Network Friendly P2P-TV: The Napa-Wine Approach

DEMO Sessio

Probe set algorithms: is there a rational best bet?

Affymetrix microarrays have become a standard experimental platform for studies of mRNA expression profiling. Their success is due, in part, to the multiple oligonucleotide features (probes) against each transcript (probe set). This multiple testing allows for more robust background assessments and gene expression measures, and has permitted the development of many computational methods to translate image data into a single normalized "signal" for mRNA transcript abundance. There are now many probe set algorithms that have been developed, with a gradual movement away from chip-by-chip methods (MAS5), to project-based model-fitting methods (dCHIP, RMA, others). Data interpretation is often profoundly changed by choice of algorithm, with disoriented biologists questioning what the "accurate" interpretation of their experiment is. Here, we summarize the debate concerning probe set algorithms. We provide examples of how changes in mismatch weight, normalizations, and construction of expression ratios each dramatically change data interpretation. All interpretations can be considered as computationally appropriate, but with varying biological credibility. We also illustrate the performance of two new hybrid algorithms (PLIER, GC-RMA) relative to more traditional algorithms (dCHIP, MAS5, Probe Profiler PCA, RMA) using an interactive power analysis tool. PLIER appears superior to other algorithms in avoiding false positives with poorly performing probe sets. Based on our interpretation of the literature, and examples presented here, we suggest that the variability in performance of probe set algorithms is more dependent upon assumptions regarding "background", than on calculations of "signal". We argue that "background" is an enormously complex variable that can only be vaguely quantified, and thus the "best" probe set algorithm will vary from project to project

Long term follow up results of sequential left internal thoracic artery grafts on severe left anterior descending artery disease

<p>Abstract</p> <p>Purpose</p> <p>Several alternative procedures have been proposed to achieve complete revascularization in the presence of diffuse left anterior descending coronary artery (LAD) disease. With the extensive use of internal thoracic artery grafts in coronary artery bypass procedures, sequential anastomosis of the left internal thoracic artery (LITA) to LAD has gained popularity in these challenging cases. The long term results of sequential LITA to LAD anstomosis were examined in this study.</p> <p>Patients and Methods</p> <p>In order to determine the long term results of the sequential revascularization of LAD by LITA graft, 41 out of 49 patients operated between January 2001 and December 2005 were selected for control coronary arteriography. The median period for control coronary arteriography was 64 months.</p> <p>Results</p> <p>Seventy five anastomoses were found to be fully patent (91,46%) among the 82 sequential LITA anastomoses (41 LITA grafts) on the LAD at a median follow-up period of 64 months (53 to 123 months). Among the 41 LITA grafts used for this purpose, 36 were found intact (complete patency of the proximal and distal anastomoses) (87,8%). Two LITA grafts (4 anastomoses) were found to be totally occluded (4,87%). The proximal anastomosis of the LITA graft was observed to be 90% stenotic in one patient (1,21%). In one patient tight stenosis of the distal anastomosis line was observed (1,21%), while in another patient 70% narrowing of LITA lumen after the proximal anastomosis was detected (1,21%).</p> <p>Conclusion</p> <p>We strongly beleive that sequential LITA grafting of LAD is a safe alternative in the presence of severe LAD disease to achieve complete revascularization of the anterior myocardium with patency rates not much differing from conventional single LITA to LAD anastomosis.</p

Genome-wide association study for acute otitis media in children identifies FNDC1 as disease contributing gene

Acute otitis media (AOM) is among the most common pediatric diseases, and the most frequent reason for antibiotic treatment in children. Risk of AOM is dependent on environmental and host factors, as well as a significant genetic component. We identify genome-wide significance at a locus on 6q25.3 (rs2932989, P(meta)=2.15 × 10(−09)), and show that the associated variants are correlated with the methylation status of the FNDC1 gene (cg05678571, P=1.43 × 10(−06)), and further show it is an eQTL for FNDC1 (P=9.3 × 10(−05)). The mouse homologue, Fndc1, is expressed in middle ear tissue and its expression is upregulated upon lipopolysaccharide treatment. In this first GWAS of AOM and the largest OM genetic study to date, we identify the first genome-wide significant locus associated with AOM

Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages

BACKGROUND: With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck. RESULTS: We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well. CONCLUSIONS: By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/]

Network Analysis of Differential Expression for the Identification of Disease-Causing Genes

Genetic studies (in particular linkage and association studies) identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize) the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved). We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes

Genome-wide association study for acute otitis media in children identifies FNDC1 as disease contributing gene

Acute otitis media (AOM) is among the most common pediatric diseases, and the most frequent reason for antibiotic treatment in children. Risk of AOM is dependent on environmental and host factors, as well as a significant genetic component. We identify genome-wide significance at a locus on 6q25.3 (rs2932989, Pmeta=2.15 × 10-09), and show that the associated variants are correlated with the methylation status of the FNDC1 gene (cg05678571, P=1.43 × 10-06), and further show it is an eQTL for FNDC1 (P=9.3 × 10-05). The mouse homologue, Fndc1, is expressed in middle ear tissue and its expression is upregulated upon lipopolysaccharide treatment. In this first GWAS of AOM and the largest OM genetic study to date, we identify the first genome-wide significant locus associated with AOM