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Relevant bibliographies by topics / Genes Bioinformatics

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Academic literature on the topic 'Genes Bioinformatics'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Genes Bioinformatics"

1

Bonetta, Laura. "Bioinformatics—from genes to pathways." Nature Methods 1, no. 2 (2004): 169–76. http://dx.doi.org/10.1038/nmeth1104-169.

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Kress, W. J., and D. L. Erickson. "DNA barcodes: Genes, genomics, and bioinformatics." Proceedings of the National Academy of Sciences 105, no. 8 (2008): 2761–62. http://dx.doi.org/10.1073/pnas.0800476105.

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Bard, J. B. L. "Using bioinformatics to identify kidney genes." Nephrology Dialysis Transplantation 17, no. 90009 (2002): 62–64. http://dx.doi.org/10.1093/ndt/17.suppl_9.62.

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Ghosh, Zhumur, Jayprokas Chakrabarti, and Bibekanand Mallick. "miRNomics—The bioinformatics of microRNA genes." Biochemical and Biophysical Research Communications 363, no. 1 (2007): 6–11. http://dx.doi.org/10.1016/j.bbrc.2007.08.030.

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Sridhar, GR, CH Divakar, T. Hanuman, and AllamAppa Rao. "Bioinformatics approach to extract information from genes." International Journal of Diabetes in Developing Countries 26, no. 4 (2006): 149. http://dx.doi.org/10.4103/0973-3930.33179.

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van Driel, Marc A., and Han G. Brunner. "Bioinformatics methods for identifying candidate disease genes." Human Genomics 2, no. 6 (2006): 429. http://dx.doi.org/10.1186/1479-7364-2-6-429.

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Basyuni, M., M. Wasilah, and Sumardi. "Bioinformatics study of the mangrove actin genes." Journal of Physics: Conference Series 801 (January 2017): 012013. http://dx.doi.org/10.1088/1742-6596/801/1/012013.

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Menon, Sudheer. "Bioinformatics methods for identifying Hirschsprung disease genes." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2974–78. http://dx.doi.org/10.22214/ijraset.2021.37045.

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Hirschsprung is a birth defect of Enteric Nervous System (ENS) which is characterized by the absence of enteric neurons along the length of intestine. Hirschsprung is one of the complex diseases which has become a important topic of human genetics. In this article we have focused on RET gene mutation that is most common cause of HSCR disease. Out of seven mutations in RET gene, one mutation S339L is found to be tolerated and have no effect on protein function.

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Bischof, Jocelyn M., and Rachel Wevrick. "Genome-wide analysis of gene transcription in the hypothalamus." Physiological Genomics 22, no. 2 (2005): 191–96. http://dx.doi.org/10.1152/physiolgenomics.00071.2005.

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As the genomic regions containing loci predisposing to obesity-related traits are mapped in human population screens and mouse genetic studies, identification of susceptibility genes will increasingly be facilitated by bioinformatic methods. We hypothesized that candidate genes can be prioritized by their expression levels in tissues of central importance in obesity. Our objective was to develop a combined bioinformatics and molecular paradigm to identify novel genes as candidates for murine or human obesity genetic modifiers based on their differential expression patterns in the hypothalamus compared with other murine tissues. We used bioinformatics tools to search publicly available gene expression databases using criteria designed to identify novel genes differentially expressed in the hypothalamus. We used RNA methods to determine their expression sites and levels of expression in the hypothalamus of the murine brain. We identified the chromosomal location of the novel genes in mice and in humans and compared these locations with those of genetic loci predisposing to obesity-related traits. We developed a search strategy that correctly identified a set of genes known to be important in hypothalamic function as well as a candidate gene for Prader-Willi syndrome that was not previously identified as differentially expressed in the hypothalamus. Using this same strategy, we identified and characterized a set of 11 genes not previously known to be differentially expressed in the murine hypothalamus. Our results demonstrate the feasibility of combined bioinformatics and molecular approaches to the identification of genes that are candidates for obesity-related disorders in humans and mice.

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Bi, De, Hua Jun Xiao, Cui Hong Zhou, and Jun Zhou. "Mining the Bioinformation of Differentially Expressed Genes in Obese Mice Treated with Chronic Intermittent Hypoxia Based on the Bioinformatics Methods." Applied Mechanics and Materials 195-196 (August 2012): 429–34. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.429.

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Objective: To analyze the differentially expressed genes expressed genes in obese mice that treated with chronic intermittent hypoxia (CIH) for getting better understanding of the molecular characteristics in the obese mice caused by CIH. Methods: Got the microarray hybridization data from the Gene Expression Omnibus (GEO) database. Identified the differentially expressed genes expressed genes in CIH obese mice and the patterns of their regulation using public bioinformatics software and database, such as BRB-Arraytools, Genecodis and DAVID, KEGG. Results and Conclusion: We found the Peroxisome proliferator activated receptors (PPARs) pathway involved in the down-regulated genes. These data mining findings between room air and CIH mice by bioinformatics methods could provide better understanding of the molecular activity change in obese caused by CIH.

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Dissertations / Theses on the topic "Genes Bioinformatics"

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Hvidsten, Torgeir R. "Predicting Function of Genes and Proteins from Sequence, Structure and Expression Data." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4490.

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Dodda, Srinivasa Rao. "Improvements and extensions of a web-tool for finding candidate genes associated with rheumatoid arthritis." Thesis, University of Skövde, School of Humanities and Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-26.

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<p>QuantitativeTraitLocus (QTL) is a statistical method used to restrict genomic regions contributing to specific phenotypes. To further localize genes in such regions a web tool called “Candidate Gene Capture” (CGC) was developed by Andersson et al. (2005). The CGC tool was based on the textual description of genes defined in the human phenotype database OMIM. Even though the CGC tool works well, the tool was limited by a number of inconsistencies in the underlying database structure, static web pages and some gene descriptions without properly defined function in the OMIM database. Hence, in this work the CGC tool was improved by redesigning its database structure, adding dynamic web pages and improving the prediction of unknown gene function by using exon analysis. The changes in database structure diminished the number of tables considerably, eliminated redundancies and made data retrieval more efficient. A new method for prediction of gene function was proposed, based on the assumption that similarity between exon sequences is associated with biochemical function. Using Blast with 20380 exon protein sequences and a threshold E-value of 0.01, 639 exon groups were obtained with an average of 11 exons per group. When estimating the functional similarity, it was found that on the average 72% of the exons in a group had at least one Gene Ontology (GO) term in common.</p>

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Jia, Yizhen, and 贾亦真. "Bioinformatics study of the lineage and tissue specificity of genes and gene expression." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45540652.

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Muhammad, Ashfaq. "Design and Development of a Database for the Classification of Corynebacterium glutamicum Genes, Proteins, Mutants and Experimental Protocols." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23.

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<p>Coryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and</p><p>management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.</p>

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Basile, Walter. "Orphan Genes Bioinformatics : Identification and properties of de novo created genes." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-149168.

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Even today, many genes are without any known homolog. These "orphans" are found in all species, from Viruses to Prokaryotes and Eukaryotes. For a portion of these genes, we might simply not have enough data to find homologs yet. Some of them are imported from taxonomically distant organisms via lateral transfer; others have homologs, but mutated beyond the point of recognition. However, a sizeable fraction of orphan genes is unambiguously created via "de novo" mechanisms. The study of such novel genes can contribute to our understanding of the emergence of functional novelty and the adaptation of species to new ecological niches. In this work, we first survey the field of orphan studies, and illustrate some of the common issues. Next, we analyze some of the intrinsic properties of orphans proteins, including secondary structure elements and Intrinsic Structural Disorder; specifically, we observe that in young proteins the relationship between these properties and the G+C content of their coding sequence is stronger than in older proteins. We then tackle some of the methodological problems often found in orphan studies. We find that using evolutionarily close species, and sensitive, state-of-the art homology recognition methods is instrumental to the identification of a set of orphans enriched in de novo created ones. Finally, we compare how intrinsic disorder is distributed in bacteria versus eukaryota. Eukaryotic proteins are longer and more disordered; the difference is to be attributed primarily to eukaryotic-specific domains and linker regions. In these sections of the proteins, a higher frequency of the disorder-promoting amino acid Serine can be observed in Eukaryotes.<br><p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Submitted. Paper 4: Manuscript.</p>

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Andersson, Malin. "A method for identification of putatively co-regulated genes." Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-705.

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<p>The genomes of several organisms have been sequenced and the need for methods to analyse the data is growing. In this project a method is described that tries to identify co-regulated genes. The method identifies transcription factor binding sites, documented in TRANSFAC, in the non-coding regions of genes. The algorithm counts the number of common binding sites and the number of unique binding sites for each pair of genes and decides if the genes are co-regulated. The result of the method is compared with the correlation between the gene expression patterns of the genes. The method is tested on 21 gene pairs from the genome of Saccharomyces cerevisiae. The algorithm first identified binding sites from all organisms. The accuracy of the program was very low in this case. When the algorithm was modified to only identify binding sites found in plants the accuracy was much improved, from 52% to 76% correct predictions.</p>

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Karjalainen, Merja. "Analysing subsets of gene expression data to find putatively co-regulated genes." Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-712.

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<p>This project is an investigation of whether analysing subsets of time series gene expression data can give additional information about putatively co-regulated genes, compared to only using the whole time series. The original gene expression data set was partitioned into subsets and similarity was computed for both the whole timed series and subsets. Pearson correlation was used as similarity measure between gene expression profiles. The results indicate that analysing co-expression in subsets of gene expression data derives true-positive connections, with respect to co-regulation, that are not detected by only using the whole time series data. Unfortunately, with the actual data set, chosen similarity measure and partitioning of the data, randomly generated connections have the same amount of true-positives as the ones derived by the applied analysis. However, it is worth to continue further analysis of the subsets of gene expression data, which is based on the multi-factorial nature of gene regulation. E.g. other similarity measures, data sets and ways of partitioning the data set should be tried.</p>

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Podowski, Raf M. "Applied bioinformatics for gene characterization /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-818-5/.

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Schaller, Susanne. "Identification of Housekeeping Genes in Human Embryonic Stem Cells." Thesis, University of Skövde, School of Life Sciences, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3508.

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Ganesan, Abhishekapriya. "The role of RFX-target genes in neurodevelopmental and psychiatric disorders." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445493.

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Neurodevelopmental disorders such as autism spectrum disorder (ASD) and psychiatric disorders, for example, schizophrenia (SCZ) represent a large spectrum of disorders that manifest through cognitive and behavioural problems. ASD and SCZ are both highly heritable, and some phenotypic similarities between ASD and SCZ have sparked an interest in understanding their genetic commonalities. The genetics of both disorders exhibit significant heterogeneity. Developments in genomics and systems biology, continually increases people’s understanding of these disorders. Recently, pathogenic genetic variants in the regulatory factor X (RFX) family of transcription factors have been identified in a number of ASD cases. In this thesis, common genetic variants and expression patterns of genes identified to have a conserved promotor X-Box motif region, a binding site of RFX factors, are studied. Significant common variants identified through expression quantitative trait loci (eQTLs) and genome wide association studies (GWAS) are mapped to the regulatory regions of these genes and analysed for putative enrichment. In addition, single-cell RNA sequencing data is utilised to examine enrichment of cell types having high X-Box gene expression in the developing human cortex. Through the study, genes that have eQTLs or SNPs in the genomic regulatory regions of the X-Box genes have been identified. While there were no eQTLs or GWAS SNPs in the X-Box motifs, in the X-Box promoter regions some common variants were found. By hypergeometric distribution testing and the subsequent p-values obtained, all of these distributions are statistically under-enriched. Further, major cell types in the cortical region with increased expression of the X-Box genes and most expressed genes among these enriched cell types have been identified. Among the 11 cell types seven were found to be enriched for X-Box genes and many of the most expressed genes in these cell-types were similar. A further study into the cell types and genes identified, along with additional systems biological data analysis, could reveal a larger list of X-Box genes involved in ASD and SCZ and the specific roles of these genes.

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Books on the topic "Genes Bioinformatics"

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Uchida, Shizuka. Annotating new genes: From in silico screening to experimental validation. Woodhead Publishing Limited, 2012.

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Frishman, Dmitrij. Modern genome annotation: The BioSapiens Network. Springer, 2009.

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Cancer systems biology. CRC Press, 2010.

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Modern genome annotation: The BioSapiens Network. Springer, 2009.

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Ralf, Hofestädt, ed. Bioinformatics: German Conference on Bioinformatics, GCB '96, Leipzig, Germany, September 30-October 2, 1996 : selected papers. Springer, 1997.

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Rashidi, Hooman H. Bioinformatics basics: Applications in biological science and medicine. CRC Press, 2000.

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Next generation microarray bioinformatics: Methods and protocols. Humana Press, 2012.

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German, Conference on Bioinformatics (2008 Dresden Germany). German Conference on Bioinformatics: GCB 2008 : 09.-12.09.2008 in Dresden, Germany. Gesellschaft für Informatik e.V. (GI), 2008.

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German Conference on Bioinformatics (2008 Dresden, Germany). German Conference on Bioinformatics: GCB 2008 : 09.-12.09.2008 in Dresden, Germany. Gesellschaft für Informatik e.V. (GI), 2008.

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German Conference on Bioinformatics (2005 Hamburg Germany). German Conference on Bioinformatics 2005: GCB 2005, October 5-7, 2005, Hamburg, Germany. Gesellschaft für Informatik, 2005.

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Book chapters on the topic "Genes Bioinformatics"

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McHardy, Alice Carolyn. "Finding Genes in Genome Sequence." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_8.

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Wang, Ruijia, and Zhanjiang Liu. "Analysis of Duplicated Genes and Multi-Gene Families." In Bioinformatics in Aquaculture. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118782392.ch6.

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Li, Chao, Qifan Zeng, Chen Jiang, Jun Yao, and Zhanjiang Liu. "Analysis of Differentially Expressed Genes and Co-expressed Genes Using RNA-Seq Datasets." In Bioinformatics in Aquaculture. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118782392.ch9.

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Wang, Zixing, Wenlong Xu, and Yin Liu. "Systematic and Integrative Analysis of Gene Expression to Identify Feature Genes Underlying Human Diseases." In Translational Bioinformatics. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7450-5_7.

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Liu, Yu-Cheng, Po-I. Chiu, Hsuan-Cheng Huang, and Vincent S. Tseng. "Prediction of Essential Genes by Mining Gene Ontology Semantics." In Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21260-4_9.

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Wang, Jiu-Yao. "From Allergen Extracts to Allergen Genes and Allergen Molecules." In Allergy Bioinformatics. Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7444-4_6.

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Crécy-Lagard, Valérie de. "Finding Missing tRNA Modification Genes: A Comparative Genomics Goldmine." In Practical Bioinformatics. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-74268-5_8.

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Rao, Nini, and Simon J. Shepherd. "Clustering Gene Expression Data for Periodic Genes Based on INMF." In Computational Intelligence and Bioinformatics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11816102_45.

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Liu, Shikai, and Zhanjiang Liu. "Analysis of MicroRNAs and Their Target Genes." In Bioinformatics in Aquaculture. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118782392.ch13.

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Jafarian, Amirali, Alioune Ngom, and Luis Rueda. "New Gene Subset Selection Approaches Based on Linear Separating Genes and Gene-Pairs." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24855-9_5.

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Conference papers on the topic "Genes Bioinformatics"

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Tian, Chunyu, Xiuli Wang, Shigui Jiang, et al. "Phylogenetic Analysis of Members of the Genus Vibrios Based on gyrB Genes and 16S rRNA Genes." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.39.

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Yang, C., E. Zeng, T. Li, and G. Narasimhan. "Clustering genes using gene expression and text literature data." In 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05). IEEE, 2005. http://dx.doi.org/10.1109/csb.2005.23.

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"Gene ontology enrichment and network analysis for differently expressed genes related to aggressive behavior." In SYSTEMS BIOLOGY AND BIOINFORMATICS. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/sbb-2019-40.

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Zeng, Erliang, Giri Narasimhan, Lisa Schneper, and Kalai Mathee. "A Functional Network of Yeast Genes Using Gene Ontology Information." In 2008 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2008. http://dx.doi.org/10.1109/bibm.2008.60.

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"Genes determining dwarfism in Cucurbitaceae." In SYSTEMS BIOLOGY AND BIOINFORMATICS (SBB-2020). Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences., 2020. http://dx.doi.org/10.18699/sbb-2020-35.

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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}. 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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AlKindy, Bassam, Christophe Guyeux, Jean-Francois Couchot, Michel Salomon, and Jacques M. Bahi. "Gene similarity-based approaches for determining core-genes of chloroplasts." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999130.

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Bragina, Elena, Alexey Rudko, Evgeny Tiys, Nadezhda Babushkina, Vladimir Ivanisenko, and Maxim Freidin. "Bioinformatics approach identified of novel genes of tuberculosis susceptibility." In ERS International Congress 2016 abstracts. European Respiratory Society, 2016. http://dx.doi.org/10.1183/13993003.congress-2016.pa2725.

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"The genes determining synthesis of pigments in cotton." In SYSTEMS BIOLOGY AND BIOINFORMATICS. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/sbb-2019-28.

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"WOX family genes in somatic embryogenesis." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Novosibirsk ICG SB RAS 2021, 2021. http://dx.doi.org/10.18699/plantgen2021-110.

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Reports on the topic "Genes Bioinformatics"

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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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