Displaying 1 to 20 from 20 results

deepvariant - DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data

  •    Python

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.DeepVariant is a suite of Python/C++ programs that run on any Unix-like operating system. For convenience the documentation refers to building and running DeepVariant on Google Cloud Platform, but the tools themselves can be built and run on any standard Linux computer, including on-premise machines. Note that DeepVariant currently requires Python 2.7 and does not yet work with Python 3.

IAMDinosaur - 🦄 An Artificial Inteligence to teach Google's Dinosaur to jump cactus

  •    Javascript

A simple artificial intelligence to teach Google Chrome's offline dinosaur to jump cactus, using Neural Networks and a simple Genetic Algorithm.Install Node.js on your computer.

AI-Programmer - Using artificial intelligence and genetic algorithms to automatically write programs

  •    CSharp

Read the research paper BF-Programmer: A Counterintuitive Approach to Autonomously Building Simplistic Programs Using Genetic Algorithms. AI-Programmer is an experiment with using artificial intelligence and genetic algorithms to automatically generate programs. Successfully created programs by the AI include: hello world, hello , addition, subtraction, reversing a string, fibonnaci sequence, 99 bottles of beer on the wall, and more. It's getting smarter. In short, it's an AI genetic algorithm implementation with self modifying code.

gago - An extensible toolkit for conceiving and running genetic algorithms

  •    Go

There is a lot of intellectual fog around the concept of genetic algorithms (GAs). It's important to appreciate the fact that GAs are composed of many nuts and bolts. There isn't a single definition of genetic algorithms. gago is intended to be a toolkit where one may run many kinds of genetic algorithms, with different evolution models and various genetic operators.

gatk - Official code repository for GATK versions 4 and up

  •    Java

Please see the GATK website, where you can download a precompiled executable, read documentation, ask questions, and receive technical support. This repository contains the next generation of the Genome Analysis Toolkit (GATK). The contents of this repository are 100% open source and released under the BSD 3-Clause license (see LICENSE.TXT).

biomartr - Genomic Data Retrieval with R

  •    R

This package is born out of my own frustration to automate the genomic data retrieval process to create computationally reproducible scripts for large-scale genomics studies. Since I couldn't find easy-to-use and fully reproducible software libraries that would allow others and me to write transparent and easy to reproduce code, I sat down and tried to implement a framework that would enable anyone to automate the genomic data retrieval process. Personally, I strongly support and believe in reproducible research, and I truly hope that this package might be useful to others as well and that it helps to promote reproducible research in genomics studies.I happily welcome anyone who wishes to contribute to this project :) Just drop me an email.

genomepy - Download genomes the easy way.

  •    Python

The goal is to have a simple and straightforward way to download and use genomic sequences. Currently, genomepy supports UCSC, Ensembl and NCBI. You can find the binaries here.

companion - Pipeline for automatic eukaryotic parasite annotation

  •    Lua

A portable, scalable eukaryotic genome annotation pipeline implemented in Nextflow. It supports parallelized execution on a single machine as well as on large cluster platforms (LSF, SGE, ...).

viral-ngs - Viral genomics analysis pipelines

  •    Python

A set of scripts and tools for the analysis of viral NGS data. More detailed documentation can be found at http://viral-ngs.readthedocs.org/ This includes installation instructions, usage instructions for the command line tools, and usage of the pipeline infrastructure.

arv - A fast 23andMe DNA parser and inferrer for Python

  •    C++

Arv (Norwegian; "heritage" or "inheritance") is a Python module for parsing raw 23andMe genome files. It lets you lookup SNPs from RSIDs. See below for software requirements.

karyoploteR - karyoploteR - An R/Bioconductor package to plot arbitrary data along the genome

  •    R

karyoploteR is an R package to plot data along the genome using a karyotype style plot. It is entirely based on R base graphics and inspired by the R base graphics API. It includes functions to plot primitive graphic elements such as points, lines, rectangles, text, etc mapped into the genome plot coordinatesand and higher level functions to plot a heatmap, the regions in a GenomicRanges object or the cumulative coverage of such regions.

OSGenome - An Open Source Library and ToolKit of Genetic Data (SNPs) using 23AndMe and Data Crawling Technologies

  •    Python

OS Genome is an open source web application that allows users to gather the information they need to make sense of their own genome without needing to rely on outside services with unknown privacy policies. OS Genome's goal is to crawl various sources and give meaning to an individual's genome. It creates a Responsive Grid of the user's specific genome. This allows for everything from filtering to excel exporting. All of which using Flask, Kendo, and Python programming. SNP, pronounced “snip,” stands for single-nucleotide polymorphism, which represents a substitution of one base for another, e.g., C to T or A to G. SNP is the most common variation in the human genome and occurs approximately once every 100 to 300 bases. SNP is terminologically distinguished from mutation based on an arbitrary population frequency cutoff value: 1%, with SNP [greater than] 1% and mutation [less than] 1%. A key aspect of research in genetics is associating sequence variations with heritable phenotypes. Because SNPs are expected to facilitate large-scale association genetics studies, there has been an increasing interest in SNP discovery and detection.

catch - A package for designing compact and comprehensive probe sets.

  •    Python

CATCH is a Python package for designing probe sets to use in hybrid capture experiments. Installing CATCH with pip, as described below, will install NumPy and SciPy if they are not already installed.

abyss - :microscope: Assemble large genomes using short reads

  •    C++

ABySS is a de novo sequence assembler intended for short paired-end reads and large genomes. Please cite our papers.

arcs - Scaffold genome sequence assemblies using 10x Genomics data

  •    C++

Scaffolding genome sequence assemblies using 10X Genomics GemCode/Chromium data. The Makefile located here: Examples/arcs-make will run the full ARCS pipeline. It will also optionally run the misassembly corrector Tigmint prior to scaffolding with ARCS.

mosdepth - fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing

  •    Nim

fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing. when appropriate, the output files are bgzipped and indexed for ease of use.

node-23andme-export - 👉 export your data from the 23andMe API for safe-keeping

  •    Javascript

A simple Express app to facilitate downloading of 23andMe data in CSV and JSON. Also provides a zip file generated in client-side JavaScript. Supports multiple 23andMe profiles per account. Licensed under the MIT license.

Anaquin - Statistical analysis of sequins in C++ and R

  •    C++

Anaquin is a C++/R bioinformatics framework for quantitative controls in next-generation sequencing experiments. Respository for the R-package is hosted by Bioconductor and available at: https://github.com/Bioconductor-mirror/Anaquin.

pileup.js - Interactive in-browser track viewer

  •    Javascript

It is built from the ground up to take advantage of the modern JavaScript ecosystem, e.g. ES2015, static type analysis, React.js and Promises. Read more about the motivations behind pileup.js in our paper. And then source either node_modules/pileup/dist/pileup.min.js or pileup.js.

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