Relevant bibliographies by topics / Bioinformatics analysis

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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 'Bioinformatics analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "Bioinformatics analysis"

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Irham, Lalu Muhammad, Danang Prasetyaning Amukti, Wirawan Adikusuma, et al. "Applied of bioinformatics in drug discovery and drug development: Bioinformatic analysis 1996-2024." BIO Web of Conferences 148 (2024): 01003. https://doi.org/10.1051/bioconf/202414801003.

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Drug discovery and drug development were two complex process to find new drugs. Advance science of medicine after human genome project were established accelerating the development of new field called bionformatics. Currently, bioinformatics integrated multidisciplinary studies including molecular biology, mathematics and information engineering. This study utilized the Biblioshiny and VosViewer databases as well as the Scopus database to evalute the study related to the bioinformatics in Drug Discovery and Drug Development. Our study were analyzed the scopus data which were retrieved from 1996-2024. We highlighted that 1581 research articles which were publised in 701 journals. Our findings showed that the annual grow up of the research related study was increased annually with the peak of study in 2023. Besides, top five most relevant sources of study was PlosOne (32 documents), international journal of molecular sciences (30 documents), BMS Bioinformatics (29 documents), Bioinfromatics (24 documents), and Frontiers in Genetics (19 documents). In conclusion, through the integration of the use of Vosviewer, biblioshiny and Scopus database software, our findings show a positive trend regarding research on the application of bioinformatics in drug discovery and drug development.
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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 (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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Parmen, Adibah, MOHD NOOR MAT ISA, FARAH FADWA BENBELGACEM, Hamzah Mohd Salleh, and Ibrahim Ali Noorbatcha. "COMPARATIVE METAGENOMICS ANALYSIS OF PALM OIL MILL EFFLUENT (POME) USING THREE DIFFERENT BIOINFORMATICS PIPELINES." IIUM Engineering Journal 20, no. 1 (2019): 1–11. http://dx.doi.org/10.31436/iiumej.v20i1.909.

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ABSTRACT: The substantial cost reduction and massive production of next-generation sequencing (NGS) data have contributed to the progress in the rapid growth of metagenomics. However, production of the massive amount of data by NGS has revealed the challenges in handling the existing bioinformatics tools related to metagenomics. Therefore, in this research we have investigated an equal set of DNA metagenomics data from palm oil mill effluent (POME) sample using three different freeware bioinformatics pipelines’ websites of metagenomics RAST server (MG-RAST), Integrated Microbial Genomes with Microbiome Samples (IMG/M) and European Bioinformatics Institute (EBI) Metagenomics, in term of the taxonomic assignment and functional analysis. We found that MG-RAST is the quickest among these three pipelines. However, in term of analysis of results, IMG/M provides more variety of phylum with wider percent identities for taxonomical assignment and IMG/M provides the highest carbohydrates, amino acids, lipids, and coenzymes transport and metabolism functional annotation beside the highest in total number of glycoside hydrolase enzymes. Next, in identifying the conserved domain and family involved, EBI Metagenomics would be much more appropriate. All the three bioinformatics pipelines have their own specialties and can be used alternately or at the same time based on the user’s functional preference.
 ABSTRAK: Pengurangan kos dalam skala besar dan pengeluaran data ‘next-generation sequencing’ (NGS) secara besar-besaran telah menyumbang kepada pertumbuhan pesat metagenomik. Walau bagaimanapun, pengeluaran data dalam skala yang besar oleh NGS telah menimbulkan cabaran dalam mengendalikan alat-alat bioinformatika yang sedia ada berkaitan dengan metagenomik. Justeru itu, dalam kajian ini, kami telah menyiasat satu set data metagenomik DNA yang sama dari sampel effluen kilang minyak sawit dengan menggunakan tiga laman web bioinformatik percuma iaitu dari laman web ‘metagenomics RAST server’ (MG-RAST), ‘Integrated Microbial Genomes with Microbiome Samples’ (IMG/M) dan ‘European Bioinformatics Institute’ (EBI) Metagenomics dari segi taksonomi dan analisis fungsi. Kami mendapati bahawa MG-RAST ialah yang paling cepat di antara ketiga-tiga ‘pipeline’, tetapi mengikut keputusan analisa, IMG/M mengeluarkan maklumat philum yang lebih pelbagai bersama peratus identiti yang lebih luas berbanding yang lain untuk pembahagian taksonomi dan IMG/M juga mempunyai bacaan tertinggi dalam hampir semua anotasi fungsional karbohidrat, amino asid, lipid, dan koenzima pengangkutan dan metabolisma malah juga paling tinggi dalam jumlah enzim hidrolase glikosida. Kemudian, untuk mengenal pasti ‘domain’ terpelihara dan keluarga yang terlibat, EBI metagenomics lebih bersesuaian. Ketiga-tiga saluran ‘bioinformatics pipeline’ mempunyai keistimewaan mereka yang tersendiri dan boleh digunakan bersilih ganti dalam masa yang sama berdasarkan pilihan fungsi penggun.
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Upadhyay, Apoorva, Andrey A. Kovalev, Elena A. Zhuravleva, et al. "A Review of Basic Bioinformatic Techniques for Microbial Community Analysis in an Anaerobic Digester." Fermentation 9, no. 1 (2023): 62. http://dx.doi.org/10.3390/fermentation9010062.

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Biogas production involves various types of intricate microbial populations in an anaerobic digester (AD). To understand the anaerobic digestion system better, a broad-based study must be conducted on the microbial population. Deep understanding of the complete metagenomics including microbial structure, functional gene form, similarity/differences, and relationships between metabolic pathways and product formation, could aid in optimization and enhancement of AD processes. With advancements in technologies for metagenomic sequencing, for example, next generation sequencing and high-throughput sequencing, have revolutionized the study of microbial dynamics in anaerobic digestion. This review includes a brief introduction to the basic process of metagenomics research and includes a detailed summary of the various bioinformatics approaches, viz., total investigation of data obtained from microbial communities using bioinformatics methods to expose metagenomics characterization. This includes (1) methods of DNA isolation and sequencing, (2) investigation of anaerobic microbial communities using bioinformatics techniques, (3) application of the analysis of anaerobic microbial community and biogas production, and (4) restriction and prediction of bioinformatics analysis on microbial metagenomics. The review has been concluded, giving a summarized insight into bioinformatic tools and also promoting the future prospects of integrating humungous data with artificial intelligence and neural network software.
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Grisham, William, Natalie A. Schottler, Joanne Valli-Marill, Lisa Beck, and Jackson Beatty. "Teaching Bioinformatics and Neuroinformatics by Using Free Web-based Tools." CBE—Life Sciences Education 9, no. 2 (2010): 98–107. http://dx.doi.org/10.1187/cbe.09-11-0079.

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This completely computer-based module's purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information's Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes—narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor's manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics .
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Kibirev, Ya A., A. V. Kuznetsovskiy, S. G. Isupov, and I. V. Darmov. "Modern Bioinformatics Solutions Used for Genetic Data Analysis." Journal of NBC Protection Corps 7, no. 4 (2024): 366–83. http://dx.doi.org/10.35825/2587-5728-2023-7-4-366-383.

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Effective counteraction to biological threats, both natural and man-made, requires the availability of means and methods for rapid and reliable microorganism identification and a comprehensive study of their basic biological properties. Over the past decade, the arsenal of domestic microbiologists has been supplemented by numerous methods for analyzing the genomes of pathogens, primarily based on nucleic acid sequencing. The purpose of this work is to provide the reader with information about capabilities of modern technical and methodological arsenal used for in-depth molecular genetic study of microorganisms, including bioinformatics solutions used for the genetic data analysis. The source base for this research is English-language scientific literature available via the Internet, bioinformation software documentation. The research method is an analysis of scientific sources from the general to the specific. We considered the features of sequencing platforms, the main stages of genetic information analysis, current bioinformation utilities, their interaction and organization into a single workflow. Results and discussion. The performance of modern genetic analyzers allows for complete decoding of the bacterial genome within one day, including the time required to prepare the sample for research. The key factor that largely determines the effectiveness of the genetic analysis methods used is the competent use of the necessary bioinformatics software utilities. Standard stages of primary genetic data analysis are assessment of the quality control, data preprocessing, mapping to a reference genome or de novo genome assembly, genome annotation, typing and identification of significant genetic determinants (resistance to antibacterial drugs, pathogenicity factors, etc.), phylogenetic analysis. For each stage bioinformation utilities have been developed, differing in implemented analysis algorithms. Conclusion. Open source utilities that do not require access to remote resources for their operation are of greatest interest due to activities specifics of NBC protection corps units.
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Lee, Young-Mee, Ji-Hae Lee, and Hyeon S. Son. "Bioinformatics analysis for drug repositioning." Korean Journal of Public Health 53, no. 2 (2016): 19–27. http://dx.doi.org/10.17262/kjph.2016.09.53.2.19.

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Woo, Patrick C. Y., Yi Huang, Susanna K. P. Lau, and Kwok-Yung Yuen. "Coronavirus Genomics and Bioinformatics Analysis." Viruses 2, no. 8 (2010): 1804–20. http://dx.doi.org/10.3390/v2081803.

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Moore, B., G. Fan, and K. Eilbeck. "SOBA: sequence ontology bioinformatics analysis." Nucleic Acids Research 38, Web Server (2010): W161—W164. http://dx.doi.org/10.1093/nar/gkq426.

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Goeta, B. "Bioinformatics-Sequence and Genome Analysis." Briefings in Bioinformatics 3, no. 1 (2002): 101–3. http://dx.doi.org/10.1093/bib/3.1.101.

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Dissertations / Theses on the topic "Bioinformatics analysis"

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Snøve, Jr Ola. "Hardware-accelerated analysis of non-protein-coding RNAs." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-713.

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<p>A tremendous amount of genomic sequence data of relatively high quality has become publicly available due to the human genome sequencing projects that were completed a few years ago. Despite considerable efforts, we do not yet know everything that is to know about the various parts of the genome, what all the regions code for, and how their gene products contribute in the myriad of biological processes that are performed within the cells. New high-performance methods are needed to extract knowledge from this vast amount of information.</p><p>Furthermore, the traditional view that DNA codes for RNA that codes for protein, which is known as the central dogma of molecular biology, seems to be only part of the story. The discovery of many non-proteincoding gene families with housekeeping and regulatory functions brings an entirely new perspective to molecular biology. Also, sequence analysis of the new gene families require new methods, as there are significant differences between protein-coding and non-protein-coding genes.</p><p>This work describes a new search processor that can search for complex patterns in sequence data for which no efficient lookup-index is known. When several chips are mounted on search cards that are fitted into PCs in a small cluster configuration, the system’s performance is orders of magnitude higher than that of comparable solutions for selected applications. The applications treated in this work fall into two main categories, namely pattern screening and data mining, and both take advantage of the search capacity of the cluster to achieve adequate performance. Specifically, the thesis describes an interactive system for exploration of all types of genomic sequence data. Moreover, a genetic programming-based data mining system finds classifiers that consist of potentially complex patterns that are characteristic for groups of sequences. The screening and mining capacity has been used to develop an algorithm for identification of new non-protein-coding genes in bacteria; a system for rational design of effective and specific short interfering RNA for sequence-specific silencing of protein-coding genes; and an improved algorithmic step for identification of new regulatory targets for the microRNA family of non-protein-coding genes.</p><br>Paper V, VI, and VII are reprinted with kind permision of Elsevier, sciencedirect.com
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Petty, Emma Marie. "Shape analysis in bioinformatics." Thesis, University of Leeds, 2009. http://etheses.whiterose.ac.uk/822/.

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In this thesis we explore two main themes, both of which involve proteins. The first area of research focuses on the analyses of proteins displayed as spots on 2-dimensional planes. The second area of research focuses on a specific protein and how interactions with this protein can naturally prevent or, in the presence of a pesticide, cause toxicity. The first area of research builds on previously developed EM methodology to infer the matching and transformation necessary to superimpose two partially labelled point configurations, focusing on the application to 2D protein images. We modify the methodology to account for the possibility of missing and misallocated markers, where markers make up the labelled proteins manually located across images. We provide a way to account for the likelihood of an increased edge variance within protein images. We find that slight marker misallocations do not greatly influence the final output superimposition when considering data simulated to mimic the given dataset. The methodology is also successfully used to automatically locate and remove a grossly misallocated marker within the given dataset before further analyses is carried out. We develop a method to create a union of replicate images, which can then be used alone in further analyses to reduce computational expense. We describe how the data can be modelled to enable the inference on the quality of a dataset, a property often overlooked in protein image analysis. To complete this line of research we provide a method to rank points that are likely to be present in one group of images but absent in a second group. The produced score is used to highlight the proteins that are not present in both image sets representing control or diseased tissue, therefore providing biological indicators which are vitally important to improve the accuracy of diagnosis. In the second area of research, we test the hypothesis that pesticide toxicity is related to the shape similarity between the pesticide molecule itself and the natural ligand of the protein to which a pesticide will bind (and ultimately cause toxicity). A ligand of aprotein is simply a small molecule that will bind to that protein. It seems intuitive that the similarities between a naturally formed ligand and a synthetically developed ligand (the pesticide) may be an indicator of how well a pesticide and the protein bind, as well as provide an indicator of pesticide toxicity. A graphical matching algorithm is used to infer the atomic matches across ligands, with Procrustes methodology providing the final superimposition before a measure of shape similarity is defined considering the aligned molecules. We find evidence that the measure of shape similarity does provide a significant indicator of the associated pesticide toxicity, as well as providing a more significant indicator than previously found biological indicators. Previous research has found that the properties of a molecule in its bioactive form are more suitable indicators of an associated activity. Here, these findings dictate that the docked conformation of a pesticide within the protein will provide more accurate indicators of the associated toxicity. So next we use a docking program to predict the docked conformation of a pesticide. We provide a technique to calculate the similarity between the docks of both the pesticide and the natural ligand. A similar technique is used to provide a measure for the closeness of fit between a pesticide and the protein. Both measures are then considered as independent variables for the prediction of toxicity. In this case the results show potential for the calculated variables to be useful toxicity predictors, though further analysis is necessary to properly explore their significance.
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Wakadkar, Sachin. "Analysis of transmembrane and globular protein depending on their solvent energy." Thesis, University of Skövde, School of Life Sciences, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-2971.

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<p>The number of experimentally determined protein structures in the protein data bank (PDB) is continuously increasing. The common features like; cellular location, function, topology, primary structure, secondary structure, tertiary structure, domains or fold are used to classify them. Therefore, there are various methods available for classification of proteins. In this work we are attempting an additional method for making appropriate classification, i.e. solvent energy. Solvation is one of the most important properties of macromolecules and biological membranes by which they remain stabilized in different environments. The energy required for solvation can be measured in term of solvent energy. Proteins from similar environments are investigated for similar solvent energy. That is, the solvent energy can be used as a measure to analyze and classify proteins. In this project solvent energy of proteins present in the Protein Data Bank (PDB) was calculated by using Jones’ algorithm. The proteins were classified into two classes; transmembrane and globular. The results of statistical analysis showed that the values of solvent energy obtained for two main classes (globular and transmebrane) were from different sets of populations. Thus, by adopting classification based on solvent energy will definitely help for prediction of cellular placement.</p><p> </p>
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Huque, Enamul. "Shape Analysis and Measurement for the HeLa cell classification of cultured cells in high throughput screening." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-27.

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<p>Feature extraction by digital image analysis and cell classification is an important task for cell culture automation. In High Throughput Screening (HTS) where thousands of data points are generated and processed at once, features will be extracted and cells will be classified to make a decision whether the cell-culture is going on smoothly or not. The culture is restarted if a problem is detected. In this thesis project HeLa cells, which are human epithelial cancer cells, are selected for the experiment. The purpose is to classify two types of HeLa cells in culture: Cells in cleavage that are round floating cells (stressed or dead cells are also round and floating) and another is, normal growing cells that are attached to the substrate. As the number of cells in cleavage will always be smaller than the number of cells which are growing normally and attached to the substrate, the cell-count of attached cells should be higher than the round cells. There are five different HeLa cell images that are used. For each image, every single cell is obtained by image segmentation and isolation. Different mathematical features are found for each cell. The feature set for this experiment is chosen in such a way that features are robust, discriminative and have good generalisation quality for classification. Almost all the features presented in this thesis are rotation, translation and scale invariant so that they are expected to perform well in discriminating objects or cells by any classification algorithm. There are some new features added which are believed to improve the classification result. The feature set is considerably broad rather than in contrast with the restricted sets which have been used in previous work. These features are used based on a common interface so that the library can be extended and integrated into other applications. These features are fed into a machine learning algorithm called Linear Discriminant Analysis (LDA) for classification. Cells are then classified as ‘Cells attached to the substrate’ or Cell Class A and ‘Cells in cleavage’ or Cell Class B. LDA considers features by leaving and adding shape features for increased performance. On average there is higher than ninety five percent accuracy obtained in the classification result which is validated by visual classification.</p>
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Chawade, Aakash. "Inferring Gene Regulatory Networks in Cold-Acclimated Plants by Combinatorial Analysis of mRNA Expression Levels and Promoter Regions." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20.

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<p>Understanding the cold acclimation process in plants may help us develop genetically engineered plants that are resistant to cold. The key factor in understanding this process is to study the genes and thus the gene regulatory network that is involved in the cold acclimation process. Most of the existing approaches1-8 in deriving regulatory networks rely only on the gene expression data. Since the expression data is usually noisy and sparse the networks generated by these approaches are usually incoherent and incomplete. Hence a new approach is proposed here that analyzes the promoter regions along with the expression data in inferring the regulatory networks. In this approach genes are grouped into sets if they contain similar over-represented motifs or motif pairs in their promoter regions and if their expression pattern follows the expression pattern of the regulating gene. The network thus derived is evaluated using known literature evidence, functional annotations and from statistical tests.</p>
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Lythgow, Kieren. "Bioinformatics analysis of mitochondrial disease." Thesis, University of Newcastle Upon Tyne, 2011. http://hdl.handle.net/10443/1174.

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Several bioinformatic methods have been developed to aid the identification of novel nuclear-mitochondrial genes involved in disease. Previous research has aimed to increase the sensitivity and specificity of these predictions through a combination of available techniques. This investigation shows the optimum sensitivity and specificity can be achieved by carefully selecting seven specific classifiers in combination. The results also show that increasing the number of classifiers even further can paradoxically decrease the sensitivity and specificity of a prediction. Additionally, text mining applications are playing a huge role in disease candidate gene identification providing resources for interpreting the vast quantities of biomedical literature currently available. A workflow resource was developed identifying a number of genes potentially associated with Lebers Hereditary Optic Neuropathy (LHON). This included specific orthologues in mouse displaying a potential association to LHON not annotated as such in humans. Mitochondrial DNA (mtDNA) fragments have been transferred to the human nuclear genome over evolutionary time. These insertions were compared to an existing database of 263 mtDNA deletions to highlight any associated mechanisms governing DNA loss from mitochondria. Flanking regions were also screened within the nuclear genome that surrounded these insertions for transposable elements, GC content and mitochondrial genes. No obvious association was found relating NUMTs to mtDNA deletions. NUMTs do not appear to be distributed throughout the genome via transposition and integrate predominantly in areas of low %GC with low gene content. These areas also lacked evidence of an elevated number of surrounding nuclear-mitochondrial genes but a further genome-wide study is required.
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Akman, Kemal. "Bioinformatics of DNA Methylation analysis." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-182873.

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Benaim, Jalfon Carlos 1966. "Analysis of the bioinformatics industry." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8882.

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Thesis (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2001.<br>Includes bibliographical references (leaf 76).<br>The rise of the commercial genomic industry and the broadening application of genomic techniques in biology and medicine together with the growing availability of DNA sequence information have created a new industry: The Bioinformatics Industry. This thesis analyzes technologies, applications market and competitors in this industry and explores potential changes to the business models that are being used today. The technology and market information indicates that this is an industry in a very early stage. On the other hand, the business models being used are very similar to the ones used traditionally in the hardware and software industry: licensing, ASP (Application Service Provider), joint developments and hardware/software solutions. The actual market size is relatively small, estimated in no more than $300M. Only by implementing strategies of horizontal or vertical integration, a company in this industry might be able to boost revenues in the long term.<br>by Carlos Benaim Jalfon.<br>S.M.M.O.T.
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Yu, Jennifer. "Bioinformatics Analysis of Vasorin in Gliomas." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1484927314447688.

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Xu, Jia. "Bioinformatics analysis of small silencing RNAs." Thesis, Boston University, 2011. https://hdl.handle.net/2144/38118.

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Thesis (Ph.D.)--Boston University<br>PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>More than 180 genomes have been deciphered, however, much remains to be learned about how genes are regulated. Transcription factors harboring promoters and distal elements are known to activate or repress downstream gene expression, and DNA methylation and histone modifications add the complexity of epigenetic regulation. Furthermore, three classes of small RNA regulators have recently been discovered to repress the target gene and transposon expression. In flies, microRNAs (miRNAs) inhibit translation and expedite degradation of the target mRNAs. Small interfering RNAs (siRNAs) participate in a self defense mechanism called RNA interference (RNAi) to silence infected virus mRNAs or endogenous transposon elements. Piwi-interaction RNAs (piRNAs) efficiently silence the transposon elements in the gonad. The advent of next generation sequencing technologies has allowed us to sequence with sufficient coverage and accuracy and perform genome-wide bioinformatics analyses on small regulatory RNAs to enrich our knowledge on regulation. In this dissertation, I developed a suite of computational algorithms and programs to study small RNAs from next generation sequencing data. First I developed a de novo miRNA discovery pipeline to discover miRNAs in sea urchin and demonstrated one of the sources of endo-siRNAs in flies was overlapping complementary mRNAs. I further investigated the question of how miRNAs and siRNAs were sorted into their own pathways. First nucleotide composition and duplex structure were shown to significantly affect the sorting protein (R2D2) to decide small RNA's destiny. Next, I described collaboration work on piRNA pathway proteins, Ago3 and Rhino. Ago3 was found to catalyze the ping-pong amplification cycle in the piRNA pathway and Rhino, a HP1 homolog, was essential for dual strand piRNA clusters. Lastly, I demonstrated a sequencing-depth independent computational approach to quantify ping-pong efficiency and illustrated the function of each piRNA pathway protein after implementing. In addition, I developed a dynamic programming for detecting piRNA clusters to better annotate the piRNAs enriched segments in the genome and revealed the expression pattern for each cluster.<br>2031-01-01
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Books on the topic "Bioinformatics analysis"

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Unger, Ron. Biyoinformaṭikah: Bioinformatics. ha-Universiṭah ha-petuḥah, 2010.

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Mewes, H. W., H. Seidel, and B. Weiss, eds. Bioinformatics and Genome Analysis. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04747-7.

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1951-, Mewes Hans-Werner, Weiss B. 1966-, and Seidel Henrik, eds. Bioinformatics and genome analysis. Springer, 2002.

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Jamous, Basel Abu. Integrative cluster analysis in bioinformatics. John Wiley & Sons Inc., 2015.

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Abu-Jamous, Basel, Rui Fa, and Asoke K. Nandi. Integrative Cluster Analysis in Bioinformatics. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118906545.

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Posada, David, ed. Bioinformatics for DNA Sequence Analysis. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-251-9.

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David, Posada, ed. Bioinformatics for DNA sequence analysis. Humana Press, 2009.

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David, Posada, ed. Bioinformatics for DNA sequence analysis. Humana Press, 2009.

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David, Posada, ed. Bioinformatics for DNA sequence analysis. Humana Press, 2009.

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R, Barnes Michael, ed. Bioinformatics for geneticists: A bioinformatics primer for the analysis of genetic data. 2nd ed. Wiley, 2007.

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Book chapters on the topic "Bioinformatics analysis"

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Moses, Alan, and Saurabh Sinha. "Regulatory Motif Analysis." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_7.

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Gowen, Christopher M., and Stephen S. Fong. "Phenome Analysis of Microorganisms." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_14.

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Ismail, Hamid D. "Shotgun Metagenomic Data Analysis." In Bioinformatics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003355205-8.

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Ismail, Hamid D. "RNA-Seq Data Analysis." In Bioinformatics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003355205-5.

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Matukumalli, Lakshmi K., and Steven G. Schroeder. "Sequence Based Gene Expression Analysis." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_9.

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Falchi, Mario. "Analysis of Quantitative Trait Loci." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-429-6_16.

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Ismail, Hamid D. "Targeted Gene Metagenomic Data Analysis." In Bioinformatics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003355205-7.

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Kim, Ju Han. "Bioinformatics for Life." In Genome Data Analysis. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_1.

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Lin, Shili, Denise Scholtens, and Sujay Datta. "Metabolomics Data Analysis." In Bioinformatics Methods. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315153728-10.

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Matthiesen, Rune, and Ole N. Jensen. "Analysis of Mass Spectrometry Data in Proteomics." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-429-6_4.

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Conference papers on the topic "Bioinformatics analysis"

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Song, Yan, Xiaorui Zhang, Yiqiong Wang, et al. "Bioinformatics-based microsatellite DNA data mining and analysis." In 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), edited by Pier Paolo Piccaluga, Ahmed El-Hashash, and Xiangqian Guo. SPIE, 2024. http://dx.doi.org/10.1117/12.3044217.

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Rout, Trilochan, Sujata Chakravarty, and Dillip Kumar Muduly. "Bioinformatics in Cancer: Key Proteins and Pathway Analysis." In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit). IEEE, 2024. https://doi.org/10.1109/globalaisummit62156.2024.10947804.

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Shree, D. Vijaya, B. L. Shiva Kumar, V. Ganesan, M. Sivaraman, and J. Sumitha. "Biological Data Analysis for Disease Prediction and Classification in Bioinformatics." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00080.

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Wei, Damu, and Canquan Mao. "Bioinformatics Analysis of -Synuclein." In 2nd International Conference on Civil, Materials and Environmental Sciences. Atlantis Press, 2015. http://dx.doi.org/10.2991/cmes-15.2015.159.

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Yu Yongkun, Ji Qianlong, and Sun Qingpeng. "Bioinformatics analysis of LeNAC protein." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5689657.

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Zhang, Jinhe, Zhengyu Liu, Minxin Cai, et al. "Bioinformatics Analysis of Integrin aVß3." In 2015 7th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2015. http://dx.doi.org/10.1109/itme.2015.105.

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Kumar, Vineet. "Cloud computing using bioinformatics MapReduce applications." In 2016 Symposium on Colossal Data Analysis and Networking (CDAN). IEEE, 2016. http://dx.doi.org/10.1109/cdan.2016.7570893.

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Sun, Qingpeng, Na Li, Fukuan Zhao, and Yongkun Yu. "Bioinformatics Analysis of Tomato WRKY2 Protein." In 2011 5th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2011. http://dx.doi.org/10.1109/icbbe.2011.5780064.

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Raut, Shital A., S. R. Sathe, and Adarsh Raut. "Bioinformatics: Trends in gene expression analysis." In 2010 International Conference on Bioinformatics and Biomedical Technology. IEEE, 2010. http://dx.doi.org/10.1109/icbbt.2010.5479003.

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Pakhaev, Khusein. "Bioinformatics As A Data Analysis Service." In International Conference "Modern trends in governance and sustainable development of socio-economic systems: from regional development to global economic growth". European Publisher, 2024. http://dx.doi.org/10.15405/epms.2024.09.69.

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Reports on the topic "Bioinformatics analysis"

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

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Muyle, Aline. Analysis of DNA Methylation. Instats Inc., 2024. http://dx.doi.org/10.61700/6ayq8hff26qxn1470.

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This three-day workshop provides comprehensive training in the analysis of DNA methylation, a key epigenetic modification influencing gene expression and controlling the spread of transposable elements in genomes. Led by Dr Aline Muyle, participants will gain theoretical knowledge and practical skills in data preprocessing, statistical analysis, and visualization using RStudio and various bioinformatic software using Bash scripts. The workshop is suited for researchers in Biostatistics, Biology, Genetics, Ecology, Evolution, Bioinformatics, Molecular Biology, and Medical Research.
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Tarozzi, Martina Elena. Next Generation Sequencing Technologies, Bioinformatics and Artificial Intelligence: A Shared Time-line. MZB Standard Enterprise, 2024. http://dx.doi.org/10.57098/scirevs.biology.3.2.2.

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This review provides a comprehensive overview of the fast-paced and intertwined evolution of three pivotal fields: next-generation sequencing (NGS) technologies, bioinformatics, and artificial intelligence (AI). The paper begins by tracing the development of sequencing technologies and highlights how advancements in genetic sequencing have led to an explosion of biological data, necessitating the rise of bioinformatics for data management and analysis. The review next covers the primary steps and methods used in bioinformatic analysis and concludes by reporting some of the technical and biological challenges in which AI methods have been applied.
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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.
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Altindis, E., R. Cozzi, B. Di Palo, et al. Protectome analysis: a new selective bioinformatics tool for bacterial vaccine candidate discovery. Cold Spring Harbor Laboratory, 2014. http://dx.doi.org/10.1101/002089.

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Wang, Pengcheng. Association between paraoxonase 1 -108C/T polymorphism and coronary heart disease:an updated meta-analysis and bioinformatics analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.7.0065.

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Xu, Mingna, Yaru Guo, and Song Yang. Prognostic and Clinicopathological Significance of TGFBI Expression in Cancer Patients: A Meta-Analysis and Bioinformatics Validation. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.10.0089.

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Srivastava, Anuj. A Statistical Theory for Shape Analysis of Curves and Surfaces with Applications in Image Analysis, Biometrics, Bioinformatics and Medical Diagnostics. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada532601.

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Xie, Xiao-Li. Identification of Key Genes and Pathways in First Acute Myocardial Infarction Based on Gene Expression Profiling by Bioinformatics Analysis. Science Repository, 2019. http://dx.doi.org/10.31487/j.jicoa.2019.02.02.

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Morse, Alexander. Bioinformatics experiments. Exp 1. Analysis of mechanisms of Cd+2 impact on MAPK-signalling through DUSPs. Part 1. Theory. ResearchHub Technologies, Inc., 2022. http://dx.doi.org/10.55277/researchhub.9y5bc33g.

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