“Worked example: Building a phylogeny in R”

Introduction

phylogenies can be used to build phylogenetic trees which can be used to show the evolutionary pathways and connections between organisms

Vocab

Make a list of at least 10 vocab terms that are important

ascension number fasta file pairwise alignment MSA (multiple sequence alignment) distance matrix reproducible work flow phylogeny double colon notation (::) /n: bioconductor

Key functions

Make a list of at least 5 key functions

rentrez::entrez_fetch Biostrings:: pairwiseAlignment Biostrings:: pid stringr::str_split Biostrings::score

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)

sequencing macromolecular sequences

here we are fetching the H. sapien protein sequences from the rentrez database.

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

the cat() function prints out to a file or screen

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 chunk shows us the 3 shroom genes present in 3 species. humans, mice, and sea urchins

# 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")

the following code chunk is telling us the number of amino acids present in each of the shroom genes specified

nchar(hShroom3)

## [1] 2070

nchar(mShroom3a)

## [1] 2083

nchar(sShroom)

## [1] 1758

nchar(hShroom2)

## [1] 1673

Prepping macromolecular sequences

this function converts a FASTA file that is stored as an object 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: 0x7f995c7a7768>
## <environment: namespace:compbio4all>

you can add fata_cleaner to your R session by activating the compbio4all then activate fasta_cleaner. if compbio4all isnt working you can define the fasta cleaner variable and then add the objects and code necessary to make it ‘do’ the same thing as defined in the code chunk below

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]])
}

takes the fasta files and cleans them up for the necessary alignemnts to be run after this

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"

paiwise alignment of sequences

the code below is lining up the sequences of human shroom and mice shroom DNA

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

this shows the aligned human and mice DNA where they are most similar and the number of base pairs they have similar to one another

align.h3.vs.m3a

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

this is giving is the percent sequence similarity for the pairwise alignment

# add necessary function
Biostrings::pid(align.h3.vs.m3a)

## [1] 70.56511

this code chunk is aligning human hemoglobin and myoglobin DNA sequences

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

this output is giving us the similarity score between hemoglobin and myoglobin compared to the similarity score of mice hemoglobin to human hemoglobin, the sequences in this alignment are more similar

score(align.h3.vs.h2)

## [1] -5673.853

pid() Calculates the percent sequence identity for a pairwise sequence alignment.score() gives the number of amino acids that the sequences have in common

Biostrings::pid(align.h3.vs.h2)

## [1] 33.83277

The shroom family of genes

this allows us to put all the species we are looking at in one table and allows us to view the specific species we are trying to collect data on

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 code chunk is creating a matrix then converting that into a dataframe and putting that information into a table with simplified species names

#did not cover in class was skipped over
# convert to XXXXXXXXXC
shroom_table_matrix <- matrix(shroom_table,
                                  byrow = T,
                                  nrow = 14)
# convert to XXXXXXXXXC
shroom_table <- data.frame(shroom_table_matrix, 
                     stringsAsFactors = F)

# XXXXXXXXXC 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 function puts our raw data into a clean table and allows us to view the accession number, and names of the species

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

cleaning multiple sequences

the $ allows us to view 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"

this function is putting all 14 sequences together so we can view them

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

it enforces the new line code. it formats into text editor format so that it can easily be downloaded.

cat(shrooms)

we are getting the proteins from the data base and matching it to the acession number and formatting it into a fasta file

it is a wrapper function

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

is(shrooms_list)

## [1] "list"             "vector"           "list_OR_List"     "vector_OR_Vector"
## [5] "vector_OR_factor"

length(shrooms_list)

## [1] 14

nchar(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

#vectorization

this function is telling us the length of the shrooms_list vector

length(shrooms_list)

## [1] 14

this function is used to clean the data again it goes through all 14 sequences and is a for loop because it does the cleaning 14 times on all 14 sequences

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

this code chunk is going to repeat the process of cleaning the code chunk. it includes intermediary cleaning steps

# XXXXXXXXCX
shrooms_vector <- rep(NA, length(shrooms_list))

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

#  XXXXXXXXCX
names(shrooms_vector) <- names(shrooms_list)

create oxford vector and convert it to an amino acid string set and its the last step to cleaning the data

shrooms_vector_ss <- Biostrings::AAStringSet(shrooms_vector)

MSA

this section of the document will convert the stringed data into an msa. it is going to format our data and arrange it into the viewable multiple sequence alignment here we are building a multiple sequence alignment re-implementation of an algorithm

Building an MSA

This code chunk is building the multiple sequence alignment.

shrooms_align <-msa(shrooms_vector_ss,
                     method = "ClustalW")

## use default substitution matrix

Viewing an MSA

this section will take our cleaned data and display it into an msa that is easier to interpret and visualize

Viewing an MSA in R

this chunk is showing us our MSA output before it is cleaned and lined up

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

this code chunk is basically taking the msa and cleaning it up

# WHAT IS THE LINE BELOW DOING? (its tricky - do your best)
class(shrooms_align) <- "AAMultipleAlignment"

# this is converting the alignment to msa format
shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment")

this output is taking our cleaned data and displaying it in a messy arrangement

print_msa(alignment = shrooms_align_seqinr, 
          chunksize = 60)

Displaying an MSA XXXXXXXX

this is showing us the multiple sequence alignment in one big organized table that is color coordinated and organized and cleaned. this is taking the previous alignment and organizing it into a neater msa with colors

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

this command is going to take the multiple sequence alignment we created in the previous chunk and export it as a pdf. it also gives the pdf a name

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

this command is telling your computer where to save the file and shows you the file path it takes in your directory

getwd()

## [1] "/Users/ghaliamalki/Desktop"

Assignment 1- Portfolio 1

Ghalia Malki

9/28/2021

A complete bioinformatics workflow in R