Sequence difference plot
Here we use the data published in Potato Research
(Chang et al. 2015) as an example.
fas <- list.files(system.file("examples","GVariation", package="seqcombo"),
pattern="fas", full.names=TRUE)
fas
## [1] "/tmp/RtmpCZkAnU/Rinst2674123d62a6/seqcombo/examples/GVariation/A.Mont.fas"
## [2] "/tmp/RtmpCZkAnU/Rinst2674123d62a6/seqcombo/examples/GVariation/B.Oz.fas"
## [3] "/tmp/RtmpCZkAnU/Rinst2674123d62a6/seqcombo/examples/GVariation/C.Wilga5.fas"
The input fasta file should contains two aligned sequences. User need to specify which sequence (1 or 2, 1 by default) as reference. The seqdiff
function will parse the fasta file and calculate the nucleotide differences by comparing the non-reference one to reference.
## sequence differences of Mont and CF_YL21
## 1181 sites differ:
## A C G T
## 286 315 301 279
We can visualize the differences by plot
method:
We can parse several files and visualize them simultaneously.
Sequence similarity plot
Session info
Here is the output of sessionInfo()
on the system on which this document was compiled:
## R version 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] igraph_1.2.5 ggplot2_3.3.0 emojifont_0.5.3 tibble_3.0.1
## [5] seqcombo_1.10.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.4.6 BiocManager_1.30.10 compiler_4.0.0
## [4] pillar_1.4.3 XVector_0.28.0 sysfonts_0.8
## [7] prettydoc_0.3.1 tools_4.0.0 zlibbioc_1.34.0
## [10] digest_0.6.25 evaluate_0.14 lifecycle_0.2.0
## [13] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.5
## [16] rvcheck_0.1.8 yaml_2.2.1 parallel_4.0.0
## [19] xfun_0.13 proto_1.0.0 withr_2.2.0
## [22] showtextdb_2.0 stringr_1.4.0 dplyr_0.8.5
## [25] knitr_1.28 Biostrings_2.56.0 S4Vectors_0.26.0
## [28] vctrs_0.2.4 IRanges_2.22.0 tidyselect_1.0.0
## [31] stats4_4.0.0 grid_4.0.0 cowplot_1.0.0
## [34] glue_1.4.0 R6_2.4.1 rmarkdown_2.1
## [37] farver_2.0.3 purrr_0.3.4 magrittr_1.5
## [40] scales_1.1.0 htmltools_0.4.0 ellipsis_0.3.0
## [43] BiocGenerics_0.34.0 showtext_0.7-1 assertthat_0.2.1
## [46] colorspace_1.4-1 labeling_0.3 stringi_1.4.6
## [49] munsell_0.5.0 crayon_1.3.4
References
Chang, Fei, Fangluan Gao, Jianguo Shen, Wenchao Zou, Shuang Zhao, and Jiasui Zhan. 2015. “Complete Genome Analysis of a PVYN-Wi Recombinant Isolate from Solanum Tuberosum in China.” Potato Research 58 (4):377–89. https://doi.org/10.1007/s11540-015-9307-3.