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https://github.com/mikeroyal/Bioinformatics-Guide

Bioinformatics Guide
https://github.com/mikeroyal/Bioinformatics-Guide

awesome awesome-list bioconductor bioinformatics bioinformatics-algorithms bioinformatics-analysis bioinformatics-pipeline bioinformatics-tool biology data-science python resources

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Bioinformatics Guide

Host: GitHub
URL: https://github.com/mikeroyal/Bioinformatics-Guide
Owner: mikeroyal
Created: 2021-07-18T19:51:27.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2021-07-25T20:26:51.000Z (over 3 years ago)
Last Synced: 2024-05-23T09:20:34.850Z (7 months ago)
Topics: awesome, awesome-list, bioconductor, bioinformatics, bioinformatics-algorithms, bioinformatics-analysis, bioinformatics-pipeline, bioinformatics-tool, biology, data-science, python, resources
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ultimate-awesome - Bioinformatics-Guide - Bioinformatics Guide. (Other Lists / Monkey C Lists)

README

Bioinformatics Guide

#### A guide covering the Bioinformatics including the applications, libraries and tools that will make you a better and more efficient with Bioinformatics development.

**Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).**

# Table of Contents

1. [Bioinformatics Learning Resources](https://github.com/mikeroyal/Bioinformatics-Guide#bioinformatics-learning-resources)

2. [Bioinformatics Tools, Libraries, and Frameworks](https://github.com/mikeroyal/Bioinformatics-Guide#bioinformatics-tools-libraries-and-frameworks)

3. [Machine Learning](https://github.com/mikeroyal/Bioinformatics-Guide#machine-learning)

4. [Algorithms](https://github.com/mikeroyal/Bioinformatics-Guide#algorithms)

5. [MATLAB Development](https://github.com/mikeroyal/Bioinformatics-Guide#matlab-development)

6. [R Development](https://github.com/mikeroyal/Bioinformatics-Guide#r-development)

7. [Python Development](https://github.com/mikeroyal/Bioinformatics-Guide#python-development)

8. [Ruby Development](https://github.com/mikeroyal/Bioinformatics-Guide#ruby-development)

9. [Java Development](https://github.com/mikeroyal/Bioinformatics-Guide#java-development)

10. [PHP Development](https://github.com/mikeroyal/Bioinformatics-Guide#php-development)

# Bioinformatics Learning Resources
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

[Bioinformatics](https://www.genome.gov/genetics-glossary/Bioinformatics) is a field of computational science that has to do with the analysis of sequences of biological molecules. This usually refers to genes, DNA, RNA, or protein, and is particularly useful in comparing genes and other sequences in proteins and other sequences within an organism or between organisms, looking at evolutionary relationships between organisms, and using the patterns that exist across DNA and protein sequences to figure out what their function is.

[European Bioinformatics Institute](https://www.ebi.ac.uk/)

[National Center for Biotechnology Information](https://www.ncbi.nlm.nih.gov)

[Online Courses in Bioinformatics |ISCB - International Society for Computational Biology](https://www.iscb.org/cms_addon/online_courses/index.php)

[Bioinformatics | Coursera](https://www.coursera.org/specializations/bioinformatics)

[Top Bioinformatics Courses | Udemy](https://www.udemy.com/topic/Bioinformatics/)

[Biometrics Courses | Udemy](https://www.udemy.com/course/biometrics/)

[Learn Bioinformatics with Online Courses and Lessons | edX](https://www.edx.org/learn/bioinformatics)

[Bioinformatics Graduate Certificate | Harvard Extension School](https://extension.harvard.edu/academics/programs/bioinformatics-graduate-certificate/)

[Bioinformatics and Biostatistics | UC San Diego Extension](https://extension.ucsd.edu/courses-and-programs/bioinformatics-and-biostatistics)

[Bioinformatics and Proteomics - Free Online Course Materials | MIT](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-092-bioinformatics-and-proteomics-january-iap-2005/)

[Introduction to Biometrics course - Biometrics Institute](https://www.biometricsinstitute.org/event/introduction-to-biometrics-short-course/)

# Bioinformatics Tools, Libraries, and Frameworks
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

[Bioconductor](https://bioconductor.org/) is an open source project that provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the [R statistical programming language](https://www.r-project.org/about.html), and is open source and open development. It has two releases each year, and an active user community. Bioconductor is also available as an [AMI (Amazon Machine Image)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AMIs.html) and [Docker images](https://docs.docker.com/engine/reference/commandline/images/).

[Bioconda](https://bioconda.github.io) is a channel for the conda package manager specializing in bioinformatics software. It has a repository of packages containing over 7000 bioinformatics packages ready to use with conda install.

[UniProt](https://www.uniprot.org/) is a freely accessible database that provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information.

[Bowtie 2](https://bio.tools/bowtie2#!) is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (mammalian) genomes.

[Biopython](https://biopython.org/) is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics.

[BioRuby](https://bioruby.open-bio.org/) is a toolkit that has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO.

[BioJava](https://biojava.org/) is a toolkit that provides an API to maintain local installations of the PDB, load and manipulate structures, perform standard analysis such as sequence and structure alignments and visualize them in 3D.

[BioPHP](https://biophp.org/) is an open source project that provides a collection of open source PHP code, with classes for DNA and protein sequence analysis, alignment, database parsing, and other bioinformatics tools.

[Avogadro](https://avogadro.cc/) is an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.

[Ascalaph Designer](https://www.biomolecular-modeling.com/Ascalaph/Ascalaph_Designer.html) is a program for molecular dynamic simulations. Under a single graphical environment are represented as their own implementation of molecular dynamics as well as the methods of classical and quantum mechanics of popular programs.

[Anduril](https://www.anduril.org/site/) is a workflow platform for analyzing large data sets. Anduril provides facilities for analyzing high-thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.

[Galaxy](https://melbournebioinformatics.github.io/MelBioInf_docs/tutorials/galaxy_101/galaxy_101/) is an open source, web-based platform for accessible, reproducible, and transparent computational biomedical research. It allows users without programming experience to easily specify parameters and run individual tools as well as larger workflows. It also captures run information so that any user can repeat and understand a complete computational analysis.

[PathVisio](https://pathvisio.github.io/) is a free open-source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins.

[Orange](https://orangedatamining.com/) is a powerful data mining and machine learning toolkit that performs data analysis and visualization.

[Basic Local Alignment Search Tool](https://blast.ncbi.nlm.nih.gov/Blast.cgi) is a tool that finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.

[OSIRIS](https://www.ncbi.nlm.nih.gov/osiris/) is public-domain, free, and open source STR analysis software designed for clinical, forensic, and research use, and has been validated for use as an expert system for single-source samples.

[NCBI BioSystems](https://www.ncbi.nlm.nih.gov/biosystems/) is a Database that provides integrated access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez.

# Machine Learning
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

**Machine Learning/Deep Learning Frameworks.**

# Learning Resources for ML

[Machine Learning](https://www.ibm.com/cloud/learn/machine-learning) is a branch of artificial intelligence (AI) focused on building apps using algorithms that learn from data models and improve their accuracy over time without needing to be programmed.

[Machine Learning by Stanford University from Coursera](https://www.coursera.org/learn/machine-learning)

[AWS Training and Certification for Machine Learning (ML) Courses](https://aws.amazon.com/training/learning-paths/machine-learning/)

[Machine Learning Scholarship Program for Microsoft Azure from Udacity](https://www.udacity.com/scholarships/machine-learning-scholarship-microsoft-azure)

[Microsoft Certified: Azure Data Scientist Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-data-scientist)

[Microsoft Certified: Azure AI Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-ai-engineer)

[Azure Machine Learning training and deployment](https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning)

[Learning Machine learning and artificial intelligence from Google Cloud Training](https://cloud.google.com/training/machinelearning-ai)

[Machine Learning Crash Course for Google Cloud](https://developers.google.com/machine-learning/crash-course/)

[JupyterLab](https://jupyterlab.readthedocs.io/)

[Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/)

[How to run Jupyter Notebooks in your Azure Machine Learning workspace](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks)

[Machine Learning Courses Online from Udemy](https://www.udemy.com/topic/machine-learning/)

[Machine Learning Courses Online from Coursera](https://www.coursera.org/courses?query=machine%20learning&)

[Learn Machine Learning with Online Courses and Classes from edX](https://www.edx.org/learn/machine-learning)

# ML Frameworks, Libraries, and Tools

[TensorFlow](https://www.tensorflow.org) is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

[Keras](https://keras.io) is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

[Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.

[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

[Apple CoreML](https://developer.apple.com/documentation/coreml) is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Apache OpenNLP](https://opennlp.apache.org/) is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).

[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.

[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.

[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[PlaidML](https://github.com/plaidml/plaidml) is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.

[Weka](https://www.cs.waikato.ac.nz/ml/weka/) is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.

[Caffe](https://github.com/BVLC/caffe) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

[Theano](https://github.com/Theano/Theano) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.

[nGraph](https://github.com/NervanaSystems/ngraph) is an open source C++ library, compiler and runtime for Deep Learning. The nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets.It provides the freedom, performance, and ease-of-use to AI developers.

[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).

[Jupyter Notebook](https://jupyter.org/) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.

[Apache Spark](https://spark.apache.org/) is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

[Apache Spark Connector for SQL Server and Azure SQL](https://github.com/microsoft/sql-spark-connector) is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.

[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

[Eclipse Deeplearning4J (DL4J)](https://deeplearning4j.konduit.ai/) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.

[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.

[Numba](https://github.com/numba/numba) is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.

[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.

[XGBoost](https://xgboost.readthedocs.io/) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.

[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.

# Algorithms
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

**[Algorithms in Bioinformatics: A Practical Introduction](https://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/)**

**[An Introduction to Bioinformatics Algorithms - UCSD (PDF)](https://bix.ucsd.edu/bioalgorithms/presentations/Ch08_GraphsDNAseq.pdf)**

[Fuzzy logic](https://www.investopedia.com/terms/f/fuzzy-logic.asp) is a heuristic approach that allows for more advanced decision-tree processing and better integration with rules-based programming.

**Architecture of a Fuzzy Logic System. Source: [ResearchGate](https://www.researchgate.net/figure/Architecture-of-a-fuzzy-logic-system_fig2_309452475)**

[Support Vector Machine (SVM)](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf) is a supervised machine learning model that uses classification algorithms for two-group classification problems.

**Support Vector Machine (SVM). Source:[OpenClipArt](https://openclipart.org/detail/182977/svm-support-vector-machines)**

[Neural networks](https://www.ibm.com/cloud/learn/neural-networks) are a subset of machine learning and are at the heart of deep learning algorithms. The name/structure is inspired by the human brain copying the process that biological neurons/nodes signal to one another.

**Deep neural network. Source: [IBM](https://www.ibm.com/cloud/learn/neural-networks)**

[Convolutional Neural Networks (R-CNN)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes.

**Convolutional Neural Networks. Source:[CS231n](https://cs231n.github.io/convolutional-networks/#conv)**

[Recurrent neural networks (RNNs)](https://www.ibm.com/cloud/learn/recurrent-neural-networks) is a type of artificial neural network which uses sequential data or time series data.

**Recurrent Neural Networks. Source: [Slideteam](https://www.slideteam.net/recurrent-neural-networks-rnns-ppt-powerpoint-presentation-file-templates.html)**

[Multilayer Perceptrons (MLPs)](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron) is multi-layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation.

**Multilayer Perceptrons. Source: [DeepAI](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron)**

[Random forest](https://www.ibm.com/cloud/learn/random-forest) is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.

**Random forest. Source: [wikimedia](https://community.tibco.com/wiki/random-forest-template-tibco-spotfirer-wiki-page)**

[Decision trees](https://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/) are tree-structured models for classification and regression.

***Decision Trees. Source: [CMU](http://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/)*

[Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) is a machine learning algorithm that is used solved calssification problems. It's based on applying [Bayes' theorem](https://www.mathsisfun.com/data/bayes-theorem.html) with strong independence assumptions between the features.

**Bayes' theorem. Source:[mathisfun](https://www.mathsisfun.com/data/bayes-theorem.html)**

# MATLAB Development
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

## MATLAB Learning Resources

[MATLAB](https://www.mathworks.com/products/matlab.html) is a programming language that does numerical computing such as expressing matrix and array mathematics directly.

[MATLAB Documentation](https://www.mathworks.com/help/matlab/)

[Getting Started with MATLAB ](https://www.mathworks.com/help/matlab/getting-started-with-matlab.html)

[MATLAB and Simulink Training from MATLAB Academy](https://matlabacademy.mathworks.com)

[MathWorks Certification Program](https://www.mathworks.com/services/training/certification.html)

[MATLAB Online Courses from Udemy](https://www.udemy.com/topic/matlab/)

[MATLAB Online Courses from Coursera](https://www.coursera.org/courses?query=matlab)

[MATLAB Online Courses from edX](https://www.edx.org/learn/matlab)

[Building a MATLAB GUI](https://www.mathworks.com/discovery/matlab-gui.html)

[MATLAB Style Guidelines 2.0](https://www.mathworks.com/matlabcentral/fileexchange/46056-matlab-style-guidelines-2-0)

[Setting Up Git Source Control with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/set-up-git-source-control.html)

[Pull, Push and Fetch Files with Git with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/push-and-fetch-with-git.html)

[Create New Repository with MATLAB & Simulink](https://www.mathworks.com/help/matlab/matlab_prog/add-folder-to-source-control.html)

[PRMLT](http://prml.github.io/) is Matlab code for machine learning algorithms in the PRML book.

## MATLAB Tools

[MATLAB Online](https://matlab.mathworks.com) allows to users to uilitize MATLAB and Simulink through a web browser such as Google Chrome.

[Simulink](https://www.mathworks.com/products/simulink.html) is a block diagram environment for Model-Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.

[MATLAB Schemer](https://github.com/scottclowe/matlab-schemer) is a MATLAB package makes it easy to change the color scheme (theme) of the MATLAB display and GUI.

[LRSLibrary](https://github.com/andrewssobral/lrslibrary) is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.

[Robotics Toolbox for MATLAB](https://www.mathworks.com/products/robotics.html) provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.

[SEA-MAT](https://sea-mat.github.io/sea-mat/) is a collaborative effort to organize and distribute Matlab tools for the Oceanographic Community.

[Gramm](https://github.com/piermorel/gramm) is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.

[hctsa](https://hctsa-users.gitbook.io/hctsa-manual) is a software package for running highly comparative time-series analysis using Matlab.

[Plotly](https://plot.ly/matlab/) is a Graphing Library for MATLAB.

[YALMIP](https://yalmip.github.io/) is a MATLAB toolbox for optimization modeling.

[GNU Octave](https://www.gnu.org/software/octave/) is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.

# R Development
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

# R Learning Resources

[R](https://www.r-project.org/) is an open source software environment for statistical computing and graphics. It compiles and runs on a wide variety of platforms such as Windows and MacOS.

[An Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf)

[Google's R Style Guide](https://google.github.io/styleguide/Rguide.html)

[R developer's guide to Azure](https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/r-developers-guide)

[Running R at Scale on Google Compute Engine](https://cloud.google.com/solutions/running-r-at-scale)

[Running R on AWS](https://aws.amazon.com/blogs/big-data/running-r-on-aws/)

[RStudio Server Pro for AWS](https://aws.amazon.com/marketplace/pp/RStudio-RStudio-Server-Pro-for-AWS/B06W2G9PRY)

[Learn R by Codecademy](https://www.codecademy.com/learn/learn-r)

[Learn R Programming with Online Courses and Lessons by edX](https://www.edx.org/learn/r-programming)

[R Language Courses by Coursera](https://www.coursera.org/courses?query=r%20language)

[Learn R For Data Science by Udacity](https://www.udacity.com/course/programming-for-data-science-nanodegree-with-R--nd118)

# R Tools

[RStudio](https://rstudio.com/) is an integrated development environment for R and Python, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.

[Shiny](https://shiny.rstudio.com/) is a newer package from RStudio that makes it incredibly easy to build interactive web applications with R.

[Rmarkdown ](https://rmarkdown.rstudio.com/) is a package helps you create dynamic analysis documents that combine code, rendered output (such as figures), and prose.

[Rplugin](https://github.com/JetBrains/Rplugin) is R Language supported plugin for the IntelliJ IDE.

[Plotly](https://plotly-r.com/) is an R package for creating interactive web graphics via the open source JavaScript graphing library [plotly.js](https://github.com/plotly/plotly.js).

[Metaflow](https://metaflow.org/) is a Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.

[Prophet](https://facebook.github.io/prophet) is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.

[LightGBM](https://lightgbm.readthedocs.io/) is a gradient boosting framework that uses tree based learning algorithms, used for ranking, classification and many other machine learning tasks.

[Dash](https://plotly.com/dash) is a Python framework for building analytical web applications in Python, R, Julia, and Jupyter.

[MLR](https://mlr.mlr-org.com/) is Machine Learning in R.

[ML workspace](https://github.com/ml-tooling/ml-workspace) is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. ML workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (Tensorflow, PyTorch, Keras, and MXnet) and dev tools (Jupyter, VS Code, and Tensorboard) perfectly configured, optimized, and integrated.

[CatBoost](https://catboost.ai/) is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

[Plumber](https://www.rplumber.io/) is a tool that allows you to create a web API by merely decorating your existing R source code with special comments.

[Drake](https://docs.ropensci.org/drake) is an R-focused pipeline toolkit for reproducibility and high-performance computing.

[DiagrammeR](https://visualizers.co/diagrammer/) is a package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into R Markdown documents, integrated with Shiny web apps, converted to other graph formats, or exported as image files.

[Knitr](https://yihui.org/knitr/) is a general-purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.

[Broom](https://broom.tidymodels.org/) is a tool that converts statistical analysis objects from R into tidy format.

# Python Development
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

## Python Learning Resources

[Python](https://www.python.org) is an interpreted, high-level programming language. Python is used heavily in the fields of Data Science and Machine Learning.

[Python Developer’s Guide](https://devguide.python.org) is a comprehensive resource for contributing to Python – for both new and experienced contributors. It is maintained by the same community that maintains Python.

[Azure Functions Python developer guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference-python) is an introduction to developing Azure Functions using Python. The content below assumes that you've already read the [Azure Functions developers guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference).

[CheckiO](https://checkio.org/) is a programming learning platform and a gamified website that teaches Python through solving code challenges and competing for the most elegant and creative solutions.

[Python Institute](https://pythoninstitute.org)

[PCEP – Certified Entry-Level Python Programmer certification](https://pythoninstitute.org/pcep-certification-entry-level/)

[PCAP – Certified Associate in Python Programming certification](https://pythoninstitute.org/pcap-certification-associate/)

[PCPP – Certified Professional in Python Programming 1 certification](https://pythoninstitute.org/pcpp-certification-professional/)

[PCPP – Certified Professional in Python Programming 2](https://pythoninstitute.org/pcpp-certification-professional/)

[MTA: Introduction to Programming Using Python Certification](https://docs.microsoft.com/en-us/learn/certifications/mta-introduction-to-programming-using-python)

[Getting Started with Python in Visual Studio Code](https://code.visualstudio.com/docs/python/python-tutorial)

[Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html)

[Google's Python Education Class](https://developers.google.com/edu/python/)

[Real Python](https://realpython.com)

[The Python Open Source Computer Science Degree by Forrest Knight](https://github.com/ForrestKnight/open-source-cs-python)

[Intro to Python for Data Science](https://www.datacamp.com/courses/intro-to-python-for-data-science)

[Intro to Python by W3schools](https://www.w3schools.com/python/python_intro.asp)

[Codecademy's Python 3 course](https://www.codecademy.com/learn/learn-python-3)

[Learn Python with Online Courses and Classes from edX](https://www.edx.org/learn/python)

[Python Courses Online from Coursera](https://www.coursera.org/courses?query=python)

## Python Frameworks and Tools

[Python Package Index (PyPI)](https://pypi.org/) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community.

[PyCharm](https://www.jetbrains.com/pycharm/) is the best IDE I've ever used. With PyCharm, you can access the command line, connect to a database, create a virtual environment, and manage your version control system all in one place, saving time by avoiding constantly switching between windows.

[Python Tools for Visual Studio(PTVS)](https://microsoft.github.io/PTVS/) is a free, open source plugin that turns Visual Studio into a Python IDE. It supports editing, browsing, IntelliSense, mixed Python/C++ debugging, remote Linux/MacOS debugging, profiling, IPython, and web development with Django and other frameworks.

[Pylance](https://github.com/microsoft/pylance-release) is an extension that works alongside Python in Visual Studio Code to provide performant language support. Under the hood, Pylance is powered by Pyright, Microsoft's static type checking tool.

[Pyright](https://github.com/Microsoft/pyright) is a fast type checker meant for large Python source bases. It can run in a “watch” mode and performs fast incremental updates when files are modified.

[Django](https://www.djangoproject.com/) is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

[Flask](https://flask.palletsprojects.com/) is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.

[Web2py](http://web2py.com/) is an open-source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.

[AWS Chalice](https://github.com/aws/chalice) is a framework for writing serverless apps in python. It allows you to quickly create and deploy applications that use AWS Lambda.

[Tornado](https://www.tornadoweb.org/) is a Python web framework and asynchronous networking library. Tornado uses a non-blocking network I/O, which can scale to tens of thousands of open connections.

[HTTPie](https://github.com/httpie/httpie) is a command line HTTP client that makes CLI interaction with web services as easy as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.

[Scrapy](https://scrapy.org/) is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.

[Sentry](https://sentry.io/) is a service that helps you monitor and fix crashes in realtime. The server is in Python, but it contains a full API for sending events from any language, in any application.

[Pipenv](https://github.com/pypa/pipenv) is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world.

[Python Fire](https://github.com/google/python-fire) is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

[Bottle](https://github.com/bottlepy/bottle) is a fast, simple and lightweight [WSGI](https://www.wsgi.org/) micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the [Python Standard Library](https://docs.python.org/library/).

[CherryPy](https://cherrypy.org) is a minimalist Python object-oriented HTTP web framework.

[Sanic](https://github.com/huge-success/sanic) is a Python 3.6+ web server and web framework that's written to go fast.

[Pyramid](https://trypyramid.com) is a small and fast open source Python web framework. It makes real-world web application development and deployment more fun and more productive.

[TurboGears](https://turbogears.org) is a hybrid web framework able to act both as a Full Stack framework or as a Microframework.

[Falcon](https://falconframework.org/) is a reliable, high-performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.

[Neural Network Intelligence(NNI)](https://github.com/microsoft/nni) is an open source AutoML toolkit for automate machine learning lifecycle, including [Feature Engineering](https://github.com/microsoft/nni/blob/master/docs/en_US/FeatureEngineering/Overview.md), [Neural Architecture Search](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/Overview.md), [Model Compression](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/Overview.md) and [Hyperparameter Tuning](https://github.com/microsoft/nni/blob/master/docs/en_US/Tuner/BuiltinTuner.md).

[Dash](https://plotly.com/dash) is a popular Python framework for building ML & data science web apps for Python, R, Julia, and Jupyter.

[Luigi](https://github.com/spotify/luigi) is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built-in.

[Locust](https://github.com/locustio/locust) is an easy to use, scriptable and scalable performance testing tool.

[spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython.

[NumPy](https://www.numpy.org/) is the fundamental package needed for scientific computing with Python.

[Pillow](https://python-pillow.org/) is a friendly PIL(Python Imaging Library) fork.

[IPython](https://ipython.org/) is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history.

[GraphLab Create](https://turi.com/) is a Python library, backed by a C++ engine, for quickly building large-scale, high-performance machine learning models.

[Pandas](https://pandas.pydata.org/) is a fast, powerful, and easy to use open source data structrures, data analysis and manipulation tool, built on top of the Python programming language.

[PuLP](https://coin-or.github.io/pulp/) is an Linear Programming modeler written in python. PuLP can generate LP files and call on use highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI, to solve these linear problems.

[Matplotlib](https://matplotlib.org/) is a 2D plotting library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.

# Ruby Development
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

# Ruby Learning Resources

[Ruby](https://www.ruby-lang.org/en/) is a dynamic, open source programming language with a focus on simplicity and productivity. It has an elegant syntax that is natural to read and easy to write.

[Ruby Documentation](https://www.ruby-lang.org/en/documentation/)

[Ruby Community](https://www.ruby-lang.org/en/community/)

[Ruby Gems](https://guides.rubygems.org/rubygems-basics/)

[Ruby courses by Coursera](https://www.coursera.org/courses?query=ruby)

[Learn Ruby course by Codecademy](https://www.codecademy.com/learn/learn-ruby)

[Ruby Glossary](https://www.codecademy.com/articles/glossary-ruby)

[Ruby in Twenty Minutes Quickstart](https://www.ruby-lang.org/en/documentation/quickstart/)

[Getting started with a Ruby on Rails application on CircleCI.](https://circleci.com/docs/2.0/language-ruby/)

[The Ruby Style Guide](https://rubystyle.guide)

[Airbnb's Ruby Style Guide](https://github.com/airbnb/ruby)

# Ruby Tools and Frameworks

[RubyMine](https://www.jetbrains.com/ruby/) is a professional IDE developed by Jet Brains that provides support for Ruby, Ruby on Rails and web development.

[Rails](https://rubyonrails.org/) is a web-application framework that includes everything needed to create database-backed web applications according to the [Model-View-Controller (MVC)](https://en.wikipedia.org/wiki/Model-view-controller) pattern. Understanding the MVC pattern is key to understanding Rails. MVC divides your application into three layers: Model, View, and Controller, each with a specific responsibility.

[rbenv](https://github.com/rbenv/rbenv) allows to pick a Ruby version for your application and guarantee that your development environment matches production. Put rbenv to work with Bundler for painless Ruby upgrades and bulletproof deployments.

[Prettier for Ruby](https://prettier.io/) is a plugin for the Ruby programming language and its ecosystem. prettier is an opinionated code formatter that supports multiple languages and integrates with most editors. The idea is to eliminate discussions of style in code review and allow developers to get back to thinking about code design instead.

[Active Admin](https://activeadmin.info/) is a Ruby on Rails framework for creating elegant backends for website administration.

[Capistrano](https://github.com/capistrano/capistrano) is a framework for building automated deployment scripts. Although Capistrano itself is written in Ruby, it can easily be used to deploy projects of any language or framework, be it Rails, Java, or PHP.

[Spree](https://spreecommerce.org/) is an open source E-commerce platform for Rails 6 with a modern UX, optional PWA frontend, REST API, GraphQL, several official extensions and 3rd party integrations.

[Sidekiq](https://sidekiq.org/) is a simple, efficient background processing for Ruby. It uses hreads to handle many jobs at the same time in the same process. It does not require Rails but will integrate tightly with Rails to make background processing dead simple.

[Kaminari](https://github.com/amatsuda/kaminari/wiki) is a Scope and Engine based, clean, powerful, and customizable paginator for modern web app frameworks and ORMs.

[React-Rails](https://github.com/reactjs/react-rails) is a flexible tool to use [React](http://facebook.github.io/react/) with Rails. By integrating React.js with Rails views and controllers, the asset pipeline, or webpacker.

[Pry](https://github.com/pry/pry) is a runtime developer console and IRB alternative with powerful introspection capabilities.

[Brakeman](https://brakemanscanner.org/) is a static analysis tool which checks Ruby on Rails applications for security vulnerabilities.

[dotenv](https://github.com/bkeepers/dotenv) is a Ruby gem to load environment variables from `.env`.

[Scientist](https://github.com/github/scientist) is a Ruby library for carefully refactoring critical paths.

[fastlane](https://fastlane.tools/) is a tool written in Ruby for iOS and Android developers to automate tedious tasks like generating screenshots, dealing with provisioning profiles, and releasing your application.

[Fluentd](https://www.fluentd.org/) collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on all written in Ruby.

# Java Development
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

## Java Learning Resources

[Java](https://www.oracle.com/java/) is a popular programming language and development platform(JDK). It reduces costs, shortens development timeframes, drives innovation, and improves application services. With millions of developers running more than 51 billion Java Virtual Machines worldwide.

[The Eclipse Foundation](https://www.eclipse.org/downloads/) is home to a worldwide community of developers, the Eclipse IDE, Jakarta EE and over 375 open source projects, including runtimes, tools and frameworks for Java and other languages.

[Getting Started with Java](https://docs.oracle.com/javase/tutorial/)

[Oracle Java certifications from Oracle University](https://education.oracle.com/java-certification-benefits)

[Google Developers Training](https://developers.google.com/training/)

[Google Developers Certification](https://developers.google.com/certification/)

[Java Tutorial by W3Schools](https://www.w3schools.com/java/)

[Building Your First Android App in Java](codelabs.developers.google.com/codelabs/build-your-first-android-app/)

[Getting Started with Java in Visual Studio Code](https://code.visualstudio.com/docs/java/java-tutorial)

[Google Java Style Guide](https://google.github.io/styleguide/javaguide.html)

[AOSP Java Code Style for Contributors](https://source.android.com/setup/contribute/code-style)

[Chromium Java style guide](https://chromium.googlesource.com/chromium/src/+/master/styleguide/java/java.md)

[Get Started with OR-Tools for Java](https://developers.google.com/optimization/introduction/java)

[Getting started with Java Tool Installer task for Azure Pipelines](https://docs.microsoft.com/en-us/azure/devops/pipelines/tasks/tool/java-tool-installer)

[Gradle User Manual](https://docs.gradle.org/current/userguide/userguide.html)

## Tools

[Java SE](https://www.oracle.com/java/technologies/javase/tools-jsp.html) contains several tools to assist in program development and debugging, and in the monitoring and troubleshooting of production applications.

[JDK Development Tools](https://docs.oracle.com/javase/7/docs/technotes/tools/) includes the Java Web Start Tools (javaws) Java Troubleshooting, Profiling, Monitoring and Management Tools (jcmd, jconsole, jmc, jvisualvm); and Java Web Services Tools (schemagen, wsgen, wsimport, xjc).

[Android Studio](https://developer.android.com/studio/) is the official integrated development environment for Google's Android operating system, built on JetBrains' IntelliJ IDEA software and designed specifically for Android development. Availble on Windows, macOS, Linux, Chrome OS.

[IntelliJ IDEA](https://www.jetbrains.com/idea/) is an IDE for Java, but it also understands and provides intelligent coding assistance for a large variety of other languages such as Kotlin, SQL, JPQL, HTML, JavaScript, etc., even if the language expression is injected into a String literal in your Java code.

[NetBeans](https://netbeans.org/features/java/index.html) is an IDE provides Java developers with all the tools needed to create professional desktop, mobile and enterprise applications. Creating, Editing, and Refactoring. The IDE provides wizards and templates to let you create Java EE, Java SE, and Java ME applications.

[Java Design Patterns ](https://github.com/iluwatar/java-design-patterns) is a collection of the best formalized practices a programmer can use to solve common problems when designing an application or system.

[Elasticsearch](https://www.elastic.co/products/elasticsearch) is a distributed RESTful search engine built for the cloud written in Java.

[RxJava](https://github.com/ReactiveX/RxJava) is a Java VM implementation of [Reactive Extensions](http://reactivex.io/): a library for composing asynchronous and event-based programs by using observable sequences. It extends the [observer pattern](http://en.wikipedia.org/wiki/Observer_pattern) to support sequences of data/events and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety and concurrent data structures.

[Guava](https://github.com/google/guava) is a set of core Java libraries from Google that includes new collection types (such as multimap and multiset), immutable collections, a graph library, and utilities for concurrency, I/O, hashing, caching, primitives, strings, and more! It is widely used on most Java projects within Google, and widely used by many other companies as well.

[okhttp](https://square.github.io/okhttp/) is a HTTP client for Java and Kotlin developed by Square.

[Retrofit](https://square.github.io/retrofit/) is a type-safe HTTP client for Android and Java develped by Square.

[LeakCanary](https://square.github.io/leakcanary/) is a memory leak detection library for Android develped by Square.

[Apache Flink](https://flink.apache.org/) is an open source stream processing framework with powerful stream- and batch-processing capabilities with elegant and fluent APIs in Java and Scala.

[Fastjson](https://github.com/alibaba/fastjson/wiki) is a Java library that can be used to convert Java Objects into their JSON representation. It can also be used to convert a JSON string to an equivalent Java object.

[libGDX](https://libgdx.com/) is a cross-platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.

[Jenkins](https://www.jenkins.io/) is the leading open-source automation server. Built with Java, it provides over 1700 [plugins](https://plugins.jenkins.io/) to support automating virtually anything, so that humans can actually spend their time doing things machines cannot.

[DBeaver](https://dbeaver.io/) is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports any database which has JDBC driver (which basically means - ANY database). EE version also supports non-JDBC datasources (MongoDB, Cassandra, Redis, DynamoDB, etc).

[Redisson](https://redisson.pro/) is a Redis Java client with features of In-Memory Data Grid. Over 50 Redis based Java objects and services: Set, Multimap, SortedSet, Map, List, Queue, Deque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Tomcat, Scheduler, JCache API, Hibernate, MyBatis, RPC, and local cache.

[GraalVM](https://www.graalvm.org/) is a universal virtual machine for running applications written in JavaScript, Python, Ruby, R, JVM-based languages like Java, Scala, Clojure, Kotlin, and LLVM-based languages such as C and C++.

[Gradle](https://gradle.org/) is a build automation tool for multi-language software development. From mobile apps to microservices, from small startups to big enterprises, Gradle helps teams build, automate and deliver better software, faster. Write in Java, C++, Python or your language of choice.

[Apache Groovy](http://www.groovy-lang.org/) is a powerful, optionally typed and dynamic language, with static-typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming.

[JaCoCo](https://www.jacoco.org/jacoco/) is a free code coverage library for Java, which has been created by the EclEmma team based on the lessons learned from using and integration existing libraries for many years.

[Apache JMeter](http://jmeter.apache.org/) is used to test performance both on static and dynamic resources, Web dynamic applications. It also used to simulate a heavy load on a server, group of servers, network or object to test its strength or to analyze overall performance under different load types.

[Junit](https://junit.org/) is a simple framework to write repeatable tests. It is an instance of the xUnit architecture for unit testing frameworks.

[Mockito](https://site.mockito.org/) is the most popular Mocking framework for unit tests written in Java.

[SpotBugs](https://spotbugs.github.io/) is a program which uses static analysis to look for bugs in Java code.

[SpringBoot](https://spring.io/projects/spring-boot) is a great tool that helps you to create Spring-powered, production-grade applications and services with absolute minimum fuss. It takes an opinionated view of the Spring platform so that new and existing users can quickly get to the bits they need.

[YourKit](https://www.yourkit.com/) is a technology leader, creator of the most innovative and intelligent tools for profiling Java & .NET applications.

# PHP Development
[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

# PHP Learning Resources

[PHP](https://www.php.net/) is a popular general-purpose scripting language that is especially suited to web development. Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

[PHP 8](https://www.php.net/releases/8.0/en.php)

[What's New in PHP 8 - Auth0](https://auth0.com/blog/whats-new-php-8/)

[PHP Manual](https://www.php.net/manual/en/index.php)

[MIT's PHP Code Style Guide](https://mitsloan.mit.edu/shared/content/PHP_Code_Style_Guide.php)

[PHP Style Guide](https://gist.github.com/ryansechrest/8138375)

[PHP tutorial by W3Schools](https://www.w3schools.com/php/)

[PHP MySQL & CodeIgniter Course on Udemy](https://www.udemy.com/course/php-mysql-codeigniter-complete-guide/)

# PHP Tools and Frameworks

[PhpStorm](https://www.jetbrains.com/phpstorm/) is a professional PHP IDE developed by Jet Brains for working with Symfony, Laravel, Drupal, WordPress, Laminas, Magento, Joomla!, CakePHP, Yii, and other frameworks.

[Laravel](https://laravel.com/) is a web application framework with expressive, elegant syntax. We believe development must be an enjoyable and creative experience to be truly fulfilling.

[PHP Intelephense](https://marketplace.visualstudio.com/items?itemName=bmewburn.vscode-intelephense-client) is a high performance PHP language server packed full of essential features for productive PHP development in Visual Studio Code.

[PHP Tools for Visual Studio/VS Code](https://www.devsense.com/en) is a an extension that turn Visual Studio into a powerful PHP development environment.

[Symfony](https://symfony.com/) is a PHP framework for web and console applications and a set of reusable PHP components. Symfony is used by thousands of web applications (including BlaBlaCar.com and Spotify.com) and most of the [popular PHP projects](https://symfony.com/projects) (including Drupal and Magento).

[CakePHP](https://cakephp.org) is a rapid development framework for PHP which uses commonly known design patterns like Associative Data Mapping, Front Controller, and MVC. CakePHP's main goal is to provide a structured framework that enables PHP users at all levels to rapidly develop robust web applications, without any loss to flexibility.

[Composer](https://getcomposer.org/) is a tools helps you declare, manage, and install dependencies of PHP projects.

[Guzzle](https://github.com/guzzle/guzzle) is a PHP HTTP client that makes it easy to send HTTP requests and trivial to integrate with web services.

[DesignPatternsPHP](https://designpatternsphp.readthedocs.io/) is a collection of known design patterns and some sample code how to implement them in PHP 7.4. Every pattern has a small list of examples.

[CodeIgniter](https://codeigniter.com/) is an Application Development Framework for people who build web sites using PHP. Its goal is to enable you to develop projects much faster than you could if you were writing code from scratch, by providing a rich set of libraries for commonly needed tasks, as well as a simple interface and logical structure to access these libraries. CodeIgniter lets you creatively focus on your project by minimizing the amount of code needed for a given task.

[HHVM](https://hhvm.com/) is an open-source virtual machine designed for executing programs written in [Hack](https://hacklang.org/). HHVM uses a just-in-time (JIT) compilation approach to achieve superior performance while maintaining amazing development flexibility. HHVM should be used together with a webserver like the built in, easy to deploy [Proxygen](https://docs.hhvm.com/hhvm/basic-usage/proxygen), or a [FastCGI-based](https://docs.hhvm.com/hhvm/advanced-usage/fastCGI) webserver on top of nginx or Apache.

[PHPUnit](https://phpunit.de/) is a programmer-oriented testing framework for PHP. It is an instance of the xUnit architecture for unit testing frameworks.

[Phalcon](https://phalcon.io/) is an open source web framework delivered as a C extension for the PHP language providing high performance and lower resource consumption.

[Swoole](https://www.swoole.co.uk/) is an event-driven asynchronous & coroutine-based concurrency networking communication engine with high performance written in C and C++ for PHP.

[Matomo](https://matomo.org/) is a full-featured PHP MySQL software program that you download and install on your own webserver. At the end of the five-minute installation process, you will be given a JavaScript code. Simply copy and paste this tag on websites you wish to track and access your analytics reports in real-time.

[Grav](https://getgrav.org/) is a Fast, Simple, and Flexible, file-based Web-platform. There is Zero installation required. Just extract the ZIP archive, and you are already up and running. It follows similar principles to other flat-file CMS platforms, but has a different design philosophy than most. Grav comes with a powerful Package Management System to allow for simple installation and upgrading of plugins and themes, as well as simple updating of Grav itself.

[Whoops](https://filp.github.io/whoops/) is an error handler framework for PHP. Out-of-the-box, it provides a pretty error interface that helps you debug your web projects, but at heart it's a simple yet powerful stacked error handling system.

[Slim](https://www.slimframework.com/) is a PHP micro framework that helps you quickly write simple yet powerful web applications and APIs.

## Contribute

- [x] If would you like to contribute to this guide simply make a [Pull Request](https://github.com/mikeroyal/Bioinformatics-Guide/pulls).

## License

[Back to the Top](https://github.com/mikeroyal/Bioinformatics-Guide#table-of-contents)

Distributed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) Public License](https://creativecommons.org/licenses/by/4.0/).