Bioinformatics Workflow

A complete bioinformatics workflow in R

By: Nathan L. Brouwer

“Worked example: Building a phylogeny in R”

Introduction

While studying biology, phylogenies may be utilized as an analytical tool to oberve the evolution of relationships between varying species.Specifically,there are diagrams used to represent physical and genetic comparisons between species, also known as a phylogenetic tree.

Vocab

MSA (multiple sequence alignment) /n accession number Fasta file function pairwise alignment package distance matrix NCBI library

Key functions

Biostrings::pid() stringr::str_split() rentrez::entrez_fetch() Biostrings::pairwiseAlignment() compbio4all::entrez_fetch_list() ## Software Preliminaires

library(compbio4all) # CRAN packages library(ape) library(rentrez) library(seqinr)

Bioconductor packages

library(Biostrings) library(msa)

Load packages into memory

# github packages
#install.packages("compbio4all")
library(compbio4all)


# CRAN packages
library(rentrez)
#install.packages("seqinr")
library(seqinr)
#install.packages("ape")
library(ape) 


# Bioconductor packages
library(msa)
library(Biostrings)

XXXXXXXXing macromolecular sequences

This code chunk assigns an accession number to a specific sequence of data as well as downloads the data for Human shroom 3 and assigns it the variable hShroom3.

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

the cat() function neatly prints out the sequence while acknowledging the newline characters.

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 designates the fasta data to their according and appropriate variables. Rentrez is the package and entrez_fetch is the function which actually downloads the data.

# 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 code here uses the nchar() function to output the number of characters in each specific shroom variable, which represents the number of amino acids.

nchar(hShroom3)

## [1] 2070

nchar(mShroom3a)

## [1] 2083

nchar(sShroom)

## [1] 1758

nchar(hShroom2)

## [1] 1673

Prepping macromolecular sequences

The purpose of this function is to align and prepare the sequence data from the fasta files as it organizes them into a proper formating for phylogenetic functions such as alignments.

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: 0x7fd5aaa52708>
## <environment: namespace:compbio4all>

By using function() then assigning it to a variable of your function name you can create a new function. This allows for this segment of code to be specific to what you are looking for this function to acomplish.

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]])
}

In this code chunk we are calling fasta_cleaner() on the original sequence variables so they can be properly prepared and fornatted.These cleaned up versions are being assigned to the same variable name to overide the old assigned sequences with the new clean ones.

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"

XXXXXXXXing sequences

TODO: give this a title. Explain what code below is doing Human shroom vs Mice Shroom

This code establishes a pairwise alignment betwen the two sequences listed in the title. They are lined up beside eachother then assigned to a variable.

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

This represents two sequences included in the pairwise alignment above. One is utilized as a subject and the other as a pattern. This also is able to determine if the alignment is good or bad.

align.h3.vs.m3a

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

This will represent how similar the two sequences are when compared using percent identity.

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

## [1] 70.56511

This code will do a pairwise alignment between 3 human shroom genes which is different from the one above because that alignment was between a mice shroom and human shroom gene.

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

This code results in a score of -5265.292 while the one above prints out 70.56511. This is a worse score.

pid(align.h3.vs.h2)

## [1] 33.83277

score(align.h3.vs.h2)

## [1] -5673.853

pid() represents percent identity of how similar the comparison of the sequences score() provides a formal mathmatical summary of the quality of the alignment

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

## [1] 33.83277

The shroom family of genes

The purpose of this table is to give an accurate summary of accession numbers, scientific names, genus as well as variable names according to each species.

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 converts the table into a matrix then into a dataframe. Once this is done it then converts it into a neat table with specified names and columns.

# 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 will display the table in a pleasantly visual format.

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

XXXXXing multiple sequences

$ allows us to access the data in a certain column by its 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"

This code downloads the data per accession number in the table then proceeds to assign it to a variable named shrooms.

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

cat() function shows the sequences for each accession number in nest form and neatly.

cat(shrooms)

This establishes the shrooms list by putting together the list of accession numbers as the ID as opposed to a table

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

TODO: briefly explain what I am doing this

length(shrooms_list)

## [1] 14

TODO: briefly explain what I am doing this. We will get into the details of for() loops in R later in the semester.

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

# 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)

TODO: explain what this is doing then add the necessary function.

# 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.

Building an XXXXXXXX (MSA)

TODO: briefly explain what this chunk does, then add the necessary function.

# add necessary function
#install.packages("BiocManager")
#library(BiocManager)
#BiocManager::install("msa")
#BiocManager::install("Biostrings")
BiocManager::install("msa")

## Bioconductor version 3.13 (BiocManager 1.30.16), R 4.1.1 (2021-08-10)

## Warning: package(s) not installed when version(s) same as current; use `force = TRUE` to
##   re-install: 'msa'

## Old packages: 'aplot', 'cpp11', 'data.table', 'desc', 'digest', 'gert', 'hms',
##   'knitr', 'lattice', 'lifecycle', 'maps', 'matrixStats', 'mgcv', 'mime',
##   'pillar', 'rcmdcheck', 'RCurl', 'readr', 'S4Vectors', 'tidyr', 'tinytex',
##   'XML'

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

## use default substitution matrix

Viewing an MSA

TODO: briefly summarize what this section will do.

Viewing an MSA in R

TODO: Briefly summarize what output is shown below

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

TODO: briefly explain what is being done in this chunk. This is tricky (and annoying) so do your best

# 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

print_msa(alignment = shrooms_align_seqinr, 
          chunksize = 60)

Displaying an MSA XXXXXXXX

TODO: explain this output and how its differnet from the prevoius

## add necessary function
library(ggmsa)

## Registered S3 methods overwritten by 'ggalt':
##   method                  from   
##   grid.draw.absoluteGrob  ggplot2
##   grobHeight.absoluteGrob ggplot2
##   grobWidth.absoluteGrob  ggplot2
##   grobX.absoluteGrob      ggplot2
##   grobY.absoluteGrob      ggplot2

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

Saving an MSA as PDF

TODO: explain what this command is doing. 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

getwd()

## [1] "/Users/anoshkabhatt/Downloads"