If you have any questions or comments, post them on one of the trackers on BRIG’s SourceForge page: http://sourceforge.net/tracker/?group_id=328245.

Features:

• Images show similarity between a central reference sequence and other sequences as concentric rings.
• BRIG will perform all BLAST comparisons and file parsing automatically via a simple GUI.
• Contig boundaries and read coverage can be displayed for draft genomes; customized graphs and annotations can be displayed.
• Using a user-defined set of genes as input, BRIG can display gene presence, absence, truncation or sequence variation in a set of complete genomes, draft genomes or even raw, unassembled sequence data.
• BRIG also accepts SAM-formatted read-mapping files enabling genomic regions present in unassembled sequence data from multiple samples to be compared simultaneously

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

Address of the bookmark: http://nemo2.sourceforge.net/index.html

There are common options for most BBMap suite programs and depending on the file extension the input/output format is automatically chosen/set.

Using BBMap

Mapping Nanopore reads

BBMap.sh has a length cap of 6kbp. Reads longer than this will be broken into 6kbp pieces and mapped independently.

More at https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html

Address of the bookmark: https://www.biostarhandbook.com/tools/bbmap/bbmap-help.html

You see, for the past couple of years, I’ve been a bit frustrated because OS X does not come with a whole lot of Perl modules pre-installed, and for all I googled, I couldn’t find an “idiot’s” guide for moderately-savvy-but-not-expert users like myself to install modules and dependencies on demand.

The only instructions I could find point to Fink, which basically installs modules in a path that isn’t included in the Perl @INC variable, meaning you have to manually specify the full path to the modules in every script — which is not a lot of fun if you’re developing on OS X and deploying on Red Hat, for instance.

Moreover, Fink doesn’t seem to make every module available, and it’s not very easy to determine which Fink package you need to install if you need a particular module.

So, with a script that called on several apparently unavailable modules, and a deadline looming, I finally decided to suck it up and figure out how to use CPAN to install them:

1) Make sure you have the Apple Developer Tools (XCode) installed.

These are on one of your install discs, or available as a huge but free download from the Apple Developer Connection [free registration required] or the Mac App Store. I thought I had them, but apparently when we upgraded that computer to Tiger, they went missing.

If you don’t have this stuff installed, your installation will fail with errors about unavailable commands.

1.5) Install Command Line Tools (Recent XCode versions only)

(Thank you to Tom Marchioro for informing me about this step.)

Older versions of XCode installed the command line tools (which are required to properly install CPAN modules) by default, but apparently newer ones do not. To check whether you have the command line tools already installed, run the following from the Terminal:

$ which make

This command checks the system for the “make” tool. If it spits out something like /usr/bin/make you’re golden and can skip ahead to Step 2. If you just get a new prompt and no output, you’ll need to install the tools:

1. Launch XCode and bring up the Preferences panel.
2. Click on the Downloads tab
3. Click to install the Command Line Tools

If you like, you can run which make again to confirm that everything’s installed correctly.

2) Configure CPAN.

$ sudo perl -MCPAN -e shell

perl> o conf init

This will prompt you for some settings. You can accept the defaults for almost everything (just hit “return”). The two things you must fill in are the path to make (which should be /usr/bin/make or the value returned when you run which make from the command line) and your choice of CPAN mirrors (which you actually choose don’t really matter, but it won’t let you finish until you select at least one). If you use a proxy or a very restrictive firewall, you may have to configure those settings as well.

If you skip Step 2, you may get errors about make being unavailable.

3) Upgrade CPAN

$ sudo perl -MCPAN -e 'install Bundle::CPAN'

Don’t forget the sudo, or it’ll fail with permissions errors, probably when doing something relatively unimportant like installing man files.

This will spend a long time downloading, testing, and compiling various files and dependencies. Bear with it. It will prompt you a few times about dependencies. You probably want to enter “yes”. I agreed to everything it asked me, and everything turned out fine. YMMV of course. If everything installs properly, it’ll give you an “OK” at the end.

4) Install your modules. For each module….

$ sudo perl -MCPAN -e 'install Bundle::Name'

or

$ sudo perl -MCPAN -e 'install Module::Name'

This will install the module and its dependencies. Nice, eh? Again, don’t forget the sudo.

The first time you run this after upgrading CPAN, it may prompt you to configure again (see Step 2). If you accept its offer to try to configure itself automatically, it may just run through everything without a problem.

There are a couple of potential pitfalls with specific modules (such as theLWP::UserAgent / HEAD issue), but most have workarounds, and I haven’t run into anything that wasn’t easily recoverable.

And that’s it!

Did you find this useful? Is there anything I missed?

THIRUVANANTHAPURAM - 695 562

Traineeship on Bioinformatics(2)

First class M.Sc. Bioinformatics/ Agriculture/ Botany/ Biotechnology @ Rs.8000

Those candidates who fulfill the above criteria may attend a walk PCC Coordinator in-interview on 28th November 2016 at Saraswathy Thangavelu center, JNTBGRI, Puthenthope, Thiruvananthapuram between at 10.00 am and 2.00 pm with all the relevant document and testimonials.

No TA/DA will be given to candidates for attending the interview.

More Info : http://jntbgri.res.in/downloads/traineeship_notification.pdf

Address of the bookmark: http://eforge.cs.ucl.ac.uk/eFORGE.v1.2/?documentation

Targeted bait capture is a technique for sequencing many loci simultaneously based on bait sequences. HybPiper pipeline starts with high-throughput sequencing reads (for example from Illumina MiSeq), and assigns them to target genes using BLASTx or BWA. The reads are distributed to separate directories, where they are assembled separately using SPAdes. The main output is a FASTA file of the (in frame) CDS portion of the sample for each target region, and a separate file with the translated protein sequence.

HybPiper also includes post-processing scripts, run after the main pipeline, to also extract the intronic regions flanking each exon, investigate putative paralogs, and calculate sequencing depth. For more information, please see our wiki.

HybPiper is run separately for each sample (single or paired-end sequence reads). When HybPiper generates sequence files from the reads, it does so in a standardized directory hierarchy. Many of the post-processing scripts rely on this directory hierarchy, so do not modify it after running the initial pipeline. It is a good idea to run the pipeline for each sample from the same directory. You will end up with one directory per run of HybPiper, and some of the later scripts take advantage of this predictable directory structure.

Address of the bookmark: https://github.com/mossmatters/HybPiper

A very nice book by Winston Chang for R ethusiast. The R code presented in these pages is the R code actually used to produce the Figures in the book. There will be differences compared to the code chunks shown in the text of the book, but in most cases the differences will be that these pages contain additional code to lay out multiple plots on a single "page".

The code presented for each figure is self-contained, i.e., all code required to produce the figure is included. This means that there is sometimes considerable overlap of code between several figures  In some cases, it may be necessary to install an add-on package from CRAN to get the code to run.

More books at http://www.e-reading.club/bookreader.php/137370/C486x_APPb.pdf

Randomness and probability are two differnet concepts: probaility is a measure (according to measure theory) which measures the randomness. Randomness is the object to be measured by probability. For example, probability is a mapping from randomness to the real number between 0 and 1. The similar examples are that the entropy measures the uncertanity; product of length and width measures the area of rectangle etc.

Please see “A mathematical theory of ability measure” by N. Kong ets for more examples to answer this question.

We present methods for the identification of protein-coding genes based on their patterns of nucleotide conservation across related species. We observed the pressure to conserve the reading frame of functional proteins and developed a test for gene identification with high sensitivity and specificity. We used this test to revisit the genome of S. cerevisiae, reducing the overall gene count by 500 genes (10% of previously annotated genes) and refining the gene structure of hundreds of genes. We present novel methods for the systematic de novo identification of regulatory motifs. The methods do not rely on previous knowledge of gene function and in that way differ from the current literature on computational motif discovery. Based on the genome-wide conservation patterns of known motifs, we developed three conservation criteria that we used to discover novel motifs. We used an enumeration approach to select strongly conserved motif cores, which we extended and collapsed into a small number of candidate regulatory motifs. These include most previously known regulatory motifs as well as several noteworthy novel motifs. The majority of discovered motifs are enriched in functionally related genes, allowing us to infer a candidate function for novel motifs.

Our results demonstrate the power of comparative genomics to further our understanding of any species. Our methods are validated by the extensive experimental knowledge in yeast, and will be invaluable in the study of complex genomes like that of human.

Address of the bookmark: http://web.mit.edu/manoli/www/publications/Kellis_JCB_04.pdf