BOL: Related items

Web Apollo: a web-based genomic annotation editing platform

Jit — Fri, 28 Jul 2017 04:48:17 -0500

Web Apollo is the first instantaneous, collaborative genomic annotation editor available on the web. One of the natural consequences following from current advances in sequencing technology is that there are more and more researchers sequencing new genomes. These researchers require tools to describe the functional features of their newly sequenced genomes. With Web Apollo researchers can use any of the common browsers (for example, Chrome or Firefox) to jointly analyze and precisely describe the features of a genome in real time, whether they are in the same room or working from opposite sides of the world.

Address of the bookmark: http://genomearchitect.github.io/

Apollo: First instantaneous, collaborative genomic annotation editor available on the Web

Jit — Fri, 31 May 2019 19:55:39 -0500

Apollo is a plug-in for the JBrowse Genome Viewer.
In addition to genes and pseudogenes, users can annotate ncRNAs (snRNA, snoRNA, tRNA, rRNA), miRNAs, repeat regions, and transposable elements; each annotation type has its own configuration of the ‘Information Editor’.
History tracking with undo/redo functions is available.
Users are able to directly set an annotation to a specific state, choosing from the ‘History’ display.
Adding and updating PubMed IDs will prompt users with a publication title to confirm their submission.
Gene Ontology (GO) terms are supported and GO ID auto-completion has been incorporated.
Users may access a ‘Recent Changes’ page.
Help page with Apollo specific content is available.

Address of the bookmark: http://genomearchitect.github.io/

SCFBio have developed Sanjeevini

Jit — Fri, 17 Nov 2017 07:55:49 -0600

SCFBio have developed a new android based application for drug design called Sanjeevini (https://play.google.com/store/apps/details?id=com.sanjeevini&hl=en). It is available free of charge. You can download it using Google play store. Just search for "Sanjeevini-SCFBIO-CADD" in Google play store. It contains all modules used by current Sanjeevini users. We have worked towards making a unified and easy to use interface. The app now supports all major small molecule file formats (pdb, mol, sdf, mol2 and xyz). The application contains inbuilt visualizer JSmol for easy analysis of results. Users can now directly download the protein files from PDB ("Get protein PDB file" in `FILE` Menu) and prepare it using the easy to use in-built module "Prepare protein/DNA".

SCFBio have worked towards making the process of Job retrieval more streamlined and user friendly. All jobs are now recorded in the "Job results". It can be accessed using the main page of the application. Job status can now be retrieved by clicking on the refresh button against the job ID.

SCFBio have also added a new feature of accessing Jobs run on different android application. Users can retrieve jobs run by other users by sharing the job ID and module name. This feature can be accessed using the Import Jobs option in File menu. We hope this feature will help collaborating groups stay in touch with each other.

The module contains all modules of Sanjeevini suite of software for structure based Drug design.

Sl No.	Module name	Activity
1	Prepare Protein/DNA	Prepares protein/DNA for other modules of Sanjeevini
2	Prepare ligand	Prepares ligands for other modules of Sanjeevini
3	Active site Prediction	Predicts biologically relevant sites in a protein
4	ParDOCK	Rigid Docking of Protein-Ligand complex
5	BAPPL	Binding affinity prediction of Protein-Ligand complex
6	BAPPL Z	Binding affinity prediction of Protein-Zinc-Ligand complex
7	DNA ligand Docking	Rigid Docking of DNA-Ligand complex
8	PreDDICTA	Binding affinity prediction of DNA-Ligand complex
9	SOM Prediction	Rigid Docking of Ligand and CYP proteins
10	Lipinski filters	Checks Lipinski's rule of five for ligand molecule
11	Molecular volume	Calculates volume of a ligand
12	RASPD	Virtual screening of protein molecule to yield hit molecules
13	AADS	Prediction and docking of top 10 biologically relevant sites on protein
14	Intercalate	Rigid Docking of DNA-Ligand complex in intercalation sites
15	DNA sequence to str.	Converts DNA sequence to DNA structure (A-DNA or B-DNA)
16	NRDBSM	Non-redundant database of small molecules
17	TPACM4	Partial charge calculator for small molecules
18	Wiener index	Wiener index calculator for small molecules

The results can be downloaded to the PC desktop for further analysis. For this you can use this accompanying website for this purpose:
http://www.scfbio-iitd.res.in/sanjapp/webSearch/Sanjeevini_webpage.html

On more information on how to use the application please visit: http://scfbio-iitd.res.in/sanjapp/webSearch/doc.html
or
http://scfbio-iitd.res.in/sanjeeviniapp/tut.html

Please email us your valuable comments and suggestions at iitd.scfbio@gmail.com

BEDOPS v2.4.26: high-performance genomic feature operations

Jit — Mon, 12 Jun 2017 10:11:01 -0500

BEDOPS v2.4.26 is a suite of tools to address common questions raised in genomic studies — mostly with regard to overlap and proximity relationships between data sets. It aims to be scalable and flexible, facilitating the efficient and accurate analysis and management of large-scale genomic data.

The overview section of the BEDOPS v2.4.26 documentation summarizes the toolkit, functionality and performance enhancements. The reference table offers documentation for all applications and scripts.

Address of the bookmark: https://github.com/bedops/bedops

Which are the best statistical programming languages to study for a bioinformatician?

Jitendra Narayan — Wed, 10 Jul 2013 14:35:34 -0500

In Bio-informatics based genome sequencing and predicting metabolic pathways research jobs I used Matlab, SAS, SPSS, R and several Bioconductor packages. Matlab had a lot of powerful tools and was easy to use, whereas SPSS is for non-programmers and R need programming skills. I am wondering what other people think is best? or there might not be one specific language but a few that lend themselves best to Bio-informatics work that is math heavy and deals with a large amount of data.

Postdoctoral Associate - Bioinformatics at Duke University Medical Center

Sat, 10 Aug 2013 18:38:38 -0500

The Department of Biostatistics and Bioinformatics at Duke University Medical Center is seeking a Postdoctoral Associate for a one year appointment to work on several high-dimensional research projects. The specific goals of the project are to identify genes or molecular markers that are predictive of clinical outcomes in renal and prostate cancer.

Candidates must have: a PhD degree in statistics, biostatistics or bioinformatics, extensive experience in analyzing high-dimensional data (microarray, SNP, CNVs) and of validation approaches. In addition, experience in penalized regression methods, data base manipulation; and strong programming skills in order to conduct Monte Carlo studies and applications (R). Candidate must have excellent communication skills (verbal, written and presentation), a strong proficiency in Linux system.

This position is available immediately and will be filled as soon as possible. Appointment could be extended beyond the first year based on additional funding.

For more information about the Department of Biostatistics and Bioinformatics, please visit our website: http://www.biostat.duke.edu.

For more info: http://biostat.duke.edu/sites/biostat.duke.edu/files/Halabi%20-%20Postdoc%20Job%20Posting%202013%20updated.pdf

Duke University is an Equal Opportunity/Affirmative Action Employer.

RNA-Seq Data Pathway and Gene-set Analysis Workflows

Jit — Fri, 25 Oct 2013 08:00:48 -0500

It describe the GAGE (Luo et al., 2009) /Pahview (Luo and Brouwer, 2013) workflows on RNA-Seq data pathway analysis and gene-set analysis. The gage package (2.12.0) now includes a new tutorial, “RNA-Seq Data Pathway and Gene-set Analysis Workflows“.

First cover a full workflow from preparation, reads counting, data preprocessing, gene set test, to pathway visualization in about 40 lines of codes. The same workflow can be used for GO analysis or other types of gene set analysis too. We also describe joint workflows, i.e. to do gene-level analysis using one of the major RNA-Seq analysis tools, DEseq/DEseq2, edgeR, limma and Cufflinks, and feed the results into GAGE/Pahview for pathway analysis or visualization. All these workflows are implemented in R/Bioconductor.

The work ows cover the most common situations and issues for RNA-Seq data pathway analysis. Issues like data quality assessment are relevant for data analysis in general yet out the scope of this tutorial. Although we focus on RNA-Seq data here, but pathway analysis work ow remains similar for microarray, particularly step 3-4 would be the same. Please check gage and pathview vigenttes for details.

Note: You need to update to current release versions of R(3.0.2)/ Bioconductor(2.13) to use all the features.

Reference:

Please check it out:
http://bioconductor.org/packages/release/bioc/html/gage.html
http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf

GrapheR !!!

John Parker — Thu, 14 Aug 2014 14:02:17 -0500

What a wonderful gem GrapheR is.... Oh yes it is. GrapheR is a GUI for base graphics in R by http://www.maximeherve.com/. The package provides a graphical user interface for creating base charts in R. It is ideal for beginners in R, as the user interface is very clear and the code is written along side into a text file, allowing users to recreate the charts directly in the console.

Adding and changing legends? Messing around with the plotting window settings? It is much easier/quicker with this GUI than reading the help file and trying to understand the various parameters.
Here is a little example using the iris data set.

library(GrapheR)
data(iris)
run.GrapheR()

This will bring up a window that helps me to create the chart and tweak the various parameters.

Finally, I find the underlying R code in a file created by GrapheR. For more details read also the package vignette, which is available in English, French and German!

RStudio

Jitendra Narayan — Sat, 27 Dec 2014 06:50:58 -0600

RStudio IDE is a powerful and productive user interface for R. It’s free and open source, and works great on Windows, Mac, and Linux.

The developers and expert trainers are the authors of several popular R packages, including ggplot2, plyr, lubridate, and others.

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

http://www.rstudio.com/products/RStudio/

Address of the bookmark: http://www.rstudio.com/

A guide for complete R beginners :- R Syntax

Archana Malhotra — Fri, 20 Feb 2015 23:41:03 -0600

R is a functional based language, the inputs to a function, including options, are in brackets. Note that all dat and options are separated by a comma

Function(data, options)

Even quit is a function

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