• 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

https://github.com/kyclark/tiny_python_projects

Address of the bookmark: https://github.com/kyclark/tiny_python_projects

As Dennis Ritchie writes in his paper, "The Development of the C Language",
C is quirky, flawed, and an enormous success. While accidents of history surely helped, it evidently satisfied a need for a system implementation language efficient enough to displace assembly language, yet sufficiently abstract and fluent to describe algorithms and interactions in a wide variety of environments.

C++ has its basis in C - extending it by supporting features meant to encourage and support the development of large programs. Perhaps most importantly, it supports object-oriented programming in a familiar setting and framework (that of C). When C++ was created, one of the initial aims was to retain compatibility with C to as large an extent as possible, and retain its spirit and efficiency. It was possible to convert from C to C++ gradually, thus making use of C++ (initally, at least) as a "better C", and moving on to using other features. This allowed many C programmers to learn C++ quickly (though using C++ effectively requires a major mind-shift for many C programmers)
Are you really interested in C/C++ language for the biological programming? If yes there is good news for you. Bio++ 1.9.0 is available with amazing libraries that can help you to solve approximately all problems related with biology.

Some of the new feature has been added in the latest version, these are as follows:

Support for codon models (including non-homogenous models),
Tools for manipulating Hidden Markov Models,
Improved numerical tools (numerical derivatives, parameter transforms...),
A new library, Bio++ RAA (Remote Acnuc Access), allowing you to fetch public databases like GenBank, EMBL or SwissProt,
Algorithms for plotting trees, with support for vector formats like SVG, Fig or LaTeX-PGF.
So get relax and solve the HMM problems with an ease with Bio++. J
Now the time has been change, the biological programmers are ready to use the C++ libraries of biology. These library are designed in order to reduce the C++ long codes in a small and handy for the biological programmers. Basically, Bio++ is a set of C++ libraries for Bioinformatics, including sequence analysis,phylogenetics, molecular evolution and population genetics.
Bio++ is designed in an extensible object-oriented way, in the C++ language.

Some of the unique features of the libraries are as follows:
Sequence analysis

Sequence and Site objects, with various Alphabet support (DNA, RNA, Proteins, Codons, any 'Word' of a given size).
Several containers available for inner storage, with several implementations. Support for alignments.
Various I/O formats supported: Fasta, Mase, CLustal, Phylip, DCSE, GenBank (sequence only).
Sequence manipulation: truncation, concatenation, sub-sequences, etc.
In silico molecular biology: (reverse) transcription, translation, replication.
Several genetic codes availables: Standard and mitochondrial (vertebrates, echinoderms and other invertabrates)
Amino acids properties: volume, polarity and charge + physico-chemical distance (Miyata and Grantham) + import from any AAIndex entry.
Consensus sequences.
Pairwise alignment.
Similarity score computation.
Sequence bootstrap.
Homogeneity test (Bowker's test).
etc.

Phylogenetics and molecular evolution 
Data structure and IO

Phylogenetic trees.
IO from newick files, with support for multiple entries.

Phylogenetic reconstuction methods

Parsimony (NNI)
Distance matrices estimation and I/O to files in Phylip format.
Distance methods: (U/W)PGMA, NJ, BioNJ.
Maximum likelihood (NNI, including a PhyML-like algorithm).
Mixed distance/ML tree reconstruction (iterative approaches).
Tree consensus methods, bipartitions, bootstrap value computations.

Substitution models

JC, K80, T92, F84, HKY85, TN93, GTR and more for nucleotides,
JC, DSO78, JTT92 + any PAML-formated model description for proteins, with possibility to estimate equilibrium frequencies.
Various codon models: Muse & Gaut 1994, Yang & Nielsen 1998, Goldman & Yang 1994 + user-defined.
Support for rate-across sites models, with virtually any probability distribution, allowing for invariant classes.
Covarion models.
Model including gaps.
Global clock tree likelihood models.
Virtually any kind of non-homogeneous model is supported!
Mixed models (beta).

Molecular evolution tools

Parameter estimation under maximum likelihood.
Ancestral states reconstructions: Marginal likelihood.
(Weighted) substitution mapping.
Sequences simulation under any substitution model, homogeneous or not.

Population genetics

A new file format to deal with codominant markers and bio-sequence data for individuals.
Import and export methods with various population genetics software.
Specific containers for polymorphism data.
Diversity and polymorphism statistics for codominant and sequence data.
Estimation of Wright F-statistics and pairwise genetic distance on codominant markers.
Statistics on synonymous and non synonymous sites for coding sequences
Various 'Neutrality' statistics on sequence data (Tajima, Fu and Li, Rand and Kann ...).
Various measures of linkage disequilibrium.
etc.

Numerical calculus

Numerical tools: extended functions (log, factorial, etc.)
Vector tools: element-wise functions, statistics (mean, var, sd, correlation, information theory)
Classes for matrices implementation.
Linear algebra: eigen decomposition, LU decomposition, inversion, etc.
Random number generation: Quick & Dirty (32bits only), Wichmann and Hill, Knuth. Samplers from probability distributions (uniform, normal, gamma, etc.).
Function object implementation, with first and second order derivatives.
Numerical derivatives computation.
Optimization algorithms: Golden section search, Brent's algorithm, Powell's and Downhill simplex method, but also methods using derivatives like conjugate gradient and Newton's method. Object implementation of these methods, using the event-driven Optmizer interface (works with Function objects).
Statistics: DataTable object, with I/O from CSV files, probability distributions.
etc.

Utils

Files: working on file paths, getting file extensions and names, testing existence, open and store in string arrays, etc.
Text: convert text to any other type and vice versa, remove spaces, tokenize, switch between upper/lower case, etc.
Applications: read options from a file or command line
etc.

Some of the libraries are under development that will be updated by Bio++ developers on there websites.
C/C++ Tutorial
http://www.cbcb.umd.edu/~jeallen/bioinfo/
Tutorial on Bio++
http://162.38.181.25/BioPP/articles/tutorial/index.html
Download Links
http://162.38.181.25/BioPP/articles/download/index.html

Reference:
http://162.38.181.25/BioPP/index.html
Dutheil J, Boussau B. Non-homogeneous models of sequence evolution in the Bio++ suite of libraries and programs. BMC Evol Biol. 2008 Sep 22;8(1):255
Dutheil J, Gaillard S, Bazin E, Glémin S, Ranwez V, Galtier N, Belkhir K. Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics. BMC Bioinformatics. 2006 Apr 4;7:188.
Dutheil JY, Ganapathy G, Hobolth A, Mailund T, Uyenoyama MK, Schierup MH. Ancestral population genomics: the coalescent hidden Markov model approach. Genetics. 2009 Sep;183(1):259-74.
Nabholz B, Mauffrey J-F, Bazin E, Galtier N, Glémin S. Determination of Mitochondrial Genetic Diversity in Mammals. Genetics. 2008 January; 178(1): 351-361.
Galtier N. A model of horizontal gene transfer and the bacterial phylogeny problem. Syst Biol. 2007 Aug;56(4):633-42.
Dutheil J, Galtier N. Detecting groups of coevolving positions in a molecule: a clustering approach. BMC Evol Biol. 2007; 7: 242.
Boussau B, Gouy M. Efficient likelihood computations with nonreversible models of evolution. Syst Biol. 2006 Oct;55(5):756-68.
Dutheil J, Pupko T, Jean-Marie A, Galtier N. A model-based approach for detecting coevolving positions in a molecule. Mol Biol Evol. 2005 Sep;22(9):1919-28.

The manual translation of many, frequently rather complex SPSS scripts often presents itself as a tedious and error-prone task, and represents a rather large obstacle for many analysts and companies to migrate to a modern, open source data management and analysis tool like R. With translate2R this hurdle will be diminished substantially.

Find at https://service.eoda.de/translater/?lang=en

Since this is your first time here, I suggest starting with something simple like Fizz Buzz.

Address of the bookmark: https://code.golf/

Research Area:
Analysis of Next Generation sequence data in cancer
Methods for analysis of structural variation in cancer genomes
Next Generation sequencing in malaria
Computational comparative genomics
Sensitive genomic sequence search techniques using hidden Markov models
Tasmanian devil facial tumour disease

Link @ http://www.wehi.edu.au/faculty_members/dr_tony_papenfuss

The importance of transcriptional control has been explored in a burgeoning line of research over several decades; nevertheless, we are still far from having a complete picture of the regulatory mechanisms of genes and non-coding RNAs, and their influences on different phenotypes and disease states of a cell. Recent shifts towards large-scale analyses of transcriptional regulation on a sequence and epigenetic level are at the forefront of research, mainly due to sequencing technology advancements and a deeper understanding of the fundamental regulatory processes involved.

Arriving at a better understanding of the influence of specific parts of the overall regulatory machinery on disease states is a high priority of the group’s research agenda.

We are seeking an enthusiastic student to join the group as a PhD student. Applicants must have a BSc(Hons) or MSc degree in a relevant discipline and a willingness to learn and apply new techniques and work in a team. Both local and international students are encouraged to apply.

The studentship covers all university fees and an annual tax-exempt stipend of NZ$22,000 for three years.

Sebastian Schmeier recently joined Massey University and started his own research group in Auckland, New Zealand, a city regularly ranked one of the most livable in the world. This is your chance to experience the amazing Auckland lifestyle and the excitement of joining a young new science team, while staying connected to world class scientific networks.

To apply for the post, please send a cover letter stating your interest in the position and why you think you would be a good candidate, a Curriculum Vitae, a copy of your academic transcript, a sample of your written scientific work, and the names of three referees. Applications will be accepted until the position is filled.

Enquiries and applications to Sebastian Schmeier (s.schmeier@massey.ac.nz).

The evolutionary connections between organisms are represented graphically through phylogenetic trees. Due to the fact that evolution takes place over long periods of time that cannot be observed directly, biologists must reconstruct phylogenies by inferring the evolutionary relationships among present-day organisms. 
Application of the techniques that make this possible can be seen in the very limited field of human genetics, such as the ever more popular use of genetic testing to determine a child's paternity, as well as the emergence of a new branch of criminal forensics focused on genetic evidence.
The effect on traditional scientific classification schemes in the biological sciences has been dramatic as well. Work that was once immensely labor- and materials-intensive can now be done quickly and easily, leading to yet another source of information becoming available for systematic and taxonomic appraisal. This particular kind of data has become so popular that taxonomical schemes based solely on molecular data may be encountered. Proponents even claim that taxonomy was previously based on morphology alone, which of course is utter fable.

For additional information on phylogenetics, see list of Phylogenetics Resources on the Internet.

Phylogeny and Reconstructing Phylogenetic Trees: http://aleph0.clarku.edu/~djoyce/java/Phyltree/cover.html
the CBRG and Department of Statistics Phylogeny tutorial: http://www.compbio.ox.ac.uk/tutorials/phylogeny/
TUTORIAL: PHYLOGENETIC ANALYSIS USING PARSIMONY:http://home.cc.umanitoba.ca/~psgendb/GDE/phylogeny/parsimony/phylip.parsimony.html

PHYLIP: http://www.umanitoba.ca/afs/plant_science/psgendb/doc/Phylip/main.html
An Introduction to Molecular Phylogeny: http://bibiserv.techfak.uni-bielefeld.de/gcb04/tutorials/hoef-emden/GCB04Tut.pdf

How to make a phylogenetic tree: http://www.hiv.lanl.gov/content/sequence/TUTORIALS/TREE_TUTORIAL/Treetutorial.html
Phylogenetic Trees: http://cnx.org/content/m11052/latest/
Phylogeny by Ron Shamir: http://www.cs.tau.ac.il/~rshamir/algmb/01/scribe08/lec08.pdf
Introduction to Phylogeny: http://www.utm.edu/departments/cens/biology/rirwin/391/391Phylog.htm
Lecturer notes on Phylogeny: http://www.sbc.su.se/~bens/course_material/phylocourse1/lecture2.pdf
Principles and Practice of Phylogenetic Systematics:http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/LectureNotes.htm

Inferring phylogenetic trees: http://www.cis.hut.fi/Opinnot/T-61.6070/slides2008/pres_6070.pdf

Lecture Notes

Chapter 1 - The Diversity, Classification, and Evolution of Vertebrates:http://academic.emporia.edu/mooredwi/nathist/chap1.htm

Algorithms for Phylogenetic Reconstructions:http://lectures.molgen.mpg.de/Algorithmische_Bioinformatik_WS0405/phylogeny_script.pdf

Phylogeny.fr is a free, simple to use web service dedicated to reconstructing and analysing phylogenetic relationships between molecular sequences. Phylogeny.fr runs and connects various bioinformatics programs to reconstruct a robust phylogenetic tree from a set of sequences. For more detail : http://www.phylogeny.fr/version2_cgi/index.cgi

A Brief Tutorial on Phylogenetics
http://bioss.ac.uk/~dirk/talks/tutorial_phylogenetics.pdf

A Brief Tutorial on Phylogenetics Human Rabbit Chicken
http://bioss.ac.uk/~dirk/talks/psnup_tutorial_phylogenetics.pdf

Phylogenetic Tree Computation Tutorial Overview
http://pga.lbl.gov/Workshop/April2002/lectures/Olken.pdf

MrBayes: A program for the Bayesian inference of phylogeny
http://golab.unl.edu/teaching/SBseminar/manual.pdf

Web sites providing software for the construction of phylogenetic trees

• BioEdit

• Coelocanth-Fish Out of Time

• Computational Biochemistry Research Group

• Digital Taxonomy

• Hennig 86

• Hyperclean from Bioinformatics Solutions, Inc.

• Memorial University of Newfoundland

• Mr. Bayes

• NONA

• Oxford University Evolutionary Biology Group

• Paleobiology Database

• PAUP

• Phylip Homepage

• Poy

• Sinauer Associates

• TNT-Tree Analysis Using New Technology

• Tree Base

• Treefinder

• Tree-Puzzle

• Tree View-Taxonomy and Systematics Group at Glasgow

• Washington University-List of Phylogeny Software

Genomics England will play a key role in building on the UK’s long track record as leader in medical science advances to push the boundaries by unlocking the power of DNA data. The UK will become the first ever country to introduce this technology in its mainstream health system – leading the global race for better tests, better drugs and above all better, more personalised care.

http://www.genomicsengland.co.uk/100k-genome-project/

http://www.interpretomics.co/