Open Source Python Bio-Informatics Software
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Python Bio-Informatics Software
View 59 business solutionsBrowse 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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Free DELTA
Software tools for processing taxonomic descriptions in DELTA format
The Free DELTA Project is a free, open-source, software alternative for a generic system to the processing of taxonomic descriptions based on the DELTA (DEscription Language for TAxonomy) format. -
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The Sashimi project hosts the Trans-Proteomic Pipeline (TPP), a mature suite of tools for mass-spec (MS, MS/MS) based proteomics: statistical validation, quantitation, visualization, and converters from raw MS data to the open mzML/mzXML formats.
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A set of very optimized tools for indexing/querying huge genomes/files. Provided so far: a very fast exact mapper, and an unconstrained split-mapper
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BioC
We describe a simple XML format to share text documents and annotation
A minimalist approach to share text documents and data annotations. Allows a large number of different annotations to be represented. Project files contain: - simple code to hold/read/write data and perform sample processing. - BioC-formatted corpora - BioC tools that work with BioC corpora BioC goals - simplicity - interoperability - broad use - reuse There should be little investment required to learn to use a format or a software module to process that format. We are interested in reuse, and we focus on common NLP tasks that are broadly useful for textmining. -
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iMir
Integrated pipeline for HT miRNA-Seq data analysis
Processing of smallRNA-Seq data to gather biologically relevant information requires application of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. The analytical workflow can be challenging for the continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once. To allow a flexible and comprehensive analysis of smallRNA-Seq data we designed a novel modular pipeline, called iMir, integrating multiple open source modules and resource in an automated workflow, devising different statistical approaches to analyze data rigorously. iMir comprises also a Graphical User Interface (GUI), so that the pipeline is particularly suited for biologist and early stage bioinformaticians and produces both graphics and text outputs. -
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A collection of bioinformatics-related software created by members of Cancer Research UK (possibly among others).
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A web-based system for upoading, managing, sharing, and analyzing bioinformatic data. The system is primarily written in python, and runs on both linux and MacOs.
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DIY Genomics is an open source bioinformatics consortium intended to bring a collection of tools and libraries into the hands of small scale genomics labs for the process of sequence assembly and annotation. Projects include DIYA, MGAP, CRISPR, and DIYGV
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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EASER
Ensembl Easy Sequence Retriever
Maldonado E, Khan I, Philip S, Vasconcelos V, Antunes A (2013) EASER: Ensembl Easy Sequence Retriever. Evolutionary Bioinformatics, 9:487-490. doi: https://doi.org/10.4137/EBO.S11335. -
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ENLY
Bioinformatic tool to improve draft genome assemblies by closing gaps
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Genetic programming environment to inference context free grammars base on positive and negative examples of language.
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IMPACT is an integrated multi-read peak calling tool for ChIP-Seq data. IMPACT utilizes multi-reads in calling peaks and provides users with high-confidence peaks. In addition, IMPACT provides a completely integrated pipeline which produces downstream analysis results such as motif discovery and peak-to-gene annotation.
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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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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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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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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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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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MutationFinder is a biomedical natural language processing (NLP) system for extracting mentions of point mutations from free text. MutationFinder achieves high performance (99% precision, 81% recall on blind test data) as an information extraction system
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Simulator of virtual animals made up of biological neural networks for research in the Computational Neuroscience field.
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PhyloTrack
PhyloTrack, D3.js and JBrowse for phylogeny and positioning of samples
PhyloTrack is a JavaScript--based software tool that integrates the D3.js library for data visualization with the JBrowse tool for genome browser representation. It requires a phylogenetic tree of the common Newick data format as input, as well as three meta data files for samples, clade-defining nodes and clade color definitions - all in tab delimited format. Functionality within PhyloTrack shows the informative markers at each node in the phylogenetic tree, therefore highlighting clade-defining polymorphism. This functionality has been implemented using the tabix tool on the server side, providing simple and rapid access to the information at each tree node, including informative SNPs stored in VCF-similar files. These informative variants have been established by comparing allele frequencies between strain-types using ancestral node comparisons and FST measures of population differentiation. -
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ReferenceFree
Scripts for reference free genomic analysis
These are Python scripts plus C/C++ programs for automating the reference free genomic analysis described in: Kua C-S, Ruan J, Harting J, Ye C-X, Helmus MR, et al. (2012) Reference-Free Comparative Genomics of 174 Chloroplasts. PLoS One 7(11). http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0048995 Analytical concept conceived by CHCannon and CSKua. Original script written in Mathematica by CHCannon. -
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Synthetic Biology Software Suite (SynBioSS) is composed of a wiki for biochemical kinetic constants, a graphical interface for simulating synthetic biological pathways, and a simulator that runs on either a desktop or supercomputer.
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hrefinder
Detection of homologous recombination events from SNP data
This software detects homologous recombination events (HREs) from SNP data. Based on SNP alleles calls and locations, it breaks the genomes into locally colinear blocks, and looks for cases where SNPs do not agree with the vertical pattern of inheritance in a phylogeny. It applies a dynamic programming algorithm to model whether changes within a block are likely a result of mutations, sequencing errors, or HRE. We use information from the nearby SNPs, so that if 1-2 alleles in a series of SNPs differs from the ancestral allele, it may be better explained as a mutation or sequencing error. But if a series of SNP alleles differ from the ancestral pattern, then it may be more likely that an HRE has occurred, particularly if the allele pattern matches that from another part of the tree better than that of the ancestral node. -
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