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3 Publications matching the given criteria: (Clear all filters)
Published year: 20143
Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.
LHA - Leipzig Health Atlas

Abstract (Expand)

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance … with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

Authors: S. A. Munro, S. P. Lund, P. S. Pine, H. Binder, D. A. Clevert, A. Conesa, J. Dopazo, M. Fasold, S. Hochreiter, H. Hong, N. Jafari, D. P. Kreil, P. P. Labaj, S. Li, Y. Liao, S. M. Lin, J. Meehan, C. E. Mason, J. Santoyo-Lopez, R. A. Setterquist, L. Shi, W. Shi, G. K. Smyth, N. Stralis-Pavese, Z. Su, W. Tong, C. Wang, J. Wang, J. Xu, Z. Ye, Y. Yang, Y. Yu, M. Salit

Date Published: 25th Sep 2014

Publication Type: Not specified

A systems biology approach for defining the molecular framework of the hematopoietic stem cell niche.
LHA - Leipzig Health Atlas

Abstract (Expand)

Despite progress in identifying the cellular composition of hematopoietic stem/progenitor cell (HSPC) niches, little is known about the molecular requirements of HSPC support. To address this issue, … we used a panel of six recognized HSPC-supportive stromal lines and less-supportive counterparts originating from embryonic and adult hematopoietic sites. Through comprehensive transcriptomic meta-analyses, we identified 481 mRNAs and 17 microRNAs organized in a modular network implicated in paracrine signaling. Further inclusion of 18 additional cell strains demonstrated that this mRNA subset was predictive of HSPC support. Our gene set contains most known HSPC regulators as well as a number of unexpected ones, such as Pax9 and Ccdc80, as validated by functional studies in zebrafish embryos. In sum, our approach has identified the core molecular network required for HSPC support. These cues, along with a searchable web resource, will inform ongoing efforts to instruct HSPC ex vivo amplification and formation from pluripotent precursors.

Authors: P. Charbord, C. Pouget, H. Binder, F. Dumont, G. Stik, P. Levy, F. Allain, C. Marchal, J. Richter, B. Uzan, F. Pflumio, F. Letourneur, H. Wirth, E. Dzierzak, D. Traver, T. Jaffredo, C. Durand

Date Published: 4th Sep 2014

Publication Type: Not specified

Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer: A Bioinformatics Perspective
LHA - Leipzig Health Atlas

Abstract (Expand)

Genome-wide ‘omics'-assays provide a comprehensive view on the molecular landscapes of healthy and diseased cells. Bioinformatics traditionally pursues a ‘gene-centered' view by extracting lists … of genes differentially expressed or methylated between healthy and diseased states. Biological knowledge mining is then performed by applying gene set techniques using libraries of functional gene sets obtained from independent studies. This analysis strategy neglects two facts: (i) that different disease states can be characterized by a series of functional modules of co-regulated genes and (ii) that the topology of the underlying regulatory networks can induce complex expression patterns that require analysis methods beyond traditional genes set techniques. The authors here provide a knowledge discovery method that overcomes these shortcomings. It combines machine learning using self-organizing maps with pathway flow analysis. It extracts and visualizes regulatory modes from molecular omics data, maps them onto selected pathways and estimates the impact of pathway-activity changes. The authors illustrate the performance of the gene set and pathway signal flow methods using expression data of oncogenic pathway activation experiments and of patient data on glioma, B-cell lymphoma and colorectal cancer.

Authors: L. Nersisyan, Henry Löffler-Wirth, A. Arakelyan, Hans Binder

Date Published: 1st Jun 2014

Publication Type: Not specified

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