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===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. *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/submit_tophat_jobs.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 -r /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. submit_tophat_jobs.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 tophat_xxx folder (one folder per sample). # After tophat mapping and cufflink quantification, the downstream analyses # are typically done on genome-miner, because it is more interactive and # less time consuming. For this, use sftp or scp to transfer all # tophat_xxx folders to genome-miner # To quickly get an overview of mapping stats and number of transcripts called, # run the following script within the GROUP_DATA_DIR that contains all tophat_xxx folders. /home/kunzhang/RNAseq/SCAP/get_mapping_stats.pl GROUP_DATA_DIR > group_name_mapping_stats.txt ====Pipeline B (~10x faster, but requires more RAM, doesn't work on genome-miner)==== *This pipeline uses STAR and Cufflink2. *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 -r /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). # To quickly get an overview of mapping stats and number of transcripts called, # transfer all xxx_mapped folders to genome-miner in a folder (GROUP_DATA_DIR) # run the following script /home/kunzhang/RNAseq/SCAP/scripts/get_STAR_mapping_stats.pl GROUP_DATA_DIR > group_name_mapping_stats.txt =====Pipeline B Version 2===== *Simply replace /home/k4zhang/bin/batch_STAR_cufflink2.pl with /home/k4zhang/bin/batch_STAR_cufflink2_HTseq.pl *What's new? **It handles both PE and SE reads. **It uses HTSeq to report raw read counts in both exonic and intronic regions, which is more appropriate for human nuclei data. *Three scripts for generating summary reports on genome-miner: **[[Media: get_STAR_TPM_matrix.txt|get_STAR_TPM_matrix.pl]]: to generate TPM matrix from the raw read counts for a set of STAR mapped data. **[[Media: get_STAR_mapping_stats.txt|get_STAR_mapping_stats.pl]]: to generate a summary report on mapping statistics and ERCC correlation. **[[Media: get_STAR_geneTypes.txt|get_STAR_geneTypes.pl]]: to generate a summary report on reads mapped to different types of genes.
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