Sle712 bioinformatics and molecular biology techniques

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Reference no: EM132500117

SLE712 Bioinformatics and Molecular Biology Techniques - Deakin University

Part 1: Importing files, data wrangling, mathematical operations, plots and saving code on GitHub

The purpose of this exercise will be for you to develop skills in problem solving, R coding. work together as a team using Rstudio and GitHub. You will be provided with two data files to work with: "gene_expression.tsv and "growth_data.csv" which are available from this URL':

To download a file with R. click on "view raw" and then you can copy the URL from the address bar and then use the download.file command in R.
• For points 1-10 below Describe how you solved the problem.
Provide the answer as directed. The answer could be a descriptive numerical, categorical, table or chart.
• Provide a link to GitHub repository with the following: The code should run without errors, and yield answers to points 1-10 below.
If working in a group, there needs to be evidence that all group members have made contributions to the code repository. This means that there needs to be -commits- and Issues-from each group member.
A README that describes the purpose of each script and their inputs and outputs.

The code should contain sufficient comments so that someone else can understand what each line or chunk of code is trying to achieve

The file "gene_expression.tsv" contains RNA-seq count data for two samples of interest.
1. Read in the file. making the gene accession numbers the row names. Show a table of values for the first six genes.
2. Make a new column which is the mean of the other columns. Show a table of values for the first six genes.
3. List the 10 genes with the highest mean expression
4. Determine the number of genes with a mean <10
5. Make a histogram plot of the mean values in png format and paste it into your report.
The file -growth_data.csy- contains measurements for tree circumference growing at two sites. control site and treatment site which were planted 20 years ago.
6. Import this csv file into an R object. What are the column names?
7. Calculate the mean and standard deviation of tree circumference at the start and end of the study at both sites.
8. Make a box plot of tree circumference at the start and end of the study at both sites.
9. Calculate the mean growth over the past 10 years at each site.
10. Use the t.test and wilcox.test functions to estimate the p-value that the 10 year growth is different at the two sites.

Part 2: Determine the limits of BLAST

In class you will be shown how to
• Download and unzip files
• Perform simple manipulations and analyses with sequence data
• Use a provided function to incorporate point mutations into a sequence
• Use provided functions to perform a BLAST search and interpret results
In this assignment we will be testing your ability to use supplied functions to perform an analysis into the limits of BLAST. Your group will be allocated one E. colt gene sequence found in the file:

• For points 1-6 below

Describe how you solved the problem.

Provide the answer as directed. The answer could be a numerical. categorical. table or chart.

• Provide a link to GitHub repository with the following:

The code should run without errors, and yield answers to questions 1-6 below.

If working in a group, there needs to be evidence that all group members have made contributions to the code repository. This means that there needs to be 'commits" and "issues" from each group member.

A README that describes the purpose of each script and their inputs and outputs.

The code should contain sufficient comments so that someone else can understand what each line or chunk of code is trying to achieve

1. Download the whole set of E. cob gene DNA sequences and use gunzip to decompress. Use the makeblast() function to create a blast database. How many sequences are present in the E.colt set?

2. Download the sample fasta sequences and read them in as above. For your allocated sequence, determine the length (in bp) and the proportion of GC bases.

3. You will be provided with R functions to create BLAST databases and perform blast searches. Use blast to identify what E. colt gene your sequence matches best. Show a table of the top 3 hits including percent identity. E-value and bit scores.

4. You will be provided with a function that enables you to make a set number of point mutations to your sequence of interest. Run the function and write an R code to check the number of mismatches between the original and mutated sequence.

5. Using the provided functions for mutating and BLASTing a sequence. determine the number and proportion of sites that need to be altered to prevent the BLAST search from matching the gene of origin. Because the mutation is random, you may need to run this test multiple times to get a reliable answer.

6. Provide a chart or table that shows how the increasing proportion of mutated bases reduces the ability for BLAST to match the gene of origin. Summarise the results in 1 to 2 sentences.

Attachment:- Bioinformatics and Molecular Biology Techniques.zip

Reference no: EM132500117

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