Accessing data from Google Docs
Nathan Brouwer
11/12/2021
The goal of this exercise is to make you familiar with how to download data from Google Sheets and to briefly review some key concepts R functions and coding concepts.
We’ll do the following things
download a list of RefSeq accessions from a Google sheet remove the NAs select out all but one isoform using duplicated()
Packages
## Google sheets download package
# comment this out when you are done
# install.packages("googlesheets4")
library(googlesheets4)
# comp bio packages
library(seqinr)
library(rentrez)
library(compbio4all)
library(Biostrings)## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Loading required package: S4Vectors## Loading required package: stats4##
## Attaching package: 'S4Vectors'## The following objects are masked from 'package:base':
##
## expand.grid, I, unname## Loading required package: IRanges## Loading required package: XVector## Loading required package: GenomeInfoDb##
## Attaching package: 'Biostrings'## The following object is masked from 'package:seqinr':
##
## translate## The following object is masked from 'package:base':
##
## strsplitDownload data
Assigns sheets link to variable
spreadsheet_sp <- "https://docs.google.com/spreadsheets/d/1spC_ZA3_cVuvU3e_Jfcj2nEIfzp-vaP7SA5f-qwQ1pg/edit?usp=sharing" Talls the package so credentials and authorization aren’t checked
# be sure to run this!
googlesheets4::gs4_deauth() # <====== MUST RUN THISThird, we download our data.
NOTE!: sometimes Google Sheets or the function gets cranky and throws this error:
“Error in curl::curl_fetch_memory(url, handle = handle) : Error in the HTTP2 framing layer”
If that happens, just re-run the code.
# I include this again in case you missed is the first time : )
googlesheets4::gs4_deauth()
# download
## NOTE: if you get an error, just run the code again
refseq_column <- read_sheet(ss = spreadsheet_sp, # the url
sheet = "RefSeq_prot", # the name of the worksheet
range = "selenoprot!H1:H364",
col_names = TRUE,
na = "", # fill in empty spaces "" w/NA
trim_ws = TRUE)## ✓ Reading from "human_gene_table".## ✓ Range ''selenoprot'!H1:H364'.## NOTE: if you get an error, just run the code again
# for reasons we won't get into I'm going to do this
protein_refseq <- refseq_column$RefSeq_protShows some of the spreadsheet
protein_refseq[1:10]## [1] "NP_000783.2" "NP_998758.1" "NP_001034804.1" "NP_001034805.1"
## [5] "NP_001311245.1" NA NA "NP_054644.1"
## [9] "NP_001353425.1" "NP_000784.3"Fills empty spaces with NA
# download
## NOTE: if you get an error, just run the code again
gene_name_column <- read_sheet(ss = spreadsheet_sp, # the url
sheet = "gene", # the name of the worksheet
range = "selenoprot!A1:A364",
col_names = TRUE,
na = "", # fill in empty spaces "" w/NA
trim_ws = TRUE)## ✓ Reading from "human_gene_table".## ✓ Range ''selenoprot'!A1:A364'.## NOTE: if you get an error, just run the code again
# for reasons we won't get into I'm going to do this
gene <- gene_name_column$geneData Checking
Shows info about spreadsheet (class, length, etc.)
is(protein_refseq)## [1] "character" "vector"
## [3] "data.frameRowLabels" "SuperClassMethod"
## [5] "character_OR_connection" "character_OR_NULL"
## [7] "atomic" "EnumerationValue"
## [9] "vector_OR_Vector" "vector_OR_factor"class(protein_refseq)## [1] "character"length(protein_refseq)## [1] 363protein_refseq[1:10]## [1] "NP_000783.2" "NP_998758.1" "NP_001034804.1" "NP_001034805.1"
## [5] "NP_001311245.1" NA NA "NP_054644.1"
## [9] "NP_001353425.1" "NP_000784.3"Checks for NAs
is.na(protein_refseq)## [1] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
## [13] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
## [37] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [73] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [253] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [289] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSESees how many NAs are in the spreadsheet
table(is.na(protein_refseq))##
## FALSE TRUE
## 334 29Assigns these values to variables
# ...
temp <- is.na(protein_refseq)
# ....
protein_refseq[temp]## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NAtemp2 <- protein_refseq[temp]
# ...
length(temp2)## [1] 29Makes a Dataframe
This makes a dataframe out of the data
seleno_df <- data.frame(gene = gene,
protein_refseq = protein_refseq)This shows some of the dataframe
summary(seleno_df)## gene protein_refseq
## Length:363 Length:363
## Class :character Class :character
## Mode :character Mode :characterhead(seleno_df)## gene protein_refseq
## 1 DIO1 NP_000783.2
## 2 DIO1 NP_998758.1
## 3 DIO1 NP_001034804.1
## 4 DIO1 NP_001034805.1
## 5 DIO1 NP_001311245.1
## 6 DIO1 <NA>Remove NAs
This takes all the NAs out
# omit NAs
seleno_df_noNA <- na.omit(seleno_df)
# check length- should be shorter
dim(seleno_df)## [1] 363 2dim(seleno_df_noNA)## [1] 334 2Selecting One Isoform
The same gene can appear multiple times because multiple isoforms are listed.
head(seleno_df_noNA)## gene protein_refseq
## 1 DIO1 NP_000783.2
## 2 DIO1 NP_998758.1
## 3 DIO1 NP_001034804.1
## 4 DIO1 NP_001034805.1
## 5 DIO1 NP_001311245.1
## 8 DIO2 NP_054644.1This selects one row for one gene
genes_unique <- unique(seleno_df_noNA$gene)
length(genes_unique)## [1] 37genes_unique## [1] "DIO1" "DIO2" "DIO3" "GPX1" "GPX2" "GPX3"
## [7] "GPX4" "GPX6" "MSRB1" "SELENOF" "SELENOH" "SELENOI"
## [13] "SELENOK" "SELENOM" "SELENON" "SELENOO" "SELENOP" "SELENOS"
## [19] "SELENOT" "SELENOV" "SELENOW" "SEPHS2" "TXNRD1" "TXNRD2"
## [25] "TXNRD3" "SELENOP1" "SELENOP2" "SELENOU" "SELENOW1" "SELENOW2"
## [31] "SELENOE" "SELENOJ" "SELENOL" "SELENOO1" "SELENOO2" "SELENOT1"
## [37] "SELENOT2"unique() just gives us the unique elements. A related function, duplicated(), gives us the location of duplicated elements in the vector. FALSE means “not duplicated yet” or “first instance so far”.
i.dups <- duplicated(seleno_df_noNA$gene)We can remove the duplicates using a form of reverse indexing where the “!” means “not”. (You don’t need to know this for the exam)
seleno_df_noNA[!i.dups, ]## gene protein_refseq
## 1 DIO1 NP_000783.2
## 8 DIO2 NP_054644.1
## 14 DIO3 NP_001353.4
## 15 GPX1 NP_000572.2
## 20 GPX2 NP_002074.2
## 24 GPX3 NP_002075.2
## 26 GPX4 NP_002076.2
## 29 GPX6 NP_874360.1
## 30 MSRB1 NP_057416.1
## 31 SELENOF NP_004252.2
## 35 SELENOH NP_734467.1
## 37 SELENOI NP_277040.1
## 39 SELENOK NP_067060.2
## 40 SELENOM NP_536355.1
## 41 SELENON NP_996809.1
## 43 SELENOO NP_113642.1
## 44 SELENOP NP_005401.3
## 47 SELENOS NP_060915.2
## 49 SELENOT NP_057359.2
## 50 SELENOV NP_874363.1
## 53 SELENOW NP_003000.1
## 54 SEPHS2 NP_036380.2
## 55 TXNRD1 NP_877393.1
## 62 TXNRD2 NP_006431.2
## 69 TXNRD3 NP_443115.1
## 232 SELENOP1 NP_001026780.2
## 233 SELENOP2 NP_001335698.1
## 236 SELENOU NP_001180447.1
## 268 SELENOW1 NP_001291715.2
## 269 SELENOW2 NP_001341647.1
## 334 SELENOE NP_001182713.2
## 338 SELENOJ NP_001180398.1
## 340 SELENOL NP_001177311.1
## 343 SELENOO1 NP_001038336.2
## 344 SELENOO2 NP_001335014.1
## 348 SELENOT1 NP_840075.2
## 350 SELENOT2 NP_001091957.2Make a dataframe of non-duplicated genes
seleno_df_noDups <- seleno_df_noNA[!i.dups, ]
dim(seleno_df_noDups)## [1] 37 2Selecting Two Random Sequences
Let’s select 2 random sequences to work with. We’ll use sample() to select a random index number to get
First, lets make a vector that contains a unique number for each row of data
indices <- 1:nrow(seleno_df_noDups)This would do the same thing
# with dim
indices <- 1:dim(seleno_df_noDups)[1]
# with length
indices <- 1:length(seleno_df_noDups$gene)or hard-coded
indices <- 1:37We can then use sample() to select 2 random numbers from this vector.
For x = we’ll use our vector of indices (1 to 37). For size we’ll use 2, since we want to pull out just 2 numbers. For replace we’ll use FALSE since we don’t want to be ale to select the same number twice.
i.random.genes <- sample(x = indices,
size = 2,
replace = FALSE)Hard coded this would be
i.random.genes <- sample(x = c(1:37),
size = 2,
replace = FALSE)This gives me two indices values.
i.random.genes## [1] 14 2I can now use these index values to pull out two rows of data
seleno_df_noNA[i.random.genes, ]## gene protein_refseq
## 18 GPX1 NP_001316432.1
## 2 DIO1 NP_998758.1Hard coded, this would be something like this for whichever genes happen to have been selected
seleno_df_noNA[c(37,15), ]## gene protein_refseq
## 47 SELENOS NP_060915.2
## 19 GPX1 NP_001316384.1Downloading genes
I will now download 2 FASTA files
rentrez::entrez_fetch(id = "NP_060915.2",
db = "protein",
rettype = "fasta")## [1] ">NP_060915.2 selenoprotein S isoform 1 [Homo sapiens]\nMERQEESLSARPALETEGLRFLHTTVGSLLATYGWYIVFSCILLYVVFQKLSARLRALRQRQLDRAAAAV\nEPDVVVKRQEALAAARLKMQEELNAQVEKHKEKLKQLEEEKRRQKIEMWDSMQEGKSYKGNAKKPQEEDS\nPGPSTSSVLKRKSDRKPLRGGGYNPLSGEGGGACSWRPGRRGPSSGGUG\n\n"rentrez::entrez_fetch(id = "NP_001316384.1",
db = "protein",
rettype = "fasta")## [1] ">NP_001316384.1 glutathione peroxidase 1 isoform 5 [Homo sapiens]\nMCAARLAAAAAAAQSVYAFSARPLAGGEPVSLGSLRGKENAKNEEILNSLKYVRPGGGFEPNFMLFEKCE\nVNGAGAHPLFAFLREALPAPSDDATALMTDPKLITWSPVCRNDVAWNFEKFLVGPDGVPLRRYSRRFQTI\nDIEPDIEALLSQGPSCA\n\n"Turns FASTA files into vectors
prot1 <- rentrez::entrez_fetch(id = "NP_060915.2",
db = "protein",
rettype = "fasta")
prot2 <- rentrez::entrez_fetch(id = "NP_001316384.1",
db = "protein",
rettype = "fasta")I can put them into a list like this
# make the list
seleno_thingy <- vector("list", 1)
# add the first fasta
seleno_thingy[[1]] <- prot1
# See the result
seleno_thingy## [[1]]
## [1] ">NP_060915.2 selenoprotein S isoform 1 [Homo sapiens]\nMERQEESLSARPALETEGLRFLHTTVGSLLATYGWYIVFSCILLYVVFQKLSARLRALRQRQLDRAAAAV\nEPDVVVKRQEALAAARLKMQEELNAQVEKHKEKLKQLEEEKRRQKIEMWDSMQEGKSYKGNAKKPQEEDS\nPGPSTSSVLKRKSDRKPLRGGGYNPLSGEGGGACSWRPGRRGPSSGGUG\n\n"# add the first fasta
seleno_thingy[[2]] <- prot2
# see the result
seleno_thingy## [[1]]
## [1] ">NP_060915.2 selenoprotein S isoform 1 [Homo sapiens]\nMERQEESLSARPALETEGLRFLHTTVGSLLATYGWYIVFSCILLYVVFQKLSARLRALRQRQLDRAAAAV\nEPDVVVKRQEALAAARLKMQEELNAQVEKHKEKLKQLEEEKRRQKIEMWDSMQEGKSYKGNAKKPQEEDS\nPGPSTSSVLKRKSDRKPLRGGGYNPLSGEGGGACSWRPGRRGPSSGGUG\n\n"
##
## [[2]]
## [1] ">NP_001316384.1 glutathione peroxidase 1 isoform 5 [Homo sapiens]\nMCAARLAAAAAAAQSVYAFSARPLAGGEPVSLGSLRGKENAKNEEILNSLKYVRPGGGFEPNFMLFEKCE\nVNGAGAHPLFAFLREALPAPSDDATALMTDPKLITWSPVCRNDVAWNFEKFLVGPDGVPLRRYSRRFQTI\nDIEPDIEALLSQGPSCA\n\n"#Assigns both files to variable
names(seleno_thingy) <- c("prot1", "prot2")
#Output
seleno_thingy## $prot1
## [1] ">NP_060915.2 selenoprotein S isoform 1 [Homo sapiens]\nMERQEESLSARPALETEGLRFLHTTVGSLLATYGWYIVFSCILLYVVFQKLSARLRALRQRQLDRAAAAV\nEPDVVVKRQEALAAARLKMQEELNAQVEKHKEKLKQLEEEKRRQKIEMWDSMQEGKSYKGNAKKPQEEDS\nPGPSTSSVLKRKSDRKPLRGGGYNPLSGEGGGACSWRPGRRGPSSGGUG\n\n"
##
## $prot2
## [1] ">NP_001316384.1 glutathione peroxidase 1 isoform 5 [Homo sapiens]\nMCAARLAAAAAAAQSVYAFSARPLAGGEPVSLGSLRGKENAKNEEILNSLKYVRPGGGFEPNFMLFEKCE\nVNGAGAHPLFAFLREALPAPSDDATALMTDPKLITWSPVCRNDVAWNFEKFLVGPDGVPLRRYSRRFQTI\nDIEPDIEALLSQGPSCA\n\n"Elements of the list are accessed like this
seleno_thingy[[1]]## [1] ">NP_060915.2 selenoprotein S isoform 1 [Homo sapiens]\nMERQEESLSARPALETEGLRFLHTTVGSLLATYGWYIVFSCILLYVVFQKLSARLRALRQRQLDRAAAAV\nEPDVVVKRQEALAAARLKMQEELNAQVEKHKEKLKQLEEEKRRQKIEMWDSMQEGKSYKGNAKKPQEEDS\nPGPSTSSVLKRKSDRKPLRGGGYNPLSGEGGGACSWRPGRRGPSSGGUG\n\n"I’ll clean them with fasta_cleaner()
# first, make a copy of the list for storing the clean data
## I'm just going to copy over the old data
seleno_thingy_clean <- seleno_thingy
for(i in 1:length(seleno_thingy_clean)){
clean_fasta_temp <- compbio4all::fasta_cleaner(seleno_thingy[[i]],
parse = T)
seleno_thingy_clean[[i]] <- clean_fasta_temp
}Now the data looks like this Shows cleaned sequences
seleno_thingy_clean## $prot1
## [1] "M" "E" "R" "Q" "E" "E" "S" "L" "S" "A" "R" "P" "A" "L" "E" "T" "E" "G"
## [19] "L" "R" "F" "L" "H" "T" "T" "V" "G" "S" "L" "L" "A" "T" "Y" "G" "W" "Y"
## [37] "I" "V" "F" "S" "C" "I" "L" "L" "Y" "V" "V" "F" "Q" "K" "L" "S" "A" "R"
## [55] "L" "R" "A" "L" "R" "Q" "R" "Q" "L" "D" "R" "A" "A" "A" "A" "V" "E" "P"
## [73] "D" "V" "V" "V" "K" "R" "Q" "E" "A" "L" "A" "A" "A" "R" "L" "K" "M" "Q"
## [91] "E" "E" "L" "N" "A" "Q" "V" "E" "K" "H" "K" "E" "K" "L" "K" "Q" "L" "E"
## [109] "E" "E" "K" "R" "R" "Q" "K" "I" "E" "M" "W" "D" "S" "M" "Q" "E" "G" "K"
## [127] "S" "Y" "K" "G" "N" "A" "K" "K" "P" "Q" "E" "E" "D" "S" "P" "G" "P" "S"
## [145] "T" "S" "S" "V" "L" "K" "R" "K" "S" "D" "R" "K" "P" "L" "R" "G" "G" "G"
## [163] "Y" "N" "P" "L" "S" "G" "E" "G" "G" "G" "A" "C" "S" "W" "R" "P" "G" "R"
## [181] "R" "G" "P" "S" "S" "G" "G" "U" "G"
##
## $prot2
## [1] "M" "C" "A" "A" "R" "L" "A" "A" "A" "A" "A" "A" "A" "Q" "S" "V" "Y" "A"
## [19] "F" "S" "A" "R" "P" "L" "A" "G" "G" "E" "P" "V" "S" "L" "G" "S" "L" "R"
## [37] "G" "K" "E" "N" "A" "K" "N" "E" "E" "I" "L" "N" "S" "L" "K" "Y" "V" "R"
## [55] "P" "G" "G" "G" "F" "E" "P" "N" "F" "M" "L" "F" "E" "K" "C" "E" "V" "N"
## [73] "G" "A" "G" "A" "H" "P" "L" "F" "A" "F" "L" "R" "E" "A" "L" "P" "A" "P"
## [91] "S" "D" "D" "A" "T" "A" "L" "M" "T" "D" "P" "K" "L" "I" "T" "W" "S" "P"
## [109] "V" "C" "R" "N" "D" "V" "A" "W" "N" "F" "E" "K" "F" "L" "V" "G" "P" "D"
## [127] "G" "V" "P" "L" "R" "R" "Y" "S" "R" "R" "F" "Q" "T" "I" "D" "I" "E" "P"
## [145] "D" "I" "E" "A" "L" "L" "S" "Q" "G" "P" "S" "C" "A"Shows element is a vector of character data
class(seleno_thingy_clean[[1]])## [1] "character"is(seleno_thingy_clean[[1]])## [1] "character" "vector"
## [3] "data.frameRowLabels" "SuperClassMethod"
## [5] "character_OR_connection" "character_OR_NULL"
## [7] "atomic" "EnumerationValue"
## [9] "vector_OR_Vector" "vector_OR_factor"is.vector(seleno_thingy_clean[[1]])## [1] TRUEMake an dotplot
For old-times sake we can make a dotplot.
Now for a dotplot
Assigning two vectors
prot1_vector <- seleno_thingy_clean[[1]]
prot2_vector <- seleno_thingy_clean[[2]]We can dotplot like this
seqinr::dotPlot(prot1_vector,
prot1_vector)
The list is being used directly to make the dotplot
seqinr::dotPlot(seleno_thingy_clean[[1]],
seleno_thingy_clean[[2]])Pairwise alignment
dotPlot likes things in a single vector, but pairwiseAlignment like a single string of characters, so as always we have to process the data.
Takes vectors and assigns them to strings The "" means blank space
prot1_str <- paste(seleno_thingy_clean[[1]],sep = "", collapse = "")
prot2_str <- paste(seleno_thingy_clean[[2]],sep = "", collapse = "")So now things look like this It is the vector with the entire sequence in a single element
prot1_str## [1] "MERQEESLSARPALETEGLRFLHTTVGSLLATYGWYIVFSCILLYVVFQKLSARLRALRQRQLDRAAAAVEPDVVVKRQEALAAARLKMQEELNAQVEKHKEKLKQLEEEKRRQKIEMWDSMQEGKSYKGNAKKPQEEDSPGPSTSSVLKRKSDRKPLRGGGYNPLSGEGGGACSWRPGRRGPSSGGUG"Protein alignments need a amino acid transition matrix, and we need to use data() to bring those up into active memory (VERY IMPORTANT STEP!)
data(BLOSUM50)The alignment
align_out <- Biostrings::pairwiseAlignment(pattern = prot1_str,
subject = prot2_str,
type = "global",
gapOpening = -9.5,
gapExtension = -0.5)This is the raw output
align_out## Global PairwiseAlignmentsSingleSubject (1 of 1)
## pattern: MERQEESLSARPALETEGLRFLHTTVGSLLATYG...-----------------ACSWRPGRRGPSSGGUG
## subject: M---------------------------------...IDIEPDIEALLSQGPSCA----------------
## score: -160.2561This shows the full alignment
compbio4all::print_pairwise_alignment(align_out)## [1] "MERQEESLSARPALETEGLRFLHTTVGSLLATYGWYIVFSCILLYVVFQKLSARLRALRQ 60"
## [1] "M---------------------------------------C----------AARL----- 6"
## [1] " "
## [1] "RQLDRAAAAVEPDVVVKRQEALAAA--------RLKMQEELNAQVEKHKEKLKQLEEEKR 112"
## [1] "-----AAAA-------------AAAQSVYAFSAR-------------------------- 22"
## [1] " "
## [1] "RQKIEMWDSMQEGKSYKGNAKKPQEEDSPGPSTSSVLKRKSDRKPLRGGGYNPLSGE--- 169"
## [1] "--------------------------------------------PLAGG-------EPVS 31"
## [1] " "
## [1] "------------------------GGG--------------------------------- 172"
## [1] "LGSLRGKENAKNEEILNSLKYVRPGGGFEPNFMLFEKCEVNGAGAHPLFAFLREALPAPS 91"
## [1] " "
## [1] "------------------------------------------------------------ 172"
## [1] "DDATALMTDPKLITWSPVCRNDVAWNFEKFLVGPDGVPLRRYSRRFQTIDIEPDIEALLS 151"
## [1] " "
## [1] "-----A 227"
## [1] "QGPSCA 211"
## [1] " "These are two randomly chosen sequences, so the alignment should be pretty bad
The score is negative, but on its own that means nothing since good alignments can be negative too
score(align_out)## [1] -160.2561The pid gives the percent identity difference, which is a better measure of how well they match
pid(align_out)## [1] 7.189542Of course, pid can be calculated several ways (this is possible because different denominators can be used)
pid(align_out,type = "PID1")## [1] 7.189542pid(align_out,type = "PID2")## [1] 91.66667pid(align_out,type = "PID3")## [1] 14.01274pid(align_out,type = "PID4")## [1] 12.71676