population was validated in the CDC Boyer/94197A1-9-2-2-2-5 population in the CDL 2010 and 2011 tests. Comparative analysis of the significant marker sequences with the rice genome database revealed 15 candidate genes for disease resistance on chromosomes 4 and 6 of rice. These genes could be potential targets for cloning from the two resistant parents.

scholarly journals In-Silico Identification of EST Based Microsatellite Markers and Snps, and Comparative Genomic Analysis of Ests in Barnyard Millet for their Omics Applications

2017 ◽  
Vol 5 (3) ◽  
pp. 279-287 ◽  
Author(s):  
B. Kalyana Babu ◽  
Rashmi Chauhan
Keyword(s):  
Microsatellite Markers ◽  
Rice Genome ◽  
Genomic Analysis ◽  
Repeat Motif ◽  
Waxy Gene ◽  
Comparative Genomic ◽  
Diabetic Patients ◽  
Genome Database ◽  
Barnyard Millet ◽  
Nucleotide Repeats

Barnyard millet belongs to the family poaceae, having good nutritional properties and is also effective for diabetic patients because of its ability to reduce the blood glucose levels. The research on genomics in barnyard millet lagging behind other millets and cereals, where there is a need of more focus towards identification of microsatellite markers. The availability of EST sequences given possibility to develop and explore the EST based SSRs and SNPs. Hence, the present study was conducted at ICAR-Vivekananda Parvateeya Krishi Anusanthan Sansthan, Almora, Uttarakhand in the year 2014-2015. In the present study, the barnyard millet EST sequences (41) were downloaded in FASTA format to find the microsatellite type, distribution, frequency and developed a total of 22 primer pairs from the ESTs. The most frequent SSR repeats found to be tetra- nucleotide repeats (50 percent) followed by the penta- and hexa- nucleotide repeats. Among the dimeric SSRs, GT was found to be the most common repeat motif, AGG was the most common repeat motif in trimeric repeat motifs. The most common tetra-, penta- and hexa nucleotide repeat motifs were AGA, CAAA, TGTTT, AGACGA respectively. The SNP mining of barnyard millet ESTs found to have 1 potential SNP and 1 reliable SNP and two haplotypes. Comparative analysis of barnyard millet EST sequences with the rice genome database showed that they were homology to the rice chromosomal regions of 2, 5, 6, 8, 9 and 12, however with maize genome showed homology with respect to Zea mays Waxy gene. Thus the identified twenty two microsatellite markers and SNPs can be effectively used for barnyard millet genomics applications to study diversity, and mapping aspects.

Rice genome unveiled

Nature ◽  
2002 ◽  
Author(s):  
John Whitfield
Keyword(s):  
Rice Genome

scholarly journals Genome-Wide Identification of the B-BOX Genes that Respond to Multiple Ripening Related Signals in Sweet Cherry Fruit

2021 ◽  
Vol 22 (4) ◽  
pp. 1622
Author(s):  
Yanyan Wang ◽  
Zefeng Zhai ◽  
Yueting Sun ◽  
Chen Feng ◽  
Xiang Peng ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Stress Responses ◽  
Fruit Development ◽  
Sweet Cherry ◽  
Anthocyanin Biosynthesis ◽  
Expression Patterns ◽  
Light Induction ◽  
Promoter Regions ◽  
Genome Database ◽  
Gene Structures

B-BOX proteins are zinc finger transcription factors that play important roles in plant growth, development, and abiotic stress responses. In this study, we identified 15 PavBBX genes in the genome database of sweet cherry. We systematically analyzed the gene structures, clustering characteristics, and expression patterns of these genes during fruit development and in response to light and various hormones. The PavBBX genes were divided into five subgroups. The promoter regions of the PavBBX genes contain cis-acting elements related to plant development, hormones, and stress. qRT-PCR revealed five upregulated and eight downregulated PavBBX genes during fruit development. In addition, PavBBX6, PavBBX9, and PavBBX11 were upregulated in response to light induction. We also found that ABA, BR, and GA3 contents significantly increased in response to light induction. Furthermore, the expression of several PavBBX genes was highly correlated with the expression of anthocyanin biosynthesis genes, light-responsive genes, and genes that function in multiple hormone signaling pathways. Some PavBBX genes were strongly induced by ABA, GA, and BR treatment. Notably, PavBBX6 and PavBBX9 responded to all three hormones. Taken together, BBX proteins likely play major roles in regulating anthocyanin biosynthesis in sweet cherry fruit by integrating light, ABA, GA, and BR signaling pathways.

scholarly journals A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chowdhury Rafeed Rahman ◽  
Ruhul Amin ◽  
Swakkhar Shatabda ◽  
Md. Sadrul Islam Toaha
Keyword(s):  
Operating Characteristic ◽  
Adenine Nucleotide ◽  
Characteristic Curve ◽  
Rice Genome ◽  
Plant Genome ◽  
Feature Importance ◽  
Importance Analysis ◽  
Motif Extraction ◽  
Site Identification ◽  
By Products

AbstractDNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network (CNN) based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC (Pseudo Amino Acid Composition) inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves auROC (area under Receiver Operating Characteristic curve) score of 0.98 with an overall accuracy of 93.97% using fivefold cross validation on benchmark dataset. Finally, we evaluate our model on three other plant genome 6mA site identification test datasets. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. An algorithm for potential motif extraction and a feature importance analysis procedure are two by products of this research. Web tool for this research can be found at: https://cutt.ly/dgp3QTR.

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