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| File | Date | Author | Commit |
|---|---|---|---|
| docs | 2019-03-19 |
j-berg
|
[ae39a9] Initial beta release |
| images | 2019-03-25 |
j-berg
|
[ddde10] Adding Docker files |
| tests | 2019-03-19 |
j-berg
|
[ae39a9] Initial beta release |
| xpresspipe | 2019-03-22 |
j-berg
|
[f59c71] Minor updates |
| .gitattributes | 2019-03-07 |
j-berg
|
[717013] Finished debugged bed_bw issue, revised git tra... |
| .gitignore | 2019-03-07 |
j-berg
|
[47ba63] Continuing tests and moding of align and ref bu... |
| .travis.yml | 2019-03-19 |
j-berg
|
[8951b5] Tuning setup.py for travis |
| LICENSE | 2019-02-15 |
Jordan Berg
|
[904409] Initial commit |
| README.md | 2019-03-20 |
j-berg
|
[4f2f11] Updated get_peaks() to accept variable number o... |
| build.sh | 2019-02-24 |
j-berg
|
[eb1590] Fixed to allow run |
| conda_upload.sh | 2019-03-05 |
j-berg
|
[db4e49] Prep documentation |
| meta.yaml | 2019-03-19 |
j-berg
|
[ae39a9] Initial beta release |
| requirements.yml | 2019-03-20 |
j-berg
|
[3ef995] Updates to makeFlat -- now grabs from UCSC sinc... |
| setup.py | 2019-03-25 |
j-berg
|
[ddde10] Adding Docker files |
Read Me
A toolkit for navigating and analyzing gene expression datasets
Find documentation here
Development Notes:
- XPRESSpipe is still in beta production
- All metagene modules are currently broken due to Picard CollectRnaSeqMetrics memory handling issues with refFlat files
Citation:
Berg, JA (2019). XPRESSyourself suite: Gene expression processing and analysis made easy. https://github.com/XPRESSyourself.
Installation:
Installation options not currently available
pip install xpresspipe
conda install -c bioconda xpresspipe
Other Requirements:
If using this package to perform batch effect normalization or differential expression analysis, you must install R
QuickStart:
$ xpresspipe riboprof -i /path/to/raw/data/ -o /path/to/output/ -r /path/to/reference/ ...
Important Notes:
In order for many of the XPRESSpipe functions to perform properly and for the output to be reliable after alignment (except for generation of a raw counts table), recommended file naming conventions must be followed.
- Download your raw sequence data and place in a folder -- this folder should contain all the sequence data and nothing else.
- Make sure files follow a pattern naming scheme. For example, if you had 3 genetic backgrounds of ribosome profiling data, the naming scheme would go as follows:
ExperimentName_BackgroundA_FP.fastq(.qz)
ExperimentName_BackgroundA_RNA.fastq(.qz)
ExperimentName_BackgroundB_FP.fastq(.qz)
ExperimentName_BackgroundB_RNA.fastq(.qz)
ExperimentName_BackgroundC_FP.fastq(.qz)
ExperimentName_BackgroundC_RNA.fastq(.qz)
- If the sample names are replicates, their sample number needs to be indicated.
- If you want the final count table to be in a particular order and the samples ordered that way are not alphabetically, append a letter in front of the sample name to force this ordering.
ExperimentName_a_WT.fastq(.qz)
ExperimentName_a_WT.fastq(.qz)
ExperimentName_b_exType.fastq(.qz)
ExperimentName_b_exType.fastq(.qz)
- If you have replicates:
ExperimentName_a_WT_1.fastq(.qz)
ExperimentName_a_WT_1.fastq(.qz)
ExperimentName_a_WT_2.fastq(.qz)
ExperimentName_a_WT_2.fastq(.qz)
ExperimentName_b_exType_1.fastq(.qz)
ExperimentName_b_exType_1.fastq(.qz)
ExperimentName_b_exType_2.fastq(.qz)
ExperimentName_b_exType_2.fastq(.qz)
