Open Source Python Bio-Informatics Software - Page 7

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/?term=29183907

Ducking is a software who is providing simulation of probable docking between two proteins using rigid body monte carlo method. It is written in python and uses the libraries wxPython, VTK, SciPy and BioPython.

A Python library bringing together utilities I've written over the years for work in bioinformatics and biostatistics, which should be generally applicable outside these fields as well. Focuses on string processing, DBI, and math.

""" E-Cell's source code repository has moved to GitHub! https://github.com/ecell The development of E-Cell now continues on GitHub. The Subversion repository was abandoned. """ E-Cell System is an object-oriented software suite for modeling, simulation, and analysis of large scale complex systems such as biological cells. It allows many components driven by multiple algorithms with different timescales to coexist in a model.

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.

EUtils is a python client-side library for querying the NCBI's Entrez life science databases through the NCBI eUtils web services.

Converts RAW Thermo Files into 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/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

This script is supplementary to the article "Time series dependent analysis of unparametrized Thomas networks" and performs the workflow steps described in section 7.

During the past few years, whole exome sequencing has imposed itself for genetic research, largely due to its use for detection of causative mutations responsible for Mendelian disorders. As a consequence of their power and of the rapidly decreasing cost of these technologies, massive amount of exome sequencing data are generated and becoming available to a broadening community of scientists. However, these data remain difficult to analyze and interpret by the general scientific community, due to the limited bioinformatics resources that are currently freely available for their analysis and the partial information that they provide. Here, we present an interactive and free analysis package, ExAM, dedicated to whole exome sequencing data analysis. It is accessible to non-computer scientists and provides a complete set of information, as well as decision support to prioritize candidate disease causing variants.

This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ExSTraCS was primarily developed to address problems in epidemiological data mining to identify complex patterns relating predictive attributes in noisy datasets to disease phenotypes of interest. ExSTraCS combines a number of recent advancements into a single algorithmic platform. It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7.

FONZIE is a bioinformatic tool written in Python developed for the genetic cartography speciality. FONZIE allow as well to find markers on a set of sequences than to find associate oligonucleotides. http://www.biomedcentral.com/1756-0500/3/322/abstra

FaceIn! is a face recognizing log in application for Linux. You will never have to enter your password anymore! Just show your face to your camera and let the system recognize you and take you to your desktop!

Tools for comparative sequence analysis. Includes FamilyRelations (GUI) and a C++ toolkit/Python wrapper for doing fixed-width-window analyses.

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).

These scripts written in Python allow you to convert fasta files into multifasta file and vice versa.

Fastq-Fasta Converter is a desktop application that converts Fastq files into Fasta files.

A database and a web front end for physiologic data on animal feeding, developed with PostgreSQL and Django at NESCent (http://www.nescent.org) for Mammalian Feeding Working Group (http://www.feedexp.org). Current development of the codebase is on Github at http://github.com/NESCent/feedingdb.

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

[NOTICE!] FingerID since version 1.4 will be hosted on github: https://github.com/icdishb/fingerid A metabolite identification software using tandem mass spectrometry and kernel methods. The related paper can be found at http://bioinformatics.oxfordjournals.org/content/early/2012/07/18/bioinformatics.bts437.abstract. Now it supports unix/linux like system.

FlexGrePPS forms the foundation of a novel antigenic screening methodology that is based on the representation of an entire proteome by near-optimal degenerate peptide pools.

Flow Investigation using N-Dimensions (FIND) is a program designed for analysis and visualization of Flow Cytometry data. FIND focuses specifically on automated population discovery (clustering) methods. The project targets both users and developers.

FreePM is an opensource physicians practice management / electronic medical record application. The company providing support for FreePM nolonger exists. You may consider TORCH http://sourceforge.net/projects/op-torch/ or one of the other FOSS EMR's.

Frowns is a chemoinformatics system written almost entirely in python.

One goal of sequencing based metagenomic analysis is the quantitative taxonomic assessment of microbial community compositions. However, the majority of approaches either quantify at low resolution (e.g. at phylum level) or have severe problems discerning highly similar species. Yet, accurate quantification on species level is desirable in applications such as metagenomic diagnostics or community comparison. GASiC is a method to correct read alignment results for the ambiguities imposed by similarities of genomes. It has superior performance over existing methods. You can find the accompanying paper here: http://nar.oxfordjournals.org/content/41/1/e10.short Thanks to the great work of the SeqAn team, you can now use GASiC as a Knime workflow: https://github.com/seqan/knime_seqan_workflows/tree/master/metagenomics_gasic_workflow