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

BACKGROUND: RNA amplification is necessary for profiling gene expression from small tissue samples. Previous studies have shown that the T7 based amplification techniques are reproducible but may distort the true abundance of targets. However, the consequences of such distortions on the ability to detect biological variation in expression have not been explored sufficiently to define the true extent of usability and limitations of such amplification techniques. RESULTS: We show that expression ratios are occasionally distorted by amplification using the Affymetrix small sample protocol version 2 due to a disproportional shift in intensity across biological samples. This occurs when a shift in one sample cannot be reflected in the other sample because the intensity would lie outside the dynamic range of the scanner. Interestingly, such distortions most commonly result in smaller ratios with the consequence of reducing the statistical significance of the ratios. This becomes more critical for less pronounced ratios where the evidence for differential expression is not strong. Indeed, statistical analysis by limma suggests that up to 87% of the genes with the largest and therefore most significant ratios (p < 10e(-20)) in the unamplified group have a p-value below 10e(-20 )in the amplified group. On the other hand, only 69% of the more moderate ratios (10e(-20 )< p < 10e(-10)) in the unamplified group have a p-value below 10e(-10 )in the amplified group. Our analysis also suggests that, overall, limma shows better overlap of genes found to be significant in the amplified and unamplified groups than the Z-scores statistics. CONCLUSION: We conclude that microarray analysis of amplified samples performs best at detecting differences in gene expression, when these are large and when limma statistics are used

The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis

The CATH database of protein domain structures (http://www.biochem.ucl.ac.uk/bsm/cath/) currently contains 43 229 domains classified into 1467 superfamilies and 5107 sequence families. Each structural family is expanded with sequence relatives from GenBank and completed genomes, using a variety of efficient sequence search protocols and reliable thresholds. This extended CATH protein family database contains 616 470 domain sequences classified into 23 876 sequence families. This results in the significant expansion of the CATHHMMmodel library to include models built from the CATH sequence relatives, giving a10%increase in coveragefor detecting remote homologues. An improved Dictionary of Homologous superfamilies (DHS) (http://www.biochem.ucl.ac.uk/bsm/dhs/) containing specific sequence, structural and functional information for each superfamily in CATH considerably assists manual validation of homologues. Information on sequence relatives in CATH superfamilies, GenBank and completed genomes is presented in the CATH associated DHS and Gene3D resources. Domain partnership information can be obtained from Gene3D (http://www.biochem.ucl.ac.uk/bsm/cath/Gene3D/). A new CATH server has been implemented (http://www.biochem.ucl.ac.uk/cgi-bin/cath/CathServer.pl) providing automatic classification of newly determined sequences and structures using a suite of rapid sequence and structure comparison methods. The statistical significance of matches is assessed and links are provided to the putative superfamily or fold group to which the query sequence or structure is assigned

Metabolic signatures differentiate ovarian from colon cancer cell lines

International audienceBackground: In this era of precision medicine, the deep and comprehensive characterization of tumor phenotypes will lead to therapeutic strategies beyond classical factors such as primary sites or anatomical staging. Recently, “-omics” approached have enlightened our knowledge of tumor biology. Such approaches have been extensively implemented in order to provide biomarkers for monitoring of the disease as well as to improve readouts of therapeutic impact. The application of metabolomics to the study of cancer is especially beneficial, since it reflects the biochemical consequences of many cancer type-specific pathophysiological processes. Here, we characterize metabolic profiles of colon and ovarian cancer cell lines to provide broader insight into differentiating metabolic processes for prospective drug development and clinical screening.Methods: We applied non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography and gas chromatography for the metabolic phenotyping of four cancer cell lines: two from colon cancer (HCT15, HCT116) and two from ovarian cancer (OVCAR3, SKOV3). We used the MetaP server for statistical data analysis.Results: A total of 225 metabolites were detected in all four cell lines; 67 of these molecules significantly discriminated colon cancer from ovarian cancer cells. Metabolic signatures revealed in our study suggest elevated tricarboxylic acid cycle and lipid metabolism in ovarian cancer cell lines, as well as increased β-oxidation and urea cycle metabolism in colon cancer cell lines.Conclusions: Our study provides a panel of distinct metabolic fingerprints between colon and ovarian cancer cell lines. These may serve as potential drug targets, and now can be evaluated further in primary cells, biofluids, and tissue samples for biomarker purposes

Additional file 6: of Metabolic signatures differentiate ovarian from colon cancer cell lines

Supplemental Figure 3. Dipeptides were significantly upregulated in ovarian cancer cells compared with colon cancer cells. The log-scaled metabolite intensities are presented as box plots representing the median values of experiments performed in 10 (HCT15, HTC116, OVCAR3) and 8 (SKOV3) replicates. Values were obtained after statistical data analysis using the metaP server

Using Runtime Traces to Improve Documentation and Unit Test Authoring for Dynamic Languages

Supplemental Figure 3. Dipeptides were significantly upregulated in ovarian cancer cells compared with colon cancer cells. The log-scaled metabolite intensities are presented as box plots representing the median values of experiments performed in 10 (HCT15, HTC116, OVCAR3) and 8 (SKOV3) replicates. Values were obtained after statistical data analysis using the metaP server

Additional file 5: of Metabolic signatures differentiate ovarian from colon cancer cell lines

Supplemental Table 3. Metabolites that significantly distinguish HCT116 from HCT15

Additional file 1: of Metabolic signatures differentiate ovarian from colon cancer cell lines

Supplemental Figure 1. Identification of outliers using PCA. PCA score plots generated by the metaP server reveal distinct clustering of HCT15 (dark blue), HCT116 (light blue), OVCAR3 (red), and SKOV3 (orange). Two sample outliers were identified in the SKOV3 cell line and were removed from the analysis

Additional file 2: of Metabolic signatures differentiate ovarian from colon cancer cell lines

Supplemental Table 1. Metabolites present in all examined cell lines. Star (*) indicates compounds that have not been officially “plexed” (based on a standard), although we are confident in their identity