International Journal of Data Mining and Bioinformatics
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International Journal of Data Mining and Bioinformatics (3 papers in press)
Herbal medicine meets bioinformatics for remedy of Tuberculosis by Mycobacterium tuberculosis RGTB423 by Arnab Kumar Chakraborty, Indrani Sarkar, Arnab Sen Abstract: Mycobacterium tuberculosis RGTB423 is the causative agent of Tuberculosis. Our aim here is to check the efficacy of a few herbal medicine to counter the rage of this highly infectious disease. The workflow here is purely data mining and bioinformatics based, here a comparative pathway analysis of the host and causative organism is done. The proteins present in these pathways is screened to obtain targets protein for in-silico drug deseigning. The ligands are selected from Withania somnifera, Oplopanax horridus and Aloe vera plants. Molecular docking of the phytochemicals is performed with the selected targets. Few ADMET parameters and in silico toxicology data was also checked for the ligands to confirm their druglikeness characteristics. Among the phytochemicals aloeemodin, a compound present in Aloe vera showed the best result. Aloeemodin even had better results than a few prescribed drugs and thus can be further considered for potent drug trail runs against tuberculosis. Keywords: Mycobacterium tuberculosis RGTB423; Phytochemicals; Metabolic Pathway; Molecular Docking; Interaction; Druglike; Lipinski’s rule; Aloe vera ;Toxicology ;Drug.
WebPORD: a web-based pipeline of RNA degradome by Jung-Im Won, JunBeom Lee, HeonWoo Lee, JaeMoon Shin, JeeHee Yoon, Dong-Hoon Jeong Abstract: The advent of high-throughput next-generation sequencing (NGS) technology has brought in a new genomics era. It is widely utilized not only for genomic but also for transcriptome sequencing. NGS-based transcriptomic methods, including RNA-seq, are widely used to study gene expression regulation. Recently, post-transcriptional gene regulation has been studied by several RNA degradome sequencing methods that differ from RNA-seq and directly profile degraded RNAs. Parallel analysis of RNA ends (PARE) sequencing is one of these methods. It is widely used for identifying microRNA targets and nonsense-mediated mRNA decay events as well as for global analysis of RNA degradation. We have developed WebPORD, a user-friendly web-based pipeline for analyzing the PARE sequencing data. This pipeline trims and applies several filters to PARE sequencing. In addition, this pipeline provides a degradome table and degradome-plots for users to further analyze RNA degradation at a genomic scale. This is the first web-based pipeline for global analysis of RNA degradation using PARE data from animals and plants, which provides mechanistic insights into post-transcriptional gene regulation. The WebPORD server is available at http://webpord.hallym.ac.kr. Keywords: Web-based pipeline;Next-generation sequencing;RNA degradome;Parallel analysis of RNA ends.
A Deep Aggregated Model for Protein Secondary Structure Prediction by Yu Hu, Tiezheng Nie, Derong Shen, Ge Yu Abstract: Protein sequence analysis is an important research subject that has
drawn increasing attention from biomedical researchers. In this research field,
Protein Secondary Structure Predication(PSSP) is a significant subproject for
studying protein spatial structure and biochemical function. However, when only
the amino acid residues sequence information can be used as the input, it is
a challenge problem to predict the spatial structure of the protein. Recently,
the deep learning technology achieves great success in information mining. In
this paper, we propose a Deep Neural Block Cascade Network(DeepNBCN) for
Protein Secondary Structure Predication. This model is constructed by stacking
multiple free-adjusted blocks, each for aggregating Feature Extractor Module
and Concate And Activate(C&A) Module. The homogeneous and multi-branch
architecture can model the complex internal relationship between amino acid
sequence and protein secondary structure sequence. We use two publicly available
protein datasets to evaluate the proposed model. Experimental results show that
our model can obtain 85% Q3 accuracy, 86% SOV score, and 75% Q8 accuracy,
respectively, achieving better performance compared with the currently popular
predictors. Keywords: Sequence Translating; Protein Secondary Structure Predication;rnMachine Learning; Cascade Model; Deep Learning.