To run this program from your own working directory, clone this repository to your workspace.

  git clone https://github.com/egeoffroy/CompBio_Mini_Project.git

To run this program, move the SRR files you would like to run the program with in the directory CompBio_Mini_Project. This can be done by running:

  mv SRR_file CompBio_Mini_Project/SRR_file

From there, set your working directory to CompBio_Mini_Project and call the function. The program requires four SRR values as input.

  cd CompBio_Mini_Project
  python run_all.py SRR1 SRR2 SRR3 SRR4

Or alternatively, you can use the flag --download_files to pull the SRA files from the database.

  cd CompBio_Mini_Project
  python run_all.py SRR1 SRR2 SRR3 SRR4 --download_files Y

Or to run it from the background,

  cd CompBio_Mini_Project
  nohup python run_all.py SRR1 SRR2 SRR3 SRR4 --download_files Y &

To run the software with the test data provided:

  cd CompBio_Mini_Project
  python run_all.py SRR5660030 SRR5660033 SRR5660044 SRR5660045

This test data consists of the first 50000 lines for the four SRR paired read files. This means there are 12500 read pairs for each SRR. When running with this tool, expect only one significant contig with length > 1000 bp after running SPades.

EF999921.gb : a genbank file for EF999921

CDS_EF999921.fasta : a fasta file for only the coding regions of EF999921

sample_covariates.txt : a file containing the sample names, paths, and conditions in order to run sleuth.R

sleuth_output.txt : a file containing the significant hits after running sleuth.

significant_contigs.txt : a file listing the contigs with more than 1000 bp.

assembly.fasta : the assembled significant contigs.

blast.xlm : the blast output file.

independently to develop a Python wrapper to automate the execution of the software tools. You will create a GitHub repo and post your code there (Introduction to GitHub presented on 2/13 in class). You should include straight-forward documentation and sample data in your repository. The code should have adequate documentation in the repository’s README.md file such that anyone could download the code, install it or get it to run, and run through the test data without encountering any problems; this should be thought of as a User’s Manual. If I cannot run the code with the test data, I cannot grade the functionality of the code. This means that you cannot hardcode paths in your code! The code will be graded as follows: • 3 pts. Code comments • 3 pts. Documentation -- including what tools need to be installed and how to use the code • 1 pts. Test data (include a small subset of input reads so I can test your code quickly, but include enough reads so the entire pipeline runs. Note, files in your GitHub repo must be less than 50MB.) • 8 pts. Functionality (include miniProject.log in your GitHub repo with the requested output from running your pipeline with all input reads, so I can check your answers.) Submit the URL to your GitHub repo to Sakai. Human herpesvirus 5 is also known as Human cytomegalovirus and is typically abbreviated as HCMV. From Wikipedia: Although they may be found throughout the body, HCMV infections are frequently associated with the salivary glands. HCMV infection is typically unnoticed in healthy people, but can be life-threatening for the immunocompromised, such as HIV-infected persons, organ transplant recipients, or newborn infants. Congenital cytomegalovirus infection can lead to significant morbidity and even death. After infection, HCMV remains latent within the body throughout life and can be reactivated at any time. Eventually, it may cause mucoepidermoid carcinoma and possibly other malignancies such as prostate cancer. Cheng et al. 2017 (https://www.ncbi.nlm.nih.gov/pubmed/29158406) produced the transcriptomes of HCMV post infection. Write a Python script to automate the following and produce the output file requested named “miniProject.log” and other output files in a folder named “miniProject_FirstName_LastName” [where you’ve indicated your first and last name]. ALL results generated by you or programs called should be written to this folder. The easiest way to guarantee this is to create the folder and then move into it via an os.system call using cd (change directory).

1. We would like to compare HCMV transcriptomes 2- and 6-days post-infection (dpi). First, retrieve the following transcriptomes from two patient donors from SRA and convert to paired-end fastq files. You can use wget (by constructing the path based on the SRR numbers for each of these samples). Donor 1 (2dpi): https://www.ncbi.nlm.nih.gov/sra/SRX2896360 Donor 1 (6dpi): https://www.ncbi.nlm.nih.gov/sra/SRX2896363 Donor 3 (2dpi): https://www.ncbi.nlm.nih.gov/sra/SRX2896374 Donor 3 (6dpi): https://www.ncbi.nlm.nih.gov/sra/SRX2896375
2. We will quantify TPM in each sample using kallisto, but first we need to build a transcriptome index for HCMV (NCBI accession EF999921). Use Biopython to retrieve and generate the appropriate input and then build the index with kallisto (https://pachterlab.github.io/kallisto/). You will need to extract the CDS features from the GenBank format. Write the following to your log file (replace # with the number of coding sequences in the HCMV genome): 2 The HCMV genome (EF99921) has # CDS.
3. Quantify the TPM of each CDS in each transcriptome using kallisto and use these results as input to find differentially expressed genes between the two timepoints (2pi and 6dpi) using the R package sleuth (https://pachterlab.github.io/sleuth/about). Write the following details for each significant transcript (FDR < 0.05) to your log file, include a header row, and tab-delimit each item: target_id test_stat pval qval
4. It has been proposed that HCMV disease and pathogenesis may be related to the genetic diversity of the virus (Renzette et al. https://www.ncbi.nlm.nih.gov/pubmed/25154343/). Which publicly available strains are most similar to these patient samples? To compare to other strains, we will assemble these transcriptome reads. We don’t expect assembly to produce the entire genome, but enough to be useful in BLAST. Virus sequencing experiments often include host DNAs. It is difficult to isolate the RNA of just the virus (as it only transcribes during infection of the host cell). Before assembly, let’s make sure our reads map to HCMV. Using Bowtie2, create an index for HCMV (NCBI accession EF999921). Next, save the reads that map to the HCMV index for use in assembly. Write to your log file the number of reads in each transcriptome before and after the Bowtie2 mapping. For instance, if I was looking at the Donor 1 (2dpi) sample, I would write to the log (numbers here are arbitrary): Donor 1 (2dpi) had 230000 read pairs before Bowtie2 filtering and 100000 read pairs after.
5. Using the Bowtie2 output reads, assemble all four transcriptomes together to produce 1 assembly via SPAdes. Write the SPAdes command used to the log file.
6. Write Python code to calculate the number of contigs with a length > 1000 and write the # out to the log file as follows: There are # contigs > 1000 bp in the assembly. For steps 7-9, you will only consider those contigs > 1000 bp in length.
7. Write Python code to calculate the length of the assembly (the total number of bp in all of the contigs > 1000 bp in length) and write this # out to the log file as follows: There are # bp in the assembly.
8. Write Python code to concatenate all of the contigs > 1000 bp in length into 1 fasta sequence. Separate each contig by a stretch of 50 N’s.
9. Using this concatenated fasta file, blast via NCBIWWW.qblast to query the nr database limited to members of the Herpesviridae family to identify the top 10 hits. For the top 10 hits, write the following to your log file: sequence title, alignment length, number of HSPs, and for the top HSP: HSP identities, HSP gaps, HSP bits, and HSP expect scores. Include the following header row and tab-delimit each item: seq_title align_len number_HSPs topHSP_ident topHSP_gaps topHSP_bits topHSP_expect