Showing 8 open source projects for "cortex-m"

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  • 1
    JSONVisor

    JSONVisor

    Visor for mass spectrometry JSON files generated by Integrator

    ...For more information, you can have a look at the README.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/jsonvisor3/code/ci/default/tree/README.md - Gallardo, Ó., Ovelleiro, D., Gay, M., Carrascal, M., & Abian, J. (2014). A collection of open source applications for mass spectrometry data mining. PROTEOMICS, 14(20), 2275–2279. https://doi.org/10.1002/pmic.201400124
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  • 2
    LymPHOS2

    LymPHOS2

    LymPHOS2 Web-App

    ...DOI: 10.1021/pr800500r - Ovelleiro, D., Carrascal, M., Casas, V., Abian, J., LymPHOS: design of a phosphosite database of primary human T cells. Proteomics 2009, 9, 3741–3751. DOI: 10.1002/pmic.200800701 - Gallardo, Ó., Ovelleiro, D., Gay, M., Carrascal, M., Abian, J., A collection of open source applications for mass spectrometry data mining. Proteomics 2014, 20, 2275-2279. DOI: 10.1002/pmic.20140012
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  • 3
    EasierMGF

    EasierMGF

    Converts RAW Thermo Files into MGF files

    ...For more information, you can have a look at the README.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/easiermgf3/code/ci/default/tree/README.md - Gallardo, Ó., Ovelleiro, D., Gay, M., Carrascal, M., & Abian, J. (2014). A collection of open source applications for mass spectrometry data mining. PROTEOMICS, 14(20), 2275–2279. https://doi.org/10.1002/pmic.201400124
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  • 4
    TCellXTalk

    TCellXTalk

    TCellXTalk Web-App from LP CSIC/UAB

    ...More detailed information on TCellXTalk and the people at the CSIC/UAB Proteomics Laboratory behind it can be obtained at https://www.tcellxtalk.org/about/ - Casanovas, A., Gallardo, Ó., Carrascal, M., Abian, J., TCellXTalk facilitates the detection of co-modified peptides for the study of protein post-translational modification cross-talk in T cells. Bioinformatics 2019, 35-8, 1404–1413, DOI 10.1093/bioinformatics/bty805 ( https://doi.org/10.1093/bioinformatics/bty805 )
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  • 5

    DualTranscriptDiscovery

    Transcript-discovery approach for gene feature delimitation by RNA-seq

    This project contains Python scripts usable for a dual transcript-discovery approach that improves the delimitation of gene features from RNA-seq data in the chicken model. Documentation: http://bio.biologists.org/content/biolopen/suppl/2018/01/17/bio.028498.DC1/BIO028498supp.pdf Citation: Orgeur M., Martens M., Börno S. T., Timmermann B., Duprez D. and Stricker S. (2018). A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model. Biology Open 7(1): bio028498. doi: 10.1242/bio.028498 PMID: 29183907. http://bio.biologists.org/content/7/1/bio028498 https://www.ncbi.nlm.nih.gov/pubmed/?...
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  • 6
    ChIP-RNA-seqPRO

    ChIP-RNA-seqPRO

    ChIP-RNA-sequencing-processing (ChIP-RNA-seqPRO)

    ChIP-RNA-seqPRO: A strategy for identifying regions of epigenetic deregulation associated with aberrant transcript splicing and RNA-editing sites. Runnable python scripts packaged together with customized annotation libraries, demo data input and README guide. 9/26 : v1.1 Updated MAIN_IV to debug error thrown by python pandas no longer supporting 'subset'. This code will no longer be actively maintained/updated here. A cloud-based resource for comparative analysis of epigenetic,...
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  • 7

    pymzML

    Python module to interface mzML data in Python

    Python module to interface mzML data in Python based on cElementTree with additional tools for MS-informatics. CITATION Please cite us when using pymzML for your publications. Bald, T., Barth, J., Niehues, A., Specht, M., Hippler, M., & Fufezan, C. (2012). pymzML - Python module for high throughput bioinformatics on mass spectrometry data. Bioinformatics, 1-2. doi: 10.1093/bioinformatics/bts066
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  • 8
    Code repository for the Laboratory for Genome Bioinformatics at Texas A&M. The LGB project was initiated primarily to support biologists at Texas A&M needing help with bioinformatics in order to use new genomic technologies.
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