Journal: Frontiers in Plant Science

Article Title: Divisions of labor in the thiamin biosynthetic pathway among organs of maize

doi: 10.3389/fpls.2014.00370

Figure Lengend Snippet: Correlated expression of thiazole, pyrimidine, transport, and salvage genes of the thiamin biosynthesis pathway in Arabidopsis and maize . (A) Arabidopsis . A profile for each gene was calculated based on Pearson rank-order correlations with other thiamin pathway genes in the AtGeneExpress development dataset (Schmid et al., ). Genes were then clustered based on the matrix of pairwise correlations (R2 values) among the gene profiles. (B) Maize. A profile for each gene was constructed by calculating Pearson rank-order correlations with other thiamin pathway genes in the QTELLER transcriptome dataset (see Methods). Genes were then clustered based on the matrix of pairwise correlations (R2 values) among the gene profiles. TPC, mitochondrial thiamin diphosphate transporter; THI4, thiazole biosynthesis protein; HETK, hydroxyethyl thiazole kinase;. TDPK, thiamin diphosphokinase paralogs; TH1, hydroxymethylpyrimidine phosphate kinase/hydroxymethylpyrimidine kinase/thiamin-phosphate pyrophosphorylase, dual function protein; COG0212, putative thiamin related 5-formyltetrahydrofolate cycloligase-like protein, function unknown; TENA1 and TENA2, thiaminase II paralogs; NUDIX, putative thiamin related NUDIX type hydrolase; THIC, hydroxymethylpyrimidine phosphate synthase.

Article Snippet: To cluster genes in the thiamin pathway of Arabidopsis and maize, a profile was first constructed for each thiamin gene by calculating its Pearson rank correlation (R-project.org) pair-wise with each gene in the pathway based on expression values in the AtGeneExpress (log2 transformed values, Schmid et al., ) and QTELLER.org databases, respectively.

Techniques: Expressing, Construct

Journal: Frontiers in Plant Science

Article Title: Divisions of labor in the thiamin biosynthetic pathway among organs of maize

doi: 10.3389/fpls.2014.00370

Figure Lengend Snippet: THIC and THI4 expression in Arabidopsis tissues . AtGeneExpress values (log2) for THIC and THI4 were normalized to the respective overall mean of each gene. Relative THI4 expression is strongly correlated with THIC expression except in the later stages of seed development (open squares, red is mature stage) where THIC expression declines sharply during embryo maturation while THI4 expression remains high. Seed at earlier stages (black open squares), stamens containing developing pollen (black open diamonds) and mature pollen (red open diamond) conform to the pattern of proportional expression of THI4 and THIC observed in other tissues (blue solid diamonds).

Article Snippet: To cluster genes in the thiamin pathway of Arabidopsis and maize, a profile was first constructed for each thiamin gene by calculating its Pearson rank correlation (R-project.org) pair-wise with each gene in the pathway based on expression values in the AtGeneExpress (log2 transformed values, Schmid et al., ) and QTELLER.org databases, respectively.

Techniques: Expressing

Journal: BMC Genomics

Article Title: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma

doi: 10.1186/1471-2164-7-252

Figure Lengend Snippet: A, experimental design. B&C&D, correlation of log2 intensities within and between protocol groups . B&C, scatter plots of log 2 signal intensities from a pair of replicates treated with the OneRA and the TwoRA protocol respectively. D, scatter plot of log 2 signal intensities averaged across the OneRA replicates versus that from the TwoRA replicates. The coefficient of correlation (r) value is given for each pair. Similar results were obtained with the SN and SA sample groups.

Article Snippet: The analysis was conducted by first determining the maximum Δlog2IN (log2 TwoRA – log2 OneRA) across the two samples for each gene.

Techniques:

Journal: BMC Genomics

Article Title: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma

doi: 10.1186/1471-2164-7-252

Figure Lengend Snippet: Deviation in log2 intensity following TwoRA (Δlog2IN) as a function of probeset 3' distance rank . Δlog2IN values on the y-axis were calculated by subtracting the mean OneRA from the mean TwoRA probeset log2 intensities. The x-axis shows the ranks of probesets locations. Probesets locations are relative to the 3' end of the transcripts (refer to methods for more details). Since the probesets locations have a skewed distribution, their ranks were plotted instead of their absolute values; this allows dispersion of data points. The actual probesets locations that correspond to the rank intervals on the x-axis are shown on the blue horizontal axis on the top of the figure. The regression line is shown in red. Only data from the DRG preparation were used, similar results were obtained with the SN and

Article Snippet: The analysis was conducted by first determining the maximum Δlog2IN (log2 TwoRA – log2 OneRA) across the two samples for each gene.

Techniques: Dispersion

Journal: BMC Genomics

Article Title: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma

doi: 10.1186/1471-2164-7-252

Figure Lengend Snippet: Dist ribution of 3' distances from probesets with the most discrepant signal intensity following TwoRA (absolute Δlog2IN >= 2) . A&B, histograms of 3' locations from the probesets at large represented on the MOE430A chip (green) versus those showing at least 2 fold change in log2 signal intensity following TwoRA (increase/decrease in signal, red/blue respectively). (Solid line), DRG. (Dashed lines), SA. (Dotted lines), SN. Arrows indicate additional peaks in the distributions apart from that of the overall population. C, a box and whisker plot showing the 0% and 100% quantiles as whiskers, the 25% and 75% quantiles as boxes and the 50% quantile as horizontal dash within the box. The plot summarises the distributions shown in A and B. One the y-axis, (+) indicates increase in signal intensity following TwoRA, (-) indicates decrease in signal intensity following TwoRA. Note that for all plots, probesets locations are in base pairs and relative to the 3' end of targets.

Article Snippet: The analysis was conducted by first determining the maximum Δlog2IN (log2 TwoRA – log2 OneRA) across the two samples for each gene.

Techniques: Whisker Assay

Journal: BMC Genomics

Article Title: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma

doi: 10.1186/1471-2164-7-252

Figure Lengend Snippet: Deviation in log2 intensity following TwoRA (Δlog2IN) versus the OneRA log2 intensity . The dashed lines in red show roughly the limits of the intensity range, thus the upper line represents saturation while the lower line represents background noise. The analysis was done on the DRG data; similar results were obtained with the SN and SA samples.

Article Snippet: The analysis was conducted by first determining the maximum Δlog2IN (log2 TwoRA – log2 OneRA) across the two samples for each gene.

Techniques:

Journal: BMC Genomics

Article Title: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma

doi: 10.1186/1471-2164-7-252

Figure Lengend Snippet: Correlation of log2 expression ratios from the OneRA and TwoRA and that of Δlog2IN from the two compared samples . A&C, scatter plots of log2 expression ratios from the OneRA and the TwoRA for the (SA,SN), (DRG,SN) pairs respectively. For instance, the log2 OneRA expression ratio for the (DRG,SN) pair is log2 OneRA DRG – log2 OneRA SN. For the (SN, SA) pair, the most significant differences in gene expression (ATF3 and SPRR1A) are consistent in both protocol groups. For the (DRG,SN), the ratios are more varying, the regression line (shown in blue) appears to be shifted from the diagonal towards smaller values of expression ratios in The TwoRA. B&D, scatter plots of Δlog2IN values for the (DRG,SN) pair and the (SA,SN) pair respectively. Δlog2IN were calculated by subtracting the log2 OneRA intensity from the log2 TwoRA intensity. Points in red in 'C' are probesets where the intensity in one sample could not be shifted as much as in the other sample because the intensity cannot lie outside the dynamic range of the scanner. These are referred to as FCE (floor & ceiling effect) probesets and have varying expression ratios following TwoRA. The FCE probesets also show the most varying DRG and SN Δlog2IN values (points in red, D). Though, for these same probesets, the Δlog2IN values in the SA and the SN are fairly consistent (points in red, B).

Article Snippet: The analysis was conducted by first determining the maximum Δlog2IN (log2 TwoRA – log2 OneRA) across the two samples for each gene.

Techniques: Expressing, Gene Expression

Journal: BMC Genomics

Article Title: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma

doi: 10.1186/1471-2164-7-252

Figure Lengend Snippet: Evidence of differential expression for the genes with the 10 most discrepant (DRG,SN) tissue ratios from the OneRA and the TwoRA.

Article Snippet: The analysis was conducted by first determining the maximum Δlog2IN (log2 TwoRA – log2 OneRA) across the two samples for each gene.

Techniques: Quantitative Proteomics, Expressing, Transgenic Assay, Immunohistochemistry, Binding Assay, Mutagenesis, In Situ, Hybridization, Dissection

Journal: Genome Biology

Article Title: Genomic alterations underlie a pan-cancer metabolic shift associated with tumour hypoxia

doi: 10.1186/s13059-016-0999-8

Figure Lengend Snippet: Independent validation of candidate metabolic drivers in the Metabric breast cancer cohort ( n = 1991). a The overlap of candidate metabolic drivers identified in the TCGA BRCA cohort and tested for the correlation between mRNA and gene copy number data (log 2 ratio) in the Metabric cohort. The numbers indicate correlated genes in the intrinsic subtypes of breast cancer (PAM50). Due to absence of subtype-specific candidate metabolic genes in the PAM50 subtype ‘ Normal-like’ breast cancer, it was not considered in subsequent analyses. b Genome-wide altered copy number fraction in the complete Metabric cohort ( n = 1991). Bottom : pink peaks indicate fractional copy number gains/amplifications; blue peaks indicate fractional homozygous or heterozygous deletions. The gene symbols of candidate metabolic genes are located at the top in the order of their genomic location ( left to right : chromosome 1 to X). Gene symbols with asterisks indicate significantly altered known breast cancer genes , MYC and ERBB2. Genes in red represent clusters of metabolic and significantly altered breast cancer genes that are in genomic proximity of each other. The red dashed lines show their approximate loci. Top : heat map showing the presence ( blue ) and absence ( grey ) of candidate metabolic genes across breast cancer intrinsic subtypes (PAM50) (as summarised in Fig. 4a). The subtype-specific significantly altered known breast cancer genes are also highlighted with asterisks . c Correlation between mRNA and copy number data for genes in chromosome 8q24 amplicon using the complete Metabric cohort ( All ) and intrinsic subtypes of breast cancer. The candidate metabolic drivers are highlighted with unique symbols to show their mRNA dependence on gene dosage. d Copy number-based Kaplan–Meier analysis of SQLE in the Metabric breast cancer cohort. There were only five cases for which copy number state = heterozygous loss and, therefore, these were merged with the copy number diploid ( NEUT , n = 1282) group. Genomic gains and amplifications were collapsed into one group ( GAIN ). e Same as ( d ) using the MYC diploid/loss subset of the Metabric breast cancer cohort. f mRNA-based Kaplan–Meier analysis of SQLE in the Metabric breast cancer cohort. Samples were split into four groups based on 75th percentile, median and 25th percentile of log 2 mRNA abundance of SQLE (lowest = Q1 , highest = Q4 ). g mRNA-based Kaplan–Meier analysis of SQLE in the MYC diploid/loss subset of the Metabric breast cancer cohort. h Median-dichotomised mRNA-based Kaplan–Meier analysis of SQLE further stratified into hypoxia high and low risk groups ( S0H0 = low SQLE and low hypoxia, S0H1 = low SQLE and high hypoxia, S1H0 = high SQLE and low hypoxia, S1H1 = high SQLE and high hypoxia). BC breast cancer, OS overall survival

Article Snippet: mRNA abundance and gene copy number log 2 ratio data from the CCLE were downloaded from the Broad Institute ( http://www.broadinstitute.org/ccle/ ).

Techniques: Biomarker Discovery, Genome Wide, Amplification