Predicting subcellular localization has become a valuable alternative to time-consuming experimental methods. Major drawbacks of many of these predictors is their lack of interpretability and the fact that they do not provide an estimate of the confidence of an individual prediction. We present YLoc, an interpretable web server for predicting subcellular localization. YLoc uses natural language to explain why a prediction was made and which biological property of the protein was mainly responsible for it. In addition, YLoc estimates the reliability of its own predictions. YLoc can, thus, assist in understanding protein localization and in location engineering of proteins. The YLoc web server is available online at www.multiloc.org/YLoc.
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http://dx.doi.org/10.1093/nar/gkq477 | DOI Listing |
The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.
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Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Research Unit of Hematologic Malignancies Genomics and Translational Research of Chinese Academy of Medical Sciences, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Physics and Astronomy, Clemson University, Clemson, SC, USA.
The ARID1A gene, frequently mutated in cancer, encodes the AT-rich interactive domain-containing protein 1 A, a key component of the chromatin remodeling SWI/SNF complex. The ARID1A protein features a conserved DNA-binding domain (ARID domain) of approximately 100 residues crucial for its function. Despite the frequency of mutations, the impact on ARID1A's stability and contribution to cancer progression remains unclear.
View Article and Find Full Text PDFActa Crystallogr B Struct Sci Cryst Eng Mater
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Department of Chemistry, University College London (UCL), 20 Gordon Street, London, WC1H 0AJ, England.
The online software server SARAh-webRepresentational Analysis is introduced. It replaces the previous Windows-versions of SARAh-Representational analysis and SARAh-Refine, and related theory. The new suite of web apps carries out a range representational analysis calculations, including those based on the works of Kovalev, Bertaut, Izyumov, Bradley, Cracknell, Birman and Landau, for magnetic structures and electronic properties within frameworks based on the crystallographic space groups and point groups.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600036 Tamil Nadu, India.
Interactions between proteins and RNAs are essential for the proper functioning of cells, and mutations in these molecules may lead to diseases. These protein mutations alter the strength of interactions between the protein and RNA, generally described as binding affinity (Δ). Hence, the affinity change upon mutation (ΔΔ) is an important parameter for understanding the effect of mutations in protein-RNA complexes.
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