to run scripts on scg3, see Src/commands_to_run.md
otherwise, just run scripts in order:
-
Src/s01.merge_fastq.sh
- merges each fastq, split between 4 lanes, into a single fastq
-
Src/s10.fastqc_pre.sh {num_parallel_jobs}
- quality control before lighter read correction/trimming
-
Src/s11.lighter.sh {num_parallel_jobs}
- or, to submit each file to scg3 individually, each using multiple cores
- ./s11.lighter_individual.sh {num_cores_per_job}
- trims/corrects reads
-
Src/s12.fastqc_post.sh {num_parallel_jobs}
- view quality of fastq files after trimming/correction
-
Src/s13.star_create_genome1.sh {num_cores}
- generate reference indexed genome to map to
-
Src/s14.star_pass1.sh {num_cores_per_job}
- note this script will only submit jobs to scg3
- map
-
Src/s15.prep_SJ.sh
- join de novo splice junctions detected during first mapping
-
Src/s16.star_create_genome2.sh {num_cores}
- create new genome to map to, including detected splice junctions from first mapping
-
Src/s17.star_pass2.sh {num_cores_per_job}
- note this script will only submit jobs to scg3
- remap to new genome, with de novo predictions from 1st mapping included
-
Src/s18.pre_filter_flagstat.sh {num_parallel_jobs}
- see how the mapping went
-
Src/s19.bam_filter.sh {num_parallel_jobs}
- filter, sort, and index
-
Src/s20.post_filter_flagstat.sh {num_parallel_jobs}
- check bams after filtering
-
Src/s22.freebayes_individual.sh {num_parallel_jobs}
-
run Src/DEseq.R in R studio session
-
run Src/vcf_to_editing_levels.ipynb in jupyter session with python3 kernel
- recommended: install Anaconda with python 3 from Continuum Analytics
- required packages to install with conda:
- pandas
- scipy
- install plotly with pip (requires signing up for plotly account)
-
All other scripts are just there because I made them, realized I didn't need them, but they work and I'm keeping them here in case I need them later
-
Software needed in path
- GNU Parallel
- bedtools
- STAR
- samtools
- fastqc
- lighter
- R
- libraries: DEseq2