Category/Biology/genetics

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genetics (12)



Aletheia
In short, Aletheia is software for getting science published and into the hands of everyone, for free. It's a decentralised and distributed database used as a publishing platform for scientific research. So, Aletheia is software. But software without people is nothing. To comprehensively answer the question what is Aletheia, Aletheia is software surrounded by a community of people who want to change the world through open access to scientific knowledge. For a more in depth explanation, Aletheia is an Ethereum Blockchain application utilising IPFS for decentralised storage that anyone can upload documents to, download documents from, that also handles the academic peer review process. The application runs on individual PCs, all forming part of the IPFS database. This gives us an open source platform that cannot be bought out by the large publishers (and any derivitive works must also be open source) that should also be hard to take down due to the database being spread across the globe in multiple legal jurisdictions. Aletheia is designed to be a resilient platform run transparently by the community, not some black box corporation or editorial board, meaning all users can see the decisions Aletheia is making and have a stake in that decision making process if they so desire. By this nature, Aletheia is decentralised, it has no key person risk. Should the core group who invented Aletheia dissapear Aletheia won't cease to exist, it will continue to be run by the community. The community moderates content through various mechanisms (peer review, reputation scores etc.,) to ensure quality of content.
Bamtools
BamTools provides both a programmer's API and an end-user's toolkit for handling genome alignment files in the BAM and SAM format. A BAM file (.bam) is the binary version of a SAM file. A SAM file (.sam) is a tab-delimited text file that contains sequence alignment data.
Bcbio-nextgen
A python toolkit providing best-practice pipelines for fully automated high throughput RNA sequencing analysis. It can work with BWA, .bam or .fastq files.
E-CELL Simulation Environment
E-Cell System is an object-oriented software suite for modelling, 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. The core library is written in C++ with a Python binding, and frontend software uses Python.
Edlib
Lightweight, super fast C/C++ library for sequence alignment using edit distance.
GAUL
The Genetic Algorithm Utility Library (GAUL) is a programming library designed to assist in the development of code requiring genetic algorithms. The steady-state, generation based and the island model of evolution are supported, using the Darwinian, Lamarkian or Baldwininan evolutionary schemes. Standard mutation, crossover and selection operators are provided, while code hooks additionally allow custom operators. It provides data structures and functions for handling and manipulation of the data required for a genetic algorithm. Additional stochastic algorithms are provided for comparison to the genetic algorithms. Much of the functionality is also available through a simple S-Lang interface.
GENtle
GENtle is bioinformatics software for everyday molecular biology tasks. It features DNA and amino acid sequence display and editing, database management, plasmid maps, restriction and ligation, alignments, sequencer data import, calculators, gel image display, primer design, virtual PCR, online database access, and more.
INFOTOPO Heckert gnu.small.png
Programs for Information Topology Data Analysis Information Topology is a program written in Python (compatible with Python 3.4.x), with a graphic interface built using TKinter [1], plots drawn using Matplotlib [2], calculations made using NumPy [3], and scaffold representations drawn using NetworkX [4]. It computes all the results on information presented in the study [5], that is all the usual information functions: entropy, joint entropy between k random variables (Hk), mutual informations between k random variables (Ik), conditional entropies and mutual informations and provides their cohomological (and homotopy) visualisation in the form of information landscapes and information paths together with an approximation of the minimum information energy complex [5]. It is applicable on any set of empirical data that is data with several trials-repetitions-essays (parameter m), and also allows to compute the undersampling regime, the degree k above which the sample size m is to small to provide good estimations of the information functions [5]. The computational exploration is restricted to the simplicial sublattice of random variable (all the subsets of k=n random variables) and has hence a complexity in O(2^n). In this simplicial setting we can exhaustively estimate information functions on the simplicial information structure, that is joint-entropy Hk and mutual-informations Ik at all degrees k=<n and for every k-tuple, with a standard commercial personal computer (a laptop with processor Intel Core i7-4910MQ CPU @ 2.90GHz * 8) up to k=n=21 in reasonable time (about 3 hours). Using the expression of joint-entropy and the probability obtained using equation and marginalization [5], it is possible to compute the joint-entropy and marginal entropy of all the variables. The alternated expression of n-mutual information given by equation then allows a direct evaluation of all of these quantities. The definitions, formulas and theorems are sufficient to obtain the algorithm [5]. We will further develop a refined interface (help welcome) but for the moment it works like this, and requires minimum Python use knowledge. Please contact pierre.baudot [at] gmail.com for questions, request, developments (etc.): [1] J.W. Shipman. Tkinter reference: a gui for python. . New Mexico Tech Computer Center, Socorro, New Mexico, 2010. [2] J.D. Hunter. Matplotlib: a 2d graphics environment. Comput. Sci. Eng., 9:22–30, 2007. [3] S. Van Der Walt, C. Colbert, and G. Varoquaux. The numpy array: a structure for efficient numerical computation. Comput. Sci. Eng., 13:22– 30, 2011. [4] A.A. Hagberg, D.A. Schult, and P.J. Swart. Exploring network structure, dynamics, and function using networkx. Proceedings of the 7th Python in Science Conference (SciPy2008). Gel Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pages 11–15, 2008. [5] M. Tapia, P. Baudot, M. Dufour, C. Formisano-Tréziny, S. Temporal, M. Lasserre, J. Gabert, K. Kobayashi, JM. Goaillard . Information topology of gene expression profile in dopaminergic neurons doi: https://doi.org/10.1101/168740 http://www.biorxiv.org/content/early/2017/07/26/168740
OASIS Heckert gnu.small.png
OASIS is a novel linkage disequilibrium clustering algorithm that can potentially address false positives and negatives in genome-wide association studies (GWAS) in complex disorders. OASIS is a unique method that can be widely applied for mining existing GWAS datasets to identify new genes and loci in an efficient, low cost way.
Open BEAGLE
Open BEAGLE is an evolutionary computation framework. It provides an Object-Oriented software environment enabling the implementation of almost any kind of evolutionary algorithm, such as genetic algorithms and genetic programming. The only necessary condition is to have a population of individuals to which a sequence of evolving operations is iteratively applied. So far, two specialized frameworks have been implemented: genetic algorithms and genetic programming. An evolution strategies framework is also planned for a future release.

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