<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/30336?offset=730 https://bioinformaticsonline.com/news/view/14024/grapher Thu, 14 Aug 2014 14:02:17 -0500 https://bioinformaticsonline.com/news/view/14024/grapher <![CDATA[GrapheR !!!]]> 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.

image

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!

]]> John Parker https://bioinformaticsonline.com/blog/view/44551/bioinformatic-tools-for-pathogens-informatics-at-cvr Sat, 08 Jun 2024 15:59:46 -0500 https://bioinformaticsonline.com/blog/view/44551/bioinformatic-tools-for-pathogens-informatics-at-cvr <![CDATA[Bioinformatic tools for pathogens informatics at CVR]]>

Novel sequencing and analytical approaches focused on studying viruses and virus-host interactions. Below you will find summaries and links to a number of bioinformatic tools that have been developed @ CVR.

The database-integrated genome-screening (DIGS) tool provides a framework for implementing automated in silico screening of sequence databases using BLAST in combination with a relational database (MySQL).

DisCVR is a Diagnostic tool for detecting known human viruses in clinical samples from Next-Generation Sequencing (NGS) data. The tool uses a simple and straightforward Graphical User Interface and is optimized on Windows OS without compromising speed and accuracy.

DiversiTools is a computational tool that is specifically tailored towards viral HTS data sets and the analysis of the underlying viral populations that they represent. It was initially developed in collaboration with a number of virologists interested in characterising the intra-host diversity of viral populations and studying their evolution across transmission chains at the micro-evolutionary scale.

GLUE is a flexible data-centric bioinformatics environment for virus sequence data, with a focus on virus evolution and genomic variation. GLUE has been applied to a range of viruses. A GLUE-based resource focused on Hepatitis C virus is HCV-GLUE.

Tanoti is a BLAST guided reference based short read aligner. It is developed for maximising alignment in highly variable next generation sequence data sets (Illumina).

ViCTree is a bioinformatic framework that automatically selects new candidate virus sequences from GenBank, generates multiple sequence alignments, calculates a maximum likelihood phylogeny and integrates the sequences into the existing phylogenetic trees. For more information click here.

Viral Host Predictor provides a fast and simple way to predict the hosts and vectors of RNA viruses from viral sequences.

GRACy is a bioinformatic tool designed for the analysis of Illumina data originated from Human cytomegalovirus samples. GRACy can be used to perform read quality filtering, genotyping, de novo assembly, variant detection, annotation and data submission to public database.

LoReTTA (Long Read Template Targeted Assembler) is a reference assisted de novo assembler specifically designed to deal with PacBio reads generated from viral genomes. 

BingleSeq is a R-package enables the user-friendly analysis of count tables obtained by both Bulk RNA-Seq and single-cell RNA-Seq protocols. The development of BingleSeq focused on providing a flexible and intuitive user experience.

]]> Abhi https://bioinformaticsonline.com/news/view/14186/pybedtools Wed, 20 Aug 2014 01:03:41 -0500 https://bioinformaticsonline.com/news/view/14186/pybedtools <![CDATA[pybedtools]]> pybedtools is a Python wrapper for Aaron Quinlan's BEDtools programs (https://github.com/arq5x/bedtools), which are widely used for genomic interval manipulation or "genome algebra". pybedtools extends BEDTools by offering feature-level manipulations from with Python. See full online documentation, including installation instructions, at http://pythonhosted.org/pybedtools/.

More at http://pythonhosted.org/pybedtools/

A powerful toolset for genome arithmetic.http://code.google.com/p/bedtools/

]]> Shruti Paniwala https://bioinformaticsonline.com/news/view/2267/added-video-feature-in-bol Tue, 13 Aug 2013 17:42:16 -0500 https://bioinformaticsonline.com/news/view/2267/added-video-feature-in-bol <![CDATA[Added video feature in BOL]]> Just in: Added video features in BOL, now you can watch and share your favourite bioinformatics video tutorials.

Share your favourite video tutorial or lectures on BOL at http://bioinformaticsonline.com/videolist/all . You can also add video in you groups.

Note: Other than bioinformatics video material/tutorial will be deleted without any prior warning.

]]> Jitendra Narayan https://bioinformaticsonline.com/researchlabs/view/14756/roderic-guigo-lab Mon, 01 Sep 2014 17:13:00 -0500 <![CDATA[Roderic Guigó Lab]]> Research in our group focuses on the investigation of the signals involved in gene specification in genomic sequences (promoter elements, splice sites, translation initiation sites, etc…). We are interested both in the mechanism of their recognition and processing, and in their evolution. In addition, but related to this basic component of our research, our group is also involved in the development of software for gene prediction and annotation in genomic sequences. Our group also actively participates in the analysis of many eukaryotic genomes and it in involved in the NIH-funded ENCODE project. Furthermore we are members of two large cancer-studies consortia (chronic lymphocytic leukemia "CLL" and Breast Cancer -Hospital del Mar/CRG/Roche-).

More at http://big.crg.cat/computational_biology_of_rna_processing

]]> https://bioinformaticsonline.com/bookmarks/view/11175/next-generation-sequencingngs-books Fri, 30 May 2014 04:48:04 -0500 https://bioinformaticsonline.com/bookmarks/view/11175/next-generation-sequencingngs-books <![CDATA[Next generation sequencing(NGS) books]]> Employing different technologies, the purpose of NGS platform is to decode the identity or modification on the nucleotides. NGS platforms evolve quickly and capture the main stream.

This bookmark is created to provide NGS online books links.

Address of the bookmark: http://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/Print_version

]]> Abhimanyu Singh https://bioinformaticsonline.com/opportunity/view/14904/bioinformatics-jrfsrf-position-at-iari Thu, 04 Sep 2014 04:14:01 -0500 <![CDATA[Bioinformatics JRF/SRF position at IARI]]> DIVISION OF NEMATOLOGY
INDIAN AGRICULTURAL RESEARCH INSTITUTE
NEW DELHI 110012
Applications are invited for the posts of one Junior
Research Fellow and one RA in the DBT funded project entitled “ Plant parasitic nematode genome informatics - insilico resource development”. The project is for a period of three years.

Essential qualifications for JRF
: M. Sc. in Bioinformatics with experience in Proteomics, genomics and structural biology. Knowledge of programming language, pearl and database – HTML, CSS,php and Java script.
Essential qualifications for Research Associate:
MSc/MTech in Bioinformatics with three years experience or Ph.D in Bioinformatics with experience in proteomics, genomics and structural biology. Knowledge of programming language, perl and database
– HTML, CSS, Java script. NGS sequence assembly and analysis and algorithm designing.
Age limit : 35 years maximum (5 year relaxation for SC/ST and women candidates)
Emoluments:
JRF: 16,000 + 30% HRA
.
Res Assoc: Rs22,000 + 30% HRA
The post is purely temporary in nature and is co-terminus with the project. The appointment would be initially for one year and may be extended further upon satisfactory performance.
Interested candidates
should send the duly filled application forms (format in the following page ) so as to reach on or before 20.9.2014 along with all the relevant documents.

More at http://www.iari.res.in/files/JRF_RA-03092014-20140903-135319.pdf

]]> https://bioinformaticsonline.com/bookmarks/view/44200/dashboard-designing-tutorial Thu, 02 Mar 2023 06:48:43 -0600 https://bioinformaticsonline.com/bookmarks/view/44200/dashboard-designing-tutorial <![CDATA[Dashboard designing tutorial !]]> Dashboard Design Tutorial

Address of the bookmark: https://github.com/dthill196/SARS-2-Dashboard-Tutorial

]]> BioStar https://bioinformaticsonline.com/poll/view/15000/which-mathstatistics-programming-languageapplication-do-you-most-frequently-use-in-bioinformatics Thu, 04 Sep 2014 17:46:41 -0500 https://bioinformaticsonline.com/poll/view/15000/which-mathstatistics-programming-languageapplication-do-you-most-frequently-use-in-bioinformatics <![CDATA[Which math/statistics programming language/application do you most frequently use in bioinformatics?]]> I'm doing a bit more statistical analysis on some bioinformatics things lately, and I'm curious if there are any programming languages that are particularly good for this NGS computation. What suggestions do you guys have? Are there any languages that have exceptionally good libraries?

]]> John Parker https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial Mon, 27 Nov 2017 16:24:29 -0600 https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial <![CDATA[Single Cell RNAseq data analysis tutorial !!]]>
  • A major breakthrough (replaced microarrays) in the late 00’s and has been widely used since
  • Measures the average expression level for each gene across a large population of input cells
  • Useful for comparative transcriptomics, e.g. samples of the same tissue from different species
  • Useful for quantifying expression signatures from ensembles, e.g. in disease studies
  • Insufficient for studying heterogeneous systems, e.g. early development studies, complex tissues (brain)
  • Does not provide insights into the stochastic nature of gene expression

    • Following are the useful links:

    Single Cell RNAseq data analysis Tutorial

    A step-by-step workflow for low-level analysis of single-cell RNA-seq data

    A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

    SCell: single-cell RNA-seq analysis software

    https://github.com/diazlab/SCell

    Beta-Poisson model for single-cell RNA-seq data analyses

    https://github.com/nghiavtr/BPSC

    Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

    https://research.cchmc.org/pbge/sincera.html

    SC3 – consensus clustering of single-cell RNA-Seq data

    http://biorxiv.org/content/early/2016/09/02/036558

    Citrus: A toolkit for single cell sequencing analysis

    http://biorxiv.org/content/early/2016/09/14/045070

    Single-Cell Resolution of Temporal Gene Expression during Heart Development

    http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

    Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

    http://biorxiv.org/content/early/2016/11/15/087775

    Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

    http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

    SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

    http://biorxiv.org/content/early/2016/11/21/088856

    SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

    https://github.com/hmatsu1226/SCOUP

    scLVM is a modelling framework for single-cell RNA-seq data

    https://github.com/PMBio/scLVM

    Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

    https://github.com/jw156605/SLICER

    SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

    http://www.morgridge.net/SinQC.html

    TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

    https://github.com/zji90/TSCAN

    Visualization and cellular hierarchy inference of single-cell data using SPADE

    http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

    OEFinder: Identify ordering effect genes in single cell RNA-seq data

    https://github.com/lengning/OEFinder

    ]]>     Robert M Willioms