RNAseq pipeline: Difference between revisions
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==TSCC pipeline for single-cell RNAseq analysis== | ==TSCC pipeline for single-cell RNAseq analysis== | ||
*The input data files should follow the SOP of UCSD SCAP center. Basically, all PE gzip compressed fastq files for one cell are stored in one folder (the name starts with "Sample_"). One folder per cell or sequencing library. | *The input data files should follow the SOP of UCSD SCAP center. Basically, all PE gzip compressed fastq files for one cell are stored in one folder (the name starts with "Sample_"). One folder per cell or sequencing library. | ||
*We are currently using hg19 for human and mm10 for mouse. LifeTech's ERCC transcripts were included. For transcript models, we are using GENCODE V14 for human, and UCSC Genes for mouse. | |||
===Smart-seq pipeline=== | ===Smart-seq pipeline=== | ||
*This pipeline is designed for mapping and transcript quantification of Smart-seq data. | *This pipeline is designed for mapping and transcript quantification of Smart-seq data. | ||
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====Pipeline B.==== | ====Pipeline B.==== | ||
*This pipeline uses STAR and Cufflink2 (much faster, but requires more RAM, doesn't work on genome-miner). | *This pipeline uses STAR and Cufflink2 (much faster, but requires more RAM, doesn't work on genome-miner). | ||
*We use one script to submit jobs for all samples in one folder, one job per sample. Here is how to start the job. | *We use one script to submit jobs for all samples in one folder, one job per sample. Here is how to start the job. | ||
# First, copy the job submission script to your own path, such as your HOME/bin | # First, copy the job submission script to your own path, such as your HOME/bin |
Revision as of 04:38, 31 October 2013
TSCC pipeline for single-cell RNAseq analysis
- The input data files should follow the SOP of UCSD SCAP center. Basically, all PE gzip compressed fastq files for one cell are stored in one folder (the name starts with "Sample_"). One folder per cell or sequencing library.
- We are currently using hg19 for human and mm10 for mouse. LifeTech's ERCC transcripts were included. For transcript models, we are using GENCODE V14 for human, and UCSC Genes for mouse.
Smart-seq pipeline
- This pipeline is designed for mapping and transcript quantification of Smart-seq data.
- It assumes that the data cover the full length of the transcripts, and have no strand specificity.
Pipeline A.
- This pipeline uses Tophat2 and Cufflink2.
Pipeline B.
- This pipeline uses STAR and Cufflink2 (much faster, but requires more RAM, doesn't work on genome-miner).
- We use one script to submit jobs for all samples in one folder, one job per sample. Here is how to start the job.
# First, copy the job submission script to your own path, such as your HOME/bin cp /home/k4zhang/bin/batch_STAR_cufflink2.pl MY_HOME/bin # Next edit this script, replace the email address "kun.zhang.ucsd@gmail.com" with your own address. # This only need to be done once. # As a rehearsal, you can copy two test data sets to your own folder. cp /home/k4zhang/my_oasis_tscc/test_data MY_FOLDER # Now you can change to this folder. cd MY_FOLDER # Then you can submit all the jobs to process the samples in this folder. # For human data, replace mm10 with hg19. batch_STAR_cufflink2.pl mm10 # To check the status, use qstat. # You should also receive emails when the jobs are started or completed. qstat | grep YOUR_USER_NAME # The results are in xxx_mapped folder (one folder per sample).