Journal: BMC Plant Biology

Article Title: Integration of proteomic and transcriptomic profiles reveals multiple levels of genetic regulation of salt tolerance in cotton

doi: 10.1186/s12870-018-1350-1

Figure Lengend Snippet: Schematic overview of proteomic and transcriptomic data generation and analysis workflow. Step 1: differential expression analysis of proteomic and Step 2: differential expression analysis of mRNA-seq data. Step 3: AS (alternative splicing) analysis using TopHat software and alternatively spliced differentially expressed genes (AS-DEGs) were obtained. Step 4: mapping of all proteins from the proteomic to mRNA-seq data and output of the two target groups. Among them, Group A contains translation repression and/or transcriptional degradation targets (contains differentially expressed genes and proteins whose levels were unchanged, both genes and proteins that were differentially expressed or no proteomic data). Group B contains potential targets of miRNA translational repression (differentially abundant proteins whose mRNA did not change or no mRNA data). Step 5: genome-wide analysis of differentially expressed conserved and novel miRNAs in both cotton genotypes under control and salt stress conditions. Step 6: psRNATarget analysis of both groups against miRNAs DE in the opposite direction combined with Group A and B data

Article Snippet: Scatterplots and Pearson correlation coefficients (r values) between proteins and mRNA expression ratios were constructed and calculated, respectively, using GraphPad Prism version 6 (GraphPad Software, Inc., San Diego, CA) with two-tailed t-tests.

Techniques: Quantitative Proteomics, Alternative Splicing, Software, Genome Wide, Control

Journal: BMC Plant Biology

Article Title: Integration of proteomic and transcriptomic profiles reveals multiple levels of genetic regulation of salt tolerance in cotton

doi: 10.1186/s12870-018-1350-1

Figure Lengend Snippet: Hierarchical clustering analysis of mRNA-seq and proteomic data based on expression data. Heatmap of the proteins and mRNA expression ratios that have the same ( a , c ) or opposite ( b , d ) change tendencies after 4 h and 24 h of salt treatment, respectively. The expression profiles shown in the left and right panels are based on standardized log 2 ratio values. The minimum and maximum displayed log 2 ratios are ±3 for the transcriptomic data and ± 0.5 for the proteomics data. Black represents no significant change in expression. The KEGG pathway of genes/proteins showing significance are represented by various symbols behind the gene ID

Article Snippet: Scatterplots and Pearson correlation coefficients (r values) between proteins and mRNA expression ratios were constructed and calculated, respectively, using GraphPad Prism version 6 (GraphPad Software, Inc., San Diego, CA) with two-tailed t-tests.

Techniques: Expressing

Journal: Nutrients

Article Title: Long-Term Coffee Consumption is Associated with Fecal Microbial Composition in Humans

doi: 10.3390/nu12051287

Figure Lengend Snippet: A heatmap showing Pearson correlations among intestinal microbial groups (Log n cells/gram feces), fecal short chain fatty acids (mM), polyphenol groups (mg/day), and alkaloids (mg/day), from coffee and other dietary sources. Columns correspond to major intestinal microbial groups and fecal SCFA; rows correspond to dietary polyphenols and alkaloids. Blue and red colors denote negative and positive association, respectively. The intensity of the colors represents the degree of association between variables. Asterisks indicate significant associations: * p ≤ 0.05; ** p ≤ 0.01.

Article Snippet: Data resulting from the Pearson correlation tests were plotted using GraphPad Prism version 8 for Windows (GraphPad Software, San Diego, CA, USA).

Techniques: