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A COMPREHENSIVE SOFTWARE SUITE FOR THE ANALYSIS OF CDNAS

Genomics, Proteomics & bioinformatics 2005年第3期3卷 179-188页

作者： KAZUHARU ARAKAWA HARUO SUZUKI KOSUKE FUJISHIMA KENJI FUJIMOTO SHO UEDA MOTOMU MATSUI MASARU TOMITA INSTITUTE FOR ADVANCED BIOSCIENCES KEIO UNIVERSITY FUJISAWA 252-8520 JAPAN BIOINFORMATICS PROGRAM OF THE GRADUATE SCHOOL OF MEDIA AND GOV- ERNANCE KEIO UNIVERSITY FUJISAWA 252-8520 JAPAN INSTITUTE FOR ADVANCED BIOSCIENCES KEIO UNIVERSITYFUJISAWA 252-8520 JAPAN

We have developed a comprehensive software suite for bioinformatics research of cDNAs; it is aimed at rapid characterization of the features of genes and the proteins they code. Methods implemented include the detection of translation initia- tion and termination signals, statistical analysis of codon usage, comparative study of amino acid composition, comparative modeling of the structures of product proteins, prediction of alternative splice forms, and metabolic pathway reconstruction. The software package is freely available under the GNU General Public License at http： / /***/ data/cdna/.

关键词： cDNA bioinformatics, software

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Flor Revisited(Again):eQTL and Mutational Analysis of NB-LRR Mediated Immunity to Powdery Mildew in Barley

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Journal of Integrative Agriculture 2014年第2期13卷 237-243页

作者： Roger Wise Priyanka Surana Greg Fuerst Ruo Xu Divya Mistry Julie Dickerson Dan Nettleton Corn Insects and Crop Genetics Research Unit U.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Plant Pathology & Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Department of Statistics Iowa State UniversityAmesIowa 50011USA Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA

Genes encoding early signaling events in pathogen defense often are identified only by their phenotype. Such genes involved in barley-powdery mildew interactions include Mla, specifying race-specific resistance; Rarl （Required for Mla12-specified resistance1）, and Roml （Restoration of Mla-specified resistancel）. The HSP90-SGT1-RAR1 complex appears to function as chaperone in MLA-specified resistance, however, much remains to be discovered regarding the precise signaling underlying plant immunity. Genetic analyses of fast-neutron mutants derived from CI 16151 （Mla6） uncovered a novel locus, designated Rar3 （R_equired for Mla6-specified resitance3）. Rar3 segregates independent of Mla6 and Rarl, and rar3 mutants are susceptible to Blumeria graminis f. sp. hordei （Bgh） isolate 5874 （A VRar）, whereas, wild-type progenitor plants are resistant. Comparative expression analyses of the rar3 mutant vs. its wild-type progenitor were conducted via Barleyl GeneChip and GAIIx paired-end RNA-Seq. Whereas Rarl affects transcription of relatively few genes; Rar3 appears to influence thousands, notably in genes controlling ATP binding, catalytic activity, transcription, and phosphorylation; possibly membrane bound or in the nucleus, eQTL analysis of a segregating doubled haploid population identified over two-thousand genes as being regulated by Mla （q value/FDR=0.00001）, a subset of which are significant in Rar3 interactions. The intersection of datasets derived from mla-loss-of-function mutants, Mla-associated eQTL, and rar3-mediated transcriptome reprogramming are narrowing the focus on essential genes required for Mla-specified immunity.

关键词： eQTL transcript profiling immunity resistance signaling barley Blumeria graminis

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Indonesian Agricultural-crops Classification Using Transfer Learning Model

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Procedia Computer Science 2023年 227卷 128-136页

作者： Mahmud Isnan Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNet- B0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation (vol 21, pg 432, 2018)

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NATURE NEUROSCIENCE 2018年第7期21卷 1015-1015页

作者： Preissl, Sebastian Fang, Rongxin Huang, Hui Zhao, Yuan Raviram, Ramya Gorkin, David U. Zhang, Yanxiao Sos, Brandon C. Afzal, Veena Dickel, Diane E. Kuan, Samantha Visel, Axel Pennacchio, Len A. Zhang, Kun Ren, Bing Ludwig Institute for Cancer Research La Jolla CA USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA US Department of Energy Joint Genome Institute Walnut Creek CA USA School of Natural Sciences University of California Merced Merced CA USA Comparative Biochemistry Program University of California Berkeley Berkeley CA USA Institute of Genomic Medicine Moores Cancer Center University of California San Diego School of Medicine La Jolla CA USA

Analysis of chromatin accessibility can reveal transcriptional regulatory sequences, but heterogeneity of primary tissues poses a significant challenge in mapping the precise chromatin landscape in specific cell types. Here we report single-nucleus ATAC-seq, a combinatorial barcoding-assisted single-cell assay for transposase-accessible chromatin that is optimized for use on flash-frozen primary tissue samples. We apply this technique to the mouse forebrain through eight developmental stages. Through analysis of more than 15,000 nuclei, we identify 20 distinct cell populations corresponding to major neuronal and non-neuronal cell types. We further define cell-type-specific transcriptional regulatory sequences, infer potential master transcriptional regulators and delineate developmental changes in forebrain cellular composition. Our results provide insight into the molecular and cellular dynamics that underlie forebrain development in the mouse and establish technical and analytical frameworks that are broadly applicable to other heterogeneous tissues.

关键词： Cell type diversity Chromatin Chromatin analysis Epigenomics

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Analysis of Single-Cell RNA Sequencing Data Unveils Novel Immune Prognostic Biomarkers

Analysis of Single-Cell RNA Sequencing Data Unveils Novel Im...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Wenjuan Zhang Alex Lee Mary Qu Yang Information Science Department MidSouth Bioinformatics Center and Bioinformatics Graduate Program University of Arkansas Little Rock and Univ. of Arkansas for Medical Sciences University of Arkansas Little Rock Little Rock USA Biology Department University of Arkansas Little Rock Little Rock USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

The immune system plays a central role in recognizing and eliminating cancerous cells. The activity and functionality of immune cells within the tumor microenvironment can significantly impact the prognosis of cancer patients. Leveraging single-cell RNA sequencing technology, we conducted an in-depth investigation into single-cell-based gene signatures for predicting cancer patient survival. The study utilized single-cell RNA sequencing data from breast cancer patients, encompassing over 14,875 immune cells. Gene signatures from major innate immune cell types, including Natural Killer (NK) cells, Macrophages, and Neutrophils, as well as adaptive immune cells, including Cytotoxic T Lymphocytes (T cells) and B lymphocytes (B cells), were identified. To elucidate their relationship in breast cancer patients, we constructed Cox proportional hazard regression models for predicting patient survival, utilizing the concordance index as a measure of prediction model performance. The evaluation incorporated three independent breast cancer patient cohorts: TCGA (The Cancer Genome Atlas program), METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), and UK breast cancer data. Our results showed that the T cell-based signature model outperformed the NK cell-based and Neutrophil-based prediction models. Moreover, the $T$ cell-based signature model exhibited robust performance across independent patient datasets. These findings suggest that gene signatures derived from cells of the adaptive immune system have a better survival prediction value than those derived from the innate immune system, underscoring the potential clinical relevance of harnessing the adaptive immune response for refining prognostic assessments in breast cancer. These insights contribute to advancing our understanding of the complex interplay between immune cells and cancer prognosis, paving the way for more tailored and effective therapeutic interventions.

关键词： Adaptation models Sequential analysis Adaptive systems RNA Predictive models Biomarkers Breast cancer

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Leveraging ChemBERTa Robustness in Drug-Drug Interaction Classification via Molecular Decomposition

Leveraging ChemBERTa Robustness in Drug-Drug Interaction Cla...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Christian Kenneth Alyssa Imani Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Understanding drug-drug interactions (DDIs) can benefit us by enhancing drug effectiveness and preventing side effects that arise from consuming multiple drugs administered together. However, as the number of novel drugs increases, predicting potential DDIs becomes more challenging. Previous studies have used the molecular language model ChemBERTa to predict potential DDIs, but the predictions were only tested on unseen drugs and not dissimilar drugs, while also performing poorly. In this study, we demonstrate that a simple molecular preprocessing method using BRICS decomposition can enhance ChemBERTa robustness in predicting potential DDIs for unseen-dissimilar drugs. Specifically, we employ fragmentation with a minimum fragment size of 5 (BRICS-5), which balances the number of fragments and decomposition speed. Using this method, we were able to improve ChemBERTa robustness, reflected by an approximate increase of 1.5% in recall and 2.2% in the f1-score compared to non-decomposed molecules. Our findings suggest that this preprocessing method could be useful in the development of future AI-based approaches for solving challenging molecular tasks across different settings and various model architectures.

关键词： Drugs Seminars Accuracy Data preprocessing Predictive models Robustness Data models Intelligent systems Information technology

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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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Software Engineering and Information Technology (ICoSEIT), International Conference on

作者： Andrea Stevens Karnyoto Mahmud Isnan Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images.

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Crop root bacterial and viral genomes reveal unexplored species and microbiome patterns

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Cell 2025年第9期188卷 2521-2539.e22页

作者： Dai, Rui Zhang, Jingying Liu, Fang Xu, Haoran Qian, Jing-Mei Cheskis, Shani Liu, Weidong Wang, Binglei Zhu, Honghui Pronk, Lotte J.U. Medema, Marnix H. de Jonge, Ronnie Pieterse, Corné M.J. Levy, Asaf Schlaeppi, Klaus Bai, Yang Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China Peking-Tsinghua Center for Life Sciences State Key Laboratory of Gene Function and Modulation Research Peking-Tsinghua-NIBS Graduate Program School of Life Sciences Peking University Beijing 100871 China CAS-JIC Centre of Excellence for Plant and Microbial Science Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China College of Advanced Agricultural Sciences University of Chinese Academy of Sciences Beijing 100101 China Department of Plant Pathology and Microbiology Institute of Environmental Science The Faculty of Agriculture Food and Environment The Hebrew University of Jerusalem Rehovot Israel State Key Laboratory of Applied Microbiology Southern China Institute of Microbiology Guangdong Academy of Sciences Guangzhou 510070 China Bioinformatics Group Wageningen University & Research Wageningen 6708 PB Netherlands Plant-Microbe Interactions Department of Biology Science for Life Utrecht University Utrecht 3584 CH Netherlands AI Technology for Life Department of Information and Computing Sciences Science for Life Utrecht University Utrecht 3584 CC Netherlands Department of Environmental Sciences University of Basel Basel 4056 Switzerland

Reference genomes of root microbes are essential for metagenomic analyses and mechanistic studies of crop root microbiomes. By combining high-throughput bacterial cultivation with metagenomic sequencing, we constructed comprehensive bacterial and viral genome collections from the roots of wheat, rice, maize, and Medicago. The crop root bacterial genome collection (CRBC) significantly expands the quantity and phylogenetic diversity of publicly available crop root bacterial genomes, with 6,699 bacterial genomes (68.9% from isolates) and 1,817 undefined species, expanding crop root bacterial diversity by 290.6%. The crop root viral genome collection (CRVC) contains 9,736 non-redundant viral genomes, with 1,572 previously unreported genus-level clusters in crop root microbiomes. From these, we identified conserved bacterial functions enriched in root microbiomes across soils and host species and uncovered previously unexplored bacteria-virus connections in crop root ecosystems. Together, the CRBC and CRVC serve as valuable resources for investigating microbial mechanisms and applications, supporting sustainable agriculture. © 2025 The Authors

关键词： bacteria crop genome genomic resources root microbiome virus

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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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Implementation of Fine-Tuned BERT for Enzyme Classification Based on Gene Ontology

Implementation of Fine-Tuned BERT for Enzyme Classification ...

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Matthew Martianus Henry Christian Kenneth Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Enzymes are biocatalysts with vital roles in biological functions and many industrial applications. Diverse enzymes are classified using Enzyme Commission (EC) nomenclature, making differentiation challenging. On the other hand, another biological information, gene ontology (GO), can describe the biological aspects of enzymes, covering related biological processes (BP), molecular functions (MF), and their locations within cells (CC). This study proposes a novel EC class and subclass classification of enzymes within the ontology subclass based on their GO semantics using a Bidirectional Encoder Representation of Transformer (BERT). The BERT model is first fine-tuned using the preprocessed GO term name and definition, with the enzymes in each ontology class (BP, MF, or CC) are also divided based on how the GO assigned, either through manual annotation (NONIEA) or electronically inferred (IEA). BERT successfully obtained 0.93, 0.60, 0.99, 0.90, 0.40, and 0.35 F1 scores during fine-tuning for BP IEA, BP NONIEA, MF IEA, MF NONIEA, CC IEA, and CC NONIEA, respectively. On the test set, the fine-tuned BERT significantly outperformed GOntoSim, a framework to calculate semantic similarity based on classical information theory, in EC class classification across all metrics with less inference time in all ontology subclass. Expanded further to the EC subclass, BERT can classify the enzyme on the EC subclass level in BP IEA and MF IEA ontology subclass. However, longer epochs are needed in fine-tuning. This result shows that the names and definitions of GO terms are distinguishable features in classifying enzymes as an alternative to the information content approach.

关键词： Measurement Enzymes Semantics Bidirectional control Manuals Ontologies Transformers Encoding Biological information theory Informatics

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