Accessing data from Google Docs
Nathan Brouwer / Mitali Belambe
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 RefSeq accession from a Google Spreadsheets document - get rid of NAs using function na.omit() and clean up the data - selecting just 1 isoform per gene - do a pairwise alignment of two random sequences
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':
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## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
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## 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':
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## 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':
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## translate## The following object is masked from 'package:base':
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## strsplitDownload data
This gets the Google spreadsheets data from the URL and sets it as a variable.
spreadsheet_sp <- "https://docs.google.com/spreadsheets/d/1spC_ZA3_cVuvU3e_Jfcj2nEIfzp-vaP7SA5f-qwQ1pg/edit?usp=sharing" This sets free access, so we don’t have to check for user access credentials / authorization
# 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$RefSeq_protThis prints out the first 10 elements of the variable protein_refseq. This variable contains RefSeq protein accession numbers.
protein_refseq[1:10]## [1] "NP_000783.2" "NP_998758.1" "NP_001034804.1" "NP_001034805.1"
## [5] "NP_001311245.1" NA NA "NP_054644.1"
## [9] "NP_001353425.1" "NP_000784.3"This section is reading the google sheet for the column for gene names and filling in empty spaces with NA
# download
## NOTE: if you get an error, just run the code again
gene_name_column <- read_sheet(ss = spreadsheet_sp, # the url
sheet = "gene", # the name of the worksheet
range = "selenoprot!A1:A364",
col_names = TRUE,
na = "", # fill in empty spaces "" w/NA
trim_ws = TRUE)## ✓ Reading from "human_gene_table".## ✓ Range ''selenoprot'!A1:A364'.## NOTE: if you get an error, just run the code again
# for reasons we won't get into I'm going to do this
gene <- gene_name_column$genechecking Data
This is identifying characteristics of the variable protein_refseq.
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"This is identifying if each element in protein_refseq is “NA” (TRUE) or not (FALSE).
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
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## [109] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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## [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
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## [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 FALSEThis is summarizing the above code into a table, telling us how many elements of protein_refseq contain NA (29) and how many do not (334).
table(is.na(protein_refseq))##
## FALSE TRUE
## 334 29This code chunk is creating a new variable to store all NAs from protein_refseq.
# storing in temp variable
temp <- is.na(protein_refseq)
# storing all NAs in a new temp2 variable
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]
#identifying length of temp2 variable
length(temp2)## [1] 29Creating Dataframe
This is creating a dataframe of the gene names and protein refseq accession numbers.
seleno_df <- data.frame(gene = gene,
protein_refseq = protein_refseq)This is providing a summary of the dataframe and then showing the first 6 rows of the data frame or the “head” of it.
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>Omitting all NAs
This code chunk is getting rid of the elements with NAs in the dataframe and then checking the length of the new and old dimensions of the dataframes to ensure they are different.
# 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 2Setting a Unique Gene (Selecting just 1 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 is creating a variable of the unique genes in the dataframe.
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 2Using sample() for 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 able 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 2 indices values.
i.random.genes## [1] 25 31I can now use these index values to pull out 2 rows of data
seleno_df_noNA[i.random.genes, ]## gene protein_refseq
## 32 SELENOF NP_976086.1
## 41 SELENON NP_996809.1Hard 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 genes using two sequences.
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"This is creating two variables of proteins from the downloaded sequences.
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_list <- vector("list", 1)
# add the first fasta
seleno_list[[1]] <- prot1
# See the result
seleno_list## [[1]]
## [1] ">NP_060915.2 selenoprotein S isoform 1 [Homo sapiens]\nMERQEESLSARPALETEGLRFLHTTVGSLLATYGWYIVFSCILLYVVFQKLSARLRALRQRQLDRAAAAV\nEPDVVVKRQEALAAARLKMQEELNAQVEKHKEKLKQLEEEKRRQKIEMWDSMQEGKSYKGNAKKPQEEDS\nPGPSTSSVLKRKSDRKPLRGGGYNPLSGEGGGACSWRPGRRGPSSGGUG\n\n"# add the first fasta
seleno_list[[2]] <- prot2
# see the result
seleno_list## [[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"# This names the elements of the list prot1 and prot2
names(seleno_list) <- c("prot1", "prot2")
#Output
seleno_list## $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_list[[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_list_clean <- seleno_list
# To make this more compact, you do not have to create as many variables.
for(i in 1:length(seleno_list_clean)){
clean_fasta_temp <- compbio4all::fasta_cleaner(seleno_list[[i]],
parse = T)
seleno_list_clean[[i]] <- clean_fasta_temp
}Now the data looks like this This is a much more cleaned up version of the data. It is showing the protein sequences as vectors.
seleno_list_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"This is giving some identifying information about the cleaned up list. Each element of the list is a character.
class(seleno_list_clean[[1]])## [1] "character"is(seleno_list_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_list_clean[[1]])## [1] TRUEMake an dotplot
For old-times sake we can make a dotplot.
Now for a dotplot
Two vectors are being created from the elements of the list, for each protein.
prot1_vector <- seleno_list_clean[[1]]
prot2_vector <- seleno_list_clean[[2]]We can dotplot like this
seqinr::dotPlot(prot1_vector,
prot1_vector)
You can also directly do this, without creating a new vector variable.
seqinr::dotPlot(seleno_list_clean[[1]],
seleno_list_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.
You are creating a string of the protein sequences. "" gets rid of the extra spaces.
prot1_str <- paste(seleno_list_clean[[1]],sep = "", collapse = "")
prot2_str <- paste(seleno_list_clean[[2]],sep = "", collapse = "")So now things look like this This is one giant string of the sequence.
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 shows the alignment output. It shows how the two sequences were aligned and their score.
align_out## Global PairwiseAlignmentsSingleSubject (1 of 1)
## pattern: MERQEESLSARPALETEGLRFLHTTVGSLLATYG...-----------------ACSWRPGRRGPSSGGUG
## subject: M---------------------------------...IDIEPDIEALLSQGPSCA----------------
## score: -160.2561This is showing the two sequences aligned against each other. This shows the whole sequence whereas it did not show the entire sequence above (only beginning and end)
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 it is hard to tell if it is good or bad.
score(align_out)## [1] -160.2561pid gives us the percent identity between two sequences (how similar they are).
pid(align_out)## [1] 7.189542Of course, pid can be calculated several ways. The pid function calculates the PID using 4 different denominators so it is possible to to get different results.
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