Portfolio 1
Varun Sundar
9/28/2021
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
library(compbio4all)
# CRAN packages
library(rentrez)
library(seqinr)
library(ape)
# Bioconductor packages
library(msa)
library(Biostrings)Downloading macromolecular sequences
In the following chunks, we are getting the Human shroom sequence and loading it into memory so that when we want to bring it up to compare it with another sequence, we can just call this object.
library(rentrez)
# Human shroom 3 (H. sapiens)
hShroom3 <- rentrez::entrez_fetch(db = "protein",
id = "NP_065910",
rettype = "fasta")cat() is simply printing whatever is in hShroom3
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
## IPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPLThis is assigning the sequences of each respective organism to an object so that we can easily compare them later on
# 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")This chunk is calculating the size of the sequence and printing it
nchar(hShroom3)## [1] 2070nchar(mShroom3a)## [1] 2083nchar(sShroom)## [1] 1758nchar(hShroom2)## [1] 1673Prepping macromolecular sequences
This function is going through the fasta file and cleaning it up so that the output display is nicer
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: 0x0000000022fe5dc0>
## <environment: namespace:compbio4all>You define a function to a name using <- function(){}. Inside the curly braces you give the code instructions for the function.
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 code chunk is putting each of the previous sequences that we loaded into the fasta cleaner function to clean them up so that they are just the sequence and nothing else
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"Comparing sequences
This code chunk is taking the human shroom sequence and the mouse shroom sequence and putting them in a pairwise alignment
# add necessary function
align.h3.vs.m3a <- Biostrings::pairwiseAlignment(
hShroom3,
mShroom3a)This function is displaying the results of the pairwise alignment by showing both sequences lined up on on top of the other and also showing the score that was calculated from the pairwise alignment.
align.h3.vs.m3a## Global PairwiseAlignmentsSingleSubject (1 of 1)
## pattern: MMRTTEDFHKPSATLN-SNTATKGRYIYLEAFLE...KAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL
## subject: MK-TPENLEEPSATPNPSRTPTE-RFVYLEALLE...KAGAISLPPALTGHATPGGTSVFGGVFPTLTSPL
## score: 2189.934This chunk is showing the percent identity similarity between the two sequences by using the pid() function on the pairwise alignment from the last chunk.
# add necessary function
Biostrings::pid(align.h3.vs.m3a)## [1] 70.56511This chunk is carrying out anothother pairwise alignment, this time with two human sequences instead of a human and mouse sequence.
align.h3.vs.h2 <- Biostrings::pairwiseAlignment(
hShroom3,
hShroom2)In this chunk, we are taking the specific score from the pairwise alignment and printing it. It is essentially the same as before, but instead of showing the sequences along with the score, it is just showing the score of the pairwise alignment.
score(align.h3.vs.h2)## [1] -5673.853Score() uses a specific grading scale to assign points to a long sequence by comparing the sequences in question. pid() is taking the sequences as they are and simply comparing them to see how close they are. in the form of a percentage.
Biostrings::pid(align.h3.vs.h2)## [1] 33.83277The shroom family of genes
This chunk is meant for organizing purposes and to have all the sequences gathered in one.
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 urchinThis next chunk takes the info that was in the “table” before and turning it into something that is a better output for a table. The last thing was just a vector, but this one makes it into an actual table.
# convert to Matrix
shroom_table_matrix <- matrix(shroom_table,
byrow = T,
nrow = 14)
# convert to Data Frame
shroom_table <- data.frame(shroom_table_matrix,
stringsAsFactors = F)
# Naming the 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"This is outputting whatever we just set up in the last chunk, which is the table
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 urchinAligning multiple sequences
The $ allows to access the specific column of the table and get the values that are in it.
shroom_table$accession## [1] "CAA78718" "NP_597713" "CAA58534" "ABD19518" "AAF13269" "AAF13270"
## [7] "NP_065910" "ABD59319" "NP_065768" "AAK95579" "ABA81834" "EAA12598"
## [13] "XP_392427" "XP_783573"This function is putting all fourteen sequences that we loaded up from the table into one long object.
# add necessary function
shrooms <- rentrez::entrez_fetch(db = "protein",
id = shroom_table$accession,
rettype = "fasta")
is(shrooms)## [1] "character" "vector"
## [3] "data.frameRowLabels" "SuperClassMethod"
## [5] "character_OR_connection" "character_OR_NULL"
## [7] "atomic" "EnumerationValue"
## [9] "vector_OR_Vector" "vector_OR_factor"nchar(shrooms)## [1] 22252This function is taking the output from the last chunk and organizing it in a better way by having each sequence be printed chunk by chunk with a new line between them.
cat(shrooms)This is taking the accession numbers from the table and outputting the sequence linked to them in a fasta file. It is much more condensed than the previous chunks.
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] 14nchar(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#vectorizationThis is simply printing out the length of the last code chunk, and it is 14 because the list utilized the accession number, of which we had 14.
length(shrooms_list)## [1] 14This is looping through the list we made and cleaning up every item using the fasta cleaner function.
for(i in 1:length(shrooms_list)){
shrooms_list[[i]] <- fasta_cleaner(shrooms_list[[i]], parse = F)
}This is getting the accession numbers and using them as labels for the sequences we got.
# Setting the vector's contents
shrooms_vector <- rep(NA, length(shrooms_list))
# Incorporating the for loop
for(i in 1:length(shrooms_vector)){
shrooms_vector[i] <- shrooms_list[[i]]
}
# Naming the vector
names(shrooms_vector) <- names(shrooms_list)This is the last little bit of cleaning we have to do with this particular data.
# add necessary function
shrooms_vector_ss <- Biostrings::AAStringSet(shrooms_vector)MSA
Readings will be assigned to explain what MSAs are. This section will go through and carry out MSAs, which are multiple sequence allignments. They will take multiple sequences at one and stack them on top of one another, while also making sure they are lined up the best they can be,
Building an XXXXXXXX (MSA)
This chunk is assigning a multiple sequence alignment between the 14 sequences to an object
# add necessary function
library(msa)
shrooms_align <- msa(shrooms_vector_ss,
method = "ClustalW")## use default substitution matrixViewing an MSA
This section will look at the MSA in greater detail by actually having an output of the aligned sequences on display
Viewing an MSA in R
The output being shown here is the multiple sequence alignment between the 14 sequences.
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 -------------------------...------------------------- ConsensusHere we are prepping the data for a better alignment
# WHAT IS THE LINE BELOW DOING? (its tricky - do your best)
##changing the class of the MSA from the previous chunk
class(shrooms_align) <- "AAMultipleAlignment"
# WHAT IS THE LINE BELOW DOING? This is simpler
##Converting the multiple sequence alignment to seqinr alignment
shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment")This is displaying a full alignment of the sequences. It is very long and blunt.
print_msa(alignment = shrooms_align_seqinr,
chunksize = 60)Displaying an MSA XXXXXXXX
This is printing the same thing out from the previous chunk but in a cleaner and more concise way. We are also specifying where exactly we want to start and stop the alignment.
## add necessary function
library(ggmsa)## Registered S3 methods overwritten by 'ggalt':
## method from
## grid.draw.absoluteGrob ggplot2
## grobHeight.absoluteGrob ggplot2
## grobWidth.absoluteGrob ggplot2
## grobX.absoluteGrob ggplot2
## grobY.absoluteGrob ggplot2ggmsa::ggmsa(shrooms_align, # shrooms_align, NOT shrooms_align_seqinr
start = 2000,
end = 2100) 
Saving an MSA as PDF
This command is taking the output from the last chunk, which is the colorful MSA, and trying to convert it into a pdf for us to save.
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)
'''Getting the working directory to save the file and tell us where it is
getwd()## [1] "C:/Users/varun/Downloads"list.files()## [1] "_23-MSA-walkthrough-shroom-code_only--1-.html"
## [2] "_23-MSA-walkthrough-shroom-code_only (1).Rmd"
## [3] "_23-MSA-walkthrough-shroom-code_only.Rmd"
## [4] "_fasta_cleaner.R"
## [5] "~$1-HWCover1to2 (1).docx"
## [6] "~$aracteristics of Living Things.doc"
## [7] "~$aracteristics_of_Living_Things (1).doc"
## [8] "~$esentation planning DWS sp21_JR.docx"
## [9] "~$exual_reproduction_packet.doc"
## [10] "~WRL1693.tmp"
## [11] "0110 F20 Recitation Worksheet 2 Send (1).pdf"
## [12] "0110 F20 Recitation Worksheet 2 Send.pdf"
## [13] "0110 F20 420 PM Syllabus Updated with Canvas Compliance.pdf"
## [14] "0110 F20 Outline of Possible Final Exam Material.pdf"
## [15] "0110 F20 Recitation Skills Inventory for Students Send.pdf"
## [16] "0110 F20 Recitation Worksheet 1 For Students (1).pdf"
## [17] "0110 F20 Recitation Worksheet 1 For Students (2).pdf"
## [18] "0110 F20 Recitation Worksheet 1 For Students.pdf"
## [19] "0110 FP20 OpenStax Chapter 4 Notes for Students to Use.pdf"
## [20] "05_Lecture_Presentation (1).pptx"
## [21] "05_Lecture_Presentation.pptx"
## [22] "1 0110 F20 Open Stax Chapter 1 Objectives.pdf"
## [23] "10slactaseenzymelab (1).doc"
## [24] "10slactaseenzymelab (2).doc"
## [25] "10slactaseenzymelab.doc"
## [26] "10th Grade First Half Transcript.pdf"
## [27] "1128551.pdf"
## [28] "1257030.pdf"
## [29] "1st Quarter - Sundar, Varun (1).pdf"
## [30] "1st Quarter - Sundar, Varun.pdf"
## [31] "2-13-21 Transcript.pdf"
## [32] "2014-2015_A1_Biological_Principles_Classroom_Quiz.pdf"
## [33] "2014-2015_A2_Chemical_Basis_of_Life_Classroom_Quiz.pdf"
## [34] "2014-2015_A3_Bioenergetics_Classroom_Quiz.pdf"
## [35] "2014-2015_A4_Homeostasis_and_Cell_Transport.pdf"
## [36] "2018-19.NBA.Roster (1).json"
## [37] "2018-19.NBA.Roster.json"
## [38] "2nd Quarter - Sundar, Varun.pdf"
## [39] "3.1_Practice_A.pdf"
## [40] "5 Significant Digits and Measurement-S.pdf"
## [41] "5saveyourears (1).dcr"
## [42] "5saveyourears.dcr"
## [43] "9-19-16.doc.docx"
## [44] "9-21-16_ATP_English.docx"
## [45] "9_Restriction Enzyme Analysis of DNA.docx"
## [46] "a world not neatly divided.pps"
## [47] "AA and Codon.pdf"
## [48] "ABC_CLIO_Solutions_-_Topic_Center_Treaty_of_Nanjing_1842.pdf"
## [49] "ABLIST21 (1).pdf"
## [50] "ABLIST21.pdf"
## [51] "ACT february 2018.pdf"
## [52] "Act_II_Questions2018.doc"
## [53] "Act_II_scene_i_quotes.docx"
## [54] "Act_III_scene_i_.docx"
## [55] "Act_III_scene_ii.doc"
## [56] "Act_III_scene_ii_Quotes.doc"
## [57] "activitymore-2F749C66-400F-4F04-8666-EE8A943D7516.png"
## [58] "activitymore-DF8C0B84-F212-4B58-8828-697CB4F05B6A.png"
## [59] "Addiction_ Is it a Choice or a Disease_.pdf"
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