56 research outputs found
ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use
<p>Abstract</p> <p>Background</p> <p>During the last decade, the use of microarrays to assess the transcriptome of many biological systems has generated an enormous amount of data. A common technique used to organize and analyze microarray data is to perform cluster analysis. While many clustering algorithms have been developed, they all suffer a significant decrease in computational performance as the size of the dataset being analyzed becomes very large. For example, clustering 10000 genes from an experiment containing 200 microarrays can be quite time consuming and challenging on a desktop PC. One solution to the scalability problem of clustering algorithms is to distribute or parallelize the algorithm across multiple computers.</p> <p>Results</p> <p>The software described in this paper is a high performance multithreaded application that implements a parallelized version of the K-means Clustering algorithm. Most parallel processing applications are not accessible to the general public and require specialized software libraries (e.g. MPI) and specialized hardware configurations. The parallel nature of the application comes from the use of a web service to perform the distance calculations and cluster assignments. Here we show our parallel implementation provides significant performance gains over a wide range of datasets using as little as seven nodes. The software was written in C# and was designed in a modular fashion to provide both deployment flexibility as well as flexibility in the user interface.</p> <p>Conclusion</p> <p>ParaKMeans was designed to provide the general scientific community with an easy and manageable client-server application that can be installed on a wide variety of Windows operating systems.</p
Differential gene expression in the salivary gland during development and onset of xerostomia in Sjögren's syndrome-like disease of the C57BL/6.NOD-Aec1Aec2 mouse
Congruences modulo prime powers of Hecke eigenvalues in level
We continue the study of strong, weak, and -weak eigenforms introduced by
Chen, Kiming, and Wiese. We completely determine all systems of Hecke
eigenvalues of level modulo , showing there are finitely many. This
extends results of Hatada and can be considered as evidence for the more
general conjecture formulated by the author together with Kiming and Wiese on
finiteness of systems of Hecke eigenvalues modulo prime powers at any fixed
level. We also discuss the finiteness of systems of Hecke eigenvalues of level
modulo , reducing the question to the finiteness of a single eigenvalue.
Furthermore, we answer the question of comparing weak and -weak eigenforms
and provide the first known examples of non-weak -weak eigenforms.Comment: 28 pages; Minor revisio
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Thymus-derived regulatory T cells control tolerance to commensal microbiota
Peripheral mechanisms preventing autoimmunity and maintaining tolerance to commensal microbiota involve CD4+Foxp3+ regulatory T cells1,2 generated in the thymus (tTregs) or extrathymically by induction of naive CD4+Foxp3− T cells (iTregs). Prior studies suggested that the T cell receptor (TCR) repertoires of tTregs and iTregs are biased towards self and non-self antigens, respectively 3–6 but their relative contribution in controlling immunopathology, e.g. colitis and other untoward inflammatory responses triggered by different types of antigens, remains unresolved 7. The intestine, and especially the colon, is a particularly suitable organ to study this question, given the variety of self-, microbiota- and food-derived antigens to which Tregs and other T cell populations are exposed. Intestinal environments can enhance conversion to a regulatory lineage 8,9 and favor tolerogenic presentation of antigens to naive CD4+ T cells 10,11, suggesting that intestinal homeostasis depends on microbiota-specific iTregs 12–15. Here, to identify the origin and antigen-specificity of intestinal Tregs, we performed single cell as well as high-throughput (HT) sequencing of the TCR repertoires of CD4+Foxp3+ and CD4+Foxp3− T cells and analyzed their reactivity against specific commensal species. We show that tTregs constitute the majority of Tregs in all lymphoid and intestinal organs, including colon, where their repertoire is heavily influenced by the composition of the microbiota. Our results suggest that tTregs, and not iTregs, dominantly mediate tolerance to antigens produced by intestinal commensals
A modified hyperplane clustering algorithm allows for efficient and accurate clustering of extremely large datasets
Motivation: As the number of publically available microarray experiments increases, the ability to analyze extremely large datasets across multiple experiments becomes critical. There is a requirement to develop algorithms which are fast and can cluster extremely large datasets without affecting the cluster quality. Clustering is an unsupervised exploratory technique applied to microarray data to find similar data structures or expression patterns. Because of the high input/output costs involved and large distance matrices calculated, most of the algomerative clustering algorithms fail on large datasets (30 000 + genes/200 + arrays). In this article, we propose a new two-stage algorithm which partitions the high-dimensional space associated with microarray data using hyperplanes. The first stage is based on the Balanced Iterative Reducing and Clustering using Hierarchies algorithm with the second stage being a conventional k-means clustering technique. This algorithm has been implemented in a software tool (HPCluster) designed to cluster gene expression data. We compared the clustering results using the two-stage hyperplane algorithm with the conventional k-means algorithm from other available programs. Because, the first stage traverses the data in a single scan, the performance and speed increases substantially. The data reduction accomplished in the first stage of the algorithm reduces the memory requirements allowing us to cluster 44 460 genes without failure and significantly decreases the time to complete when compared with popular k-means programs. The software was written in C# (.NET 1.1)
Alterations of renal phenotype and gene expression profiles due to protein overload in NOD-related mouse strains
BACKGROUND: Despite multiple causes, Chronic Kidney Disease is commonly associated with proteinuria. A previous study on Non Obese Diabetic mice (NOD), which spontaneously develop type 1 diabetes, described histological and gene expression changes incurred by diabetes in the kidney. Because proteinuria is coincident to diabetes, the effects of proteinuria are difficult to distinguish from those of other factors such as hyperglycemia. Proteinuria can nevertheless be induced in mice by peritoneal injection of Bovine Serum Albumin (BSA). To gain more information on the specific effects of proteinuria, this study addresses renal changes in diabetes resistant NOD-related mouse strains (NON and NOD.B10) that were made to develop proteinuria by BSA overload. METHODS: Proteinuria was induced by protein overload on NON and NOD.B10 mouse strains and histology and microarray technology were used to follow the kidney response. The effects of proteinuria were assessed and subsequently compared to changes that were observed in a prior study on NOD diabetic nephropathy. RESULTS: Overload treatment significantly modified the renal phenotype and out of 5760 clones screened, 21 and 7 kidney transcripts were respectively altered in the NON and NOD.B10. Upregulated transcripts encoded signal transduction genes, as well as markers for inflammation (Calmodulin kinase beta). Down-regulated transcripts included FKBP52 which was also down-regulated in diabetic NOD kidney. Comparison of transcripts altered by proteinuria to those altered by diabetes identified mannosidase 2 alpha 1 as being more specifically induced by proteinuria. CONCLUSION: By simulating a component of diabetes, and looking at the global response on mice resistant to the disease, by virtue of a small genetic difference, we were able to identify key factors in disease progression. This suggests the power of this approach in unraveling multifactorial disease processes
Frequent HPV-independent p16/INK4A overexpression in head and neck cancer
Objectives
p16INK4A (p16) is the most widely used clinical biomarker for Human Papillomavirus (HPV) in head and neck squamous cell cancer (HNSCC). HPV is a favourable prognostic marker in HNSCC and is used for patient stratification. While p16 is a relatively accurate marker for HPV within the oropharynx, recent reports suggest it may be unsuitable for use in other HNSCC subsites, where a smaller proportion of tumors are HPV-driven.
Materials and methods
We integrated reverse phase protein array (RPPA) data for p16 with HPV status based on detection of viral transcripts by RNA-seq in a set of 210 HNSCCs profiled by The Cancer Genome Atlas project. Samples were queried for alterations in CDKN2A, and other pathway genes to investigate possible drivers of p16 expression.
Results
While p16 levels as measured by RPPA were significantly different by HPV status, there were multiple HPV (?) samples with similar expression levels of p16 to HPV (+) samples, particularly at non-oropharyngeal subsites. In many cases, p16 overexpression in HPV (?) tumors could not be explained by mutation or amplification of CDKN2A or by RB1 mutation. Instead, we observed enrichment for inactivating mutations in the histone H3 lysine 36 methyltransferase, NSD1 in HPV (?)/p16-high tumors.
Conclusions
RPPA data suggest high p16 protein expression in many HPV (?) non-oropharyngeal HNSCCs, limiting its potential utility as an HPV biomarker outside of the oropharynx. HPV-independent overexpression of wild-type p16 in non-oropharyngeal HNSCC may be linked to global deregulation of chromatin state by inactivating mutations in NSD1
The Effect of Blindfolding and Swaddling on The Stress Response to Handling in Domestic Rabbits
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