bcbio-nextgen
Dask
| bcbio-nextgen | Dask | |
|---|---|---|
| 2 | 32 | |
| 973 | 11,999 | |
| 0.2% | 0.6% | |
| 6.2 | 9.6 | |
| 3 days ago | 5 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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bcbio-nextgen
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Deep Sleep May Be the Best Defense Against Alzheimer’s
Re WGS there are a lot of well established tool chains that are FLOSS (eg https://github.com/bcbio/bcbio-nextgen). You could run alignment and variant calling on a beefy workstation. A laptop would potentially work. Easy to test this with publicly available raw data. Another option: The sequencing provider often will run alignment and some default variant calling for you. Annotating and analysing these variants can be done on pretty much any computer, all with open source software. A SNP chip is even easier to deal with as the computational requirements are less.
Interpreting the results is a more manual process. Really depends on what you are interested in.
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Advices on how to advance in the field to a beginner
There are plenty of workshops and free documentation online, a few from the top of my head : https://hbctraining.github.io/main/, https://software-carpentry.org/lessons/, https://github.com/bcbio/bcbio-nextgen.
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Numba - NumPy aware dynamic Python compiler using LLVM
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
NumPy - The fundamental package for scientific computing with Python.
NetworkX - Network Analysis in Python
Biopython - Official git repository for Biopython (originally converted from CVS)
PyDy - Multibody dynamics tool kit.
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python