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scripts
track_hub
README.md
directory_structure.png
main.sh
user_config.txt

README.md

The Horse Transcriptome project

Most of the gene models of the current horse genome lack transcriptional evidence support. This project develops a pipeline for analysis of RNAseq to fulfill these aims:

  1. Make use of RNAseq as a transcriptional evidence to provide more accurate gene models.
  2. Compare tissue specific gene models
  3. Test different approaches for effective integrative analysis of several RNAseq experiments
  4. Use GitHub and UCSC genome browser to provide a scientific collaborative platform for laboratories interested in the horse functional genomics and provide a replicable model for other organisms.
  5. Allow continuous update of transcriptomes with new RNAseq experiments. Also facilitate reproduction of the whole annotation pipeline with newer versions of the genome

This github project contains all the scripts required to reproduce the analysis of the horse transcriptome project on the HPC of Michigan State University. Also it has the basic tree structure of the UCSC track hub. This design will allow the community to share in developing the track hub and also they can fork to have their own version with different view options.

Coding guidelines in this project

  • Fork the horse_trans repository to your Github account, clone it to your local machine, and Configure Git to sync your fork with the original repository (if you want). To learn more about doing this, you can check the github documentation
  • Edit the user_config.txt file to determine your environment and pipeline choices
  • The "main.sh" script represents the index page of the whole project. The first section of the main script should construct the basic directory structure for you.
  • Preparation of the input data:

    • Every tissue has a separate folder carrying its name (maximum 14 letter). All tissue folders should be housed in the prepdata folder (a folder that was created in the previous step)
    • Every RNAseq library should have a separate folder in the corresponding tissue folder.
    • The library folder name should have this format:
      • start with PE_ or SE_ according to the sequencing type
      • Then it should should have the read length followed by underscore
      • Then it should have fr.unstranded_ , fr.firststrand_ , fr.secondstrand_ according to lib type
      • Then the owner name (or names separated by dots) followed by underscore
      • Then the date of sequencing as MMDDYYYY
    • The raw data files should be kept in a folder named fastq_data in the library folder
    • The raw data files should fulfill these criteria
      • The file names should fit the format *_R1_*.fastq.gz & *_R2_*.fastq.gz for PE reads or *_SR_*.fastq.gz for SE
      • All reads in every given file should belong to ONE sequencing lane.
      • If there are sample replicates from different lanes, you can add a text file called "replicates.txt". Each line in this file should have the names of one sample replicates with space separation. (only the *_R1_*.fastq.gz for PE reads or *_SR_*.fastq.gz for SE)
      • The first syllabus (the part of the file name before _R1_ , _R2_ or _SR_) should be unique
      • All samples should be prepared so that they have encoding "Sanger / Illumina 1.9"
    • prep_proj.sh script can automatically convert an SRA repository into the appropriate format (the script assumes no sample replicates)
  • All the analysis was done on the High performance computer of Michigan State University. The main script represents an abstraction administrator. It does not run any of the programs but always initiates daughter scripts to do the job. This allows new forks or branches to provide different implementations of the daughter scripts to run the same pipeline with different versions or on different machines.
  • With such a large scale analysis, it is difficult to check that every job ended successfully. I tried to add a check point after every step to make sure that I am getting out what I really expect. I believe adding more of these tests would be a good practice
  • Every step in the analysis receives the input data as a sample list. This enables the users to run the pipeline for selected samples. Also enables easier re-running of failed samples. On the long term this would allow the community to keep the transcriptome list updated without re-analysis or re-coding.
  • Track hubs:
    • Each hub should include tracks for set of samples that underwent the same "non-branching" analytical pipeline.
    • Each hub should contain tracks for every tissue. Tissues with multiple libraries would have a special composite track which allow the visualization of the separate libraries. The hub also should contain track that represent the integration of all tissues.
    • I tried to automate the creation and edition of the tracks so that the user just pass a list of all the annotation files or even only the newer files to the "edit_trackDb.sh" script which takes care of arranging the libraries and tissues into the appropriate format. Hopefully it is going to work the way I like.

Current available track hubs

You can add them to your hubs on UCSC using these URLs:

To add a hub, go to the genome browser home page. In the My Data menu, open the Track Hubs page and click the My Hubs tab. This tab lists the unlisted track hubs that you have loaded into your browser. To import a new hub, type its URL into the text box, then click the Add Hub button.

Links for Downloads:

  • GTF file of primitive assembly. This is our initial RNAseq supported assembly before any filtration.
  • GTF file of filtered assembly. This is our final "refined assembly" after application of four filters to remove the unlikely true transcripts.
  • GTF file of merged assembly. The is a version of the refined assembly merged with NCBI and ENSEMBL annotations to achieve breadth not covered by our tissues
  • Annotation table of the new assembly. For each transcript in our assembly, the table identifies the corresponding transcripts in the assemblies of Hestand et al (2014), ISME (2015), NCBI, ENSEMBL, and refGene track. The table uses class codes of Cuffcompare to describe the type of similarity between our transcripts and those in each assembly.

Updates:

  • This pipeline was developed to run on the HPC of Michigan State University. Currently we are working on the pipeline to run on Amazon instance as well. Building the Tophat transcriptome-index "run_buildTransIndex.sh" was the last step to be updated.