customized rice mirna microarray combimatrix custom array 4 × 2 k Search Results


customized rice mirna microarray combimatrix custom array 4 × 2 k  (CombiMatrix)
90
CombiMatrix customized rice mirna microarray combimatrix custom array 4 × 2 k
Architecture of the RiceATM platform. Step 1: Eight agronomic traits are represented in the RiceATM web server. The user can select an interesting trait and identify the associated miRNAs. Step 2: After selecting the agronomic trait, the user must fill in the ‘High cumulative percentage’ and “Low cumulative percentage” fields to identify the high- and low-quantity groups. The <t>miRNA</t> expression data on these two groups are selected for analysis. Step 3: In the <t>microarray</t> data pretreatment step, the user can select quantile normalization and data adjustment to normalize the microarray data. Step 4: To identify the miRNAs associated with the agronomic trait in the two groups of cultivars, RiceATM supports Student’s t -tests or ANOVAs. Step 5: Finally, the user can select the miRanda or psRNATarget algorithm to predict the target genes of the associated miRNAs.
Customized Rice Mirna Microarray Combimatrix Custom Array 4 × 2 K, supplied by CombiMatrix, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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customized rice mirna microarray combimatrix custom array 4 × 2 k - by Bioz Stars, 2026-03
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Architecture of the RiceATM platform. Step 1: Eight agronomic traits are represented in the RiceATM web server. The user can select an interesting trait and identify the associated miRNAs. Step 2: After selecting the agronomic trait, the user must fill in the ‘High cumulative percentage’ and “Low cumulative percentage” fields to identify the high- and low-quantity groups. The miRNA expression data on these two groups are selected for analysis. Step 3: In the microarray data pretreatment step, the user can select quantile normalization and data adjustment to normalize the microarray data. Step 4: To identify the miRNAs associated with the agronomic trait in the two groups of cultivars, RiceATM supports Student’s t -tests or ANOVAs. Step 5: Finally, the user can select the miRanda or psRNATarget algorithm to predict the target genes of the associated miRNAs.

Journal: Database: The Journal of Biological Databases and Curation

Article Title: RiceATM: a platform for identifying the association between rice agronomic traits and miRNA expression

doi: 10.1093/database/baw151

Figure Lengend Snippet: Architecture of the RiceATM platform. Step 1: Eight agronomic traits are represented in the RiceATM web server. The user can select an interesting trait and identify the associated miRNAs. Step 2: After selecting the agronomic trait, the user must fill in the ‘High cumulative percentage’ and “Low cumulative percentage” fields to identify the high- and low-quantity groups. The miRNA expression data on these two groups are selected for analysis. Step 3: In the microarray data pretreatment step, the user can select quantile normalization and data adjustment to normalize the microarray data. Step 4: To identify the miRNAs associated with the agronomic trait in the two groups of cultivars, RiceATM supports Student’s t -tests or ANOVAs. Step 5: Finally, the user can select the miRanda or psRNATarget algorithm to predict the target genes of the associated miRNAs.

Article Snippet: The mature miRNA sequences and six control probes (four positive and two negative) were used to produce the customized rice miRNA microarray (Combimatrix Custom Array 4 × 2 K, CA, USA).

Techniques: Expressing, Microarray

Example of browsing the RiceATM platform. (A) Eight agronomic traits affecting yield are represented in RiceATM, including the heading date, plant height, panicle number, panicle length, panicle weight, spikelet number, seed-set %, and 1000-seed weight. Here, we select ‘Heading Date’ as a demonstration. (B) RiceATM includes 187 rice cultivars: 155 japonica and 32 indica. The user can select total (japonica plus indica), japonica or indica cultivars to analyse by checking the ‘Variety’ box. In this example, we select the k-means clustering algorithm to select the high and low heading date groups for the total cultivars. (C) In the data pretreatment step, we use quantile normalization and then clip the minimum value at 800 to normalize the microarray data. (D) Differentially expressed miRNAs are evaluated by ANOVA and then subjected to target gene prediction by the psRNATarget algorithm. Thus, RiceATM shows the regulatory miRNA network. Large orange circles, miRNAs with high expression in the high-quantity group; large green circles, miRNAs with high expression in the low-quantity group; small blue circles, targeted mRNAs.

Journal: Database: The Journal of Biological Databases and Curation

Article Title: RiceATM: a platform for identifying the association between rice agronomic traits and miRNA expression

doi: 10.1093/database/baw151

Figure Lengend Snippet: Example of browsing the RiceATM platform. (A) Eight agronomic traits affecting yield are represented in RiceATM, including the heading date, plant height, panicle number, panicle length, panicle weight, spikelet number, seed-set %, and 1000-seed weight. Here, we select ‘Heading Date’ as a demonstration. (B) RiceATM includes 187 rice cultivars: 155 japonica and 32 indica. The user can select total (japonica plus indica), japonica or indica cultivars to analyse by checking the ‘Variety’ box. In this example, we select the k-means clustering algorithm to select the high and low heading date groups for the total cultivars. (C) In the data pretreatment step, we use quantile normalization and then clip the minimum value at 800 to normalize the microarray data. (D) Differentially expressed miRNAs are evaluated by ANOVA and then subjected to target gene prediction by the psRNATarget algorithm. Thus, RiceATM shows the regulatory miRNA network. Large orange circles, miRNAs with high expression in the high-quantity group; large green circles, miRNAs with high expression in the low-quantity group; small blue circles, targeted mRNAs.

Article Snippet: The mature miRNA sequences and six control probes (four positive and two negative) were used to produce the customized rice miRNA microarray (Combimatrix Custom Array 4 × 2 K, CA, USA).

Techniques: Microarray, Expressing

Expression trend of candidate miRNAs in the early and late heading date groups of rice cultivars. Four miRNA derived from RiceATM analysis and associated with heading date were subjected to a real-time PCR assay. Early, early heading date group, n = 4; Late, late heading date group, n = 4. Actin served as the internal control. (A) miR172d-3p; (B) miR818c; (C) miR820c and (D) miR399f. * P < 0.05, compared with the early group.

Journal: Database: The Journal of Biological Databases and Curation

Article Title: RiceATM: a platform for identifying the association between rice agronomic traits and miRNA expression

doi: 10.1093/database/baw151

Figure Lengend Snippet: Expression trend of candidate miRNAs in the early and late heading date groups of rice cultivars. Four miRNA derived from RiceATM analysis and associated with heading date were subjected to a real-time PCR assay. Early, early heading date group, n = 4; Late, late heading date group, n = 4. Actin served as the internal control. (A) miR172d-3p; (B) miR818c; (C) miR820c and (D) miR399f. * P < 0.05, compared with the early group.

Article Snippet: The mature miRNA sequences and six control probes (four positive and two negative) were used to produce the customized rice miRNA microarray (Combimatrix Custom Array 4 × 2 K, CA, USA).

Techniques: Expressing, Derivative Assay, Real-time Polymerase Chain Reaction, Control