Globaltest and GOEAST: two different approaches for Gene Ontology analysis

Background Gene set analysis is a commonly used method for analysing microarray data by considering groups of functionally related genes instead of individual genes. Here we present the use of two gene set analysis approaches: Globaltest and GOEAST. Globaltest is a method for testing whether sets of genes are significantly associated with a variable of interest. GOEAST is a freely accessible web-based tool to test GO term enrichment within given gene sets. The two approaches were applied in the analysis of gene lists obtained from three different contrasts in a microarray experiment conducted to study the host reactions in broilers following Eimeria infection. Results The Globaltest identified significantly associated gene sets in one of the three contrasts made in the microarray experiment whereas the functional analysis of the differentially expressed genes using GOEAST revealed enriched GO terms in all three contrasts. Conclusion Globaltest and GOEAST gave different results, probably due to the different algorithms and the different criteria used for evaluating the significance of GO terms

Consistency, comprehensiveness, and compatibility of pathway databases

<p>Abstract</p> <p>Background</p> <p>It is necessary to analyze microarray experiments together with biological information to make better biological inferences. We investigate the adequacy of current biological databases to address this need.</p> <p>Description</p> <p>Our results show a low level of consistency, comprehensiveness and compatibility among three popular pathway databases (KEGG, Ingenuity and Wikipathways). The level of consistency for genes in similar pathways across databases ranges from 0% to 88%. The corresponding level of consistency for interacting genes pairs is 0%-61%. These three original sources can be assumed to be reliable in the sense that the interacting gene pairs reported in them are correct because they are curated. However, the lack of concordance between these databases suggests each source has missed out many genes and interacting gene pairs.</p> <p>Conclusions</p> <p>Researchers will hence find it challenging to obtain consistent pathway information out of these diverse data sources. It is therefore critical to enable them to access these sources via a consistent, comprehensive and unified pathway API. We accumulated sufficient data to create such an aggregated resource with the convenience of an API to access its information. This unified resource can be accessed at <url>http://www.pathwayapi.com</url>.</p

Radical palliative surgery: new limits to pursue

This case report describes the radical subtotal palliative resection of a massive recurrent desmoid tumor encompassing the abdomen, pelvis, and groin in a child who was 13 years old at the time of initial resection. Given the extensive distribution of the tumor en bloc resection, which is the standard treatment of desmoid tumors, would have meant performing a hemipelvectomy and repair of a large abdominal wall defect, likely with skin grafts and mesh. The patient’s personal goals however were to alleviate the pain and limited mobility that would allow her to re-attend high school and appear normal to her peers. Therefore, palliative surgery was pursued and currently the patient is 5 years out from her last surgery doing well. We believe that the option of surgical palliation in this case was warranted and should be an option for similar cases in the future

Cross-laboratory evaluation of multiplex bead assays including independent common reference standards for immunological monitoring of observational and interventional human studies

© 2018 van Meijgaarden et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Multiplex assays are increasingly applied to analyze multicomponent signatures of human immune responses, including the dynamics of cytokine and chemokine production, in observational as well as interventional studies following treatment or vaccination. However, relatively limited information is available on the performance of the different available multiplex kits, and comparative evaluations addressing this important issue are lacking. Study design To fill this knowledge gap we performed a technical comparison of multiplex bead assays from 4 manufacturers, each represented by 3 different lots, and with the assays performed by 3 different laboratories. To cross compare kits directly, spiked samples, biological samples and a newly made reference standard were included in all assays. Analyses were performed on 324 standard curves to allow for evaluation of the quality of the standard curves and the subsequent interpretation of biological specimens. Results Manufacturer was the factor which contributed most to the observed variation whereas variation in lots, laboratory or type of detection reagent contributed minimally. Inclusion of a common reference standard allowed us to overcome observed differences in cytokine and chemokine levels between manufacturers. Conclusions We strongly recommend using multiplex assays from the same manufacturer within a single study and across studies that are likely to compare results in a quantitative manner. Incorporation of common reference standards, and application of the same analysis method in assays can overcome many analytical biases and thus could bridge comparison of independent immune profiling (e.g. vaccine immunogenicity) studies. With these recommendations taken into account, the multiplex bead assays performed as described here are useful tools in capturing complex human immune-signatures in observational and interventional studies.FP7 EURIPRED (FP7-INFRA-2012 Grant Agreement No. 312661 to HMcS, HMD, THMO, MMH) and EC HORIZON2020 TBVAC2020 (Grant Agreement No. 643381EC to HMcS, HMD, THMO)

Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges

Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis

What is Intellectual Freedom Today? An Invitation to Think the Event

The pubmed search term “pastoris[Title] AND (express[Title] OR produced[Title] OR expression[Title] OR production[Title])” yielded 877 hits in December 2008, dated from 1987 to 2009. At the same time, the search term “pastoris[Title] AND (bioreactor[Title] OR fed-batch[Title] OR continuous[Title] OR fermentations[Title] OR large-scale[Title] OR fermentation[Title] OR pilot[Title])” returned 92 hits –published between 1990 and 2009. This analysis is somewhat superficial and ostentatious, but it suggests that the majority of researchers publishing on Pichia use it as a tool for rather than an object of their work. This is not to say that the majority should change their focus, but in fact researchers sometimes face difficulties when the need to obtain useful amounts of a target protein produced in Pichia calls for scale-up from the benchtop protocols to a bioreactor-based process. This chapter attempts to provide a reliable protocol for AOX1-driven bioreactor production of secreted scFvs or other proteins

Noncentral bimatrix variate generalised beta distributions

In this paper, we determine the density functions of nonsymmetrised doubly noncentral matrix variate beta type I and II distributions. The nonsymetrised density functions of doubly noncentral and noncentral bimatrix variate generalised beta type I and II distributions are also obtained.Comment: 14 page

Finding consistent disease subnetworks across microarray datasets

<p>Abstract</p> <p>Background</p> <p>While contemporary methods of microarray analysis are excellent tools for studying individual microarray datasets, they have a tendency to produce different results from different datasets of the same disease. We aim to solve this reproducibility problem by introducing a technique (SNet). SNet provides both quantitative and descriptive analysis of microarray datasets by identifying specific connected portions of pathways that are significant. We term such portions within pathways as “subnetworks”.</p> <p>Results</p> <p>We tested SNet on independent datasets of several diseases, including childhood ALL, DMD and lung cancer. For each of these diseases, we obtained two independent microarray datasets produced by distinct labs on distinct platforms. In each case, our technique consistently produced almost the same list of significant nontrivial subnetworks from two independent sets of microarray data. The gene-level agreement of these significant subnetworks was between 51.18% to 93.01%. In contrast, when the same pairs of microarray datasets were analysed using GSEA, t-test and SAM, this percentage fell between 2.38% to 28.90% for GSEA, 49.60% tp 73.01% for t-test, and 49.96% to 81.25% for SAM. Furthermore, the genes selected using these existing methods did not form subnetworks of substantial size. Thus it is more probable that the subnetworks selected by our technique can provide the researcher with more descriptive information on the portions of the pathway actually affected by the disease.</p> <p>Conclusions</p> <p>These results clearly demonstrate that our technique generates significant subnetworks and genes that are more consistent and reproducible across datasets compared to the other popular methods available (GSEA, t-test and SAM). The large size of subnetworks which we generate indicates that they are generally more biologically significant (less likely to be spurious). In addition, we have chosen two sample subnetworks and validated them with references from biological literature. This shows that our algorithm is capable of generating descriptive biologically conclusions.</p