文件列表:

DESeq-analysis.R (4756, 2021-05-26)
DESeq-launch.py (1817, 2021-05-26)
RSEM (0, 2021-05-26)
RSEM\AlignerRefSeqPolicy.h (383, 2021-05-26)
RSEM\BamConverter.h (8734, 2021-05-26)
RSEM\BamWriter.h (3781, 2021-05-26)
RSEM\Buffer.h (2089, 2021-05-26)
RSEM\COPYING (35147, 2021-05-26)
RSEM\EBSeq (0, 2021-05-26)
RSEM\EBSeq\EBSeq_1.2.0.tar.gz (1028380, 2021-05-26)
RSEM\EBSeq\KernSmooth_2.23-15.tar.gz (24572, 2021-05-26)
RSEM\EBSeq\Makefile (386, 2021-05-26)
RSEM\EBSeq\bitops_1.0-6.tar.gz (8734, 2021-05-26)
RSEM\EBSeq\blockmodeling_0.1.8.tar.gz (67273, 2021-05-26)
RSEM\EBSeq\caTools_1.17.1.tar.gz (63358, 2021-05-26)
RSEM\EBSeq\calcClusteringInfo.cpp (3401, 2021-05-26)
RSEM\EBSeq\gdata_2.17.0.tar.gz (1041264, 2021-05-26)
RSEM\EBSeq\gplots_2.17.0.tar.gz (629789, 2021-05-26)
RSEM\EBSeq\gtools_3.5.0.tar.gz (64855, 2021-05-26)
RSEM\EBSeq\install (1179, 2021-05-26)
RSEM\EBSeq\rsem-for-ebseq-find-DE (2598, 2021-05-26)
RSEM\EBSeq\rsem-for-ebseq-generate-ngvector-from-clustering-info (484, 2021-05-26)
RSEM\EM.cpp (19129, 2021-05-26)
RSEM\GTFItem.h (6059, 2021-05-26)
RSEM\Gibbs.cpp (13618, 2021-05-26)
RSEM\GroupInfo.h (1086, 2021-05-26)
RSEM\HitContainer.h (2617, 2021-05-26)
RSEM\HitWrapper.h (618, 2021-05-26)
RSEM\LenDist.h (6840, 2021-05-26)
RSEM\Makefile (8926, 2021-05-26)
RSEM\Model.h (75, 2021-05-26)
RSEM\ModelParams.h (737, 2021-05-26)
RSEM\NoiseProfile.h (3165, 2021-05-26)
RSEM\NoiseQProfile.h (4304, 2021-05-26)
RSEM\Orientation.h (753, 2021-05-26)
RSEM\PairedEndHit.h (682, 2021-05-26)
RSEM\PairedEndModel.h (12520, 2021-05-26)
... ...

# rna-pipeline ## Project Status **This project is now deprecated. For a RNA-Seq pipeline that works on SLURM clusters like Stanford's Sherlock with minimal setup, see [here](https://github.com/tjbencomo/bulk-rnaseq)** I've recently discovered Cromwell, a workflow engine that easily scales tasks like RNASeq processing on HPC clusters. `variant-discovery-pipeline` uses Cromwell to abstract large amounts of pipeline code. `rna-pipeline` needs a major update to take advantage of Cromwell features. RNA-Seq analysis pipeline for the [Lee Lab](http://leelab.stanford.edu/) at Stanford University. The pipeline is designed to mimic dnanexus function on Stanford's Sherlock HPC cluster. The pipeline receives as input paired-end FASTQ files and performs alignment as well as several analysis functions. ## Pipeline Design The pipeline consists of 3 different steps: 1. [Alignment](https://github.com/tjbencomo/rna-pipeline/blob/master/README.md#star-aligner) 2. [RNA Expression Quantification](https://github.com/tjbencomo/rna-pipeline/blob/master/README.md#rsem-expression-quantification) 3. [Differential Expression Analysis](https://github.com/tjbencomo/rna-pipeline/blob/master/README.md#deseq2-differential-expression-analysis) Alignment is first completed using the [STAR Aligner](https://github.com/alexdobin/STAR). Its settings mimic those found in ENCODE's [long-rna-seq-pipeline](https://github.com/ENCODE-DCC/long-rna-seq-pipeline/blob/f9ff54ddf1d955382a1f0aa50b55c8627702f6e1/dnanexus/align-star-pe/resources/usr/bin/lrna_align_star_pe.sh). RNA Expression Quantification and Gene Expression Analysis are performed in parallel. RNA Expression Quantification is computed using [RSEM](https://github.com/deweylab/RSEM) software package. Gene Expression Analysis is performed first by counting RNA reads with [HTSeq-count](http://htseq.readthedocs.io/en/master/count.html) and then analyzing counts with [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html). ## Pipeline Usage `pipeline.py` is the master program to fully automate the pipeline. The user enters a directory containing paired end FASTQ files they wish to process, and the pipeline runs creates an output directory containing the aligned bam files, rsem quantification files, and htseq-count read counts file. Once the rsem and htseq jobs have all finished, use the `DESeq-launch.py` command to analyze the differential expression of all the samples. ### Input `-I`|`--inputDirectory` Input directory containing paired end gzipped FASTQ files. FASTQ files must end in .gz extension `-gDir`|`--genomeDirectory` Location of the genome directory required by STAR. Must be generated by the user prior to program use. Consult STAR documentation to generate the directory. `-rDir`|`rsemReferenceDirectory` Location of the reference directory required by RSEM. Must be generated by the user prior to program use. Consult RSEM documentation to generate the directory. `-output`|`--outputDirectory` (Optional) Directory location for where to store all files generated by the pipeline. If not specified, the pipeline will create a new directory, named `*input_directory_path*-pipeline-output` Example command: `python pipeline.py -I /scratch/PI/carilee/tomas/CACYBP_KD_fastqs/ -rDir /scratch/users/tbencomo/RNA_seq/refs/out/rsem -gDir /scratch/users/tbencomo/RNA_seq/refs/out` ## Individual Pipeline Components ### STAR Aligner `star-aligner.py` performs sequence alignment with the STAR aligner `star-aligner.py` acts as a wrapper for `star.sh` the bash script that executes the star aligner. `star-aligner.py` submits a slurm sbatch job to run the STAR aligner. #### Inputs `-wd`|`--workingDirectory` Working Directory: Where all output files will be located `-f1`|`--fastq1` FASTQ file 1: 1 of the two paired end fastq files to align `-f2`|`--fastq2` FASTQ file 2: 1 of the two paired end fastq files to align `-gDir`|`--genomeDirectory` Genome Directory: Location of reference files for star aligner `-prefix`|`--outFilePrefix` Output File Prefix: file prefix that will be appended to start of output files. Optional. By default, the prefix is set to the filename of FASTQ1 #### Outputs (Files are prefixed with specified or default prefix above) * `_Aligned.sortedByCoord.out.bam` Aligned BAM file sorted by coordinate. Used for programs such as HTSeq-count * `_Aligned.toTranscriptome.out.bam` Aligned BAM of translated coordinates. Used for programs such as RSEM * `_Log.out` Main file with information about run * `_Log.progress.out` Job progress statistics Example command: `python star-aligner.py -wd /scratch/users/tbencomo/RNA_seq/pipeline-tests -gDir /scratch/users/tbencomo/RNA_seq/refs/out -f1 /scratch/users/tbencomo/RNA_seq/input_files/SG13_004_004_CGCTCATT-ATAGAGGC_R1.fastq -f2 /scratch/users/tbencomo/RNA_seq/input_files/SG13_004_004_CGCTCATT-ATAGAGGC_R2.fastq` ### RSEM Expression Quantification `rsem-calculate.py` performs rna expression quantification with the RSEM software package. `rsem-calculate.py` is a wrapper for rsem.sh, which actually calls the rsem-calculate-expression program. `rsem-calculate.py` submits a SLURM sbatch job of rsem.sh #### Inputs `-I`|`--input-bam-file` Input Bam: RNA-Seq aligned transcriptome bam file `-rDir`|`--rsem-ref-directory` RSEM Reference Directory: Prebuilt files RSEM needs to run `wd`|`--working-directory` Working Directory: Where all output files are located #### Outputs (Files are prefixed with prefix computated from input bam file - contains filename until 'Aligned') * `.genes.results` Gene level expression estimates * `.isoforms.results` Isoform level expression estimates * `.stat` Folder containing model statistics * `.transcript.bam` Read alignments in transcript coordinates Example command: `python rsem-calculate.py -wd /scratch/users/tbencomo/RNA_seq/pipeline-tests/ -I /scratch/users/tbencomo/RNA_seq/pipeline-tests/SG13_004_004_CGCTCATT-ATAGAGGC_Aligned.toTranscriptome.out.bam -rDir /scratch/users/tbencomo/RNA_seq/refs/out/rsem` ### HTSeq Read Counts `htseq-launch.py` performs a count of all the read sequences from the aligned sorted by coordinate bam input file via the HTSeq software package. `htseq-launch.py` is a wrapper for `htseq.sh,` which actually calls the htseq-count program. `htseq-launch.py` submits a SLURM sbatch job of htseq.sh #### Inputs `-I`|`--input-bam-file` Input Bam: RNA-Seq aligned sorted by coordinates bam file `wd`|`--working-directory` Working Directory: Where all output files are located #### Outputs (Files are prefixed with prefix computated from input bam file - contains filename until 'Aligned') * `_counts.txt` Read counts for each gene Example command: `python htseq-launch.py -I SG13_004_004_CGCTCATT-ATAGAGGC_Aligned.sortedByCoord.out.bam -wd /scratch/users/tbencomo/RNA_seq/pipeline-tests/` ### DESeq2 Differential Expression Analysis `DESeq-launch.py` performs differential expression analysis on several samples, reporting which genes are upregulated and downregualted. `DESeq-launch.py` is a wrapper for desq.sh. deseq.sh is a shell script to submit a SLURM sbatch job. The actual analysis is performed by an R script `DESeq-analysis.R` NOTE: As of now, the R script determines if a sample is tumor or normal based on its filename. If the file contains 'NS' or 'ctrl', it is classified as normal. Otherwise, it is classified as a tumor. In the future, the user will be able to enter strings for the program to classify each condition. #### Inputs `-counts`|`--counts-Directory` Directory containing HTSeq-count counts files `-results`|`--results-Directory` Directory to store results files #### Outputs `unfiltered_normal_tumor_results.csv` CSV file containing all genes. Not filtered for pvalues or log2FoldChange levels `upregulated_genes.csv` CSV file containing all genes with log2FoldChange scores > 0 and with adjusted p-values <= .05 `downregulated_genes.csv` CSV file containing all genes with log2FoldChange scores < 0 and with adjusted p-values <= .05 Example command: `python DESeq-launch.py -counts /scratch/PI/carilee/NatComm-Analysis/counts/ -results /scratch/PI/carilee/NatComm-Analysis/results/`

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