<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/32076?offset=80 https://bioinformaticsonline.com/bookmarks/view/32190/dbg2olcefficient-assembly-of-large-genomes-using-long-erroneous-reads-of-the-third-generation-sequencing-technologies Wed, 19 Apr 2017 10:09:51 -0500 https://bioinformaticsonline.com/bookmarks/view/32190/dbg2olcefficient-assembly-of-large-genomes-using-long-erroneous-reads-of-the-third-generation-sequencing-technologies <![CDATA[DBG2OLC:Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies]]> DBG2OLC:Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies

Our work is published in Scientific Reports:

Ye, C. et al. DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci. Rep. 6, 31900; doi: 10.1038/srep31900 (2016).

http://www.nature.com/articles/srep31900

The manual can be downloaded from:

https://github.com/yechengxi/DBG2OLC/raw/master/Manual.docx

To use precompiled versions,please go to:

https://github.com/yechengxi/DBG2OLC/tree/master/compiled

 

Address of the bookmark: https://github.com/yechengxi/DBG2OLC

]]> Jit https://bioinformaticsonline.com/researchlabs/view/32713/salzberg-lab Mon, 15 May 2017 05:14:01 -0500 <![CDATA[Salzberg lab]]> We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.

https://salzberg-lab.org/

]]> https://bioinformaticsonline.com/bookmarks/view/36592/lachesis-genome-assembly-with-hi-c-based-contact-probability-maps-lachesis Mon, 14 May 2018 04:26:30 -0500 https://bioinformaticsonline.com/bookmarks/view/36592/lachesis-genome-assembly-with-hi-c-based-contact-probability-maps-lachesis <![CDATA[LACHESIS: Genome Assembly with Hi-C-based Contact Probability Maps (LACHESIS)]]> LACHESIS is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale de novo genome assembly.

Further information about LACHESIS, including source code, documentation and a user's guide are available at: http://shendurelab.github.io/LACHESIS.

Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nature Biotechnology 2013 Dec;31(12):1119-25. doi: 10.1038/nbt.272. PubMed PMID: 24185095.

 

http://shendurelab.github.io/LACHESIS/

Address of the bookmark: http://shendurelab.github.io/LACHESIS/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/42672/introduction-to-bioinformatics-and-computational-biology Mon, 25 Jan 2021 01:32:30 -0600 https://bioinformaticsonline.com/bookmarks/view/42672/introduction-to-bioinformatics-and-computational-biology <![CDATA[Introduction to Bioinformatics and Computational Biology]]> This is the course material for STAT115/215 BIO/BST282 at Harvard University.

Xiaole Shirley Liu (lead instructor)
Joshua Starmer
Martin Hemberg
Ting Wang
Feng Yue

Ming Tang
Yang Liu
Jack Kang
Scarlett Ge
Jiazhen Rong
Phillip Nicol
Maartin De Vries

We thank many colleagues in the community, who helped Dr. Liu in prepare the STAT115/215 BIO/BST282 course over the years. 

Address of the bookmark: https://liulab-dfci.github.io/bioinfo-combio/

]]> Jit https://bioinformaticsonline.com/opportunity/view/43292/bioinformatics-scientist-production-bioinformatics-south-san-francisco-ca Thu, 19 Aug 2021 08:45:24 -0500 <![CDATA[Bioinformatics Scientist, Production Bioinformatics @ South San Francisco, CA]]> wist is looking for a Bioinformatics Scientist to join our Production Bioinformatics Team. You will work alongside research scientists, software engineers and data scientists to further deliver on our mission to expand access to best-in-class synthetic biology and next-generation sequencing applications. You will be developing and engineering tools to better evaluate and build hardened, production quality pipelines, optimize data quality, and automate lab and bioinformatics processes. Our ideal candidate is an organized problem solver with a background in developing and building novel production-quality bioinformatics tools and packages. Equally excellent communication skills and a proven ability to work independently are required.

More at https://boards.greenhouse.io/twistbioscience/jobs/3135495?gh_src=9ecc0b941us

]]> https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

]]> Jit https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective Mon, 14 May 2018 16:33:03 -0500 https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective <![CDATA[Learning Python Programming - a bioinformatician perspective !]]> Python Programming is a general purpose programming language that is open source, flexible, powerful and easy to use. One of the most important features of python is its rich set of utilities and libraries for data processing and analytics tasks. In the current era of big biological data, python and biopython is getting more popularity due to its easy-to-use features which supports big data processing.

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

  • It's widely used in the scientific community
  • It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
  • It lend itself well to being integrated with other, existing tools
  • It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

  • Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
  • Python is dynamically typed, so the variables types are defined automatically.
  • Python is strongly typed. So the developers need to cast the type manually.
  • Less code and more use makes it more acceptable.
  • Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.

]]> Rahul Nayak https://bioinformaticsonline.com/news/view/39606/amity-university-bioinformatics-summer-program-kolkata Tue, 11 Jun 2019 21:27:10 -0500 https://bioinformaticsonline.com/news/view/39606/amity-university-bioinformatics-summer-program-kolkata <![CDATA[Amity University Bioinformatics Summer Program - Kolkata]]> Registrations are now open for the 2019 Summer Bioinformatics Training program at Amity University, Kolkata. The program will focus on introductory topics for life science students. We will review important history, topics and challenges bioinformatics can help address in the context of basic research, discovery and industry.

Read more: https://edu.t-bio.info/amity-university-summer-bioinformatics-program-registrations-are-open/

]]> eliabrodsky https://bioinformaticsonline.com/bookmarks/view/42552/bioinformatics-workbook Tue, 05 Jan 2021 22:42:32 -0600 https://bioinformaticsonline.com/bookmarks/view/42552/bioinformatics-workbook <![CDATA[bioinformatics workbook]]> This books assumes that the reader has some knowledge of biology and basic understanding of the Unix command line. However, for the beginner, the appendix contains introductory material and tips/tricks for common bioinformatic problems, that is referred to for more information throughout the book.

https://bioinformaticsworkbook.org/

Address of the bookmark: https://bioinformaticsworkbook.org/

]]> biogeek https://bioinformaticsonline.com/bookmarks/view/43042/bioinformatics-in-thailand Wed, 28 Apr 2021 02:04:56 -0500 https://bioinformaticsonline.com/bookmarks/view/43042/bioinformatics-in-thailand <![CDATA[Bioinformatics in Thailand !]]> Our international PhD and master programs are designed for students who desire focused training in the elements of biology, computer science, and information technology needed for a successful career in the exciting new discipline of Bioinformatics & Systems Biology. Students in our program will receive comprehensive training in omics analysis, database design and management, software engineering and programming (including web-based development), simulation techniques and modeling, and data integration. Each student will apply their skills to a practical project, where they will design and implement a solution to a real-world problem under the guidance of an experienced mentor in industry or academia.

https://bioinformatics.kmutt.ac.th/about.html

Duangrudee Tanramluk (Ajarn Wi) uses computational biology and machine learning to tackle the key to drug design problems via MANORAA webserver.

https://mb.mahidol.ac.th/en/bioinformatics/

https://graduate.mahidol.ac.th/inter/

This international Doctorate programme is designed to further broaden students’ knowledge in Bioinformatics and Molecular Biology to their maximum capability. 

http://www.mbb.psu.ac.th/programmes/phd

Ph.D. program in Bioinformatics and Computational Biology is a joint effort of the Faculty of Science and Faculty of Medicine, Chulalongkorn University. The program has study plans for both applicants who hold a bachelor’s degree and applicants who hold a master’s degree in any related fields of study.

http://www.bioinfo.sc.chula.ac.th/ph-d-program-specialization/

Additional detail 

https://www.biotec.or.th/en/index.php/research/research-units/genome-technology-research-unit

https://tbrcnetwork.org/labtbrc/index.php/bioinformatics-and-chemoinformatics/

https://genomicsthailand.com/Genomic/home

Address of the bookmark: https://bioinformatics.kmutt.ac.th/

]]> Shruti Paniwala