BackgroundEukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used.ResultsIn this paper, a novel approach is presented in which 128 4-mer motifs in conjunction with a non-linear machine-learning algorithm utilising a Support Vector Machine (SVM) are used to distinguish between promoter and non-promoter DNA sequences. By applying this approach to plant, Drosophila, human, mouse and rat sequences, the classification model has showed 7-fold cross-validation percentage accuracies of 83.81%, 94.82%, 91.25%, 90.77% and 82.35% respectively. The high sensitivity and specificity value of 0.86 and 0.90 for plant; 0.96 and 0.92 for Drosophila; 0.88 and 0.92 for human; 0.78 and 0.84 for mouse and 0.82 and 0.80 for rat demonstrate that this technique is less prone to false positive results and exhibits better performance than many other tools. Moreover, this model successfully identifies location of promoter using TATA weight matrix.ConclusionThe high sensitivity and specificity indicate that 4-mer frequencies in conjunction with supervised machine-learning methods can be beneficial in the identification of RNA pol II promoters comparative to other methods. This approach can be extended to identify promoters in sequences for other eukaryotic genomes.

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There is paucity of knowledge on the histological features of the intestinal mucosa in malnourished adults of Bangladesh. The purpose of the study was to explore the histological features of the intestinal mucosa in malnourished adults of Bangladesh and to compare the findings with their well-nourished counterparts. 64 adults (37 malnourished with body mass index, BMI<18.5 kg/m2 and 27 controls with BMI>18.5 kg/m2) from the Bangladesh Environmental Enteric Dysfunction (BEED) study, who underwent upper-gastrointestinal endoscopy, were selected for this study. With a view to address the association of environmental enteric dysfunction (EED) with malnutrition, upper-gastrointestinal endoscopy was performed and mucosal biopsies from the distal duodenum were studied for histopathology. Villous height, crypt depth, and presence of inflammatory infiltrates in lamina propria were investigated. Bivariate analysis was performed to quantify the relation between malnutrition and the histological features. About 95% adults, irrespective of nutritional status, were diagnosed to have chronic non-specific duodenitis on histopathology. Malnourished adults suffered significantly more from chronic active duodenitis compared to their well-nourished counterparts (p=0.003). Malnourished adults also had significantly higher frequency of subtotal villous atrophy, crypt hyperplasia and marked cellular infiltration in the lamina propria than the healthy controls (p<0.05).

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Fast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository (https://github.com/saeed344/SCORPION).