The easiest way to install Gooey is via pip

pip install Gooey 

Alternatively, you can install Gooey by cloning the project to your local directory

git clone https://github.com/chriskiehl/Gooey.git

run setup.py

python setup.py install

Address of the bookmark: https://github.com/chriskiehl/Gooey

Additional Information:

If you have any questions about scientific aspects of this position, please contact Prof. Lars Bertram, head of LIGA (lars.bertram@uni-luebeck.de).

Please contact Ms. Anna Wolbert for further questions about administrative details (recruiting@uksh.de).

Weitere Informationen erhalten Sie auch unter www.uksh.de/karriere.

Wir freuen uns auf Ihre Bewerbung bis zum 15.03.2020 unter Angabe unserer Ausschreibungsnummer 20190570.119.CL.

Example:

if you want to run fastqc for QC of fastq file:

from subprocess import Popen,PIPE,call

p=Popen(["fastqc","-f","fastq","-o", "/home/name/result/","/dev/stdin"],stdin=fopen("read.fastq","r") ,stdout=PIPE,stderr=PIPE)

print p.stderr

p.stdout.close()

More:

http://pymotw.com/2/subprocess/

• 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

Massimiliano Orsini and Simone Carcangiu develope a new and fast fast package "BlaSTorage" to parse and store BLAST results. BlaSTorage shows comparable speed of more basic parser written in compiled languages as C++ and can be easily integrated into web applications or software pipelines.

Find more @ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571973/

http://biowiki.crs4.it/biowiki/MassimilianoOrsini

Code in almost all popular languages using Coding Ground. Edit, compile, execute and share your projects, 100% cloud.

http://www.tutorialspoint.com/codingground.htm

Address of the bookmark: http://www.tutorialspoint.com/codingground.htm

1. Understand the Landscape

Before diving into applications, take the time to understand the bioinformatics job market. Common roles include:

• Bioinformatics Analyst/Scientist: Focused on data analysis and interpretation.
• Computational Biologist: Combines computational techniques with biological research.
• Data Scientist in Genomics: Applies machine learning and statistical models to genomic data.
• Software Developer in Bioinformatics: Designs and develops tools and pipelines for biological research.

Familiarize yourself with the key industries hiring bioinformaticians, such as academia, biotech, pharmaceuticals, healthcare, and agriculture.

2. Build a Strong Foundation

Bioinformatics demands a diverse skill set. Ensure you have a solid foundation in the following areas:

• Programming Skills: Proficiency in Python, R, or Perl is often required. Familiarity with tools like Bash scripting and version control systems (e.g., Git) is a plus.
• Statistics and Data Analysis: Knowledge of statistical methods, machine learning, and data visualization is crucial.
• Biological Knowledge: Understanding genomics, transcriptomics, and proteomics will help you communicate effectively with biologists.
• Specialized Tools and Databases: Be comfortable using tools like BLAST, Bowtie, and databases like NCBI and Ensembl.

3. Create a Winning Resume and Portfolio

Highlight your technical skills, biological knowledge, and relevant experience. Tips for a standout application:

• Tailor your resume to each job, emphasizing skills mentioned in the job description.
• Showcase your experience with real-world datasets by linking to your GitHub profile or online portfolio.
• Include details of any publications, presentations, or significant projects.

4. Network Actively

Networking is often the key to discovering opportunities. Here’s how to build connections:

• Attend Conferences and Workshops: Events like ISMB or specialized bioinformatics workshops are great for meeting professionals.
• Engage Online: Join LinkedIn groups, participate in bioinformatics forums, and follow relevant hashtags on Twitter.
• Leverage Alumni Networks: Connect with alumni from your university who are working in the field.

5. Gain Relevant Experience

Experience is a major factor for hiring managers. Ways to enhance your profile include:

• Internships: Seek out internships in research labs or biotech companies.
• Collaborations: Volunteer to work on projects with professors or peers.
• Open Source Contributions: Participate in bioinformatics software development on platforms like GitHub.

6. Prepare for Interviews

Bioinformatics interviews often combine technical and behavioral questions. Prepare by:

• Reviewing Key Concepts: Refresh your knowledge of algorithms, sequence analysis, and statistical methods.
• Practicing Coding: Be ready to solve coding challenges or discuss code snippets.
• Understanding the Organization: Research their recent projects, publications, or products.
• Preparing Questions: Demonstrate interest by asking about their tools, workflows, or team structure.

7. Stay Resilient and Persistent

Job hunting can be a long process, but persistence pays off. Tips to keep moving forward:

• Keep improving your skills by taking online courses or certifications.
• Stay updated with advancements in bioinformatics by following journals and blogs.
• Apply to multiple positions and don’t get discouraged by rejections. Each application is a learning experience.

Closing Thoughts

Landing a bioinformatics job requires a mix of technical expertise, networking, and resilience. By understanding the market, showcasing your skills effectively, and continuously learning, you’ll be well on your way to a rewarding career in this dynamic field. Remember, the key to cracking the code is perseverance—stay curious, stay determined, and success will follow.

Research Area

We are interested in the development of the statistics and computational methods for the analysis of this data in breast cancer.
We have worked on probabilistic models for subcellular localization, protein-protein interactions, and problems related to chemical genomics.
We are interested in the development of bioinformatics/biostatistical methodology in the analysis of epigenetic/epigenomic data.
We are interested in integrative bioinformatics approaches to learn the gene, gene products, interactions, and regulatory mechanisms involved in mental retardation.

Link @ http://www.mcgill.ca/mcb/

Faculty facilitator/Founder Director for two start-up companies (Leadinvent incubated at IIT, Delhi from 2006-2009 & Novoinformatics, under incubation at IIT Delhi since 2011).

Research Interest
Genome Analysis, Protein Structure Prediction and Drug Design.

Link @ http://www.scfbio-iitd.res.in/