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/RtmpDoH7jq/Rinst5c6f5f7d9211/seqcombo/examples/GVariation/A.Mont.fas"
## [2] "/tmp/RtmpDoH7jq/Rinst5c6f5f7d9211/seqcombo/examples/GVariation/B.Oz.fas"
## [3] "/tmp/RtmpDoH7jq/Rinst5c6f5f7d9211/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 3.5.2 (2018-12-20)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-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_1.2.2 ggplot2_3.1.0 emojifont_0.5.2 tibble_1.4.2
## [5] seqcombo_1.4.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.0 compiler_3.5.2 pillar_1.3.1
## [4] plyr_1.8.4 XVector_0.22.0 bindr_0.1.1
## [7] sysfonts_0.8 prettydoc_0.2.1 tools_3.5.2
## [10] zlibbioc_1.28.0 digest_0.6.18 evaluate_0.12
## [13] gtable_0.2.0 pkgconfig_2.0.2 rlang_0.3.0.1
## [16] rvcheck_0.1.3 yaml_2.2.0 parallel_3.5.2
## [19] xfun_0.4 proto_1.0.0 bindrcpp_0.2.2
## [22] withr_2.1.2 showtextdb_2.0 stringr_1.3.1
## [25] dplyr_0.7.8 knitr_1.21 Biostrings_2.50.1
## [28] S4Vectors_0.20.1 IRanges_2.16.0 tidyselect_0.2.5
## [31] stats4_3.5.2 grid_3.5.2 cowplot_0.9.3
## [34] glue_1.3.0 R6_2.3.0 rmarkdown_1.11
## [37] purrr_0.2.5 magrittr_1.5 scales_1.0.0
## [40] htmltools_0.3.6 BiocGenerics_0.28.0 showtext_0.5-1
## [43] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3
## [46] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0
## [49] 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.