Relevant bibliographies by topics / Open Bioinformation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Open Bioinformation'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Open Bioinformation"

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Pavithra, Senkuttavan. "Challenges and other linked features in promoting open access to bioinformation literature over about 2 decades." Bioinformation 18, no. 6 (2022): 525–30. http://dx.doi.org/10.6026/97320630018525.

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Open access to known literature is critical for creating a harmonious society across continents on planet earth. However, this objective is not simple. Therefore, it is of interest to document the challenges and linked features in promoting open access to bioinformation literature over about 2 decades.
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Kibirev, Ya A., A. V. Kuznetsovskiy, S. G. Isupov, and I. V. Darmov. "Modern Bioinformatics Solutions Used for Genetic Data Analysis." Journal of NBC Protection Corps 7, no. 4 (2024): 366–83. http://dx.doi.org/10.35825/2587-5728-2023-7-4-366-383.

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Effective counteraction to biological threats, both natural and man-made, requires the availability of means and methods for rapid and reliable microorganism identification and a comprehensive study of their basic biological properties. Over the past decade, the arsenal of domestic microbiologists has been supplemented by numerous methods for analyzing the genomes of pathogens, primarily based on nucleic acid sequencing. The purpose of this work is to provide the reader with information about capabilities of modern technical and methodological arsenal used for in-depth molecular genetic study of microorganisms, including bioinformatics solutions used for the genetic data analysis. The source base for this research is English-language scientific literature available via the Internet, bioinformation software documentation. The research method is an analysis of scientific sources from the general to the specific. We considered the features of sequencing platforms, the main stages of genetic information analysis, current bioinformation utilities, their interaction and organization into a single workflow. Results and discussion. The performance of modern genetic analyzers allows for complete decoding of the bacterial genome within one day, including the time required to prepare the sample for research. The key factor that largely determines the effectiveness of the genetic analysis methods used is the competent use of the necessary bioinformatics software utilities. Standard stages of primary genetic data analysis are assessment of the quality control, data preprocessing, mapping to a reference genome or de novo genome assembly, genome annotation, typing and identification of significant genetic determinants (resistance to antibacterial drugs, pathogenicity factors, etc.), phylogenetic analysis. For each stage bioinformation utilities have been developed, differing in implemented analysis algorithms. Conclusion. Open source utilities that do not require access to remote resources for their operation are of greatest interest due to activities specifics of NBC protection corps units.
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Jin Su Kim, Gyu Ho Choi, and Sung Bum Pan. "A Study on ECG-based Biometrics using Open Source Hardware." Research Briefs on Information and Communication Technology Evolution 3 (November 15, 2017): 165–71. http://dx.doi.org/10.56801/rebicte.v3i.56.

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Biometric technology uses unique physical features as bioinformation. Electrocardiogram(ECG) isa signal recording potential changes related to heartbeats in the form of voltage, and each individualhas unique ECG signals in terms of heart location and size thereof. Therefore, they are used to diagnosediseases and examine personal identity. The ECG device can be made compact in comparisonwith other biosignal measurement instruments and the signals measured in locations easily accessible.This paper aims to implement a personal identification system based on ECG signals. Noiseremoval and signal split is conducted as a pre-processing process by using the Band Pass Filter(BPF),Median Filter(MF) and R-Peak. Features are extracted by using the morphological method from thetime domain. Euclidean Distance(ED) is used to analyze the verification data and registration data. Inthe experiment result, For 18 verification data with the lowest EER 1.67%, driving was 113.4 seconds.
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Xue, Yongbiao, Yiming Bao, Zhang Zhang, et al. "Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022." Nucleic Acids Research 50, no. D1 (2021): D27—D38. http://dx.doi.org/10.1093/nar/gkab951.

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Abstract The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.
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Okido, Toshihisa, Yuichi Kodama, Jun Mashima, Takehide Kosuge, Takatomo Fujisawa, and Osamu Ogasawara. "DNA Data Bank of Japan (DDBJ) update report 2021." Nucleic Acids Research 50, no. D1 (2021): D102—D105. http://dx.doi.org/10.1093/nar/gkab995.

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Abstract The Bioinformation and DDBJ (DNA Data Bank of Japan) Center (DDBJ Center; https://www.ddbj.nig.ac.jp) operates archival databases that collect nucleotide sequences, study and sample information, and distribute them without access restriction to progress life science research as a member of the International Nucleotide Sequence Database Collaboration (INSDC), in collaboration with the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute. Besides the INSDC databases, the DDBJ Center also provides the Genomic Expression Archive for functional genomics data and the Japanese Genotype-phenotype Archive for human data requiring controlled access. Additionally, the DDBJ Center started a new public repository, MetaboBank, for experimental raw data and metadata from metabolomics research in October 2020. In response to the COVID-19 pandemic, the DDBJ Center openly shares SARS-CoV-2 genome sequences in collaboration with Shizuoka Prefecture and Keio University. The operation of DDBJ is based on the National Institute of Genetics (NIG) supercomputer, which is open for large-scale sequence data analysis for life science researchers. This paper reports recent updates on the archival databases and the services of DDBJ.
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Zhu, Yan-Fang, Hong-Hong Fan, Da-Hui Li, et al. "MOLECULAR CLONING, BIOINFORMATION ANALYSIS AND EXPRESSION OF THE STRICTOSIDINE SYNTHASE IN Dendrobium officinale." Acta Scientiarum Polonorum Hortorum Cultus 19, no. 3 (2020): 111–24. http://dx.doi.org/10.24326/asphc.2020.3.10.

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The enzyme strictosidine synthase (STR, EC: 4.3.3.2) plays a key role in the biosynthetic pathway of terpenoid indole alkaloid (TIA). It catalyzes the condensation of the tryptamine and secologanin to form 3α(S)-strictosidine, which is the common precursor of all TIAs. In this paper, a STR gene designated as DoSTR (GenBank: KX068707) was first cloned and characterized from Dendrobium officinale with rapid amplified cDNA ends method (RACE). DoSTR has a length of 1380bp with 1179bp open reading frame encoding 392 amino acids. BlastP analyses showed that its amino acid sequence was classified into Str_synth superfamily. qRT-PCR showed that DoSTR was expressed in all tissues tested, with a significantly higher level in flower and the lowest in stem. Four different treatments with MeJA, SA, ABA and AgNO3, respectively, could induce the DoSTR expression to a different extent. And the effect of MeJA was the most obvious and transcript level of DoSTR induced by MeJA was 20.7 times greater than that of control at 48 hours after treatment. Furthermore, it was found that DoSTR was localized in vacuole through transient expression in tobacco. The characterization and expression of DoSTR can help in further studying the role of DoSTR in the biosynthesis of TIAs in D. officinale. This study may throw light on the alkaloid biosynthesis pathway of D. officinale.
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Lee, Byungwook, Seungwoo Hwang, Pan-Gyu Kim, et al. "Introduction of the Korea BioData Station (K-BDS) for sharing biological data." Genomics & Informatics 21, no. 1 (2023): e12. http://dx.doi.org/10.5808/gi.22073.

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A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.
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Jamison, D. C. "Open Bioinformatics." Bioinformatics 19, no. 6 (2003): 679–80. http://dx.doi.org/10.1093/bioinformatics/btg214.

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Gao, Xiangqian, Guiyan Yang, Dapei Li, et al. "Potential Role of WIP Family Genes in Drought Stress Response in Rubus idaeus." Agriculture 14, no. 11 (2024): 2047. http://dx.doi.org/10.3390/agriculture14112047.

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Rubus idaeus is one of the primary cultivated species of raspberries, renowned for its appealing color, distinctive flavor and numerous health benefits. WIP proteins, which contain three conserved amino acids (W: Tryptophan, I: Isoleucine, P: Proline) and four zinc finger motifs in a highly conserved C-terminal region, are members of the A1d subgroup of C2H2 zinc finger proteins. Drought is one of the main limiting factors of plant growth and development, which restricts the cultivation and utilization of raspberry in northwest China. In this study, to obtain candidate genes for drought resistance, we identified key related genes, RiWIPs, from R. idaeus and analyzed their bioinformation and tissue stress response expression to drought. We found that there are three RiWIPs in R. idaeus and they are located on chromosomes 3, 4 and 6 of R. idaeus, respectively. The open reading frames (ORFs) of the RiWIPs ranged from 870 to 1056 bp in length, encoding 289 to 372 amino acid residues. The proteins were highly conserved and feature diverse conserved motifs. The promoters of the RiWIPs contained abundant cis-elements related to growth, development and stress response. Tissue-specific expression analysis revealed that the RiWIPs were expressed in the leaves, stems and roots of both drought-susceptible and drought-tolerant cultivars, except for RiWIP2, which was only expressed in the roots of the drought-tolerant one. Under drought stress, the transcriptional activity of the RiWIPs was increased to different degrees with specificity in the leaves, stems and roots. Our study demonstrated the role of WIP genes in raspberry drought response and provided a marker gene, RiWIP2, for drought resistance and candidate genes for subsequent drought-resistant breeding of R. idaeus.
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Vlagioiu, Constantin, Vlad Vuta, Florica Barbuceanu, Gabriel Predoi, and Nicolae Tudor. "Open-source bioinformatics software." Journal of Biotechnology 256 (August 2017): S53. http://dx.doi.org/10.1016/j.jbiotec.2017.06.979.

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Dissertations / Theses on the topic "Open Bioinformation"

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Rönnbrant, Anders. "Implementing a visualization tool for myocardial strain tensors." Thesis, Linköping University, Department of Biomedical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5173.

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<p>The heart is a complex three-dimensional structure with mechanical properties that are inhomogeneous, non-linear, time-variant and anisotropic. These properties affect major physiological factors within the heart, such as the pumping performance of the ventricles, the oxygen demand in the tissue and the distribution of coronary blood flow.</p><p>During the cardiac cycle the heart muscle tissue is deformed as a consequence of the active contraction of the muscle fibers and their relaxation respectively. A mapping of this deformation would give increased understanding of the mechanical properties of the heart. The deformation induces strain and stress in the tissue which are both mechanical properties and can be described with a mathematical tensor object.</p><p>The aim of this master's thesis is to develop a visualization tool for the strain tensor objects that can aid a user to see and/or understand various differences between different hearts and spatial and temporal differences within the same heart. Preferably should the tool be general enough for use with different types of data.</p>
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Dunning, Mark J. "Genome-wide analyses using bead-based microarrays." Thesis, University of Cambridge, 2008. https://www.repository.cam.ac.uk/handle/1810/218542.

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Microarrays are now an established tool for biological research and have a wide range of applications. In this thesis I investigate the BeadArray microarray technology developed by Illumina. The design of this technology is unique and gives rise to many computational and statistical challenges. However, I show how knowledge from other microarray technologies can be used to our advantage. I describe the beadarray software package, which is now used by researchers around the world. The development of this software was motivated by the fact that Illumina's software (BeadStudio) gives a summarised view of Illumina data and does not gives users any control over several processing steps that were found to be crucial for other microarray technologies. A main feature of beadarray is the ability to access raw data. The advantages of such data include the ability to perform more detailed quality assessment and greater control over the analysis at all stages. The analysis of a control experiment shows that the processing steps used in BeadStudio can be improved. In particular, utilising variances calculated from the raw data can increase the ability to detect genes which have different expression levels between samples, a common goal for microarray studies. The data from the control experiment are made available for other researchers to use and validate their own analysis methods. One issue discovered during the analysis of the control experiment was that only half of the intended genes could be reliably measured due to problems in the design of the probes targetting particular genes. By considering a large set of publicly available Illumina arrays, I show how such unreliable measurements can affect the analysis of Illumina data. I also show how potential problems can be identified in advance of an experiment and incorporated into an analysis pipeline.
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Liu, Xiao. "Comprehensive bioinformatic analysis of kinesin classification and prediction of structural changes from a closed to an open conformation of the motor domain." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-108430.

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Drlík, Radovan. "Databázový systém pro správu biologických dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237252.

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This thesis describes the problems of storage and management of biological data, particularly of Haloalkane Dehalogenase enzymes. Furthermore, the thesis aims at project HADES (HAloalkane DEhalogenase databaSe) initiated by protein engineering group of Loschmidt Laboratories, Masaryk University in Brno. This is a project whose main goal is simply to store, preserve and display a wide variety of proteins data. The result of this work is a flexible database system allowing easy extensibility and maintainability, which is built on technologies Apache, PostgreSQL and PHP using the Zend Framework.
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Heyer, Erin E. "Optimizing RNA Library Preparation to Redefine the Translational Status of 80S Monosomes: A Dissertation." eScholarship@UMMS, 2015. https://escholarship.umassmed.edu/gsbs_diss/810.

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Deep sequencing of strand-specific cDNA libraries is now a ubiquitous tool for identifying and quantifying RNAs in diverse sample types. The accuracy of conclusions drawn from these analyses depends on precise and quantitative conversion of the RNA sample into a DNA library suitable for sequencing. Here, we describe an optimized method of preparing strand-specific RNA deep sequencing libraries from small RNAs and variably sized RNA fragments obtained from ribonucleoprotein particle footprinting experiments or fragmentation of long RNAs. Because all enzymatic reactions were optimized and driven to apparent completion, sequence diversity and species abundance in the input sample are well preserved. This optimized method was used in an adapted ribosome-profiling approach to sequence mRNA footprints protected either by 80S monosomes or polysomes in S. cerevisiae. Contrary to popular belief, we show that 80S monosomes are translationally active as demonstrated by strong three-nucleotide phasing of monosome footprints across open reading frames. Most mRNAs exhibit some degree of monosome occupancy, with monosomes predominating on upstream ORFs, canonical ORFs shorter than ~590 nucleotides and any ORF for which the total time required to complete elongation is substantially shorter than the time required for initiation. Additionally, endogenous NMD targets tend to be monosome-enriched. Thus, rather than being inactive, 80S monosomes are significant contributors to overall cellular translation.
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Heyer, Erin E. "Optimizing RNA Library Preparation to Redefine the Translational Status of 80S Monosomes: A Dissertation." eScholarship@UMMS, 2010. http://escholarship.umassmed.edu/gsbs_diss/810.

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Deep sequencing of strand-specific cDNA libraries is now a ubiquitous tool for identifying and quantifying RNAs in diverse sample types. The accuracy of conclusions drawn from these analyses depends on precise and quantitative conversion of the RNA sample into a DNA library suitable for sequencing. Here, we describe an optimized method of preparing strand-specific RNA deep sequencing libraries from small RNAs and variably sized RNA fragments obtained from ribonucleoprotein particle footprinting experiments or fragmentation of long RNAs. Because all enzymatic reactions were optimized and driven to apparent completion, sequence diversity and species abundance in the input sample are well preserved. This optimized method was used in an adapted ribosome-profiling approach to sequence mRNA footprints protected either by 80S monosomes or polysomes in S. cerevisiae. Contrary to popular belief, we show that 80S monosomes are translationally active as demonstrated by strong three-nucleotide phasing of monosome footprints across open reading frames. Most mRNAs exhibit some degree of monosome occupancy, with monosomes predominating on upstream ORFs, canonical ORFs shorter than ~590 nucleotides and any ORF for which the total time required to complete elongation is substantially shorter than the time required for initiation. Additionally, endogenous NMD targets tend to be monosome-enriched. Thus, rather than being inactive, 80S monosomes are significant contributors to overall cellular translation.
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Sharma, Kanika. "Identification of micro-RNAs and their messenger RNA targets in Prostate cancer and Biological fluids." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3551.

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Prostate cancer is the second most common cancer in the United States that affects men today. To better treat this disease accurate biomarkers and successful therapeutic treatments are needed. A novel approach to understand the mechanisms behind prostate cancer tumor formation lies in identifying dysregulated micro-RNAs (miRNAs), which are a class of small (18-24 nucleotides) non-coding RNAs that regulate gene expression posttranscriptionally by either inhibiting protein synthesis or signaling messenger-RNA for degradation. Multiple miRNAs were discovered in our highly tumorigenic and metastatic prostate cancer progression model M12 cell line compared to its weakly tumorigenic P69 parental cell line. Various analyses such as human panel analyses, single-miR analyses and patient tumor biopsy samples were analyzed to determine dysregulated miRNAs that contributed to the progression and metastasis of prostate cancer. Together with performing experiments to identify miRNAs, a de novo next generation sequencing approach was applied to identify miRNAs naturally present in biological fluids of normal and healthy subjects. Since, these miRNAs are highly dysregulated in many diseases, including cancer, they can act as potential biomarkers or therapeutic targets to improve treatments for prostate cancer. Essential miRNAs studied for this research were miR-17-3p that is known to target the ErbB2 mRNA; miR-299-5p that directly targets osteopontin (OPN) mRNA, and miR-147b that directly targets many mRNAs, such as COL4A2, ALDH5A1, NDUFA4, SDHD, and IER5. A wide range of miRNAs were identified in six biological fluids: venous blood, menstrual blood, vaginal fluid, semen, saliva, and feces. There were some miRNAs that were common to all 6 body fluids, some unique to each body fluid, and some miRNAs that literature suggested could potentially be biomarkers or normalizers for body fluid characterization.
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Stigliani, Arnaud. "Modélisation de la liaison à l'ADN et des mécanismes d'action de facteurs de transcription floraux Building Transcription Factor Binding Site Models to Understand Gene Regulation in Plants JASPAR 2018: Update of the open-access database of transcription factor binding profiles and its web framework." Thesis, Université Grenoble Alpes (ComUE), 2019. https://thares.univ-grenoble-alpes.fr/2019GREAV032.pdf.

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Chez les angiospermes, la floraison est un processus qui prend part en plusieurs étapes. Le méristème caulinaire, un réservoir de cellule souche d’où émergent la totalité des organes aériens de la plante, va d’abord se différencier en méristème d’inflorescence. Des méristèmes floraux vont alors émerger des flancs du méristème d’inflorescence pour donner naissance aux différents organes qui composent la fleur : les pétales, les sépales, les étamines et le carpelle. Chacune de ces phases est régulée avec finesse par des facteurs de transcription, une famille de protéines se liant à l’ADN pour induire l’activation ou la répression des gènes. Si cette thèse nous a permis de contribuer à la mise à jour de JASPAR, une base de données qui recense des profils liaison de facteurs de transcription, elle a avant tout pour but d’apporter un regard nouveau sur la compréhension des phénomènes qui contrôlent le développement des fleurs à travers l’étude d’une poignée de facteurs de transcription clé dans ce processus. Nous essayerons au mieux d’expliquer les paramètres qui influent sur la liaison de ces facteurs de transcription en utilisant des modèles bioinformatiques associés à des expériences de génomique.Nous nous pencherons d’abord sur les facteurs de réponse à l’auxine à travers l’étude de deux représentants de cette famille de 23 protéines : ARF2 et ARF5. Si les facteurs de transcription de cette famille sont connus pour se lier en dimères, nous avons montré que ARF2 et ARF5 préféraient des espacements différents entre les sites de liaison monomériques sur l’ADN. Nous avons également montré que certaines configurations semblent favoriser l’activation des gènes liés.Ensuite, nous avons étudié LFY, un facteur de transcription maître du développement floral. Nous avons amélioré un modèle de liaison existant et nous avons pu voir que l’intégration de données génomiques de natures diverse permettait de mieux comprendre la liaison du facteur de transcription in vivo.Enfin, nous avons analysé les préférences des facteurs de transcription à boîte MADS, connus pour lier les mêmes séquences d’ADN et dont le rôle est de déterminer l’identité des organes floraux. À travers l’étude du complexe SEP3/AG, qui contrôle la formation du carpelle, nous avons montré que le domaine de tétramérisation de ces facteurs confère une spécificité de liaison expliquant potentiellement que des groupes de facteurs de transcription à boîte MADS régulent la formation d’organes floraux différents en activant des gènes distincts<br>In angiosperms, the development of flowers takes place in several stages. The meristem, a stem cell reservoir from which all the plant’s aerial organs emerge, first differentiate into an inflorescence meristem. Floral meristems then emerge from the flanks of the inflorescence meristem to give birth to the different organs that compose the flower : the petals, sepals, stamens and carpel. Each of these phases is finely regulated by transcription factors, a family of proteins that bind to DNA to induce gene activation or repression. If this thesis allowed us to contribute to the JASPAR database, which gather transcription factor binding profiles, its main goal is to provide a new perspective on the understanding of the phenomena that control flower development through the study of a handful of key transcription factors in the regulation of floral development. We have tried to explain the parameters that influence the binding of these transcription factors using bioinformatics models associated with genomics experiments.We have analysed the auxin response factors (ARF) through the study of two representatives of this family of 23 proteins : ARF2 and ARF5. The transcription factors of this family are known to bind in dimers and we have shown that ARF2 and ARF5 prefer different spacings between monomeric binding sites on DNA. We have also shown that some configurations seem to favour the activation of bound genes.Then, we have studied LFY, a master transcription factor of floral development. We have improved an existing binding model and have seen that the integration of genomic data of various kinds provides a better understanding of the binding of the transcription factor in vivo.Finally, we have analyzed the preferences of MADS box transcription factors, known to bind the same DNA sequences and whose role is to determine the identity of floral organs. Through the study of the SEP3/AG complex, which controls the formation of the carpel, we have found that the tetramerization domain of these factors confers binding specificity, potentially explaining that groups of MADS box transcription factors regulate the formation of different floral organs by activating distinct genes
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Junker, Romane. "Comprehensive study of the microbiome of fermented vegetables using integrative approaches." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL050.

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Le développement du séquençage à haut débit a suscité un regain d'intérêt pour l'étude de divers microbiomes. Parmi eux, le microbiome des légumes fermentés est particulièrement intéressant en raison de son établissement spontané et de sa grande diversité. Cela pose des défis en matière de contrôle de la production ainsi que des propriétés organoleptiques et nutritionnelles. La gestion de la fermentation des aliments repose encore largement sur des connaissances empiriques, car la compréhension des dynamiques des communautés microbiennes et des réseaux métaboliques produisant des produits sûrs et nutritifs reste incomplète. Le paradigme de la science ouverte, notamment la disponibilité de nombreux jeux de données, offre l'opportunité de caractériser ce microbiome de manière plus approfondie. Cette thèse présente diverses méthodes intégratives pour analyser le microbiome des légumes fermentés, en intégrant divers jeux de données publiques afin de caractériser et de comparer de manière exhaustive les microbiomes des légumes fermentés. Tout d'abord, nous avons étudié les dynamiques de succession des communautés bactériennes lors de la fermentation des légumes. Pour cela, nous avons conçu une approche basée sur les réseaux pour comparer dix jeux de données métataxonomiques 16S publiques, comprenant 931 échantillons, et ciblant différents légumes fermentés au fil du temps. Les réseaux construits à partir des jeux de données individuels sont intégrés dans un “core réseau”, mettant en évidence des associations significatives. Cette méthode a permis de mettre en lumière les dynamiques de la fermentation des légumes en caractérisant les processus de succession des communautés parmi différents assemblages bactériens. En second lieu, nous avons utilisé une approche métataxonomique multi-marqueurs pour étudier l'influence des facteurs de production sur les communautés bactériennes lors de la fermentation. Nous avons ensuite examiné la relation entre le core microbiote et les voies métaboliques des légumes fermentés. Pour cela, nous avons analysé de manière exhaustive la diversité microbienne, la composition taxonomique et les profils métaboliques de neuf jeux de données indépendants, incluant 142 échantillons. En utilisant un workflow reproductible, nous avons identifié un ensemble de core espèces et souches microbiennes. Nous les avons reliées à un ensemble de fonctions métaboliques partagées qui peuvent représenter un réseau d'activités importantes durant la fermentation des légumes<br>The development of high-throughput sequencing has sparked renewed interest in studying various microbiomes. Among them, the microbiome of fermented vegetables is particularly intriguing due to its spontaneous establishment and high diversity. This poses challenges in control of production and organoleptic and nutritional properties. The management of food fermentation still relies heavily on empirical knowledge, as understanding the dynamics of microbial communities and the metabolic networks producing safe and nutritious products still needs to be explored. The open science paradigm, especially the availability of numerous datasets, provides an opportunity to characterize this microbiome more in-depth. This thesis presents various integrative methods for analyzing the microbiome of fermented vegetables, incorporating diverse public data to characterize and compare microbiomes across fermented vegetables comprehensively. Firstly, we investigated the succession dynamics of bacterial communities during the fermentation of vegetables. Therefore, we designed a network-based approach to compare ten public metataxonomic 16S datasets, including 931 samples, and targeting different fermented vegetables throughout time. Networks for individual datasets are integrated into a core network, highlighting significant associations. This method sheds light on the dynamics of vegetable fermentation by characterizing the processes of community succession among different bacterial assemblages. On a secondary level, we used a multi-marker metataxonomic approach to study the influence of processing factors on the bacterial communities during the fermentation. We then investigated the relationship between the core microbiota and the core metabolic pathways of fermented vegetables. To this end, we comprehensively analyzed the microbial diversity, taxonomic composition, and metabolic profiles of nine independent datasets, including 142 samples. Using a reproducible analytical workflow with a read-based approach, we identified a core set of microbial species and strains. We linked them to a set of shared metabolic functions that may represent a network of activities important during the fermentation of vegetables
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Schlaffner, Christoph Norbert. "Proteogenomics for personalised molecular profiling." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275137.

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Technological advancements in mass spectrometry allowing quantification of almost complete proteomes make proteomics a key platform for generating unique functional molecular data. Furthermore, the integrative analysis of genomic and proteomic data, termed proteogenomics, has emerged as a new field revealing insights into gene expression regulation, cell signalling, and disease processes. However, the lack of software tools for high-throughput integration and unbiased modification and variant detection hinder efforts for large-scale proteogenomics studies. The main objectives of this work are to address these issues by developing and applying new software tools and data analysis methods. Firstly, I address mapping of peptide sequences to reference genomes. I introduce a novel tool for high-throughput mapping and highlight its unique features facilitating quantitative and post-translational modification mapping alongside accounting for amino acid substitutions. The performance is benchmarked. Furthermore, I offer an additional tool that permits generation of web accessible hubs of genome wide mappings. To enable unbiased identification of post-translational modifications and amino acid substitutions for high resolution mass spectrometry data, I present algorithmic updates the mass tolerant blind spectrum comparison tool ’MS SMiV’. I demonstrate the applicability of the changes by benchmarking against a published mass tolerant database search of a high resolution tandem mass spectrometry dataset. I then present the application of ‘MS SMiV’ on a panel of 50 colorectal cancer cell lines. I show that the adaption of ‘MS SMiV’ outperforms traditional sequence database based identification of single amino acid variants. Furthermore, I highlight the utility of mass tolerant spectrum matching in combination with isobaric labelled quantitative proteomics in distinguishing between post-translational modifications and amino acid variants of similar mass. In the last part of this work I integrate both tools with a high-throughput proteogenomic identification pipeline and apply it to a pilot study of chondrocytes derived from 12 osteoarthritic individuals. I show the value of this approach in identifying variation between individuals and molecular levels and highlight them with individual examples. I show that multi-plexed proteogenomics can be used to infer genotypes of individuals.
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Books on the topic "Open Bioinformation"

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Kilmartin, Gerard. An electronic notebook for sequencing. The Author], 2004.

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Bleasby, Alan. EMBOSS administrator's guide: Bioinformatics software management. Cambridge University Press, 2011.

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G, Smolinski Tomasz, Milanova Mariofanna G, and Hassanien Aboul Ella, eds. Applications of computational intelligence in biology: Current trends and open problems. Springer, 2008.

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G, Smolinski Tomasz, Milanova Mariofanna G, and Hassanien Aboul Ella, eds. Applications of computational intelligence in biology: Current trends and open problems. Springer, 2008.

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Teetor, Paul. R cookbook. O'Reilly, 2011.

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Bessant, Conrad, Ian Shadforth, and Darren Oakley. Building Bioinformatics Solutions. Oxford University Press, 2014.

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Bessant, Conrad, Ian Shadforth, and Darren Oakley. Building Bioinformatics Solutions. Oxford University Press, 2014.

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Buffalo, Vince. Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools. O'Reilly Media, Incorporated, 2015.

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Buffalo, Vince. Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools. O'Reilly Media, Incorporated, 2015.

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Buffalo, Vince. Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools. O'Reilly Media, Incorporated, 2015.

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Book chapters on the topic "Open Bioinformation"

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Caso, Roberto, and Rossana Ducato. "Open Bioinformation in the Life Sciences as a Gatekeeper for Innovation and Development." In Law, Development and Innovation. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13311-9_7.

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Gururaj, T., and A. P. Pavithra. "Open-Source Software Tools for Bioinformatics." In Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2445-5_6.

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Winfield, Mark, Paul Wilkinson, Amanda Burridge, et al. "CerealsDB: A Whistle-Stop Tour of an Open Access SNP Resource." In Plant Bioinformatics. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2067-0_6.

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Hsiao, Yun-Hua Esther, Ashley A. Cass, Jae Hoon Bahn, Xianzhi Lin, and Xinshu Xiao. "Global Approaches to Alternative Splicing and Its Regulation—Recent Advances and Open Questions." In Translational Bioinformatics. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7450-5_2.

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Brinkrolf, Christoph, and Lennart Ochel. "Comprehensive Open-Source Petri Net Toolchain for Modeling and Simulation in Systems Biology." In Integrative Bioinformatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6795-4_13.

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Garg, Vanika, and Rajeev K. Varshney. "Analysis of Small RNA Sequencing Data in Plants." In Plant Bioinformatics. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2067-0_26.

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AbstractOver the past decades, next-generation sequencing (NGS) has been employed extensively for investigating the regulatory mechanisms of small RNAs. Several bioinformatics tools are available for aiding biologists to extract meaningful information from enormous amounts of data generated by NGS platforms. This chapter describes a detailed methodology for analyzing small RNA sequencing data using different open source tools. We elaborate on various steps involved in analysis, from processing the raw sequencing reads to identifying miRNAs, their targets, and differential expression studies.
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Keeble-Gagnère, Gabriel, Johan Nyström-Persson, Matthew I. Bellgard, and Kenji Mizuguchi. "An Open Framework for Extensible Multi-stage Bioinformatics Software." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34123-6_10.

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de Souza, Eduardo Vieira, and Cristiano Valim Bizarro. "Identification of Novel Bacterial Microproteins Encoded by Small Open Reading Frames Using a Computational Proteogenomics Workflow." In Protein Bioinformatics. Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-4007-4_2.

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Rady, Brooks J., and Stéphane Mesnage. "PGFinder, an Open-Source Software for Peptidoglycomics: The Structural Analysis of Bacterial Peptidoglycan by LC-MS." In Protein Bioinformatics. Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-4007-4_8.

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Kodysh, Julia, and Alex Rubinsteyn. "OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction." In Bioinformatics for Cancer Immunotherapy. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0327-7_10.

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Conference papers on the topic "Open Bioinformation"

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Li, Zijian, Sendong Zhao, Haochun Wang, Haoming Xu, Bing Qin, and Ting Liu. "CMCOQA: A Chinese Medical Complex Open-Question Answering Benchmark." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821873.

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Zhao, Rui-Wei, Yiqian Xu, Ying Zhang, and Rui Feng. "Evidential Open Set Recognition for Imbalanced Medical Images via Multi-level Data Augmentation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822558.

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Cui, Kunbo, Mingqi Zhao, Di Liu, Minxin He, Qinglin Zhao, and Bin Hu. "Heterogeneous Effects of Eye-close and Eye-open on Electroencephalographic Microstates of Depressed Brain in Resting State." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822864.

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Ghosh, Pampa Saha, and Souvik Sengupta. "SentiGrad: A New Hindi-English Code Mixed Sentiment Analysis Dataset with Preliminary Results and Open Challenges." In 2024 International Conference on Big Data Analytics in Bioinformatics (DABCon). IEEE, 2024. https://doi.org/10.1109/dabcon63472.2024.10919296.

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Lakes, Thanos, Vasiliki Efstathiou, and Nikos Perdikopanis. "Evaluating the Impact of Variants on Translation Initiation Sites for Accurate Identification of Functional Open Reading Frames (ORFs)." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822565.

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Li, Xubin, Wenrui Jiang, Yi Jiang, and Quan Zou. "Hadoop Applications in Bioinformatics." In 2012 7th Open Cirrus Summit (OCS). IEEE, 2012. http://dx.doi.org/10.1109/ocs.2012.40.

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Shah, Abad Ali, Fatima Maqbool, and Muhammad Usman Ghani Khan. "Dynamic ontology management for bioinformatics domain." In 2013 International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2013. http://dx.doi.org/10.1109/icosst.2013.6720596.

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Shabaga, K., and D. M. German. "BioFOSS: a survey of Free/Open Source Software in Bioinformatic." In Proceedings. 19th IEEE International Symposium on Computer-Based Medical Systems. IEEE, 2006. http://dx.doi.org/10.1109/cbms.2006.60.

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Celebi, Remzi, Ozgur Gumus, and Yesim Aydin Son. "Use of open linked data in bioinformatics space: A case study." In 2013 8th International Symposium on Health Informatics and Bioinformatics (HIBIT). IEEE, 2013. http://dx.doi.org/10.1109/hibit.2013.6661679.

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Zhang, Yucheng, Payas Bhutra, and Xue Li. "nf-core on Open OnDemand: community-curated bioinformatics pipelines for everyone." In PEARC '24: Practice and Experience in Advanced Research Computing. ACM, 2024. http://dx.doi.org/10.1145/3626203.3670559.

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Reports on the topic "Open Bioinformation"

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.
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