“Worked example: Building a phylogeny in R”

Introduction

Understand how to use R.Markdown Create a phylogenetic tree for the shroom family

Vocab

Accession number: unique number assigned by a particular database as an additional means to find the article Fasta File: test-based format for representing either nucleotide sequences or amino acid sequence Pairwise alignment: identify regions of similarity between files Distance Matrix: summarize Reproducible workflow: R code that can be adaptable to compute Double colon (::): get the function from that package (“”): creates a new line

Key functions

rentrez::entrez_fetch

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

library(devtools)
# github packages
library(compbio4all)
library(ggmsa)

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

# Bioconductor packages
library(msa)
library(Biostrings)

Downloading macromolecular sequences

Here we are downloading the human shroom sequence from NCBI by using the entrez_fetch function, which accesses the NCBI database to download the sequences we’d like to have.

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

slash n is a new line character

hShroom3

## [1] ">NP_065910.3 protein Shroom3 [Homo sapiens]\nMMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKL\nQAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAA\nWSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYL\nRRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGR\nERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNI\nPRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSL\nGSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGM\nLYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFC\nSVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDF\nQEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTK\nAEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQ\nGFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEP\nLGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGS\nPESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPH\nTPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFP\nESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAY\nLQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLS\nGANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSG\nFGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTP\nCPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGP\nRGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTS\nDPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPV\nYSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEA\nESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDK\nSLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYY\nSVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEA\nLISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHED\nARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDF\nIPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL\n\n"

cat() does some fomatting for us from the raw sequence we’ve downloaded; actually creates a new line when it sees a

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 downloads the other shroom gene sequences we want using the same entrez_fetch function we’ve used for the human shroom 3 sequence

# 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")

This code chunk counts the amount of characters for each gene sequence we have downloaded. The purpose of this is to have a number to reference back after we run other cleaning and formatting functions to make sure that they’ve been shortened and summarized to the essential parts we want to look at.

nchar(hShroom3)

## [1] 2070

nchar(mShroom3a)

## [1] 2083

nchar(sShroom)

## [1] 1758

nchar(hShroom2)

## [1] 1673

Cleaning / Prepping macromolecular sequences

This function cleans the current sequence files that we have to be able to use them for our purposes. What fasta_cleaner does is it removes the header and the newline character from the sequences.

library(compbio4all)
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: 0x7f8555837790>
## <environment: namespace:compbio4all>

If you’ve had problems downloading the compbio4all package, you can add the fasta_cleaner function by running 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]])
}

This should clean the fasta files to be able to do alignments the way we want do them using functions from the libraries we called at the very start.

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"

nchar(hShroom3)

## [1] 1996

Aligning the sequences

Here we align one sequence against another using a function called pairwiseAlignment() from the Bioconductor package. Pairwise alignment is a global alignment algorithm that aligns both sequences in the best way to get the most amount of matching bases by aligning indels in the best way and trying to match bases as much as possible.

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

This just shows us the result of the pairwise alignment we have carried out

align.h3.vs.m3a

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

# This code chunk is just marking the stopping point from
## 9/21/21
######################################
######################################
######################################
######################################
######################################
######################################
######################################
######################################
######################################

The function pid() gives us a score and that indicates the percentage that both sequences are identical by counting the amount of base pairs aligned in the same position and the amount of indels present as well.

All R packages are always in lower case, but the Biostrings package is an exception.

Biostrings::pid(align.h3.vs.m3a)

## [1] 70.56511

The last code chunk with the pairwiseAlignment() function was comparing the shroom 3 gene from humans to the one from rats. This code chunk compares human shroom 2 vs human shroom 3.

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

The following chunk shows you the percent match between both genes of human 2 and 3 from the pid() function, and the score() function shows how closely two sequences are aligned in a way that is less orderly than having a percentage (a simple number between zero and a hundred). In essence, the higher the score, the more similar two sequences are.

pid(align.h3.vs.h2)

## [1] 33.83277

score(align.h3.vs.h2)

## [1] -5673.853

The score is negative because there is a lot of indels. The pid() offers a easier and more indicative number of how similar both sequences are.

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

## [1] 33.83277

The shroom family of genes

This big table is just a copied list of accession numbers that we will use for an MSA later on in this program. They comprise of shroom genes, some from the same organism and some from different organisms.

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 chunk just does a bit of formatting for each of the 15 sequences.

# convert the vector to a matrix
shroom_table_matrix <- matrix(shroom_table,
                                  byrow = T,
                                  nrow = 14)
# convert to dataframe
shroom_table <- data.frame(shroom_table_matrix, 
                     stringsAsFactors = F)

# Name columns of dataframe using the names() function
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 shows us the finished table after all formatting is properly done

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

Downloading multiple sequences

Instead of getting one sequence at a time we can load up several by accessing the accession column from the table.

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"

entrez_fetch is once again here to take the accession numbers in and dowload the sequences from the NCBI database. It can do that because it is a vectorized function and can process a whole vector of inputs

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

is(shrooms)

##  [1] "character"               "vector"                 
##  [3] "data.frameRowLabels"     "SuperClassMethod"       
##  [5] "EnumerationValue"        "character_OR_connection"
##  [7] "character_OR_NULL"       "atomic"                 
##  [9] "vector_OR_Vector"        "vector_OR_factor"

length(shrooms)

## [1] 1

nchar(shrooms)

## [1] 22252

cat() just simply creates a new line when it encounters a new line operator in the sequence code

cat(shrooms)

entrez_fetch_list is a function that downloads all the sequences given to it and stores them in a an R list.

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

lists data structure

Excel = “workbook”- worksheets

running length() here serves as our confirmation that all sequences have been downloaded and stored successfully in an R list

length(shrooms_list)

## [1] 14

This for loop pulls in all the sequences individually, runs fasta_cleaner on them to take out the unnecessary characters such as the one for making new lines, then stores them all in a list called shrooms_list

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

This chunk creates a vector ready to house the clean sequences from the list, and puts them in it in preparation for carrying out an MSA

# creation of an empty vector
shrooms_vector <- rep(NA, length(shrooms_list))

# filling up the vector with the sequences from the list
for(i in 1:length(shrooms_vector)){
  shrooms_vector[i] <- shrooms_list[[i]]
}

# assign names to the vector using the names() function
names(shrooms_vector) <- names(shrooms_list)

Converting the vector to a string set, which is useful to organize and annotate data.

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

MSA

This section of the document will compare all the sequences downloaded to build a phylogenetic tree.

Building a Multiple Sequence Alignment (MSA)

This code chunk will use the software msa, to implement the ClustalW multiple alignment sequence algorithm

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

## use default substitution matrix

re-implementation of algorithm

Viewing an MSA

There’s more than one way to visualize an MSA, and this section will demostrate that.

Viewing an MSA in R

This chunk shows the direct output from msa(), which is not very helpful

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 following code chunk reclassifies shrooms_align to be a part of the “AAMultipleAlignment” class, in order to have it work well with the other functions.

#reclassifying shrooms_align to the class AAMultipleAlignment
class(shrooms_align) <- "AAMultipleAlignment"

# making a small tweak to the object shrooms_align to make it work with functions from the seqinr package. Also renaming the tweaked package to shrooms_align_seqinr
shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment")

Shows the raw output of all 14 shroom genes

print_msa(alignment = shrooms_align_seqinr, 
          chunksize = 60)

Displaying an MSA as an R plot

This will show nice looking figures of parts of an MSA, with most of them having alignment on the base pairs.

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

Saving an MSA as PDF

This saves the produced MSA plot on R as a pdf by using the function msaPrettyPrint

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

This command just shows you where R on your device saves any files, so you can get to the created MSA PDF if you want to access it and use it on a scientific paper.

getwd()

## [1] "/Users/kareemzein/OneDrive - University of Pittsburgh/College/Junior Year/Comp Bio/MSA workflow"

list.files

## function (path = ".", pattern = NULL, all.files = FALSE, full.names = FALSE, 
##     recursive = FALSE, ignore.case = FALSE, include.dirs = FALSE, 
##     no.. = FALSE) 
## .Internal(list.files(path, pattern, all.files, full.names, recursive, 
##     ignore.case, include.dirs, no..))
## <bytecode: 0x7f854395e6a0>
## <environment: namespace:base>

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Portfoliio 1 (A stage)

Kareem Zein

9/28/2021

A complete bioinformatics workflow in R