README.md
In vincenzoaellis/malaviR: An R interface to MalAvi
malaviR
This package is an R interface to the global avian haemosporidian database MalAvi (http://mbio-serv2.mbioekol.lu.se/Malavi/). The package includes functions for downloading data from the MalAvi website directly into your R environment and some basic utility functions for manipulating those data. Furthermore, you can use malaviR to BLAST your haemosporidian DNA sequences against the MalAvi database programmatically, which should facilitate comparisons with MalAvi.
The package also includes a key for linking the host taxonomic classifications in MalAvi with the avian taxonomic classifications found on http://birdtree.org/.
Installation
You can install malaviR from github with:
# install.packages("devtools")
devtools::install_github("vincenzoaellis/malaviR", build_vignettes = TRUE)
Then you can load it in your R session with:
library(malaviR)
The tutorial is accessible by calling the package vignette:
browseVignettes("malaviR")
This will open a web browser window. Click the "HTML" link next to the tutorial name (Getting_Started_with_malaviR) to see the tutorial.
Citation
You can cite malaviR as follows:
citation("malaviR")
#>
#> To cite package 'malaviR' in publications use:
#>
#> Vincenzo A Ellis, Staffan Bensch and Björn Canbäck (NA).
#> malaviR: An R interface to MalAvi. R package version 0.1.0.
#> https://github.com/vincenzoaellis/malaviR
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {malaviR: An R interface to MalAvi},
#> author = {Vincenzo A Ellis and Staffan Bensch and Björn Canbäck},
#> note = {R package version 0.1.0},
#> url = {https://github.com/vincenzoaellis/malaviR},
#> }
Download tables from MalAvi
There are nine tables that can be downloaded from the MalAvi website that summarize the database. Here's how you would download the table linking morphological species to genetic lineages:
morph <- extract_table("Morpho Species Summary")
head(morph) # check it out
#> #no Lineage_Name genus species
#> 1 1 ACCFRA01 Leucocytozoon Leucocytozoon toddi
#> 2 2 ACCOP01 Leucocytozoon Leucocytozoon mathisi
#> 3 3 ACNI04 Leucocytozoon Leucocytozoon mathisi
#> 4 4 ALARV01 Haemoproteus Haemoproteus tartakovskyi
#> 5 5 ALARV02 Haemoproteus Haemoproteus tartakovskyi
#> 6 6 ALARV03 Haemoproteus Haemoproteus tartakovskyi
#> Reference_Name comment
#> 1 Barraclough et al 2008
#> 2 Valkiunas et al 2010a
#> 3 Valkiunas et al 2010a
#> 4 Zehtindjiev et al 2012
#> 5 Zehtindjiev et al 2012
#> 6 Zehtindjiev et al 2012
The extract_table() help file lists all nine tables you can download directly into R. Or you can specify "all" and get them all as a list.
BLAST a sequence to MalAvi
You can BLAST your own sequences against the MalAvi database using the blast_malavi() function, which leverages the MalAvi website's already existing BLAST capabilities. Your input sequence just needs to be specified as a character string.
## define a sequence. This is the Plasmodium parasite ACAGR1
ACAGR1 <- "GCAACTGGTGCTTCATTTGTATTTATT
TTAACTTATTTACATATTTTAAGAGGATTAAATTATTC
ATATTCATATTTACCTTTATCATGGATATCTGGATTAA
TAATATTTTTAATATCTATAGTAACAGCTTTTATGGGT
TACGTATTACCTTGGGGTCAAATGAGTTTCTGGGGTGC
TACCGTAATAACTAATTTATTATATTTTATACCTGGAC
TAGTTTCATGGATATGTGGTGGATATCTTGTAAGTGAC
CCAACCTTAAAAAGATTCTTTGTACTACATTTTACATT
TCCTTTTATAGCTTTATGTATTGTATTTATACATATAT
TCTTTCTACATTTACAAGGTAGCACAAATCCTTTAGGG
TATGATACAGCTTTAAAAATACCCTTCTATCCAAATCT
TTTAAGTCTTGATATTAAAGGATTTAATAATGTATTAG
TATTATTTTTAGCACAAAGTTTATTTGGAATACT"
## BLAST it against the MalAvi database and save the top five hits to a data frame
hits <- blast_malavi(ACAGR1)
hits # check it out
#> Lineage Score Identities Gaps Strand Coverage Perfect.Match
#> 1 ACAGR1 885 479/479 0/479 Plus/Plus 479 Yes
#> 2 SGS1 880 478/479 0/479 Plus/Plus 479 No
#> 3 CXPIP20 876 476/477 0/477 Plus/Plus 477 No
#> 4 YWT4 874 477/479 0/479 Plus/Plus 479 No
#> 5 SERCAN01 874 477/479 0/479 Plus/Plus 479 No
Download the MalAvi sequence alignments
Using the extract_alignment() function, you can download all of the sequences on MalAvi, or the complete or nearly complete sequences only, or the sequences associated with morphological species only. These alignments will appear as objects of the class DNAbin, defined in the ape package.
## download all sequences
all.seqs <- extract_alignment("all seqs")
all.seqs #check it out
#> 2825 DNA sequences in binary format stored in a matrix.
#>
#> All sequences of same length: 479
#>
#> Labels:
#> H_ACAED01
#> P_ACAGR1
#> H_ACAGR2
#> H_ACATEN01
#> L_ACCBRE01
#> L_ACCBRE02
#> ...
#>
#> Base composition:
#> a c g t
#> 0.294 0.133 0.135 0.438
The extract_alignment() help file lists the names of the sequence alignments that the function understands. You can also identify and remove repeated haplotypes (several typically exist due to ambiguous base pairs) in the alignments with the clean_alignment() function.
Clean lineage names from MalAvi sequence alignments
The lineage names in the MalAvi sequence alignments have been modified to include extra information and therefore do not match the lineage names in the rest of the database. The clean_names() function removes the extra information so that the sequence alignment lineage names match those in the rest of the database.
For example, the lineage names in the alignment files look like this:
## lineage names are stored in the rownames of a DNAbin object
six.names <- head(rownames(all.seqs))
six.names
#> [1] "H_ACAED01" "P_ACAGR1" "H_ACAGR2" "H_ACATEN01" "L_ACCBRE01"
#> [6] "L_ACCBRE02"
After cleaning those names, they look like this:
clean_names(six.names)
#> [1] "ACAED01" "ACAGR1" "ACAGR2" "ACATEN01" "ACCBRE01" "ACCBRE02"
Taxonomic key for host species
The package includes a taxonomic key linking (nearly all) host species names from MalAvi with the species names used in the phylogenetic analysis found on http://birdtree.org/. The latter species names are in a column labeled Jetz.species and have an underscore between the genus and species as they would appear if you were to download trees from the website. The key is stored in a data object called taxonomy which can be called directly once the package is loaded.
## check out taxonomic host key
head(taxonomy)
#> species Jetz.species match
#> 1 Nectarinia sperata Nectarinia_sperata yes
#> 2 Prinia inornata Prinia_inornata yes
#> 3 Acrocephalus aedon Acrocephalus_aedon yes
#> 4 Emberiza elegans Emberiza_elegans yes
#> 5 Emberiza pusilla Emberiza_pusilla yes
#> 6 Parus major Parus_major yes
The taxonomy help file notes when the comparison was made (I will update it occasionally) and lists the multiple MalAvi host species that correspond to single host species in the phylogenetic analysis (there are several).
Identifying sister lineages from a given node
Phylogenetic analysis of the MalAvi lineages is difficult at the moment due to the limited sequence data available for most lineages. Often, the best one can do is to study sister lineages in the phylogeny that are linked by well supported nodes. The function sister_taxa() gives a list of lineages on either side of a specific node in a phylogeny.
## simulate a phylogenetic tree with 10 taxa using the rtree() function in the ape package
library(ape)
#> Warning: package 'ape' was built under R version 3.4.2
tree <- rtree(n=10)
## the node labels of the tree can then be examined
plot(tree)
nodelabels()
## the root of the tree has node label "11" and we can extract sister lineages from the root;
## the sister taxa are grouped into two clades with arbitrary labels of "1" and "2"
sis.tax.df <- sister_taxa(tree, 11)
sis.tax.df # check it out
#> ancestral.node sister.clade taxa
#> 1 11 1 t4
#> 2 11 1 t2
#> 3 11 1 t5
#> 4 11 2 t9
#> 5 11 2 t6
#> 6 11 2 t1
#> 7 11 2 t7
#> 8 11 2 t10
#> 9 11 2 t3
#> 10 11 2 t8
In general, this function can be used to identify lineages for further analysis or for visualization purposes. For analyses of MalAvi data in particular, you might want to identify all pairs of sister lineages in a phylogeny. This could be done with one line (after calling the dplyr package):
## load dplyr package
library(dplyr)
## identify all pairs of sister lineages in the phylogenetic tree
sister_taxa(tree, 1:tree$Nnode + length(tree$tip.label)) %>%
group_by(ancestral.node) %>%
mutate(no.lins = length(unique(taxa))) %>%
filter(no.lins == 2) %>%
select(-no.lins) %>%
as.data.frame
#> ancestral.node sister.clade taxa
#> 1 13 1 t2
#> 2 13 2 t5
#> 3 19 1 t7
#> 4 19 2 t10
Reporting Issues
Please report any issues with the package here.
vincenzoaellis/malaviR documentation built on Oct. 10, 2019, 10:55 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
malaviR
This package is an R interface to the global avian haemosporidian database MalAvi (http://mbio-serv2.mbioekol.lu.se/Malavi/). The package includes functions for downloading data from the MalAvi website directly into your R environment and some basic utility functions for manipulating those data. Furthermore, you can use malaviR to BLAST your haemosporidian DNA sequences against the MalAvi database programmatically, which should facilitate comparisons with MalAvi.
The package also includes a key for linking the host taxonomic classifications in MalAvi with the avian taxonomic classifications found on http://birdtree.org/.
Installation
You can install malaviR from github with:
# install.packages("devtools")
devtools::install_github("vincenzoaellis/malaviR", build_vignettes = TRUE)
Then you can load it in your R session with:
library(malaviR)
The tutorial is accessible by calling the package vignette:
browseVignettes("malaviR")
This will open a web browser window. Click the "HTML" link next to the tutorial name (Getting_Started_with_malaviR) to see the tutorial.
Citation
You can cite malaviR as follows:
citation("malaviR")
#>
#> To cite package 'malaviR' in publications use:
#>
#> Vincenzo A Ellis, Staffan Bensch and Björn Canbäck (NA).
#> malaviR: An R interface to MalAvi. R package version 0.1.0.
#> https://github.com/vincenzoaellis/malaviR
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {malaviR: An R interface to MalAvi},
#> author = {Vincenzo A Ellis and Staffan Bensch and Björn Canbäck},
#> note = {R package version 0.1.0},
#> url = {https://github.com/vincenzoaellis/malaviR},
#> }
Download tables from MalAvi
There are nine tables that can be downloaded from the MalAvi website that summarize the database. Here's how you would download the table linking morphological species to genetic lineages:
morph <- extract_table("Morpho Species Summary")
head(morph) # check it out
#> #no Lineage_Name genus species
#> 1 1 ACCFRA01 Leucocytozoon Leucocytozoon toddi
#> 2 2 ACCOP01 Leucocytozoon Leucocytozoon mathisi
#> 3 3 ACNI04 Leucocytozoon Leucocytozoon mathisi
#> 4 4 ALARV01 Haemoproteus Haemoproteus tartakovskyi
#> 5 5 ALARV02 Haemoproteus Haemoproteus tartakovskyi
#> 6 6 ALARV03 Haemoproteus Haemoproteus tartakovskyi
#> Reference_Name comment
#> 1 Barraclough et al 2008
#> 2 Valkiunas et al 2010a
#> 3 Valkiunas et al 2010a
#> 4 Zehtindjiev et al 2012
#> 5 Zehtindjiev et al 2012
#> 6 Zehtindjiev et al 2012
The extract_table() help file lists all nine tables you can download directly into R. Or you can specify "all" and get them all as a list.
BLAST a sequence to MalAvi
You can BLAST your own sequences against the MalAvi database using the blast_malavi() function, which leverages the MalAvi website's already existing BLAST capabilities. Your input sequence just needs to be specified as a character string.
## define a sequence. This is the Plasmodium parasite ACAGR1
ACAGR1 <- "GCAACTGGTGCTTCATTTGTATTTATT
TTAACTTATTTACATATTTTAAGAGGATTAAATTATTC
ATATTCATATTTACCTTTATCATGGATATCTGGATTAA
TAATATTTTTAATATCTATAGTAACAGCTTTTATGGGT
TACGTATTACCTTGGGGTCAAATGAGTTTCTGGGGTGC
TACCGTAATAACTAATTTATTATATTTTATACCTGGAC
TAGTTTCATGGATATGTGGTGGATATCTTGTAAGTGAC
CCAACCTTAAAAAGATTCTTTGTACTACATTTTACATT
TCCTTTTATAGCTTTATGTATTGTATTTATACATATAT
TCTTTCTACATTTACAAGGTAGCACAAATCCTTTAGGG
TATGATACAGCTTTAAAAATACCCTTCTATCCAAATCT
TTTAAGTCTTGATATTAAAGGATTTAATAATGTATTAG
TATTATTTTTAGCACAAAGTTTATTTGGAATACT"
## BLAST it against the MalAvi database and save the top five hits to a data frame
hits <- blast_malavi(ACAGR1)
hits # check it out
#> Lineage Score Identities Gaps Strand Coverage Perfect.Match
#> 1 ACAGR1 885 479/479 0/479 Plus/Plus 479 Yes
#> 2 SGS1 880 478/479 0/479 Plus/Plus 479 No
#> 3 CXPIP20 876 476/477 0/477 Plus/Plus 477 No
#> 4 YWT4 874 477/479 0/479 Plus/Plus 479 No
#> 5 SERCAN01 874 477/479 0/479 Plus/Plus 479 No
Download the MalAvi sequence alignments
Using the extract_alignment() function, you can download all of the sequences on MalAvi, or the complete or nearly complete sequences only, or the sequences associated with morphological species only. These alignments will appear as objects of the class DNAbin, defined in the ape package.
## download all sequences
all.seqs <- extract_alignment("all seqs")
all.seqs #check it out
#> 2825 DNA sequences in binary format stored in a matrix.
#>
#> All sequences of same length: 479
#>
#> Labels:
#> H_ACAED01
#> P_ACAGR1
#> H_ACAGR2
#> H_ACATEN01
#> L_ACCBRE01
#> L_ACCBRE02
#> ...
#>
#> Base composition:
#> a c g t
#> 0.294 0.133 0.135 0.438
The extract_alignment() help file lists the names of the sequence alignments that the function understands. You can also identify and remove repeated haplotypes (several typically exist due to ambiguous base pairs) in the alignments with the clean_alignment() function.
Clean lineage names from MalAvi sequence alignments
The lineage names in the MalAvi sequence alignments have been modified to include extra information and therefore do not match the lineage names in the rest of the database. The clean_names() function removes the extra information so that the sequence alignment lineage names match those in the rest of the database.
For example, the lineage names in the alignment files look like this:
## lineage names are stored in the rownames of a DNAbin object
six.names <- head(rownames(all.seqs))
six.names
#> [1] "H_ACAED01" "P_ACAGR1" "H_ACAGR2" "H_ACATEN01" "L_ACCBRE01"
#> [6] "L_ACCBRE02"
After cleaning those names, they look like this:
clean_names(six.names)
#> [1] "ACAED01" "ACAGR1" "ACAGR2" "ACATEN01" "ACCBRE01" "ACCBRE02"
Taxonomic key for host species
The package includes a taxonomic key linking (nearly all) host species names from MalAvi with the species names used in the phylogenetic analysis found on http://birdtree.org/. The latter species names are in a column labeled Jetz.species and have an underscore between the genus and species as they would appear if you were to download trees from the website. The key is stored in a data object called taxonomy which can be called directly once the package is loaded.
## check out taxonomic host key
head(taxonomy)
#> species Jetz.species match
#> 1 Nectarinia sperata Nectarinia_sperata yes
#> 2 Prinia inornata Prinia_inornata yes
#> 3 Acrocephalus aedon Acrocephalus_aedon yes
#> 4 Emberiza elegans Emberiza_elegans yes
#> 5 Emberiza pusilla Emberiza_pusilla yes
#> 6 Parus major Parus_major yes
The taxonomy help file notes when the comparison was made (I will update it occasionally) and lists the multiple MalAvi host species that correspond to single host species in the phylogenetic analysis (there are several).
Identifying sister lineages from a given node
Phylogenetic analysis of the MalAvi lineages is difficult at the moment due to the limited sequence data available for most lineages. Often, the best one can do is to study sister lineages in the phylogeny that are linked by well supported nodes. The function sister_taxa() gives a list of lineages on either side of a specific node in a phylogeny.
## simulate a phylogenetic tree with 10 taxa using the rtree() function in the ape package
library(ape)
#> Warning: package 'ape' was built under R version 3.4.2
tree <- rtree(n=10)
## the node labels of the tree can then be examined
plot(tree)
nodelabels()
## the root of the tree has node label "11" and we can extract sister lineages from the root;
## the sister taxa are grouped into two clades with arbitrary labels of "1" and "2"
sis.tax.df <- sister_taxa(tree, 11)
sis.tax.df # check it out
#> ancestral.node sister.clade taxa
#> 1 11 1 t4
#> 2 11 1 t2
#> 3 11 1 t5
#> 4 11 2 t9
#> 5 11 2 t6
#> 6 11 2 t1
#> 7 11 2 t7
#> 8 11 2 t10
#> 9 11 2 t3
#> 10 11 2 t8
In general, this function can be used to identify lineages for further analysis or for visualization purposes. For analyses of MalAvi data in particular, you might want to identify all pairs of sister lineages in a phylogeny. This could be done with one line (after calling the dplyr package):
## load dplyr package
library(dplyr)
## identify all pairs of sister lineages in the phylogenetic tree
sister_taxa(tree, 1:tree$Nnode + length(tree$tip.label)) %>%
group_by(ancestral.node) %>%
mutate(no.lins = length(unique(taxa))) %>%
filter(no.lins == 2) %>%
select(-no.lins) %>%
as.data.frame
#> ancestral.node sister.clade taxa
#> 1 13 1 t2
#> 2 13 2 t5
#> 3 19 1 t7
#> 4 19 2 t10
Reporting Issues
Please report any issues with the package here.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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