Enhancers are sequences with short motifs that exhibit high positional variability and free scattering properties. Identification of these noncoding DNA fragments and their strength are extremely important because they play a key role in controlling gene regulation on a cellular basis. The identification of enhancers is more complex than that of other factors in the genome because they are freely scattered, and their location varies widely. In recent years, bioinformatics tools have enabled significant improvement in identifying this biological difficulty. Cell line-specific screening is not possible using these existing computational methods based solely on DNA sequences. DNA segment chromatin accessibility may provide useful information about its potential function in regulation, thereby identifying regulatory elements based on its chromatin accessibility. In chromatin, the entanglement structure allows positions far apart in the sequence to encounter each other, regardless of their proximity to the gene to be acted upon. Thus, identifying enhancers and assessing their strength is difficult and time-consuming. The goal of our work was to overcome these limitations by presenting a convolutional neural network (CNN) with attention-gated recurrent units (AttGRU) based on Deep Learning. It used a CNN and one-hot coding to build models, primarily to identify enhancers and secondarily to classify their strength. To test the performance of the proposed model, parallels were drawn between enhancer-CNNAttGRU and existing state-of-the-art methods to enable comparisons. The proposed model performed the best for predicting stage one and stage two enhancer sequences, as well as their strengths, in a cross-species analysis, achieving best accuracy values of 87.39% and 84.46%, respectively. Overall, the results showed that the proposed model provided comparable results to state-of-the-art models, highlighting its usefulness.
In the quest for bioactive natural products of fungal origin, Aspergillus flavus was isolated from rhizosphere of Mentha piperita using Potato Dextrose Agar (PDA) and Czapec Yeast Broth (CYB) nutrient media for metabolites production. In total, three different metabolites were purified using HPLC/LCMS and the structures were established using 500 Varian NMR experiments. Further the isolated metabolites in different concentrations (10, 100, 1000 μg/mL) were tested for herbicidal activity using Completely Randomized design (CRD) against the seeds of Silybum marianum and Avena fatua which are major threats to wheat crop in Pakistan. Among the isolated metabolites, one compound was found active against the test weed species whose activity is reported in the present work. The chemical name of the compound is 2-(1, 4-dihydroxybutan-2-yl)-1, 3-dihydroxy-6, 8-dimethoxyanthracene-9, 10(4aH, 9aH)-dione with mass of 388. Results showed that all seeds germinated in control treatment; however, with the metabolite treated, the growth was retarded to different levels in all parts of the weeds. At a dose of 1000 μg/mL of the pure compound, 100% seeds of S. marianum and 60% seeds of A. fatua were inhibited. Interestingly, the pure compound exhibited less inhibition of 10% towards the seeds of common wheat (Triticum aestivum).
Background: Peptidases are a group of enzymes which catalyzes the cleavage of peptide bonds. Around 2-3% of the whole genome codes for proteases and about one third of all known proteases are serine proteases which are divided into 13 clans and 40 families. They are involved in diverse physiological roles such as digestion, coagulation of blood, fibrinolysis, processing of proteins and prohormones, signaling pathways, complement fixation and have a vital role in immune defense system. Based on their functions, they can broadly be divided into two classes; GASPIDs (Granule Associated Serine Peptidases involved in Immune Defense System) and Non-GASPIDs. GASPIDs, in particular are involved in immune associated functions i.e. initiating apoptosis to kill virally infected and cancerous cells, cytokine modulation for generation of inflammatory responses and direct killing of pathogens through phagosomes. Methods: In this study, sequence-based characterization of these two types of serine proteases is performed. We first identified sequences by analyzing multiple online databases as well as by analyzing whole genomes of different species from different orthologous and nonorthologous species. Sequences were identified by devising a distinct criterion to differentiate GASPIDs from Non-GASPIDs. The translated version of these sequences were then subjected to feature extraction. Using these distinctive features, we differentiated GASPIDs from NonGASPIDs by applying multiple supervised machine learning models. Results and Conclusion: Our results show that, among the three classifiers used in this study, SVM classifier coupled with tripeptide as feature method, has shown the best accuracy in classification of sequences as GASPIDs and Non-GASPIDs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.