The script takes as input a reference genome and annotations, a bedtools coverage map for a collection of one or more mapped samples and a set of spliced leader (SL) acceptor sites and polyadenylation sites, and attempts to determine the most likely primary UTR boundaries for each gene for which evidence exists, while at the same time detecting novel ORFs with transcriptional and SL/Poly(A) support in the genome.

It is designed primarily for use with trypanasome RNA-Seq data, for which most genes contain only a single exon, and genes exists in the genome in long tracts on the same strand.

The above screenshot shows the basic goal of this script. Given a reference genome with known CDS's, a set of RNA-Seq reads, and detected SL and Poly(A) sites, can we choose the most likely primary SL/Poly(A) sites (and thus 5' and 3'UTR boundaries), for as many genes as possible?

In the above IGV screenshot, detected SL sites are shown in blue (with the height indicating the number of RNA-Seq reads supporting that site), and the polyadenylation sites are shown in red. While there appears to be an extended block of mapped RNA-Seq reads between the two existing annotated CDS's, and also potential SL and Poly(A) sites to go along with the inter-CDS coverage region, there is currently no annotation for that region.

This script attempts to simultaneously detect novel ORFs (which may correspond to unnanotated genes, pseudogenes, or other types of features), and assign primary UTR boundaries to the both the novel ORFs and existing gene annotations. This way we can arrive at a better understanding of the parasite gene structure, and obtain more accurate distributions for 5' and 3'UTR lengths, as well as intergenic lengths.

• BioPython
• Numpy
• Matplotlib
• bcbio-gff

This script requires the following files for input:

1. Reference FASTA
2. Reference GFF
3. Trans-splicing acceptor site GFF
4. Polyadenylation site GFF
5. RNA-Seq coverage map

In addition to the parasite genome sequence and annotations, which usually comes from TriTrypDB, this script requires a set of detected spliced leader and polyadenylation sites.

In order to generate these, a separate pipeline has been developed:

• https://github.com/elsayed-lab/utr_analysis

The UTR analysis pipeline takes a collection of RNA-Seq reads, the same genome sequence and annotations used in this analysis, and a known SL DNA sequence and attempts to detect trans-splicing and polyadenylation events in the data. Each site for which a putative event was detected is saved as a separate entry an output GFF file, with the score corresponding to the number of RNA-Seq reads supporting that specific site.

Since it is often useful to detect trans-splicing acceptor sites and polyadenylation sites separately for different developmental stages, we provide a script (combine_site_data.py which can be used to combine data across multiple stages. By using data from all stages measured, we can be arrive at more accurate estimates of the general UTR lengths.

For more information, see the README.me for the repository above.

A genome coverage map is a file which contains counts of reads at each position in a genome, e.g.:

...
TcChr1-S	98	0
TcChr1-S	99	0
TcChr1-S	100	0
TcChr1-S	101	1
TcChr1-S	102	3
TcChr1-S	103	3
TcChr1-S	104	3
TcChr1-S	105	6
...

You can use the bedtools genomecov function to create such a map.

First, of you have multiple samples you wish you wish you use, first combine and sort them using samtools view:

samtools merge - */accepted_hits.bam | samtools sort -o combined.bam -

Next, call genomecov with the -d (single-nt resolution) option:

bedtools genomecov -d -ibam combined.bam | gzip > combined.coverage.gz

You should now have a gzip-compressed coverage map spanning the entire parasite genome.

Example call to gene_structure_determination.py:

./gene_structure_determination.py \
    -c $SCRATCH/tcruzi-hsapiens/tophat/tcruzi/tcruzi_all_samples_sorted.coverage.gz \
    -g $REF/tcruzi_clbrener_esmeraldo-like/annotation/TriTrypDB-27_TcruziCLBrenerEsmeraldo-like.gff \
    -f $REF/tcruzi_clbrener_esmeraldo-like/genome/TriTrypDB-27_TcruziCLBrenerEsmeraldo-like_Genome.fasta \
    -s $RESEARCH/2015/110-utr-lengths/input/tcruzi_infecting_hsapiens_combined_sl_sorted.gff \
    -p $RESEARCH/2015/110-utr-lengths/input/tcruzi_infecting_hsapiens_combined_polya_sorted.gff \
    tcruzi_all

Full list of command-line parameters:

usage: gene_structure_determination.py [-h] -c COVERAGE -f FASTA -g GFF [-s SL_GFF]
                              [-p POLYA_GFF] -l MIN_PROTEIN_LENGTH
                              [-t PLOT_TYPE]
                              OUTDIR

Detect novel ORFs using RNA-Seq data.

positional arguments:
  OUTFILE               Location to save output GFF to

optional arguments:
  -h, --help            show this help message and exit
  -c COVERAGE, --coverage COVERAGE
                        Single nucleotide resolution genome coverage map
  -f FASTA, --fasta FASTA
                        Input genome FASTA file
  -g GFF, --gff GFF     Input genome GFF file
  -s SL_GFF, --sl-gff SL_GFF
                        Spliced leader site GFF
  -p POLYA_GFF, --polya-gff POLYA_GFF
                        Polyadenylation site GFF
  -l MIN_PROTEIN_LENGTH, --min-protein-length MIN_PROTEIN_LENGTH
                        Minimum size in amino acids allowed for novel ORFs.
                        (default=30)
  -t PLOT_TYPE, --plot-type PLOT_TYPE
                        Type of colormap to use when generating coverage
                        plots. [discrete|continuous]

TODO...

TODO...

Note: utr5_stats.csv and utr3_stats.csv include only data on primary UTR boundaries.