BOL: Related items

Powerful books for learning data analysis with R

LEGE — Tue, 28 May 2024 07:42:56 -0500

R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R today:

https://csgillespie.github.io/efficientR/

https://r-graphics.org/

https://rstudio-education.github.io/hopr/

https://r-pkgs.org/

https://r4ds.had.co.nz/

Address of the bookmark: https://r-graphics.org/

Postdoc at UBritishColumbia in TroutGenomics

Thu, 22 Dec 2016 08:18:46 -0600

Landscape Genomics Postdoc at UBC A research team at the University of British Columbia’s Department of Zoology and Biodiversity Research Centre is seeking a postdoctoral researcher in landscape genetics of native rainbow trout (Oncorhynchus mykiss).

This project is part of a larger Genome Canada project on genetics and physiology of adaptation to climate change in rainbow trout, and the population genomics component is in the labs of Eric Taylor and Michael Whitlock. The landscape genomics component primarily involves whole genome sequencing approaches to understanding the genomic basis of adaptation to features of habitat, but also to provide insights into phylogeography and the influence of watershed structure on population subdivision in rainbow trout. A PhD in a related field with expertise in basic theory and bioinformatic analysis of population genomics data is required.

The position is available for one year with renewal for up to three additional years. Salary is $55,000 per year plus benefits. To apply, please send a brief cover letter summarizing your qualifications for the position, a CV, and the names, addresses, phone numbers and emails of three references.

Review of applications will begin January 16, 2017. Address application materials to etaylor@zoology.ubc.ca to whom any questions can also be addressed. etaylor@zoology.ubc.ca

Comparison of Short Read De Novo Alignment Algorithms

Rahul Agarwal — Wed, 21 Aug 2013 07:56:01 -0500

Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.

Title: Comparison of Short Read De Novo Alignment Algorithms

Author: Nikhil Gopal

Address of the bookmark: http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf

GenomeRing: alignment visualization based on SuperGenome coordinates

Neel — Wed, 18 Jan 2017 10:24:10 -0600

The number of completely sequenced genomes is continuously rising, allowing for comparative analyses of genomic variation. Such analyses are often based on whole-genome alignments to elucidate structural differences arising from insertions, deletions or from rearrangement events. Computational tools that can visualize genome alignments in a meaningful manner are needed to help researchers gain new insights into the underlying data. Such visualizations typically are either realized in a linear fashion as in genome browsers or by using a circular approach, where relationships between genomic regions are indicated by arcs. Both methods allow for the integration of additional information such as experimental data or annotations. However, providing a visualization that still allows for a quick and comprehensive interpretation of all important genomic variations together with various supplemental data, which may be highly heterogeneous, remains a challenge.

More at https://academic.oup.com/bioinformatics/article/28/12/i7/268598/GenomeRing-alignment-visualization-based-on

Address of the bookmark: http://it.informatik.uni-tuebingen.de/?page_id=185

coursera genome assembly tutorial

Jit — Sat, 25 Nov 2017 08:57:25 -0600

Solutions to Coursera Genome Sequencing (Bioinformatics II)

Address of the bookmark: https://github.com/iansealy/coursera-assembly

Studentship

Tue, 24 Jan 2017 04:26:14 -0600

Advt. No. ACBR/BTBI/BIF/Manpower/ADV/16-17
Research Associate/ Studentship/ Traineeship Jobs opportunity in University of Delhi - Dr. B.R. Ambedkar Center for Biomedical Research (ACBR) on temporary basis
Studentship
No. of Post : 01
Qualifications : Applicants who are pursuing Post Graduate Degree course/ Diploma in Bioinformatics/ Biomedical Sciences/ Biotechnology/Life Sciences/Chemistry/ Biochemistry with a programme in Bioinformatics to carry out the project.
Remuneration : Rs. 8,000/-
Traineeship
No. of Post : 01
Qualifications : Applicants who have completed their Post Graduate degree in Bioinformatics /Biomedical Sciences/Biotechnology/Life Sciences/Chemistry/Biochemistry or any other branch of Life Sciences with knowledge in Bioinformatics
Remuneration : Rs. 8,000/-
Research Associate
No. of Post : 01
Qualifications : Ph.D. in Bio-Medical Sciences/Bioinformatics/ Biotechnology/Life Sciences/ Chemistry/ Bio-Chemistry and related areas
Remuneration : Rs. 36,000/-

Applicant are required to bring applications on plain paper, stating the name, address, date of birth. educational qualifications and experiences and Institute, along with attested photocopies of mark sheets and certificates etc. at the time of Interview. Applications should also be sent by Entail to Dr. Madhu Chopra, Coordinator, BIF Facility. Email: acbrdu.blisnet@nic.in; mchopradu@gmail.com.
Interview Date : 13.02.2017
Time : 10:00 am-12:00 noon
Venue : ACBR

COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly

Jit — Wed, 06 Dec 2017 02:08:14 -0600

An efficient tool called Connecting Overlapped Pair-End (COPE) reads, to connect overlapping pair-end reads using k-mer frequencies. We evaluated our tool on 30× simulated pair-end reads from Arabidopsis thaliana with 1% base error. COPE connected over 99% of reads with 98.8% accuracy, which is, respectively, 10 and 2% higher than the recently published tool FLASH. When COPE is applied to real reads for genome assembly, the resulting contigs are found to have fewer errors and give a 14-fold improvement in the N50 measurement when compared with the contigs produced using unconnected reads.

Address of the bookmark: ftp://ftp.genomics.org.cn/pub/cope

Many-Core Engine (MCE) for Perl example

Jit — Tue, 31 Jan 2017 05:37:50 -0600

MCE spawns a pool of workers and therefore does not fork a new process per each element of data. Instead, MCE follows a bank queuing model. Imagine the line being the data and bank-tellers the parallel workers. MCE enhances that model by adding the ability to chunk the next n elements from the input stream to the next available worker.

CORE MODULES

Three modules make up the core engine for MCE.

MCE::Core: Provides the Core API for Many-Core Engine. The various MCE options are described here.
MCE::Signal: Temporary directory creation, cleanup, and signal handling.
MCE::Util: Utility functions for Many-Core Engine.

MCE EXTRAS

There are 4 add-on modules for use with MCE.

MCE::Candy: Provides a collection of sugar methods and output iterators for preserving output order.
MCE::Mutex: Provides a simple semaphore implementation supporting threads and processes.
MCE::Queue: Provides a hybrid queuing implementation for MCE supporting normal queues and priority queues from a single module. MCE::Queue exchanges data via the core engine to enable queuing to work for both children (spawned from fork) and threads.
MCE::Relay: Enables workers to receive and pass on information orderly with zero involvement by the manager process while running.

MCE MODELS

The models take Many-Core Engine to a new level for ease of use. Two options (chunk_size and max_workers) are configured automatically as well as spawning and shutdown.

MCE::Loop: Provides a parallel loop utilizing MCE for building creative loops.
MCE::Flow: A parallel flow model for building creative applications. This makes use of user_tasks in MCE. The author has full control when utilizing this model. MCE::Flow is similar to MCE::Loop, but allows for multiple code blocks to run in parallel with a slight change to syntax.
MCE::Grep: Provides a parallel grep implementation similar to the native grep function.
MCE::Map: Provides a parallel map model similar to the native map function.
MCE::Step: Provides a parallel step implementation utilizing MCE::Queue between user tasks. MCE::Step is a spin off from MCE::Flow with a touch of MCE::Stream. This model, introduced in 1.506, allows one to pass data from one sub-task into the next transparently.
MCE::Stream: Provides an efficient parallel implementation for chaining multiple maps and greps together through user_tasks and MCE::Queue. Like with MCE::Flow, MCE::Stream can run multiple code blocks in parallel with a slight change to syntax from MCE::Map and MCE::Grep.

MISCELLANEOUS

Miscellaneous additions included with the distribution.

MCE::Examples: Describes various demonstrations for MCE including a Monte Carlo simulation.
MCE::Subs: Exports functions mapped directly to MCE methods; e.g. mce_wid. The module allows 3 options; :manager, :worker, and :getter.

REQUIREMENTS

Perl 5.8.0 or later. PDL::IO::Storable is required in scripts running PDL.

SOURCE AND FURTHER READING

The source, cookbook, and examples are hosted at GitHub.

3d-dna: 3D de novo assembly (3D DNA) pipeline

Jit — Thu, 28 Dec 2017 10:09:37 -0600

This code is designed to enable anyone to reproduce the Hs2-HiC and the AaegL4 genomes reported in: Dudchenko et al., De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science, 2017.

Unless otherwise noted, all terminology below is consistent with this paper, and all references to figures and tables in this readme refer to this paper. Specifically, some of the terminology used below is outlined in Figure S2. The assembly procedure is described in detail in the Supporting Online Materials, specifically in the section labelled “Pipeline description”.

In addition, the pipeline uses tools and methods from Juicer (Durand & Shamim et al., Cell Systems, 2016) and Juicebox (Durand & Robinson et al., Cell Systems, 2016), as well as additional dependencies noted below.

Feel free to post your questions and comments at: http://www.aidenlab.org/forum.html

http://aidenlab.org/documentation.html

Address of the bookmark: https://github.com/theaidenlab/3d-dna

Open Positions in Pasini’s lab

Sat, 04 Feb 2017 08:17:18 -0600

Computational Biologists
Open to PhD-student and Post-doc candidates
We are looking for wet and computational biologists to work on an ERC funded project in our
laboratory located at the Department of Experimental Oncology of the European Institute of
Oncology in Milan (Italy). The project will focus on different aspects of the function of Polycomb
Group proteins and other chromatin modifying activities in relation to their role in regulating cellular
identity in the development of adult tissues.
The candidates will be in charge of computational analysis and data management related to the
project. She/he will directly interact with the wet scientists working in our laboratory while working
embedded in the community of computational biologists present at our institution. The work will
involve the analysis of sequencing data produced with cutting edge technologies to study gene
expression and chromatin environment including data produced on rare cell populations and single
cells. The applicants must have a good knowledge of programming in python/perl/java along with
strong statistical background and performing analysis in R platform. A biological background is
also recommended however it’s not mandatory for application.
Each applicant should submit a full CV (with a detailed description of her/his background,
expertise, achievements and publication records) together with a letter of intent and at least two
contacts for recommendations (for a post-doc position). Competitive salary will be offered based
on the experience of the candidate. Non Italian as well as Italian applicants that have been working
outside Italy (>3yrs.) will have the opportunity to benefit of a full tax deduction for the first three
years of contract.
Applications should be submitted as single PDF to diego.pasini@ieo.it

Lab https://www.ieo.it/en/RESEARCH/People/Researchers/Pasini-Diego/