Similarity Score Matrix and HeatMap for Nucleic and Amino acid K-mers
kmeRs: K-mers Similarity Score Matrix and HeatMap
| Similarity Score Matrix and HeatMap for nucleic and amino acid k-mers. Similarity score is evaluated by Point Accepted Mutation (PAM) and BLOcks SUbstitution Matrix (BLOSUM). The 30, 40, 70, 120, 250 and 62, 45, 50, 62, 80, 100 matrix versions are available for PAM and BLOSUM, respectively. Alignment is evaluated by local 1 and global 2 alignment. |  |
- 2.1 Functions
- 2.2 Examples
- 2.3.1 How to display BLOSUM matrix used for amino acides calculation?
- 2.3.2 How to find the most ‘different’ k-mer from the ‘given’ set of k-mers?
- 2.3.3 How to find the most ‘different’ k-mer to ‘whole’ given set of k-mers?
- 2.3.4 How to calculate basic statistics for the matrix?
- 2.3.5 How to display a similarity matrix as a heatmap?
- 2.3 Tutorials
1. Installation
Bioconductor Packages need to be installed first,
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Biostrings")
BiocManager::install("pwalign")
BiocManager::install("BiocGenerics")
then install from CRAN
install.packages("kmeRs")
or development version from GitHub
install.packages("devtools")
devtools::install_github("urniaz/kmeRs/kmeRs/")
2. Documentation
2.1 Functions
The documentation for package including user guides, package vignettes, descriptions of the functions and other documentation is simply accessible by running command below.
# To show the manual and functions' descriptions
help(package = "kmeRs")
2.2 Examples
2.3.1 How to display BLOSUM matrix used for amino acides calculation?
Simply apply the kmeRs_similarity_matrix function and mark the appropriate matrix, here BLOSUM62.
# Simple BLOSUM62 similarity matrix for all amino acid nucleotides
BLOSUM62 <- kmeRs_similarity_matrix(submat = "BLOSUM62")
# Fancy knitr table
knitr::kable(BLOSUM62)
| G | A | L | M | F | W | K | Q | E | S | P | V | I | C | Y | H | R | N | D | T | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| G | 6 | 0 | -4 | -3 | -3 | -2 | -2 | -2 | -2 | 0 | -2 | -3 | -4 | -3 | -3 | -2 | -2 | 0 | -1 | -2 |
| A | 0 | 4 | -1 | -1 | -2 | -3 | -1 | -1 | -1 | 1 | -1 | 0 | -1 | 0 | -2 | -2 | -1 | -2 | -2 | 0 |
| L | -4 | -1 | 4 | 2 | 0 | -2 | -2 | -2 | -3 | -2 | -3 | 1 | 2 | -1 | -1 | -3 | -2 | -3 | -4 | -1 |
| M | -3 | -1 | 2 | 5 | 0 | -1 | -1 | 0 | -2 | -1 | -2 | 1 | 1 | -1 | -1 | -2 | -1 | -2 | -3 | -1 |
| F | -3 | -2 | 0 | 0 | 6 | 1 | -3 | -3 | -3 | -2 | -4 | -1 | 0 | -2 | 3 | -1 | -3 | -3 | -3 | -2 |
| W | -2 | -3 | -2 | -1 | 1 | 11 | -3 | -2 | -3 | -3 | -4 | -3 | -3 | -2 | 2 | -2 | -3 | -4 | -4 | -2 |
| K | -2 | -1 | -2 | -1 | -3 | -3 | 5 | 1 | 1 | 0 | -1 | -2 | -3 | -3 | -2 | -1 | 2 | 0 | -1 | -1 |
| Q | -2 | -1 | -2 | 0 | -3 | -2 | 1 | 5 | 2 | 0 | -1 | -2 | -3 | -3 | -1 | 0 | 1 | 0 | 0 | -1 |
| E | -2 | -1 | -3 | -2 | -3 | -3 | 1 | 2 | 5 | 0 | -1 | -2 | -3 | -4 | -2 | 0 | 0 | 0 | 2 | -1 |
| S | 0 | 1 | -2 | -1 | -2 | -3 | 0 | 0 | 0 | 4 | -1 | -2 | -2 | -1 | -2 | -1 | -1 | 1 | 0 | 1 |
| P | -2 | -1 | -3 | -2 | -4 | -4 | -1 | -1 | -1 | -1 | 7 | -2 | -3 | -3 | -3 | -2 | -2 | -2 | -1 | -1 |
| V | -3 | 0 | 1 | 1 | -1 | -3 | -2 | -2 | -2 | -2 | -2 | 4 | 3 | -1 | -1 | -3 | -3 | -3 | -3 | 0 |
| I | -4 | -1 | 2 | 1 | 0 | -3 | -3 | -3 | -3 | -2 | -3 | 3 | 4 | -1 | -1 | -3 | -3 | -3 | -3 | -1 |
| C | -3 | 0 | -1 | -1 | -2 | -2 | -3 | -3 | -4 | -1 | -3 | -1 | -1 | 9 | -2 | -3 | -3 | -3 | -3 | -1 |
| Y | -3 | -2 | -1 | -1 | 3 | 2 | -2 | -1 | -2 | -2 | -3 | -1 | -1 | -2 | 7 | 2 | -2 | -2 | -3 | -2 |
| H | -2 | -2 | -3 | -2 | -1 | -2 | -1 | 0 | 0 | -1 | -2 | -3 | -3 | -3 | 2 | 8 | 0 | 1 | -1 | -2 |
| R | -2 | -1 | -2 | -1 | -3 | -3 | 2 | 1 | 0 | -1 | -2 | -3 | -3 | -3 | -2 | 0 | 5 | 0 | -2 | -1 |
| N | 0 | -2 | -3 | -2 | -3 | -4 | 0 | 0 | 0 | 1 | -2 | -3 | -3 | -3 | -2 | 1 | 0 | 6 | 1 | 0 |
| D | -1 | -2 | -4 | -3 | -3 | -4 | -1 | 0 | 2 | 0 | -1 | -3 | -3 | -3 | -3 | -1 | -2 | 1 | 6 | -1 |
| T | -2 | 0 | -1 | -1 | -2 | -2 | -1 | -1 | -1 | 1 | -1 | 0 | -1 | -1 | -2 | -2 | -1 | 0 | -1 | 5 |
2.3.2 How to find the most ‘different’ k-mer from the ‘given’ set of k-mers?
In this example, the most ‘different’ k-mer to “GATTACA” sequence will be indicated from given set of heptamers. Here, 7 heptamer (being an anagram of the movie title “GATTACA”) are given, as follow:
# Given hexamers
kmers_given <- c("GATTACA", "ACAGATT", "GAATTAC", "GAAATCT", "CTATAGA", "GTACATA", "AACGATT")
# Matrix calculation
kmers_mat <- kmeRs_similarity_matrix(q = c("GATTACA"), x = kmers_given , submat = "BLOSUM62")
# Fancy knitr table
knitr::kable(kmers_mat)
| GATTACA | |
|---|---|
| GATTACA | 37 |
| ACAGATT | 1 |
| GAATTAC | 15 |
| GAAATCT | 19 |
| CTATAGA | 7 |
| GTACATA | 12 |
| AACGATT | 4 |
Now, applying kmeRs_score function the total score is calculated and the matrix is sorted by increasing score value. The lowest value (in case of BLOSUM) indicates the most ‘different’ sequence from given k-mers, in contrast to the highest value which indicates the most similar one.
# Score and sort the matrix
kmers_res <- kmeRs_score(kmers_mat)
# Fancy knitr table
knitr::kable(kmers_res)
| GATTACA | Sum | |
|---|---|---|
| ACAGATT | 1 | 1 |
| AACGATT | 4 | 4 |
| CTATAGA | 7 | 7 |
| GTACATA | 12 | 12 |
| GAATTAC | 15 | 15 |
| GAAATCT | 19 | 19 |
| GATTACA | 37 | 37 |
As can be observed, the most ‘different’ sequence to GATTACA is ACAGATT with total score equal to 1 and the most similar to GATTACA sequence is of course GATTACA sequence with the highest score equal to 37.
2.3.3 How to find the most ‘different’ k-mer to ‘whole’ given set of k-mers?
In this example, the most ‘different’ k-mer to whole given set of heptamers will be indicated. The same heptamers as in example 2 are used.
# Given hexamers
kmers_given <- c("GATTACA", "ACAGATT", "GAATTAC", "GAAATCT", "CTATAGA", "GTACATA", "AACGATT")
# Matrix calculation
kmers_mat <- kmeRs_similarity_matrix(q = kmers_given, submat = "BLOSUM62")
# Score the matrix and sort by decreasing score
kmers_res <- kmeRs_score(kmers_mat)
# Fancy knitr table
knitr::kable(kmers_res)
| GATTACA | ACAGATT | GAATTAC | GAAATCT | CTATAGA | GTACATA | AACGATT | Sum | |
|---|---|---|---|---|---|---|---|---|
| CTATAGA | 7 | 3 | 6 | -2 | 37 | 11 | 0 | 62 |
| AACGATT | 4 | 24 | 1 | 8 | 0 | 6 | 37 | 80 |
| ACAGATT | 1 | 37 | 1 | 8 | 3 | 9 | 24 | 83 |
| GAATTAC | 15 | 1 | 37 | 18 | 6 | 9 | 1 | 87 |
| GTACATA | 12 | 9 | 9 | 9 | 11 | 37 | 6 | 93 |
| GATTACA | 37 | 1 | 15 | 19 | 7 | 12 | 4 | 95 |
| GAAATCT | 19 | 8 | 18 | 37 | -2 | 9 | 8 | 97 |
As can be observed, the most ‘different’ sequence to all given heptamers is CTATAGA with score equal to 62 and the most similar sequence is GAAATCT with the highest score equal to 97.
2.3.4 How to calculate basic statistics for the matrix?
Applying function kmeRs_statistics to the result matrix the basic statistics can be calculated as additional rows. When summary_statistics_only is set to TRUE only summary table is returned. It is much more elegant way to present results, especially in case of ‘big data’ output.
# Calculate stats
kmers_stats <- kmeRs_statistics(kmers_res)
# Fancy knitr table
knitr::kable(kmers_stats[ ,1:(dim(kmers_stats)[2] - 4) ])
| GATTACA | ACAGATT | GAATTAC | GAAATCT | CTATAGA | GTACATA | AACGATT | Sum | |
|---|---|---|---|---|---|---|---|---|
| CTATAGA | 7.00 | 3.00 | 6.00 | -2.00 | 37.00 | 11.00 | 0.00 | 62.00 |
| AACGATT | 4.00 | 24.00 | 1.00 | 8.00 | 0.00 | 6.00 | 37.00 | 80.00 |
| ACAGATT | 1.00 | 37.00 | 1.00 | 8.00 | 3.00 | 9.00 | 24.00 | 83.00 |
| GAATTAC | 15.00 | 1.00 | 37.00 | 18.00 | 6.00 | 9.00 | 1.00 | 87.00 |
| GTACATA | 12.00 | 9.00 | 9.00 | 9.00 | 11.00 | 37.00 | 6.00 | 93.00 |
| GATTACA | 37.00 | 1.00 | 15.00 | 19.00 | 7.00 | 12.00 | 4.00 | 95.00 |
| GAAATCT | 19.00 | 8.00 | 18.00 | 37.00 | -2.00 | 9.00 | 8.00 | 97.00 |
| Min | 1.00 | 1.00 | 1.00 | -2.00 | -2.00 | 6.00 | 0.00 | 62.00 |
| Max | 37.00 | 37.00 | 37.00 | 37.00 | 37.00 | 37.00 | 37.00 | 97.00 |
| Mean | 13.57 | 11.86 | 12.43 | 13.86 | 8.86 | 13.29 | 11.43 | 85.29 |
| SD | 12.08 | 13.64 | 12.62 | 12.40 | 13.16 | 10.63 | 13.83 | 12.04 |
2.3.5 How to display a similarity matrix as a heatmap?
Simply applying function kmeRs_heatmap to the result matrix.
# Heatmap without sum column
kmeRs_heatmap(kmers_res[, -8])
2.3 Tutorials
Package vignettes are avaliable in /kmeRs/vignettes/ folder of the repository or directly from the package by command:
# To show the introduction vignette
vignette("Introduction_to_kmeRs_package", package="kmeRs")
3. Tests
Test are implemented as continuous integration by CircleCI. Continuous integration is a DevOps software development practice where code changes are regularly merge into a central repository, after which automated builds and tests are run.
The source of tests covering the code of the package are available in /kmeRs/tests/ folder of the repository.
4. How to cite
When applying kmeRs please cite both:
- CRAN repository : https://doi.org/10.32614/cran.package.kmers
- Original paper : TBA
5. Acknowledgement
Special thanks to Jason Lin, PhD from Chiba Cancer Center Research Institute, Chiba, Japan for contribution in 2021 as implementing of heatmap function and update deprecated functions. The kmeRs_generate_kmers and kmeRs_twoSeqSim base on orginal source from tcR and rDNAse, respectively.
6. References
-
Needleman, Saul B., and Christian D. Wunsch. 1970. “A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins.” Journal of Molecular Biology 48 (3): 443–53. https://doi.org/10.1016/0022-2836(70)90057-4.
-
Smith, T. F., and M. S. Waterman. 1981. “Identification of Common Molecular Subsequences.” Journal of Molecular Biology 147 (1): 195–97. https://doi.org/10.1016/0022-2836(81)90087-5.