From GitHub

Start R and enter:

devtools::install_github('pliu55/pram')

From Bioconductor

Start R and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("pram")

For different versions of R, please refer to the appropriate Bioconductor release.

Description

PRAM provides a function named runPRAM() to let you conveniently run through the whole workflow.

For a given gene annotation and RNA-seq alignments, you can predict transcript models in intergenic genomic regions:

##
## assuming the stringtie binary is in folder /usr/local/stringtie-1.3.3/
##
pram::runPRAM(  in_gtf, in_bamv, out_gtf, method='plst',
                stringtie='/usr/loca/stringtie-1.3.3/stringtie')

• 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.
• method: prediction method. For the command above, we were using pooling RNA-seq datasets and building models by stringtie. For a list of available PRAM methods, please check Table @ref(tab:methods) below.
• stringtie: location of the stringtie binary. PRAM’s model-building step depends on external software. For more information on this topic, please see Section @ref(id:required-external-software) below.

Examples

PRAM has included input examples files in its extdata/demo/ folder. The table below provides a quick summary of all the example files.

You can access example files by system.file() in R, e.g. for the argument in_gtf, you can access its example file by

system.file('extdata/demo/in.gtf', package='pram')
#> [1] "/tmp/RtmpcDHRPs/Rinst133261bc29f7/pram/extdata/demo/in.gtf"

Below shows the usage of runPRAM() with example input files:

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')

##
## assuming the stringtie binary is in folder /usr/local/stringtie-1.3.3/
##
pram::runPRAM(  in_gtf, in_bamv, pred_out_gtf, method='plst',
                stringtie='/usr/loca/stringtie-1.3.3/stringtie')

Description

To predict intergenic transcripts, we must first define intergenic regions by defIgRanges(). This function requires a GTF file containing known gene annotation supplied for its in_gtf argument. This GTF file should contain an attribue of gene_id in its ninth column. We provided an example input GTF file in PRAM package: extdata/gtf/defIGRanges_in.gtf.

In addition to gene annotation, defIgRanges() also requires user to provide chromosome sizes so that it would know the maximum genomic ranges. You can provide one of the following arguments:

• chromgrs: a GRanges object, or
• genome: a genome name, currently supported ones are: hg19, hg38, mm9, and mm10, or
• fchromsize: a UCSC genome browser-style size file, e.g. hg19

By default, defIgRanges() will define intergenic ranges as regions 10 kb away from any known genes. You can change it by the radius argument.

Example

pram::defIgRanges(system.file('extdata/gtf/defIgRanges_in.gtf', package='pram'),
                genome = 'hg38')
#> GRanges object with 456 ranges and 0 metadata columns:
#>                 seqnames          ranges strand
#>                    <Rle>       <IRanges>  <Rle>
#>     [1]             chr1 30001-248956422      *
#>     [2]             chr2     1-242193529      *
#>     [3]             chr3     1-198295559      *
#>     [4]             chr4     1-190214555      *
#>     [5]             chr5     1-181538259      *
#>     ...              ...             ...    ...
#>   [452] chrUn_KI270753v1         1-62944      *
#>   [453] chrUn_KI270754v1         1-40191      *
#>   [454] chrUn_KI270755v1         1-36723      *
#>   [455] chrUn_KI270756v1         1-79590      *
#>   [456] chrUn_KI270757v1         1-71251      *
#>   -------
#>   seqinfo: 455 sequences from an unspecified genome; no seqlengths

Description

Once intergenic regions were defined, prepIgBam() will extract corresponding RNA-seq alignments from input BAM files. In this way, transcript models predicted at later stage will solely from intergenic regions. Also, with fewer RNA-seq alignments, model prediction will run faster.

Three input arguments are required by prepIgBam():

• finbam: an input RNA-seq BAM file sorted by genomic coordinate. Currently, we only support strand-specific paired-end RNA-seq data with the first mate on the right-most of transcript coordinate, i.e. ‘fr-firststrand’ by Cufflinks’s definition.
• iggrs: a GRanges object to define intergenic regions.
• foutbam: an output BAM file.

Example

finbam =system.file('extdata/bam/CMPRep2.sortedByCoord.raw.bam', 
                    package='pram')

iggrs = GenomicRanges::GRanges('chr10:77236000-77247000:+')

foutbam = tempfile(fileext='.bam')

pram::prepIgBam(finbam, iggrs, foutbam)
#> Extracted RNA-seq alignments are saved in /tmp/RtmpbHe8z7/file13e84cf1a656.bam

Description

buildModel() predict transcript models from RNA-seq BAM file(s). This function requires two arguments:

• in_bamv: a vector of input BAM file(s)
• out_gtf: an output GTF file containing predicted transcript models

Transcript prediction methods

buildModel() has implemented seven transcript prediction methods. You can specify it by the method argument with one of the keywords: plcf, plst, cfmg, cftc, stmg, cf, and st. The first five denote meta-assembly methods that utilize multiple RNA-seq datasets to predict a single set of transcript models. The last two represent methods that predict transcript models from a single RNA-seq dataset.

The table below compares prediction steps for these seven methods. By default, buildModel() uses plcf to predict transcript models.

Required external software

Depending on your specified prediction method, buildModel() requires external software: Cufflinks, StringTie and/or TACO, to build and/or assemble transcript models. You can either specify the software location using the cufflinks, stringtie, and taco arguments in buildModel(), or simply leave these three arugments undefined and let PRAM download them for you automatically. The table below summarized software versions buildModel() would download when required software was not specified. Please note that, for macOS, pre-compiled Cufflinks binary versions 2.2.1 and 2.2.0 appear to have an issue on processing BAM files, therefore we recommend to use version 2.1.1 instead.

Example

fbams = c(  system.file('extdata/bam/CMPRep1.sortedByCoord.clean.bam', 
                        package='pram'),
            system.file('extdata/bam/CMPRep2.sortedByCoord.clean.bam', 
                        package='pram') )

foutgtf = tempfile(fileext='.gtf')

##
## assuming the stringtie binary is in folder /usr/local/stringtie-1.3.3/
##
pram::buildModel(fbams, foutgtf, method='plst',
                stringtie='/usr/loca/stringtie-1.3.3/stringtie')

Description

Once transcript models were built, you may want to select a subset of them by their genomic features. selModel() was developed for this purpose. It allows you to select transcript models by their total number of exons and total length of exons and introns.

selModel() requires two arguments:

• fin_gtf:input GTF file containing to-be-selected transcript models. This file is required to have transcript_id attribute in the ninth column.
• fout_gtf: output GTF file containing selected transcript models.

By default: selModel() will select transcript models with ≥ 2 exons and ≥ 200 bp total length of exons and introns. You can change the default using the min_n_exon and min_tr_len arguments.

Example

fin_gtf = system.file('extdata/gtf/selModel_in.gtf', package='pram')

fout_gtf = tempfile(fileext='.gtf')

pram::selModel(fin_gtf, fout_gtf)
#> Selected transcript models are saved in /tmp/RtmpbHe8z7/file13e86c859013.gtf

Motivation

After PRAM has predicted a number of transcript models, you may wonder how accurate these models are. To answer this question, you can compare PRAM models with real transcripts (i.e., positive controls) that you know should be predicted. PRAM’s evalModel() function will help you to make such comparison. It will calculate precision and recall rates on three features of a transcript: exon nucleotides, individual splice junctions, and transcript structure (i.e., whether all splice junctions within a transcript were constructed in a model).

Input

evalModel() requires two arguments:

• model_exons: genomic coordinates of transcript model exons.
• target_exons: genomic coordinates of real transcript exons.

The two arguments can be in multiple formats:

• both are GRanges objects
• both are character objects denoting names of GTF files
• both are data.table objects containing the following five columns for each exon:
  • chrom: chromosome name
  • start: starting coordinate
  • end: ending coordinate
  • strand: strand information, either ‘+’ or ‘-’
  • trid: transcript ID
• model_exons is the name of a GTF file and target_exons is a data.table object.

Output

The output of evalModel() is a data.table object, where columns are evaluation results and each row is three transcript features.

Example

fmdl = system.file('extdata/benchmark/plcf.tsv', package='pram')
ftgt = system.file('extdata/benchmark/tgt.tsv',  package='pram')

mdldt = data.table::fread(fmdl, header=TRUE, sep="\t")
tgtdt = data.table::fread(ftgt, header=TRUE, sep="\t")

pram::evalModel(mdldt, tgtdt)
#>        feat     ntp    nfn   nfp precision    recall
#>      <char>   <num>  <num> <num>     <num>     <num>
#> 1: exon_nuc 1723581 109337  8424 0.9951363 0.9403481
#> 2: indi_jnc    2889    162   192 0.9376826 0.9469027
#> 3:   tr_jnc    1138    118   252 0.8187050 0.9060510