Open Source Python Bio-Informatics Software - Page 9

Python Bio-Informatics Software

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Browse free open source Python Bio-Informatics Software and projects below. Use the toggles on the left to filter open source Python Bio-Informatics Software by OS, license, language, programming language, and project status.

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  • 1
    LGTmate

    LGTmate

    Lateral Gene Transfer utility

    LGTmate is a simple and powerful bioinformatic software tool to identify lateral gene transfer events and contaminants in Eukaryotic proteomes. It is available as a GUI or command-line application, with no dependencies and no installation required.
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  • 2
    LP CSIC/UAB Apps and Code

    LP CSIC/UAB Apps and Code

    Software and Code from Laboratori de Proteòmica CSIC/UAB

    Software, Code and Documents from Laboratori de Proteòmica CSIC/UAB ( LP-CSIC/UAB: http://proteomica.uab.cat )
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  • 3
    An input GUI for parameterizing simulation with the LSD Simulator
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  • 4
    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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  • 5
    LaiaMotifs

    LaiaMotifs

    Search for MHC-like motifs in peptide collections

    Search for MHC-like motifs in peptide collections (Python 3 version). 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/laiamotifs3/code/ci/default/tree/README.md
    Downloads: 0 This Week
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  • 6
    This is a c-library that provides tools for advanced analysis of electrophysiological data. It features denoising, unsupervised classification, time-frequency analysis, phase-space analysis, neural networks, time-warping and more.
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  • 7
    This project houses software to analyze data acquired from electrophysiology experiments. Currently, we have an Octave/MATLAB program to analyze electroneurogram traces of coupled oscillators, and a Perl library for the analysis of voltage trace data
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  • 8
    A GUI-based search tool that allows the exploration of the relationships between Metabolites.
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  • 9

    LipidWrapper

    Create lipid-bilayer models of arbitrary geometry.

    LATEST VERSION NOW LOCATED AT http://git.durrantlab.com/jdurrant/lipidwrapper As ever larger and more complex biological systems are modeled in silico, approximating physiological lipid bilayers with simple planar models becomes increasingly unrealistic. When building large-scale models of whole subcellular environments, models of lipid membranes with carefully considered, biologically relevant curvature are essential. LipidWrapper, written by Jacob Durrant in the lab of Rommie E. Amaro, is capable of creating curved membrane models with geometries derived from various possible sources, both experimental and theoretical. We are hopeful that this utility will be a useful tool for the computational-biology community.
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  • 10
    LipidXplorer
    LipidXplorer is an uniqe software to identify lipids from high to low resolution mass spectra.
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  • 11
    LymPHOS2

    LymPHOS2

    LymPHOS2 Web-App

    LymPHOS2 is a web-based Application at www.LymPHOS.org containing peptidic and protein sequences and spectrometric information on the PhosphoProteome of human T-Lymphocytes. - Nguyen, TD., Vidal-Cortes, O., Gallardo, Ó., Abian, J., Carrascal, M., LymPHOS 2.0: an update of a phosphosite database of primary human T cells. Database 2015, 2015. DOI: 10.1093/database/bav115 - Carrascal, M., Ovelleiro, D., Casas, V., Gay, M., Abian, J., Phosphorylation analysis of primary human T lymphocytes using sequential IMAC and titanium oxide enrichment. J. Proteome Res. 2008, 7, 5167-5176. 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
    Downloads: 0 This Week
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  • 12
    MAXIMUS is a genome assembly pipeline which takes the best out of multiple reference assemblies and de novo assembly. The benefits of this approach include better assembled repetitive regions, less gaps and higher accuracy for the resultant assembly.
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  • 13
    This program has been written having in mind to create an useful tool to evaluate the Motor Evoked Potentials (MEPs) generated by Transcranial Magnetic Stimulation (TMS) and recorded with the program "Signal" (version 2.xx).
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  • 14
    MGFVisor

    MGFVisor

    Visor for mass spectrometry MGF files

    Visor for mass spectrometry MGF files (Python 3 version). 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/mgfvisor3/code/ci/default/tree/README.md
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  • 15
    MIVF - Medical Imaging and Visualization Factory, is a framework for medical applications. It supplies a platform, in which image processing and 3D visualization algorithms can be employed as reusable components (functional modules or plugins).
    Downloads: 0 This Week
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  • 16
    Artificial plants laboratory.
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  • 17
    Magnolya

    Magnolya

    De novo CNV detection by co-assembly

    Magnolya enables copy number variation (CNV) detections without using a reference genome. Magnolya directly compares two next-generation sequencing datasets.
    Downloads: 0 This Week
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  • 18

    MaryGold

    Variation analysis of metagenomic samples

    The package enables detection of sequence variation between metagenomic samples.
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  • 19
    Maximal Clique Motif Reduction (MCMR) is a software program for running and then combining the output of multiple motif finder programs, such as MEME, AlignACE and Weeder, into a set of consensus predictions with associated confidence rankings.
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  • 20
    Maximum Common Genome Alignment (MCGA)

    Maximum Common Genome Alignment (MCGA)

    Pipeline for creating core genome alignments for phylogenetic analysis

    Maximum Common Genome Alignment (MCGA) Tool MCGA is a bioinformatics analysis tool written in Python for generating core genome alignment for bacterial whole genome sequences which can be used to construct phylogenetic trees.
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  • 21
    MePrId is a Python program which can be used to classify a protein, depending whether it is a membrane or a globular protein. Download link: http://meprid.svn.sourceforge.net/viewvc/meprid.tar.gz?view=tar
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  • 22
    The MeVisLab Community project provides add-on modules that extend the functionality of the MeVisLab software. The modules are provided and maintained by the MeVisLab developer community and can be compiled with the public MeVisLab SDK.
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  • 23

    MethyMer

    Design of specific primer combinations for bisulfite sequencing

    MethyMer is a Python-based tool aimed at selecting specific primers for amplification of complete CpG islands. These regions are difficult in terms of selection appropriate primers because of their low-complexity, polyN-, CG-richness, etc. MethyMer have a flexible scoring system capable of selecting primers in problematic regions (e.g. SpG islands) and includes specificity test (based on bowtie alignment against bisulfite-treated genome). It also incorporates TCGA CpG methylation (microarrays) and gene expression (RNA-Seq) data, as well as methylation-expression correlation analysis results for 20 human cancer types. ENCODE genome regions annotation data are also integrated in MethyMer
    Downloads: 0 This Week
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  • 24

    Microarray Integrator (MAI)

    Microarray Integrator for heterogeneous human/mouse Affymetrix arrays

    The Microarray Integrator is freely available programs to integrate heterogeneous Affymetrix microarrays for human and mouse. The cell-type-specific genes or condition-specific genes can be screened in the integrated microarrays by MAI result.
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  • 25

    Microarray assosiated motif analyzer

    Cis-element prediction tool from microarray data

    We developed a novel clustering-free method, microarray-associated motif analyzer (MAMA), to predict novel cis-acting elements based on weighted sequence similarities and gene expression profiles in microarray analyses. Simulation of gene expression was performed using a support vector machine and based on the presence of predicted motifs and motif pairs. The accuracy of simulated gene expression was used to evaluate the quality of prediction and to optimize the parameters used in this method. After optimization, MAMA accurately simulated more than 87% of gene expression. See Kakei Y, Ogo Y, Itai RN, et al. (2013) Development of a novel prediction method of cis-elements to hypothesize collaborative functions of cis-element pairs in iron-deficient. Rice 6(1): 22.
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