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!
Add YAML header!!! Give it a title — Title: MSA Walkthrough Assignment Output: html_document — # DONT TOUCH THE KNITR HEADER PLEASE
A Complete Bioinformatics Workflow in R
By: Jared H Harris
“Worked example: Building a Phylogeny in R”
Introduction
Describe how phylogeneies can be used in biology (readings will be assigned)
Phylogenies are used in biology to hypothesize the evolutionary relationship between genes, species, or families of organisms. This can be useful in understanding evolution and how genes and species have changed and evolved over time.
Vocab
Make a list of at least 10 vocab terms that are important (don’t have to define) 1. Accession Number 2. Fasta Files 3. Multiple Sequence Alignment 4. Reproducible Work Flow 5. NCIB 6. Pairwise Alignment 7. Genome
8. Amino acid (single letter shortcut) 9. Mutation (types) 10. Phylogeny
Key functions
Make a list of at least 5 key functions Put in the format of package::function 1. Bioconductor :: Biostrings 2. Bioconductor :: msa 3. CRAN :: rentrez 4. CRAN :: entrez_fetch 5. ggmsa :: ggmsa
Software Preliminaires
Add the necessary calls to library() to load call packages Indicate which packages cam from Bioconductor, CRAN, and GitHub
Load packages into memory
# github packages
# devtools::install_github("brouwern/compbio4all")
# devtools::install_github("YuLab-SMU/ggmsa")
library(compbio4all)
library(ggmsa)
# CRAN packages
library(rentrez)
library(seqinr)
library(ape)
# Bioconductor packages
library(BiocManager)
library(Biostrings)
library(msa)Downloading Macromolecular Sequences
The code chunk here is calling up the protein sequence from the human shroom file in NCIB database and saving it as hShroom3. The entrez_fetch function from the Rentrez package calls up the data using the genetic code’s accession number with the NCIB.
# Human shroom 3 (H. sapiens)
hShroom3 <- rentrez::entrez_fetch(db = "protein",
id = "NP_065910",
rettype = "fasta")The cat() function is allowing the output of hShroom3 to be viewed in a way that respects the new line command (/n). The output is a continuous string of the amino acids that the shroom protein is comprised of in this organism. This format looks nicer on the screen which will allow it to be read more easily.
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
## IPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPLThis code chuck is calling up the mouse shroom genome sequence from NCBI. The fasta files of the amino acid sequence will be stored in each of the files, mShroom3a, hShroom2, and sShroom. This will allow us to later compare the different sets of data side by side.
# Mouse shroom 3a (M. musculus)
mShroom3a <- entrez_fetch(db = "protein",
id = "AAF13269",
rettype = "fasta")
# Human shroom 2 (H. sapiens)
hShroom2 <- entrez_fetch(db = "protein",
id = "CAA58534",
rettype = "fasta")
# Sea-urchin shroom
sShroom <- entrez_fetch(db = "protein",
id = "XP_783573",
rettype = "fasta")This code chunk is outputting the number of characters in each of the saved shroom files. This will output the number of amino acids in each fasta file.
nchar(hShroom3)## [1] 2070nchar(mShroom3a)## [1] 2083nchar(sShroom)## [1] 1758nchar(hShroom2)## [1] 1673Prepping macromolecular sequences
The fasta_cleaner function takes the fasta files that we uploaded and edits them into the proper format for sequences and phylogenetic trees.
library(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: 0x7ff776269608>
## <environment: namespace:compbio4all>IM NOT REALLY SURE ABOUT THIS PART!!
TODO: explain how to add the function to your R session even if you can’t download compbio4all # To add fasta_cleaner to R if you don’t have compbio4all downloaded, you can input devtools :: install_github(“brouwern/compbio4all”) which will install the file to the current R session.
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]])
}TODO: briefly explain what this code chunk is doing # This chunk of code is “cleaning up the shroom files so that the data will be properly formatted to make sequences and phylogentic trees. It is”scrubbing" our data.
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 Alignments
This code chunk is creating a pairwise alignment using the human and mouse shroom sequences. This will allow us to create a mulitple sequence alignment in later code chunks.
align.h3.vs.m3a <- Biostrings::pairwiseAlignment(
hShroom3,
mShroom3a)This object shows the pairwise alignment of the human shroom amino acids and mouse shroom amino acids. It also shows the score of the alignment.
align.h3.vs.m3a## Global PairwiseAlignmentsSingleSubject (1 of 1)
## pattern: MMRTTEDFHKPSATLN-SNTATKGRYIYLEAFLE...KAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL
## subject: MK-TPENLEEPSATPNPSRTPTE-RFVYLEALLE...KAGAISLPPALTGHATPGGTSVFGGVFPTLTSPL
## score: 2189.934The pid() function shows the percent identification of the aligned sequences. This basically means that the output is the percentage of amino acids that the two sequences have in common.
# add necessary function *No idea if this is what he wanted to be added here-check at office hours
Biostrings:: pid(align.h3.vs.m3a)## [1] 70.56511This code chunk is aligning the sequences of human shroom protein amino acids. It is creating a new save file for the alignment between the two different human shroom proteins that will give the output of the pairwise alignment.
align.h3.vs.h2 <- Biostrings::pairwiseAlignment(
hShroom3,
hShroom2)This code chunk gives the score of the pairwise alignment.
score(align.h3.vs.h2)## [1] -5673.853The score is a mathematical summary of the alignment. The score is used to compare with other scores. PID is percent identity. It is a rough estimate of how similar two sequences are and can be reported as a decimal or a percentage.
Biostrings::pid(align.h3.vs.h2)## [1] 33.83277The Shroom Family of Genes
There is a whole table here in order to create a vector of a bunch of the shroom sequences from different organisms. It takes the raw data and makes a nice table. It will allow us to create a multiple sequence alignment with the data.
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 urchinThis next code chunk is converting the data into different forms. We create a matrix first, then a data frame. From there we change the names of the columns and change the scientific names in the data to simplified species names that will make the data easier to read.
# 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)
# Naming Columns
names(shroom_table) <- c("accession", "name.orig","name.new")
# Create simplified species names
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"This code chunk is will output the table that was created in the previous chunk, displaying the matrix with the updated names from the last 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
The $ allows us to call up a specific column within the table and display it in R by itself. In this case, the output is the “accession” column.
shroom_table$accession## [1] "CAA78718" "NP_597713" "CAA58534" "ABD19518" "AAF13269" "AAF13270"
## [7] "NP_065910" "ABD59319" "NP_065768" "AAK95579" "ABA81834" "EAA12598"
## [13] "XP_392427" "XP_783573"This code chunk contains the entrez_fetch function which is taking the accession column from the saved file “shroom_table” and inputting that data into a new save file called “shrooms.”
# add necessary function *I think this is right
shrooms <- rentrez :: entrez_fetch(db = "protein",
id = shroom_table$accession,
rettype = "fasta")This code chunk uses the cat() function in order to create a better looking output of the data.
cat(shrooms)TODO: in a brief sentence explain what this is doing and if/how its different from the previous code chunks # This code chunk is using the entrez_fetch_list function to_____ and it is different from the previous chunks because it —
shrooms_list <- entrez_fetch_list(db = "protein",
id = shroom_table$accession,
rettype = "fasta")This code chunk is necessary to see the length of the list created (shrooms_list) and it allows us to see the length of the sequence.
length(shrooms_list)## [1] 14The for() function allows for a task to be repeated mulitple times. This used to allow the fasta_cleaner to clean the data mulitple times.
for(i in 1:length(shrooms_list)){
shrooms_list[[i]] <- fasta_cleaner(shrooms_list[[i]], parse = F)
}TODO: summarize what is going on in this code chunk, then annotate each line of code with what its doing # The rep() function is replicating the _______. The for() function is used to repeat a task mulitple times, and in this instance it is ______. The names() function is being used to add names to _____/
# Replication
shrooms_vector <- rep(NA, length(shrooms_list))
# For loop cleaning
for(i in 1:length(shrooms_vector)){
shrooms_vector[i] <- shrooms_list[[i]]
}
# Adding names
names(shrooms_vector) <- names(shrooms_list)The function AAStringSet is used to clean the data. It converts our vector into a string set. It is not important to know how it works because it is not very straight forward.
#
shrooms_vector_ss <- Biostrings:: AAStringSet (shrooms_vector)MSA
An MSA is a Multiple Sequence Alignment that allows us to comapre data side by side. It will output many different genetic sequnces that we can input and the function will output them in an MSA that compiles them all side by side.
Building a Multple Sequence Alignment (MSA)
This sequence is creating a mulitple sequence alignment from the data set “shrooms_align” that we created earlier.
shrooms_align <- msa(shrooms_vector_ss,
method = "ClustalW")## use default substitution matrixViewing an MSA
This section will allow us to see the MSA that we created and actually view the data we collected in one single dataframe.
Viewing an MSA in R
The output below is a mulitple sequence alignment of the shroom data.
shrooms_align## CLUSTAL 2.1
##
## Call:
## 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 -------------------------...------------------------- ConsensusTODO: briefly explain what is being done in this chunk. This is tricky (and annoying) so do your best
This code chunk is more data preparation. The first function is…..The second function is converting the mulitple sequence alignment into a different format.
# WHAT IS THE LINE BELOW DOING? (its tricky - do your best)
class(shrooms_align) <- "AAMultipleAlignment"
# WHAT IS THE LINE BELOW DOING? This is simpler
shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment")TODO: what is the output this produces # The output of this is an ugly version of the MSA.
print_msa(alignment = shrooms_align_seqinr,
chunksize = 60)Displaying an MSA XXXXXXXX
The ggmsa function creates a colorful MSA using the data. This is far different from the output of the last code chunk which was a long, confusing string of characters.
library(ggmsa)
ggmsa :: ggmsa(shrooms_align, # shrooms_align, NOT shrooms_align_seqinr
start = 2000,
end = 2100) 
Saving an MSA as PDF
This command works to edit the MSA and allow us to download it as a PDF. This did not run for me due to a problem with latex (office hours 9/27).
msa::msaPrettyPrint(shrooms_align, # alignment
file = "shroom_msa.pdf", # file name
y=c(2000, 2100), # range
askForOverwrite=FALSE)This command is the get working directory funciton. The working directory is where everything is saved in R. This will save the MSA to R.
getwd()## [1] "/Users/jaredharris/Downloads"