<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/32076?offset=70 https://bioinformaticsonline.com/bookmarks/view/31375/cocacola-binning-metagenomic-contigs-using-sequence-composition-read-coverage-co-alignment-and-paired-end-read-linkage Tue, 07 Mar 2017 08:50:57 -0600 https://bioinformaticsonline.com/bookmarks/view/31375/cocacola-binning-metagenomic-contigs-using-sequence-composition-read-coverage-co-alignment-and-paired-end-read-linkage <![CDATA[COCACOLA (binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge)]]> COCACOLA is a general framework that combines different types of information: sequence COmposition, CoverAge across multiple samples, CO-alignment to reference genomes and paired-end reads LinkAge to automatically bin contigs into OTUs. Furthermore, COCACOLA seamlessly embraces customized prior knowledge to facilitate binning accuracy.

News: Python version of COCACOLA is available now!

Address of the bookmark: https://github.com/younglululu/COCACOLA

]]> Jit 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/35798/an-introduction-to-applied-bioinformatics Fri, 02 Mar 2018 04:26:38 -0600 https://bioinformaticsonline.com/bookmarks/view/35798/an-introduction-to-applied-bioinformatics <![CDATA[An Introduction to Applied Bioinformatics]]> IAB is primarily being developed by Greg Caporaso(GitHub/Twitter: @gregcaporaso) in the Caporaso Lab at Northern Arizona University. You can find information on the courses I teach on my teaching website and information on my research and lab on my lab website.

 

Address of the bookmark: http://readiab.org/

]]> Jit https://bioinformaticsonline.com/researchlabs/view/43913/lsugenomics-lab Thu, 07 Jul 2022 05:26:37 -0500 <![CDATA[lsugenomics Lab]]> In our lab, we seek to characterize and to compare genomes in order to better understand genetic and evolutionary processes linking genotypes to phenotypes.

Sequencing and decoding plant genomes have been integral in our approaches.

The overarching goal of our research is to understand how to interpret complex and fascinating messages embedded in genomes.

https://www.lsugenomics.org/

]]> https://bioinformaticsonline.com/pages/view/42810/bioinformatics-in-africa-part3-mali Sat, 06 Feb 2021 13:28:44 -0600 https://bioinformaticsonline.com/pages/view/42810/bioinformatics-in-africa-part3-mali <![CDATA[Bioinformatics in Africa: Part3 - Mali]]> International Center for Excellence in Research (ICER):

The ICER is a research center composed of the following three programs: 1. The Malaria Research and Training Center ­ Parasitology Group,  2. The Malaria Research and Training Center ­ Entomology Group  3. The SEREFO Group.

The first two programs develop biomedical researches in malaria, Filariasis and Leishmaniasis. The  third program develops biomedical researches in tuberculosis and HIV.

Bioinformatics was introduced recently to the ICER and is constantly growing. The ICER has one  team, headed by Sidy SOUMARE, which supports the three programmes in all their needs in  informatics and bioinformatics. This team can beneficiate from some computational facilities (4  blast servers, 15 other servers and around 200 terminals), but the ICER staff needs some training in  order to be able to administrate these facilities.

Research Interest and Activities: The following are the present areas of research interest: 1. Functional genomics 2. Analysis of microarray data 3. Interaction between the vector and the parasite.

]]> BioStar 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