Assignment: Your assignment is to use your notes from class - along with help from classmates, UTAs, and me - to turn this script into a fleshed-out description of what is going on.
This is a substantial project - we’ll work on it in steps over the rest of the unit.
We are currently focused on the overall process and will cover the details over the rest of this unit.
Your first assignment is to get this script to run from top to bottom by adding all of the missing R commands. Once you have done that, you can knit it into an HTML file and upload it to RPubs. (Note - you’ll need to add the YAML header!)
Your second assignment, which will be posted later, is to answer all the TODO and other prompts to add information. You can start on this, but you don’t have to do this on your first time through the code.
Delete all the prompts like TODO() as you compete them. Use RStudio’s search function to see if you’ve missed any - there are a LOT!
title: “Portfolio 1 Assignment” author: “Vignesh Senguttuvan” date: “9-27-21” output: html_document
A complete bioinformatics workflow in R
functions for prepwork; only used if packages are not already installed
devtools
install.packages(“devtools”)
compbio4all
devtools::install_github(“brouwern/compbio4all”)
ggmsa
devtools::install_github(“YuLab-SMU/ggmsa”)
Bioconductor / BiocManager
install.packages(“BiocManager”)
Biostrings
BiocManager::install(“Biostrings”) BiocManager::install(“msa”)
R bioinformatics software
install.packages(“rentrez”)
install.packages(“seqinr”)
install.packages(“ape”)
By: Vignesh Senguttuvan
“Worked example: Building a phylogeny in R”
Introduction
Phylogenies are used in biology to describe changes over time in nucleotide coding sequences or protein sequences due to mutations from evolution. Phylogenies are used to provide a hierarchal taxonomical structure for the evolutionary development of species over time by showcasing the changes in genomes due to mutations which then affect protein sequences, affecting phenotype.
Vocab
protein sequence sequence fetch ncbi protein data base accession number pairwise alignment multiple sequence alignment shroom family of proteins pid fasta file format alignment score stringset file format
Key functions
rentrez::entrez_fetch() compbio4all::fasta_cleaner() Biostrings::pairwiseAlignment() Biostrings::pid() compbio4all::entrez_fetch_list() Biostrings::AAStringSet() msa::msa() msa::msaConvert() compbio4all::print_msa() ggmsa::ggmsa() msa::msaPrettyPrint()
Software Preliminaires
Add the necessary calls to library() to load call packages Indicate which packages cam from Bioconducotr, CRAN, and GitHub
Load packages into memory
# github packages
library(devtools)
library(compbio4all)
library(ggmsa)
# CRAN packages
library(rentrez)
library(seqinr)
library(ape)
# Bioconductor packages
library(BiocManager)
library(Biostrings)
library(msa)Fetching macromolecular sequences
##We are using the entrez_fetch function in the rentrez package to fetch the fasta gene sequence data for the human shroom 3 gene from the NCBI protein database. We then assign the sequence to the object: hShroom3
# Human shroom 3 (H. sapiens)
hShroom3 <- rentrez::entrez_fetch(db = "protein",
id = "NP_065910",
rettype = "fasta")##cat() function outputs the entire hShroom3 protein string with accession # and scientific name
cat(hShroom3)## >NP_065910.3 protein Shroom3 [Homo sapiens]
## MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKL
## QAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAA
## WSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYL
## RRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGR
## ERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNI
## PRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSL
## GSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGM
## LYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFC
## SVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDF
## QEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTK
## AEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQ
## GFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEP
## LGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGS
## PESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPH
## TPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFP
## ESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAY
## LQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLS
## GANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSG
## FGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTP
## CPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGP
## RGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTS
## DPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPV
## YSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEA
## ESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDK
## SLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYY
## SVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEA
## LISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHED
## ARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDF
## IPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPLFetches the protein sequence fasta files for each of the respective shroom types from ncbi protein data base using accession numbers
# Mouse shroom 3a (M. musculus)
mShroom3a <- rentrez::entrez_fetch(db = "protein",
id = "AAF13269",
rettype = "fasta")
# Human shroom 2 (H. sapiens)
hShroom2 <- rentrez::entrez_fetch(db = "protein",
id = "CAA58534",
rettype = "fasta")
# Sea-urchin shroom
sShroom <- rentrez::entrez_fetch(db = "protein",
id = "XP_783573",
rettype = "fasta")##Counts number of residues in each protein sequence
nchar(hShroom3)## [1] 2070nchar(mShroom3a)## [1] 2083nchar(sShroom)## [1] 1758nchar(hShroom2)## [1] 1673Prepping macromolecular sequences
Cleaning/Prepping raw fasta macromolecular sequences for msa
compbio4all::fasta_cleaner## function (fasta_object, parse = TRUE)
## {
## fasta_object <- sub("^(>)(.*?)(\\n)(.*)(\\n\\n)", "\\4",
## fasta_object)
## fasta_object <- gsub("\n", "", fasta_object)
## if (parse == TRUE) {
## fasta_object <- stringr::str_split(fasta_object, pattern = "",
## simplify = FALSE)
## }
## return(fasta_object[[1]])
## }
## <bytecode: 0x000000002da505f8>
## <environment: namespace:compbio4all>##Essentially takes existing function, fasta_object, and adds to it; then, the new function is applied to the object fasta_cleaner, which is the new function
fasta_cleaner <- function(fasta_object, parse = TRUE){
fasta_object <- sub("^(>)(.*?)(\\n)(.*)(\\n\\n)","\\4",fasta_object)
fasta_object <- gsub("\n", "", fasta_object)
if(parse == TRUE){
fasta_object <- stringr::str_split(fasta_object,
pattern = "",
simplify = FALSE)
}
return(fasta_object[[1]])
}##Cleans up raw FASTA sequences using newly created fasta_cleaner function; puts them in appropriate format for allignments and phylogenetic trees
hShroom3 <- fasta_cleaner(hShroom3, parse = F)
mShroom3a <- fasta_cleaner(mShroom3a, parse = F)
hShroom2 <- fasta_cleaner(hShroom2, parse = F)
sShroom <- fasta_cleaner(sShroom, parse = F)hShroom3## [1] "MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKLQAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAAWSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYLRRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGRERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNIPRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSLGSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGMLYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFCSVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDFQEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTKAEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQGFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEPLGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGSPESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPHTPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFPESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAYLQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLSGANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSGFGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTPCPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGPRGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTSDPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPVYSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEAESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDKSLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYYSVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEALISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHEDARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDFIPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL"Aligning sequences
##Pairwise alignment of human shroom 3 and mouse shroom 3;aligns both sequences as string objects and assigns to the object: align.h3.vs.m3a
align.h3.vs.m3a <- Biostrings::pairwiseAlignment(
hShroom3,
mShroom3a)##Shows pairwise alignment of hShroom3 vs mShroom3
align.h3.vs.m3a## Global PairwiseAlignmentsSingleSubject (1 of 1)
## pattern: MMRTTEDFHKPSATLN-SNTATKGRYIYLEAFLE...KAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL
## subject: MK-TPENLEEPSATPNPSRTPTE-RFVYLEALLE...KAGAISLPPALTGHATPGGTSVFGGVFPTLTSPL
## score: 2189.934Percentage Alignment of human shroom 3 protein sequence vs mouse shroom 3 protein sequence
Biostrings::pid(align.h3.vs.m3a)## [1] 70.56511#Assigns pairwise alignment of human shroom 3 vs. human shroom 2 to the object: align.h3.vs.h2
align.h3.vs.h2 <- Biostrings::pairwiseAlignment(
hShroom3,
hShroom2)##Assigns a score describing the alignment of h3 vs. h2
score(align.h3.vs.h2)## [1] -5673.853##Alignment score describes how many matches, mismatches, and indels there are between two sequences; PID describes percentage of matches over matches and mismatches but not indels
Biostrings::pid(align.h3.vs.h2)## [1] 33.83277The shroom family of genes
##Essentially takes the NCBI accession #s, scientific names, and common names for the shroom family of genes and turns them into a list
shroom_table <- c("CAA78718" , "X. laevis Apx" , "xShroom1",
"NP_597713" , "H. sapiens APXL2" , "hShroom1",
"CAA58534" , "H. sapiens APXL", "hShroom2",
"ABD19518" , "M. musculus Apxl" , "mShroom2",
"AAF13269" , "M. musculus ShroomL" , "mShroom3a",
"AAF13270" , "M. musculus ShroomS" , "mShroom3b",
"NP_065910", "H. sapiens Shroom" , "hShroom3",
"ABD59319" , "X. laevis Shroom-like", "xShroom3",
"NP_065768", "H. sapiens KIAA1202" , "hShroom4a",
"AAK95579" , "H. sapiens SHAP-A" , "hShroom4b",
#"DQ435686" , "M. musculus KIAA1202" , "mShroom4",
"ABA81834" , "D. melanogaster Shroom", "dmShroom",
"EAA12598" , "A. gambiae Shroom", "agShroom",
"XP_392427" , "A. mellifera Shroom" , "amShroom",
"XP_783573" , "S. purpuratus Shroom" , "spShroom") #sea urchin##Converting the list from the previous code chunk into a matrix, then into a dataframe
# convert to matrix
shroom_table_matrix <- matrix(shroom_table,
byrow = T,
nrow = 14)
# convert to data frame
shroom_table <- data.frame(shroom_table_matrix,
stringsAsFactors = F)
# Adds names to columns
names(shroom_table) <- c("accession", "name.orig","name.new")
# Adds common names to column name:name.orig
shroom_table$spp <- "Homo"
shroom_table$spp[grep("laevis",shroom_table$name.orig)] <- "Xenopus"
shroom_table$spp[grep("musculus",shroom_table$name.orig)] <- "Mus"
shroom_table$spp[grep("melanogaster",shroom_table$name.orig)] <- "Drosophila"
shroom_table$spp[grep("gambiae",shroom_table$name.orig)] <- "mosquito"
shroom_table$spp[grep("mellifera",shroom_table$name.orig)] <- "bee"
shroom_table$spp[grep("purpuratus",shroom_table$name.orig)] <- "sea urchin"##Outputs shroom table dataframe created in previous code chunk
shroom_table## accession name.orig name.new spp
## 1 CAA78718 X. laevis Apx xShroom1 Xenopus
## 2 NP_597713 H. sapiens APXL2 hShroom1 Homo
## 3 CAA58534 H. sapiens APXL hShroom2 Homo
## 4 ABD19518 M. musculus Apxl mShroom2 Mus
## 5 AAF13269 M. musculus ShroomL mShroom3a Mus
## 6 AAF13270 M. musculus ShroomS mShroom3b Mus
## 7 NP_065910 H. sapiens Shroom hShroom3 Homo
## 8 ABD59319 X. laevis Shroom-like xShroom3 Xenopus
## 9 NP_065768 H. sapiens KIAA1202 hShroom4a Homo
## 10 AAK95579 H. sapiens SHAP-A hShroom4b Homo
## 11 ABA81834 D. melanogaster Shroom dmShroom Drosophila
## 12 EAA12598 A. gambiae Shroom agShroom mosquito
## 13 XP_392427 A. mellifera Shroom amShroom bee
## 14 XP_783573 S. purpuratus Shroom spShroom sea urchinAligning multiple sequences
##Outputs accession numbers in the shroom_table dataframe
shroom_table$accession## [1] "CAA78718" "NP_597713" "CAA58534" "ABD19518" "AAF13269" "AAF13270"
## [7] "NP_065910" "ABD59319" "NP_065768" "AAK95579" "ABA81834" "EAA12598"
## [13] "XP_392427" "XP_783573"##Fetches the residue sequences for each of the shroom proteins for different species from the NCBI using the accession numbers in the datatable from the previous code chunks, downloads the sequences as fasta files
shrooms <-entrez_fetch(db = "protein",
id = shroom_table$accession,
rettype = "fasta")##Lists each of the residue sequences for each species
cat(shrooms)##Collects the fasta residue sequence data for each shroom protein from NCBI using the accession #s from the previously created data frame;Assigns residue sequences to object shrooms_list; sequence data includes line breaks and is vectorized
shrooms_list <- compbio4all::entrez_fetch_list(db = "protein",
id = shroom_table$accession,
rettype = "fasta")#is() describes what object shrooms_list is; length() describes how many shroom proteins are being analyzed; nchar() describes how many aa residues are in each protein
is(shrooms_list)## [1] "list" "vector" "list_OR_List" "vector_OR_Vector"
## [5] "vector_OR_factor"length(shrooms_list)## [1] 14nchar(shrooms_list)## CAA78718 NP_597713 CAA58534 ABD19518 AAF13269 AAF13270 NP_065910 ABD59319
## 1486 915 1673 1543 2083 1895 2070 1864
## NP_065768 AAK95579 ABA81834 EAA12598 XP_392427 XP_783573
## 1560 778 1647 750 2230 1758##Applies fasta_cleaner to each shroom protein in shrooms_list
for(i in 1:length(shrooms_list)){
shrooms_list[[i]] <- fasta_cleaner(shrooms_list[[i]], parse = F)
}##Creates a vector, shrooms_vector, and applies shrooms_list to each newly named column in shrooms_vector;
# Creates same # of columns in shrooms_vector as there are proteins in shrooms_list
shrooms_vector <- rep(NA, length(shrooms_list))
# Inserts shroom aa sequence data to shrooms_vector
for(i in 1:length(shrooms_vector)){
shrooms_vector[i] <- shrooms_list[[i]]
}
# Adds shroom names to each sequence in shrooms_vector
names(shrooms_vector) <- names(shrooms_list)##Converts shrooms_vector in to proper stringset format and creates new object: shrooms_vector_ss
shrooms_vector_ss <- Biostrings::AAStringSet(shrooms_vector)MSA
##This section of the rmarkdown file is preparing and running a multiple sequence alignment (msa); essentially, we are running a pairwise alignment for all of the shroom proteins and analyzing the sequence data
Building an Multiple Sequence Alignment (MSA)
#msa() function aligns all of the sequences in shrooms_vector_ss object; ClustalW is a computer program used for performing multiple sequence alignments; then assigns msa to shrooms_align object
shrooms_align <-msa::msa(shrooms_vector_ss,
method = "ClustalW")## use default substitution matrixViewing an MSA
##This section will show the results of the msa
Viewing an MSA in R
##shrooms_align object contains the msa
shrooms_align## CLUSTAL 2.1
##
## Call:
## msa::msa(shrooms_vector_ss, method = "ClustalW")
##
## MsaAAMultipleAlignment with 14 rows and 2252 columns
## aln names
## [1] -------------------------...------------------------- NP_065768
## [2] -------------------------...------------------------- AAK95579
## [3] -------------------------...SVFGGVFPTLTSPL----------- AAF13269
## [4] -------------------------...SVFGGVFPTLTSPL----------- AAF13270
## [5] -------------------------...CTFSGIFPTLTSPL----------- NP_065910
## [6] -------------------------...NKS--LPPPLTSSL----------- ABD59319
## [7] -------------------------...------------------------- CAA58534
## [8] -------------------------...------------------------- ABD19518
## [9] -------------------------...LT----------------------- NP_597713
## [10] -------------------------...------------------------- CAA78718
## [11] -------------------------...------------------------- EAA12598
## [12] -------------------------...------------------------- ABA81834
## [13] MTELQPSPPGYRVQDEAPGPPSCPP...------------------------- XP_392427
## [14] -------------------------...AATSSSSNGIGGPEQLNSNATSSYC XP_783573
## Con -------------------------...------------------------- Consensus##Renames the class of msa from “MsaAAMultipleAlignment” to “AAMultipleAlignment”; then converts msa into seqinr::alignment format using msaConvert() function
##Renames the class of msa from "MsaAAMultipleAlignment" to "AAMultipleAlignment""
class(shrooms_align) <- "AAMultipleAlignment"
# converts the format of msa using msaConvert() function; converts to seqinr package type alignment
shrooms_align_seqinr <- msa::msaConvert(shrooms_align, type = "seqinr::alignment")##prints msa in 60 chunks to more easily observe sequence data in seqinr format
compbio4all::print_msa(alignment = shrooms_align_seqinr,
chunksize = 60)Displaying an MSA as an R plot
##The output graph uses shrooms_align object, not shrooms_align_seqinr, to plot sequence data using ggmsa() function in ggmsa package; only plots residues from 2000 to 2100
ggmsa::ggmsa(shrooms_align, # shrooms_align, NOT shrooms_align_seqinr
start = 2000,
end = 2100) 
Saving an MSA as PDF
##msaPrettyPrint() function customizes the formatting of an msa; then converts into a specific file type, in this instance a pdf
#msa::msaPrettyPrint(shrooms_align,
# file = "shroom_msa.pdf", # file name
# y=c(2000, 2100), # range
# askForOverwrite=FALSE)##Outputs R’s current working directory
getwd()## [1] "C:/Users/vigne/Downloads"