Accessing data from Google Docs
Deeksha Sesha
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
(TODO: MAKE YOUR OWN OUTLINE)
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
WHAT IS THIS? This is the web address/URL of the google sheets spreadsheet with all the data.
spreadsheet_sp <- "https://docs.google.com/spreadsheets/d/1spC_ZA3_cVuvU3e_Jfcj2nEIfzp-vaP7SA5f-qwQ1pg/edit?usp=sharing" WHAT DOES THIS DO? Ensures that we don’t call a package that checks user access credentials or 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$RefSeq_protWHAT’S THIS? Accessing a snippet of the results, range from index 1 to 10.
protein_refseq[1:10]## [1] "NP_000783.2" "NP_998758.1" "NP_001034804.1" "NP_001034805.1"
## [5] "NP_001311245.1" NA NA "NP_054644.1"
## [9] "NP_001353425.1" "NP_000784.3"WHAT’S THIS? Gets another column of data.
# download
## NOTE: if you get an error, just run the code again
gene_name_column <- read_sheet(ss = spreadsheet_sp, # the url
sheet = "gene", # the name of the worksheet
range = "selenoprot!A1:A364",
col_names = TRUE,
na = "", # fill in empty spaces "" w/NA
trim_ws = TRUE)## ✓ Reading from "human_gene_table".## ✓ Range ''selenoprot'!A1:A364'.## NOTE: if you get an error, just run the code again
# for reasons we won't get into I'm going to do this
gene <- gene_name_column$geneChecking the Data
Viewing the properties/characteristics of the downloaded data. Running the code tells us that we downloaded a character vector.
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"WHAT’S THIS DOING? Checking the presence of “NA” values.
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 FALSEWHAT’S THIS DOING? Counts how many instances of “false” we get using the table function, where “false” represents an NA.
table(is.na(protein_refseq))##
## FALSE TRUE
## 334 29WHAT’S THIS DOING? Another way of how many false values we have, this time by subsetting the true values and counting how many are left.
# ...
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] 29Creating a Dataframe
WHAT’S THIS DOING? WHY? This code creates a dataframe, we do so because dataframes can store multiple vectors.
seleno_df <- data.frame(gene = gene,
protein_refseq = protein_refseq)WHAT’S THIS DOING? Giving us important information about our newly created 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>Omitting NA Values from Dataframe
WHAT’S THIS DOING? This code omits the NAs and checks the length of the modified dataframe to ensure that elements were removed.
# 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 2Choosing 1 Isoform Of a Gene
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_054644.1WHAT’S THIS DOING? Picking only one row per gene, representing only the unique elements.
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 2 Random Sequences to Work With
Let’s select 2 random sequences to work with. We’ll use WHICH FUNCTION? 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 WHAT? 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 2 indices values.
i.random.genes## [1] 36 13I can now use these index values to pull out 2 rows of data
seleno_df_noNA[i.random.genes, ]## gene protein_refseq
## 46 SELENOP NP_001087195.1
## 17 GPX1 NP_001316431.1Hard 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 the fasta files of two genes using their accession numbers.
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"Saving the data for each gene into a vector.
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 WHAT?
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"# Sets the names of the objects in the list.
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
# HOW TO MAKE THIS MORE COMPACT? Using a for loop.
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 HOW WOULD YOU DESCRIBE THIS? A character vector
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"HOW WOULD YOU DESCRIBE THIS? character vector
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
WHAT AM I DOING HERE? Separating the list into 2 vector objects.
prot1_vector <- seleno_thingy_clean[[1]]
prot2_vector <- seleno_thingy_clean[[2]]We can dotplot like this
seqinr::dotPlot(prot1_vector,
prot1_vector)
WHAT DID I DO DIFFERENTLY HERE? Directly accessing the elements of the list as opposed to making 2 objects first.
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.
WHAT AM I DOING HERE? Turning the vector into a single string of characters using paste(). WHAT DOES “” MEAN? Empty string
prot1_str <- paste(seleno_thingy_clean[[1]],sep = "", collapse = "")
prot2_str <- paste(seleno_thingy_clean[[2]],sep = "", collapse = "")So now things look like this HOW WOULD YOU DESCRIBE THIS? String vector of length 1.
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)What is this? The raw output of the pairwise alignment.
align_out## Global PairwiseAlignmentsSingleSubject (1 of 1)
## pattern: MERQEESLSARPALETEGLRFLHTTVGSLLATYG...-----------------ACSWRPGRRGPSSGGUG
## subject: M---------------------------------...IDIEPDIEALLSQGPSCA----------------
## score: -160.2561WHAT IS THIS? Prints the entire alignment. HOW IS IT DIFFERNT FROM THE LAST CHUNK? Shows all the gaps/indels.
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 doesn’t really mean anything significant, the sign of the score doesn’t necessarily correlate to whether the sequences are highly similar or not.
score(align_out)## [1] -160.2561pid gives us a more accurate depiction of how well the sequences match to each other, a PID of 7.19% is very low.
pid(align_out)## [1] 7.189542Of course, pid can be calculated several ways (WHY IS THIS AN ISSUE / POSSIBLE?) It is possible due to calculations using different denominators. It’s an issue because as shown, the 4 PID values are very different and you can accidentally use the incorrect value as the PID.
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