This is a short post giving steps on how to actually install R packages. Let’s suppose you want to install the ggplot2 package. Well nothing could be easier. We just fire up an R shell and type:

> install.packages("ggplot2")

In theory the package should just install, however:

• if you are using Linux and don’t have root access, this command won’t work.
• you will be asked to select your local mirror, i.e. which server should you use to download the package.

Installing packages without root access

First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory /data/Rpackages/ After creating a package directory, to install a package we use the command:

> install.packages("ggplot2", lib="/data/Rpackages/")
> library(ggplot2, lib.loc="/data/Rpackages/")


It’s a bit of a pain having to type /data/Rpackages/ all the time. To avoid this burden,  we create a file .Renviron in our home area, and add the line R_LIBS=/data/Rpackages/ to it. This means that whenever you start R, the directory /data/Rpackages/ is added to the list of places to look for R packages and so:

> install.packages("ggplot2")
> library(ggplot2)

just works!

Setting the repository

Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:

• create a file .Rprofile in your home area.
• Add the following piece of code to it:


cat(".Rprofile: Setting UK repositoryn")
r = getOption("repos") # hard code the UK repo for CRAN
r["CRAN"] = "http://cran.uk.r-project.org"
options(repos = r)
rm(r)


I found this tip in a stackoverflow answer .

More at http://www.binc.co.in/College/Index_New.aspx

BINC notification http://www.binc.co.in/PdfDocuments/Notification.pdf

Few dates to remember:

Starting of online submission of application: March 9, 2015
Last date for submission of application: April 30,2015
Examination consists of two parts:
Part I (Paper I) : June 7, 2015 (10 AM-12 PM)
Part II ( Paper II & III) :June 28, 2015 (9 AM-12 PM & 2 PM-4 PM)

Upgrading to R 3.2.0 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) #load / install+load installr
 
updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.).

If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

NEW FEATURES

• anyNA() gains a recursive argument.
• When x is missing and names is not false (including the default value), Sys.getenv(x, names) returns an object of class "Dlist" and hence prints tidily.
• (Windows.) shell() no longer consults the environment variable SHELL: too many systems have been encountered where it was set incorrectly (usually to a path where software was compiled, not where it was installed). R_SHELL, the preferred way to select a non-default shell, can be used instead.
• Some unusual arguments to embedFonts() can now be specified as character vectors, and the defaults have been changed accordingly.
• Functions in the Summary group duplicate less. (PR#15798)
• (Unix-alikes.) system(cmd, input = ) now uses ‘shell-execution-environment’ redirection, which will be more natural if cmd is not a single command (but requires a POSIX-compliant shell). (Wish of PR#15508)
• read.fwf() and read.DIF() gain a fileEncoding argument, for convenience.
• Graphics devices can add attributes to their description in .Device and .Devices. Several of those included with R use a "filepath" attribute.
• pmatch() uses hashing in more cases and so is faster at the expense of using more memory. (PR#15697)
• pairs() gains new arguments to select sets of variables to be plotted against each other.
• file.info(, extra_cols = FALSE) allows a minimal set of columns to be computed on Unix-alikes: on some systems without properly-configured caching this can be significantly faster with large file lists.
• New function dir.exists() in package base to test efficiently whether one or more paths exist and are directories.
• dput() and friends gain new controls hexNumeric and digits17 which output double and complex quantities as, respectively, binary fractions (exactly, see sprintf("%a")) and as decimals with up to 17 significant digits.
• save(), saveRDS() and serialize() now support ascii = NA which writes ASCII files using sprintf("%a") for double/complex quantities. This is read-compatible with ascii = TRUE but avoids binary->decimal->binary conversions with potential loss of precision. Unfortunately the Windows C runtime’s lack of C99 compliance means that the format cannot be read correctly there in R before 3.1.2.
• The default for formatC(decimal.mark =) has been changed to be getOption("OutDec"); this makes it more consistent with format() and suitable for use in print methods, e.g. those for classes "density", "ecdf", "stepfun" and "summary.lm".

  getOption("OutDec") is now consulted by the print method for class "kmeans", by cut(), dendrogram(), plot.ts() and quantile() when constructing labels and for the report fromlegend(trace = TRUE).

  (In part, wish of PR#15819.)

• printNum() and hence format() and formatC() give a warning if big.mark and decimal.mark are set to the same value (period and comma are not uncommonly used for each, and this is a check that conventions have not got mixed).
• merge() can create a result which uses long vectors on 64-bit platforms.
• dget() gains a new argument keep.source which defaults to FALSE for speed (dput() and dget() are most often used for data objects where this can make dget() many times faster).
• Packages may now use a file of common macro definitions in their help files, and may import definitions from other packages.
• A number of macros have been added in the new ‘share/Rd’ directory for use in package overview help pages, and promptPackage() now makes use of them.
• tools::parse_Rd() gains a new permissive argument which converts unrecognized macros into text. This is used by utils:::format.bibentry to allow LaTeX markup to be ignored.
• options(OutDec =) can now specify a multi-byte character, e.g., options(OutDec = "u00b7") in a UTF-8 locale.
• is.recursive(x) is no longer true when x is an external pointer, a weak reference or byte code; the first enables all.equal(x, x) when x .
• ls() (aka objects()) and as.list.environment() gain a new argument sorted.
• The "source" attribute (which has not been added to functions by R since before R version 2.14.0) is no longer treated as special.
• Function returnValue() has been added to give on.exit() code access to a function’s return value for debugging purposes.
• crossprod(x, y) allows more matrix coercions when x or y are vectors, now equalling t(x) %*% y in these cases (also reported by Radford Neal). Similarly, tcrossprod(x,y) and %*% work in more cases with vector arguments.
• Utility function dynGet() useful for detecting cycles, aka infinite recursions.
• The byte-code compiler and interpreter include new instructions that allow many scalar subsetting and assignment and scalar arithmetic operations to be handled more efficiently. This can result in significant performance improvements in scalar numerical code.
• apply(m, 2, identity) is now the same as the matrix m when it has named row names.
• A new function debuggingState() has been added, allowing to temporarily turn off debugging.
• example() gets a new optional argument run.donttest and tools::Rd2ex() a corresponding commentDonttest, with a default such that example(..) in help examples will run donttest code only if used interactively (a change in behaviour).
• rbind.data.frame() gains an optional argument make.row.names, for potential speedup.
• New function extSoftVersion() to report on the versions of third-party software in use in this session. Currently reports versions of zlib, bzlib, the liblzma from xz, PCRE, ICU, TRE and the iconv implementation.

  A similar function grSoftVersion() in package grDevices reports on third-party graphics software.

  Function tcltk::tclVersion() reports the Tcl/Tk version.

• Calling callGeneric() without arguments now works with primitive generics to some extent.
• vapply(x, FUN, FUN.VALUE) is more efficient notably for large length(FUN.VALUE); as extension of PR#16061.
• as.table() now allows tables with one or more dimensions of length 0 (such as as.table(integer())).
• names(x) now clears the names of call and ... objects.
• library() will report a warning when an insufficient dependency version is masking a sufficient one later on the library search path.
• A new plot() method for class "raster" has been added.
• New check_packages_in_dir_changes() function in package tools for conveniently analyzing how changing sources impacts the check results of their reverse dependencies.
• Speed-up from Peter Haverty for ls() and methods:::.requirePackage() speeding up package loading. (PR#16133)
• New get0() function, combining exists() and get() in one call, for efficiency.
• match.call() gains an envir argument for specifying the environment from which to retrieve the ... in the call, if any; this environment was wrong (or at least undesirable) when thedefinition argument was a function.
• topenv() has been made .Internal() for speedup, based on Peter Haverty’s proposal in PR#16140.
• getOption() no longer calls options() in the main case.
• Optional use of libcurl (version 7.28.0 from Oct 2012 or later) for Internet access:
  • capabilities("libcurl") reports if this is available.
  • libcurlVersion() reports the version in use, and other details of the "libcurl" build including which URL schemes it supports.
  • curlGetHeaders() retrieves the headers for http://, https://, ftp:// and ftps:// URLs: analysis of these headers can provide insights into the ‘existence’ of a URL (it might for example be permanently redirected) and is so used in R CMD check --as-cran.
  • download.file() has a new optional method "libcurl" which will handle more URL schemes, follow redirections, and allows simultaneous downloads of multiple URLs.
  • url() has a new method "libcurl" which handles more URL schemes and follows redirections. The default method is controlled by a new option url.method, which applies also to the opening of URLs via file() (which happens implicitly in functions such as read.table.)
  • When file() or url() is invoked with a https:// or ftps:// URL which the current method cannot handle, it switches to a suitable method if one is available.
• (Windows.) The DLLs ‘internet.dll’ and ‘internet2.dll’ have been merged. In this version it is safe to switch (repeatedly) between the internal and Windows internet functions within an Rsession.

  The Windows internet functions are still selected by flag –internet2 or setInternet2(). This can be overridden for an url() connection via its new method argument.

  download.file() has new method "wininet", selected as the default by –internet2 or setInternet2().

• parent.env<- can no longer modify the parent of a locked namespace or namespace imports environment. Contributed by Karl Millar.
• New function isLoadedNamespace() for readability and speed.
• names(env) now returns all the object names of an environment env, equivalently to ls(env, all.names = TRUE, sorted = FALSE) and also to the names of the corresponding list,names(as.list(env, all.names = TRUE)). Note that although names() returns a character vector, the names have no particular ordering.
• The memory manager now grows the heap more aggressively. This reduces the number of garbage collections, in particular while data or code are loaded, at the expense of slightly increasing the memory footprint.
• New function trimws() for removing leading/trailing whitespace.
• cbind() and rbind() now consider S4 inheritance during S3 dispatch and also obey deparse.level.
• cbind() and rbind() will delegate recursively to methods::cbind2 (methods::rbind2) when at least one argument is an S4 object and S3 dispatch fails (due to ambiguity).
• (Windows.) download.file(quiet = FALSE) now uses text rather than Windows progress bars in non-interactive use.
• New function hsearch_db() in package utils for building and retrieving the help search database used by help.search(), along with functions for inspecting the concepts and keywords in the help search database.
• New function .getNamespaceInfo(), a no-check version of getNamespaceInfo() mostly for internal speedups.
• The help search system now takes keyword entries in Rd files which are not standard keywords (as given in ‘KEYWORDS’ in the R documentation directory) as concepts. For standard keyword entries the corresponding descriptions are additionally taken as concepts.
• New lengths() function for getting the lengths of all elements in a list.
• New function toTitleCase() in package tools, tailored to package titles.
• The matrix methods of cbind() and rbind() allow matrices as inputs which have 2^31 or more elements. (For cbind(), wish of PR#16198.)
• The default method of image() has an explicit check for a numeric or logical matrix (which was always required).
• URLencode() will not by default encode further URLs which appear to be already encoded.
• BIC(mod) and BIC(mod, mod2) now give non-NA numbers for arima() fitted models, as nobs(mod) now gives the number of “used” observations for such models. This fixes PR#16198, quite differently than proposed there.
• The print() methods for "htest", "pairwise.htest" and "power.htest" objects now have a digits argument defaulting to (a function of) getOption("digits"), and influencing all printed numbers coherently. Unavoidably, this changes the display of such test results in some cases.
• Code completion for namespaces now recognizes all loaded namespaces, rather than only the ones that are also attached.
• The code completion mechanism can now be replaced by a user-specified completer function, for (temporary) situations where the usual code completion is inappropriate.
• unzip() will now warn if it is able to detect truncation when unpacking a file of 4GB or more (related to PR#16243).
• methods() reports S4 in addition to S3 methods; output is simplified when the class argument is used. .S3methods() and methods::.S4methods() report S3 and S4 methods separately.
• Higher order functions such as the apply functions and Reduce() now force arguments to the functions they apply in order to eliminate undesirable interactions between lazy evaluation and variable capture in closures. This resolves PR#16093.

More at http://cran.rstudio.com/

Reference: http://www.r-bloggers.com/r-3-2-0-is-released-using-the-installr-package-to-upgrade-in-windows-os/

Bioinformatics Textbook Track

Find more about Rosalind puzzle at http://rosalind.info/problems/list-view/?location=bioinformatics-textbook-track

I will provide solution of all the Rosalind problem with Perl for community.

Check out the right sidebar for more links ...

It was first developed to transfer annotations between different genome assembly versions. However, it can also transfer annotations between strains and even different species, like Plasmodium chabaudi onto P. berghei, between different Leishmania species or Salmonella enterica onto other Salmonella serotypes. RATT is able to transfer any entries present on a reference sequence, such as the systematic id or an annotator's notes; such information would be lost in a de novo annotation.

More at http://ratt.sourceforge.net/

Address of the bookmark: http://ratt.sourceforge.net/

• Automatically improve draft assemblies
• Find variation among strains, including large event detection

Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:

• Single base differences
• Small indels
• Larger indel or block substitution events
• Gap filling
• Identification of local misassemblies, including optional opening of new gaps

More at https://github.com/broadinstitute/pilon/wiki

Address of the bookmark: https://github.com/broadinstitute/pilon/wiki

1. Make friends with local bioinformatics groups
2. Talk to your computing group
3. Obtain clear expectations
4. Rewrite your job description
5. Papers
6. Attend bioinformatics meetings
7. Try first, ask later

Address of the bookmark: http://biomickwatson.wordpress.com/2013/04/23/a-guide-for-the-lonely-bioinformatician/

The Job detail

Advancing the SNP-discovery and polymorphism assessment work across several germplasm panels representing global genetic diversity
Population genetic and genomic analyses, testing the hypothesis related to adaptation in multiple geographic regions
Develop SNP assays from large scale GBS and other re-sequencing data for several target traits utilizing available phenotyping data
Combined analyses of genotypic and phenotypic data for discovery of marker-trait associations, and conducting GWAS
Processing, analyzing, and archiving large-scale genomic data sets, assessing data quality, conducting analyses, interpreting findings, and communicating findings to others including preparation of reports, presentations, posters and journal articles
Providing support to MSc and PhD students on topic related to its major core of research
Any other work assigned by the supervisor

The Person:

PhD in bioinformatics, genetics, computational biology preferably with 1 to 2 years of experience;
familiar with standard bioinformatics tools and scripting languages and emerging and evolving software platforms relevant to bioinformatics and computational biology;
ability to create new analytical pipelines; experience with handling large data sets;
ability to program in at least two of the following: C++, PERL, Python, R, Java.
will use next-generation sequencing technologies to generate marker data for genetic mapping and transcriptome data for expression QTL mapping, and will be responsible for data generation as well as data analysis.

Period and Remuneration: The assignment is for a period of two years, and can be extended for another year depending on performance. ICRISAT pays a very attractive all inclusive lump sum assignment fee payable in Indian Rupees.

How to Apply: Please send your application by email to icrisatjobs@cgiar.org, stating the job title (Special project Scientist-Sorghum Genomics) clearly in the subject column, addressed to the Director, Human Resources and Operations, ICRISAT, Patancheru, Andhra Pradesh 502 324, India, latest by 10 June 2014. The application should include an up-to-date Curriculum Vitae, a short statement of competencies and experience for the position, and the names and addresses (including phone/e-mail) of three referees. Only short-listed candidates will be contacted.

More at: http://www.icrisat.org/careers/Special-Project-Scientist-Sorghum-Genomics.htm

Walk- in- Test cum Interview (based on test) for the selection of Research Associate

under the scheme “Distributed Information Sub Centre –DISC” & Research Assistant under scheme “Phytophthora, Fusarium and Ralstonia diseases of Horticultural and Field Crops” will be held at this Institute as per details indicated below.

WALK -IN- TEST CUM INTERVIEW

Name of the post : Research Associate

Date of Interview : 21-05-2014 at 10.00 AM

No. of posts : One

Qualifications : a)Essential

Ph.D Degree in Bioinformatics OR : Masters degree in Bioinformatics with a minimum of
60% marks or equivalent OGPA with at least two years research experience as evidenced from fellowship/ associateship/training/published papers etc.

b)Desirable: Experience in NGS data analysis.

Emoluments : Rs. 23,000/- per month + HRA (Masters Degree Holders)

Rs. 24,000/- per month + HRA (Ph.D Degree Holders)

Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)

Duration of Project : Till 31-03-2017.

Title of Assigment : Research Assistant (on contract basis)

No. of vacancy : One

Qualification : Essential : Post Graduation in Bioinformatics and Minimum one year experience in NGS data analysis

Desirable : Experience in Perl/Python/R

Remuneration : Rs. 20,000/- per month (consolidated)

Scope of work :

1. Analysis of different file formats and their conversions.

2. Assessing the quality of data and filtering of raw reads.
3. Assembling the raw reads-de novo as well as reference mapping.
4. Compression of aligned reads using Jam tools
5. RNA-seq. Analysis
6. Differential expression testing involving Normalization, Statistical testing, heat map generation & hierarchical clustering
7. Annotating the assembled genome and geneet testing and their validation
8. Metabolic pathway analysis
9. Comparative genomics
10. Setting up of genome browsers.

Period of Assigment : Initially for six months.

Date & Venue of Interview : 21-05-2014 at IISR, Kozhikode at 10.00 AM

More at http://www.spices.res.in/pdf/disc-advtmnt.pdf