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PRAM: Pooling RNA-seq and Assembling Models
===========================================
Table of Contents
-----------------
* [Introduction](#Introduction)
* [Installation](#Installation)
* [Reference](#Reference)
* [Contact](#Contact)
* [License](#License)
* * *
## <a name='Introduction'></a> Introduction
Pooling RNA-seq and Assembling Models (__PRAM__) is an __Bioconductor__ __R__
package that
utilizes multiple RNA-seq datasets to predict transcript models. The workflow
of PRAM contains four steps, which is shown in
the figure below with function names and associated key parameters. PRAM has a
[vignette](https://bioconductor.org/packages/devel/bioc/vignettes/pram/inst/doc/pram.pdf) that describes each function in details.
<p align='center'>
<img src="vignettes/workflow_noScreen.jpg" width="400" height="407">
</p>
## <a name='Installation'></a> Installation
### From GitHub
Start __R__ and enter:
```r
devtools::install_github('pliu55/pram')
```
### From Bioconductor
Start __R__ and enter:
```r
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("pram")
```
<!--
- Cufflinks v2.2.1 macOS binary have some issues
- it will report segmentation fault for the same bam file, which Linux
Cufflinks runs ok
- Have to use Cufflinks v2.1.1 for macOS instead
-->
<!--
## <a name='Quick-start'></a>Quick start
PRAM provides a function `runPRAM()` to let you run through the whole workflow.
### <a name='predict-only'></a> Predict transcript models only
For a given gene annotation and RNA-seq alignments, you can predict transcript
models in intergenic genomic regions:
```R
runPRAM(in_gtf, in_bamv, out_gtf)
```
- `in_gtf`: an input GTF file defining genomic coordinates of existing genes.
Required to have an attribute of __gene_id__ in the ninth column.
- `in_bamv`: a vector of input BAM file(s) containing RNA-seq alignments.
Currently,
PRAM only supports strand-specific paired-end RNA-seq with the
first mate on the right-most of transcript coordinate, i.e.,
'fr-firststrand' by Cufflinks definition.
- `out_gtf`: an output GTF file of predicted transcript models
### <a name='predict-screen'></a> Predict transcript models and screen them by ChIP-seq
If you are interested to predict intergenic transcripts for a particular cell
or tissue type, you can use epigenetic ChIP-seq
data together with known transcripts and their expression levels to further
screen intergenic transcript models:
```
runPRAM(in_gtf, in_bamv, out_gtf, in_bedv, training_tpms, training_gtf)
```
- `in_gtf`, `in_bamv`, and `out_gtf` are the same as described above
- `in_bedv`: A vector of BED file(s) containing ChIP-seq alignments.
- `training_tpms`: A vector of RSEM quantification results for known
transcripts
- `training_gtf`: A GTF file defining genomic coordinates of known
transcripts
### <a name='Examples'></a> Examples
PRAM has included input examples files in its `extdata/demo/`
folder. The table below provides a quick summary of all the example files.
| input argument | file name(s) |
|:--------------:|:------------:|
| `in_gtf` | [in.gtf](inst/extdata/demo/in.gtf) |
| `in_bamv` | [SZP.bam](inst/extdata/demo/SZP.bam), [TLC.bam](inst/extdata/demo/TLC.bam) |
| `in_bedv` | H3K79me2.bed.gz, POLR2.bed.gz |
| `training_tpms`| AED1.isoforms.results, AED2.isoforms.results |
| `training_gtf` | training.gtf |
You can access example files by `system.file()` in __R__, e.g. for the
argument `in_gtf`, you can access its example file by
```R
system.file('extdata/demo/in.gtf', package='pram')
```
Below shows usage of `runPRAM()` with example input files:
##
## Predict transcript models only
##
```R
in_gtf = system.file('extdata/demo/in.gtf', package='pram')
in_bamv = c( system.file('extdata/demo/SZP.bam', package='pram'),
system.file('extdata/demo/TLC.bam', package='pram') )
pred_out_gtf = tempfile(fileext='.gtf')
runPRAM(in_gtf, in_bamv, pred_out_gtf)
```
##
## Predict transcript models and screen them by ChIP-seq data
##
in_bedv = c( system.file('extdata/demo/H3K79me2.bed.gz', package='pram'),
system.file('extdata/demo/POLR2.bed.gz', package='pram') )
training_tpms = c( system.file('extdata/demo/AED1.isoforms.results', package='pram'),
system.file('extdata/demo/AED2.isoforms.results', package='pram') )
training_gtf = system.file('extdata/demo/training.gtf', package='pram')
screen_out_gtf = tempfile(fileext='.gtf')
runPRAM(in_gtf, in_bamv, screen_out_gtf, in_bedv, training_tpms, training_gtf)
-->
## <a name="Reference"></a> Reference
PRAM identifies novel hematopoietic transcripts. Peng Liu, Alexandra A. Soukup, Emery H. Bresnick, Colin N. Dewey, and Sündüz Keleş. Manuscript in preparation.
For key results reported in the PRAM manuscript and scripts for
reproducibility, please check out
[this GitHub repository](https://github.com/pliu55/pram_paper).
## <a name="Contact"></a> Contact
Got a question? Please report it at the [issues tab](https://github.com/pliu55/pram/issues) in this repository.
## <a name="license"></a> License
PRAM is licensed under the [GNU General Public License v3](LICENSE).
