bitotech and ms access

Capstone in Biotechnology

BTMN 670 – 9042

Project: RNA-Seq transcriptomic analysis in Arabidopsis thaliana

Members: Stacy Campbell, Ronak Gandhi, Jazmine Gooding, John Snowball, Shekinah Summers

Project Goals/Objectives

· Align RNA-Seq fastq files to Arabidopsis thaliana reference genome.

We have been provided with controls and mutant Arabidopsis thaliana RNA-Seq datasets which correspond to different time points of the day. In the mutant plants the lux gene, which is known to be involved in circadian rhythm, has been silenced. By aligning the reads generated by each RNA-Seq experiment to Arabidopsis thaliana’s reference genome we will be able to calculate the amount of reads, in each sample, that correspond to a particular gene in the reference genome.

· Conduct differential expressions analysis comparing mutant pants to control plants.

After the fastq files have been aligned the resulting alignment files will be normalized and used to calculate the RNA levels of genes in each dataset. The RNA levels of genes can then be used to infer gene expressions levels. By comparing the gene expressions levels, between control and mutant plants of the same time point, the genes that are differentially expressed between the mutant and control plants can be determined. The fold change of these genes can also be determined.

· Build a searchable database of the data provided by our differential expressions analysis.

Due to the sheer number of genes that are in Arabidopsis thaliana it will be extremely useful to build a searchable database that will allow researchers to query which genes are differentially expressed as well as allow researchers to look up the expressions levels of particular genes within one certain or all datasets.

· Further analysis (if time permits)

Using GO ontologies and the KEGG pathway database we want to try and identify key genes that might be affected by the silencing of the lux gene in our mutant plant. These genes can then be marked for future interrogation and analysis to determine if they play a role in circadian rhythm or plant defense.

Background

· Our team is working with Dr. Nadim Alkharouf, and the Alkharouf Lab at Towson University, to identify genes and pathways in plants that are involved in resistance responses against pathogens.

· Towson University is located in Towson, Maryland, a public university founded in 1866, which offers over 100 degrees, including bachelors, masters and doctoral programs.

· As a team we will be working with Nadim Alkharouf using next generation sequencing (RNA-Seq) to analyze Arabidopsis thaliana.

· Contact information

· Contact Address: Towson University, Towson, MD Contact Email: [email protected]

· Contact Phone: 410-704-3149

· This project is also under the leadership with Dr. Hua Lu from the University of Maryland Baltimore County (UMBC). However we will not be working directly with Dr. Hua Lu.

· Contact Information

· Contact Address: 10000 Hilltop Cir, Baltimore, MD 21250

· Contact Email: [email protected]

· Contact Phone: 410-455-5972

Updated Objectives / Tools and Resources

1. We will use a tool called Bowtie to align our RNA-Seq data. The latter are in files called fastq files. They need to be aligned to a reference genome. These are Illumina sequence files.

2. First download and run Bowtie using the example data

3. Once you have Bowtie up and running you can align the actual samples. The sample descriptions are attached in the Excel file. They come from a plant named Arabidopsis thaliana.

4. You will need to download the genome of A. thaliana to align the reads against

5. Next download the fastq files, I will be sending you a separate email sharing those files with you from my OneDrive.

6. Align all the samples with the reference genome

7. Write a script, Perl or Phyton, to get the read counts for every gene\cds

8. Do differential expression analysis comparing the treatment samples against the controls.

9. Put the data, raw read counts, and differential expression results in a database.

10. Develop search forms to allow user to query data for specific genes and\or gene descriptions.

We will rely on various software programs in order to complete this project. The first is Bowtie. Bowtie will align our DNA sequences. Perl is an additional tool. Once the alignments are completed, we will write a program using Perl that will perform the alignment counts. We need to know which genes are expressed at which amounts, so this will give us that answer. Then we will use DEGseq in order to perform the differential expression analysis. Once we have our data, we will construct a searchable database using SQL.

At this point, foreseeable problems we may come across include determining the amount of storage necessary to hold the files we create once we carry out the analyses. The usage of external USB drives will be necessary in order to store some of these files, which may wind up being quite large. Also, some of the software programs and tasks are fairly new to our member, which means that although completion will not be a problem, it will be challenge.

We have begun the necessary research by reading several articles about plant defense. We will most likely find a few more sources containing similar information, as well as sources that will help us utilize the software we will be downloading as well as any other necessary background information.

Division of Work

John Snowball- Alignment of RNA – Seq fastq reads to Arabidopsis thaliana genome using the aligner tool Bowtie. Secondary – will help writing the script and building the database.

Stacy Campbell – Align the samples with the reference genome, write a script using Perl to get the read counts for counting the amount of mRNA reads. Secondary will work building the database.

Jazmine Gooding / Ronak Gandhi – Building the searchable database using the data, raw read counts, and differential expression results. This will allow users (scientists) to search all data for useful information.

Shekinah Summers – Conduct differential expression analysis, comparing the treatment samples against the controls (using DEGSeq / DESeq).

Timeline

DATE	MILESTONE
7-Sep	Week 1--Project Start
14-Sep	Week 2--Kick-off Meeting with Professor, team and Nadim*** Download software Bowtie and Biolinux--work through examples
21-Sep	Week 3--Follow-up meeting with Nadim Tuesday at 7PM EST*** Contnue getting familiar with software*** download all necessary software
28-Sep	Week 4--Alignment initiated and completed no later than week 5*** Database building initiated
5-Oct	Week 5-- Presentation: Thursday at 7pm EST
12-Oct	Week 6--Research, script and counts initiated here or in week 5; dependent on alignment*** Differential Expression Analysis initiated
19-Oct	Week 7-- Research, scripts and counts completed
26-Oct	Week 8--Differential Expression Analysis Completed
2-Nov	Week 9--Database building completed
9-Nov	Week 10-- Begin Written Report***Compile teams setions and initiate final presentaion; make final touches
16-Nov	Week 11-- Continue final touches for presentation***omplete written report and submit***practice run through final presentation as team
23-Nov	Week 12--Final Presentation