Open Source Python Bio-Informatics Software - Page 3
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Python Bio-Informatics Software
View 69 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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Copy Number Analysis for Targeted Resequencing (CONTRA) is a tool for copy number variation (CNV) detection for targeted resequencing data such as those from whole-exome capture data.
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CoNIFER
Homepage for CoNIFER (Copy Number Inference From Exome Reads)
CoNIFER uses exome sequencing data to find copy number variants (CNVs) and genotype the copy-number of duplicated genes. -
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FineSplice
Enhanced splice junction detection and estimation from RNA-Seq data
FineSplice is a Python wrapper to TopHat2 geared towards a reliable identification of expressed exon junctions from RNA-Seq data, at enhanced detection precision with small loss in sensitivity. Following alignment with TopHat2 using known transcript annotations, FineSplice takes as input the resulting BAM file and outputs a confident set of expressed splice junctions with the corresponding read counts. Potential false positives arising from spurious alignments are filtered out via a semi-supervised anomaly detection strategy based on logistic regression. Multiple mapping reads with a unique location after filtering are rescued and reallocated to the most reliable candidate location. FineSplice requires Python 2.x (>= 2.6) with the following modules installed: pysam (http://code.google.com/p/pysam/) and scikit-learn (http://scikit-learn.org/). For further details check out our publication: Nucl. Acids Res. (2014) doi: 10.1093/nar/gku166 -
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MicrobeGPS
The Explorative Taxonomic Profiling Tool for Metagenomic Data
MicrobeGPS is a bioinformatics tool for the analysis of metagenomic sequencing data. The goal is to profile the composition of metagenomic communities as accurately as possible and present the results to the user in a convenient manner. One main focus is reliability: the tool calculates quality metrics for the estimated candidates and allows the user to identify false candidates easily. -
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reditools
RNA editing detection by NGS data
REDItools are python scripts developed with the aim to study RNA editing at genomic scale by next generation sequencing data. RNA editing is a post-transcriptional phenomenon involving the insertion/deletion or substitution of specific bases in precise RNA localizations. In human, RNA editing occurs by deamination of cytosine to uridine (C-to-U) or mostly by the adenosine to inosine (A-to-I) conversion through ADAR enzymes. A-to-I substitutions may have profound functional consequences and have been linked to a variety of human diseases including neurological and neurodegenerative disorders or cancer. NGS technologies offer a unique opportunity to investigate RNA editing. REDItools are simple python scripts conceived to investigate RNA editing at large-scale. REDItools are now hosted at https://github.com/BioinfoUNIBA/REDItools -
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AMBIENT
Find active modules in metabolic networks using high-throughput data
IMPORTANT: Since publication of the AMBIENT method in BMC Sys Bio, several updates have been made. If you wish to use the version used in the paper it is v0.6.3, however I recommend using the latest version which works in the same way but with additional options and has stability and performance improvements. Thanks for your interest! AMBIENT (Active Modules for Bipartite Networks) is a Python module that uses simulated annealing to find areas of a metabolic network (modules) that have some consistent characteristic. AMBIENT does not require predefined pathways and gives highly specific predictions of affected areas of metabolism. For example, scores for reactions based on transcriptional data of their annotated encoding genes can be used in the network and modules of coordinated expression changes can be found. This provides an alternative to pathway/gene set enrichment analyses which is simultaneously more flexible and more specific. -
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DEBay
Deconvolutes qPCR data to estimate cell-type-specific gene expression
DEBay: Deconvolution of Ensemble through Bayes-approach DEBay estimates cell type-specific gene expression by deconvolution of quantitative PCR data of a mixed population. It will be useful in experiments where the segregation of different cell types in a sample is arduous, but the proportion of different cell types in the sample can be measured. DEBay uses the population distribution data and the qPCR data to calculate the relative expression of the target gene in different cell types in the sample. The user manual of DEBay: https://sourceforge.net/projects/debay/files/UserManual.pdf Sample data: https://sourceforge.net/projects/debay/files/Test_data/ Citation Information: Vimalathithan Devaraj, Biplab Bose. DEBay: A computational tool for deconvolution of quantitative PCR data for estimation of cell type-specific gene expression in a mixed population. Heliyon, 2020, 6(7), e04489. https://doi.org/10.1016/j.heliyon.2020.e04489 -
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PyMod
(OBSOLETE) PyMOL Plugin for Homology Modeling of Protein Structures
This is the UNOFFICIAL mirror to PyMod. Since the release of PyMod2.0, this repository will no longer be maintained. -
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openModeller is a complete C++ framework for species potential distribution modelling. The project also includes a graphical user interface, a web service interface and an API for Python.
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This is a sophisticated & integrated simulation and analysis environment for dynamical systems models of physical systems (ODEs, DAEs, maps, and hybrid systems). It supports symbolic math, optimization, continuation, data analysis, biological apps...
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Biskit is a python library for structural bioinformatics research. It simplifies the analysis of macromolecular structures, protein complexes, and molecular dynamics trajectories and offers a platform for the rapid integration of external programs. PLEASE NOTE: The Biskit source code as well as any later releases are now hosted on https://github.com/graik/biskit The sourceforge repo is only kept here for reference.
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PBSuite
Software for Long-Read Sequencing Data from PacBio
This currently hosts two projects created and maintained by Adam English. PBJelly - the genome upgrading tool. PBHoney - the structural variation discovery tool Both are contained within the PBSuite code found in downloads. ----- PBJelly ----- Read The Paper http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0047768 PBJelly is a highly automated pipeline that aligns long sequencing reads (such as PacBio RS reads or long 454 reads in fasta format) to high-confidence draft assembles. PBJelly fills or reduces as many captured gaps as possible to produce upgraded draft genomes. ----- PBHoney ----- Read The Paper http://www.biomedcentral.com/1471-2105/15/180/abstract PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants. -
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ADTEx
Aberration detection in tumour exome
Aberration Detection in Tumour Exome (ADTEx) is a tool for copy number variation (CNV) detection for whole-exome data from paired tumour/matched normal samples. -
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NetBuilder' is a tool for building, visualizing, and simulating multi-layered biological networks, structured as hierarchical Petri-nets. It has a built-in stochastic simulator, but also provides easy access to other simulation and analysis.
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The RDXplorer is a computational tool for copy number variants (CNV) detection in whole human genome sequence data using read depth (RD) coverage. CNV detection is based on the Event-Wise Testing (EWT) algorithm recently published by our group.
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The igraph library
Library for creating and manipulating graphs
This is a library for creating and manipulating graphs with focus on speedy operations for large, sparse graphs. -
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CodonU
A python project for analysis of codon usage for gene or genome analys
With CodonU, you can now easily read, manipulate, and analyze various file formats used in this kind of analysis, and generate high-quality graphics suitable for publishing. This open-source project incorporates various statistical measures necessary for codon usage analysis, including codon adaptation index, codon bias index, Gravy score, and correspondence analysis for both nucleotide and protein sequences. Start using CodonU today and see how it can revolutionize your genomics research! Find more at: https://github.com/SouradiptoC/CodonU Documentation link: https://souradiptoc.github.io/CodonU Example Link: https://github.com/SouradiptoC/CodonU/tree/master/Examples -
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DANGLE: A Bayesian inferential prediction method for protein backbone dihedral angles and secondary structure assignments, solely from sequence information, experimental chemical shifts and a database of known protein structures and their shifts.
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SAT-Assembler
Scalable and accurate targeted gene assembly for large-scale NGS data
SAT-Assembler is a scalable and accurate gene assembly tool for large-scale RNA-Seq and metagenomic data. It recovers genes from gene families of particular interest to biologists with high coverage, low chimera rate, and extremely low memory usage compared with exiting gene assembly tools. Moreover, it is naturally compatible with parallel computing platforms. -
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StochPy - Stochastistic modeling in Python - is an easy-to-use package, which provides several stochastic simulation algorithms and unique analysis techniques, which can be used for stochastic simulations of biochemical systems.
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CONTIGuator is a Python script for Linux environments whose purpose is to speed-up the bacterial genome assembly process and to obtain a first insight of the genome structure using the well-known artemis comparison tool (ACT).
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This is a Python-based efficient implementation of several semantic similarity measures. The target is to enable fast and easy calculation of similarity between proteins and genes using the Gene Ontology (GO).
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"Blue Planet" is a research project simulating the behaviour and darwinian evolution of unicellular lifeforms, each controlled by its own genetic program. Moreover, "Blue Planet Inhabitants" are suited for swarm intelligence and swarm research.
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The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.
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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
