knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of rsrm is to generate the distribution of Restriction Sites on given DNA sequence and also construct the Restriction Map with given single and double digests reaction result.
You can install the development version from GitHub with:
# install.packages("devtools") require("devtools") install_github("LoadingBFX/rsrm", build_vignettes = TRUE) library("rsrm")
To run the shinyApp:
runRsrmApp()
browseVignettes("rsrm")
findre()
, run the Shiny app byrunRsrmApp()
findre()
will find all of the positions can be cut by the enzymes in dataset. You can set the number of enzymes to display in the plot. Default value is 6, which means 6 enzymes will be displayed on the plot for both side of target sequence (totally 12 sites, 6 for left, 6 for right, 1 for target, if there are enough sites.).
example:
r
seq1 <- 'GGCAGATTCCCCCTAACGTCGGACCCGCCCGCACCATGGTCAGGCATGCCCCTCCTCATCGCTGGGCACAGCCCAGAGGGT
ATAAACAGTGCTGGAGGCTGGCGGGGCAGGCCAGCTGAGTCCTGAGCAGCAGCCCAGCGCAGCCACCGAGACACC
ATGAGAGCCCTCACACTCCTCGCCCTATTGGCCCTGGCCGCACTTTGCATCGCTGGCCAGGCAGGTGAGTGCCCC'
name1 <- 'Example gene for test findre (EGFTF)'
seq2 <- 'ACGTCG'
name2 <- 'Target'
result <- findre(name1, seq1, name2, seq2)
result
Second function is to construct RM for unknown sequence which is also useful for DNA Sequencing.
Unknown sequence and fragment obtained in single and double digests reaction were:
EcoRI: 70, 30kb
HaeIII: 60, 40kb
EcoRI + HaeIII: 40, 30, 20, 10kb
Q: construct a restriction map of unknown sequence.
Now you can easily put single and double digests reaction result in rsmap()
function to construct thr RM for you
r
frag1 <- c(70, 30)
frag2 <- c(60, 40)
dou_dig <- c(40, 30, 20, 10)
enz1 <- "EcoRI"
enz2 <- "HaeIII"
rsmap(enz1, frag1, enz2, frag2, dou_dig)
library("rsrm") lsf.str("package:rsrm")
The author of the package is Fanxing Bu. sanitizeSeq() is adapted from Dr. Steipe's function dbSanitizeSequence() in course BCH441 ABC-units. It has been indicated and referenced in the utility.R file. The rest functions were authored by Fanxing.
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models The R Journal 8/1, pp. 205-233 https://cran.r-project.org/web/packages/mclust/vignettes/mclust.html
Boris Steipe BCH441 - Bioinformatics http://steipe.biochemistry.utoronto.ca/abc/index.php/Bioinformatics_Main_Page
This package was developed as part of an assessment for 2019 BCB410H: Applied Bioinformatics, University of Toronto, Toronto, CANADA.
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