Relevant bibliographies by topics / Bioinformatic

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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 'Bioinformatic'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 July 2024

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Journal articles on the topic "Bioinformatic"

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Bottomley, S. "Bioinformatics: guide for evaluating bioinformatic software." Drug Discovery Today 4, no. 5 (May 1, 1999): 240–43. http://dx.doi.org/10.1016/s1359-6446(99)01352-5.

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Moreews, François, Olivier Sallou, Hervé Ménager, Yvan Le bras, Cyril Monjeaud, Christophe Blanchet, and Olivier Collin. "BioShaDock: a community driven bioinformatics shared Docker-based tools registry." F1000Research 4 (December 14, 2015): 1443. http://dx.doi.org/10.12688/f1000research.7536.1.

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Linux container technologies, as represented by Docker, provide an alternative to complex and time-consuming installation processes needed for scientific software. The ease of deployment and the process isolation they enable, as well as the reproducibility they permit across environments and versions, are among the qualities that make them interesting candidates for the construction of bioinformatic infrastructures, at any scale from single workstations to high throughput computing architectures. The Docker Hub is a public registry which can be used to distribute bioinformatic software as Docker images. However, its lack of curation and its genericity make it difficult for a bioinformatics user to find the most appropriate images needed. BioShaDock is a bioinformatics-focused Docker registry, which provides a local and fully controlled environment to build and publish bioinformatic software as portable Docker images. It provides a number of improvements over the base Docker registry on authentication and permissions management, that enable its integration in existing bioinformatic infrastructures such as computing platforms. The metadata associated with the registered images are domain-centric, including for instance concepts defined in the EDAM ontology, a shared and structured vocabulary of commonly used terms in bioinformatics. The registry also includes user defined tags to facilitate its discovery, as well as a link to the tool description in the ELIXIR registry if it already exists. If it does not, the BioShaDock registry will synchronize with the registry to create a new description in the Elixir registry, based on the BioShaDock entry metadata. This link will help users get more information on the tool such as its EDAM operations, input and output types. This allows integration with the ELIXIR Tools and Data Services Registry, thus providing the appropriate visibility of such images to the bioinformatics community.
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Kim, Min Cheol, Jaclyn M. Winter, Reiko Cullum, Alexander J. Smith, and William Fenical. "Expanding the Utility of Bioinformatic Data for the Full Stereostructural Assignments of Marinolides A and B, 24- and 26-Membered Macrolactones Produced by a Chemically Exceptional Marine-Derived Bacterium." Marine Drugs 21, no. 6 (June 20, 2023): 367. http://dx.doi.org/10.3390/md21060367.

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Marinolides A and B, two new 24- and 26-membered bacterial macrolactones, were isolated from the marine-derived actinobacterium AJS-327 and their stereostructures initially assigned by bioinformatic data analysis. Macrolactones typically possess complex stereochemistry, the assignments of which have been one of the most difficult undertakings in natural products chemistry, and in most cases, the use of X-ray diffraction methods and total synthesis have been the major methods of assigning their absolute configurations. More recently, however, it has become apparent that the integration of bioinformatic data is growing in utility to assign absolute configurations. Genome mining and bioinformatic analysis identified the 97 kb mld biosynthetic cluster harboring seven type I polyketide synthases. A detailed bioinformatic investigation of the ketoreductase and enoylreductase domains within the multimodular polyketide synthases, coupled with NMR and X-ray diffraction data, allowed for the absolute configurations of marinolides A and B to be determined. While using bioinformatics to assign the relative and absolute configurations of natural products has high potential, this method must be coupled with full NMR-based analysis to both confirm bioinformatic assignments as well as any additional modifications that occur during biosynthesis.
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Brazas, M. D., J. T. Yamada, and B. F. F. Ouellette. "Evolution in bioinformatic resources: 2009 update on the Bioinformatics Links Directory." Nucleic Acids Research 37, Web Server (June 15, 2009): W3—W5. http://dx.doi.org/10.1093/nar/gkp531.

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SoRelle, Jeffrey A., Megan Wachsmann, and Brandi L. Cantarel. "Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays." Archives of Pathology & Laboratory Medicine 144, no. 9 (February 11, 2020): 1118–30. http://dx.doi.org/10.5858/arpa.2019-0476-ra.

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Context.— Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories. Objective.— To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay. Data Sources.— This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation. Conclusions.— This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.
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García-García, Natalia, Javier Tamames, and Fernando Puente-Sánchez. "M&Ms: a versatile software for building microbial mock communities." Bioinformatics 38, no. 7 (January 12, 2022): 2057–59. http://dx.doi.org/10.1093/bioinformatics/btab882.

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Abstract Summary Advances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze 16S rDNA sequencing data. As these tools need to be tested, it is important to simulate datasets that resemble samples from different environments. Here, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce different 16S rDNA datasets from reference sequences, based on pragmatic ecological parameters. It creates sequence libraries for ‘in silico’ microbial communities with user-controlled richness, evenness, microdiversity and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools. Availability and implementation The source code of M&Ms is freely available at https://github.com/ggnatalia/MMs (GPL-3.0 License). Supplementary information Supplementary data are available at Bioinformatics online.
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Hsu, Pei-Chun, Evaristus Nwulia, and Akira Sawa. "Using Bioinformatic Tools." American Journal of Psychiatry 166, no. 8 (August 2009): 854. http://dx.doi.org/10.1176/appi.ajp.2009.09060908.

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Chen, Runsheng. "On Bioinformatic Resources." Genomics, Proteomics & Bioinformatics 13, no. 1 (February 2015): 1–3. http://dx.doi.org/10.1016/j.gpb.2015.02.002.

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Mshvidobadze, Tinatin. "Bioinformatics as Emerging Tool and Pipeline Frameworks." Science Progress and Research 1, no. 4 (October 23, 2021): 411–15. http://dx.doi.org/10.52152/spr/2021.162.

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In this article, we will discuss the areas of origin of bioinformatics in the human health care system. Due to the growing network of biological information databases such as human genomes, transcriptomics and proteomics, bioinformatics has become the approach of choosing forensic sciences. High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Here we survey and compare the design philosophies of several current pipeline frameworks.
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Chen, Ray, Hon Wong, and Brendan Burns. "New Approaches to Detect Biosynthetic Gene Clusters in the Environment." Medicines 6, no. 1 (February 25, 2019): 32. http://dx.doi.org/10.3390/medicines6010032.

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Microorganisms in the environment can produce a diverse range of secondary metabolites (SM), which are also known as natural products. Bioactive SMs have been crucial in the development of antibiotics and can also act as useful compounds in the biotechnology industry. These natural products are encoded by an extensive range of biosynthetic gene clusters (BGCs). The developments in omics technologies and bioinformatic tools are contributing to a paradigm shift from traditional culturing and screening methods to bioinformatic tools and genomics to uncover BGCs that were previously unknown or transcriptionally silent. Natural product discovery using bioinformatics and omics workflow in the environment has demonstrated an extensive distribution of BGCs in various environments, such as soil, aquatic ecosystems and host microbiome environments. Computational tools provide a feasible and culture-independent route to find new secondary metabolites where traditional approaches cannot. This review will highlight some of the advances in the approaches, primarily bioinformatic, in identifying new BGCs, especially in environments where microorganisms are rarely cultured. This has allowed us to tap into the huge potential of microbial dark matter.
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Dissertations / Theses on the topic "Bioinformatic"

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Hedlund, Joel. "Bioinformatic protein family characterisation." Doctoral thesis, Linköpings universitet, Bioinformatik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-61754.

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Biological research is necessary; not only to further our understanding of the processes of life, but also to combat disease, hunger and environmental damage. Bioinformatics is the science of handling biological information. It entails integrating, structuring and analysing the ever-increasing amounts of available biological data. In practise it means using computers to analyse huge amounts of very complicated data taken from a field that is only partially understood, to see the hidden trends and connections, and to draw useful conclusions. My thesis work has mainly concerned the study of protein families, which are groups of evolutionarily related proteins. I have analysed known protein families and created predictive models for them, and developed algorithms for defining new protein families. My principal techniques have been sequence alignments and hidden Markov models (HMM). To aid my work, I have written a lot of software, including MSAView, a visualiser for multiple sequence alignments (MSA). In this thesis, the protein family of inorganic pyrophosphatases (H+-PPases) is studied, as well as the two protein superfamilies BRICHOS and MDR (medium-chain dehydrogenases/reductases). The H+-PPases are tightly membrane bound, proton pumping, dimeric enzymes with ~700-residue subunits and found in bacteria, plants and eukaryotic parasites, and which use pyrophosphate as an alternative to ATP. The BRICHOS superfamily is only present in higher eukaryotes, but encompasses at least 8 protein families with a wide range of functions and disease associations, such as respiratory distress syndrome, dementia and cancer. The sequences are typically ~200 residues with even shorter functional forms. Finally, MDR, is a large and complex protein superfamily; it currently has over 16000 members, it is present in all kingdoms of life, the pairwise sequence identity is typically around 25 %, the chain lengths vary as does the oligomericity, and the members are partaking in a multitude of biological processes. The member families include the classical liver alcohol dehydrogenase (ADH), quinone reductase, leukotriene B4 dehydrogenase, and many more forms. There are at least 25 human MDR genes excluding close homologues. There are HMMs available for detecting MDR superfamily membership, but none for the individual families. For the H+-PPase family, we characterised member sequences found using an HMM of a conserved 57-residue region thought to form part of the active site. This region was found to contain two highly conserved nonapeptides, mainly consisting of the four “very early” residues Gly, Ala, Val and Asp, compatible with an ancient origin of the family. The two patterns have charged amino acid residues at positions 1, 5 and 9, are apparent binding sites for the substrate and parts of the active site, and were shown to be so specific for these enzymes that they can be used for automated annotation of new sequences. For the BRICHOS superfamily, we were able to find three previously unknown member families; group A, which may be ancestral to the ITM2 families (integral membrane protein 2); group B, which is a close relative to the gastrokine families, and group C, which appears to be a truly novel, disjoint BRICHOS family. The C-terminal region of group C has nearly identical sequences in all species ranging from fish to man and is seemingly unique to this family, indicating critical functional or structural properties. For the MDR superfamily, we characterised and built stable HMMs for 17 member families using an empiric approach. From our experiences we were able to develop an algorithm for automated HMM refinement that uses relationships in data to produce stable and reliable classifiers, and we used it to produce HMMs for 86 distinct MDR families. We have made the program freely available and it can be readily applied to other protein families. We also developed a web site (http://mdr–enzymes.org) that makes our findings directly useful also for non-bioinformaticians. In our analyses of the 86 families, we found that MDR forms with 2 Zn2+ ions in general are dehydrogenases, while MDR forms with no Zn2+ in general are reductases. Furthermore, in Bacteria, MDRs without Zn2+ are more frequent than those with Zn2+, while the opposite is true for eukaryotic MDRs, indicating that Zn2+ has been recruited into the MDR superfamily after the initial life kingdom separations. Multiple sequence alignments (MSA) play a central part in most work on protein families, and are integral to many bioinformatic methods. With the ongoing explosive increase of available sequence data, the scales of bioinformatic projects are growing, and efficient and human-friendly data visualisation becomes increasingly challenging, but is still essential for making new interpretations and discovering unexpected properties of the data. Ideally, visualisation should be comprehensive and detailed, and never distract with irrelevant information. It needs to offer natural and responsive ways of exploring the data, as well as provide consistent views in order to facilitate comparisons between datasets. I therefore developed MSAView, which is a fast, modular, configurable and extensible package for analysing and visualising MSAs and sequence features. It has a graphical user interface and a powerful command line client, and can be imported as a package into any Python program. It has a plugin architecture and a user extendable preset library. It can integrate and display data from online sources and launch external viewers for showing additional details. It also includes two new conservation measures; alignment divergences, which indicate atypical residues or deletions, and sequence conformances, which highlight sequences that differ from their siblings at crucial positions. In conclusion, this thesis details my work in analysing two protein superfamilies and one protein family using bioinformatic methods; developing an algorithm for automated generation of stable and reliable HMMs, as well as a new conservation measure, and a software platform for working with aligned sequences.
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Kallberg, Yvonne. "Bioinformatic methods in protein characterization /." Stockholm, 2002. http://diss.kib.ki.se/2002/91-7349-370-8/.

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Li, Yvonne Yiyuan. "Bioinformatic approaches to drug repositioning." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/39934.

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Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. However, most successful repositioning cases to date have been serendipitous; the goal of my thesis was to use computational methods to rationally discover drug repositioning candidates. I first virtually screened (VS) 4621 drugs against 252 drug targets with molecular docking. This method emphasized removing potential false positives using stringent criteria from known interaction docking, consensus scores, and rank information. Published literature indicated experimental evidence for 31 top predicted interactions, supporting the approach. The chemotherapeutic nilotinib was validated as a potent MAPK14 inhibitor in vitro (IC50 40nM), suggesting a potential use in inflammatory diseases. I then applied this method to the cancer target EGFR, predicting the anti-HIV drug tenofovir disoproxil fumarate (TDF) as a novel inhibitor. In vitro, TDF inhibited the proliferation and EGFR-signaling of an EGFR-overexpressing cell line, but did not inhibit EGFR in direct kinase binding assays. This study highlighted limitations of computational and experimental methodologies that should be considered when interpreting or designing other studies. We then screened 1,120 off-patent drugs against the triple-negative breast cancer (TNBC) target p90RSK using both VS and high-throughput (HTS) methods. VS predicted a set of compounds 26-times enriched for known RSK inhibitors and 11 times enriched for HTS hits, underscoring its efficiency. In secondary screens, the chemotherapeutic ellipticine and the bioflavonoids luteolin and apigenin inhibited RSK activity (IC50 0.50-4.77μM), blocked RSK signaling, and inhibited TNBC cell proliferation. These drugs thus have potential to be repositioned to TNBC. Finally, we rationally repositioned renal cell carcinoma drugs for a patient with a rare tongue adenocarcinoma. Whole genome and transcriptome sequencing of the patient’s tumor and normal cells detected sequence, copy number, and expression aberrations, and analysis suggested that the tumor was driven by the RET oncogene. Treatment with RET-inhibiting drugs stabilized the disease for eight months, after which the disease progressed. We also sequenced the post-treatment tumor and found changes consistent with acquired therapeutic resistance. Overall, this thesis details two novel high-throughput approaches for drug repositioning: virtual screening of drugs and targets and personalized medicine via sequencing.
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Weinstein, Earl G. 1974. "MicroRNA cloning and bioinformatic analysis." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8390.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Biology, 2002.
Includes bibliographical references.
Part I. Two gene-regulatory noncoding RNAs (ncRNAs), let-7 RNA and lin-4 RNA, were previously discovered in the C. elegans genome. The let-7 gene is conserved across a wide range of genomes, suggesting that these ncRNAs represent a wider class of gene-regulatory RNAs. Both lin-4 and let-7 RNAs are generated from stem-loop precursor RNAs, and share a common biochemical signature, namely 5'-terminal phosphate and 3'-terminal hydroxyl groups. We refer to ncRNAs that share the characteristic size, biochemical signature, and precursor structures of let-7 and lin-4 as microRNAs (miRNAs). The size of this class of genes, and its prevalence in other genomes, are unknown. Therefore, we developed an experimental and bioinformatics strategy to identify novel miRNA genes. We discovered a total of 75 miRNA genes in the C. elegans genome, and orthologues for a majority of these were computationally identified in the C. briggsae, D. melanogaster or H. sapiens genomes. Northern analysis was used to confirm and analyze the expression of these miRNAs. The data set has implications for understanding miRNA gene regulation, miRNA processing, and regulation of miRNA genes. Part II. Directed molecular evolution has previously been applied to generate RNAs with novel structures and functions. This method works because nucleic acids can be selected, randomized, amplified and characterized using polymerase chain reaction (PCR)-based methods. Here we present a novel method for extending directed molecular evolution to the realm of peptide selections by linking a peptide to its encoding mRNA.
(cont.) A proof of principle selection for two different peptides indicates that this tRNA should prove useful in discovering more complex protein molecules using directed molecular evolution.
by Earl G. Weinstein.
Ph.D.
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Leonardi, Emanuela. "Bioinformatic Analysis of Protein Mutations." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3426280.

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Many gene defects have been associated to genetic disorders, but the details of molecular mechanisms by which they contribute to the disease are often unclear. The study of mutation effects at the protein level can help elucidate the biological processes involved in the disease and the role of the protein in it. Bioinformatics can help to address this problem, being the connection between different disciplines including clinical, genetics, structural biology, and biochemistry. By using a computational approach I tackled the analysis of some examples of biomedical interesting proteins integrating various sources of data and addressing experimental and clinical investigations. Experimentally defined structures and molecular modelling were used as a basis to determine the protein structure-function relationship, which is essential to gain insights into disease genotype-phenotype correlation. Proteins have been further analyzed in their context, considering interactions that they take in specific cellular compartments. The results have been used to formulate functional hypotheses, which in some cases have been tested and confirmed by further investigations performed by cooperation groups. Mutations found in genes encoding these proteins have been evaluated for their impact on the protein structure and function by using several available prediction methods. These studies provided the idea for developing novel approaches, using residue interaction networks and an ensemble of methods. A novel strategy has been also designed to evaluate genomic data obtained by next generation sequencing technology. This consists in using available resources and software to prioritize rare functional variants and estimate their contribution to the disease. The novel approaches developed in this thesis have been applied and assessed at the Critical Assessment of Genome Interpretation (CAGI) experiment in 2011, providing in some cases very successful results
Alterazioni genetiche sono state identificate per molte malattie di natura genetica, ma in molti casi i meccanismi molecolari che contribuiscono all’insorgere della malattia non sono ancora chiari. Lo studio degli effetti delle mutazioni a livello della proteina permette di chiarire i processi biologici coinvolti nella malattia e il ruolo della proteina in essa. La bioinformatica può aiutare a affrontare questo problema rappresentando il punto di connessione tra diverse discipline quali la clinica, la genetica, la biologia strutturale e la biochimica. In questa tesi ho impiegato un approccio computazionale per affrontare l’analisi di alcuni esempi di proteine di interesse biomedico, integrando diverse risorse di dati e indirizzando la ricerca sperimentale e clinica. Strutture proteiche determinate sperimentalmente o mediante il modelling molecolare sono state utilizzate come base per determinare la relazione tra struttura e funzione, essenziale per ottenere informazioni sulla correlazione genotipo-fenotipo. Le proteine prese in esame sono state inoltre analizzate nel loro contesto, considerando le interazioni che avvengono con altre proteine o ligandi nei diversi compartimenti cellulari. I risultati dell’analisi bioinformatica sono stati poi utilizzati per formulare ipotesi funzionali che in alcuni casi sono state verificate e confermate sperimentalmente da altri gruppi di ricerca. Le mutazioni identificate nei geni codificanti per le proteine in esame sono state valutate per il loro impatto sulla struttura e funzione della proteina utilizzando numerosi metodi di predizione disponibili online. Le diverse applicazioni descritte in questa tesi hanno fornito l’idea per lo sviluppo di nuovi approcci computazionali per lo caratterizzazione strutturale e funzionale di proteine e dei loro mutanti. Si è visto che la predizione migliora utilizzando un ensemble dei diversi metodi di predizione disponibili. Inoltre, per la predizione degli effetti di mutazioni è stato ideato un nuovo approccio computazionale che utilizza le reti di interazione tra residui per rappresentare la struttura proteica. Questi metodi sono stati utilizzati anche nell’analisi di dati genomici originati da nuove tecnologie di sequenziamento. Questo ambito necessita di nuove strategie di indagine per l’individuazione di poche varianti causative in un’enorme quantità di varianti identificate di dubbio significato. A questo scopo viene proposta una strategia di analisi che utilizza informazioni derivanti dalle reti di interazioni proteiche. I nuovi approcci formulati in questa tesi sono stati applicati e valutati ad un nuovo esperimento internazionale, chiamato Critical Assessment of Genome Interpretation (CAGI), fornendo in alcuni casi ottimi risultati
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Bertoldi, Loris. "Bioinformatics for personal genomics: development and application of bioinformatic procedures for the analysis of genomic data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421950.

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In the last decade, the huge decreasing of sequencing cost due to the development of high-throughput technologies completely changed the way for approaching the genetic problems. In particular, whole exome and whole genome sequencing are contributing to the extraordinary progress in the study of human variants opening up new perspectives in personalized medicine. Being a relatively new and fast developing field, appropriate tools and specialized knowledge are required for an efficient data production and analysis. In line with the times, in 2014, the University of Padua funded the BioInfoGen Strategic Project with the goal of developing technology and expertise in bioinformatics and molecular biology applied to personal genomics. The aim of my PhD was to contribute to this challenge by implementing a series of innovative tools and by applying them for investigating and possibly solving the case studies included into the project. I firstly developed an automated pipeline for dealing with Illumina data, able to sequentially perform each step necessary for passing from raw reads to somatic or germline variant detection. The system performance has been tested by means of internal controls and by its application on a cohort of patients affected by gastric cancer, obtaining interesting results. Once variants are called, they have to be annotated in order to define their properties such as the position at transcript and protein level, the impact on protein sequence, the pathogenicity and more. As most of the publicly available annotators were affected by systematic errors causing a low consistency in the final annotation, I implemented VarPred, a new tool for variant annotation, which guarantees the best accuracy (>99%) compared to the state-of-the-art programs, showing also good processing times. To make easy the use of VarPred, I equipped it with an intuitive web interface, that allows not only a graphical result evaluation, but also a simple filtration strategy. Furthermore, for a valuable user-driven prioritization of human genetic variations, I developed QueryOR, a web platform suitable for searching among known candidate genes as well as for finding novel gene-disease associations. QueryOR combines several innovative features that make it comprehensive, flexible and easy to use. The prioritization is achieved by a global positive selection process that promotes the emergence of the most reliable variants, rather than filtering out those not satisfying the applied criteria. QueryOR has been used to analyze the two case studies framed within the BioInfoGen project. In particular, it allowed to detect causative variants in patients affected by lysosomal storage diseases, highlighting also the efficacy of the designed sequencing panel. On the other hand, QueryOR simplified the recognition of LRP2 gene as possible candidate to explain such subjects with a Dent disease-like phenotype, but with no mutation in the previously identified disease-associated genes, CLCN5 and OCRL. As final corollary, an extensive analysis over recurrent exome variants was performed, showing that their origin can be mainly explained by inaccuracies in the reference genome, including misassembled regions and uncorrected bases, rather than by platform specific errors.
Nell’ultimo decennio, l’enorme diminuzione del costo del sequenziamento dovuto allo sviluppo di tecnologie ad alto rendimento ha completamente rivoluzionato il modo di approcciare i problemi genetici. In particolare, il sequenziamento dell’intero esoma e dell’intero genoma stanno contribuendo ad un progresso straordinario nello studio delle varianti genetiche umane, aprendo nuove prospettive nella medicina personalizzata. Essendo un campo relativamente nuovo e in rapido sviluppo, strumenti appropriati e conoscenze specializzate sono richieste per un’efficiente produzione e analisi dei dati. Per rimanere al passo con i tempi, nel 2014, l’Università degli Studi di Padova ha finanziato il progetto strategico BioInfoGen con l’obiettivo di sviluppare tecnologie e competenze nella bioinformatica e nella biologia molecolare applicate alla genomica personalizzata. Lo scopo del mio dottorato è stato quello di contribuire a questa sfida, implementando una serie di strumenti innovativi, al fine di applicarli per investigare e possibilmente risolvere i casi studio inclusi all’interno del progetto. Inizialmente ho sviluppato una pipeline per analizzare i dati Illumina, capace di eseguire in sequenza tutti i processi necessari per passare dai dati grezzi alla scoperta delle varianti sia germinali che somatiche. Le prestazioni del sistema sono state testate mediante controlli interni e tramite la sua applicazione su un gruppo di pazienti affetti da tumore gastrico, ottenendo risultati interessanti. Dopo essere state chiamate, le varianti devono essere annotate al fine di definire alcune loro proprietà come la posizione a livello del trascritto e della proteina, l’impatto sulla sequenza proteica, la patogenicità, ecc. Poiché la maggior parte degli annotatori disponibili presentavano errori sistematici che causavano una bassa coerenza nell’annotazione finale, ho implementato VarPred, un nuovo strumento per l’annotazione delle varianti, che garantisce la migliore accuratezza (>99%) comparato con lo stato dell’arte, mostrando allo stesso tempo buoni tempi di esecuzione. Per facilitare l’utilizzo di VarPred, ho sviluppato un’interfaccia web molto intuitiva, che permette non solo la visualizzazione grafica dei risultati, ma anche una semplice strategia di filtraggio. Inoltre, per un’efficace prioritizzazione mediata dall’utente delle varianti umane, ho sviluppato QueryOR, una piattaforma web adatta alla ricerca all’interno dei geni causativi, ma utile anche per trovare nuove associazioni gene-malattia. QueryOR combina svariate caratteristiche innovative che lo rendono comprensivo, flessibile e facile da usare. La prioritizzazione è raggiunta tramite un processo di selezione positiva che fa emergere le varianti maggiormente significative, piuttosto che filtrare quelle che non soddisfano i criteri imposti. QueryOR è stato usato per analizzare i due casi studio inclusi all’interno del progetto BioInfoGen. In particolare, ha permesso di scoprire le varianti causative dei pazienti affetti da malattie da accumulo lisosomiale, evidenziando inoltre l’efficacia del pannello di sequenziamento sviluppato. Dall’altro lato invece QueryOR ha semplificato l’individuazione del gene LRP2 come possibile candidato per spiegare i soggetti con un fenotipo simile alla malattia di Dent, ma senza alcuna mutazione nei due geni precedentemente descritti come causativi, CLCN5 e OCRL. Come corollario finale, è stata effettuata un’analisi estensiva su varianti esomiche ricorrenti, mostrando come la loro origine possa essere principalmente spiegata da imprecisioni nel genoma di riferimento, tra cui regioni mal assemblate e basi non corrette, piuttosto che da errori piattaforma-specifici.
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Markstedt, Olof. "Kubernetes as an approach for solving bioinformatic problems." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-330217.

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The cluster orchestration tool Kubernetes enables easy deployment and reproducibility of life science research by utilizing the advantages of the container technology. The container technology allows for easy tool creation, sharing and runs on any Linux system once it has been built. The applicability of Kubernetes as an approach to run bioinformatic workflows was evaluated and resulted in some examples of how Kubernetes and containers could be used within the field of life science and how they should not be used. The resulting examples serves as proof of concepts and the general idea of how implementation is done. Kubernetes allows for easy resource management and includes automatic scheduling of workloads. It scales rapidly and has some interesting components that are beneficial when conducting life science research.
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Hull, Duncan. "Semantic matching of bioinformatic web services." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497578.

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Understanding bioinformatic data on the Web often requires the interoperation of heterogeneous and autonomous services. Unfortunately, getting many different services to interoperate is problematic, and frequently requires cumbersome shim components which can be difficult to describe and discover using existing techniques. The use of description logic reasoning has been proposed as a method for improving discovery of services, by classifying advertisements and matchmaking them with requests on the semantic Web. However, theoretical approaches to reasoning with semantic Web services have not been adequately tested on realistic scenarios while practical approaches have not fully investigated or applied useful aspects of current theory.
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Cova, Marta Alexandra Mendonça Nóbrega. "Bioinformatic analysis of the neuronal phosphoproteome." Master's thesis, Universidade de Aveiro, 2013. http://hdl.handle.net/10773/11623.

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Mestrado em Biomedicina Molecular
A fosforilação anormal de proteínas é uma das características chave da Doença de Alzheimer (DA) que pode estar envolvida tanto na patogénese como na progressão da doença. A fosforilação reversível de proteínas representa um importante mecanismo regulador que envolve a atividade de fosfoproteínas fosfatases (FPF) e proteínas cinases (PC). Um desequilíbrio intracelular entre a actividade de FPF e PC pode alterar a atividade, localização subcelular e interacções de proteínas, contribuindo para a desregulação da função e sinalização neuronal e, consequentemente para a neurodegeneração. Assim, o estudo do fosfoproteoma neuronal da DA tornase relevante tanto do ponto de vista fisiológico como patológico. Culturas primárias corticais foram expostas ao ácido ocadáico (AO, um inibidor de PPP) ou ao péptido β amilóide (Aβ) para mimetizar as condições da DA. Os lisados celulares foram aplicados numa coluna de afinidade para fosfoproteínas. As frações enriquecidas em fosfoproteínas foram analisadas por espetrometria de massa tendo sido desenvolvido um script em linguagem python (http://sourceforge.net/projects/protdb/) para análise das proteínas identificadas. Os resultados provenientes das condições Controlo vs AO indicam que o tratamento com este inibidor de FPF leva a um aumento do número de fosfoproteínas (174 vs 242 proteínas totais e 32 vs 100 proteínas exclusivas). Os resultados do tratamento com Aβ indicam uma alteração qualitativa do fosfoproteoma neuronal (174 vs 166 proteínas totais) com um número considerável de proteínas exclusivas (42 vs 34 proteínas exclusivas). Subsequentemente, para a obtenção de informação detalhada e caracterização das proteínas identificadas em cada condição, foi realizada uma análise exploratória das fosfoproteínas organizando-as por classe proteica, processos biológicos, localização subcelular e funções moleculares. Os tratamentos com AO e Aβ levam a alterações em proteínas envolvidas em processos celulares que se encontram comprometidos na DA, tais como a actividade das PC e FPF, degradação proteica, stress oxidativo, folding proteico, dinâmica do citoesqueleto, síntese proteica e apoptose. A caracterização do fosfoproteoma neuronal da DA pode revelar ou elucidar os mecanismos moleculares subjacentes à transdução de sinais anormal associada com a patogénese da doença. A análise das fosfoproteínas exclusivas poderá, também, contribuir para a identificação de potenciais novos biomarcadores ou alvos terapêuticos para a DA.
Abnormal protein phosphorylation is a characteristic hallmark of Alzheimer’s disease (AD) and may be implicated both in pathogenesis or disease progression. Reversible protein phosphorylation represents a key regulatory mechanism involving the activity of protein phosphatases (PPP) and protein kinases (PK). Imbalanced PPP and PK activity can alter protein action, subcellular localization and protein interactions, thus contributing to abnormal neuronal function and signaling and consequently to neurodegeneration. Hence, the study of the AD neuronal phosphoproteome is of physiological and pathological relevance. Primary cortical cultures were exposed to okadaic acid (OA, a PPP inhibitor) or amyloid-β peptide (Aβ), in order to mimic AD conditions. Cell lysates were applied to a phosphoprotein affinity column and phosphoprotein enriched fractions analyzed by mass spectrometry. A protein database management framework (http://sourceforge.net/projects/protdb/) was set up allowing for the development of a script to analyze the identified proteins. Data from Control vs OA conditions indicates that OA treatment leads to an increase in phosphoproteins (174 vs 242 proteins and 32 vs 100 exclusive proteins). Data indicates that Aβ treatment leads to a shift in neuronal phosphoproteome pool (174 vs 166 proteins) with noteworthy alterations in the exclusive neurophosphoproteome (42 vs 34 exclusive proteins). Subsequently, analysis of the protein classes, biological processes, subcellular localization and molecular functions allowed for detailed information regarding the proteins obtained in the different groups. Upon treatments an alteration in the proteins involved in critical processes impaired in AD such as PK and PPP activities, protein degradation, oxidative stress, protein folding, cytoskeleton network dynamics, protein synthesis and apoptosis was observed. The characterization of AD neuronal phosphoproteome may reveal or elucidate the molecular mechanisms underlying abnormal signal transduction associated with AD pathogenesis. Further, by analyzing the pool of exclusive proteins, this work may also contribute to identify potential novel biomarker candidates or AD targets for therapeutic intervention.
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Atkinson, Samantha Nicole. "Bioinformatic assessment of disrupted microbial communities." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6696.

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Bioinformatics is a unique field in that it incorporates many different disciplines, including biology, computer science, and statistics, to study biological data. There is a vast array of techniques that utilize bioinformatics, including pangenomics, RNASeq, whole genome metagenomics, and 16S sequencing. To study bacterial interactions, we used a model system of species interactions, Myxococcus xanthus. M. xanthus is a soil bacterium that is a known predator of other bacteria. It has one of the largest repertoires of two component systems (TCS) to respond to external stresses. TCS are a pair of proteins, one that senses environmental stress (histidine kinase, HK) and another that usually acts as a transcriptional regulator (response regulators, RR). We studied a class of RRs, NtrC-like, reliant on an alternative sigma factor, sigma54. The oligomerization of NtrC-like RRs is regulated to modulate activation of the protein, which would change the bacterium’s ability to respond to its environment. We studied HsfA, a NtrC-like RR that regulates specialized metabolites. Specialized metabolites are used in bacterial interactions. In predation interactions they are used to kill prey. Our goal was to find genes that might be involved in specialized metabolite production that would aid in predation. We used prediction tools to find putative binding sites of HsfA to find potentially new metabolites. We used two motifs to attempt to predict if the oligomerization of these response regulators is positively or negatively regulated. We found that the presence of a motif in the receiver domain to be associated with negative regulation of oligomerization, but further studies are needed to experimentally confirm this finding. One environment in which bacterial interactions occur is in the gut. The gut microbiome is the consortium of organisms and their genomic content in the gastrointestinal tract. The gut microbiome is sensitive to aspects of a person’s lifestyle, such as diet and medication. Here we studied the effect of two different diets and two drugs on the gut microbiome. Risperidone, an antipsychotic used to treat schizophrenia and bipolar disorder, has been shown to cause obesity and diabetes. We studied the effect of diet and risperidone usage on weight gain and the microbiome using a C57Bl/6J female mouse model. Our results show that diet has a strong impact on the microbial composition of the gut in response to risperidone. As many mental health patients stop and restart their medication, we examined the effect of stopping and restarting risperidone on the microbiome. When risperidone is stopped the microbiome reverts to a state similar to the control group but diverges into a different microbial composition upon restarting treatment. Interestingly, mice did not gain significantly more weight than their control group upon the second risperidone treatment. Further studies are needed to examine the functional changes occurring with the stop and restart of risperidone to determine the mechanism of mice resisting weight gain during the second round of treatment. Captopril is used to treat hypertension, a very common disease in the United States. Here we studied the effect of captopril on weight gain, metabolic phenotypes, and the gut microbiome. Our results showed that captopril caused an increase in resting metabolic rate (RMR) in mice. This occurred through an increase in energy expenditure. This increase in RMR had the effect of captopril-treated mice being resistant to weight gain. Our group has previously shown that the gut microbiome can directly affect RMR. Therefore, we studied the gut microbiome of captopril-treated mice. We observed a shift in their gut microbiome to organisms Akkermansia muciniphila and Lactobacillus, associated with lean body mass. Captopril therefore has the potential to be a better medication to treat patients with both hypertension and obesity. Further studies are needed to determine the effect of captopril on the microbiome in a hypertension mouse model.
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Books on the topic "Bioinformatic"

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1950-, Tsigelny Igor F., ed. Protein structure prediction: Bioinformatic approach. La Jolla, Calif: International University Line, 2002.

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Xia, Yinglin, and Jun Sun. Bioinformatic and Statistical Analysis of Microbiome Data. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21391-5.

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Roy, Kunal, ed. Multi-Target Drug Design Using Chem-Bioinformatic Approaches. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8733-7.

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Gorodkin, Jan, and Walter L. Ruzzo, eds. RNA Sequence, Structure, and Function: Computational and Bioinformatic Methods. Totowa, NJ: Humana Press, 2014. http://dx.doi.org/10.1007/978-1-62703-709-9.

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Bock, Gregory, and Jamie Goode, eds. Immunoinformatics: Bioinformatic Strategies for Better Understanding of Immune Function. Chichester, UK: John Wiley & Sons, Ltd, 2003. http://dx.doi.org/10.1002/0470090766.

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RNA sequence, structure, and function: Computational and bioinformatic methods. New York: Humana Press, 2014.

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McMeekin, Andrew. The formation of bioinformatic knowledge markets: An 'economies of knowledge' approach. Manchester: Centre for Research on Innovation and Competition, 2002.

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Ignacimuthu, S. Basic bioinformatics. Harrow, U.K: Alpha Science International, 2005.

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Jana, Sperschneider, and Scheubert Lena, eds. Bioinformatics: Problem solving paradigms. Berlin: Springer, 2008.

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Andrew, French, and Westhead David R, eds. Bioinformatics. 2nd ed. Milton Park, Abingdon [Oxon]: Taylor & Francis, 2010.

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Book chapters on the topic "Bioinformatic"

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Ötleş, Semih, Bahar Bakar, and Burcu Kaplan Türköz. "Bioinformatic Analysis." In Bioactive Peptides from Food, 321–46. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003106524-20.

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Garfias-Gallegos, Diego, Claudia Zirión-Martínez, Edder D. Bustos-Díaz, Tania Vanessa Arellano-Fernández, José Abel Lovaco-Flores, Aarón Espinosa-Jaime, J. Abraham Avelar-Rivas, and Nelly Sélem-Mójica. "Metagenomics Bioinformatic Pipeline." In Methods in Molecular Biology, 153–79. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2429-6_10.

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Dailey, Allyson L. "Metabolomic Bioinformatic Analysis." In Methods in Molecular Biology, 341–52. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6990-6_22.

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Meshram, B. B. "Building Bioinformatic Database Systems." In Bioinformatics: Applications in Life and Environmental Sciences, 44–61. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-8880-3_6.

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Holstein, Tanja, and Thilo Muth. "Bioinformatic Workflows for Metaproteomics." In Methods in Molecular Biology, 187–213. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3910-8_16.

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Bilal, A. Mir, H. Mir Sajjad, Inho Choi, and Yoon-Bo Shim. "Bioinformatic Techniques on Marine Genomics." In Hb25_Springer Handbook of Marine Biotechnology, 295–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-53971-8_10.

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Adams, Josephine C., and Juergen Engel. "Bioinformatic Analysis of Adhesion Proteins." In Adhesion Protein Protocols, 147–71. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-353-0_12.

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De Filippis, L. F. "Bioinformatic Tools in Crop Improvement." In Crop Improvement, 49–122. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-7028-1_2.

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Zhang, Zhaiyi, and Stefan Stamm. "Bioinformatic Analysis of Splicing Events." In Alternative pre-mRNA Splicing, 566–73. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527636778.ch52.

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Fernández, José M., and Alfonso Valencia. "Bioinformatic Software Developments in Spain." In Bioinformatics for Personalized Medicine, 108–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28062-7_13.

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Conference papers on the topic "Bioinformatic"

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Hiew, Hong Liang, Matthew Bellgard, Tuan D. Pham, and Xiaobo Zhou. "A Bioinformatics Reference Model: Towards a Framework for Developing and Organising Bioinformatic Resources." In COMPUTATIONAL MODELS FOR LIFE SCIENCES/CMLS '07. AIP, 2007. http://dx.doi.org/10.1063/1.2816640.

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Fando, Roman. "The history of bioinformatic in Russia." In 2020 International Conference Engineering Technologies and Computer Science (EnT). IEEE, 2020. http://dx.doi.org/10.1109/ent48576.2020.00022.

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Grewal, H. K., Parvinder Sandhu, and Manpreet Singh. "A Bioinformatic Approach to Genetic Diversity." In 2006 International Conference on Emerging Technologies. IEEE, 2006. http://dx.doi.org/10.1109/icet.2006.335940.

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Havre, S. L., B. J. Webb-Robertson, A. Shah, C. Posse, B. Gopalan, and F. J. Brockma. "Bioinformatic insights from metagenomics through visualization." In 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05). IEEE, 2005. http://dx.doi.org/10.1109/csb.2005.19.

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Wang, Xiran, Jiangang He, and Haoru Tang. "Bioinformatic Analysis of Strawberry Rbsc Gene." In 2018 International Workshop on Bioinformatics, Biochemistry, Biomedical Sciences (BBBS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/bbbs-18.2018.33.

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Wang, Xiran, Jiangang He, and Haoru Tang. "Bioinformatic Analysis of Strawberry PGR5 Gene." In 2018 International Workshop on Bioinformatics, Biochemistry, Biomedical Sciences (BBBS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/bbbs-18.2018.49.

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Wang, Xiran, Jiangang He, and Haoru Tang. "Bioinformatic Analysis of Strawberry PTOX Gene." In 2018 International Workshop on Bioinformatics, Biochemistry, Biomedical Sciences (BBBS 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/bbbs-18.2018.7.

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Fang, Hao, Cheng Shi, and Chi-Hua Chen. "BioExpDNN: Bioinformatic Explainable Deep Neural Network." In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313113.

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Seo, Jiwon. "Datalog Extensions for Bioinformatic Data Analysis." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512571.

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Shchyogolev, S. Yu, G. L. Burygin, and M. G. Pyatibratov. "Prokaryotic cell surface biopolymers: bioinformatic analysis." In 2nd International Scientific Conference "Plants and Microbes: the Future of Biotechnology". PLAMIC2020 Organizing committee, 2020. http://dx.doi.org/10.28983/plamic2020.221.

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Using the example of a number of representatives of bacteria and archaea, the structure of their cell surface biopolymers is considered, taking into account post-translational modifications of proteins and contemporary views on the features of protein folding.
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Reports on the topic "Bioinformatic"

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Robinson, Aaron. Fungal Research at LANL and Bioinformatic Resources. Office of Scientific and Technical Information (OSTI), October 2023. http://dx.doi.org/10.2172/2202600.

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, February 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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Lawrence, Charles E., and Lee Ann McCue. Development of Bioinformatic and Experimental Technologies for Identification of Prokaryotic Regulatory Networks. Office of Scientific and Technical Information (OSTI), July 2008. http://dx.doi.org/10.2172/935264.

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Beckstrom-Sternberg, Stephen. Bioinformatic Tools for Metagenomic Analysis of Pathogen Backgrounds and Human Microbial Communities. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada581677.

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Zhang, Dan, Jingting Liu, Mengxia zheng, Chunyan Meng, and Jianhua Liao. Prognostic and Clinicopathological significance of CD155 Expression in Cancer Patients: A Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0087.

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Review question / Objective: The present study aimed to comprehensively explore the relationship between CD155 expression and clinical characteristics and prognosis of cancer patients. The study was based on comprehensive search of relevant literature. In particular, the study attempted to define the role of CD155 in various cancer types. Eligibility criteria: The pre-established inclusion criteria were as follows: (1) all subjects were cancer patients who received standard treatment; (2) The expression of CD155 in the cancer patients was well-examined, and all patients were assigned into two groups based on the expression; (3) survival analysis was performed based on these two groups, and provided sufficient data to estimate the risk ratio (HR) and 95% confidence interval (CI) for overall survival (OS); and (4) scientific and reasonable research. Case reports, reviews, abstracts, letters, bioinformatic analysis, TCGA analysis, and articles that did not meet the inclusion criteria were excluded from analyses.
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Davenport, Karen Walston, Chien-Chi Lo, Po-E. Li, Migun Shakya, and Patrick Sam Guy Chain. EDGE Bioinformatics. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1503175.

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Berube, Paul M., Scott M. Gifford, Bonnie Hurwitz, Bethany Jenkins, Adrian Marchetti, and Alyson E. Santoro. Roadmap Towards Communitywide Intercalibration and Standardization of Ocean Nucleic Acids ‘Omics Measurements. Woods Hole Oceanographic Institution, March 2022. http://dx.doi.org/10.1575/1912/28054.

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In January 2020, the US Ocean Carbon & Biogeochemistry (OCB) Project Office funded the Ocean Nucleic Acids 'omics Intercalibration and Standardization workshop held at the University of North Carolina in Chapel Hill. Thirty-two participants from across the US, along with guests from Canada and France, met to develop a framework for standardization and intercalibration (S&I) of ocean nucleic acid ‘omics (na’omics) approaches (i.e., amplicon sequencing, metagenomics and metatranscriptomics). During the three-day workshop, participants discussed numerous topics, including: a) sample biomass collection and nucleic acid preservation for downstream analysis, b) extraction protocols for nucleic acids, c) addition of standard reference material to nucleic acid isolation protocols, d) isolation methods unique to RNA, e) sequence library construction, and f ) integration of bioinformatic considerations. This report provides a summary of these and other topics covered during the workshop and a series of recommendations for future S&I activities for na’omics approaches.
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Carr, Peter A., Darrell O. Ricke, and Anna Shcherbina. Bioinformatics Challenge Days. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada591640.

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Gary J. Olsen. Bioinformatics for Genome Analysis. Office of Scientific and Technical Information (OSTI), June 2005. http://dx.doi.org/10.2172/956994.

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Holm, Bruce. NYS Center of Excellence in Bioinformatics. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada441201.

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