Relevant bibliographies by topics / Bioinformatic methods development

Academic literature on the topic 'Bioinformatic methods development'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Bioinformatic methods development"

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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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Quan, Yuan, Zhong-Yi Wang, Min Xiong, Zheng-Tao Xiao, and Hong-Yu Zhang. "Dissecting Traditional Chinese Medicines by Omics and Bioinformatics." Natural Product Communications 9, no. 9 (September 2014): 1934578X1400900. http://dx.doi.org/10.1177/1934578x1400900942.

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Traditional Chinese medicines (TCM) are a rich source of potential leads for drug development. However, there are fundamental differences between traditional Chinese medical concepts and modern pharmacology, which greatly hinder the modern development of TCM. To address this challenge, new techniques associated with genomics, transcriptomics, proteomics, metabolomics and bioinformatics have been used to dissect the pharmacological mechanisms of TCM. This review article provides an overview of the current research in this area, and illustrates the potential of omic and bioinformatic methods in TCM-based drug discovery.
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Droit, Arnaud, Guy G. Poirier, and Joanna M. Hunter. "Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function." Journal of Molecular Endocrinology 34, no. 2 (April 2005): 263–80. http://dx.doi.org/10.1677/jme.1.01693.

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An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein–protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein–protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein–protein interactions.
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Lang, E. "Section 2: Patient Records: Integrating Bioinformatics into Clinical Practice: Progress and Evaluation." Yearbook of Medical Informatics 16, no. 01 (August 2007): 106–8. http://dx.doi.org/10.1055/s-0038-1638534.

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SummaryTo summarize current excellent research in the field of bioinformatics.Synopsis of the articles selected for the IMIA Yearbook 2007.Current research in the field of bioinformatics is characterized by careful evaluation of methods and by improved integration of methods into clinical workflows. Ongoing research on genetic causes of diseases is performed on more and better sources of reference data (genome sets and respective annotations), but is still hampered by insufficient, lacking or biased patient data. The application area of bioinformatics has been broadened, leading to amendment or even replacement of traditional methods in fields like characterization of microorganisms. Researchers carry out thorough statistical analyses in order to ensure quality and methodological correctness of new methods based on bioinformatic approaches which are more and more competitive compared to well-established techniques.The best paper selection of articles on bioinformatics shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. The crucial role of reliable and comprehensive data sources is affirmed, while technical development draws attention to the increasing problem of comparability of data derived some years ago with weaker equipment and those that are of up-to-date quality.
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Soanes, Darren M., Wendy Skinner, John Keon, John Hargreaves, and Nicholas J. Talbot. "Genomics of Phytopathogenic Fungi and the Development of Bioinformatic Resources." Molecular Plant-Microbe Interactions® 15, no. 5 (May 2002): 421–27. http://dx.doi.org/10.1094/mpmi.2002.15.5.421.

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Genomic resources available to researchers studying phytopathogenic fungi are limited. Here, we briefly review the genomic and bioinformatic resources available and the current status of fungal genomics. We also describe a relational database containing sequences of expressed sequence tags (ESTs) from three phytopathogenic fungi, Blumeria graminis, Magnaporthe grisea, and Mycosphaerella graminicola, and the methods and underlying principles required for its construction. The database contains significant annotation for each EST sequence and is accessible at http://cogeme.ex.ac.uk . An easy-to-use interface allows the user to identify gene sequences by using simple text queries or homology searches. New querying functions and large sequence sets from a variety of phytopathogenic species will be incorporated in due course.
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Hynst, Jakub, Veronika Navrkalova, Karol Pal, and Sarka Pospisilova. "Bioinformatic strategies for the analysis of genomic aberrations detected by targeted NGS panels with clinical application." PeerJ 9 (March 31, 2021): e10897. http://dx.doi.org/10.7717/peerj.10897.

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Molecular profiling of tumor samples has acquired importance in cancer research, but currently also plays an important role in the clinical management of cancer patients. Rapid identification of genomic aberrations improves diagnosis, prognosis and effective therapy selection. This can be attributed mainly to the development of next-generation sequencing (NGS) methods, especially targeted DNA panels. Such panels enable a relatively inexpensive and rapid analysis of various aberrations with clinical impact specific to particular diagnoses. In this review, we discuss the experimental approaches and bioinformatic strategies available for the development of an NGS panel for a reliable analysis of selected biomarkers. Compliance with defined analytical steps is crucial to ensure accurate and reproducible results. In addition, a careful validation procedure has to be performed before the application of NGS targeted assays in routine clinical practice. With more focus on bioinformatics, we emphasize the need for thorough pipeline validation and management in relation to the particular experimental setting as an integral part of the NGS method establishment. A robust and reproducible bioinformatic analysis running on powerful machines is essential for proper detection of genomic variants in clinical settings since distinguishing between experimental noise and real biological variants is fundamental. This review summarizes state-of-the-art bioinformatic solutions for careful detection of the SNV/Indels and CNVs for targeted sequencing resulting in translation of sequencing data into clinically relevant information. Finally, we share our experience with the development of a custom targeted NGS panel for an integrated analysis of biomarkers in lymphoproliferative disorders.
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Coy, Samantha, Eric Gann, Helena Pound, Steven Short, and Steven Wilhelm. "Viruses of Eukaryotic Algae: Diversity, Methods for Detection, and Future Directions." Viruses 10, no. 9 (September 11, 2018): 487. http://dx.doi.org/10.3390/v10090487.

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The scope for ecological studies of eukaryotic algal viruses has greatly improved with the development of molecular and bioinformatic approaches that do not require algal cultures. Here, we review the history and perceived future opportunities for research on eukaryotic algal viruses. We begin with a summary of the 65 eukaryotic algal viruses that are presently in culture collections, with emphasis on shared evolutionary traits (e.g., conserved core genes) of each known viral type. We then describe how core genes have been used to enable molecular detection of viruses in the environment, ranging from PCR-based amplification to community scale “-omics” approaches. Special attention is given to recent studies that have employed network-analyses of -omics data to predict virus-host relationships, from which a general bioinformatics pipeline is described for this type of approach. Finally, we conclude with acknowledgement of how the field of aquatic virology is adapting to these advances, and highlight the need to properly characterize new virus-host systems that may be isolated using preliminary molecular surveys. Researchers can approach this work using lessons learned from the Chlorella virus system, which is not only the best characterized algal-virus system, but is also responsible for much of the foundation in the field of aquatic virology.
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Han, Xiaoyan, Lei Cai, Yi Lu, Dan Li, and Jin Yang. "Identification of tRNA-derived fragments and their potential roles in diabetic cataract rats." Epigenomics 12, no. 16 (August 2020): 1405–18. http://dx.doi.org/10.2217/epi-2020-0193.

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Aim: To illustrate the expression profile of transfer RNA-derived fragments and reveal their putative role in the pathogenesis of diabetic cataract (DC) rats. Materials & methods: Small RNA sequencing was conducted in the lens epithelium of rats lens. The data were validated by quantitative real-time PCR, and bioinformatic analysis was performed to explore the roles of the fragments in DC pathogenesis. Results: A total of 213 differentially expressed tRNA-related fragments were identified, in which 111 were upregulated and 102 were downregulated in DC rats. Bioinformatics analysis revealed that several associated pathways might participate in the development of DC rats. Conclusion: tRNA-derived fragments may be involved in the pathogenesis of DC rats.
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Waite, David W., Lia Liefting, Catia Delmiglio, Anastasia Chernyavtseva, Hye Jeong Ha, and Jeremy R. Thompson. "Development and Validation of a Bioinformatic Workflow for the Rapid Detection of Viruses in Biosecurity." Viruses 14, no. 10 (September 30, 2022): 2163. http://dx.doi.org/10.3390/v14102163.

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The field of biosecurity has greatly benefited from the widespread adoption of high-throughput sequencing technologies, for its ability to deeply query plant and animal samples for pathogens for which no tests exist. However, the bioinformatics analysis tools designed for rapid analysis of these sequencing datasets are not developed with this application in mind, limiting the ability of diagnosticians to standardise their workflows using published tool kits. We sought to assess previously published bioinformatic tools for their ability to identify plant- and animal-infecting viruses while distinguishing from the host genetic material. We discovered that many of the current generation of virus-detection pipelines are not adequate for this task, being outperformed by more generic classification tools. We created synthetic MinION and HiSeq libraries simulating plant and animal infections of economically important viruses and assessed a series of tools for their suitability for rapid and accurate detection of infection, and further tested the top performing tools against the VIROMOCK Challenge dataset to ensure that our findings were reproducible when compared with international standards. Our work demonstrated that several methods provide sensitive and specific detection of agriculturally important viruses in a timely manner and provides a key piece of ground truthing for method development in this space.
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SONG, JIANGNING, HAO TAN, SARAH E. BOYD, HONGBIN SHEN, KHALID MAHMOOD, GEOFFREY I. WEBB, TATSUYA AKUTSU, JAMES C. WHISSTOCK, and ROBERT N. PIKE. "BIOINFORMATIC APPROACHES FOR PREDICTING SUBSTRATES OF PROTEASES." Journal of Bioinformatics and Computational Biology 09, no. 01 (February 2011): 149–78. http://dx.doi.org/10.1142/s0219720011005288.

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Proteases have central roles in "life and death" processes due to their important ability to catalytically hydrolyze protein substrates, usually altering the function and/or activity of the target in the process. Knowledge of the substrate specificity of a protease should, in theory, dramatically improve the ability to predict target protein substrates. However, experimental identification and characterization of protease substrates is often difficult and time-consuming. Thus solving the "substrate identification" problem is fundamental to both understanding protease biology and the development of therapeutics that target specific protease-regulated pathways. In this context, bioinformatic prediction of protease substrates may provide useful and experimentally testable information about novel potential cleavage sites in candidate substrates. In this article, we provide an overview of recent advances in developing bioinformatic approaches for predicting protease substrate cleavage sites and identifying novel putative substrates. We discuss the advantages and drawbacks of the current methods and detail how more accurate models can be built by deriving multiple sequence and structural features of substrates. We also provide some suggestions about how future studies might further improve the accuracy of protease substrate specificity prediction.
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Dissertations / Theses on the topic "Bioinformatic methods development"

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Rossini, Roberto. "Development and validation of bioinformatic methods for GRC assembly and annotation." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414739.

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This thesis presents the work done during my master degree projects under the supervision of Alexander Suh and Francisco J. Ruiz-Ruano. My work focused on the development of in-silico methods to improve the assembly of the Germline Restricted Chromosome (GRC) of songbirds, more specifically that of zebra finch.GRCs are a good example of the popular saying "The exception that proves the rule". For a very long time, it was assumed that every cell in a healthy multicellular organism carries the same genetic information. Cytogenetic evidence dating back as far as early XX century suggests that this is not always the case, as it has been documented that certain organisms carry supernumerary B chromosomes, which are dispensable chromosomes that are not part of the normal karyotype of a species. GRCs are often regarded as a special case of B chromosomes, where every individual from a species carries an additional chromosome whose presence is restricted to germline cells only. GRCs presence has been documented in insects, hagfishes and songbirds. A peculiar case of GRCs is that of zebra finch, whose GRC has an estimated size of over 150 Mb, accounting for over 10% of zebra finch total genome size. Despite the first cytogenetic evidence of zebra finch GRC dating back to 1998, it was only last year that the first comprehensive genomic study about this relatively large chromosome was published. This study shed some light on the gene content of the GRC in zebra finch, revealing that the GRC of zebra finch mostly consists of paralogs of A chromosomal genes. The GRC assembly and annotation that were published as part of this study included 115 GRC-linked genes that were identified through germline/soma read mapping, as well as 36 manually curated scaffolds with a median length of 3.6 kb. Considering the conspicuous size of the GRC of zebra finch, it is clear that this is a very fragmented and likely incomplete GRC assembly. There are many factors that can have a negative impact on assembly completeness and contiguity. In the GRC case, these factors collectively affect coverage in ways that are not properly handled by available genome assemblers. In the course of my master degree project I developed kFish, a bioinformatic software to perform alignment-free enrichment of GRC-linked barcodes from a 10x Genomics linked-read DNA Chromium library. kFish uses an iterative approach where the k-mer content of a set of GRC-linked sequences is compared with that of reads corresponding to each individual 10x Genomics barcode. This comparison allows kFish to identify likely GRC-linked barcodes, and then only use reads corresponding to these barcodes when trying to assemble the GRC. First benchmarking results generated using five GRC-linked genes from zebra finch as reference sequences, show that kFish is not only capable of assembling already known GRC-linked sequences, but also new ones with high confidence. kFish can do all of this in a matter of hours, using only few gigabytes of system memory, while previous efforts took over two days to assemble zebra finch genome and identify GRC-linked scaffolds using an approach based on read mapping. High quality genome assemblies and annotations are the foundations of modern genomics research, the lack of which greatly limits the breadth of the questions that can be answered. There is still a lot that we do not understand about GRCs, and part of this is due to the lack of high quality GRC assemblies and annotations. Producing such an assembly will likely require an integrated approach, where multiple sequencing technologies as well as bleeding edge bioinformatic tools such as kFish, are combined together to produce an high quality assembly, which will be crucial to unravel the mystery of GRCs function and evolutionary history.
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Ruiz, Arenas Carlos 1990. "Methods and bioinformatic tools to study polymorphic inversions in complex diseases." Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666582.

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Las inversiones cromosómicas son variantes estructurales donde un segmento de ADN cambia su orientación. Las inversiones cromosómicas reducen la recombinación homóloga y producen diferentes haplotipos en los cromosomas estándar e invertidos. Como resultado, influyen en la adaptación y la selección y desempeñan un papel en la susceptibilidad a las enfermedades humanas. Las inversiones se pueden estudiar con métodos experimentales y bioinformáticos. Los datos de SNP array se pueden usar para determinar genotipos de inversión mediante el uso de diferencias de haplotipos entre cromosomas invertidos y estándares. Sin embargo, estos métodos no están optimizados para grandes cohortes (con miles de individuos, como dbGaP o UK Biobank). Además, los métodos actuales solo pueden genotipar las inversiones con dos haplotipos y la clasificación es difícil de armonizar entre cohortes. Finalmente, se conoce que las inversiones cromosómicas afectan la expresión génica y la metilación del ADN. Sin embargo, no existen métodos precisos para evaluar globalmente el efecto de las inversiones en la expresión génica local o la metilación del ADN. El objetivo principal de esta tesis es desarrollar nuevos métodos robustos y escalables así como herramientas bionformáticas para estudiar los efectos fenotípicos y funcionales de las inversiones cromosómicas, superando las limitaciones existentes. Con este fin, he desarrollado un nuevo método para genotipar las inversiones cromosómicas que se puede usar en grandes cohortes, con inversiones con múltiples haplotipos y que utiliza haplotipos de referencia que permite el análisis conjunto de múltiples cohortes. En segundo lugar, he implementado un método multivariante basado en el análisis de la redundancia para estudiar los efectos de las inversiones cromosómicas en la metilación del ADN y la expresión génica locales. A continuación, he aplicado ambos métodos para estudiar el papel de las inversiones cromosómicas en dos grupos de enfermedades complejas: trastornos del neurodesarrollo y cáncer. Finalmente, he desarrollado un nuevo método para estudiar cómo las inversiones cromosómicas afectan los patrones de recombinación. Este método es aplicable a cualquier región genómica que contenga subpoblaciones con diferentes patrones de recombinación, lo que permite asociar estas subpoblaciones a rasgos fenotípicos.
Chromosomal inversions are structural variants where a segment changes its orientation. Chromosomal inversions reduce homologous recombination, producing different haplotypes in standard and inverted chromosomes. As a result, they influence adaptation and selection and play a role in susceptibility to human diseases. Inversions can be studied using experimental and bioinformatic methods. SNP array data can be used to call inversion genotypes by using haplotype differences between inverted and standard chromosomes. However, these methods are not optimized for large cohorts (thousands of individuals from existing databases such as dbGaP or UK Biobank). Also, current methods can only genotype inversions with two haplotypes and the inversion calling is difficult to be harmonized among cohorts. Finally, it is recognized that chromosomal inversions affect gene expression and DNA methylation. However, there are no accurate methods to globally assess the effect of inversions on local gene expression or DNA methylation. The main aim of this thesis is to develop new robust and scalable methods and bioinformatic tools to study the phenotypic and functional effects of chromosomal inversions by overcoming the existing limitations. To this end, I have developed a new method to genotype chromosomal inversions that can be used in large cohorts, inversions with multiple haplotypes and that uses reference haplotypes allowing the integrative analysis of multiple cohorts. Second, I have implemented a multivariate method based on redundancy analysis to study the effects of chromosomal inversions on local DNA methylation and gene expression. Then, I applied both methods to study the role of chromosomal inversions in two groups of complex diseases: neurodevelopmental disorders and cancer. Finally, I developed a new method to study how chromosomal inversions affect recombination patterns. This method is extendable to any genomic regions containing subpopulations with different recombination patterns, allowing associating these subpopulations to phenotypic traits.
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Mastick, Kellen J. "Identification of candidate genes involved in fin/limb development and evolution using bioinformatic methods." Thesis, University of South Dakota, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1566765.

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Key to understanding the transition that vertebrates made from water to land is determining the developmental and genomic bases for the changes. New bioinformatic tools provide an opportunity to automate the discovery, broaden the number of, and provide an evidence-based ranking for potential candidate genes. I sought to explore this potential for the fin/limb transition, using the substantial genetic and phenotypic data available in model organism databases. Model organism data was used to hypothesize candidate genes for the fin/limb transition. In addition, 131 fin/limb candidate genes from the literature were extracted and used as a basis for comparison with candidates from the model organism databases. Additionally, seven genes specific to limb and 24 genes specific to fin were identified as future fin/limb transition candidates.

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Zierep, Paul [Verfasser], and Stefan [Akademischer Betreuer] Günther. "Development of bioinformatic methods for the prediction and understanding of biosynthesis and activity of natural products." Freiburg : Universität, 2020. http://d-nb.info/1231711752/34.

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Besnier, Francois. "Development of Variance Component Methods for Genetic Dissection of Complex Traits." Doctoral thesis, Uppsala universitet, Centrum för bioinformatik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-101399.

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This thesis presents several developments on Variance component (VC) approach for Quantitative Trait Locus (QTL) mapping. The first part consists of methodological improvements: a new fast and efficient method for estimating IBD matrices, have been developed. The new method makes a better use of the computer resources in terms of computational power and storage memory, facilitating further improvements by resolving methodological bottlenecks in algorithms to scan multiple QTL. A new VC model have also been developed in order to consider and evaluate the correlation of the allelic effects within parental lines origin in experimental outbred crosses. The method was tested on simulated and experimental data and revealed a higher or similar power to detect QTL than linear regression based QTL mapping. The second part focused on the prospect to analyze multi-generational pedigrees by VC approach. The IBD estimation algorithm was extended to include haplotype information in addition to genotype and pedigree to improve the accuracy of the IBD estimates, and a new haplotyping algorithm was developed for limiting the risk of haplotyping errors in multigenerational pedigrees. Those newly developed methods where subsequently applied for the analysis of a nine generations AIL pedigree obtained after crossing two chicken lines divergently selected for body weight. Nine QTL described in a F2 population were replicated in the AIL pedigree, and our strategy to use both genotype and phenotype information from all individuals in the entire pedigree clearly made efficient use of the available genotype information provided in AIL.
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Jauhiainen, Alexandra. "Evaluation and Development of Methods for Identification of Biochemical Networks." Thesis, Linköping University, The Department of Physics, Chemistry and Biology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2811.

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Systems biology is an area concerned with understanding biology on a systems level, where structure and dynamics of the system is in focus. Knowledge about structure and dynamics of biological systems is fundamental information about cells and interactions within cells and also play an increasingly important role in medical applications.

System identification deals with the problem of constructing a model of a system from data and an extensive theory of particularly identification of linear systems exists.

This is a master thesis in systems biology treating identification of biochemical systems. Methods based on both local parameter perturbation data and time series data have been tested and evaluated in silico.

The advantage of local parameter perturbation data methods proved to be that they demand less complex data, but the drawbacks are the reduced information content of this data and sensitivity to noise. Methods employing time series data are generally more robust to noise but the lack of available data limits the use of these methods.

The work has been conducted at the Fraunhofer-Chalmers Research Centre for Industrial Mathematics in Göteborg, and at the division of Computational Biology at the Department of Physics and Measurement Technology, Biology, and Chemistry at Linköping University during the autumn of 2004.

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Hedberg, Lilia. "Identification of obesity-associated SNPs in the human genome : Method development and implementation for SOLiD sequencing data analysis." Thesis, Linköpings universitet, Institutionen för klinisk och experimentell medicin, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57932.

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Over the last few years, genome-wide association studies (GWAS) have been used to identify numerous obesity associated SNPs in the human genome. By using linkage studies, candidate obesity genes have been identified. When SNPs in the first intron of FTO were found to be associated to BMI, it became the first gene to be linked to common obesity. In order to look for causative explanations behind the associated SNPs, a re-sequencing of FTO had been performed on the SOLiD sequencing platform. In-house candidate gene, SLCX, was also sequenced in order to evaluate a potential obesity association. The purpose of this project was to analyse the sequences and also to evaluate the quality of the SOLiD sequencing. A part of the project consisted in performing PCRs and selecting genomic regions for future sequencing projects. I developed and implemented a sequence analysis strategy to identify obesity associated SNPs. I found 39 obesity-linked SNPs in FTO, a majority of which were located in introns 1 and 8. I also identified 3 associated intronic SNPs in SLCX. I found that the SOLiD sequencing coverage varies between non-repetitive and repetitive genomic regions, and that it is highest near amplicon ends. Interestingly, coverage varies significantly between different amplicons even after repetitive sequences have been removed, which indicates that it is affected by features inherent to the sequence. Still, the observed allele frequencies for known SNPs were highly correlated with the SNP frequencies documented in HapMap. In conclusion, I verify that SNPs in FTO are associated with obesity and also identify a previously unassociated gene, SLCX, as a potential obesity gene. Re-sequencing of genomic regions on the SOLiD platform was proven to be successful for SNP identification, although the difference in sequencing coverage might be problematic.
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Li, Miaoxin, and 李淼新. "Development of a bioinformatics and statistical framework to integratebiological resources for genome-wide genetic mapping and itsapplications." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43572030.

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Patel, Hitesh [Verfasser], and Irmgard [Akademischer Betreuer] Merfort. "Use and development of chem-bioinformatics tools and methods for drug discovery and target identification." Freiburg : Universität, 2015. http://d-nb.info/1115495917/34.

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Pennington, Steven. "Pulsed induction, a method to identify genetic regulators of determination events." Thesis, Oklahoma State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3727701.

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Abstract: Determination is the process in which a stem cell commits to differentiation. The process of how a cell goes through determination is not well understood. Determination is important for proper regulation of cell turn-over in tissue and maintaining the adult stem cell population. Deregulation of determination or differentiation can lead to diseases such as several forms of cancer. In this study I will be using microarrays to identify candidate genes involved in determination by pulse induction of mouse erythroleukemia (MEL) cells with DMSO and looking at gene expression changes as the cells go through the early stages of erythropoiesis. The pulsed induction method I have developed to identify candidate genes is to induce cells for a short time (30 min, 2 hours, etc.) and allow them then to grow for the duration of their differentiation time (8 days). For reference, cells were also harvested at the time when the inducer is removed from the media. Results show high numbers of genes differentially expressed including erythropoiesis specific genes such as GATA1, globin genes and many novel candidate genes that have also been indicated as playing a role in the dynamic early signaling of erythropoiesis. In addition, several genes showed a pendulum effect when allowed to recover, making these interesting candidate genes for maintaining self-renewal of the adult stem cell population.

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Books on the topic "Bioinformatic methods development"

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S, Larson Richard, ed. Bioinformatics and drug discovery. Totowa, N.J: Humana Press, 2005.

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E, MacCuish Norah, ed. Clustering in bioinformatics and drug discovery. Boca Raton: Taylor & Francis, 2011.

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Bioinformatics and Computational Biology in Drug Discovery and Development. Cambridge University Press, 2016.

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Poroikov, Vladimir, and Roman Efremov, eds. PROCEEDINGS BOOK OF THE XXVIII SYMPOSIUM "BIOINFORMATICS AND COMPUTER-AIDED DRUG DISCOVERY", MOSCOW, 2022. Institute of Biomedical Chemistry, Moscow, Russia, 2022. http://dx.doi.org/10.18097/bcadd2022.

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The materials of the XXVIII Symposium "Bioinformatics and Computer-Aided Drug Discovery" are presented. This Symposium is dedicated to the emerging challenges and opportunities for in silico drug discovery.The Symposium's main topics: development and practical application of computational methods for finding and validation of new pharmacological targets, in silico design of potent and safe pharmaceutical agents, optimization of the structure and properties of drug-like compounds, rational approaches to the utilization of pharmacotherapeutic remedies in medical practice. This information will be useful for researchers whose investigations are dedicated to creating computational methods and their application to drug research and development using bio- and chemoinformatics methods based on post-genomic technologies. It can also be useful for undergraduate, graduate, and postgraduate students specializing in the relevant fields.
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Wagner, Roland, and Sepp Hochreiter. Bioinformatics Research and Development: First International Conference, BIRD 2007, Berlin, Germany, March 12-14, 2007, Proceedings. Springer London, Limited, 2007.

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(Editor), Sepp Hochreiter, and Roland Wagner (Editor), eds. Bioinformatics Research and Development: First International Conference, BIRD 2007, Berlin, Germany, March 12-14, 2007, Proceedings (Lecture Notes in Computer Science). Springer, 2007.

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Ross, John, Igor Schreiber, and Marcel O. Vlad. Determination of Complex Reaction Mechanisms. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780195178685.001.0001.

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In a chemical system with many chemical species several questions can be asked: what species react with other species: in what temporal order: and with what results? These questions have been asked for over one hundred years about simple and complex chemical systems, and the answers constitute the macroscopic reaction mechanism. In Determination of Complex Reaction Mechanisms authors John Ross, Igor Schreiber, and Marcel Vlad present several systematic approaches for obtaining information on the causal connectivity of chemical species, on correlations of chemical species, on the reaction pathway, and on the reaction mechanism. Basic pulse theory is demonstrated and tested in an experiment on glycolysis. In a second approach, measurements on time series of concentrations are used to construct correlation functions and a theory is developed which shows that from these functions information may be inferred on the reaction pathway, the reaction mechanism, and the centers of control in that mechanism. A third approach is based on application of genetic algorithm methods to the study of the evolutionary development of a reaction mechanism, to the attainment given goals in a mechanism, and to the determination of a reaction mechanism and rate coefficients by comparison with experiment. Responses of non-linear systems to pulses or other perturbations are analyzed, and mechanisms of oscillatory reactions are presented in detail. The concluding chapters give an introduction to bioinformatics and statistical methods for determining reaction mechanisms.
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Pontarotti, Pierre. Evolutionary Biology: Convergent Evolution, Evolution of Complex Traits, Concepts and Methods. Springer, 2016.

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Pontarotti, Pierre. Evolutionary Biology: Convergent Evolution, Evolution of Complex Traits, Concepts and Methods. Springer London, Limited, 2016.

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Pontarotti, Pierre. Evolutionary Biology: Convergent Evolution, Evolution of Complex Traits, Concepts and Methods. Springer, 2018.

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Book chapters on the topic "Bioinformatic methods development"

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Perera-Bel, Júlia, Andreas Leha, and Tim Beißbarth. "Bioinformatic Methods and Resources for Biomarker Discovery, Validation, Development, and Integration." In Predictive Biomarkers in Oncology, 149–64. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95228-4_11.

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Ghosh, Arpita, and Aditya Mehta. "Concept, Development, and Application of Computational Methods for the Analysis and Integration of Omics Data." In Plant Bioinformatics, 241–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67156-7_9.

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Malm, Johan, and György Marko-Varga. "The Role of Proteomics in the Development of Personalized Medicine, Diagnostic Methods and Large Scale Biobanking." In Translational Bioinformatics, 243–55. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9202-8_12.

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Sevigny, Joseph L., Jon L. Norenburg, and Francesca Leasi. "A Bioinformatics Tutorial for Comparative Development Genomics in Diverse Meiofauna." In Methods in Molecular Biology, 289–305. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0974-3_19.

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Jones, Wendell. "Genomics and Bioinformatics in Biological Discovery and Pharmaceutical Development." In Quantitative Methods in Pharmaceutical Research and Development, 105–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48555-9_3.

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Sorathiya, Anil, Tadas Jucikas, Stephanie Piecewicz, Shiladitya Sengupta, and Pietro Liò. "Searching for Glycomics Role in Stem Cell Development." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 198–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02504-4_18.

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Hartmann, Morghan, Norman Fenton, and Ruth Dobson. "Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 13–24. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20837-9_2.

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Perkins, Randa M., and Joseph Markowitz. "Development and Optimization of Clinical Informatics Infrastructure to Support Bioinformatics at an Oncology Center." In Methods in Molecular Biology, 1–19. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0849-4_1.

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Sarr, Abdoulaye, Alexandra Fronville, Pascal Ballet, and Vincent Rodin. "French Flag Tracking by Morphogenetic Simulation Under Developmental Constraints." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 90–106. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09042-9_7.

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Perkins, Randa M., and Joseph Markowitz. "Correction to: Development and Optimization of Clinical Informatics Infrastructure to Support Bioinformatics at an Oncology Center." In Methods in Molecular Biology, C1. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0849-4_17.

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Conference papers on the topic "Bioinformatic methods development"

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"Development of Prediction Models under Multiple Imputation for Coronary Heart Disease in Type 2 Diabetes Mellitus." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004244303120315.

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Gerasimoska, Teodora, Milka Ljoncheva, and Monika Simjanoska. "MSL-ST: Development of Mass Spectral Library Search Tool to Enhance Compound Identification." In 12th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010424101950203.

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Rodrigues, José, Letícia Raposo, and Flavio Nobre. "Development of HIV-1 Coreceptor Tropism Classifiers: An Approach to Improve X4 and R5X4 Viruses Prediction." In 11th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009096101830187.

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Mi, Leilei, Nairui Liu, and Bo Zhou. "Disposal Methods for Municipal Solid Wastes and Its Development Trend." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5517879.

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Liu, Zhongzhou, and Wenbin Hu. "FSM: A Fast Similarity Measurement for Gene Regulatory Networks via Genes' Influence Power." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/632.

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The problem of graph similarity measurement is fundamental in both complex networks and bioinformatics researches. Gene regulatory networks (GRNs) describe the interactions between the molecules in organisms, and are widely studied in the fields of medical AI. By measuring the similarity between GRNs, significant information can be obtained to assist the applications like gene functions prediction, drug development and medical diagnosis. Most of the existing similarity measurements have been focusing on the graph isomorphisms and are usually NP-hard problems. Thus, they are not suitable for applications in biology and clinical research due to the complexity and large-scale features of real-world GRNs. In this paper, a fast similarity measurement method called FSM for GRNs is proposed. Unlike the conventional measurements, it pays more attention to the differences between those influential genes. For the convenience and reliability, a new index defined as influence power is adopted to describe the influential genes which have greater position in a GRN. FSM was applied in nine datasets of various scales and is compared with state-of-art methods. The results demonstrated that it ran significantly faster than other methods without sacrificing measurement performance.
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"Development of sweet pepper F1 hybrids based on MAS methods by fruit quality and resistance genes." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-016.

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Jing, Ping, and Hai-feng Jia. "The Integrated Forewarning Method Research for Urban Sustainable Development." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08). IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.585.

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Holcapek, Michal. "Comprehensive analysis of lipids: From plant oil analysis towards lipidomic cancer screening." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/wqnw2781.

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The analysis of lipids as biomolecules involved in numerous metabolic pathways has attracted my attention since the beginning of my scientific carrier. My first article was published in 1999 on the monitoring of biodiesel production by LC/MS [1] and then continued with further work on the detailed characterization of triacylglycerols in plant oils and animal fats. We developed various LC/MS methods for the analysis of all existing types of triacylglycerol isomers, including regioisomers, double bond (DB) positional isomers [2], DB geometrical isomers [3] up to enantiomers [4]. Later, we moved towards comprehensive lipidomic analysis of multiple lipid classes of phospholipid, sphingolipid, glycerolipid, fatty acyl, and sterol categories with the emphasis on validated quantitative workflows applicable in clinical analysis [5]. The development of LipidQuant 1.0 tool for automated processing of lipidomic data [6] was essential for high-throughput analysis of large clinical cohorts. Finally, the lipidomic profiling of human serum enabled the early detection of pancreatic cancer patients [7]. A similar pattern of lipid dysregulation was observed for other types of cancer [8]. References [1] Holčapek, M. et al., Anal. Chem., 858, 13–31. [2] Lísa, M.; Holčapek, M., Anal. Chem. 2008, 1998–1999, 115–130. [3] Lísa, M.; Velínská, H.; Holčapek, M. Anal. Chem., 81, 3903–3910. [4] Lísa, M.; Holčapek, M. Anal. Chem. 2013, 85, 1852–1859. [5] Holčapek, M.; Liebisch, G.; Ekroos, K. Anal. Chem. 2018, 90, 4249–4257. [6] Wolrab D. et al., Bioinformatics 2021, in press. DOI: 10.1093/bioinformatics/btab644. [7] Wolrab D. et al., Nat. Com. 2021, final revision. Preprint at medRxiv DOI: 10.1101/2021.01.22.21249767. [8] Wolrab D., 11, 20322.
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Jing, Ping, and Hai-Feng Jia. "The Forecasting and Assessing Method for Urban Sustainable Development Tendency." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08). IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.586.

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"Development of a method for recognizing biomedical entities in the texts of scientific articles." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-092.

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Reports on the topic "Bioinformatic methods development"

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Cytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598174.bard.

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Control of agro-associated pathogens is becoming increasingly difficult due to increased resistance and mounting restrictions on chemical pesticides and antibiotics. Likewise, in veterinary and human environments, there is increasing resistance of pathogens to currently available antibiotics requiring discovery of novel antibiotic compounds. These drawbacks necessitate discovery and application of microorganisms that can be used as biocontrol agents (BCAs) and the isolation of novel biologically-active compounds. This highly-synergistic one year project implemented an innovative pipeline aimed at detecting BCAs and associated biologically-active compounds, which included: (A) isolation of multidrug-resistant desert soil bacteria and root-associated bacteria from medicinal plants; (B) invitro screening of bacterial isolates against known plant, animal and human pathogens; (C) nextgeneration sequencing of isolates that displayed antagonistic activity against at least one of the model pathogens and (D) in-planta screening of promising BCAs in a model bean-Sclerotiumrolfsii system. The BCA genome data were examined for presence of: i) secondary metabolite encoding genes potentially linked to the anti-pathogenic activity of the isolates; and ii) rhizosphere competence-associated genes, associated with the capacity of microorganisms to successfully inhabit plant roots, and a prerequisite for the success of a soil amended BCA. Altogether, 56 phylogenetically-diverse isolates with bioactivity against bacterial, oomycete and fungal plant pathogens were identified. These strains were sent to Auburn University where bioassays against a panel of animal and human pathogens (including multi-drug resistant pathogenic strains such as A. baumannii 3806) were conducted. Nineteen isolates that showed substantial antagonistic activity against at least one of the screened pathogens were sequenced, assembled and subjected to bioinformatics analyses aimed at identifying secondary metabolite-encoding and rhizosphere competence-associated genes. The genome size of the bacteria ranged from 3.77 to 9.85 Mbp. All of the genomes were characterized by a plethora of secondary metabolite encoding genes including non-ribosomal peptide synthase, polyketidesynthases, lantipeptides, bacteriocins, terpenes and siderophores. While some of these genes were highly similar to documented genes, many were unique and therefore may encode for novel antagonistic compounds. Comparative genomic analysis of root-associated isolates with similar strains not isolated from root environments revealed genes encoding for several rhizospherecompetence- associated traits including urea utilization, chitin degradation, plant cell polymerdegradation, biofilm formation, mechanisms for iron, phosphorus and sulfur acquisition and antibiotic resistance. Our labs are currently writing a continuation of this feasibility study that proposes a unique pipeline for the detection of BCAs and biopesticides that can be used against phytopathogens. It will combine i) metabolomic screening of strains from our collection that contain unique secondary metabolite-encoding genes, in order to isolate novel antimicrobial compounds; ii) model plant-based experiments to assess the antagonistic capacities of selected BCAs toward selected phytopathogens; and iii) an innovative next-generation-sequencing based method to monitor the relative abundance and distribution of selected BCAs in field experiments in order to assess their persistence in natural agro-environments. We believe that this integrated approach will enable development of novel strains and compounds that can be used in large-scale operations.
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