Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous 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.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa 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.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

The Fission Yeast Homeodomain Protein Yox1p Binds to MBF and Confines MBF-Dependent Cell-Cycle Transcription to G1-S via Negative Feedback

The regulation of the G1- to S-phase transition is critical for cell-cycle progression. This transition is driven by a transient transcriptional wave regulated by transcription factor complexes termed MBF/SBF in yeast and E2F-DP in mammals. Here we apply genomic, genetic, and biochemical approaches to show that the Yox1p homeodomain protein of fission yeast plays a critical role in confining MBF-dependent transcription to the G1/S transition of the cell cycle. The yox1 gene is an MBF target, and Yox1p accumulates and preferentially binds to MBF-regulated promoters, via the MBF components Res2p and Nrm1p, when they are transcriptionally repressed during the cell cycle. Deletion of yox1 results in constitutively high transcription of MBF target genes and loss of their cell cycle-regulated expression, similar to deletion of nrm1. Genome-wide location analyses of Yox1p and the MBF component Cdc10p reveal dozens of genes whose promoters are bound by both factors, including their own genes and histone genes. In addition, Cdc10p shows promiscuous binding to other sites, most notably close to replication origins. This study establishes Yox1p as a new regulatory MBF component in fission yeast, which is transcriptionally induced by MBF and in turn inhibits MBF-dependent transcription. Yox1p may function together with Nrm1p to confine MBF-dependent transcription to the G1/S transition of the cell cycle via negative feedback. Compared to the orthologous budding yeast Yox1p, which indirectly functions in a negative feedback loop for cell-cycle transcription, similarities but also notable differences in the wiring of the regulatory circuits are evident

Long Span DNA Paired-End-Tag (DNA-PET) Sequencing Strategy for the Interrogation of Genomic Structural Mutations and Fusion-Point-Guided Reconstruction of Amplicons

10.1371/journal.pone.0046152PLoS ONE79

In-Vitro Evidences for the Formulation, Development, and Evaluation of Budesonide Oral Nano-sponges

Idiopathic inflammatory bowel disease is quite common, with ulcerative colitis and Crohn's disease being the most common forms. Due to its high receptor affinity and quick diversion, the anti-inflammatory effects of the glucocorticoid budesonide are limited, but it is nevertheless useful in some situations. A unique BUD nano-sponges was developed by employing quasi-solvent diffusion and Eudragit S-100 as the polymer to address the problem of low efficacy and accessibility. Material and Methods: Drug release profile and percentage of drug entrapment in BUD Nano sponges were also measured and analyzed. Clinical activity score, colon/body weight ratio, and macroscopic ulceration activity were among the criteria used in an in vivo investigation of the formulation in male Wistar rats. Finally, histological investigation was done on colon tissue samples. Results: When compared to other BUD formulations on the market, this one performed exceptionally well, suggesting that the designed nanosponges are highly effective. The Wistar rats' clinical activity score was reduced when treated with the formed nanosponges. When compared to the placebo group, those who took this supplement saw a dramatic decrease in their colon weight ratio. Nanosponge colon histology revealed healthy colon anatomy and architecture. Conclusion: The findings of this study have substantiated the efficacy of BUD nano-sponges as innovative carriers in the treatment of inflammatory bowel diseas

Personality profiles of cultures: aggregate personality traits

Personality profiles of cultures can be operationalized as the mean trait levels of culture members. College students from 51 cultures rated an individual from their country whom they knew well (N = 12, 156). Aggregate scores on Revised NEO Personality Inventory scales generalized across age and gender groups, approximated the individual-level Five-Factor Model, and correlated with aggregate self-report personality scores and other culture-level variables. Results were not attributable to national differences in economic development or to acquiescence. Geographical differences in scale variances and mean levels were replicated, with Europeans and Americans generally scoring higher in Extraversion than Asians and Africans. Findings support the rough scalar equivalence of NEO-PI-R factors and facets across cultures, and suggest that aggregate personality profiles provide insight into cultural differences

ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species

Saccharomyces cerevisiae is a primary model for studies of transcriptional control, and the specificities of most yeast transcription factors (TFs) have been determined by multiple methods. However, it is unclear which position weight matrices (PWMs) are most useful; for the roughly 200 TFs in yeast, there are over 1200 PWMs in the literature. To address this issue, we created ScerTF, a comprehensive database of 1226 motifs from 11 different sources. We identified a single matrix for each TF that best predicts in vivo data by benchmarking matrices against chromatin immunoprecipitation and TF deletion experiments. We also used in vivo data to optimize thresholds for identifying regulatory sites with each matrix. To correct for biases from different methods, we developed a strategy to combine matrices. These aligned matrices outperform the best available matrix for several TFs. We used the matrices to predict co-occurring regulatory elements in the genome and identified many known TF combinations. In addition, we predict new combinations and provide evidence of combinatorial regulation from gene expression data. The database is available through a web interface at http://ural.wustl.edu/ScerTF. The site allows users to search the database with a regulatory site or matrix to identify the TFs most likely to bind the input sequence

The combined molecular adjuvant CASAC enhances the CD8+ T cell response to a tumor-associated self-antigen in aged, immunosenescent mice

Background: Ineffective induction of T cell mediated immunity in older individuals remains a persistent challenge for vaccine development. Thus, there is a need for more efficient and sophisticated adjuvants that will complement novel vaccine strategies for the elderly. To this end, we have investigated a previously optimized, combined molecular adjuvant, CASAC (Combined Adjuvant for Synergistic Activation of Cellular immunity), incorporating two complementary Toll-like receptor agonists, CpG and polyI:C, a class-II epitope, and interferon (IFN)-γ in aged mice. Findings: In aged mice with typical features of immunosenescence, antigen specific CD8+ T cell responses were stimulated after serial vaccinations with CASAC or Complete/Incomplete Freund's Adjuvant (CFA/IFA) and a class I epitope, deriving either from ovalbumin (SIINFEKL, SIL) or the melanoma-associated self-antigen, tyrosinase-related protein-2 (SVYDFFVWL, SVL). Pentamer analysis revealed that aged, CASAC/SIL-vaccinated animals had substantially higher frequencies of H-2Kb/SIL-specific CD8+ T cells compared to the CFA/IFA-vaccinated groups. Similarly, higher frequencies of H-2Kb/SVL-pentamer+ and IFN-γ+ CD8+ T cells were detected in the aged, CASAC + SVL-vaccinated mice than in their CFA/IFA-vaccinated counterparts. In both antigen settings, CASAC promoted significantly better functional CD8+ T cell activity. Conclusion: These studies demonstrate that functional CD8+ T cells, specific for both foreign and tumour-associated self-antigens, can be effectively induced in aged immunosenescent mice using the novel multi-factorial adjuvant CASAC.</p

Cross-Platform Microarray Data Normalisation for Regulatory Network Inference

Background Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences. Methods We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets. Conclusions Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem