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COMPARATIVE-STUDIES OF UV-INDUCED dna CLEAVAGE BY ANALOGS OF IODOHOECHST-33258

international JOURNAL OF RADIATION BIOLOGY 1994年第5期66卷 517-521页

作者： MARTIN, RF KELLY, DP ROBERTS, M NEL, P TURSI, J DENISON, L ROSE, M REUM, M PARDEE, M UNIV MELBOURNE SCH CHEMMELBOURNEVICAUSTRALIA UNIV MELBOURNE DEPT PHARMACOLMELBOURNEVICAUSTRALIA

Following the earlier demonstration that iodo-Hoechst 33258 sensitizes dna and cells to UV,, presumably mediated by formation of a carbon-centred radical on the ligand upon dehalogenation, three isomeric analogues of iodo-Hoechst 33258 have now been studied. the isomers differ in the location of the iodine atom in the phenyl ring of the ligand, relative to the site of attachment of the bibenzimidazole moiety, and are accordingly denoted ortho-, meta- and para-iodoHoechst. Comparison of the ligands with respect to induction of dna ssb in pBR322 dna revealed a wide range of activity;(D-37)'s vary by a factor of 37), decreasing in the order: ortho- > meta- and para- > iodoHoechst 33258. Preliminary dehalogenation studies suggest that the higher activity of the ortho isomer results more from increased cross-section for dehalogenation than from increased efficiency of strand breakage per dehalogenation event. However, the chemistry of strand breakage by the ortho-isomer is distinctive, and tentatively assigned to initial attack at the I'-deoxyribosyl carbon;the other two isomers, like iodoHoechst 33258, attack the 5'-carbon. the results are discussed in terms of the spectrum of dna strand breakage chemistry associated with ionizing radiation, and the potential of dna strand breaking agents such as the iodoHoechst compounds to study the chemical and biological consequences of the different subclasses of initial dna damage.

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TRStalker: an efficient heuristic for finding fuzzy tandem repeats

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BIOINFORMATICS 2010年第12期26卷 i358-i366页

作者： Pellegrini, Marco Renda, M. Elena Vecchio, Alessio Ist Informat & Telemat CNR I-56124 Pisa Italy Univ Pisa Dip Ingn Informaz I-56122 Pisa Italy

Motivation: Genomes in higher eukaryotic organisms contain a substantial amount of repeated sequences. Tandem Repeats (TRs) constitute a large class of repetitive sequences that are originated via phenomena such as replication slippage and are characterized by close spatial contiguity. they play an important role in several molecular regulatory mechanisms, and also in several diseases (e.g. in the group of trinucleotide repeat disorders). While for TRs with a low or medium level of divergence the current methods are rather effective, the problem of detecting TRs with higher divergence (fuzzy TRs) is still open. the detection of fuzzy TRs is propaedeutic to enriching our view of their role in regulatory mechanisms and diseases. Fuzzy TRs are also important as tools to shed light on the evolutionary history of the genome, where higher divergence correlates with more remote duplication events. Results: We have developed an algorithm (christened TRStalker) with the aim of detecting efficiently TRs that are hard to detect because of their inherent fuzziness, due to high levels of base substitutions, insertions and deletions. To attain this goal, we developed heuristics to solve a Steiner version of the problem for which the fuzziness is measured with respect to a motif string not necessarily present in the input string. this problem is akin to the 'generalized median string' that is known to be an NP-hard problem. Experiments with both synthetic and biological sequences demonstrate that our method performs better than current state of the art for fuzzy TRs and that the fuzzy TRs of the type we detect are indeed present in important biological sequences. Availability: TRStalker will be integrated in the web-based TRs Discovery Service (TReaDS) at ***. Contact: ***@*** Supplementary information: Supplementary data are available at Bioinformatics online.

关键词： Algorithms Genome Sequence Analysis, dna/methods Tandem Repeat Sequences/genetics

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Combinatorial optimization of dna sequence analysis on heterogeneous systems

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第7期29卷

作者： Memeti, Suejb Pllana, Sabri Linnaeus Univ Dept Comp Sci Vaxjo Sweden

Analysis of dna sequences is a data and computational intensive problem, and therefore, it requires suitable parallel computing resources and algorithms. In this paper, we describe our parallel algorithm for dna sequence analysis that determines how many times a pattern appears in the dna sequence. the algorithm is engineered for heterogeneous platforms that comprise a host with multi-core processors and one or more many-core devices. For combinatorial optimization, we use the simulated annealing algorithm. the optimization goal is to determine the number of threads, thread affinities, and dna sequence fractions for host and device, such that the overall execution time of dna sequence analysis is minimized. We evaluate our approach experimentally using real-world dna sequences of various organisms on a heterogeneous platform that comprises two Intel Xeon E5 processors and an Intel Xeon Phi 7120P co-processing device. By running only about 5% of possible experiments, our optimization method finds a near-optimal system configuration for dna sequence analysis that yields with average speedup of 1.6x and 2x compared with the host-only and device-only execution. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： dna sequence analysis simulated annealing combinatorial optimization heterogeneous systems

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Digital droplet ELOHA for nucleic acid molecule counting and analysis

Digital droplet ELOHA for nucleic acid molecule counting and...

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international conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Weihua Guan Liben Chen Tushar Rane Aniruddha Kaushik Jeff Tza-Huei Wang Department of Electrical Engineering Pennsylvania State University PA USA Department of Mechanical Engineering Johns Hopkins University MD USA Department of Biomedical Engineering Johns Hopkins University MD USA

We present a droplet digital Enzyme-Linked Oligonucleotide Hybridization Assay (droplet digital ELOHA) for reference-free absolute quantification of nucleic acids. Droplet digital ELOHA is able to accurately quantify as few as several hundred copies of nucleic acids in a large, mL-sized sample volume (equivalent to 1 aM or lower) without molecular replication. Droplet digital ELOHA provides a linear response to the number of copies present in a sample to allow for detecting a small fold change unattainable by real time quantitative PCR. Its capability to cope with a large sample volume benefits from the use of magnetic beads to streamline capturing of nucleic acids from the sample and subsequent surface hybridization reactions in droplets. Droplet digital ELOHA is particularly attractive for RNA quantification since it does not require reverse transcription. the absolute quantification ability of droplet digital ELOHA is demonstrated with the analysis of clinical Neisseria Gonorrhoeae 16S rRNA to show its potential diagnostic value.

关键词： dna Magnetic resonance imaging Biochemistry RNA Microfluidics Testing Dynamic range

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Convolutional Neural Network for Cancer Treatment Response Prediction

Convolutional Neural Network for Cancer Treatment Response P...

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international Computer Engineering conference (ICENCO)

作者： Hanan Ahmed Howida A. Shedeed Safwat Hamad Ashraf S. Hussein Scientific Computing Faculty of computer and Information science Cairo Egypt

ISBN: (纸本)9781665458962

Recently, efforts are exerted on cancer treatment prediction based on the biomarkers related to the tumor. Many biomarkers are used to discriminate cancer but there is no accurate evidence which is more accurate. To save resources and due to the enormous computational power, the analysis of huge sequences such as dna (DesoxyriboNucleic Acid) becomes a valid process. Machine learning and deep learning algorithms have been used to predict drug response. the indicators show that the performance of deep learning models is better than the performance of machine learning based one. In this paper, we introduce the use of Convolutional Neural Network (CNN) to predict the response of 265 different drugs using the gene expression profile and mutation profile of 19702 genes. DeepInsight algorithm used to convert the input data to images to be more suitable as input to the CNN. We proposed eight CNNs with different architectures. At first, the whole data input passes through the DeepInsight model which converts the gene expression data and mutation data to images. Dimension reduction is then applied using the helper technique inside the DeepInsignt. the CNN are trained with different settings such as image size, no of nodes in layers…etc. Comparative analysis shows that the proposed architectures improve the prediction accuracy in a range between 16% and 24% as a reduction in mean squared error (MSE).

关键词： Drugs Deep learning Machine learning algorithms Cancer treatment Computer architecture Biomarkers Prediction algorithms

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Unifying Genetics and Imaging: MRI-Based Classification of MGMT Genetic Subtypes Using Visual Transformers

Unifying Genetics and Imaging: MRI-Based Classification of M...

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international conference on Emerging Technologies, ICET

作者： Ibrar Babar Asjid Tahir Arslan Haider Hafeez Ur Rehman Moutaz Alazab Department of Computer Science National University of Computer and Emerging Sciences Peshawar Pakistan Department of Computing and Data Science Oryx Universal College Doha Qatar

Brain cancer can take many forms, but glioblastoma (GBM) is one of the most aggressive. To treat it effectively, doctors need to know the genetic subtype of a specific part of the tumor called the O-6-methylguanine-dna-methyltransferase (MGMT) promoter. this information is critical for making treatment decisions and predicting outcomes. However, traditional methods for predicting MGMT status have significant limitations. One of the main reasons for this poor performance is that these methods fail to capture small variations in MRI signatures that are essential for accurately predicting MGMT promoter methylation status. In this work, we propose a two-part approach to predict the MGMT methylation status. First, we use autoencoders to extract features from the MRI images, capturing intricate spatial patterns and representations. then, we feed these encoded features into a visual transformer architecture to learn long-range dependencies and contextually significant relationships among image regions. this innovative fusion of autoencoders and visual transformers allows us to harness both local and global information present in the MRI data. We utilized a dataset consisting of 1010 3D MRI images from patients with GBM. Half of the patients had MGMT promoter methylation, while the other half did not. the model achieved impressive results, including an overall accuracy score of 0.904, recall score of 0.902, precision score of 0.914, F1 score of 0.906, and specificity score of 0.906. these metrics far surpass the accuracy of conventional imaging techniques.

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Predicting dna Content Abnormalities in Barrett’s Esophagus: A Weakly Supervised Learning Paradigm 7

Predicting DNA Content Abnormalities in Barrett’s Esophagus...

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7th international conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Ercan, Caner Pan, Xiaoxi Paulson, thomas G. Stachler, Matthew D. Maley, Carlo C. Grady, William M. Yuan, Yinyin Department of Translational Molecular Pathology The University of Texas MD Anderson Cancer Center HoustonTX United States Institute for Data Science in Oncology FA1-Quantitative Pathology and Medical Imaging The University of Texas MD Anderson Cancer Center HoustonTX United States Translational Science and Therapeutics Division Fred Hutchinson Cancer SeattleWA United States Department of Pathology University of California San Francisco San FranciscoCA United States Arizona Cancer Evolution Center Biodesign Center for Biocomputing Security and Society School of Life Sciences Arizona State University TempeAZ United States Department of Medicine University of Washington School of Medicine SeattleWA United States

Barrett’s esophagus (BE) is the sole precursor to esophageal adenocarcinoma (EAC), and is an opportunity for developing biomarkers for cancer risk assessment. dna content abnormalities, including aneuploidy, have been implicated in the progression to EAC in BE patients, but molecular assays require valuable tissue for its detection. We propose utilizing images from routine histology to detect ploidy status using deep learning. Employing a weakly supervised deep learning approach, multi-instance learning (MIL), we trained a model to predict ploidy using hematoxylin and eosin-stained whole slide images of endoscopic biopsies and flow cytometry results. the study introduces a novel image augmentation method for MIL, sequentially altering features from original and augmented images during training loops. this method improved the average area under curve (AUC) from 0.43, 0.64 and 0.81 for ResNet50, DenseNet121 and REMEDIS foundation model, respectively (training without any augmentation), to 0.61, 0.87 and 0.91 with the proposed augmentation strategy. the top-performing model, employing foundation model as the backbone, achieved 0.93 AUC and 83% balanced accuracy to predict aneuploidy in the test cohort biopsies (n=279). Across all the patients (n=123), predicted aneuploidy status was correlated with progression to EAC (p=6.55e-06), similar to correlation with ploidy status based on flow cytometry results (p=2.84e-7). Supporting the findings, histologic nuclear features typically associated with dna content abnormalities such as enlarged and hyperchromatic nuclei were seen in the samples called abnormal compared to the control diploid samples. In conclusion, our deep learning model efficiently predicts aneuploidy, a mechanism that has been shown to underpin BE progression to EAC. this method, preserving precious biopsy tissues, complements routine histology, offering potential for identifying individuals at high risk of progression through molecular-based advancements. ©

关键词： Risk assessment

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Reduced leaflet pathway controls leaf complexity and leaflet polarity in Lotus japonicus

Reduced leaflet pathway controls leaf complexity and leaflet...

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18th international conference on Arabidopsis Research(第十八届国际拟南芥大会)

作者： Jun Yan Xuefei Cai Shusei Sato Satoshi Tabata Jun Yang Xianglin Cao Wei Ma Da Luo National Laboratory of Plant Molecular Genetics Shanghai Institute of Plant Physiology and EcologyShanghai Institute for Biological SciencesShanghai 200032China Kazusa DNA Research Institute2-6-7 Kazusa-kamatariKisarazuChiba 292-0818Japan School of Life Science and BiotechnologyShanghai Jiao Tong UniversityShanghai 200030China National Laboratory of Plant Molecular GeneticsShanghai Institute of Plant Physiology and EcologyShanghai Institute for Biological SciencesShanghai 200032China School of Life Science and Biotechnology Shanghai Jiao Tong UniversityShanghai 200030China

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High performance computational tools for Motif discovery

High performance computational tools for Motif discovery

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international Symposium on Parallel and Distributed Processing (IPDPS)

作者： N.E. Baldwin R.L. Collins M.A. Langston C.T. Symons M.R. Leuze B.H. Voy Department of Computer Science University of Tennessee Knoxville USA Oak Ridge National Laboratory USA

Summary form only given. We highlight a fruitful interplay between biology and computation. the sequencing of complete genomes from multiple organisms has revealed that most differences in organism complexity are due to elements of gene regulation that reside in the non protein coding portions of genes. Both within and between species, transcription factor binding sites and the proteins that recognize them govern the activity of cellular pathways that mediate adaptive responses and survival. Experimental identification of these regulatory elements is by nature a slow process. the availability of complete genomic sequences, however, opens the door for computational methods to predict binding sites and expedite our understanding of gene regulation at a genomic level. Just as with traditional experimental approaches, the computational identification of the molecular factors that control a gene's expression level has been problematic. As a case in point, the identification of putative motifs is a challenging combinatorial task. For it, powerful new motif finding algorithms and high performance implementations are described. Heavy use is made of graph algorithms, some of which are exceedingly computationally intensive and involve the use of emergent mathematical methods. An approach to fully dynamic load balancing is developed in order to make effective use of highly parallel platforms.

关键词： High performance computing Sequences dna Genomics Bioinformatics Gene expression Proteins Computational Intelligence Society Mice Humans

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HALF-LIFE VALUES FOR dna DOUBLE-STRAND BREAK REJOINING IN YEAST CAN VARY BY MORE thAN AN ORDER OF MAGNITUDE DEPENDING ON thE IRRADIATION CONDITIONS

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international JOURNAL OF RADIATION BIOLOGY 1994年第5期66卷 543-547页

作者： FRANKENBURGSCHWAGER, M HARBICH, R BECKONERT, S FRANKENBERG, D UNIV GOTTINGEN KLINIKUM STRAHLENPHYSD-37075 GOTTINGENGERMANY

Yeast cells in stationary phase were exposed under oxic or anoxic conditions to sparsely (30MeV electrons) or densely (35 MeV alpha-particles) ionizing radiation. For all four experimental set-ups postirradiation treatment of cells was the same, i.e. cells were kept under oxic conditions in non-growth medium at 30 degrees C. Double-strand break (dsb) rejoining was measured during this treatment yielding the fallowing results: (1) half-life values ranged from < 60 min (electrons, anoxia) to 3.8h (low doses of electrons, oxia), 7h (alpha-particles, anoxia), 10 h (high doses of electrons, oxia) and 13 h (alpha-particles, oxia). (2) In the case of exposure of oxic cells to electrons a biphasic rejoining kinetics is observed with a dose-dependent increase of the fraction of the slow component. these results suggest that half-life values of dsb rejoining in a given cell depend on physical, chemical and biological parameters. the rejoining of dsb slows down with increasing LET, being probably due to the increasing complexity of dsb. Oxygenation of cells at the time of irradiation affects half-life values, indicating that radiation chemistry plays an important role. the biphasic rejoining kinetics observed for dsb induced by electrons in oxic cells is interpreted in terms of a dose-dependent change of chromatin structure hindering the interaction between damaged chromatin and the rejoining enzymes rather than by two chemically distinct types of dsb with differing half-life values.

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