We introduce a web-based tool, Peak Annotation and Visualization (PAVIS), for annotating and visualizing ChIP-seq peak data. PAVIS is designed with non-bioinformaticians in mind and presents a straightforward user interface to facilitate biological interpretation of ChIP-seq peak or other genomic enrichment data. PAVIS, through association with annotation, provides relevant genomic context for each peak, such as peak location relative to genomic features including transcription start site, intron, exon or 5'/3'-untranslated region. PAVIS reports the relative enrichment P-values of peaks in these functionally distinct categories, and provides a summary plot of the relative proportion of peaks in each category. PAVIS, unlike many other resources, provides a peak-oriented annotation and visualization system, allowing dynamic visualization of tens to hundreds of loci from one or more ChIP-seq experiments, simultaneously. PAVIS enables rapid, and easy examination and cross-comparison of the genomic context and potential functions of the underlying genomic elements, thus supporting downstream hypothesis generation.
Background: Retinoids have been studied extensively for their potential as therapeutic and chemopreventive agents for a variety of cancers, including nonmelanoma skin cancer (NMSC). Despite their use for many years, the mechanism of action of retinoids in the prevention of NMSC is still unclear. In this study we have attempted to understand the chemopreventive mechanism of all-trans retinoic acid (ATRA), a primary biologically active retinoid, in order to more efficiently utilize retinoids in the clinic.
Sorghum has been proposed as a potential energy crop. However, it has been traditionally bred for grain yield and forage quality, not traits related to bioenergy production. To develop tools for genetic improvement of bioenergy-related traits such as height, genetic markers associated with these traits have to be identified first. Association mapping has been extensively used in humans and in some crop plants for this purpose. However, genome-wide association mapping using the whole association panel is costly and time-consuming. A variation of this method called pool-based genome-wide association mapping has been extensively used in humans. In this variation, pools of individuals with contrasting phenotypes, instead of the whole panel, are screened with genetic markers and polymorphic markers are confirmed by screening the individuals in the pools. Here we identified several new simple sequence repeats (SSR) markers associated with height using this pool-based genome-wide association mapping in sorghum.After screening the tall and short pools of sorghum accessions from the sorghum mini core collection developed at the International Crops Research Institute for the Semi-Arid Tropics with 703 SSR markers, we have identified four markers that are closely associated with sorghum height on chromosomes 2, 6, and 9. Comparison with published maps indicates that all four markers are clustered with markers previously mapped to height or height-related traits and with candidate genes involved in regulating plant height such as FtsZ, Ugt, and GA 2-oxidase. The mapping method can be applied to other crop plants for which a high throughput genome-wide association mapping platform is not yet available.
While cancer is a heterogeneous complex of distinct diseases, the common underlying mechanism for uncontrolled tumor growth is due to mutations in protooncogenes and the loss of the regulatory function of tumor suppression genes. In this paper we propose a novel deep learning model for predicting tumor suppression genes (TSGs) and proto-oncogenes (OGs) from their Protein Data Bank (PDB) three dimensional structures. Specifically, we develop a convolutional neural network (CNN) to classify the feature map sets extracted from the tertiary protein structures. Each feature map set represents particular biological features associated with the atomic coordinates appearing on the outer surface of protein's three dimensional structure. The experimental results on the collected dataset for classifying TSGs and OGs demonstrate promising performance with 82.57% accuracy and 0.89 area under ROC curve. The initial success of the proposed model warrants further study to develop a comprehensive model to identify the cancer driver genes or events using the principle cancer genes (TSG and OG).
Background: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultaneously. The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes. A large number of clustering algorithms and distance measuring matrices are proposed in the literature. The popular ones among them include hierarchal clustering and k-means clustering. These algorithms have often used the Euclidian distance or Pearson correlation distance. The biologists or the practitioners are often confused as to which algorithm to use since there is no clear winner among algorithms or among distance measuring metrics. Several validation indices have been proposed in the literature and these are based directly or indirectly on distances; hence a method that uses any of these indices does not relate to any biological features such as biological processes or molecular functions.
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