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: Bioinformatics Workflow author: Nick Moshgat date: September 26, 2021 output: html_document —
A complete bioinformatics workflow in R
By: Nathan L. Brouwer
“Worked example: Building a phylogeny in R”
Introduction
Describe how phylogeneies can be used in biology (readings will be assigned)
Vocab
Make a list of at least 10 vocab terms that are important (don’t have to define)
Key functions
Make a list of at least 5 key functions Put in the format of package::function
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(compbio4all)
# CRAN packages
library(rentrez)
library(seqinr)
library(ape)
# Bioconductor packages
library(msa)
library(Biostrings)Creating macromolecular sequences
We are taking a sequence from the NCBI database and storing it into hShroom3. entrez_fetch is from package rentrez.
TODO: Fill in the XXXXXs and write a 1-2 sentence of what is going on here.
Add the package that is where entrez_fetch is from using :: notation
# Human shroom 3 (H. sapiens)
hShroom3 <- entrez_fetch(db = "protein",
id = "NP_065910",
rettype = "fasta")TODO:explain what cat() is doing cat() is making the output similar to that of a web browser/word processor and making it easier to read with new lines.
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
## IPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPLTODO: explain what this code chunk is doing This code chunk is taking the specific sequences from the NCBI database and storing them into various variables.
# 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")TODO: Explain what this code chunk is doing This code chunk shows the number of characters (number of amino acids) in the fasta file.
nchar(hShroom3)## [1] 2070nchar(mShroom3a)## [1] 2083nchar(sShroom)## [1] 1758nchar(hShroom2)## [1] 1673Prepping macromolecular sequences
TODO: Explain what this function does This function takes a fasta file and turns it into a vector.
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: 0x00000000230ca1c0>
## <environment: namespace:compbio4all>TODO: explain how to add the function to your R session even if you can’t download compbio4all YOu could manually turn the fasta file into a vector without using fasta_cleaner.
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 code takes the fasta files and puts them in proper format for making phylogentic 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"Aligninging sequences
TODO: give this a title. Explain what code below is doing This code is aligning the two specified sequences together in the most accurate way.
# add necessary function
align.h3.vs.m3a <- Biostrings:: pairwiseAlignment(
hShroom3,
mShroom3a)TODO: In 1-2 sentence explain what this object shows This shows the alignment score for the two sequences (ie. how similar they are).
align.h3.vs.m3a## Global PairwiseAlignmentsSingleSubject (1 of 1)
## pattern: MMRTTEDFHKPSATLN-SNTATKGRYIYLEAFLE...KAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL
## subject: MK-TPENLEEPSATPNPSRTPTE-RFVYLEALLE...KAGAISLPPALTGHATPGGTSVFGGVFPTLTSPL
## score: 2189.934TODO: explain what this is showing This is showing the percent identity of the two sequences. Percent identity is the percentage that the two sequences share the same sequence.
# add necessary function
Biostrings::pid(align.h3.vs.m3a)## [1] 70.56511TODO: briefly explain what is going on here versus the previous code chunk This code is aligning one of the same sequences used above with another sequence not used yet, giving a new score.
align.h3.vs.h2 <- Biostrings::pairwiseAlignment(
hShroom3,
hShroom2)TODO: explain what is going on here and compare and contrast with previous ouput Again, the score is given for the new compared sequences. This score is smaller, therefore is less similar. The number is very negative, suggesting that there are many indels in one of the sequences.
score(align.h3.vs.h2)## [1] -5673.853TODO: briefly explian the difference between the output of score() and pid() (can be very brief - we’ll get into the details later) Percent identity does not take indels into consideration while score does.
Biostrings::pid(align.h3.vs.h2)## [1] 33.83277The shroom family of genes
TODO: briefly explain why I have this whole table here This table shows various species of shrooms as well as their specific accession number for easy visibility.
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 urchinTODO: write a short sentence explaining what this next code chunk will do, then annotate each line with what was done. This code turns the tabel above into a readable dataframe and labels the columns.
# 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)
# Label 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"TODO: in a brief sentence explain what this is doing This is taking each row creating in the dataframe, and turning it into an easy to read table.
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 urchinCreating multiple sequences
TODO: in a brief sentence explain what the $ allows us to do The $ allows us to see an entire row or column in a table. In the case below, we can see all of the accession numbers.
shroom_table$accession## [1] "CAA78718" "NP_597713" "CAA58534" "ABD19518" "AAF13269" "AAF13270"
## [7] "NP_065910" "ABD59319" "NP_065768" "AAK95579" "ABA81834" "EAA12598"
## [13] "XP_392427" "XP_783573"TODO: briefly explain what this chunk is doing and add the correct function This code is taking the accession numbers in the table made previously, and using the NCBI database to add every sequence in variabloe shrooms.
# add necessary function
shrooms <-entrez_fetch (db = "protein",
id = shroom_table$accession,
rettype = "fasta")TODO: in a very brief sentence explain what this is doing. This is breaking up the various sequences put into shrooms, making it much easier to read.
cat(shrooms)TODO: in a brief sentence explain what this is doing and if/how its different from the previous code chunks This code is breaking up each sequence into new lines.
shrooms_list <- entrez_fetch_list(db = "protein",
id = shroom_table$accession,
rettype = "fasta")TODO: briefly explain what I am doing this This code is getting the number of sequences in shrooms_list.
length(shrooms_list)## [1] 14TODO: briefly explain what I am doing this. We will get into the details of for() loops in R later in the semester. This is cleaning the data and making it easier to read.
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 A new vector is being made that contains the accession numbers embedded.
# Vectorization
shrooms_vector <- rep(NA, length(shrooms_list))
# Storing shrooms_list into shrooms_vector
for(i in 1:length(shrooms_vector)){
shrooms_vector[i] <- shrooms_list[[i]]
}
# Naming Vector
names(shrooms_vector) <- names(shrooms_list)TODO: explain what this is doing then add the necessary function. This code gives the amino acids for each accession number.
# add necessary function
shrooms_vector_ss <- Biostrings:: AAStringSet(shrooms_vector)MSA
TODO: briefly summarize what this section of the document will do.
Readings will be assigned to explain what MSAs are. This section of the code will create a multiple sequence alignment.
Building an MSA
TODO: briefly explain what this chunk does, then add the necessary function. The code provides an interface of shroom_align, preparing to create a msa.
# add necessary function
shrooms_align <- msa(shrooms_vector_ss,
method = "ClustalW")## use default substitution matrixViewing an MSA
TODO: briefly summarize what this section will do. We will be able to see the multiple sequence alignments of the desired sequences.
Viewing an MSA in R
TODO: Briefly summarize what output is shown below This output is showing where there are similar sequences.
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 The code is turning each sequence into the same class so that it can go through and determine a full comparison of each sequence.
# 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 This prints regions of similarity.
print_msa(alignment = shrooms_align_seqinr,
chunksize = 60)Displaying an MSA
TODO: explain this output and how its differnet from the prevoius This output is the actual MSA and shows a visual depiction of sequence alignment, unlike the previous output.
## add necessary function
ggmsa:: ggmsa(shrooms_align, # shrooms_align, NOT shrooms_align_seqinr
start = 2000,
end = 2100) ## Registered S3 methods overwritten by 'ggalt':
## method from
## grid.draw.absoluteGrob ggplot2
## grobHeight.absoluteGrob ggplot2
## grobWidth.absoluteGrob ggplot2
## grobX.absoluteGrob ggplot2
## grobY.absoluteGrob ggplot2
Saving an MSA as PDF
TODO: explain what this command is doing. This is trying to save the MSA as a PDF. Add the package the function is coming from using :: notation This may not work for everyone. If its not working you can comment it out.
# msaPrettyPrint(shrooms_align, # alignment
# file = "shroom_msa.pdf", # file name
# y=c(2000, 2100), # range
#askForOverwrite=FALSE)TODO: explain what this command is doing This gets everything in the working directory of R.
getwd()## [1] "C:/Users/nmosh/OneDrive"