SVbyEye, an open-source R package to visualize and annotate sequence-to-sequence alignments along with various functionalities to process alignments in PAF format. The tool facilitates the characterization of complex SVs in the context of sequence homology helping resolve the mechanisms underlying their formation. Availability and implementation SVbyEye is available at https://github.com/daewoooo/SVbyEye.

Author: David Porubsky

Address of the bookmark: https://github.com/daewoooo/SVbyEye

# plumber.R

#* Echo back the input
#* @param msg The message to echo
#* @get /echo
function(msg=""){
  list(msg = paste0("The message is: '", msg, "'"))
}

#* Plot a histogram
#* @png
#* @get /plot
function(){
  rand <- rnorm(100)
  hist(rand)
}

#* Return the sum of two numbers
#* @param a The first number to add
#* @param b The second number to add
#* @post /sum
function(a, b){
  as.numeric(a) + as.numeric(b)
}

Address of the bookmark: https://www.rplumber.io/

• multi-class semantic segmentation using U-Net architecture
• multi-class object detection using YOLOv3 architecture

You can install the latest version of platypus with remotes package:

Note that in order to install platypus you need to install keras and tensorflow packages and Tensorflow version >= 2.0.0 ( Tensorflow 1.x will not be supported!)

Address of the bookmark: https://github.com/maju116/platypus

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

Some of the prominent features of the package are:

• visualizing polyploidy simultaneously on the same plot.
• annotating groups of elements as distinct colors.
• creating chromosome heatmaps.
• adjusting chromosome range or visualizing chromosome regions such as genes
• adding labels to the plot
• adding hyperlinks to each element

Address of the bookmark: https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html

This guide contains three different sets of instructions.  If you use RStudio, you can follow the “Ba-sic Instructions” in Section 2 which involve using RStudio’s interface. 

Address of the bookmark: http://web.mit.edu/insong/www/pdf/rpackage_instructions.pdf

https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1

Address of the bookmark: https://github.com/wanyuac/GeneMates

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

If you use circlize in your publications, I would be appreciated if you can cite:

Gu, Z. (2014) circlize implements and enhances circular visualization in R. Bioinformatics. DOI: 10.1093/bioinformatics/btu393

Address of the bookmark: http://zuguang.de/circlize_book/book/