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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

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) We can use phylogeny to compare and contrast how different species evolve over time. This can help us see if species are closely related, genetically or in some other way. In this project, we use the multiple sequence alignment technique to simultaneously compare many different sequences of amino acids, which gives us insight into species similarity.

Vocab - Make a list of at least 10 vocab terms that are important (don’t have to define)

  • wrapper function
  • dependency
  • accession number
  • fasta file
  • multiple sequence alignment
  • percent identity
  • for loop
  • gap/deletion
  • phylogenetic tree
  • homology

Key functions - Make a list of at least 5 key functions, Put in the format of package::function

rentrez::entrez_fetch() compbio4all::entrez_fetch_list() msa::msa() Biostrings::pid() compbio4all::fasta_cleaner

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)
library(ggmsa)


# CRAN packages
library(devtools)
library(BiocManager)
library(rentrez)
library(seqinr)
library(ape)

# Bioconductor packages

library(Biostrings)
#library(msa)

retrieving macromolecular sequences

This function accesses data from NCBI databases and receives it. The code here returns the received data in the form of a character string.

# Human shroom 3 (H. sapiens)
hShroom3 <- rentrez::entrez_fetch(db = "protein", 
                          id = "NP_065910", 
                          rettype = "fasta")

This code here outputs the hShroom3 object, and it concatenates the data in the object to a single character vector.

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
## IPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL

This code here is getting the amino acid sequence data for mouse, human, and sea-urchin shroom and assigns it to 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")

Outputs the length of each of these character vectors, which is the number of amino acids.

nchar(hShroom3)
## [1] 2070
nchar(mShroom3a)
## [1] 2083
nchar(sShroom)
## [1] 1758
nchar(hShroom2)
## [1] 1673

Prepping macromolecular sequences

Cleans fasta file and converts 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: 0x12e1513d0>
## <environment: namespace:compbio4all>

Use compbio4all::fasta_cleaner to use the function without previously downloading the package.

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 all the fasta files of each variable and converts them into vectors.

hShroom3  <- fasta_cleaner(hShroom3,  parse = F)
mShroom3a <- fasta_cleaner(mShroom3a, parse = F)
hShroom2  <- fasta_cleaner(hShroom2,  parse = F)
sShroom   <- fasta_cleaner(sShroom,   parse = F)

Prints out the contents of hShroom3

hShroom3
## [1] "MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKLQAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAAWSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYLRRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGRERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNIPRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSLGSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGMLYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFCSVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDFQEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTKAEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQGFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEPLGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGSPESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPHTPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFPESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAYLQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLSGANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSGFGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTPCPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGPRGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTSDPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPVYSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEAESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDKSLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYYSVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEALISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHEDARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDFIPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL"

aligning sequences

Lines up amino acid sequences in the best possible way, so that the amino acids of one line up with the amino acid sequence of the other in the closest way possible.

align.h3.vs.m3a <- Biostrings::pairwiseAlignment(
                  hShroom3,
                  mShroom3a)

Prints out the matched up amino acid sequences of both objects.

align.h3.vs.m3a
## Global PairwiseAlignmentsSingleSubject (1 of 1)
## pattern: MMRTTEDFHKPSATLN-SNTATKGRYIYLEAFLE...KAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL
## subject: MK-TPENLEEPSATPNPSRTPTE-RFVYLEALLE...KAGAISLPPALTGHATPGGTSVFGGVFPTLTSPL
## score: 2189.934

Displays the percent identity, which is the simplest way of comparing two sequences. It goes down the sequence, and it calculates (number of same amino acids/total length).

Biostrings::pid(align.h3.vs.m3a)
## [1] 70.56511

Aligns the amino acid sequence of human shroom 3 and human shroom 2.

align.h3.vs.h2 <- Biostrings::pairwiseAlignment(
                  hShroom3,
                  hShroom2)

Presents the “score” of this alignment, which has no intrinsic value. It is just used to compare scores of different alignments - positive score is a better match than a negative score.

score(align.h3.vs.h2)
## [1] -5673.853

Score is more of a formal mathematical summary, and its meaning isn’t interpreted very easily. It is mainly used for comparison. PID is for finding the percent match of two samples, and it is simpler to interpret.

Biostrings::pid(align.h3.vs.h2)
## [1] 33.83277

The shroom family of genes

Creates vector of shroom family of genes with info such as accession numbers and new and original gene names.

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

Takes the data from the previous vector and organizes it into a neat table as 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)

# sets up 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"

Displays the 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 urchin

Comparing multiple sequences

The $ marks the category of the table that is to be displayed in full

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 creates an object of 14 sequences and assigns it to the variable “shrooms”

# add necessary function
shrooms <-rentrez::entrez_fetch(db = "protein", 
                          id = shroom_table$accession, 
                          rettype = "fasta")

Displays the concatenated, text file form of “shrooms”

cat(shrooms)

entrez_fetch_list is an edited version of the function entrez_fetch called a wrapper function compbio4all is a dependency of rentrez!

shrooms_list <- compbio4all::entrez_fetch_list(db = "protein", 
                          id = shroom_table$accession, 
                          rettype = "fasta")

prints out the length of the new shrooms_list

length(shrooms_list)
## [1] 14

cleans up the 14 sequences in the shrooms_list loops through each sequence in the list

for(i in 1:length(shrooms_list)){
  shrooms_list[[i]] <- fasta_cleaner(shrooms_list[[i]], parse = F)
}

converts our shrooms list into vector form

# creates an empty vector with the length of the shrooms_list
shrooms_vector <- rep(NA, length(shrooms_list))

# loops through the vector and copies each sequence of the shrooms_list into shrooms_vector
for(i in 1:length(shrooms_vector)){
  shrooms_vector[i] <- shrooms_list[[i]]
}

#  puts the accession numbers of the shrooms_list into the new vector
names(shrooms_vector) <- names(shrooms_list)

Converts our vector to a string set, which is pretty much the final step in cleaning up our sequences.

# add necessary function
shrooms_vector_ss <- Biostrings::AAStringSet(shrooms_vector)

##note to self: up to here works!

MSA

builds our msa where we can compare many different sequences of amino acids.

Building an alignment (MSA)

Converts our sequences that were in the form of a string set into a multiple sequence alignment.

# # add necessary function
# #library(msa)
# shrooms_align <- msa(shrooms_vector_ss,
#                      method = "ClustalW")

Viewing an MSA

creates and outputs an organized multiple sequence alignment

Viewing an MSA in R

outputs our kind of messy shrooms_align msa

# shrooms_align

gets our shrooms_align to the desired form which is seqinr::alignment

# # WHAT IS THE LINE BELOW DOING? sets the class an shrooms_align inherits from, which is AAMultipleAlignment
# class(shrooms_align) <- "AAMultipleAlignment"
# 
# # WHAT IS THE LINE BELOW DOING? converts shrooms_align to the type seqinr::alignment
# shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment")

prints out the msa

# print_msa(alignment = shrooms_align_seqinr, 
#           chunksize = 60)

Displaying an MSA XXXXXXXX

prints out the msa in a prettier fashion

# ## add necessary function
# library(ggmsa)
# ggmsa::ggmsa(shrooms_align,   # shrooms_align, NOT shrooms_align_seqinr
#       start = 2000, 
#       end = 2100)

Saving an MSA as PDF

saves the msa to a file on the hard drive.

# msa::msaPrettyPrint(shrooms_align,             # alignment
#                file = "shroom_msa.pdf",   # file name
#                y=c(2000, 2100),           # range
#                askForOverwrite=FALSE)

prints the path/location of where the msa is saved

#getwd()