Current Issue : October-December Volume : 2021 Issue Number : 4 Articles : 5 Articles
Background: Osteoarthritis (OA) is a multifactorial joint degenerative disease with low-grade inflammation. The gut microbiome has recently emerged as an pathogenic factor of OA, and prebiotics supplementation could alleviate OA symptoms in animal models. However, the relationship between the gut microbiome and OA in the older female adults is hitherto not clear. Results: Here we studied the gut microbiome of 57 OA patients and their healthy controls by metagenome-wide association study based on previously published data. A significant reduction in the richness and diversity of gut microbiome were observed in OA patients. Bifidobacterium longum and Faecalibacterium prausnitzii were decreased while Clostridium spp. was increased in the OA group. The functional modules, particularly the energetic metabolism and acetate production were also decreased in the OA patients. To evaluate the diagnostic value of identified species for elderly patients with OA, we constructed a set of random forest disease classifiers based on species differences between the two groups. Among them, 9 species reached the lowest classification error in the random forest cross validation, and the area under ROC of the model was 0.81. Conclusions: Significant alterations in the gut microbial composition and function were observed between the older patients with OA and their controls, and a random forest classifier model for OA were constructed based on the differences in our study. Our study have identified several potential gut microbial targets in the elderly females with OA, which will facilitate the treatment of OA based on gut microbiota, is of great value in alleviating pain and improving the quality of life for them....
Background: Feature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions. Results: In this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods. Conclusion: The FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses....
Background: Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them. Methods: To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points: (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation. Results: Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results. Conclusions: We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER....
Background: RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement. Results: Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species. Conclusions: RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software....
Sequence files formats (FASTA and FASTQ) are commonly used in bioinformatics, molecular biology and biochemistry. With the advent of next-generation sequencing (NGS) technologies, the number of FASTQ datasets produced and analyzed has grown exponentially, urging the development of dedicated software to handle, parse, and manipulate such files efficiently. Several bioinformatics packages are available to filter and manipulate FASTA and FASTQ files, yet some essential tasks remain poorly supported, leaving gaps that any workflow analysis of NGS datasets must fill with custom scripts. This can introduce harmful variability and performance bottlenecks in pivotal steps. Here we present a suite of tools, called SeqFu (Sequence Fastx utilities), that provides a broad range of commands to perform both common and specialist operations with ease and is designed to be easily implemented in high-performance analytical pipelines. SeqFu includes high-performance implementation of algorithms to interleave and deinterleave FASTQ files, merge Illumina lanes, and perform various quality controls (identification of degenerate primers, analysis of length statistics, extraction of portions of the datasets). SeqFu dereplicates sequences from multiple files keeping track of their provenance. SeqFu is developed in Nim for high-performance processing, is freely available, and can be installed with the popular package manager Miniconda....
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