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Direct RNA sequencing in plants:Practical applications and future perspectives

Plant Communications 2024年第11期5卷 54-71页

作者： Xi-Tong Zhu Pablo Sanz-Jimenez Xiao-Tong Ning Muhammad Tahir ul Qamar Ling-Ling Chen State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources College of Life Science and TechnologyGuangxi UniversityNanning 530004China National Key Laboratory of Crop Genetic Improvement College of InformaticsHuazhong Agricultural UniversityWuhan 430070China Integrative Omics and Molecular Modeling Laboratory Department of Bioinformatics and BiotechnologyGovernment College University Faisalabad(GCUF)Faisalabad 38000Pakistan These authors contributed equally to this article

The transcriptome serves as a bridge that links genomic variation to phenotypic diversity.A vast number of studies using next-generation RNA sequencing(RNA-seq)over the last 2 decades have emphasized the essential roles of the plant transcriptome in response to developmental and environmental conditions,providing numerous insights into the dynamic changes,evolutionary traces,and elaborate regulation of the plant *** substantial improvement in accuracy and throughput,direct RNA sequencing(DRS)has emerged as a new and powerful sequencing platform for precise detection of native and full-length transcripts,overcoming many limitations such as read length and PCR bias that are inherent to short-read ***,we review recent advances in dissecting the complexity and diversity of plant transcriptomes using DRS as the main technological approach,covering many aspects of RNA metabolism,including novel isoforms,poly(A)tails,and RNA modification,and we propose a comprehensive workflow for processing of plant DRS *** challenges to the application of DRS in plants,such as the need for machine learning tools tailored to plant transcriptomes,remain to be overcome,and together we outline future biological questions that can be addressed by DRS,such as allele-specific RNA *** technology provides convenient support on which the connection of distinct RNA features is tightly built,sustainably refining our understanding of the biological functions of the plant transcriptome.

关键词： direct RNA sequencing novel isoforms fusion transcripts poly(A)tail RNA modifications

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The Importance of Prognostic Variables to Monitoring Heart Failure Using Health Management Systems 2nd

The Importance of Prognostic Variables to Monitoring Heart...

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2nd International Symposium on Biomedical and Computational Biology, BECB 2022

作者： Dias, Alexandre Davi Santos Almeida, Fernanda Nascimento Federal University of ABC São Bernardo do Campo Brazil Bioinformatics and Health Informatics Group - BHIG Center for Engineering Modeling and Applied Social Science Federal University of ABC SP São Bernardo do Campo Brazil

ISBN: (纸本)9783031251900

Heart Failure is a syndromal disease affecting about 2% of the world’s adult population and about 10% of adults over 70 years of age. Because of the progressive nature of this disease, continuous and close monitoring of disease progression is an important approach to managing patient quality of life once data suggest that patients who develop Heart Failure have difficulty maintaining self-care routines. The use of technology to assist physicians and patients in managing Heart Failure offers great opportunities related to the challenges of managing the disease. Several papers have shown that the use of follow-up protocols by patients from Heart Failure reduces hospital readmissions by approximately 26%. Therefore, regular follow-up of patients is crucial to keep the disease stable and maintain the quality of life of these people. However, through the use of technologies like electronic follow-up protocols and Decision Support Technologies, disease management could be more assertive and precise. For this reason, in this paper we present an overview of how prognostic variables can be important for system development and patient monitoring. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision support systems

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CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs

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Applied Network Science 2022年第1期7卷 1-12页

作者： Omranian, Sara Nikoloski, Zoran Technical University of Munich Petersgasse 18 Campus Straubing for Biotechnology and Sustainability Bioinformatics Straubing94315 Germany Weihenstephan-Triesdorf University of Applied Sciences Petersgasse 18 Straubing94315 Germany SynBiofoundry@TUM Technical University of Munich Schulgasse 22 Straubing94315 Germany Bioinformatics Institute of Biochemistry and Biology University of Potsdam Potsdam14476 Germany Systems Biology and Mathematical Modeling Max Planck Institute of Molecular Plant Physiology Potsdam14476 Germany

High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with a high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity, CUBCO+ enables us to predict more accurate protein complexes in 36% of the cases in comparison to CUBCO as its predecessor. © 2022, The Author(s).

关键词： Forecasting

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pasta: Pattern Analysis for Spatial Omics Data

arXiv

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arXiv 2024年

作者： Emons, Martin Gunz, Samuel Crowell, Helena L. Mallona, Izaskun Furrer, Reinhard Robinson, Mark D. Department of Molecular Life Sciences SIB Swiss Institute of Bioinformatics University of Zurich Zurich Switzerland Barcelona Spain Department of Mathematical Modeling and Machine Learning University of Zurich Zurich Switzerland

Spatial omics assays allow for the molecular characterisation of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, can give rise to very different data modalities. The characteristics of the two data types are well known in adjacent fields such as spatial statistics as point patterns and lattice data, and there is a wide range of tools available. This paper discusses the application of spatial statistics to spatially-resolved omics data and in particular, discusses various advantages, challenges, and nuances. This work is accompanied by a vignette, pasta, that showcases the usefulness of spatial statistics in biology using several R packages. © 2024, CC BY.

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Compression rates of microbial genomes are associated with genome size and base composition

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Genomics and Informatics 2024年第1期22卷 1-9页

作者： Bohlin, Jon Pettersson, John H.-O. Norwegian Institute of Public Health Domain for Infection Control Section for Modeling and Bioinformatics Oslo Norway Zoonosis Science Center Clinical Microbiology Department of Medical Sciences University of Uppsala Uppsala 751 85 Sweden Clinical Microbiology and Hospital Hygiene Uppsala University Hospital Uppsala 751 85 Sweden Department of Microbiology and Immunology Peter Doherty Institute for Infection and Immunity University of Melbourne Melbourne VIC Australia

Background: To what degree a string of symbols can be compressed reveals important details about its complexity. For instance, strings that are not compressible are random and carry a low information potential while the opposite is true for highly compressible strings. We explore to what extent microbial genomes are amenable to compression as they vary considerably both with respect to size and base composition. For instance, microbial genome sizes vary from less than 100,000 base pairs in symbionts to more than 10 million in soil-dwellers. Genomic base composition, often summarized as genomic AT or GC content due to the similar frequencies of adenine and thymine on one hand and cytosine and guanine on the other, also vary substantially;the most extreme microbes can have genomes with AT content below 25% or above 85% AT. Base composition determines the frequency of DNA words, consisting of multiple nucleotides or oligonucleotides, and may therefore also influence compressibility. Using 4,713 RefSeq genomes, we examined the association between compressibility, using both a DNA based- (MBGC) and a general purpose (ZPAQ) compression algorithm, and genome size, AT content as well as genomic oligonucleotide usage variance (OUV) using generalized additive models. Results: We find that genome size (p < 0.001) and OUV (p < 0.001) are both strongly associated with genome redundancy for both type of file compressors. The DNA-based MBGC compressor managed to improve compression with approximately 3% on average with respect to ZPAQ. Moreover, MBGC detected a significant (p < 0.001) compression ratio difference between AT poor and AT rich genomes which was not detected with ZPAQ. Conclusion: As lack of compressibility is equivalent to randomness, our findings suggest that smaller and AT rich genomes may have accumulated more random mutations on average than larger and AT poor genomes which, in turn, were significantly more redundant. Moreover, we find that OUV is a strong proxy

关键词： Base composition Compression Information potential MBGC Microbial Genomics ZPAQ

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INTEGRATION OF GRAPH NEURAL NETWORK AND NEURAL-ODES FOR TUMOR DYNAMICS PREDICTION

arXiv

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arXiv 2023年

作者： Bazgir, Omid Wang, Zichen Park, Ji Won Hafner, Marc Lu, James Modeling & Simulation/ Clinical Pharmacology Genentech Prescient Design/ Computational Sciences Genentech Oncology Bioinformatics/ Computational Sciences Genentech Discovery Oncology/ Research Genentech

In the development of anti-cancer drugs, a major scientific challenge is disentangling the complex interplay between high-dimensional genomics data derived from patient tumor samples, the organ of origin of the tumor, the drug targets associated with the specified treatments, and the ensuing treatment response. Furthermore, to realize the aspirations of precision medicine in identifying and adjusting treatments for patients depending on the therapeutic response, there is a need for building tumor dynamics models that can integrate the longitudinal tumor size measurements with multimodal, high-throughput data. In this work, we take a step towards enhancing personalized tumor dynamics predictions by proposing a heterogeneous graph encoder that utilizes a bipartite Graph Convolutional Neural networks (GCNs) combined with Neural Ordinary Differential Equations (Neural-ODEs). We apply the methodology to a large collection of patient-derived xenograft (PDX) data, spanning a wide variety of treatments (as well as their combinations) and tumor organs of origin. We first show that the methodology is able to discover a tumor dynamic model that significantly improves upon an empirical model in current use. Additionally, we show that the graph encoder is able to effectively incorporate multimodal data to enhance tumor predictions. Our findings indicate that the methodology holds significant promise and offers potential applications in pre-clinical settings. © 2023, CC BY-NC-ND.

关键词： Tumors

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Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits

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园艺研究（英文） 2022年第1期9卷 1524-1534页

作者： Hao Tong Amol N.Nankar Jintao Liu Velichka Todorova Daniela Ganeva Stanislava Grozeva Ivanka Tringovska Gancho Pasev Vesela Radeva-Ivanova Tsanko Gechev Dimitrina Kostova Zoran Nikoloski Center of Plant Systems Biology and Biotechnology Plovdiv4000Bulgaria Systems Biology and Mathematical Modeling Max Planck Institute of Molecular Plant PhysiologyPotsdam14476Germany Bioinformatics Institute of Biochemistry and BiologyUniversity of PotsdamPotsdam14476Germany Center of Plant Systems Biology and Biotechnology Plovdiv4000Bulgaria Systems Biology and Mathematical Modeling Max Planck Institute of Molecular Plant PhysiologyPotsdam14476Germany Maritsa Vegetable Crops Research Institute Plovdiv4003Bulgaria. Center of Plant Systems Biology and Biotechnology Plovdiv4000Bulgaria Maritsa Vegetable Crops Research Institute Plovdiv4003Bulgaria.

Selection of high-performance lines with respect to traits of interest is a key step in plant *** prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers,***-nucleotide polymorphisms(SNPs),and machine learning approaches,which can therefore shorten breeding cycles,referring to genomic selection(GS).Here,we applied GS approaches in two populations of Solanaceous crops,*** and pepper,to predict morphometric and colorimetric *** traits were measured by using scoring-based conventional descriptors(CDs)as well as by Tomato Analyzer(TA)tool using the longitudinally and latitudinally cut fruit *** GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits,*** results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous *** highest predictability of 0.89 was achieved for fruit width in pepper,with an average predictability of 0.69 over all *** multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in *** model validation performs poorly on wild tomato accessions,the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits(*** shape for which predictability in unseen scenario increased from zero to 0.6).Overall,GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding.

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Opening Amyloid-Windows to the secondary structure of proteins: The amyloidogenecity increases tenfold inside beta-sheets

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Computers in Biology and Medicine 2024年 179卷 108863-108863页

作者： Takács, Kristóf Varga, Bálint Farkas, Viktor Perczel, András Grolmusz, Vince PIT Bioinformatics Group Eötvös University BudapestH-1117 Hungary Uratim Ltd. BudapestH-1118 Hungary HUN-REN -ELTE Protein Modeling Research Group BudapestH-1117 Hungary Laboratory of Structural Chemistry and Biology Eötvös University BudapestH-1117 Hungary

Methods from artificial intelligence (AI), in general, and machine learning, in particular, have kept conquering new territories in numerous areas of science. Most of the applications of these techniques are restricted to the classification of large data sets, but new scientific knowledge can seldom be inferred from these tools. Here we show that an AI-based amyloidogenecity predictor can strongly differentiate the border- and the internal hexamers of β-pleated sheets when screening all the Protein Data Bank-deposited homology-filtered protein structures. Our main result shows that more than 30% of internal hexamers of β sheets are predicted to be amyloidogenic, while just outside the border regions, only 3% are predicted as such. This result may elucidate a general protection mechanism of proteins against turning into amyloids: if the borders of β-sheets were amyloidogenic, then the whole β sheet could turn more easily into an insoluble amyloid-structure, characterized by periodically repeated parallel β-sheets. We also present that no analogous phenomenon exists on the borders of α-helices or randomly chosen subsequences of the studied protein structures. © 2024 Elsevier Ltd

关键词： Support vector machines

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InterCriteria Analysis of the Financial System in the EU Countries 10

InterCriteria Analysis of the Financial System in the EU Cou...

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10th IEEE International Conference on Intelligent Systems, IS 2020

作者： Danailova-Veleva, Slaviiana Doukovska, Lyubka Atanassova, Vassia IICT-BAS Intelligent Systems Department Sofia Bulgaria IBBE-BAS Bioinformatics and Mathematical Modeling Department Sofia Bulgaria

ISBN: (纸本)9781728154565

The paper presents an analysis of the financial systems of the EU member states which is an actual problem. The paper showcases a new method of analyzing and grading of the connection between the criteria defining the state of the financial systems. The presented multicriteria decision making method is based on two fundamental concepts: Intuitionistic Fuzzy Sets (IFSs) and Index Matrices (IMs). We have named it 'InterCriteria' decision making approach. The data for the financial systems of the EU member states is sourced from the World Economic Forum's Global Competitiveness Reports for 2019. The assessment was made using 12 main drivers, so called Pillars. The main goal is to assess the competitiveness of each EU country. © 2020 IEEE.

关键词： Intelligent systems

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Navigating Homogeneous Paths through Amyloidogenic and Non-Amyloidogenic Hexapeptides

arXiv

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arXiv 2023年

作者： Keresztes, László Szögi, Evelin Varga, Bálint Farkas, Viktor Perczel, András Grolmusz, Vince PIT Bioinformatics Group Eötvös University BudapestH-1117 Hungary Uratim Ltd. BudapestH-1118 Hungary ELKH-ELTE Protein Modeling Research Group BudapestH-1117 Hungary Laboratory of Structural Chemistry and Biology Eötvös University BudapestH-1117 Hungary

Hexapeptides are increasingly applied as model systems for studying the amyloidogenecity properties of oligo- and polypeptides. It is possible to construct 64 million different hexapeptides from the twenty proteinogenic amino acid residues. Today’s experimental amyloid databases contain only a fraction of these annotated hexapeptides. For labeling all the possible hexapeptides as"amyloidogenic" or"non-amyloidogenic" there exist several computational predictors with good accuracies. It may be of interest to define and study a simple graph structure on the 64 million hexapeptides as nodes when two hexapeptides are connected by an edge if they differ by only a single residue. For example, in this graph, HIKKLM is connected to AIKKLM, or HIKKNM, or HIKKLC, but it is not connected with an edge to VVKKLM or HIKNPM. In the present contribution, we consider our previously published artificial intelligence-based tool, the Budapest Amyloid Predictor (BAP for short), and demonstrate a spectacular property of this predictor in the graph defined above. We show that for any two hexapeptides predicted to be"amyloidogenic" by the BAP predictor, there exists an easily constructible path of length at most 6 that passes through neighboring hexapeptides all predicted to be"amyloidogenic" by BAP. For example, the predicted amyloidogenic ILVWIW and FWLCYL hexapeptides can be connected through the length-6 path ILVWIW-IWVWIWIWVCIW-IWVCIL-FWVCIL-FWLCIL-FWLCYL in such a way that the neighbors differ in exactly one residue, and all hexapeptides on the path are predicted to be amyloidogenic by BAP. The symmetric statement also holds true for non-amyloidogenic predicted hexapeptides: for any such pair, there exists a path of length at most 6, traversing only predicted non-amyloidogenic hexapeptides. It is noted that the mentioned property of the Budapest Amyloid Predictor https://***/bap is not proprietary;it is also true for any linear © 2023, CC BY.

关键词： Glycoproteins

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