y a number of metrics, including:

• Overall assembly length
• Number of scaffolds/contigs
• Length of longest scaffold/contig
• Scaffold/contig N50 and N90Assembly base composition, in particular percentage GC and percentage Ns
• CEGMA completeness
• Scaffold/contig length/count distribution

assembly-stats supports two widely used presentations of these values, tabular and cumulative length plots, and introduces an additional circular plot that summarises most commonly used assembly metrics in a single visualisation. Each of these presentations is generated using javascript from a common (JSON) data structure, allowing toggling between alternative views, and each can be applied to a single or multiple assemblies to allow direct comparison of alternate assemblies.

Tabular presentation allows direct comparison of exact values between assemblies, the limitations of this approach lie in the necessary omission of distributions and the challenge of interpreting ratios of values that may vary by several orders of magnitude.

Address of the bookmark: https://github.com/rjchallis/assembly-stats

If you currently use a spreadsheet like Microsoft Excel for data analysis, you might be interested in taking a look at this tutorial on how to transition from Excel to R by Tony Ojeda. The tutorial explains how to use R functions in place of Excel formulas, including tools like =AVERAGE and =VLOOKUP. For the most part, it uses modern R packages to keep the R code clear and concise.

You'll likely still be using Excel as a data source, though, so you'll also want to check out this guide to importing data from Excel to R from MilanoR.

Reference http://www.r-bloggers.com/an-r-tutorial-for-microsoft-excel-users/

The assembly process can be summarized as follows:

1. overlap
2. patch reads
3. overlap (again)
4. scrubbing
5. assembly graph construction and touring
6. optional read correction
7. fasta file creation

Address of the bookmark: https://github.com/schloi/MARVEL

• Function(data, options)

Even quit is a function

• q()

So is help

help(read.table)

Provides the help page for the FUNCTION ‘read.table’

help.search(“t test”)

Searches for help pages that might relate to the phrase ‘t test’

NOTE: quotes are needed for search strings, they are not needed when referring to data objects or function names.

There is a short cut for help,

? shows the help page on a function name, same as help(function)

?read.table

?? searches for help pages on functions, same as help.search(‘phrase’)

??“t test”

Information is usually returned from a function, by default this is printed to screen

read.table(‘data.tsv’)

This can always be stored, we call what it is stored in an ‘object’

mydata

here mydata is an object of type dataframe

Reminder:

• Vector: a list of numbers, equivalent to a column in a table
• Data Frame = a collection of vectors. Equivalent to a table

Hint:

• Up/Down arrow keys can be use to cycle through previous commands

Address of the bookmark: https://github.com/Reedwarbler/GAPPadder

Central University of Jharkhand has been granted funds by the Department of Biotechnology (DBT), Govt. of India to establish “DBT-Boost to CUJ Interdisciplinary Life Sciences Departments for Education and Research” Applications are invited for the Assistant Professor on purely temporary basis. The appointments shall be initially for a period of one year, renewable every year depending on the satisfactory performance, till the end of project.

Position: ASSISTANT PROFESSOR (Total 03)
Salary: 45,000/- (fixed) per month
Essential Qualifications: i. Good academic record with at least 55% marks (or an equivalent grade in a point scale wherever grading system is followed) at the master’s degree level with specialization in Biodiversity and Systematic/ Systems Biology/ Biophysics/ Bioinformatics from an Indian University, or an equivalent degree from an accredited foreign university. ii. Besides fulfilling the above qualifications, the candidates must have cleared the National Eligibility Test (NET) conducted by the UGC, CSIR or similar test accredited by the UGC like SLET/SET. iii. Notwithstanding anything contained in i. and ii. candidates, who are or have been awarded Ph.D Degree in accordance with the University Grants Commission (Minimum Standards and Procedure for Award of Ph.D. Degree) Regulation, 2009, shall be exempted from therequirement of the minimum eligibility condition of NET/SLET/SET for recruitment and appointment of Assistant Professor. iv. NET/SLET/SET shall also not be required for such disciplines for which NET/SLET/SET in not conducted.
Desirable: Preference will be given to candidates having Ph.D in any of the above mentioned areas with NET

IMPORTANT DATES TO REMEMBER :

Last Date to Apply for this job 24/3/2015

REFERENCE:

Central University of Jharkhand (CUJ) Recruitment 2015 – Advt. No.: CUJ/Advt./14-15/15 Date: 26th Feb. 2015.

More at http://cuj.ac.in/careers.php

http://molbiolabccsumrt.webs.com/

Applications are invited for one post of RA in a DBT funded research project “Creation of Bioinformatics Infrastructure Facility (BIF) for the promotion of Biology Teaching through Bioinformatics (BTBI) Scheme of BTISet”.

Candidate should have a Ph.D. degree in Bioinformatics/Biotechnology/Genetics and Plant Breeding with adequate experience in the area of Bioinformatics. If a suitable candidate for the post of RA is not available, a JRF/SRF may be appointed.

Candidate for the post of JRF/SRF should have Master’s degree in relevant subject with adequate experience in the area of Bioinformatics and should be NET/DBT-BINC qualified

Interested candidates may send their bio-data to Prof. H. S. Balyan (hsbalyan@gmail.com) (in exceptional case, bio-data may also be submitted at the time of interview) and attend the interview on Monday, March 30, 2015 at 11:00 AM in the Department of Genetics & Plant Breeding, Ch. Charan Singh University, Meerut. Candidates shall bring their original documents at the time of interview for verification. No interview letters will be issued and no TA/DA will be paid.

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

Given the potential relevance of efficient haplotyping in several analysis pipelines, we have designed and engineered pWhatsHap, a parallel, high-performance version of WhatsHap. pWhatsHap is embedded in a toolkit developed in Python and supports genomics datasets in standard file formats. Building on WhatsHap, pWhatsHap exhibits the same complexity exploring a number of possible solutions which is exponential in the coverage of the dataset. The parallel implementation on multi-core architectures allows for a relevant reduction of the execution time for haplotyping, while the provided results enjoy the same high accuracy as that provided by WhatsHap, which increases with coverage.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1170-y

Address of the bookmark: https://bitbucket.org/whatshap/whatshap