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Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

Genomics, Proteomics & bioinformatics 2020年第1期18卷 52-64页

作者： Fuyi Li Andre Leier Quanzhong Liu Yanan Wang Dongxu Xiang Tatsuya Akutsu Geoffrey IWebb A.Ian Smith Tatiana Marquez-Lago Jian Li Jiangning Song Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash UniversityMelbourneVIC 3800Australia Monash Centre for Data Science Faculty of Information TechnologyMonash UniversityMelbourneVIC 3800Australia School of Medicine University of Alabama at BirminghamBirminghamAL 35233USA College of Information Engineering Northwest A&F UniversityYangling 712100China Bioinformatics Center Institute for Chemical ResearchKyoto UniversityUjiKyoto 611-0011Japan ARC Centre of Excellence in Advanced Molecular Imaging Monash UniversityMelbourneVIC 3800Australia Biomedicine Discovery Institute and Department of Microbiology Monash UniversityMelbourneVIC 3800Australia

Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate ***-controlled proteolysis plays a key role in the degradation and recycling of proteins,which is essential for various physiological ***,solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases,as well as for therapeutic target identification and pharmaceutical ***,there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural *** this study,we present Procleave,a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural *** features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method,which turned out to be critical for the performance of *** optimal approximations of all structural parameter values were encoded in a conditional random field(CRF)computational framework,alongside sequence and chemical group-based ***,we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent *** is capable of correctly identifying most cleavage sites in the case ***,when applied to the human structural proteome encompassing 17,628 protein structures,Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different *** is implemented as a webserver and is freely accessible at http://***/.

关键词： Protease Cleavage site prediction Machine learning Conditional random field Structural determinants

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Hybrid feature selection to identify important single nucleotide polymorphism for asthma classification

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AIP Conference Proceedings 2024年第1期2991卷

作者： Wan Ni Wong Weng Howe Chan Hui Wen Nies Kauthar Mohd Daud Artificial Intelligence and Bioinformatics Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

Single nucleotide polymorphism (SNP), being the most common genetic variation, is found to be responsible for traits and complex diseases such as asthma. Polygenic risk scoring (PRS) and machine learning (ML) are the current two common types of genetic risk prediction models. PRS employs statistical association testing to identify alleles with risk. However, it was found not performing well in unraveling the SNP-SNP interactions and attaining low predictive value. In contrast, ML algorithms are more robust in detecting disease associated SNPs, resulting in an improvement in prediction performance. The challenge remains as the high dimensionality nature of SNP tends to cause overfitting problems and impact the classifier performance. This paper introduces a two-stage hybrid feature selection that integrates Mutual Information and Random Forest Recursive Feature Elimination for identifying informative SNPs from 176,287 SNPs in a dataset of 128 asthmatic and non-asthmatic samples. The reduced samples based on the identified informative SNPs are then used with Support Vector Machine to train a classification model. The model from this paper outperforms other ML models with an average accuracy, precision, recall and area under the curve of 98.91%, 98.36%, 99.72% and 0.99 respectively. Additional analysis shows that the genes associated with the top selected SNPs, namely Ring Finger Protein 217 (RNF217) and A-kinase-anchoring protein 6 (AKAP6) are correlated with respiratory diseases. This shows that the proposed method can identify important SNPs that are able to differentiate between asthmatic and non-asthmatic samples.

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Characterization of Rare Germline Variants in Familial Multiple Myeloma

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BLOOD 2020年 136卷 45-46页

作者： Catalano, Calogerina Blocka, Joanna Huhn, Stefanie Paramasivam, Nagarajan Schlesner, Matthias Weinhold, Niels Sijmons, Rolf Langer, Christian Preuss, Klaus Durie, Brian G. M. Nilsson, Bjorn Goldschmidt, Hartmut Bandapalli, Obul Reddy Hemminki, Kari Forsti, Asta Department of Internal Medicine V Heidelberg University Hospital Heidelberg Germany Division of Molecular Genetic Epidemiology German Cancer Research Center (DKFZ) Heidelberg Germany Department of Hematology Oncology and Rheumatology Heidelberg University Hospital Heidelberg Germany Computational Oncology Molecular Diagnostics Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg Germany Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany Clinical Cooperation Unit (CCU) Molecular Hematology/Oncology German Cancer Research Center (DKFZ) Heidelberg Germany University Medical Center Groningen University of Groningen Groningen Netherlands Kempten Clinic Kempten Germany Kempten Germany José Carreras Center for Immuno and Gene Therapy Universität Des Saarlandes Homburg/Saar Germany Cedars Sinai Cancer Center Los Angeles CA Division of Hematology and Transfusion Medicine Lund University Lund Sweden National Center for Tumor Diseases Heidelberg Heidelberg Germany Hopp Children's Cancer Center (KiTZ) Heidelberg Germany Heidelberg Germany Division of Pediatric Neurooncology German Consortium for Translational Cancer Research (DKTK) Heidelberg Germany Division of Cancer Epidemiology German Cancer Research Center Heidelberg DEU Faculty of Medicine and Biomedical Center in Pilsen Charles University in Prague Pilsen Czech Republic Pilsen Czech Republic

Introduction: The risk of developing Multiple Myeloma (MM) is 2-4 fold higher in first-degree relatives of patients with MM compared to the general population, suggesting genetic predisposition to this cancer. Indeed, recent genome-wide association studies have identified common risk alleles that predispose for MM. Yet, the impact of these variants on MM risk is too low to explain familial aggregation of MM. High-impact alleles have been identified for other cancers such as ovarian and breast cancer ( BRCA1,-2 ) and melanoma ( CDKN2A ) but the search for such alleles in MM is still in its infancy. In order to identify high-impact alleles in MM we have performed whole genome/exon sequencing (WGS/WES) in members of MM high risk families. Methods: We included 21 families with multiple cases of MM/MGUS. Whole genome/exome sequencing was performed on a total of 46 affected and 20 unaffected family members. Filtering and prioritization of the variants were performed in accordance with the criteria of our in-house familial cancer variant prioritization pipeline version 2 (FCVPPv2). Loss-of-function variants were further screened using MutPred-LOF, Translate tool and IntOGen/c-BioPortal in order to discriminate pathogenic and neutral variants, to translate a nucleotide sequence to a protein sequence and to visualize the domain affected by the variant and the portion of the protein lost after the newly formed stop codon. Variants were analyzed for predicted effects on splicing by using Human Splicing Finder. Results: We found a total of 148 potentially pathogenic variants, 109 non-synonymous and 39 LOF, in 18 out of 21 MM families. Among our genes, many affect protein metabolism, immune system, and other have known links to carcinogenesis. Additionally, some of them are known to interact with key signaling pathways in MM, including PI3K/Akt/mTOR, Ras/Raf/MEK/MAPK, JAK/STAT, NF-κB, Wnt/β-catenin, and RANK/RANKL/OPG, showing congruency with previously reported literature. Inte

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Provoking Tweets by Indonesia Media Twitter in the Initial Month of Coronavirus Disease Hit

Provoking Tweets by Indonesia Media Twitter in the Initial M...

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International Conference on Information Management and Technology (ICIMTech)

作者： Ika Nurlaila Reza Rahutomo Kartika Purwandari Bens Pardamean BINUS Online Learning Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

After months in denial, on 2 nd March 2020 the first case of COVID-19 in Indonesia was announced and soon was declared as a national pandemic. COVID-19 was since being the headlines of the news offline and online. This trigger either positive or negative responses from the readers, which in turn affect at least the depth of understanding and furthermore the effectiveness of any enforced intervention taken with the purpose to control the transmission of the COVID-19. In order to assess how likely the COVID-19-containing news being forwarded to reach more readers and how the pertinent news bridge further virtual interaction, we crawled data from Twitter as the major online microblogging platform to analyze how likely COVID-19-containing tweets were echoed and what typical tweet about COVID-19 that gained attentions. This is critical to evaluate how people virtually responded against the tweets. Our analyzed data is visualized using Matplotlib by Python and Graph Prism8 accordingly. We figured out that timing and number of followers are not determinative for the tweets being retweeted. Instead, provoking headline add likelihood for the tweets to be moving forward. Also appeared in our analysis that the top two media (Detik and Kompas) shared the same proportion on their tweet type fractions on where information dominated the fraction of their tweets.

关键词： Twitter Media Corona Tagging Diseases data visualization

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The Ser7 phosphorylation of RNA polymerase II-CTD is required for the recruitment of E3 ubiquitin ligase Asr1 and subtelomeric gene silencing

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Journal of Biological Chemistry 2025年

作者： Nikita Sudarshan Mohd. Asalam Ashutosh Kumar Neha Singh Adity Gupta Ishita De Sanjeev Kumar Singh Kam Y.J. Zhang Md. Sohail Akhtar Biochemistry and Structural Biology Division CSIR-Central Drug Research Institute Jankipuram Extension Sitapur Road Lucknow 226031 India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad- 201002 India Laboratory for Structural Bioinformatics Center for Biosystems Dynamics Research RIKEN 1-7-22 Suehiro Tsurumi Yokohama Kanagawa 230-0045 Japan Department of Data Sciences Centre of Bio Medical Research Lucknow 226014 India

The carboxy terminal domain (CTD) of the largest subunit of RNA Polymerase II (RNAPII) is composed of a tandem heptad sequence of Tyr1Ser2Pro3Thr4Ser5Pro6Ser7, which helps facilitate the transcription of all mRNA and the majority of non-coding RNA. The serines of RNAPII-CTD undergo differential phosphorylation, with Ser5 phosphorylation (Ser5P) being predominant at the 5’ end, Ser2P towards the 3’ end, and Ser7P present throughout the ORF during transcription. The phosphorylation of Ser2 and Ser5 coordinates the recruitment of proteins involved in the progression of transcription. The Ser7P has been shown to play a role in the processing and termination of snRNA transcription in both budding yeast and humans. Nevertheless, the effect of this phosphorylation mark on protein-coding genes remains unclear. This is despite the fact that substitution of Ser7 with phosphomimetic Glu does not support growth and highly transcribed mRNA genes show high levels of this phosphorylation mark. In this study, we demonstrate that the interaction between E3 ubiquitin ligase Asr1 and RNAPII is influenced by the Ser7P in both in vitro and in vivo conditions. Asr1 appears to interact with the CTD in a distinct manner, where Ser7 is phosphorylated in the first heptad and Ser5 in the third heptad, involving key residues such as Lys43, Arg48, Arg168, and Arg252. The Ser7P is important for the recruitment of Asr1 to RNAPII and Ser7 mutation leads to the upregulation of subtelomeric genes. Ubc2 has been identified as the canonical ubiquitin-conjugating enzyme associated with Asr1.

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Orientation of Fitch graphs and detection of horizontal gene transfer in gene trees

arXiv

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arXiv 2021年

作者： Schaller, David Hellmuth, Marc Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Leipzig Research Center for Civilization Diseases Leipzig University Germany Inst. f. Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Horizontal gene transfer events partition a gene tree T and thus, its leaf set into subsets of genes whose evolutionary history is described by speciation and duplication events alone. Indirect phylogenetic methods can be used to infer such partitions p from sequence similarity or evolutionary distances without any a priory knowledge about the underlying tree T. In this contribution, we assume that such a partition p of a set of genes X is given and that, independently, an estimate T of the original gene tree on X has been derived. We then ask to what extent T and the xenology information, i.e., p can be combined to determine the horizontal transfer edges in T. We show that for each pair of genes x and y with x,y being in different parts of p, it can be decided whether there always exists or never exists a horizontal gene transfer in T along the path connecting y and the most recent common ancestor of x and y. This problem is equivalent to determining the presence or absence of the directed edge (x,y) in so-called Fitch graphs;a more fine-grained version of graphs that represent the dependencies between the sets in p. We then consider the generalization to insufficiently resolved gene trees and show that analogous results can be obtained. We show that the classification of (x,y) can be computed in constant time after linear-time preprocessing. Using simulated gene family histories, we observe empirically that the vast majority of horizontal transfer edges in the gene tree T can be recovered unambiguously. © 2021, CC BY-NC-SA.

关键词： Clustering algorithms

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Regression with a right-censored predictor, using inverse probability weighting methods

arXiv

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arXiv 2020年

作者： Matsouaka, Roland A. Atem, Folefac D. Department of Biostatistics and Bioinformatics & Program for Comparative Effectiveness Methodology Duke Clinical Research Institute Duke University DurhamNC United States Department of Biostatistics and Data Science University of Texas Health Science Center at Houston HoustonTX United States

In a longitudinal study, measures of key variables might be incomplete or partially recorded due to drop-out, loss to follow-up, or early termination of the study occurring before the advent of the event of interest. In this paper, we focus primarily on the implementation of a regression model with a randomly censored predictor. We examine, particularly, the use of inverse probability weighting methods in a generalized linear model (GLM), when the predictor of interest is right-censored, to adjust for censoring. To improve the performance of the complete-case analysis and prevent selection bias, we consider three different weighting schemes: inverse censoring probability weights, Kaplan-Meier weights, and Cox proportional hazards weights. We use Monte Carlo simulation studies to evaluate and compare the empirical properties of different weighting estimation methods. Finally, we apply these methods to the Framingham Heart Study data as an illustrative example to estimate the relationship between age of onset of a clinically diagnosed cardiovascular event and low-density lipoprotein (LDL) among cigarette smokers. Copyright © 2020, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Quasi-Best Match Graphs

arXiv

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arXiv 2021年

作者： Korchmaros, Annachiara Schaller, David Hellmuth, Marc Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Quasi-best match graphs (qBMGs) are a hereditary class of directed, properly vertex-colored graphs. They arise naturally in mathematical phylogenetics as a generalization of best match graphs, which formalize the notion of evolutionary closest relatedness of genes (vertices) in multiple species (vertex colors). They are explained by rooted trees whose leaves correspond to vertices. In contrast to BMGs, qBMGs represent only best matches at a restricted phylogenetic distance. We provide characterizations of qBMGs that give rise to polynomial-time recognition algorithms and identify the BMGs as the qBMGs that are color-sink-free. Furthermore, two-colored qBMGs are characterized as directed graphs satisfying three simple local conditions, two of which have appeared previously, namely bi-transitivity in the sense of Das et al. (2021) and a hierarchy-like structure of out-neighborhoods, i.e., N(x) ∩ N(y) ∈ {N(x), N(y), ∅} for any two vertices x and y. Further results characterize qBMGs that can be explained by binary phylogenetic trees. Copyright © 2021, The Authors. All rights reserved.

关键词： Phylogenetic

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The Sensorium competition on predicting large-scale mouse primary visual cortex activity

arXiv

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arXiv 2022年

作者： Willeke, Konstantin F. Fahey, Paul G. Bashiri, Mohammad Pede, Laura Burg, Max F. Blessing, Christoph Cadena, Santiago A. Ding, Zhiwei Lurz, Konstantin-Klemens Ponder, Kayla Muhammad, Taliah Patel, Saumil S. Ecker, Alexander S. Tolias, Andreas S. Sinz, Fabian H. International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Institute for Theoretical Physics University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Electrical and Computer Engineering Rice University Houston United States

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the SENSORIUM benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (SENSORIUM) or stimulus plus behavior (SENSORIUM+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. © 2022, CC BY-NC-ND.

关键词： Neural networks

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Relative Timing Information and Orthology in Evolutionary Scenarios

arXiv

引用

arXiv 2022年

作者： Schaller, David Hartmann, Tom Lafond, Manuel Wieseke, Nicolas Stadler, Peter F. Hellmuth, Marc Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Computer Science Université de Sherbrooke 2500 boul. de l’Université SherbrookeQCJ1K 2R1 Canada Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Universität Leipzig Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States Swarm Intelligence and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 LeipzigD-04109 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Evolutionary scenarios describing the evolution of a family of genes within a collection of species comprise the mapping of the vertices of a gene tree T to vertices and edges of a species tree S. The relative timing of the last common ancestors of two extant genes (leaves of T) and the last common ancestors of the two species (leaves of S) in which they reside is indicative of horizontal gene transfers (HGT) and ancient duplications. Orthologous gene pairs, on the other hand, require that their last common ancestors coincides with a corresponding speciation event. The relative timing information of gene and species divergences is captured by three colored graphs that have the extant genes as vertices and the species in which the genes are found as vertex colors: the equal-divergence-time (EDT) graph, the later-divergence-time (LDT) graph and the prior-divergence-time (PDT) graph, which together form an edge partition of the complete graph. Here we give a complete characterization in terms of informative and forbidden triples that can be read off the three graphs and provide a polynomial time algorithm for constructing an evolutionary scenario that explains the graphs, provided such a scenario exists. While both LDT and PDT graphs are cographs, this is not true for the EDT graph in general. We show that every EDT graph is perfect. While the information about LDT and PDT graphs is necessary to recognize EDT graphs in polynomial-time for general scenarios, this extra information can be dropped in the HGT-free case. However, recognition of EDT graphs without knowledge of putative LDT and PDT graphs is NP-complete for general scenarios. In contrast, PDT graphs can be recognized in polynomial-time. We finally connect the EDT graph to the alternative definitions of orthology that have been proposed for scenarios with horizontal gene transfer. With one exception, the corresponding graphs are shown to be colored cographs. © 2022, CC BY.

关键词： Genes

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