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Ovarian-Cancer-Associated Extracellular Vesicles: Microenvironmental Regulation along with Probable Clinical Apps.
This method was tested on microarray expression data from the M3D database, corresponding to subnetworks on one of the best researched model organisms, Escherichia coli. Results show a surprisingly high correlation between the observed states and the inferred system's behavior under various experimental conditions. AVAILABILITY Processed data and software implementation using Matlab are freely available at https//github.com/kotiang54/PgmGRNs. Full dataset available from the M3D database. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email [email protected] Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces the challenges in prediction accuracy and program accessibility. RESULTS This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens. To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested five-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model, Vaxign-ML, was compared to three publicly available RV programs with a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting bacterial protective antigens. Vaxign-ML is deployed in a publicly available web server. AVAILABILITY Vaxign-ML website at http//www.violinet.org/vaxign/vaxign-ml. Docker standalone Vaxign-ML available at https//hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https//github.com/VIOLINet/Vaxign-ML-docker. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email [email protected]/BACKGROUND Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large scale metagenome assemblies. RESULTS We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications. CONCLUSIONS DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modelling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects. AVAILABILITY DeepMAsED is available from GitHub at https//github.com/leylabmpi/DeepMAsED. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email [email protected] MUM&Co is a single bash script to detect Structural Variations (SVs) utilizing Whole Genome Alignment (WGA). Using MUMmer's nucmer alignment, MUM&Co can detect insertions, deletions, tandem duplications, inversions and translocations greater than 50bp. Its versatility depends upon the WGA and therefore benefits from contiguous de-novo assemblies generated by 3rd generation sequencing technologies. Benchmarked against 5 WGA SV-calling tools, MUM&Co outperforms all tools on simulated SVs in yeast, plant and human genomes and performs similarly in two real human datasets. Additionally, MUM&Co is particularly unique in its ability to find inversions in both simulated and real datasets. Lastly, MUM&Co's primary output is an intuitive tabulated file containing a list of SVs with only necessary genomic details. AVAILABILITY https//github.com/SAMtoBAM/MUMandCo. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email [email protected] injury after spinal cord injury (SCI) is one reversible pathological change mainly involving excessive inflammatory response and neuro-apoptosis. Since in recent years, microRNAs (miRNAs) have been proposed as novel regulators of inflammation in different disease conditions. However, the role of miRNAs in the inflammatory response and apoptosis of secondary injury after SCI remains to be fully elucidated. Here, we tried to explore the influence and mechanism of miRNAs on the neuron inflammatory response and apoptosis after SCI. The expression profiles of miRNA were examined using miRNA microarray, and among the candidate miRNAs, miR-129-5p was found to be the most down-regulated miRNA in spinal tissues. Overexpression of miR-129-5p using agomir-miR-129-5p promoted injury mice functional recovery, suppressed the apoptosis and alleviated inflammatory response in spinal tissues. Using LPS-induced BV-2 cell model, we found miR-129-5p was also proved in protecting inflammatory response and cell apoptosis in vitro. High-mobility group protein B1 (HMGB1), a well-known inflammatory mediator, was found to be directly targeted by miR-129-5p and it was associated with the inhibitory effect of miR-129-5p on the activation of toll-like receptor (TLR)-4 (TLR4)/ nuclear factor-κB (NF-κB) pathway in vitro and in vivo. find more Further experiments revealed that the anti-apoptosis and anti-inflammatory effects of miR-129-5p were reversed by HMGB1 overexpression in BV-2 cells. Collectively, these data revealed that miR-129-5p alleviated SCI in mice via suppressing the apoptosis and inflammatory response through HMGB1//TLR4/NF-κB pathway. Our data suggest that up-regulation of miR-129-5p may be a novel therapeutic target for SCI. © 2020 The Author(s).
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