Relevant bibliographies by topics / Bioinformatic package

Academic literature on the topic 'Bioinformatic package'

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Published: 10 March 2023

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Journal articles on the topic "Bioinformatic package"

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Theil, Sebastien, and Etienne Rifa. "rANOMALY: AmplicoN wOrkflow for Microbial community AnaLYsis." F1000Research 10 (January 7, 2021): 7. http://dx.doi.org/10.12688/f1000research.27268.1.

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Bioinformatic tools for marker gene sequencing data analysis are continuously and rapidly evolving, thus integrating most recent techniques and tools is challenging. We present an R package for data analysis of 16S and ITS amplicons based sequencing. This workflow is based on several R functions and performs automatic treatments from fastq sequence files to diversity and differential analysis with statistical validation. The main purpose of this package is to automate bioinformatic analysis, ensure reproducibility between projects, and to be flexible enough to quickly integrate new bioinformatic tools or statistical methods. rANOMALY is an easy to install and customizable R package, that uses amplicon sequence variants (ASV) level for microbial community characterization. It integrates all assets of the latest bioinformatics methods, such as better sequence tracking, decontamination from control samples, use of multiple reference databases for taxonomic annotation, all main ecological analysis for which we propose advanced statistical tests, and a cross-validated differential analysis by four different methods. Our package produces ready to publish figures, and all of its outputs are made to be integrated in Rmarkdown code to produce automated reports.
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Love, Michael I., Charlotte Soneson, and Rob Patro. "Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification." F1000Research 7 (June 27, 2018): 952. http://dx.doi.org/10.12688/f1000research.15398.1.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.
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Love, Michael I., Charlotte Soneson, and Rob Patro. "Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification." F1000Research 7 (September 14, 2018): 952. http://dx.doi.org/10.12688/f1000research.15398.2.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.
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Love, Michael I., Charlotte Soneson, and Rob Patro. "Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification." F1000Research 7 (October 1, 2018): 952. http://dx.doi.org/10.12688/f1000research.15398.3.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.
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Li, Xiaoying, Xin Lin, Huiling Ren, and Jinjing Guo. "Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study." Journal of Medical Internet Research 22, no. 7 (July 20, 2020): e20443. http://dx.doi.org/10.2196/20443.

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Background Licensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs. Objective This paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications. Methods Based on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis. Results We collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs. Conclusions Ontological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.
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Zhbannikov, Ilya Y., Konstantin Arbeev, Svetlana Ukraintseva, and Anatoliy I. Yashin. "haploR: an R package for querying web-based annotation tools." F1000Research 6 (May 15, 2017): 97. http://dx.doi.org/10.12688/f1000research.10742.2.

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We developed haploR, an R package for querying web based genome annotation tools HaploReg and RegulomeDB. haploR gathers information in a data frame which is suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.
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Zhbannikov, Ilya Y., Konstantin Arbeev, and Anatoliy I. Yashin. "haploR: an R-package for querying web-based annotation tools." F1000Research 6 (February 1, 2017): 97. http://dx.doi.org/10.12688/f1000research.10742.1.

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There exists a set of web-based tools for integration and exploring information linked to annotated genetic variants. We developed haploR, an R-package for querying such web-based genome annotation tools (currently implementing on HaploReg and RegulomeDB) and gathering information in a format suitable for downstream bioinformatic analyses. This will facilitate post-genome wide association studies streamline analysis for rapid discovery and interpretation of genetic associations.
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Rinchai, Darawan, Jessica Roelands, Mohammed Toufiq, Wouter Hendrickx, Matthew C. Altman, Davide Bedognetti, and Damien Chaussabel. "BloodGen3Module: blood transcriptional module repertoire analysis and visualization using R." Bioinformatics 37, no. 16 (February 24, 2021): 2382–89. http://dx.doi.org/10.1093/bioinformatics/btab121.

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Abstract Motivation We previously described the construction and characterization of fixed reusable blood transcriptional module repertoires. More recently we released a third iteration (‘BloodGen3’ module repertoire) that comprises 382 functionally annotated modules and encompasses 14 168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here an R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability and implementation The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module. Supplementary information Supplementary data are available at Bioinformatics online.
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Fasterius, Erik, and Cristina Al-Khalili Szigyarto. "seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing data." F1000Research 7 (August 12, 2019): 1466. http://dx.doi.org/10.12688/f1000research.16083.2.

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High throughput sequencing technologies are flourishing in the biological sciences, enabling unprecedented insights into e.g. genetic variation, but require extensive bioinformatic expertise for the analysis. There is thus a need for simple yet effective software that can analyse both existing and novel data, providing interpretable biological results with little bioinformatic prowess. We present seqCAT, a Bioconductor toolkit for analysing genetic variation in high throughput sequencing data. It is a highly accessible, easy-to-use and well-documented R-package that enables a wide range of researchers to analyse their own and publicly available data, providing biologically relevant conclusions and publication-ready figures. SeqCAT can provide information regarding genetic similarities between an arbitrary number of samples, validate specific variants as well as define functionally similar variant groups for further downstream analyses. Its ease of use, installation, complete data-to-conclusions functionality and the inherent flexibility of the R programming language make seqCAT a powerful tool for variant analyses compared to already existing solutions. A publicly available dataset of liver cancer-derived organoids is analysed herein using the seqCAT package, corroborating the original authors' conclusions that the organoids are genetically stable. A previously known liver cancer-related mutation is additionally shown to be present in a sample though it was not listed in the original publication. Differences between DNA- and RNA-based variant calls in this dataset are also analysed revealing a high median concordance of 97.5%. SeqCAT is an open source software under a MIT licence available at https://bioconductor.org/packages/release/bioc/html/seqCAT.html.
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Dai, Fangfang, Jinglin Wu, Zhimin Deng, Hengxing Li, Wei Tan, Mengqin Yuan, Dongyong Yang, et al. "Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer." BioMed Research International 2022 (June 20, 2022): 1–13. http://dx.doi.org/10.1155/2022/5119411.

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Background. Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis. Methods. The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using “MethylMix” and “corrplot” R packages. The connection between methylated genes and the expression of screened driven genes were explored using “survminer” and “beeswarm” packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using “CIBERSORT” package. Results. In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell–cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration. Conclusions. Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.
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Dissertations / Theses on the topic "Bioinformatic package"

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Mbah-Mbole, Georgia Fru. "Comparative study of topology based pathway enrichment analysis methods for cardiac hypertrophy from a stem cell model using : ToPASeq and EnrichmentBrowser packages." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19465.

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Pathway enrichment analysis is an approach extensively used when analyzing high throughput data to identify pathways enriched within a group of differentially expressed genes. Furthermore, different methods utilizing the topology of the pathway offer a unique way of analyzing and interpreting gene expression data. These methods usually offer pathway topologies with a limited number of methods and visualization of results. Also, the use of different methods individually and comparison of their results can be very cumbersome, time-consuming and prone to errors due to the need for repeated data conversion and transfer. Packages that offer a common interface to multiple methods are therefore necessary, to provide a uniform way of calling these methods or specifying parameters, and making simultaneous application of the methods easier. In this study topology-based pathway enrichment analysis was performed by using the R packages EnrichmentBrowser and ToPASeq on a time series RNA-Seq data for cardiac hypertrophy in order to compare their usability. Additionally, different topology-based enrichment analysis methods included in the packages were compared with a non-topology-based pathway enrichment analysis method as well as the combination of their results in order to assess biological insights. Regarding usability, the available instructions for how to use both EnrichmentBrowser and ToPASeq were easy to understand and apply in the R workspace. Furthermore, both packages were easy to install and adjust to various parameters. However, ToPASeq returned errors when some parameters other than the default ones were used. Also, one of the differences between the tools was the flexibility of options for visualization and interpretation of the results, where EnrichmentBrowser had clear advantages. Regarding biological insights, the methods SPIA and DEGraph produced significant pathways linked to the phenotype cardiac hypertrophy, with a clear advantage for SPIA that performed well in both tested data setups. Finally, combining results from both SPIA and GSEA (non-topology-based pathway enrichment analysis method) improved individual ranking by increasing confidence in specific target pathways and eliminating irrelevant pathways.
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Jin, Lu. "Building Matlab Standalone Package from Java for Differential Dependence Network Analysis Bioinformatics Toolkit." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/33488.

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This thesis reports a software development effort to transplant Matlab algorithm into a Matlab license-free, platform dependent Java based software. The result is almost equivalent to a direct translation of Matlab source codes into Java or any other programming languages. Since compiled library is platform dependent, an MCR (Matlab Compiler Runtime environment) is required and has been developed to deploy the transplanted algorithm to end user. As the result, the implemented MCR is free to distribution and the streamline transplantation process is much simpler and more reliable than manually translation work. In addition, the implementation methodology reported here can be reused for other similar software engineering tasks. There are mainly 4 construction steps in our software package development. First, all Matlab *.m files or *.mex files associated with the algorithms of interest (to be transplanted) are gathered, and the corresponding shared library is created by the Matlab Compiler. Second, a Java driver is created that will serve as the final user interface. This Java based user interface will take care of all the input and output of the original Matlab algorithm, and prepare all native methods. Third, assisted by JNI, a C driver is implemented to manage the variable transfer between Matlab and Java. Lastly, Matlab mbuild function is used to compile the C driver and aforementioned shared library into a dependent library, ready to be called from the standalone Java interface. We use a caBIGTM (Cancer Biomedical Informatics Grid) data analytic toolkit, namely, the DDN (differential dependence network) algorithm as the testbed in the software development. The developed DDN standalone package can be used on any Matlab-supported platform with Java GUI (Graphic User Interface) or command line parameter. As a caBIGTM toolkit, the DDN package can be integrated into other information systems such as Taverna or G-DOC. The major benefits provided by the proposed methodology can be summarized as follows. First, the proposed software development framework offers a simple and effective way for algorithm developer to provide novel bioinformatics tools to the biomedical end-users, where the frequent obstacle is the lack of language-specific software runtime environment and incompatibility between the compiled software and available computer platforms at userâ s sites. Second, the proposed software development framework offers software developer a significant time/effort-saving method for translating code between different programming languages, where the majority of software developerâ s time/effort is spent on understanding the specific analytic algorithm and its language-specific codes rather than developing efficient and platform/user-friendly software. Third, the proposed methodology allows software engineers to focus their effort on the quality of software rather than the details of original source codes, where the only required information is the inputs and outputs of the algorithm. Specifically, all used variables and functions are mapped between Matlab, C and Java, handled solely by our designated C driver.
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FONDI, MARCO. "Bioinformatics of genome evolution: from ancestral to modern metabolism." Doctoral thesis, 2010. http://hdl.handle.net/2158/546258.

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Books on the topic "Bioinformatic package"

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Molecular Biology of the Gene Plus MasteringBiology with EText -- Access Card Package. Pearson Education, Limited, 2013.

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LeCao, Kim-Anh, and Zoe Marie Welham. Multivariate Data Integration Using R: Methods and Applications with the MixOmics Package. Taylor & Francis Group, 2021.

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LeCao, Kim-Anh, and Zoe Marie Welham. Multivariate Data Integration Using R: Methods and Applications with the MixOmics Package. Taylor & Francis Group, 2021.

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Cristianini, Nello, and John Shawe-Taylor. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.

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Cristianini, Nello, and John Shawe-Taylor. Kernel Methods for Pattern Analysis. Cambridge University Press, 2006.

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Cristianini, Nello, and John Shawe-Taylor. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.

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Cristianini, Nello, and John Shawe-Taylor. Kernel Methods for Pattern Analysis. Cambridge University Press, 2011.

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Cristianini, Nello, and John Shawe-Taylor. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.

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Cristianini, Nello, and John Shawe-Taylor. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.

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Book chapters on the topic "Bioinformatic package"

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Ismail, Hamid D. "R and Python Packages for the NCBI E-Utilities." In Bioinformatics, 343–82. New York: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003226611-6.

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Staden, Rodger, David P. Judge, and James K. Bonfield. "Analyzing Sequences Using the Staden Package and EMBOSS." In Introduction to Bioinformatics, 393–410. Totowa, NJ: Humana Press, 2003. http://dx.doi.org/10.1007/978-1-59259-335-4_24.

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LeMeur, Nolweim, Michael Lawrence, Merav Bar, Muneesh Tewari, and Robert Gentleman. "R and Bioconductor Packages in Bioinformatics: Towards Systems Biology." In Statistical Bioinformatics, 309–38. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470567647.ch13.

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Feng, Gang, Pamela Shaw, Steven T. Rosen, Simon M. Lin, and Warren A. Kibbe. "Using the Bioconductor GeneAnswers Package to Interpret Gene Lists." In Next Generation Microarray Bioinformatics, 101–12. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-400-1_7.

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Nehra, Kiran, Vijay Nehra, Bhupinder Singh, Sunil Kumar, and Mahesh Kumar. "Computer Simulation Using GPSC Package MATLAB, Simulink for Bioinformatics Professional." In Advances in Intelligent Systems and Computing, 251–62. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6602-3_25.

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Abdullah, Azian Azamimi, and Shigehiko Kanaya. "Machine Learning Using H2O R Package: An Application in Bioinformatics." In Proceedings of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017), 375–81. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7279-7_46.

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Castelo, Robert, and Alberto Roverato. "Inference of Regulatory Networks from Microarray Data with R and the Bioconductor Package qpgraph." In Next Generation Microarray Bioinformatics, 215–33. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-400-1_14.

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Vishnevsky, O. V., E. A. Ananko, E. V. Ignatieva, O. A. Podkolodnaya, and I. L. Stepanenko. "Argo_Viewer: A Package for Recognition and Analysis of Regulatory Elements in Eukaryotic Genes." In Bioinformatics of Genome Regulation and Structure, 71–80. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-7152-4_8.

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Pollard, K. S., S. Dudoit, and M. J. van der Laan. "Multiple Testing Procedures: the multtest Package and Applications to Genomics." In Bioinformatics and Computational Biology Solutions Using R and Bioconductor, 249–71. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/0-387-29362-0_15.

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Sturm, Gregor, Francesca Finotello, and Markus List. "Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA-Sequencing Data." In Bioinformatics for Cancer Immunotherapy, 223–32. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0327-7_16.

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Conference papers on the topic "Bioinformatic package"

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"Pygenomics: Python package for processing genomic intervals and bioinformatic data formats." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-672.

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Peng, Wei, and Tao Li. "IntClust: A Software Package for Clustering Replicated Microarray Data." In Sixth IEEE Symposium on BioInformatics and BioEngineering (BIBE'06). IEEE, 2006. http://dx.doi.org/10.1109/bibe.2006.253322.

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Li, Zhenzhi, Yiming Zuo, Chaohui Xu, Rency S. Varghese, and Habtom W. Ressom. "INDEED: R package for network based differential expression analysis." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621426.

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Barton, Vojtech, Marketa Nykrynova, and Helena Skutkova. "MANASIG: Python Package to Manipulate Nanopore Signals From Sequencing Files." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669821.

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"Software package for retrosynthesis-based prediction of metabolic pathways." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-306.

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Jeong-Hyeon Choi, Dong-Sung Ryu, S. Sureshchandra, Huidong Shi, and Hwan-Gue Cho. "A software package for next-gen bisulfite sequencing data analysis." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112561.

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Thai, Quang Tung, Seokjong Yu, and Yongseong Cho. "A distributed software package for global sensitivity analysis of biological models." In 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE). IEEE, 2012. http://dx.doi.org/10.1109/bibe.2012.6399724.

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Angelin-Bonnet, Olivia, Patrick J. Biggs, and Matthieu Vignes. "The sismonr Package: Simulation of In Silico Multi-Omic Networks in R." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621131.

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Chen, Yao, Libo Huang, Jiong He, Kunyao Zhao, Ruichu Cai, and Zhifeng Hao. "HASS: High Accuracy Spike Sorting with Wavelet Package Decomposition and Mutual Information." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621401.

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"Assignment of Orthologous Genes by Utilization of Multiple Databases - The Orthology Package in R." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004193201050110.

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Reports on the topic "Bioinformatic package"

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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.
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