Live online courses on statistics with R based on this book, led by the author, are offered regularly; see this page for more information and dates.

The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit. It teaches you:

• Data wrangling - importing, formatting, reshaping, merging, and filtering data in R.
• Exploratory data analysis - using visualisations and multivariate techniques to explore datasets.
• Statistical inference - modern methods for testing hypotheses and computing confidence intervals.
• Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting.
• Simulation - using simulation techniques for sample size computations and evaluations of statistical methods.
• Ethics in statistics - ethical issues and good statistical practice.
• R programming - writing code that is fast, readable, and (hopefully!) free from bugs.

The book includes plenty of examples and more than 200 exercises with worked solutions. The datasets used for the examples and the exercises can be downloaded here.

Address of the bookmark: https://www.modernstatisticswithr.com/

https://cov-lineages.org/resources/pangolin/tutorial.html

Address of the bookmark: https://cov-lineages.org/resources/pangolin/tutorial.html

Background/Motivation:

The dearth of structural information on alpha helical membrane protein (MPs) has hindered thus far the development of reliable knowledge –based potentials that can be used for automatic prediction of trans-membrane (TM) protein structure. While algorithm for identification of TM segments is available, modelling of the domains of alpha helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains. 

Results: In a cross validated test on single sequences, our trans-membrane MM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 86 proteins with supervised topology. These results compare favorably with existing methods. 

Source Code: Matlab

Conclusion/Implementation: Here discriminant data mining approach was used to predict the location and orientation of alpha helices in membrane-spanning proteins. It is based on a first order Markov model (MM) with an architecture that corresponds closely to the biological systems. The model is enriched with three types of states for the loop on the cytoplasmic side (outer loop), loop for the non-cytoplasmic side (inner side), and trans-membrane part. The closed association between the biological and Markov states allows us to infer which part of the model architecture are important to capture the information which encodes the membrane topology, and gain a better understanding of the mechanism and constraints involved. Predictor Model was established by various  Markov decoder , and assignment of the membrane helix boundaries was apparent.

Deadline: 30.09.2019

Requirement:
Microsoft SQL Server: Administration, Transact-SQL
Machine Learning
Know Deutsch und Englisch langauge

Find more @ https://developers.google.com/edu/python/

python -V

at the terminal. For purposes of this example we'll assume you have 3.1 installed. You'll next need to execute the following commands:

sudo apt-get install python2.6 idle-python2.6
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.1 1
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2.6 10
sudo update-alternatives --config python

This last command will allow you to choose which version of python to use by default. If you have done everything above correctly, python2.6 should already be set as the default. If it is not, choose it to be the default. From now on, running python should start version 2.6.

sudo update-alternatives --remove-all python
sudo ln -s python3.1 /usr/bin/python

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

Run pip install luigi to install the latest stable version from PyPI. Documentation for the latest release is hosted on readthedocs.

Run pip install luigi[toml] to install Luigi with TOML-based configs support.

Address of the bookmark: https://github.com/spotify/luigi

• DNA-mapping*
• ChIP-seq*
• RNA-seq*
• ATAC-seq*
• scRNA-seq
• Hi-C
• Whole Genome Bisulfite Seq/WGBS

(*Also available in "allele-specific" mode)

snakePipes can be installed via conda :

'conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'.

Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online.

Address of the bookmark: https://github.com/maxplanck-ie/snakepipes

MitoHiFi was first developed to assemble the mitogenomes for a wide range of species in the Darwin Tree of Life Project (DToL)

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05385-y 

Address of the bookmark: https://github.com/marcelauliano/MitoHiFi