A complete bioinformatics workflow in R

By: Nathan L. Brouwer

“Worked example: Building a phylogeny in R”

Introduction

Phylogenies can be used to determine relationships between species and how they have evolved over time. Through phylogenetics, gene sequences are analyzed to determine their alignment and thus their similarity, so that species can be grouped based on when they evolved certain traits.

Vocab

Pairwise alignment, multiple sequence alignment, nucleotide, amino acid, percent identity, consensus sequence, indel, accession number, gene, conserved, mutation

Key functions

Biostrings::pid, ggmsa::ggmsa, rentrez::entrez_fetch, compbio4all::entrez_fetch_list, Biostrings::AAStringSet

Software Preliminaires

Load packages into memory

# github packages
library(compbio4all)


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


# Bioconductor packages
library(msa)
#library(ggmsa)
library(Biostrings)

Downloading macromolecular sequences

The human shroom 3 gene sequence is being retrieved as a FASTA file from the NCBI database using its accession number

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

The cat() function concatenates the human shroom 3 gene file into 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

3 different gene sequences, each from a different species mouse, human, sea-urchin) are being retrieved and downloaded from NCBI database

# 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")

Determining the length of each gene by returning the number of characters in each vector

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

Prepping macromolecular sequences

This function takes the FASTA file which is stored as an object 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: 0x00000000230e4ac0>
## <environment: namespace:compbio4all>

Use an if statement to return the object as a variable

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]])
}

Cleaning the FASTA file but not parsing it into a character vector

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

Printing the hShroom3 AA sequence

hShroom3
## [1] "MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKLQAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAAWSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYLRRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGRERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNIPRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSLGSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGMLYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFCSVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDFQEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTKAEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQGFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEPLGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGSPESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPHTPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFPESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAYLQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLSGANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSGFGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTPCPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGPRGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTSDPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPVYSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEAESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDKSLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYYSVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEALISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHEDARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDFIPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL"

Aligning sequences

This returns the pairwise alignment between the human shroom 3 gene and the mouse shroom 3 gene sequences

# add necessary function
align.h3.vs.m3a <- Biostrings::pairwiseAlignment(
                  hShroom3,
                  mShroom3a)

This shows the alignment of amino acids between the human shroom 3 gene and the mouse shroom 3a gene, with gaps representing indels.

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

Determining percent identity between human shroom 3 and mouse shroom 3a gene amino acid sequences

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

Aligning two human shroom genes (human shroom 3 vs. human shroom 2) rather than a human vs. mouse gene

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

This shows the score of the pairwise alignment between the human shroom 3 and human shroom 2 genes

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

The similarity score takes indels into account unlike percent identity

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

The shroom family of genes

This is converting the table to a vector

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

This takes the data from the vector and makes turns it into a data frame so that specific columns and rows can be accessed

# 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"

Displaying the table in an organized way with 3 separated labeled columns

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

Aligning multiple sequences

The $ allows you to access all the data from a specific column in the data frame (in this case, “accession” is the column name)

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"

Retrieving the accession number column from the shroom table as a FASTA file

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

Concatenating the column accessed into a single character vector

cat(shrooms)

This is using a different package than rentrez to use the modified function to retrieve the accession numbers as a list

devtools::install_github("brouwern/compbio4all")
## Skipping install of 'compbio4all' from a github remote, the SHA1 (dbb3cdf4) has not changed since last install.
##   Use `force = TRUE` to force installation
library(compbio4all)
shrooms_list <- compbio4all::entrez_fetch_list(db = "protein", 
                          id = shroom_table$accession, 
                          rettype = "fasta")

Finding out the number of gene sequences in the list

length(shrooms_list)
## [1] 14

Cleaning each sequence in the list one at a time

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

Cleaning the sequences and converting them to vectors

# appending shrooms_list to a vector
shrooms_vector <- rep(NA, length(shrooms_list))

# accessing specific columns
for(i in 1:length(shrooms_vector)){
  shrooms_vector[i] <- shrooms_list[[i]]
}

#  naming the vector
names(shrooms_vector) <- names(shrooms_list)

Last step to clean the data

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

MSA

This part of the document will build a multiple sequence alignment (MSA). This involves aligning the gene or protein sequences of 3 from 3 or more different species.

Building an multiple sequence alignment (MSA)

This function takes the data in string set format and creates the MSA

# add necessary function
shrooms_align <- msa(shrooms_vector_ss,
                     method = "ClustalW")
## use default substitution matrix

Viewing an MSA

This section will show the MSA as a substitution matrix

Viewing an MSA in R

The initial output in R is a somewhat unhelpful way of viewing the MSA, not processed

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 -------------------------...------------------------- Consensus

Data preparation

# naming the shrooms_align table in R
class(shrooms_align) <- "AAMultipleAlignment"

# character string converting the R sequence to an alignment
shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment")

Now that the data has been prepared the MSA can be printed in a more readable way and printing it to the console

print_msa(alignment = shrooms_align_seqinr, 
          chunksize = 60)

Displaying an MSA XXXXXXXX

This uses the ggmsa() function to print the MSA in a useful format, and generate a specific useful part of the alignment, where “start” and “end” specify the limits of that section of the alignment

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

Saving an MSA as PDF

This prints the MSA and creates a PDF rather than just viewing it in R, where the y=c() function specifies the residues at which to start and stop

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

This function retrieves your current working directory

getwd()
## [1] "C:/Users/peter/Downloads"