Improvement of marker-based predictability of Apparent Amylose Content in japonica rice through GBSSI allele mining - Wikidata - awgldk - TriplyDB

Improvement of marker-based predictability of Apparent Amylose Content in japonica rice through GBSSI allele mining

Improvement of marker-based predictability of Apparent Amylose Content in japonica rice through GBSSI allele mining

http://www.wikidata.org/entity/Q30392414

about

Phenology, sterility and inheritance of two environment genic male sterile (EGMS) lines for hybrid rice Deep sequencing transcriptional fingerprinting of rice kernels for dissecting grain quality traits.Genome-Wide Association Study for Traits Related to Plant and Grain Morphology, and Root Architecture in Temperate Rice Accessions Proteome Profile of Starch Granules Purified from Rice (Oryza sativa) Endosperm Variation in cooking and eating quality traits in Japanese rice germplasm accessions High-resolution QTL mapping for grain appearance traits and co-localization of chalkiness-associated differentially expressed candidate genes in rice.Rice Chloroplast Genome Variation Architecture and Phylogenetic Dissection in Diverse Oryza Species Assessed by Whole-Genome Resequencing.Ability of Rf5 and Rf6 to Restore Fertility of Chinsurah Boro II-type Cytoplasmic Male Sterile Oryza Sativa (ssp. Japonica) Lines Association Analysis of Markers Derived from Starch Biosynthesis Related Genes with Starch Physicochemical Properties in the USDA Rice Mini-Core Collection.Progress and challenges in improving the nutritional quality of rice (Oryza sativa L.).Rapid identification of a new gene influencing low amylose content in rice landraces (Oryza sativa L.) using genome-wide association study with specific-locus amplified fragment sequencing.Dose-dependent response of Trichoderma harzianum in improving drought tolerance in rice genotypes.Ascorbate biosynthesis and its involvement in stress tolerance and plant development in rice (Oryza sativa L.).Natural variations in OsγTMT contribute to diversity of the α-tocopherol content in rice.Isolation and identification of five cold-inducible promoters from Oryza sativa.Deletion of a target gene in Indica rice via CRISPR/Cas9.Use of CRISPR/Cas Genome Editing Technology for Targeted Mutagenesis in Rice.Fine mapping and candidate gene analysis of qHD5, a novel major QTL with pleiotropism for yield-related traits in rice (Oryza sativa L.).Diversity analysis and genome-wide association studies of grain shape and eating quality traits in rice (Oryza sativa L.) using DArT markers.

P2860

Q33854067-ED69641E-936E-4506-A4B4-FABC7139A054 Q35874298-C5D2EB6D-C10A-44AC-97A9-EAD0A549ED30 Q36030726-DC7CB5B2-721E-4B2C-A684-7BE430089851 Q36228269-9F472446-26AD-45AE-95C3-BCEE1A128D95 Q36669481-F6060EEF-2398-4584-854A-503412EC3D92 Q37277153-773FC676-9DFA-4AE7-A298-DC8617AC9E2B Q37348273-D4976CA4-6333-4261-847C-4AE4A835AEDB Q37597652-32588E80-6F8A-451D-94D5-7F44DA8BE530 Q37733768-316DB675-4F44-4B1C-80A6-DE85324454A4 Q38619166-7F5F0684-664E-4B1A-9D61-50D04CBD2A4C Q38972086-77B50C13-187C-479E-8B58-93CA47F90B24 Q39336253-1843FC1B-52F2-425B-98B7-53019467DA09 Q40780523-FA325B77-DA72-4F67-A1A2-AD8878B81204 Q40923096-B35CBC77-0A67-4A17-9694-D94FFDABA85A Q47707138-6FD07D2B-0326-466B-AD51-137C862E4922 Q48324625-5EE22AFC-C962-4804-B63B-F4D42BEA8F4C Q51144634-34E90A8F-93F9-4E0D-B016-B20C212F391F Q51792296-728165F4-C615-4AB2-837F-CF88B8363713 Q55177584-9E6400A7-0B2A-4D60-B574-134D7E347533

P2860

Improvement of marker-based predictability of Apparent Amylose Content in japonica rice through GBSSI allele mining

http://www.wikidata.org/entity/Q30392414

description

2014 nî lūn-bûn

@nan

2014 թուականի Յունուարին հրատարակուած գիտական յօդուած

@hyw

2014 թվականի հունվարին հրատարակված գիտական հոդված

@hy

2014年の論文

@ja

2014年論文

@yue

2014年論文

@zh-hant

2014年論文

@zh-hk

2014年論文

@zh-mo

2014年論文

@zh-tw

2014年论文

@wuu

name

Improvement of marker-based pr ...... ce through GBSSI allele mining

@ast

Improvement of marker-based pr ...... ce through GBSSI allele mining

@en

type

label

Improvement of marker-based pr ...... ce through GBSSI allele mining

@ast

Improvement of marker-based pr ...... ce through GBSSI allele mining

@en

prefLabel

Improvement of marker-based pr ...... ce through GBSSI allele mining

@ast

Improvement of marker-based pr ...... ce through GBSSI allele mining

@en

P1433

Q30392414-FEF457D7-1691-4D9B-907A-F001B7208A3E

P1476

Q30392414-3FD78FF4-291A-4879-BFA7-8A7DC6C594DB

P2093

Q30392414-225C5FAF-43A4-41C0-B967-9C6D02F37544

Q30392414-39C58DD5-B84B-4B23-BEF5-ED2C61F570DF

Q30392414-58CB7F23-9035-4267-A5BA-6FA1D1E27A04

Q30392414-8AE21113-1C67-4BCA-8886-31C6EF71CD53

Q30392414-D21F117A-0781-4E55-BA59-B38128C5C1A3

Q30392414-DE682F50-0355-481B-AFCE-6C3283257B23

Q30392414-E71C4A63-E9DE-474E-88D6-0ADF5F659C45

P2860

Q30392414-01F357D4-E8D9-4D82-B9FF-D724A5219AF8

Q30392414-10B76EEF-F369-4628-A219-8FB1BDEA5D43

Q30392414-1533E4DD-E1F4-47DF-B292-16BB03DB16CB

Q30392414-21FDFC3F-AF39-464D-8FCB-3AC910935B06

Q30392414-33C6E4A5-FAB3-4D65-A9CD-B0A9EF8B0947

Q30392414-3B5CEA11-FAB4-498C-BB13-04CCDCA5DE48

Q30392414-6D51F379-608C-45B6-9B90-D73EDC3CBCFE

Q30392414-83DF427B-66B6-4D7C-8454-05554BA55257

Q30392414-8EA5B47F-49D9-4ADF-8CDC-FE5A15682122

Q30392414-90EF012C-477C-4BED-AE13-4E18D4668C25

P2888

Q30392414-2AFC9550-1DEB-4A12-B301-4F3CA5D7E59F

P304

Q30392414-B409D115-498E-4043-B7B5-0CA651E1DFB5

P31

Q30392414-880C0F54-B149-416F-9FE6-9FBDE4F1D938

P356

Q30392414-93B6E05C-74D0-4971-9BA0-7F956E9F7AC8

P433

Q30392414-38F09376-33C8-4AF3-9E30-4E41E8B1709E

P478

Q30392414-EECCE8D4-516B-48E2-99F5-D67F1B72C1A8

P50

Q30392414-5B586D50-826C-4439-B5A7-546BC22C53A2

Q30392414-96E4C3EF-0580-43C7-9B65-29ED2E59052F

Q30392414-A8B7B255-8DA5-4542-98B6-821C6209B499

Q30392414-E4ABBC67-8E99-4965-BFE2-B74F330F4A1F

P577

Q30392414-6723648A-9FBE-4E23-AAAC-F53DBA506AF2

P5875

Q30392414-4EE6B212-D6CC-4835-9639-8912DFA3178A

P6179

Q30392414-186261C0-2F96-4797-8D94-9BAA8157702F

P698

Q30392414-ED66A169-D7D7-4B18-843C-E9471D898D10

P932

Q30392414-7970C76F-30DD-4C51-B4BF-6B2A8CE2BE01

P356

P1433

P1476

Improvement of marker-based pr ...... ce through GBSSI allele mining

@en

P2093

Chiara Biselli

http://www.w3.org/2001/XMLSchema#string

Daniela Cavalluzzo

http://www.w3.org/2001/XMLSchema#string

Elisabetta Lupotto

http://www.w3.org/2001/XMLSchema#string

Francesca Desiderio

http://www.w3.org/2001/XMLSchema#string

Paolo Bagnaresi

http://www.w3.org/2001/XMLSchema#string

Rosaria Perrini

http://www.w3.org/2001/XMLSchema#string

Simona Urso

http://www.w3.org/2001/XMLSchema#string

P2860

Improved methods for the structural analysis of the amylose-rich fraction from rice flour.

Deep sequencing reveals the complex and coordinated transcriptional regulation of genes related to grain quality in rice cultivars.

Correlational selection and the evolution of genomic architecture.

Evolutionary history of GS3, a gene conferring grain length in rice.

Genetic bases of rice grain shape: so many genes, so little known.

Understanding rice domestication and implications for cultivar improvement.

Use of alternate splice sites in granule-bound starch synthase mRNA from low-amylose rice varieties.

Genetic variance and correlation after selection for two traits by index, independent culling levels and extreme selection.

An efficient computational method for screening functional SNPs in plants.

Identification and characterization of a novel Waxy allele from a Yunnan rice landrace.

P2888

P304

1

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1186/1939-8433-7-1

http://www.w3.org/2001/XMLSchema#string

P433

1

http://www.w3.org/2001/XMLSchema#string

P478

7

http://www.w3.org/2001/XMLSchema#string

P50

Giampiero Valè

Luigi Cattivelli

Gabriele Orasen

Alberto Gianinetti

P577

2014-01-02T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P5875

259565619

http://www.w3.org/2001/XMLSchema#string

P6179

1034382073

http://www.w3.org/2001/XMLSchema#string

P698

24383761

http://www.w3.org/2001/XMLSchema#string

P932

3904453

http://www.w3.org/2001/XMLSchema#string