Труды сотрудников ИВМ СО РАН

[Text] : научное издание / Gorban A.N.,Zinovyev A.Y., Popova T. // Institut des Hautes Etudes Scientifiques Preprint . - 2001
   Перевод заглавия: Статистический подход к автоматической идентификации генов без учителя
Аннотация: Overview of statistical methods of gene identification is made. Particular attention is given to the methods which need not a training set of already known genes. After analysis several statistical approaches are proposed for computational exon identification in whole genomes. For several genomes an optimal window length for averaging GC-content function and calculating codon frequencies has been found. Self-training procedure based on clustering in multidimensional codon frequencies space is proposed.

http://icm.krasn.ru/refextra.php?id=2108


Доп.точки доступа:
Zinovyev, A.Y.; Зиновьев, Андрей Юрьевич; Popova, T.G.; Попова Т.Г.; Горбань, Александр Николаевич

[Text] : статья / A. Zinovyev [et al.] // Bioinformatics. - 2008. - Vol. 24, Iss. 6. - p. 876-877DOI 10.1093/bioinformatics/btm553 . -
Аннотация: BiNoM (Biological Network Manager) is a new bioinformatics software that significantly facilitates the usage and the analysis of biological networks in standard systems biology formats (SBML, SBGN, BioPAX). BiNoM implements a full-featured BioPAX editor and a method of ‘interfaces’ for accessing BioPAX content. BiNoM is able to work with huge BioPAX files such as whole pathway databases. In addition, BiNoM allows the analysis of networks created with CellDesigner software and their conversion into BioPAX format. BiNoM comes as a library and as a Cytoscape plugin which adds a rich set of operations to Cytoscape such as path and cycle analysis, clustering sub-networks, decomposition of network into modules, clipboard operations and others.

Полный текст на сайте журнала

Держатели документа:
ИВМ СО РАН : 660036, Красноярск, Академгородок, 50, стр.44

Доп.точки доступа:
Zinovyev, Andrei; Зиновьев, Андрей Юрьевич; Viara, Eric; Calzone, Laurence; Barillot, Emmanuel

[Текст] : статья / Егор Александрович Морозов [и др.] // Исследования наукограда. - 2017. - Т. 1, № 1. - С. 38-43 . - ISSN 2225-9449
   Перевод заглавия: Improving the system’s efficiency of information and analysis support in the development of satellites: questions of identifying and protection of intellectual property

УДК

338.2

Аннотация: Проведение научно-исследовательских работ, которые являются локомотивом деятельности наукоёмких предприятий, невозможно без анализа научно-технической информации. Производство современной конкурентоспособной продукции основано на постоянных научных исследованиях, ориентированных на долгосрочную перспективу. Поэтому так важно использовать системный подход к анализу научно-технической информации, который позволяет не только выявить научно-технический задел, созданный в заданной области научно-технического и инженерного поиска, но и обозначить контуры предполагаемых решений, определить технические решения, способные к последующей защите в виде объектов интеллектуальной собственности. В статье обозначены проблемы сохранения и развития интеллектуальной базы предприятий космической отрасли. Предложен путь решения - разработка комплекса эффективных мер, направленных на повышение системы информационно-аналитического обеспечения. Рассмотрена одна из задач, помогающих решить вышеуказанную проблему, - отбор перспективных технических решений, и предложено решение данной задачи методом кластеризации, относящимся к интеллектуальному анализу данных. Научная новизна заключается в использовании кластеризации в новой для данного метода сфере - в космической отрасли при отборе перспективных технических решений. Перечислены основные этапы реализации метода кластеризации на примере акционерного общества «Информационные спутниковые системы» имени академика М.Ф. Решетнёва». Разработанный и апробированный на практике метод кластеризации для отбора перспективных технических решений позволит решить одну из задач глобальной цели, стоящей перед предприятиями космической отрасли, - разработка комплекса эффективных мер, направленных на повышение системы информационно-аналитического обеспечения.
Conducting science and research work, which are the engine of activity of high technology enterprises, it is impossible without an analysis of the scientific and technical information. Production of modern competitive products based on regular, focused on long-term perspective scientific research. Therefore, it is so important to use a systematic approach to the analysis of scientific and technical information that makes it possible not only to identify the technological advance, created in a given field of scientific, technical and engineering research, but also to shape the circuit of the supposed solutions, to identify technical solutions, capable of subsequent protection in the form of intellectual property. In the article indicated problems of preservation and development of the intellectual base of enterprises of space industry. The way of solving problems is develop a set of effective measures, aimed to improving the system of information and analytical support. It is considered one of the tasks, helping to solve the above problem - the selection of promising technical solutions and provides a solution to this problem by clustering method, related on data mining. Scientific novelty consists in using of clustering method in the new field for this method - in the space industry for selecting of promising technical solutions. List the main stages of the clustering method by the example of JSC «Information Satellite Systems» academician M.F. Reshetnev». Developed and tested in practice, clustering method for the selection of promising technical solutions will solve only one of the tasks of the global target of the space industry enterprises - development of effective measures aimed at improving information and analytical support systems.

РИНЦ

Держатели документа:
АО «Информационные спутниковые системы» им. академика М. Ф. Решетнёва»
Институт вычислительного моделирования СО РАН
Сибирский государственный университет науки и технологий им. академика М. Ф. Решетнёва

Доп.точки доступа:
Морозов, Егор Александрович; Morozov E.A.; Вилков, Юрий Вячеславович; Vilkov Yu. V.; Киселёва, Анастасия Николаевна; Kiseleva A.N.; Двирный, Валерий Васильевич; Dvirnyi V.V.; Крушенко, Генрих Гаврилович; Krushenko G.G.

[Текст] : статья / С. В. Исаев // Решетневские чтения. - 2017. - № 21-2. - С. 408-410 . - ISSN 1990-7702
   Перевод заглавия: Identifying groups of internet users based on the PROXY server log

УДК

004.738

Аннотация: Автором предложен подход выделения кластеров пользователей для контроля использования интернет-ресурсов. Его применение может повысить кибербезопасность организаций ракетно-космической отрасли за счет обнаружения аномалий использования ресурсов.
The author proposes an approach of clustering users to control the use of Internet resources. Its use can improve the cybersecurity at organizations of rocket-space industry through the detection of anomalies in the use of resources.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН

Доп.точки доступа:
Исаев, С.В.; Isaev S.V.

[Текст] : статья / Артем Александрович Быков, Михаил Александрович Курако, Константин Васильевич Симонов // Информационные и математические технологии в науке и управлении. - 2017. - № 3. - С. 85-92 . - ISSN 2413-0133

УДК

519.6

Аннотация: Исследование посвящено разработке алгоритмического обеспечения для обработки и сравнительного анализа различных моделей оценки параметров источника цунами, которые имеют в своей основе данные геомониторинга процесса подготовки цунамигенного землетрясения для выделенной очаговой области. Адекватная оценка параметров источника цунами от ожидаемого цунамигенного землетрясения и, соответственно, предвычисление распространения цунами обеспечивают предварительную оценку опасности цунами. В рамках информационного обеспечения решения поставленной задачи рассмотрены классические способы оценки параметров источника цунами на основе анализа форшокового процесса, блочно-клавишной и поршневой моделей. Для поиска и анализа вариантов конфигурации и местоположения изучаемого источника предлагаются алгоритмы и методика расчетов, включающая нелинейную регрессию для построения аппроксимационных функций и метод построения упругих сеток для кластеризации пространственных данных.
The study is devoted to the development of algorithmic support for the data processing and comparative analysis of various models of tsunami source parameter estimation, which are based on the geomonitoring data of the tsunamigenic earthquake preparation for the selected source area. An adequate assessment of the tsunami source parameters from the expected tsunamigenic earthquake and, accordingly, the precomputation of tsunami distribution provide a preliminary assessment of the tsunami hazard. Within the information support framework for the solution of the problem, classical methods for tsunami source parameters estimation are analyzed, based on foreshock process analysis, block-key and piston source models. For the search and analysis of configuration options and the location of the source, algorithms and calculation techniques are proposed, including nonlinear regression for constructing approximation functions and a method for constructing elastic grids for clustering spatial data.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН
Институт космических и информационных технологий Сибирского федерального университета

Доп.точки доступа:
Быков, Артем Александрович; Курако, Михаил Александрович; Симонов, Константин Васильевич

[Текст] : статья / Михаил Александрович Курако, Артём Александрович Быков, Константин Васильевич Симонов // Информатизация и связь. - 2017. - № 4. - С. 88-93 . - ISSN 2078-8320
   Перевод заглавия: Geomoitoring data approximation and clustering algorithms for tsunami source parameters estimation

УДК

550.3

Аннотация: The study is devoted to the development o/ algorithmic support /or the data processing and comparative analysis o/ various models o/ tsunami source parameter estimation, which are based on the geomonitoring data o/ the tsunamigenic earthquake preparation /or the selected source area. An adequate assessment o/ the tsunami source parameters /rom the expected tsunamigenic earthquake and, accordingly, the precomputation o/ tsunami distribution provide a preliminary assessment o/ the tsunami hazard. Within the in- /ormation support /ramework /or the solution o/ the problem, classical methods /or tsunami source parameters estimation are analyzed, based on /oreshock process analysis, block-key and piston source models. For the search and analysis o/ configuration options and the location o/ the source, algorithms and calculation techniques are proposed, including nonlinear regression /or constructing approximation /unctions and a method /or constructing elastic grids /or clustering spatial data.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН
Сибирский федеральный университет

Доп.точки доступа:
Курако, Михаил Александрович; Kurako M.A.; Быков, Артём Александрович; Bykov A.A.; Симонов, Константин Васильевич; Simonov K.V.

: статья / Александр Геннадьевич Зотин [и др.] // Медицина и высокие технологии. - 2018. - № 1. - С. 20-28 . - ISSN 2306-3645
   Перевод заглавия: Mri brain’s tumor edge detection based on fuzzy c-means

УДК

004.932

Аннотация: В настоящее время обработка медицинских изображений является наиболее сложной и развивающейся областью. При этом выявление границ объектов интереса на снимках МРТ является одним из наиболее важных элементов этой области. В настоящей статье предлагается методика обнаружения границ опухоли головного мозга по МРТ пациента. Эта методика включает несколько этапов: во-первых - удаления шума, а затем улучшение медицинского изображения с использованием метода улучшения контрастности (Balance Contrast Enhancement Technique, BCET), во-вторых - сегментация изображения с использованием метода нечеткой кластеризации С-средних (Fuzzy c-Means, FCM), и наконец, в-третьих, применение детектора Кэнни для выявления тонких границ. Для экспериментального исследования использованы изображения, содержащие опухоли головного мозга, которые характеризовались разным особенностями: расположением, типом патологии, формой, размером и плотностью, а также размером площади пораженной ткани около опухолевого пространства. Обнаружение и выделение опухоли на снимках МРТ головного мозга осуществлялось с использованием программного обеспечения MATLAB. Результат исследований экспериментального материала с использованием предлагаемой методики демонстрирует достаточно хорошую устойчивость к шуму. Кроме того, было обнаружено, что повышение точности решения задач геометрического анализа и сегментации, в некоторых случаях опухолевой патологии, на 10-15% лучше, чем соответствующие оценки экспертов.
Medical image processing is the most challenging and emerging field nowadays. Edge detection of MRI images is one of the most important elements of this field. This paper describes the proposed strategy to detect the edges of brain tumor from patient’s MRI scan images of the brain. This method incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using BCET. The result of second stage is subjected to image segmentation by using Fuzzy c-Means (FCM) clustering method. Finally, Canny edge detection method is applied to detect the fine edges. For the experimental study we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done by using MATLAB software. The result of studies of the experimental material with usage of the proposed methodology demonstrates some resistivity to a noise. Also, an increase in the accuracy of solving the problems of geometric analysis and segmentation, in some cases of tumor pathology, was found to be up to 10-15% better relative to the corresponding expert estimates.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН
Институт космических и информационных технологий Сибирского федерального университета
Сибирский государственный университет науки и технологии им. академика М.Ф. Решетнева

Доп.точки доступа:
Зотин, Александр Геннадьевич; Zotin Alexander Gennadievich; Хамад, Юсиф Ахмед; Hamad Yousif Ahmed; Кириллова, Светлана Владимировна; Kirillova Svetlana Vladimirovna; Курако, Михаил Александрович; Kurako Mikhail Aleksandrovich; Симонов, Константин Васильевич; Simonov Konstantin Vasilyevich

/ A. Zotin [et al.] // Procedia Computer Science : Elsevier B.V., 2018. - Vol. 126: 22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2018 (3 September 2018 through 5 September 2018, ) Conference code: 141492. - P1261-1270, DOI 10.1016/j.procS.2018.08.069 . -
Аннотация: Nowadays, medical image processing is the most challenging and emerging field. Edge detection of MRI images is one of the most important stage in this field. The paper describes the proposed strategy to detect the edges of brain tumor from patient's MRI scan images of the brain. At the first stage, this method includes some noise removal functions improving features that provides better characteristics of medical images for reliable diagnosis using Balance Contrast Enhancement Technique (BCET). The result of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method. Finally, Canny edge detection method is applied to detect the fine edgeS. During the experimental study, we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done using MATLAB software. The obtained results demonstrate some resistivity to a noise. Also, the accuracy of segmentation, in some cases of tumor pathology, was increased up to 10-15% regarding the expert estimateS. © 2018 The Author(s).

Scopus,
Смотреть статью

Держатели документа:
Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky rabochy av., Krasnoyarsk, 660037, Russian Federation
Institute of Computational Modeling of the Siberian Branch, Russian Academy of Sciences, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation
Siberian Federal University, 79 Svobodny st., Krasnoyarsk, 660041, Russian Federation

Доп.точки доступа:
Zotin, A.; Simonov, K.; Kurako, M.; Hamad, Y.; Kirillova, S.

/ M. G. Sadovsky, M. Y. Senashova, Y. A. Putintseva // : Nova Science Publishers, Inc., 2018. - P25-96 . -
Аннотация: We studied the features and characters of various chloroplast genomes that could be retrieved solely from the analysis of triplet composition. To do that, two types of triplet dictionaries were developed: the former lists all the triplets (with overlapping), so that each nucleotide yields a start for a triplet, and the latter is the entity where triplets do not overlap, but also have no gaps between them. Two main cores were studied: the former is the structuredness of a genome that manifests in the statistical properties of small fragments of the genome, each of them converted into a triplet frequency dictionary, and the latter is the relation between the triplet frequencies of a genome, and their phylogeny, when determined over a significant ensemble of genomes. It was found that the great majority of chloroplast genomes exhibit a specific eight-cluster pattern comprising these fragments (converted into triplet frequency dictionaries). The first cluster corresponds to junk fragments, and six more clusters correspond to the fragments corresponding to coding regions, so that each entity corresponds to the specific reading frame shift, and the strand (leading vs. ladder). Finally, the eighth cluster (called the "tail") differs from all those mentioned above, and comprises the fragments with excessive GC-content values. In the observed pattern, two clusters corresponding to the third position of a reading frame but belonging to opposite strands always project one over the other, while the other four clusters do not. Moreover, there is a mirroring symmetry in the orientation of these two coincidental clusters against four others: each genome has either left-hand or right-hand orientation of these six clusters. The cluster structuredness of the chloroplasts found here differs from a similar one observed for bacterial or eukaryotic genomes. The aim of the second core investigation was to establish the relation between the triplet composition of chloroplast genomes and the taxonomy of their bearers; the latter was determined morphologically, by nuclear genomes. To reveal the relation, all the chloroplast genomes (approx. 900 entries) were converted into triplet frequency dictionaries of the first type, and then they were clustered by K-means, elastic maps and some other clustering techniques into two, three, four, five, six and seven classes, respectively. The composition of the classes was the subject of interest: it was found that the distribution of clades over the classes that developed due to clustering was very non-random, and followed, in general, a natural taxonomy of the bearers. Some further perspectives and problems are discussed. © 2018 Nova Science Publishers, Inc. All rights reserved.

Scopus

Держатели документа:
Institute of Computational Modeling, SB RAS, Krasnoyarsk, Russian Federation
Siberian Federal University, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Sadovsky, M. G.; Senashova, M. Y.; Putintseva, Y. A.

[Текст] : статья / С. В. Исаев // Решетневские чтения. - 2018. - Т. 2, № 22. - С. 330-332 . - ISSN 1990-7702
   Перевод заглавия: DETECTION OF CYBER THREATS SOURCES BASED ON CLUSTER ANALYSIS OF INTERNET SERVICE LOGS

УДК

004.738

Аннотация: Предложен подход для кластеризации потребителей интернет-ресурсов c целью обнаружения и идентификации источников киберугроз. Его применение может повысить кибербезопасность организаций за счет анализа и своевременного реагирования на обнаруженные аномалии использования ресурсов.
The author suggests an approach for clustering Internet users in order to detect and identify sources of cyberthreats. Its application can enhance the cybersecurity of organizations by analyzing and timely responding to discovered anomalies of resource use.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН

Доп.точки доступа:
Исаев, С.В.; Isaev S.V.

[Текст] : статья / Юсиф Ахмед Хамад [и др.] // Медицина и высокие технологии. - 2018. - № 3. - С. 5-13 . - ISSN 2306-3645
   Перевод заглавия: COMPUTATIONAL PROCESSING TECHNIQUE MEDICAL IMAGES USING WAVELET AND NEURAL NETWORKS

УДК

004.932

Аннотация: В статье представлен подход к диагностике опухоли молочной железы - вычислительная методика поэтапной классификации с использованием искусственной нейронной сети (машинное обучение) и выявление опухоли молочной железы для медицинской визуализации с помощью методов пороговой сегментации и метода нечеткой кластеризации С-средних.
This paper presents an innovative approach to the diagnosis of breast tumor - a computational methodology for stage classification using artificial neural network (learning machine) and to detect Breast Tumor through thresholding and fuzzy C-means clustering methods for medical imaging application.

РИНЦ

Держатели документа:
Институт вычислительного моделирования СО РАН
Институт космических и информационных технологий Сибирского федерального университета

Доп.точки доступа:
Хамад, Юсиф Ахмед; Hamad Yousif Ahmed; Кириллова, Светлана Владимировна; Kirillova Svetlana Vladimirovna; Курако, Михаил Александрович; Kurako Mikhail Aleksandrovich; Симонов, Константин Васильевич; Simonov Konstantin Vasilyevich

/ Y. A. Hamad, K. Simonov, M. B. Naeem // 2018 1ST ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION AND SCIENCES : IEEE, 2018. - 1st Annual International Conference on Information and Sciences (AiCIS) (NOV 20-21, 2018, Univ Fallujah, Fallujah, IRAQ). - P45-50, DOI 10.1109/AiCIS.2018.00021. - Cited References:15 . - ISBN 978-1-5386-9188-5РУБ Engineering, Multidisciplinary + Multidisciplinary Sciences

Аннотация: Medical image processing is the most challenging and emerging field nowadays. Edge detection of MRI images is one of the most important elements of this field. This paper describes the proposed strategy to detect the edges of brain tumor from patient's MRI scan images of the brain. This method incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using BCET. The result of second stage is subjected to image segmentation by using Fuzzy c-Means (ECM) clustering method. Finally, canny edge detection method is applied to detect the fine edges. For the experimental study we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done by using MATLAB software. The result of studies of the experimental material with usage of the proposed methodology demonstrates some resistivity to a noise. Also, an increase in the accuracy of solving the problems of geometric analysis and segmentation, in some cases of tumor pathology, was found to be up to 10-15% better relative to the corresponding expert estimates.

WOS,
Смотреть статью,
Scopus,
РИНЦ,
Источник статьи

Держатели документа:
Siberian Fed Univ, Inst Space & Informat Sci, Krasnoyarsk, Russia.
Russian Acad Sci, Siberian Branch, Inst Computat Modeling, Krasnoyarsk, Russia.
Al Maaref Univ Coll, Dept Comp Sci, Ramadi, Iraq.

Доп.точки доступа:
Hamad, Yousif A.; Simonov, Konstantin; Naeem, Mohammad B.

/ Y. A. Hamad, K. Simonov, M. B. Naeem // 2018 1ST ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION AND SCIENCES : IEEE, 2018. - 1st Annual International Conference on Information and Sciences (AiCIS) (NOV 20-21, 2018, Univ Fallujah, Fallujah, IRAQ). - P51-57, DOI 10.1109/AiCIS.2018.00022. - Cited References:22 . - ISBN 978-1-5386-9188-5РУБ Engineering, Multidisciplinary + Multidisciplinary Sciences

Аннотация: Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. Breast Cancer detection of medical images is one of the most important elements of this field. Because of low contrast and ambiguous the structure of the tumor cells in breast images, it is still a challenging task to automatically segment the breast tumors. Our method presents an innovative approach to the diagnosis of breast tumor incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using balance contrast enhancement techniques (BCET). The results of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method and Thresholding method to segment the out boundaries of the breast and to locate the Breast Tumor boundaries (shape, area, spatial sizes, etc.) in the images. The third stage feature extraction using Discrete Wavelet Transform (DWI). Finally the artificial neural network will be used to classify the stage of Breast Tumor that is benign, malignant or normal. The early detection of Breast tumor will improves the chances of survival for the patient Probabilistic Neural Network (PNN) with radial basis function will be employed to implement an automated breast tumor classification. The simulated results shown that classifier and segmentation algorithm provides better accuracy than previous method. Proper segmentation is mandatory for efficient feature extraction and classification.

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Siberian Fed Univ, Inst Space & Informat Sci, Krasnoyarsk, Russia.
Russian Acad Sci, Siberian Branch, Inst Computat Modeling, Krasnoyarsk, Russia.
Al Maaref Univ Coll, Dept Comp Sci, Ramadi, Iraq.

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Hamad, Yousif A.; Simonov, Konstantin; Naeem, Mohammad B.

/ M. Sadovsky [et al.] // (8 May 2019 through 10 May 2019 : Springer Verlag, 2019. - Vol. 11465 LNBI. - P335-345, DOI 10.1007/978-3-030-17938-0_30 . -
Аннотация: We studied the relations between triplet composition of the family of mitochondrial atp6, atp8 and atp9 genes, their function, and taxonomy of the bearers. The points in 64-dimensional metric space corresponding to genes have been clustered. It was found the points are separated into three clusters corresponding to those genes. 223 mitochondrial genomes have been enrolled into the database. © 2019, Springer Nature Switzerland AG.

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Institute of Computational Modelling of SB RAS, Akademgorodok, Krasnoyarsk, 660036, Russian Federation
Institute of Fundamental Biology and Biotechnology, Siberian Federal University, Svobodny prosp., 79, Krasnoyarsk, 660049, Russian Federation
Laboratory of Genomics and Biotechnology, Federal Research Center RAS, Krasnoyarsk, Russian Federation

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Sadovsky, M.; Fedotovskaya, V.; Kolesnikova, A.; Shpagina, T.; Putintseva, Y.

/ M. Sadovsky, T. Guseva, V. Biriukov // (8 May 2019 through 10 May 2019 : Springer Verlag, 2019. - Vol. 11465 LNBI. - P370-378, DOI 10.1007/978-3-030-17938-0_33 . -
Аннотация: We studied the structuredness inA total transcriptome of Siberian larch. To do that, the contigs from total transcriptome has been labeled with the reads comprising the tissue specific transcriptomes, and the distribution of the contigs from the total transcriptome has been developed with respect to the mutual entropy of the frequencies of occurrence of reads from tissue specific transcriptomes. It was found that a number of contigs contain comparable amounts of reads from different tissues, so the chimeric transcripts to be extremely abundant. On the contrary, the transcripts with high tissue specificity do not yield a reliable clustering revealing the tissue specificity. This fact makes usage of total transcriptome for the purposes of differential expression arguable. © 2019, Springer Nature Switzerland AG.

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Institute of Computational Modelling of SB RAS, Akademgorodok, Krasnoyarsk 660036, Russian Federation
Institute of Fundamental Biology and Biotechnology, Siberian Federal University, Svobodny prosp., 79, Krasnoyarsk, 660049, Russian Federation

Доп.точки доступа:
Sadovsky, M.; Guseva, T.; Biriukov, V.

/ M. Sadovsky [et al.] // (8 May 2019 through 10 May 2019 : Springer Verlag, 2019. - Vol. 11465 LNBI. - P346-355, DOI 10.1007/978-3-030-17938-0_31 . -
Аннотация: We studied the statistical properties of non-coding regions of chloroplast genomes of 391 plants. To do that, each non-coding region has been tiled with a set of overlapping fragments of the same length, and those fragments were transformed into triplet frequency dictionaries. The dictionaries were clustered in 64-dimensional Euclidean space. Five types of the distributions were identified: ball, ball with tail, ball with two tails, lens with tail, and lens with two tails. Besides, the multi-genome distribution has been studied: there are ten species performing an isolated and distant cluster; surprisingly, there is no immediate and simple relation in taxonomy composition of these clusters. © 2019, Springer Nature Switzerland AG.

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Institute of Computational Modelling of SB RAS, Akademgorodok, Krasnoyarsk 660036, Russian Federation
Institute of Fundamental Biology and Biotechnology, Siberian Federal University, Svobodny prosp., 79, Krasnoyarsk, 660049, Russian Federation

Доп.точки доступа:
Sadovsky, M.; Senashova, M.; Gorban, I.; Gustov, V.

[Text] / M. G. Sadovsky, M. Y. Senashova, Y. A. Putintseva // Chloroplasts and Cytoplasm: Structure and Functions : Nova Science Publishers, Inc., 2018. - P25-96
Аннотация: We studied the features and characters of various chloroplast genomes that could be retrieved solely from the analysis of triplet composition. To do that, two types of triplet dictionaries were developed: the former lists all the triplets (with overlapping), so that each nucleotide yields a start for a triplet, and the latter is the entity where triplets do not overlap, but also have no gaps between them. Two main cores were studied: the former is the structuredness of a genome that manifests in the statistical properties of small fragments of the genome, each of them converted into a triplet frequency dictionary, and the latter is the relation between the triplet frequencies of a genome, and their phylogeny, when determined over a significant ensemble of genomes. It was found that the great majority of chloroplast genomes exhibit a specific eight-cluster pattern comprising these fragments (converted into triplet frequency dictionaries). The first cluster corresponds to junk fragments, and six more clusters correspond to the fragments corresponding to coding regions, so that each entity corresponds to the specific reading frame shift, and the strand (leading vs. ladder). Finally, the eighth cluster (called the "tail") differs from all those mentioned above, and comprises the fragments with excessive GC-content values. In the observed pattern, two clusters corresponding to the third position of a reading frame but belonging to opposite strands always project one over the other, while the other four clusters do not. Moreover, there is a mirroring symmetry in the orientation of these two coincidental clusters against four others: each genome has either left-hand or right-hand orientation of these six clusters. The cluster structuredness of the chloroplasts found here differs from a similar one observed for bacterial or eukaryotic genomes. The aim of the second core investigation was to establish the relation between the triplet composition of chloroplast genomes and the taxonomy of their bearers; the latter was determined morphologically, by nuclear genomes. To reveal the relation, all the chloroplast genomes (approx. 900 entries) were converted into triplet frequency dictionaries of the first type, and then they were clustered by K-means, elastic maps and some other clustering techniques into two, three, four, five, six and seven classes, respectively. The composition of the classes was the subject of interest: it was found that the distribution of clades over the classes that developed due to clustering was very non-random, and followed, in general, a natural taxonomy of the bearers. Some further perspectives and problems are discussed.

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Institute of Computational Modeling|SB RAS
Siberian Federal University

Доп.точки доступа:
Sadovsky, M.G.; Senashova, M.Y.; Putintseva, Y.A.
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[Text] / M. G. Sadovsky, M. Y. Senashova, Y. A. Putintseva // Chloroplasts and Cytoplasm: Structure and Functions : Nova Science Publishers, Inc., 2018. - P25-96
Аннотация: We studied the features and characters of various chloroplast genomes that could be retrieved solely from the analysis of triplet composition. To do that, two types of triplet dictionaries were developed: the former lists all the triplets (with overlapping), so that each nucleotide yields a start for a triplet, and the latter is the entity where triplets do not overlap, but also have no gaps between them. Two main cores were studied: the former is the structuredness of a genome that manifests in the statistical properties of small fragments of the genome, each of them converted into a triplet frequency dictionary, and the latter is the relation between the triplet frequencies of a genome, and their phylogeny, when determined over a significant ensemble of genomes. It was found that the great majority of chloroplast genomes exhibit a specific eight-cluster pattern comprising these fragments (converted into triplet frequency dictionaries). The first cluster corresponds to junk fragments, and six more clusters correspond to the fragments corresponding to coding regions, so that each entity corresponds to the specific reading frame shift, and the strand (leading vs. ladder). Finally, the eighth cluster (called the "tail") differs from all those mentioned above, and comprises the fragments with excessive GC-content values. In the observed pattern, two clusters corresponding to the third position of a reading frame but belonging to opposite strands always project one over the other, while the other four clusters do not. Moreover, there is a mirroring symmetry in the orientation of these two coincidental clusters against four others: each genome has either left-hand or right-hand orientation of these six clusters. The cluster structuredness of the chloroplasts found here differs from a similar one observed for bacterial or eukaryotic genomes. The aim of the second core investigation was to establish the relation between the triplet composition of chloroplast genomes and the taxonomy of their bearers; the latter was determined morphologically, by nuclear genomes. To reveal the relation, all the chloroplast genomes (approx. 900 entries) were converted into triplet frequency dictionaries of the first type, and then they were clustered by K-means, elastic maps and some other clustering techniques into two, three, four, five, six and seven classes, respectively. The composition of the classes was the subject of interest: it was found that the distribution of clades over the classes that developed due to clustering was very non-random, and followed, in general, a natural taxonomy of the bearers. Some further perspectives and problems are discussed.

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Держатели документа:
Institute of Computational Modeling|SB RAS
Siberian Federal University

Доп.точки доступа:
Sadovsky, M.G.; Senashova, M.Y.; Putintseva, Y.A.
Нет сведений об экземплярах (Источник в БД не найден)

[Text] / M. G. Sadovsky, M. Y. Senashova, Y. A. Putintseva // Chloroplasts and Cytoplasm: Structure and Functions : Nova Science Publishers, Inc., 2018. - P25-96
Аннотация: We studied the features and characters of various chloroplast genomes that could be retrieved solely from the analysis of triplet composition. To do that, two types of triplet dictionaries were developed: the former lists all the triplets (with overlapping), so that each nucleotide yields a start for a triplet, and the latter is the entity where triplets do not overlap, but also have no gaps between them. Two main cores were studied: the former is the structuredness of a genome that manifests in the statistical properties of small fragments of the genome, each of them converted into a triplet frequency dictionary, and the latter is the relation between the triplet frequencies of a genome, and their phylogeny, when determined over a significant ensemble of genomes. It was found that the great majority of chloroplast genomes exhibit a specific eight-cluster pattern comprising these fragments (converted into triplet frequency dictionaries). The first cluster corresponds to junk fragments, and six more clusters correspond to the fragments corresponding to coding regions, so that each entity corresponds to the specific reading frame shift, and the strand (leading vs. ladder). Finally, the eighth cluster (called the "tail") differs from all those mentioned above, and comprises the fragments with excessive GC-content values. In the observed pattern, two clusters corresponding to the third position of a reading frame but belonging to opposite strands always project one over the other, while the other four clusters do not. Moreover, there is a mirroring symmetry in the orientation of these two coincidental clusters against four others: each genome has either left-hand or right-hand orientation of these six clusters. The cluster structuredness of the chloroplasts found here differs from a similar one observed for bacterial or eukaryotic genomes. The aim of the second core investigation was to establish the relation between the triplet composition of chloroplast genomes and the taxonomy of their bearers; the latter was determined morphologically, by nuclear genomes. To reveal the relation, all the chloroplast genomes (approx. 900 entries) were converted into triplet frequency dictionaries of the first type, and then they were clustered by K-means, elastic maps and some other clustering techniques into two, three, four, five, six and seven classes, respectively. The composition of the classes was the subject of interest: it was found that the distribution of clades over the classes that developed due to clustering was very non-random, and followed, in general, a natural taxonomy of the bearers. Some further perspectives and problems are discussed.

РИНЦ,
Источник статьи

Держатели документа:
Institute of Computational Modeling|SB RAS
Siberian Federal University

Доп.точки доступа:
Sadovsky, M.G.; Senashova, M.Y.; Putintseva, Y.A.
Нет сведений об экземплярах (Источник в БД не найден)

[Text] / M. G. Sadovsky, M. Y. Senashova, Y. A. Putintseva // Chloroplasts and Cytoplasm: Structure and Functions : Nova Science Publishers, Inc., 2018. - P25-96
Аннотация: We studied the features and characters of various chloroplast genomes that could be retrieved solely from the analysis of triplet composition. To do that, two types of triplet dictionaries were developed: the former lists all the triplets (with overlapping), so that each nucleotide yields a start for a triplet, and the latter is the entity where triplets do not overlap, but also have no gaps between them. Two main cores were studied: the former is the structuredness of a genome that manifests in the statistical properties of small fragments of the genome, each of them converted into a triplet frequency dictionary, and the latter is the relation between the triplet frequencies of a genome, and their phylogeny, when determined over a significant ensemble of genomes. It was found that the great majority of chloroplast genomes exhibit a specific eight-cluster pattern comprising these fragments (converted into triplet frequency dictionaries). The first cluster corresponds to junk fragments, and six more clusters correspond to the fragments corresponding to coding regions, so that each entity corresponds to the specific reading frame shift, and the strand (leading vs. ladder). Finally, the eighth cluster (called the "tail") differs from all those mentioned above, and comprises the fragments with excessive GC-content values. In the observed pattern, two clusters corresponding to the third position of a reading frame but belonging to opposite strands always project one over the other, while the other four clusters do not. Moreover, there is a mirroring symmetry in the orientation of these two coincidental clusters against four others: each genome has either left-hand or right-hand orientation of these six clusters. The cluster structuredness of the chloroplasts found here differs from a similar one observed for bacterial or eukaryotic genomes. The aim of the second core investigation was to establish the relation between the triplet composition of chloroplast genomes and the taxonomy of their bearers; the latter was determined morphologically, by nuclear genomes. To reveal the relation, all the chloroplast genomes (approx. 900 entries) were converted into triplet frequency dictionaries of the first type, and then they were clustered by K-means, elastic maps and some other clustering techniques into two, three, four, five, six and seven classes, respectively. The composition of the classes was the subject of interest: it was found that the distribution of clades over the classes that developed due to clustering was very non-random, and followed, in general, a natural taxonomy of the bearers. Some further perspectives and problems are discussed.

РИНЦ,
Источник статьи

Держатели документа:
Institute of Computational Modeling|SB RAS
Siberian Federal University

Доп.точки доступа:
Sadovsky, M.G.; Senashova, M.Y.; Putintseva, Y.A.
Нет сведений об экземплярах (Источник в БД не найден)