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!

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)

Vocab

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

Key functions

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

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
getwd()

## [1] "/Users/haydenbash"

library(compbio4all)



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



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

XXXXXXXXing macromolecular sequences

TODO: Fill in the XXXXXs and write a 1-2 sentence of what is going on here.
Add the package that is where entrez_fetch is from using :: notation

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

TODO:explain what cat() is doing

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

TODO: explain what this code chunk is doing

# 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")

TODO: Explain what this code chunk is doing

nchar(hShroom3)

## [1] 2070

nchar(mShroom3a)

## [1] 2083

nchar(sShroom)

## [1] 1758

nchar(hShroom2)

## [1] 1673

Prepping macromolecular sequences

TODO: Explain what this function does

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: 0x7f888ef713d0>
## <environment: namespace:compbio4all>

TODO: explain how to add the function to your R session even if you can’t download 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]])
}

TODO: briefly explain what this code chunk is doing

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

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

TODO: In 1-2 sentence explain what this object shows

align.h3.vs.m3a

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

TODO: explain what this is showing

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

## [1] 70.56511

TODO: briefly explain what is going on here versus the previous code chunk

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

TODO: explain what is going on here and compare and contrast with previous ouput

score(align.h3.vs.h2)

## [1] -5673.853

TODO: briefly explian the difference between the output of score() and pid() (can be very brief - we’ll get into the details later)

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

## [1] 33.83277

The shroom family of genes

TODO: briefly explain why I have this whole table here

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

TODO: write a short sentence explaining what this next code chunk will do, then annotate each line with what was done.

# 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"

TODO: in a brief sentence explain what this is doing

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

TODO: in a brief sentence explain what the $ allows us to do

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"

TODO: briefly explain what this chunk is doing and add the correct function

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

TODO: in a very brief sentence explain what this is doing.

cat(shrooms)

TODO: in a brief sentence explain what this is doing and if/how its different from the previous code chunks

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
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
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/haydenbash"

A subset of sequences

To make things easier we’ll move forward with just a subset of sequences:

XP_392427: amShroom (bee shroom)
EAA12598: agShroom (mosquito shroom)
ABA81834: dmShroom (Drosophila shroom)
XP_783573: spShroom (sea urchin shroom)
CAA78718: xShroom1 (frog shroom)

Our main working object shrooms_vector_ss has the names of our genes listed

names(shrooms_vector_ss)

##  [1] "CAA78718"  "NP_597713" "CAA58534"  "ABD19518"  "AAF13269"  "AAF13270" 
##  [7] "NP_065910" "ABD59319"  "NP_065768" "AAK95579"  "ABA81834"  "EAA12598" 
## [13] "XP_392427" "XP_783573"

We can select the ones we want to focus on be first making a vector of the names

names.focal <- c("XP_392427","EAA12598","ABA81834","XP_783573","CAA78718")

We can use this vector and bracket notation to select the what we want from shrooms_vector_ss:

shrooms_vector_ss[names.focal]

## AAStringSet object of length 5:
##     width seq                                               names               
## [1]  2126 MTELQPSPPGYRVQDEAPGPPSC...GREIQDKVKLGEEQLAALREAID XP_392427
## [2]   674 IPFSSSPKNRSNSKASYLPRQPR...ADKIKLGEEQLAALKDTLVQSEC EAA12598
## [3]  1576 MKMRNHKENGNGSEMGESTKSLA...AVRIKGSEEQLSSLSDALVQSDC ABA81834
## [4]  1661 MMKDAMYPTTTSTTSSSVNPLPK...TSSSSNGIGGPEQLNSNATSSYC XP_783573
## [5]  1420 MSAFGNTIERWNIKSTGVIAGLG...KNLEEKIKVYEEQFESIHNSLPP CAA78718

Let’s assign the subset of sequences to a new object called shrooms_vector_ss_subset.

shrooms_vector_ss_subset <- shrooms_vector_ss[names.focal]

Let’s make another MSA with just this subset. If msa isn’t working for you you can comment this out.

shrooms_align_subset <- msa(shrooms_vector_ss_subset,
                     method = "ClustalW")

## use default substitution matrix

To view it using ggmsa we need to do those annoying conversions again.

class(shrooms_align_subset) <- "AAMultipleAlignment"
shrooms_align_subset_seqinr <- msaConvert(shrooms_align_subset, type = "seqinr::alignment")

THen we can plot it

ggmsa::ggmsa(shrooms_align_subset,   # shrooms_align, NOT shrooms_align_seqinr
      start = 2030, 
      end = 2100)

We can save our new smaller MSA like this.

msaPrettyPrint(shrooms_align_subset,             # alignment
               file = "shroom_msa_subset.pdf",   # file name
               y=c(2030, 2100),           # range
               askForOverwrite=FALSE)

Genetic distances of sequence in subset

While an MSA is a good way to examine a sequence its hard to assess all of the information visually. A phylogenetic tree allows you to summarize patterns in an MSA. The fastest way to make phylogenetic trees to is first summarize an MSA using a genetic distance matrix. The more amino acids that are identical to each other, the smaller the genetic distance is between them and the less evolution has occurred.

We usually work in terms of difference or genetic distance (a.k.a. evolutionary distance), though often we also talk in terms of similarity or identity.

Calculating genetic distance from an MSA is done using the seqinr::dist.alignment() function.

shrooms_subset_dist <- seqinr::dist.alignment(shrooms_align_subset_seqinr, 
                                       matrix = "identity")

This produces a “dist” class object.

is(shrooms_subset_dist)

## [1] "dist"     "oldClass"

class(shrooms_subset_dist)

## [1] "dist"

If you’ve been having trouble with the MSA software, the data necessary to build the distance matrix directly in R is in this code chunk (you can ignore the details).

shrooms_subset_dist_alt <- matrix(data = NA,
                              nrow = 5, 
                              ncol = 5)

distances <- c(0.8260049, 
               0.8478722, 0.9000568, 
               0.9244596, 0.9435187, 0.9372139, 
               0.9238779, 0.9370038, 0.9323225,0.9413209)
shrooms_subset_dist_alt[lower.tri(shrooms_subset_dist_alt)] <- distances

seqnames <- c("EAA12598","ABA81834","XP_392427", "XP_783573","CAA78718")
colnames(shrooms_subset_dist_alt) <- seqnames
row.names(shrooms_subset_dist_alt)  <- seqnames
shrooms_subset_dist_alt <- as.dist(shrooms_subset_dist_alt)
shrooms_subset_dist <- shrooms_subset_dist_alt

We’ve made a matrix using dist.alignment(); let’s round it off so its easier to look at using the round() function.

shrooms_subset_dist_rounded <- round(shrooms_subset_dist,
                              digits = 3)

If we want to look at it we can type

shrooms_subset_dist_rounded

##           EAA12598 ABA81834 XP_392427 XP_783573
## ABA81834     0.826                             
## XP_392427    0.848    0.944                    
## XP_783573    0.900    0.937     0.937          
## CAA78718     0.924    0.924     0.932     0.941

Not that we have 5 sequence, but the matrix is 4 x 4. This is because redundant information is dropped, including distances from one sequence to itself. This makes it so that the first column is EAA12598, but the first row is ABA81834.

Phylognetic trees of subset sequences (finally!)

We got our sequences, built a multiple sequence alignment, and calculated the genetic distance between sequences. Now we are - finally - ready to build a phylogenetic tree.

First, we let R figure out the structure of the tree. There are MANY ways to build phylogenetic trees. We’ll use a common one used for exploring sequences called neighbor joining algorithm via the function nj(). Neighbor joining uses genetic distances to cluster sequences into clades.

nj() is simple function that takes only a single argument, a distance matrix.

# Note - not using rounded values
tree_subset <- nj(shrooms_subset_dist)

Plotting phylogenetic trees

Now we’ll make a quick plot of our tree using plot() (and add a little label using an important function called mtext()).

# plot tree
plot.phylo(tree_subset, main="Phylogenetic Tree", 
            type = "unrooted", 
            use.edge.length = F)

# add label
mtext(text = "Shroom family gene tree - UNrooted, no branch lengths")

This is an unrooted tree with no outgroup defined. For the sake of plotting we’ve also ignored the evolutionary distance between the sequences, so the branch lengths don’t have meaning.

To make a rooted tree we remove type = "unrooted. In the case of neighbor joining, the algorithm tries to figure out the outgroup on its own.

# plot tree
plot.phylo(tree_subset, main="Phylogenetic Tree", 
            use.edge.length = F)

# add label
mtext(text = "Shroom family gene tree - rooted, no branch lenths")

We can include information about branch length by setting use.edge.length = ... to T.

# plot tree
plot.phylo(tree_subset, main="Phylogenetic Tree", 
            use.edge.length = T)

# add label
mtext(text = "Shroom family gene tree - rooted, with branch lenths")

Now the length of the branches indicates the evolutionary distance between sequences and correlate to the distances reported in our distance matrix. The branches are all very long, indicating that these genes have been evolving independently for many millions of years.

An important note: the vertical lines on the tree have no meaning, only the horizontal ones.

Because the branch lengths are all so long I find this tree a bit hard to view when its rooted. Let’s make it unrooted again.

# plot tree
plot.phylo(tree_subset, main="Phylogenetic Tree", 
           type = "unrooted",
            use.edge.length = T)

# add label
mtext(text = "Shroom family gene tree - rooted, with branch lenths")

Now you can see that the ABA and EAA sequences form a clade, and that the distance between them is somewhat smaller than the distance between other sequences. If we go back to our original distance matrix, we can see that the smallest genetic distance is between ABA and EAA at 0.826.

shrooms_subset_dist_rounded

##           EAA12598 ABA81834 XP_392427 XP_783573
## ABA81834     0.826                             
## XP_392427    0.848    0.944                    
## XP_783573    0.900    0.937     0.937          
## CAA78718     0.924    0.924     0.932     0.941

We can confirm that this is the minimum using the min() function.

min(shrooms_subset_dist_rounded)

## [1] 0.826

Bioinformatics Work Flow

Hayden Bash

9/28/2021