Python Resources for Bioinformatics
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Python Resources for Bioinformatics
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Why women in science should learn to code - SoapboxScience

Why women in science should learn to code - SoapboxScience | Python Resources for Bioinformatics | Scoop.it
Tweet TweetBy Vicky Butt, who is a PhD student studying bioinformatics and metagenomics at King’s College London.   With this ever-increasing amount of biological data, such as genomics, the only way to make sense of it is to write computer programmes. … Continue reading →
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How to do common Excel and SQL tasks in Python - code(love)

How to do common Excel and SQL tasks in Python - code(love) | Python Resources for Bioinformatics | Scoop.it
This is a step-by-step, code and example-filled tutorial on how to do common queries and transformations in SQL and Excel in Python.
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Python Practice: DNA RNA Bioinformatics Converter

Using Python to convert RNA to DNA and DNA to RNA. Bioinformatics. This uses lists, appending lists, slicing lists, finding and replacing information i
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PyPDB: a Python API for the Protein Data Bank | Bioinformatics | Oxford Academic

Summary: We have created a Python programming interface for the RCSB Protein Data Bank (PDB) that allows search and data retrieval for a wide range of result types, including BLAST and sequence motif queries. The API relies on the existing XML-based API and operates by creating custom XML requests from native Python types, allowing extensibility and straightforward modification. The package has the ability to perform many types of advanced search of the PDB that are otherwise only available through the PDB website.
Availability and implementation: PyPDB is implemented exclusively in Python 3 using standard libraries for maximal compatibility. The most up-to-date version, including iPython notebooks containing usage tutorials, is available free-of-charge under an open-source MIT license via...
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GlucoSim

GlucoSim Using Python to simulate the effects of insulin, glucagon, and metabolic activity on blood glucose in diabetic patients John Kozlosky Lakeland Jr-Sr H…
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A review of bioinformatic pipeline frameworks

High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Modern implementations of these frameworks differ on three key dimensions: using an implicit or explicit syntax, using a configuration, convention or class-based design paradigm and offering a command line or workbench interface. Here I sur- vey and compare the design philosophies of several current pipeline frameworks. I provide practical recommendations based on analysis requirements and the user base.
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Why scientists must share their research code

Why scientists must share their research code | Python Resources for Bioinformatics | Scoop.it
'Reproducibility editor' Victoria Stodden explains the growing movement to make code and data available to others.
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Python for Bioinformatics for learning Python | Bioinformatics Training

Python for Bioinformatics for learning Python | Bioinformatics Training | Python Resources for Bioinformatics | Scoop.it
Martin Schweitzer https://2016.pycon-au.org/schedule/89/view_talk In this talk I will explain why Python is an excellent language for learning abou | Bioinformatics Training
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4 Best Python Programming Language Courses of All Times!

4 Best Python Programming Language Courses of All Times! | Python Resources for Bioinformatics | Scoop.it
If you’ve been working for a while or are still searching for an ideal job, it would be best to develop yourself a bit more before you apply further. In order to emerge successful and break through these highly competitive doors, it is essential to learn a few programming skills that will certainly help you…
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An Introduction to Programming for Bioscientists: A Python-Based Primer

An Introduction to Programming for Bioscientists: A Python-Based Primer | Python Resources for Bioinformatics | Scoop.it
Author Summary Contemporary biology has largely become computational biology, whether it involves applying physical principles to simulate the motion of each atom in a piece of DNA, or using machine learning algorithms to integrate and mine “omics” data across whole cells (or even entire ecosystems). The ability to design algorithms and program computers, even at a novice level, may be the most indispensable skill that a modern researcher can cultivate. As with human languages, computational fluency is developed actively, not passively. This self-contained text, structured as a hybrid primer/tutorial, introduces any biologist—from college freshman to established senior scientist—to basic computing principles (control-flow, recursion, regular expressions, etc.) and the practicalities of programming and software design. We use the Python language because it now pervades virtually every domain of the biosciences, from sequence-based bioinformatics and molecular evolution to phylogenomics, systems biology, structural biology, and beyond. To introduce both coding (in general) and Python (in particular), we guide the reader via concrete examples and exercises. We also supply, as Supplemental Chapters, a few thousand lines of heavily-annotated, freely distributed source code for personal study.
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Junnplus/awesome-python-books

awesome-python-books - :books: Directory of Python books

awesome-python-books - :books: Directory of Python books

 
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metric-learn: Metric Learning in Python — metric-learn 0.1.0 documentation

Distance metrics are widely used in the machine learning literature. Traditionally, practicioners would choose a standard distance metric (Euclidean, City-Block, Cosine, etc.) using a priori knowledge of the domain. Distance metric learning (or simply, metric learning) is the sub-field of machine learning dedicated to automatically constructing optimal distance metrics. This package contains efficient Python implementations of several popular metric learning algorithms.
Distance metrics are widely used in the machine learning literature. Traditionally, practicioners would choose a standard distance metric (Euclidean, City-Block, Cosine, etc.) using a priori knowledge of the domain. Distance metric learning (or simply, metric learning) is the sub-field of machine learning dedicated to automatically constructing optimal distance metrics. This package contains efficient Python implementations of several popular metric learning algorithms.
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ResidentMario/missingno

ResidentMario/missingno | Python Resources for Bioinformatics | Scoop.it
missingno - Missing data visualization module for Python.

missingno - Missing data visualization module for Python.

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Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers

Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers | Python Resources for Bioinformatics | Scoop.it
Author summary Galaxy users can utilize a large number of tools and workflows. What they could not previously do is run ad hoc scripts and arbitrary tools within their Galaxy instance. This was very limiting, as initial analyses of data often involve interactive exploration with tools like Jupyter or RStudio—powerful platforms that are becoming increasingly popular in life sciences. Here, we showcase Galaxy Interactive Environment framework, designed to combine Galaxy's tools and workflows with environments such as Jupyter.
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Introducing Python Fire, a library for automatically generating command line interfaces

Introducing Python Fire, a library for automatically generating command line interfaces | Python Resources for Bioinformatics | Scoop.it
Today we are pleased to announce the open-sourcing of Python Fire . Python Fire generates command line interfaces (CLIs) from any Pytho
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3 Interactive Drug Discovery Dashboards

RT @plotlygraphs: New post:
3 interactive drug discovery dashboards
https://t.co/CeBA8Rhpyf
� � #plotcon https://t.co/o51uC0pfku
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7 Interactive Bioinformatics Plots made in Python and R

7 Interactive Bioinformatics Plots made in Python and R | Python Resources for Bioinformatics | Scoop.it
7 Interactive Bioinformatics Plots made in Python and R https://t.co/XEZW9ERjQQ
Great resource for #healthinformatics too #ddj #dataviz
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vcfpy - python package for reading and writing VCF files.

vcfpy - Python 3 library with good support for both reading and writing VCF
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Python in Viral Bioinformatics Research

Trevor Bell https://2016.za.pycon.org/talks/25/ The field of bioinformatics -- using computer technology to store, process and analyze biological data -- i
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Democratic databases: science on GitHub

Democratic databases: science on GitHub | Python Resources for Bioinformatics | Scoop.it
Scientists are turning to a software–development site to share data and code.
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A Focus on Allostery, Programming for Bioscientists, Ten Simple Rules for Effective Stats Practice: the PLOS Comp Biol June Issue | PLOS Biologue

A Focus on Allostery, Programming for Bioscientists, Ten Simple Rules for Effective Stats Practice: the PLOS Comp Biol June Issue | PLOS Biologue | Python Resources for Bioinformatics | Scoop.it
Check out our highlights from the PLOS Computational Biology June 2016 Issue:   History of allostery. Image Credit: Liu, Nussinov. Allostery Focus Feature This Focus Feature, led by Jin Liu and Ruth Nussinov, turns the spotlight on allostery.
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thombashi/pytablewriter

pytablewriter is a python library to write a table in various formats: CSV / HTML / JavaScript / JSON / Markdown / Excel / Pandas / Python / reStructuredText

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vinta/awesome-python

awesome-python - A curated list of awesome Python frameworks, libraries, software and resources

awesome-python - A curated list of awesome Python frameworks, libraries, software and resources

 
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Virus trading cards

Virus trading cards | Python Resources for Bioinformatics | Scoop.it
Deadly but beautiful - The trading cards you probably don’t want to collect.

Deadly but beautiful - The trading cards you probably don’t want to collect.

 
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