<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36360?offset=10 https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn Mon, 29 Feb 2016 17:39:24 -0600 https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn <![CDATA[scikit-learn]]> Machine Learning in Python

Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license

More at http://scikit-learn.org/stable/index.html

 

Address of the bookmark: http://scikit-learn.org/stable/auto_examples/index.html

]]> Jitendra Prajapati https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

]]> Jit https://bioinformaticsonline.com/blog/view/37586/julia-programming-language-a-python-and-r-rival Sat, 25 Aug 2018 04:46:39 -0500 https://bioinformaticsonline.com/blog/view/37586/julia-programming-language-a-python-and-r-rival <![CDATA[Julia Programming Language, a Python and R rival]]> Big data has grown to become one of the most lucrative fields. In fact, data scientists are some of the most sought people. They are usually hired to analyze, control and parse large chunks of data. Implementing these actions using traditional techniques is not a walk in the park. This is why most data scientists prefer using programming languages such as R and Python. However, there is one more programming language that can do the job. That is Julia programming language.

What Is Julia Language?

Julia is a programming language that came into the limelight in 2012. It is a general-purpose programming language that was designed for solving scientific computations. Julia was meant to be an alternative to Python, R and other programming languages that were mainly used for manipulating data. This is because it has numerous features that can minimize the complexities of numerical computations. 

Julia optimizes on the best features of Python and R while at the same time overlooks their weaknesses. This explains why it is viewed as an alternative to these programming languages. For instance, it utilizes the readability and simplicity of Python then performs faster.

Julia is the most preferred programming language for data scientists and mathematicians. This is because its core features are similar to the ones that are used on most data software. Also, the language is ideal for these two subjects because its syntax is similar to the standard mathematical formulas.

Key Features Of Julia Language
Uses JIT Compilation
Parallelism
Dynamic Typing
Simple Syntax
Allows Metaprogramming
Accessible to Libraries
-1-Array Indexing

Julia Vs Python And R Programming Languages
1. Speed
Julia is faster than both Python and R. This is a very critical aspect that is given special attention in the big data programming. The high speed of Julia is because of JIT compilers. You will need to install external libraries on Python to achieve similar speed.

2. Syntax
Julia has a math-friendly syntax. The syntax of this programming language is similar to the mathematical formulas hence can be used to perform mathematical and scientific computations. This syntax makes it easier to learn than Python.

3. Parallelism
Although both Python and R use parallelism, Julia uses a top-level parallelism. Julia allows the processor to perform to the optimum level than what Python and R can achieve.

4. Versatility
Julia programming language is more versatile than Python and R. It allows a programmer to move from different codes and functions with ease.

The only area that Python and R are superior to Julia is in terms of community. Given that Julia is a new programming language, it has a small community as compared to others which have been around for years.

In overall Julia programming language is a better alternative that you can use to handle Big data projects. Despite having a small community, it is one of those programming languages that you can easily learn.

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/40834/nucleus-python-and-c-code-for-reading-and-writing-genomics-data Sun, 02 Feb 2020 08:14:19 -0600 https://bioinformaticsonline.com/bookmarks/view/40834/nucleus-python-and-c-code-for-reading-and-writing-genomics-data <![CDATA[Nucleus: Python and C++ code for reading and writing genomics data.]]> Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF. In addition, Nucleus enables painless integration with the TensorFlow machine learning framework, as anywhere a genomics file is consumed or produced, a TensorFlow tfrecords file may be used instead.

Address of the bookmark: https://github.com/google/nucleus

]]> Jit https://bioinformaticsonline.com/bookmarks/view/42923/flanker Sat, 27 Feb 2021 22:04:53 -0600 https://bioinformaticsonline.com/bookmarks/view/42923/flanker <![CDATA[Flanker]]> Flanker, a Python package which performs alignment-free clustering of gene flanking sequences in a consistent format, allowing investigation of mobile genetic elements (MGEs) without prior knowledge of their structure. Flanker can be flexibly parameterised to finetune outputs by characterising upstream and downstream regions separately and investigating variable lengths of flanking sequence.

image

Address of the bookmark: https://github.com/wtmatlock/flanker

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/34567/jobtree-based-python-wrapper-to-run-the-genome-simulation-tool-suite-evolver Fri, 08 Dec 2017 16:26:32 -0600 https://bioinformaticsonline.com/bookmarks/view/34567/jobtree-based-python-wrapper-to-run-the-genome-simulation-tool-suite-evolver <![CDATA[jobTree based python wrapper to run the genome simulation tool suite Evolver]]> evolverSimControl (eSC) can be used to simulate multi-chromosome genome evolution on an arbitrary phylogeny (Newick format). In addition to simply running evolver, eSC also automatically creates statistical summaries of the simulation as it runs including text and image files. Also included are convenience scripts to: check on a running simulation and see detailed status and logging information; extract fasta sequence files from the leaf nodes of a completed simulation; extract pairwise multiple alignment files (.maf) from leaf and branch nodes from a completed simulation and with the help of mafJoin, join them together into a single maf covering the entire simulation.

Address of the bookmark: https://github.com/dentearl/evolverSimControl

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40099/contiguator Fri, 04 Oct 2019 01:27:58 -0500 https://bioinformaticsonline.com/bookmarks/view/40099/contiguator <![CDATA[CONTIGuator !]]> 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).

 

Address of the bookmark: https://sourceforge.net/projects/contiguator/

]]> BioStar https://bioinformaticsonline.com/pages/view/44284/tools-for-geospatial-data-analysis Wed, 22 Mar 2023 02:10:28 -0500 https://bioinformaticsonline.com/pages/view/44284/tools-for-geospatial-data-analysis <![CDATA[Tools for Geospatial data analysis !]]>

Geospatial data is becoming increasingly important in many fields, including urban planning, environmental science, public health, and more. These tools can help you work with data from a variety of sources, including satellite imagery, GPS data, and other forms of spatial data. They can help you visualize data, perform complex analysis, and even create maps and other visualizations.

The list includes some of the most popular and widely used geospatial tools available in Python. These tools can help you work with data from a variety of sources and in a variety of formats. Some of the tools are focused on visualization, such as Cartopy, Folium, and Contextily, which allow you to create interactive maps and other visualizations. Other tools are more focused on data manipulation and analysis, such as Fiona, GeoPandas, and Rasterio, which allow you to manipulate and analyze spatial data in a variety of ways.

The list also includes some tools for working with specific types of geospatial data. For example, the H3 library is designed specifically for working with hexagonal grids, while PySAL is focused on spatial econometrics and spatial analysis. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.

In summary, this list is an excellent resource for anyone working with geospatial data in Python. It contains a wide range of tools for working with different types of data, and can help you visualize data, perform complex analysis, and create maps and other visualizations. If you're looking to enhance your skills in geospatial analysis, this list is definitely worth checking out.

These tools are:

These tools offer a wide range of capabilities for working with geospatial data, from visualizing and manipulating data to performing complex analysis and modeling. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/31024/dagchainer-computing-chains-of-syntenic-genes-in-complete-genomes Fri, 17 Feb 2017 16:13:35 -0600 https://bioinformaticsonline.com/bookmarks/view/31024/dagchainer-computing-chains-of-syntenic-genes-in-complete-genomes <![CDATA[DAGchainer: Computing Chains of Syntenic Genes in Complete Genomes]]> The DAGchainer software computes chains of syntenic genes found within complete genome sequences. As input, DAGchainer accepts a list of gene pairs with sequence homology along with their genome coordinates. Using a scoring function which accounts for the distance between neighboring genes on each DNA molecule and the BLAST E-value score between homologs, maximally scoring chains of ordered gene pairs are computed and reported. This algorithm can be used to mine large evolutionary conserved regions of genomes between two organisms. Alternatively, by examining colinear sets of homologous genes found within a single genome, segmental genome duplications can be revealed.

This software distribution includes both the DAGchainer utility and a Java-based graphical interface that allows the inputs and outputs to be navigated and interrogated dynamically.

Address of the bookmark: http://dagchainer.sourceforge.net/

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/37529/bokeh-an-interactive-visualization-library-that-targets-modern-web-browsers-for-presentation Fri, 10 Aug 2018 18:43:08 -0500 https://bioinformaticsonline.com/bookmarks/view/37529/bokeh-an-interactive-visualization-library-that-targets-modern-web-browsers-for-presentation <![CDATA[Bokeh: An interactive visualization library that targets modern web browsers for presentation]]> Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.

To get started using Bokeh to make your visualizations, see the User Guide.

To see examples of how you might use Bokeh with your own data, check out the Gallery.

A complete API reference of Bokeh is at Reference Guide.

If you are interested in contributing to Bokeh, or extending the library, see the Developer Guide.

Address of the bookmark: https://bokeh.pydata.org/en/latest/

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