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

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

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

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research

Article Title: Deep Learning based multi-omics integration robustly predicts survival in liver cancer

doi: 10.1158/1078-0432.CCR-17-0853

Figure Lengend Snippet: (A) Autoencoder architecture used to integrate 3 omics of HCC data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and miRNA features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.

Article Snippet: 166 pairs of HCC/matched noncancerous normal tissue samples were downloaded, with CapitalBio custom Human miRNA array data ( {"type":"entrez-geo","attrs":{"text":"GSE31384","term_id":"31384"}} GSE31384 ) ( 33 ).

Techniques: DNA Methylation Assay, Transformation Assay, Methylation

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research

Article Title: Deep Learning based multi-omics integration robustly predicts survival in liver cancer

doi: 10.1158/1078-0432.CCR-17-0853

Figure Lengend Snippet: Performance of classifier for the five external confirmation cohorts.

Article Snippet: 166 pairs of HCC/matched noncancerous normal tissue samples were downloaded, with CapitalBio custom Human miRNA array data ( {"type":"entrez-geo","attrs":{"text":"GSE31384","term_id":"31384"}} GSE31384 ) ( 33 ).

Techniques: Microarray, DNA Methylation Assay

Journal: The FASEB Journal

Article Title: Extracellular vesicles extracted from young donor serum attenuate inflammaging via partially rejuvenating aged T-cell immunotolerance

doi: 10.1096/fj.201800059R

Figure Lengend Snippet: Characteristics of serum-extracted EVs. A) Comparison of the sizes of EVs extracted from young murine serum by the ExoQuick reagent pretreated with and without using 0.2-μm filters. B) Morphology of EVs from young murine serum used in this project for rejuvenation of inflammaging, photographed by atomic force microscopy (AFM). C, D) Different miRNA expression profiles in heatmap (C) and quantified summary (D) of EVs from young vs. old murine serum, analyzed by murine miRNA microarray with Mus musculus miRBase version-21 arrays that contained 1900 unique mature miRNA probes (miRNA microarray service via LC Sciences).

Article Snippet: Then, 1–3 μg of total RNAs for each sample were used for customized miRNA microarray service from LC Sciences (Houston, TX, USA).

Techniques: Comparison, Microscopy, Expressing, Microarray

Journal: BMC Genomics

Article Title: Evaluation of microRNA expression in a sheep model for lung fibrosis

doi: 10.1186/s12864-021-08073-4

Figure Lengend Snippet: Heatmap of significantly expressed miRNA microarray expression profile in the sheep model for lung fibrosis. The expression level of each miR was presented as fold-change relative to the control/saline group. Average intensity from the 21 replicates were taken for each miRNA probes tested for bleomycin and saline/control lung segments. Student’s t-test was performed and p < 0.01 was considered statistically significant. Hierarchical clustering was performed by applying One minus Pearson correlation to cluster differentially expressed miRNAs. Blue indicates under expression and red indicates over expression

Article Snippet: A custom designed ovine miRNA microarray was performed by a service provider LC Sciences, Houston, USA.

Techniques: Microarray, Expressing, Control, Saline, Over Expression

Journal: BMC Genomics

Article Title: Evaluation of microRNA expression in a sheep model for lung fibrosis

doi: 10.1186/s12864-021-08073-4

Figure Lengend Snippet: Significantly expressed microRNA microarray profile in sheep model for lung fibrosis

Article Snippet: A custom designed ovine miRNA microarray was performed by a service provider LC Sciences, Houston, USA.

Techniques: Microarray

Journal: BMC Genomics

Article Title: Evaluation of microRNA expression in a sheep model for lung fibrosis

doi: 10.1186/s12864-021-08073-4

Figure Lengend Snippet: Customized primer sequences miRCURY LNA miRNA PCR assay for sheep model of lung fibrosis

Article Snippet: A custom designed ovine miRNA microarray was performed by a service provider LC Sciences, Houston, USA.

Techniques: Sequencing