Improving the Genome Annotation of Rhizoctonia solani Using Proteogenomics
Current Genomics
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2y ago
Background: Rhizoctonia solani is a pathogenic fungus that causes serious diseases in many crops, including rice, wheat, and soybeans. In crop production, it is very important to understand the pathogenicity of this fungus, which is still elusive. It might be helpful to comprehensively understand its genomic information using different genome annotation strategies. Methods: Aiming toimprove the genome annotation of R. solani, we performed a proteogenomic study based on the existing data. Based on our study, a total of 1060 newly identified genes, 36 revised genes, 139 single amino acid variant ..read more
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Splice Junction Identification using Long Short-Term Memory Neural Networks
Current Genomics
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2y ago
Background: Splice junctions are the key to move from pre-messenger RNA to mature messenger RNA in many multi-exon genes due to alternative splicing. Since the percentage of multi- exon genes that undergo alternative splicing is very high, identifying splice junctions is an attractive research topic with important implications. Objective: The aim of this paper is to develop a deep learning model capable of identifying splice junctions in RNA sequences using 13,666 unique sequences of primate RNA. Methods: A Long Short-Term Memory (LSTM) Neural Network model is developed that classifies a given ..read more
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Quantitative Trait Loci Identification by Estimating the Genetic Model based on the Extremal Samples
Current Genomics
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2y ago
Background: In genetic association studies with quantitative trait loci (QTL), the association between a candidate genetic marker and the trait of interest is commonly examined by the omnibus F test or by the t-test corresponding to a given genetic model or mode of inheritance. It is known that the t-test with a correct model specification is more powerful than the F test. However, since the underlying genetic model is rarely known in practice, the use of a model-specific t-test may incur substantial power loss. Robustefficient tests, such as the Maximin Efficiency Robust Test (MERT) and MAX3 ..read more
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In Silico Analysis of CCGAC and CATGTG Cis-regulatory Elements Across Genomes Reveals their Roles in Gene Regulation under Stress
Current Genomics
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2y ago
Background: Plant yield closely depends on its environment and is negatively affected by abiotic stress conditions like drought, salinity, heat, and cold. Analysis of the stress-inducible genes in Arabidopsis has previously shown that CCGAC and CATGTG play a crucial role in controlling the gene expression through the binding of DREB/CBF and NAC TFs under various stress conditions, mainly drought and salinity. Methods: The pattern of these motifs is conserved, which has been analyzed in this study to find the mechanism of gene expression through spacer specificity, inter motif distance preferen ..read more
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Accumulating Impact of Smoking and Co-morbidities on Severity and Mortality of COVID-19 Infection: A Systematic Review and Meta-analysis
Current Genomics
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2y ago
Background: High prevalence, severity, and formidable morbidity have marked the recent emergence of the novel coronavirus disease (COVID-19) pandemic. The significant association with the pre-existing co-morbid conditions has increased the disease burden of this global health emergency, pushing the patients, healthcare workers and facilities to the verge of complete disruption. Methods: Meta-analysis of pooled data was undertaken to assess the cumulative risk assessment of multiple co-morbid conditions associated with severe COVID-19. PubMed, Scopus, and Google Scholar were searched from Janua ..read more
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Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery
Current Genomics
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2y ago
The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Among ..read more
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An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation
Current Genomics
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2y ago
Single cell RNA-Seq technology enables the assessment of RNA expression in individual cells. This makes it popular in experimental biology for gleaning specifications of novel cell types as well as inferring heterogeneity. Experimental data conventionally contains zero counts or dropout events for many single cell transcripts. Such missing data hampers the accurate analysis using standard workflows, designed for massive RNA-Seq datasets. Imputation for single cell datasets is done to infer the missing values. This was traditionally done with ad-hoc code but later customized pipelines, workflow ..read more
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Indicators of Successful Career Transitions from Physical Sciences and Engineering to Biomedical Research
Current Genomics
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2y ago
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Proteasome Activator Blm10 Regulates Transcription Especially During Aging
Current Genomics
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2y ago
Background: Histones are basic elements of the chromatin and are critical to controlling chromatin structure and transcription. The proteasome activator PA200 promotes the acetylation- dependent proteasomal degradation of the core histones during spermatogenesis, DNA repair, transcription, and cellular aging and maintains the stability of histone marks. Objective: The study aimed to explore whether the yeast ortholog of PA200, Blm10, promotes degradation of the core histones during transcription and regulates transcription especially during aging. Methods: Protein degradation assays were perfo ..read more
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Machine Learning in Healthcare
Current Genomics
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2y ago
Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinfor ..read more
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