GEN242 – Homework Assignments

Assignments: HW1 - Online Exercise and Basic GitHub Usage

Mon, 01 Jan 0001 00:00:00 +0000

A. Online Excercise: Databases and Software Tools

This is an easy warm-up homework exposing students to a variety of online databases and software tools.

Go to http://www.ncbi.nlm.nih.gov, select Protein database in dropdown, and then run query: P450 & hydroxylase & human [organism], select under Source databases UniProtKB/Swiss-Prot
1. Report final query syntax from Search Details field.

Save GIs of the final query result to a file. For this select under Send to dropdown GI List format.
1. Report the number of retrieved GIs.

Retrieve the corresponding sequences through Batch-Entrez using GI list file as query input -> save sequences in FASTA format

Generate multiple alignment and tree of these sequences using MultAalin
1. Save multiple alignment and tree to file
2. Identify putative heme binding cysteine in multiple alignment

Open corresponding UniProt page and search for first P450 sequence in your list.
1. Compare putative heme binding cysteine with consensus pattern from Prosite database (Syntax)
2. Report corresponding Pfam ID

BLASTP against the PDB database (use again first P450 in your list); on result page click first entry in BLAST hit list (here 3K9V_A); then select ‘Identify Conserved Domains’ on side bar; click grey bar labelled ‘CYP24A1’; then select ‘Interactive View’ under ‘Structure’ menu which will download a file named ‘pfam00067.cn3’.
1. Compare resulting alignment with result from MultAlin
2. View 3D structure (pfam00067.cn3) in Cn3D*, save structure (screen shot) and highlight heme binding cysteine. Note, Cn3D* can be downloaded from here.

*If there are problems in the last step (6.2) with the install of Cn3D, then please use this online only alternative: (i) click in the 3K9V_A page ‘Protein 3D Structure’ instead of ‘Identify Conserved Domains’; (ii) choose one of the two structure entries provided on the subsequent page; (iii) select option “full-featured 3D viewer” in the bottom right corner of the structure image; (iv) choose the ‘Details’ tab on the right; (v) after this the structure of the protein is shown on the left and the underlying protein sequence on the right; (vi) highlight the heme binding cysteine in the structure by selecting it in the sequence; and (vii) then save the structure view to a PNG file or take a screenshot.

B. Homework Submission to a Private GitHub Repository

Please assemble the results of this homework in one PDF file and upload it to your private course GitHub repository under Homework/HW1/HW1.pdf.

Due date

Most homework will be due one week after they are assigned. This one is due on Thu, April 11th at 6:00 PM. You have unlimited attempts. Students can edit and re-upload files anytime before the deadline.

Homework solution

A solution for this homework is not required since the tasks are identical to the steps described above under sections HW1A-B.

Assignments: HW2 - Introduction to Biocluster and Linux

Mon, 01 Jan 0001 00:00:00 +0000

Topic: Linux Basics

Log into your user account on the HPCC cluster, and from there into a compute node with srun.

srun --x11 --partition=short --mem=2gb --cpus-per-task 4 --ntasks 1 --time 1:00:00 --pty bash -l

Download code from this page

wget https://cluster.hpcc.ucr.edu/~tgirke/Linux.sh --no-check-certificate

Download Halobacterium proteome and inspect it

wget https://ftp.ncbi.nlm.nih.gov/genomes/genbank/archaea/Halobacterium_salinarum/all_assembly_versions/GCA_004799605.1_ASM479960v1/GCA_004799605.1_ASM479960v1_protein.faa.gz
gunzip GCA_004799605.1_ASM479960v1_protein.faa.gz
mv GCA_004799605.1_ASM479960v1_protein.faa halobacterium.faa
less halobacterium.faa # press q to quit

How many protein sequences are stored in the downloaded file?

grep '>' halobacterium.faa | wc
grep '^>' halobacterium.faa --count

How many proteins contain the pattern WxHxxH or WxHxxHH?
```
egrep 'W.H..H{1,2}' halobacterium.faa --count
```

Use less to find IDs for pattern matches or use awk

awk --posix -v RS='>' '/W.H..(H){1,2}/ { print ">" $0;}' halobacterium.faa | less
awk --posix -v RS='>' '/W.H..(H){1,2}/ { print ">" $0;}' halobacterium.faa | grep '^>' | cut -c 2- | cut -f 1 -d\ > myIDs

Create a BLASTable database with formatdb

module load ncbi-blast/2.2.31+
makeblastdb -in halobacterium.faa -out halobacterium.faa -dbtype prot -hash_index -parse_seqids

Query BLASTable database by IDs stored in a file (e.g. myIDs)

blastdbcmd -db halobacterium.faa -dbtype prot -entry_batch myIDs -get_dups -out myseq.fasta

Run BLAST search for sequences stored in myseq.fasta

blastp -query myseq.fasta -db halobacterium.faa -outfmt 0 -evalue 1e-6 -out blastp.out
blastp -query myseq.fasta -db halobacterium.faa -outfmt 6 -evalue 1e-6 -out blastp.tab

Return system time and host name
```
date
hostname
```

Additional exercise material in Linux Manual

Homework assignment

Perform above analysis on the protein sequences from E. coli. A right click on the link will allow you to copy the URL so that it can be used together with wget. Record result from final BLAST command (with outfmt 6) in text file named myresult.txt.

Homework submission

Upload result file (myresult.txt) to your private course GitHub repository under Homework/HW2/HW2.txt.

Due date

Most homeworks will be due one week after they are assigned. This one is due on Thu, April 11th at 6:00 PM.

Homework solution

See here.

Assignments: HW3 - Introduction to R

Mon, 01 Jan 0001 00:00:00 +0000

The tasks (steps) within each of the following homework assignments (A to C) are expected to be performed in the order stated, since an earlier task will often generate the input for the next task.

A. Object Subsetting, Import and Export

Task 1: Sort the rows of the iris data frame by its first column and sort its columns alphanumerically by column names.
Task 2: Subset the first 12 rows of the sorted iris dataset, export the result to a tabular file and view it in a spreadsheet program like Excel or Google Sheets.
Task 3: Change some column titles in your spreadsheet program, save the result to a tab delimited text file and import it back into R. Note, for this task you only want to include the read.table command in the homework result (here R script).

Before you start it can be helpful to evaluate the structure of the iris data set with the following commands:

class(iris)
dim(iris)
colnames(iris)

B. Scatter Plots

Task 1: Generate a scatter plot for the first two columns of the iris data frame and color the dots by the Species column.
Task 2: Use the xlim/ylim arguments to set limits on the x- and y-axes so that all data points are restricted to the bottom left quadrant of the plot.

Again before you start, evaluate the structure of iris data set. The following commands are useful:

iris[1:4,]
table(iris$Species)

C. Bar Plots

Task 1: Calculate the mean values for the Species components of the first four columns in the iris data frame. Organize the results in a matrix where the row names are the unique values from the iris Species column and the column names are the names of the first four iris columns.
Task 2: Generate two bar plots for the matrix generated in the previous step: one with stacked bars and one with horizontally arranged bars.

D-H. Analysis Worflow

The instructions for these homework assignments are here.

Homework submission

Assemble the code for solving the homework assignments A-H in a single R script (HW3.R) and upload it to your private GitHub repository under Homework/HW3/HW3.R. Note, the preassembled workflow script provided for HW3H (here) does not include the solutions for HW3A-C.

Due date

This homework is due on Thu, April 23th at 6:00 PM.

Homework Solutions

See here.

Assignments: HW4: Pairwise Alignments

Mon, 01 Jan 0001 00:00:00 +0000

A. Choice of Sequence Type

Task 1: Which sequence type - amino acid or nucleotide - is more appropriate to search databases for remotely related sequences? Provide at least three reasons for your decision.

B. Dynamic Programming for Pairwise Alignments

Task 2: Create manually (or write an R script for it) one global and one local alignment for the following two protein sequences using the Needleman-Wusch and Smith-Waterman algorithms, respectively:

O15528: PFGFGKRSCMGRRLA
P98187: FIPFSAGPRNCIGQK

Use in each case BLOSUM50 as substitution matrix and -8 as gap extension penalty (no extra penalty for gap opening). Note, here is some helper code in R to create the initial matrix programmatically for upload to a spreadsheet program. Alternatively, solve the entire homework by writing an R script. Your answers should contain the following components:

Manually populated dynamic programming matrices
The optimal pairwise alignments created by traceback
The final scores of the alignments

C. Alignments with Different Substitution Matrices

Task 1: Load the Biostrings package in R, import the following two cytochrome P450 sequences O15528 and P98187 from NCBI (save as myseq.fasta), and create a global alignment with the pairwiseAlignment function from Biostrings as follows:

library(Biostrings)
myseq <- readAAStringSet("myseq.fasta", "fasta")
(p <- pairwiseAlignment(myseq[[1]], myseq[[2]], type="global", substitutionMatrix="BLOSUM50"))
writePairwiseAlignments(p)

Your answers should address the following:

Record the scores for the scoring matrices BLOSUM50, BLOSUM62 and BLOSUM80.
How and why do the scores differ for the three scoring matrices?

Homework submission

Assemble the results from this homework in one PDF file (HW4.pdf) and upload it to your private GitHub repository under Homework/HW4/HW4.pdf.

Due date

This homework is due on Fri, April 26 at 6:00 PM (~11 days).

Homework Solutions

See here.

Assignments: HW4: Pairwise Alignment Solutions

Mon, 01 Jan 0001 00:00:00 +0000

Source code downloads: [ .Rmd ] [ pairwiseAlign_Fct.R ]

Rendering Instructions

To render this R Markdown document, one needs to download the following files to the same directory.

HW4_key.Rmd: Rmd source file for this document
pairwiseAlign_Fct.R: R script defining pairwise alignment functions
bibtex.bib: references cited in the text in BibTeX format

Next, one can render the report to HTML, PDF and other formats following the instructions below. Both the HTML and PDF versions are linked. Note, the rendering needs to be performed from the same directory where the downloaded *.Rmd, *.R and *.bib files are located.

HTML: this report in HTML format
PDF: corresponding PDF version

The HTML report can be rendered with rmarkdown::render() as follows.

rmarkdown::render('HW4_key.Rmd') # From R
Rscript -e "rmarkdown::render('HW4_key.Rmd')" # From command-line

To render a PDF file instead of HTML, one can instruct the rendering function to do so like this: rmarkdown::render('HW4_key.Rmd', c('pdf_document'). To render to several formats with a single command, one can concatenate the formatting values with c('html_document', 'pdf_document').

A. Choice of Sequence Type

Task 1: Which sequence type - amino acid or nucleotide - is more appropriate to search databases for remotely related sequences? Provide at least three reasons for your decision.

Answer: When coding sequences are expected to have weak similarities then one should use protein sequences rather than DNA sequences for database searching, because of (1) their higher information content (20 versus 4 letter alphabet), as well as (2) the better scoring and (3) functional classification systems available for amino acids.

B. Dynamic Programming for Pairwise Alignments

Task 2: Create manually (or write an R script for it) one global and one local alignment for the following two protein sequences using the Needleman-Wusch and Smith-Waterman algorithms, respectively (Smith and Waterman 1981; Needleman and Wunsch 1970).

O15528: PFGFGKRSCMGRRLA
P98187: FIPFSAGPRNCIGQK

Source functions

All alignment functions used in the following sections are defined in the downloaded R script file that is named pairwiseAlign_Fct.R. These functions are loaded with the source() command below.

source("pairwiseAlign_Fct.R")

Input sequences

Define within R or import them (here former).

S1 <- "PFGFGKRSCMGRRLA"
S2 <- "FIPFSAGPRNCIGQK"

Additional test sequences

# S1 <- "HEAGAWGHEE"
# S2 <- "PAWHEAE"

Global alignment

The alignment type choice is passed on to all following functions.

align_type <- "global"
# align_type <- "local"

Dynamic programming matrices

dynMA <- dynProgMatrix(S1, S2, align_method=align_type, gap_penalty=8, substitutionMA="BLOSUM50")

The matrices are stored in a list and returned below. The path is indicated by three numbers in the glob_ma_path matrix. Their meaning is:

1: diagonal
2: vertical (up)
3: horizontal (left)

dynMA

## $glob_ma
##      gp    P   F   G   F   G   K   R   S   C   M   G   R    R    L    A
## gp    0   -8 -16 -24 -32 -40 -48 -56 -64 -72 -80 -88 -96 -104 -112 -120
## F    -8   -4   0  -8 -16 -24 -32 -40 -48 -56 -64 -72 -80  -88  -96 -104
## I   -16  -11  -4  -4  -8 -16 -24 -32 -40 -48 -54 -62 -70  -78  -86  -94
## P   -24   -6 -12  -6  -8 -10 -17 -25 -33 -41 -49 -56 -64  -72  -80  -87
## F   -32  -14   2  -6   2  -6 -14 -20 -28 -35 -41 -49 -57  -65  -71  -79
## S   -40  -22  -6   2  -6   2  -6 -14 -15 -23 -31 -39 -47  -55  -63  -70
## A   -48  -30 -14  -6  -1  -6   1  -7 -13 -16 -24 -31 -39  -47  -55  -58
## G   -56  -38 -22  -6  -9   7  -1  -2  -7 -15 -19 -16 -24  -32  -40  -48
## P   -64  -46 -30 -14 -10  -1   6  -2  -3 -11 -18 -21 -19  -27  -35  -41
## R   -72  -54 -38 -22 -17  -9   2  13   5  -3 -11 -19 -14  -12  -20  -28
## N   -80  -62 -46 -30 -25 -17  -6   5  14   6  -2 -10 -18  -15  -16  -21
## C   -88  -70 -54 -38 -32 -25 -14  -3   6  27  19  11   3   -5  -13  -17
## I   -96  -78 -62 -46 -38 -33 -22 -11  -2  19  29  21  13    5   -3  -11
## G  -104  -86 -70 -54 -46 -30 -30 -19 -10  11  21  37  29   21   13    5
## Q  -112  -94 -78 -62 -54 -38 -28 -27 -18   3  13  29  38   30   22   14
## K  -120 -102 -86 -70 -62 -46 -32 -25 -26  -5   5  21  32   41   33   25
## 
## $glob_ma_path
##    gp P F G F G K R S C M G R R L A
## gp  0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## F   2 1 1 3 1 3 3 3 3 3 3 3 3 3 3 3
## I   2 1 1 1 1 3 3 3 3 3 1 3 3 3 1 3
## P   2 1 2 1 1 1 1 3 1 3 3 1 3 3 3 1
## F   2 2 1 3 1 3 1 1 1 1 1 3 3 3 1 3
## S   2 2 2 1 2 1 1 3 1 3 3 3 3 3 3 1
## A   2 2 2 1 1 1 1 3 1 1 1 1 3 3 3 1
## G   2 2 2 1 2 1 3 1 1 3 1 1 3 3 3 3
## P   2 1 2 2 1 2 1 3 1 1 1 1 1 1 3 1
## R   2 2 2 2 1 2 1 1 3 3 3 3 1 1 3 3
## N   2 2 2 2 2 1 2 2 1 3 3 3 3 1 1 1
## C   2 2 2 2 1 2 2 2 2 1 3 3 3 3 3 1
## I   2 2 2 2 1 2 2 2 2 2 1 3 3 3 1 3
## G   2 2 2 1 2 1 2 2 2 2 2 1 3 3 3 3
## Q   2 2 2 2 2 2 1 2 2 2 2 2 1 1 3 3
## K   2 2 2 2 2 2 1 1 2 2 2 2 1 1 3 3

Compute alignment

The following alignList stores all relevant results in a list, including dynamic programming matrices, as well as the coordinates (named path_coor) to highlight path in dynamic progamming matrix (see below).

alignList <- alignmentTraceback(ma=dynMA[[1]], ma_path=dynMA[[2]], align_method=align_type) 
names(alignList)

## [1] "ma"        "ma_path"   "path_coor" "as1"       "consensus" "as2"      
## [7] "score"

# alignList$ma # dyn ma with scores
# alignList$ma_path # dyn ma with path
# alignList$path_coor # coordinates for path to auto highlight path in HTML/PDF table

Return results

Traceback in matrix

The following prints the fully populated dynamic programming matrix where the traceback path is highlighted in color.

printColMa(alignList)

	gp	P	F	G	F	G	K	R	S	C	M	G	R	R	L	A
gp	0	-8	-16	-24	-32	-40	-48	-56	-64	-72	-80	-88	-96	-104	-112	-120
F	-8	-4	0	-8	-16	-24	-32	-40	-48	-56	-64	-72	-80	-88	-96	-104
I	-16	-11	-4	-4	-8	-16	-24	-32	-40	-48	-54	-62	-70	-78	-86	-94
P	-24	-6	-12	-6	-8	-10	-17	-25	-33	-41	-49	-56	-64	-72	-80	-87
F	-32	-14	2	-6	2	-6	-14	-20	-28	-35	-41	-49	-57	-65	-71	-79
S	-40	-22	-6	2	-6	2	-6	-14	-15	-23	-31	-39	-47	-55	-63	-70
A	-48	-30	-14	-6	-1	-6	1	-7	-13	-16	-24	-31	-39	-47	-55	-58
G	-56	-38	-22	-6	-9	7	-1	-2	-7	-15	-19	-16	-24	-32	-40	-48
P	-64	-46	-30	-14	-10	-1	6	-2	-3	-11	-18	-21	-19	-27	-35	-41
R	-72	-54	-38	-22	-17	-9	2	13	5	-3	-11	-19	-14	-12	-20	-28
N	-80	-62	-46	-30	-25	-17	-6	5	14	6	-2	-10	-18	-15	-16	-21
C	-88	-70	-54	-38	-32	-25	-14	-3	6	27	19	11	3	-5	-13	-17
I	-96	-78	-62	-46	-38	-33	-22	-11	-2	19	29	21	13	5	-3	-11
G	-104	-86	-70	-54	-46	-30	-30	-19	-10	11	21	37	29	21	13	5
Q	-112	-94	-78	-62	-54	-38	-28	-27	-18	3	13	29	38	30	22	14
K	-120	-102	-86	-70	-62	-46	-32	-25	-26	-5	5	21	32	41	33	25

Alignment and score

printAlign(x=alignList)

## 
##  S1:  --PFGFGKRSCMGRRLA 
##         ||  | | | |     
##  S2:  FIPFSAGPRNCIGQK-- 
## 
##  Score of alignment: 25

Local alignment

The alignment type choice is passed on to all following functions.

# align_type <- "global"
align_type <- "local"

Dynamic programming matrices

dynMA <- dynProgMatrix(S1, S2, align_method=align_type, gap_penalty=8, substitutionMA="BLOSUM50")

The matrices are stored in a list and returned below.

dynMA

## $loc_ma
##    gp  P  F  G  F  G  K  R  S  C  M  G  R  R  L  A
## gp  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## F   0  0  8  0  8  0  0  0  0  0  0  0  0  0  1  0
## I   0  0  0  4  0  4  0  0  0  0  2  0  0  0  2  0
## P   0 10  2  0  0  0  3  0  0  0  0  0  0  0  0  1
## F   0  2 18 10  8  0  0  0  0  0  0  0  0  0  1  0
## S   0  0 10 18 10  8  0  0  5  0  0  0  0  0  0  2
## A   0  0  2 10 15 10  7  0  1  4  0  0  0  0  0  5
## G   0  0  0 10  7 23 15  7  0  0  1  8  0  0  0  0
## P   0 10  2  2  6 15 22 14  6  0  0  0  5  0  0  0
## R   0  2  7  0  0  7 18 29 21 13  5  0  7 12  4  0
## N   0  0  0  7  0  0 10 21 30 22 14  6  0  6  8  3
## C   0  0  0  0  5  0  2 13 22 43 35 27 19 11  4  7
## I   0  0  0  0  0  1  0  5 14 35 45 37 29 21 13  5
## G   0  0  0  8  0  8  0  0  6 27 37 53 45 37 29 21
## Q   0  0  0  0  4  0 10  2  0 19 29 45 54 46 38 30
## K   0  0  0  0  0  2  6 13  5 11 21 37 48 57 49 41
## 
## $loc_ma_path
##    gp P F G F G K R S C M G R R L A
## gp  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## F   0 1 1 3 1 3 1 1 1 1 1 1 1 1 1 1
## I   0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## P   0 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1
## F   0 2 1 3 1 3 1 1 1 1 1 1 1 1 1 1
## S   0 1 2 1 3 1 1 1 1 1 1 1 1 1 1 1
## A   0 1 2 1 1 1 1 3 1 1 1 1 1 1 1 1
## G   0 1 1 1 2 1 3 3 1 1 1 1 3 1 1 1
## P   0 1 3 2 1 2 1 3 1 3 1 2 1 1 1 1
## R   0 2 1 1 1 2 1 1 3 3 3 1 1 1 3 1
## N   0 1 2 1 3 1 2 2 1 3 3 3 1 1 1 1
## C   0 1 1 2 1 1 2 2 2 1 3 3 3 3 1 1
## I   0 1 1 1 1 1 1 2 2 2 1 3 3 3 1 3
## G   0 1 1 1 3 1 3 1 2 2 2 1 3 3 3 3
## Q   0 1 1 2 1 2 1 3 1 2 2 2 1 1 3 3
## K   0 1 1 1 1 1 1 1 3 2 2 2 1 1 3 3

Compute alignment

Note: alignList stores all relevant results in a list, including dynamic programming matrices, as well as the coordinates (named path_coor) to highlight the path in the dynamic progamming matrix. This way one can easily generate a single dynamic programming matrix with the traceback path highlighted by colors or arrows in an HTML or PDF document (see below).

alignList <- alignmentTraceback(ma=dynMA[[1]], ma_path=dynMA[[2]], align_method=align_type) 
names(alignList)

## [1] "ma"        "ma_path"   "path_coor" "as1"       "consensus" "as2"      
## [7] "score"

# alignList$ma # dyn ma with scores
# alignList$ma_path # dyn ma with path
# alignList$path_coor # coordinates for path to auto highlight path in HTML/PDF table

Return results

Traceback in matrix

The following prints the fully populated dynamic programming matrix where the traceback path is highlighted in color.

printColMa(alignList)

	P	F	G	F	G	K	R	S	C	M	G	R	R	L	A
gp	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
F	0	8	0	8	0	0	0	0	0	0	0	0	0	1	0
I	0	0	4	0	4	0	0	0	0	2	0	0	0	2	0
P	10	2	0	0	0	3	0	0	0	0	0	0	0	0	1
F	2	18	10	8	0	0	0	0	0	0	0	0	0	1	0
S	0	10	18	10	8	0	0	5	0	0	0	0	0	0	2
A	0	2	10	15	10	7	0	1	4	0	0	0	0	0	5
G	0	0	10	7	23	15	7	0	0	1	8	0	0	0	0
P	10	2	2	6	15	22	14	6	0	0	0	5	0	0	0
R	2	7	0	0	7	18	29	21	13	5	0	7	12	4	0
N	0	0	7	0	0	10	21	30	22	14	6	0	6	8	3
C	0	0	0	5	0	2	13	22	43	35	27	19	11	4	7
I	0	0	0	0	1	0	5	14	35	45	37	29	21	13	5
G	0	0	8	0	8	0	0	6	27	37	53	45	37	29	21
Q	0	0	0	4	0	10	2	0	19	29	45	54	46	38	30
K	0	0	0	0	2	6	13	5	11	21	37	48	57	49	41

Alignment and score

printAlign(x=alignList)

## 
##  S1:  PFGFGKRSCMGRR 
##       ||  | | | |   
##  S2:  PFSAGPRNCIGQK 
## 
##  Score of alignment: 57

C. Different Substitution Matrices

Task 1: Load the Biostrings package in R, import the following two cytochrome P450 sequences O15528 and P98187 from NCBI (save as myseq.fasta), and create a global alignment with the pairwiseAlignment function from Biostrings as follows.

library(Biostrings)
myseq <- readAAStringSet("myseq.fasta", "fasta")
(p <- pairwiseAlignment(myseq[[1]], myseq[[2]], type="global", substitutionMatrix="BLOSUM50"))
writePairwiseAlignments(p)

Your answers should address the following items:

Record the scores for the scoring matrices BLOSUM50, BLOSUM62 and BLOSUM80. How and why do the scores differ for the three scoring matrices?

Answer 1: The scores for the three BLOSUM substitutions matrices are:

BLOSUM50: 227
BLOSUM62: 54
BLOSUM80: -52

Answer 2: Since the two sequences are relatively dissimilar (as determined by alignment view from writePairwiseAlignments(p)) it is expected that the BLOSUM matrices trained on less similar sequences (e.g. BLOSUM50) result in higher scores than those trained on more similar sequences (e.g. BLOSUM80).

Session Info

sessionInfo()

## R version 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 11 (bullseye)
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/Los_Angeles
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] kableExtra_1.4.0    Biostrings_2.70.3   GenomeInfoDb_1.38.8
## [4] XVector_0.42.0      IRanges_2.36.0      S4Vectors_0.40.2   
## [7] BiocGenerics_0.48.1
## 
## loaded via a namespace (and not attached):
##  [1] jsonlite_1.8.8          compiler_4.3.3          crayon_1.5.2           
##  [4] xml2_1.3.6              stringr_1.5.1           bitops_1.0-7           
##  [7] jquerylib_0.1.4         systemfonts_1.0.6       scales_1.3.0           
## [10] yaml_2.3.8              fastmap_1.1.1           R6_2.5.1               
## [13] knitr_1.45              bookdown_0.38           munsell_0.5.0          
## [16] GenomeInfoDbData_1.2.11 svglite_2.1.3           bslib_0.7.0            
## [19] rlang_1.1.3             cachem_1.0.8            stringi_1.8.3          
## [22] xfun_0.43               sass_0.4.9              viridisLite_0.4.2      
## [25] cli_3.6.2               magrittr_2.0.3          zlibbioc_1.48.2        
## [28] digest_0.6.35           rstudioapi_0.16.0       lifecycle_1.0.4        
## [31] vctrs_0.6.5             evaluate_0.23           glue_1.7.0             
## [34] blogdown_1.19           RCurl_1.98-1.14         colorspace_2.1-0       
## [37] rmarkdown_2.26          tools_4.3.3             htmltools_0.5.8

References

Needleman, S B, and C D Wunsch. 1970. “A general method applicable to the search for similarities in the amino acid sequence of two proteins.” J. Mol. Biol. 48 (3): 443–53. https://doi.org/10.1016/0022-2836(70)90057-4.

Smith, T F, and M S Waterman. 1981. “Identification of common molecular subsequences.” J. Mol. Biol. 147 (1): 195–97. http://www.ncbi.nlm.nih.gov/pubmed/7265238.

Assignments: HW5 - Programming in R

Mon, 01 Jan 0001 00:00:00 +0000

Source code downloads: [ .R ]

A. Reverse and complement of DNA

Task 1: Write a RevComp function that returns the reverse and complement of a DNA sequence string. Include an argument that will allow to return only (i) the reversed sequence, (ii) the complemented sequence, or (iii) the reversed and complemented sequence. The following R functions will be useful for the implementation:

Generate a short test DNA sequence

x <- c("ATGCATTGGACGTTAG")  
x

## [1] "ATGCATTGGACGTTAG"

Vectorize sequence

x <- substring(x, 1:nchar(x), 1:nchar(x)) 
x

##  [1] "A" "T" "G" "C" "A" "T" "T" "G" "G" "A" "C" "G" "T" "T" "A" "G"

Reverse sequence

x <- rev(x) 
x

##  [1] "G" "A" "T" "T" "G" "C" "A" "G" "G" "T" "T" "A" "C" "G" "T" "A"

Collapse sequence back to character string

x <- paste(x, collapse="")
x

## [1] "GATTGCAGGTTACGTA"

Form complement of sequence

chartr("ATGC", "TACG", x)

## [1] "CTAACGTCCAATGCAT"

Task 2: Write a function that applies the RevComp function to many sequences stored in a vector. In addition, write an export function that saves the sequences generated under Tasks 1 and 2 to a file in FASTA format.

B. Translate DNA into Protein

Task 3: Write a function that will translate one or many DNA sequences in all three reading frames into proteins. The following commands will simplify this task:

Import lookup table of genetic code

AAdf <- read.table(file="http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/AA.txt", header=TRUE, sep="\t") 
AAdf[1:4,]

##   Codon AA_1 AA_3 AA_Full AntiCodon
## 1   TCA    S  Ser  Serine       TGA
## 2   TCG    S  Ser  Serine       CGA
## 3   TCC    S  Ser  Serine       GGA
## 4   TCT    S  Ser  Serine       AGA

Generated named vector of relevant components

AAv <- as.character(AAdf[,2]) 
names(AAv) <- AAdf[,1] 
AAv

## TCA TCG TCC TCT TTT TTC TTA TTG TAT TAC TAA TAG TGT TGC TGA TGG CTA CTG CTC CTT CCA CCG CCC CCT CAT 
## "S" "S" "S" "S" "F" "F" "L" "L" "Y" "Y" "*" "*" "C" "C" "*" "W" "L" "L" "L" "L" "P" "P" "P" "P" "H" 
## CAC CAA CAG CGA CGG CGC CGT ATT ATC ATA ATG ACA ACG ACC ACT AAT AAC AAA AAG AGT AGC AGA AGG GTA GTG 
## "H" "Q" "Q" "R" "R" "R" "R" "I" "I" "I" "M" "T" "T" "T" "T" "N" "N" "K" "K" "S" "S" "R" "R" "V" "V" 
## GTC GTT GCA GCG GCC GCT GAT GAC GAA GAG GGA GGG GGC GGT 
## "V" "V" "A" "A" "A" "A" "D" "D" "E" "E" "G" "G" "G" "G"

Tripletize sequence and translate by name subsetting/sorting of AAv

y <- gsub("(...)", "\\1_", x) 
y <- unlist(strsplit(y, "_")) 
y <- y[grep("^...$", y)] 
AAv[y]

## GAT TGC AGG TTA CGT 
## "D" "C" "R" "L" "R"

Homework submission

Submit the 3 functions in one well structured and annotated R script to your private GitHub repository under Homework/HW5/HW5.R. The script should include instructions on how to use the functions.

Due date

This homework is due on Fri, April 26th at 6:00 PM.

Homework Solutions

See here.

Assignments: HW6 - NGS Analysis Basics

Mon, 01 Jan 0001 00:00:00 +0000

Source code downloads: [ .R ]

A. Demultiplexing

Write a demultiplexing function that accepts any number of barcodes and splits a FASTQ file into as many subfiles as there are barcodes. At the same time the function should remove low quality tails from the reads. In both cases arguments should be provided so that the barcodes and cutoff can be specified by the user. The following function accomplishes the first step. Extend this function so that it performs the second step as well. As test data set one can use the FASTQ test files downloaded in the corresponding tutorial section here.

demultiplex <- function(x, barcode, nreads) {
	f <- FastqStreamer(x, nreads) 
	while(length(fq <- yield(f))) {
		for(i in barcode) {
			pattern <- paste("^", i, sep="")
			fqsub <- fq[grepl(pattern, sread(fq))] 
			if(length(fqsub) > 0) {
				writeFastq(fqsub, paste(x, i, sep="_"), mode="a", compress=FALSE)
			}
		}
	}
	close(f)
}
demultiplex(x=fastq[1], barcode=c("TT", "AA", "GG"), nreads=50)

B. Sequence Parsing

Download GFF from Halobacterium sp here
Download genome sequence from Halobacterium sp here
Task 1 Extract gene ranges, parse their sequences from genome and translate them into proteins
Task 2 Reduce overlapping genes and parse their sequences from genome
Task 3 Generate intergenic ranges and parse their sequences from genome

Useful commands

download.file("https://ftp.ncbi.nlm.nih.gov/genomes/archive/old_genbank/Bacteria/Halobacterium_sp_uid217/AE004437.gff", "data/AE004437.gff")
download.file("https://ftp.ncbi.nlm.nih.gov/genomes/archive/old_genbank/Bacteria/Halobacterium_sp_uid217/AE004437.fna", "data/AE004437.fna")
chr <- readDNAStringSet("data/AE004437.fna")
gff <- import("data/AE004437.gff")
gffgene <- gff[values(gff)[,"type"]=="gene"]
gene <- DNAStringSet(Views(chr[[1]], IRanges(start(gffgene), end(gffgene))))
names(gene) <- values(gffgene)[,"locus_tag"]
pos <- values(gffgene[strand(gffgene) == "+"])[,"locus_tag"]
p1 <- translate(gene[names(gene) %in% pos])
names(p1) <- names(gene[names(gene) %in% pos])
neg <- values(gffgene[strand(gffgene) == "-"])[,"locus_tag"]
p2 <- translate(reverseComplement(gene[names(gene) %in% neg]))
names(p2) <- names(gene[names(gene) %in% neg])
writeXStringSet(c(p1, p2), "./data/mypep.fasta")

Homework submission

Please submit the homework results in one well structured and annotated R script to your private GitHub repository under Homework/HW6/HW6.R. The script should include instructions on how to use the functions.

Due date

This homework is due on Thu, May 2nd at 6:00 PM.

Homework Solutions

See here.

Assignments: HW7 - RNA-Seq Analysis

Mon, 01 Jan 0001 00:00:00 +0000

Source code downloads: [ .R ]

EXCEPTION: Due to the cluster outage (May 7-8th), the read count tables required for HW7 have been made available for download from this GEN242_Data repository created for this purpose. To work with this data set, clone this repository (same way as HW repos), and then cd into the rnaseq subdirectory located under GEN242/HW7_data/ of this repos, or set your RStudio session to this directory. Next open the provided HW07.Rmd file under HW7_data/rnaseq and follow the instructions given in this file. I contains quite a bit of additional helper code, well beyond what is given below.

A. Unstranded and strand-specific read counting

Task 1: Rerun or reload the RNA-Seq workflow with the toy data sets up to the read quantification step here. Note, the toy data set gets automatically loaded when intializing a workflow environment (directory structure) with the genWorkenvir function (see tutorial here).

In the read quantification step with summarizeOverlaps generate count tables for exons by genes (eByg) of the following three strand modes:
1. Unstranded
2. Strand-specific for positive (sense) strand
3. Strand-specific for negative (antisense) strand
The solution for generating the unstranded read counts is given below. Note, the upstream steps of the RNA-Seq workflow only need to be rerun to generate the proper inputs for the read counting. Thus, they are not required to be included in the homework results (see HW7.R below).

unstranded <- summarizeOverlaps(eByg, bfl, mode="Union", 
                                                ignore.strand=TRUE, 
                                                # preprocess.reads=invertStrand,
                                                inter.feature=FALSE, 
                                                singleEnd=FALSE)
unstranded <- assays(unstranded)$counts
unstranded[1:4,]

Before attempting to solve this homework task please read the vignette Counting reads with summarizeOverlaps (here) from the GenomicAlignments package that defines the summarizeOverlap function. In addition, the help file for ?summarizeOverlaps provides useful information.

Task 2: Provide R code that demonstrates that the two strand-specific count tables sum up to very similar values as the unstranded count table.
Task 3: Explain the utility (biological relevance) of the different strand counting modes used under Task 1. Include your explanation as comment text in your homework script (see HW7.R below).

Note, for Tasks 1-3 only the code and/or text needs to be included in the homework submission (no data/result files). For details see below.

B. Read counting for different feature types

Task 4: Compute strand-specific count tables for the positive (sense) strand of the following feature types. The help files of ?exonsBy and ?transcripts provide useful information for solving these tasks.
1. Genes
2. Exons
3. Exons by genes
4. Introns by transcripts
5. 5’-UTRs by transcripts

Note, for Tasks 4 only include the code and/or text in your homework submission (no data/result files).

C. DEG analysis

Task 5: Perform the DEG analysis with edgeR as outlined under section 6 of the RNA-Seq workflow here. Use in one case for the DEG analysis the unstranded count table as input (from Task 1.1) and in another the sense strand count table (from Task 1.2). Compare the DEG result of the two methods in two separate 4-way Venn diagrams for the same sample comparisons used in the workflow example here.
1. 4-way Venn diagram for unstranded count table
2. 4-way Venn diagram for sense strand count table

Note, for Tasks 5 include both the code and the resulting images in your homework submission.

Homework submission

Please submit the homework results in one well structured and annotated R script to your private GitHub repository under Homework/HW7/HW7.R. Instead of an R script the homework can be submitted in form of an R Markdown (*Rmd) file.

Due date

This homework is due on Tue, May 14th at 6:00 PM.

Homework Solutions

See here.

Assignments: HW8 - R Markdown Exercises

Mon, 01 Jan 0001 00:00:00 +0000

Learning R Markdown

To get started with the following homework tasks, log in to your HPCC account and then cd into /bigdata/gen242/<user_name>. Then clone your new project repos linked under your name in Colum M of the Course Planning Sheet. After this, cd into your project’s repos and create as subdirectory called HW8. Subsequentially, download into the HW8 directory with wget the sample.Rmd and bibtex.bib files linked from the R Markdown Tutorial here. Next, make the following changes (Tasks 1.-8.) to the downloaded R Markdown (Rmd) file. After this render the modified Rmd to HTML format, and then submit both files to your project repos on GitHub. The changes to include in the R Markdown are:

Homework Tasks

At the beginning of the R Markdown report, add a short section describing the analysis steps you have chosen to perform as part of your challenge project.
In the challenge project section, cite the reference(s) of the paper(s) you have chosen to present in class as part of your course project. For this add the reference in BibTeX format to the downloaded bibtex.tex file, and then cite it in the text of the Rmd file so that the properly rendered citation shows up in the text and the corresponding reference is automatically added to the reference list at the end of the R Markdown when running rmarkdown::render. Note, references in BibTeX format can be obtained from Google Scholar, Paperpile or most other reference management software. More detailed information about managing references in R Markdown files is here.
Add a mathematical equation in LaTeX format to the challenge project section and make sure it renders properly when generating the HTML report.
Evaluate an R expression in the text as inline R code (see here) of your challenge project, e.g. mathematical or number from an existing R oject such as mean value of the first column of the iris data.frame.
Add a code chunk that auto-generates the barplot for HW3C. This barplot should be embedded in the rendered report without saving it intermediately to a file.
Insert the targets file of your workflow (default toy data is sufficient) as an interactive table using the DT package. An example is given in the table section of the R Markdown manual here.
Use the rmarkdown::render() function to render the report to an HTML file (details are here). To embed the targets file under step 6 into the report, the interactive table generated by the DT package can only be included in the HTML version of the report. For including this table in the PDF version of the report, use the static knitr::kable option instead as outlined here.
Submit the Rmd and HTML files for the report to the corresponding project repos on GitHub. Remember the details for adding, committing and pushing new files to a GitHub respos are given in the GitHub tutorial of GEN242 here.

Homework submission

Please submit both the final Rmd and HTML files, where the above Tasks 1-8 have been fully addressed, to the HW8 subdirectory of your project repos on GitHub.

Due date

This homework is due in one week on Thu, June 6th at 6:00 PM.

Homework Solution

All solutions for HW08 can be looked up in the R Markdown tutorial of this class. The source code is available in the Rmd file and the rendered result in the corresponding HTML file. As in all tutorial pages of this site, the Rmd files can also be accessed via a link located in the header section of each tutorial page.

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