BOL: Related items

CSBB-v1.0

Neel — Wed, 29 Jun 2016 07:33:05 -0500

CSBB is a command line based bioinformatics suite to analyze biological data acquired through varied avenues of biological experiments. CSBB is implemented in Perl, while it also leverages the use of R and python in background for specific modules. Major focus of CSBB is to allow users from biology and bioinformatics community, to get benefited by performing down-stream analysis tasks while eliminating the need to write programming code. CSBB is currently available on Linux, UNIX, MAC OS and Windows platforms.

Currently CSBB provides 13 modules focused on analytical tasks like performing upper-quantile normalization on expression data or convert genome wide gene expression to z-scores when comparing expression data from different platforms.

More at https://github.com/skygenomics/CSBB-v1.0

Address of the bookmark: https://github.com/skygenomics/CSBB-v1.0

Sushi: An R/Bioconductor package for visualizing genomic data

Jit — Wed, 31 Aug 2016 08:29:12 -0500

Sushi: An R/Bioconductor package for visualizing genomic data

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/Sushi/inst/doc/Sushi.pdf

Shinyheatmap

Jit — Fri, 21 Oct 2016 05:12:11 -0500

Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap.

More at http://biorxiv.org/content/early/2016/09/21/076463

Address of the bookmark: http://shinyheatmap.com/

R Graphical Cookbook by Winston Chang

Abhimanyu Singh — Fri, 04 Nov 2016 12:50:30 -0500

R Graphical Cookbook by Winston Chang

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

Method in Comparative genomics !!

Jit — Wed, 09 Nov 2016 16:29:24 -0600

We present methods for the automatic determination of genome correspondence. The algorithms enabled the automatic identification of orthologs for more than 90% of genes and intergenic regions across the four species despite the large number of duplicated genes in the yeast genome. The remaining ambiguities in the gene correspondence revealed recent gene family expansions in regions of rapid genomic change.

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

Welch Lab

Mon, 15 Jul 2013 18:21:13 -0500

They are based in the Department of Genetics at the University of Cambridge.

The research covers diverse areas of evolutionary biology, and molecular evolution in particular. It combines theoretical and empirical approaches, and particularly evolutionary inference from genome sequence data.

Links @ http://www.gen.cam.ac.uk/research/welch/GroupPage/Home.html

Computational Proteomics : Lets remember the basics

Jitendra Narayan — Thu, 01 Aug 2013 17:24:20 -0500

I spend some of my valuable time in computational drug designing sector. I remember my initial proteomics days, playing with interactive protein visualization software and dreaming big. Fortunately or unfortunately, I switched to genomics and handling the genomic floods in Petabytes which is expected to be in Brontobytes in coming years. Did I mention Brontobytes ??? Let me call to my server personnel … it gonna tsunami !!!!!

Today, refreshing my old memories I decided to blog about the basic knowledge of biochemistry and computational proteomics skills, but after I found several article on internet saying exactly what I had wanted to say I thought I might as well just redirect BOL's blog readers there instead:

Here is the list of website and videos links which provide a good resource for you basic chemistry need:

http://tecreativ.blogspot.co.uk/2012/09/funny-shortcut-remember-periodic-table.html

This blog have some specific hindi word to remember entire periodic table. I really like

Group 14 (C Si Ge Sn Pb) -> Sentence “Chemistry Sir Gives Sanki Problems”

Sanki is a hindi word which mean crazy :P

I found this link useful as well http://www.wikihow.com/Memorise-the-Periodic-Table

The eagle genomics group provide an element of bioinformatics in periodic tables. Yes you got it, this is not periodic table rather bioinformatics tools with periodicals

http://elements.eaglegenomics.com/

You can also try this video links, which provide you an overview with tricks on periodic tables:

http://www.youtube.com/watch?v=fLSfgNxoVGk

http://www.youtube.com/user/periodicvideos

For drug design educational material, software, tools, databses, viewer, file format and many more stuff at one place http://www.allfordrugs.com/drug-design/ I highly recommend you all computational drug designer to bookmark this page for future studies as well.

I just remember one of my mini project in which I use my flash knowledge (flash .. oh ya flash) to explain amino acids in interactive and user friendly manner. I can’t provide It right now, but promise you to provide a link in near future. I hope that you will enjoy my flashy creative skills :).

Moreover, I found some of very interesting tricks to remember all amino acids chemical formulae on youtube at

http://www.youtube.com/watch?v=gqrWb0fmzQ&list=PL6132651E70BB5575

http://www.youtube.com/watch?v=C2GfoGXfySQ&list=PL6132651E70BB5575

Key points for computer added drug designers?
1. A shortage of biochemistry skills means that you absolutely nowhere in understanding the key concept and do research.
2. Keep handy with complex mathematical formula, before merely running tools or software.
3. Dig it better and deeper guys .. design it.

Personalised Medicine - Animation

Tue, 27 Aug 2013 10:07:24 -0500

Two animated case scenarios set now and in the future. These highlight potential differences in the way patients are treated now, and how they might be treated as healthcare becomes more tailored.

IMTECH Lab

Sun, 15 Sep 2013 09:41:04 -0500

Computer Aided Protein Structure Prediction; Identification of Vaccine
Candidates (T-Epitope prediction); Analysis of Nucleotide/Protein Sequences; Development of Web Server/

Software; Creation of Public Domain Resources in Biology
Present Status::

Developing prediction methods for gene, beta-turn, secondary structure and MHC-binding sites.
Area of Interest ::

Comparison of force field simulations. Analysis of DNA-protein interactions using molecular mechanics methods.Drug Target Identification using in silico biology.

More @ http://www.imtech.res.in/bic/index.php?option=com_content&view=article&id=65

PIs: http://www.imtech.res.in/bic/index.php?option=com_content&view=article&id=69

Taverna Workflow Management System

Madhvan Reddy — Thu, 15 Aug 2013 19:34:32 -0500

Taverna is an open source domain independent Workflow Management System – a suite of tools used to design and execute scientific workflows. Taverna has been created by the myGrid project and is funded through a range of organisations and projects.

The Taverna suite is written in Java and includes the Taverna Engine(used for enacting workflows) that powers both the Taverna Workbench(the desktop client application) and the Taverna Server (which allows remote execution of workflows). Taverna is also available as a Command Line Tool for a quick execution of workflows from a terminal.

Address of the bookmark: http://www.taverna.org.uk/