pantas

This repository contains the codebase of pantas, a pangenomic approach for performing differential AS events quantification across RNA-Seq conditions. pantas is based on the notion of annotated spliced pangenomes, which are spliced pangenomes augmented with additional information needed for AS events inference and quantification.

Alongside pantas, we provide a set of utilities to build and index a (annotated) spliced pangenome. We devised two alternative construction pipelines, one for full genome analysis and one for the analysis of a panel of genes of interest (reduced indexing).

Installation

git clone https://github.com/AlgoLab/pantas.git

# Install dependencies (all available from bioconda)
mamba create -c bioconda -c conda-forge -n pantas \
             python=3.10 biopython gffutils intervaltree bcftools samtools gffread vg=1.50.1 snakemake-minimal
mamba activate pantas

pantas pipeline

# Augment the annotated spliced pangenome with alignment information (run this for each replicate)
./pantas augment [condition1-rep1.gaf] [spliced-pangenome.annotated.gfa] > [condition1-rep1.gfa]

# Call events from each **augmented** graph
./pantas call [sample.gfa] [annotation.gtf] > [sample-events.csv]

# Quantify events across conditions (provide the two conditions with comma-separated path to the events csv)
./pantas quant condition1-rep1.csv,condition1-rep2.csv,condition1-rep3.csv \
             condition2-rep1.csv,condition2-rep2.csv,condition2-rep3.csv > [quantification.csv]

Event calling

The call mode of pantas provides several arguments that can be used to tweak the event calling:

  -w <INT>       Minimum read count (default: 3)
  -W <INT>       Minimum read count for annotated events (default: -1)
  -n             Call novel events (default: False)
  -a             Do not call known annotated events (default: False)

To call events from a reduced spliced pangenome, it is necessary to use the -p argument to provide the list of reference paths in the reduced graph (this file is created during the graph construction/indexing):

python3 ./scripts/call.py --rp [spliced-pangenome.refpath] [sample.gfa] [annotation.gtf] > [sample-events.csv]

Input preparation

The input of pantas are: an annotated spliced pangenome and the replicates aligned to this graph.

To build and index an annotated spliced pangenome, we provide a snakemake pipeline (index.smk):

snakemake -s index.smk -c4 --config fa=/path/to/reference.fa gtf=/path/to/annotation.gtf vcf=/path/to/variants.vcf.gz wd=/path/to/out/dir

The annotated spliced pangenome and the index are stored in the wd directory:

# Annotated spliced pangenome in GFA format:
spliced-pangenome.annotated.gfa
# Compressed graph:
spliced-pangenome.xg
# Index:
spliced-pangenome.dist         
spliced-pangenome.gcsa
spliced-pangenome.gcsa.lcp

To build/index a reduced annotated spliced pangenomes, i.e., a graph representing a panel of genes of interest:

snakemake -s index-reduced.smk -c4 --config fa=/path/to/reference.fa gtf=/path/to/panel.gtf vcf=/path/to/variants.vcf.gz wd=/path/to/out/dir

The reduced annotated spliced pangenome and the index are stored in the wd directory:

# Annotated spliced pangenome in GFA format:
spliced-pangenes.annotated.gfa
# Compressed graph:
spliced-pangenes.xg
# Index:
spliced-pangenes.dist         
spliced-pangenes.gcsa
spliced-pangenes.gcsa.lcp
# Reduced reference paths:
spliced-pangenes.refpath

Note: using reduced annotated spliced pangenomes (when possible) is recommended since it hugely improves running times and RAM usage.

To map each replicate to the annotated spliced pangenome, we suggest to use vg mpmap:

vg mpmap -x [spliced-pangenome.xg] -g [spliced-pangenome.gcsa] -d [spliced-pangenome.dist] -f [sample_1.fq] -f [sample_2.fq] -F GAF > [sample.gaf]

Example

The example subdirectory contains example data that can be used to test pantas:

# Prepare the graph
# This should take ~1 minute
snakemake -s index.smk -c4 --config fa=example/4.fa gtf=example/4.gtf vcf=example/4.vcf.gz wd=example/pantas-index

# Align the RNA-Seq sample to the graph
# This should take ~10 seconds
vg mpmap -x example/pantas-index/spliced-pangenome.xg \
         -g example/pantas-index/spliced-pangenome.gcsa \
	 -d example/pantas-index/spliced-pangenome.dist \
	 -f example/reads_1.fq -f example/reads_2.fq -F GAF > example/reads.gaf

# Augment the annotated spliced pangenome with alignment information
# This should be immediate
./pantas augment example/reads.gaf example/pantas-index/spliced-pangenome.annotated.gfa > example/reads.gfa

# Call all annotated events with minimum support 0 (since example RNA-Seq sample is very small)
# Note that using -W 0 is equivalent to extract all events from the graph
# This should take less than 2 seconds
./pantas call -W 0 example/reads.gfa example/4.gtf > example/reads.events.csv

# Quantify the events across the two conditions (an an example here we are using the same file twice)
# This should be immediate
./pantas quant example/reads.events.csv example/reads.events.csv > example/quant.csv

Custom output format

Annotated and augmented spliced pangenome

The annotated spliced pangenome augmented with alignment information (output of augment mode of pantas) is stored in a GFA file where optional fields are used to store the annotation. We refer to the documentation.

Events

The events (output of call mode) are stored in a CSV file:

• event type (ES, A3, A5, IR)
• annotated/novel
• chromosome (e.g., 4)
• gene name (e.g., FBgn0004859)
• strand (e.g., +)
• junction1, based on annotation (e.g., FBtr0308074.4.5 or ? if novel)
• junction1, in graph space (e.g., 2057>2065, meaning the junction link segments 2057 and 2065)
• junction1, on linear reference (e.g., 4:50614-50744)
• support for junction1 (e.g., 3)
• junction2, junction2 based on annotation, junction2 in graph space, junction2 on linear reference, support for junction2
• junction3, junction3 based on annotation, junction3 in graph space, junction3 on linear reference, support for junction3

We note that in the case of an exon skipping (or a cassette exon), all three junctions will be reported. In the case of an alternative splice site events, only two junctions are reported (1 and 2). A point (.) indicates that the junction is not used in the event.

Quantification

The differential quantification across conditions (output of quant mode of pantas) is stored in a CSV file:

• event type
• annotated/novel
• chromosome
• gene name
• strand
• junction1, on linear reference
• junction2, on linear reference
• junction3, on linear reference
• support for canonical isoform involved in the event (one value per condition, separated by /)
• support for minor isoform involved in the event (one value per condition, separated by /)
• PSI value for condition 1
• PSI value for condition 2
• ΔPSI

Experiments

Experimental evaluation scripts can be found in the ./exps subdirectory of this repository. We provide three snakemake pipelines which also contain more information on how to use pantas.

• ./exps/dm-sim/ is the evaluation on simulated data from Drosophila Melanogaster (here we used an annotated spliced pangenome)
• ./exps/dm-sim/ is the evaluation on real data from Drosophila Melanogaster using (here we used an annotated spliced pangenome)
• ./exps/homo-real/ is the evaluation on real data from human (here we used a reduced annotated spliced pangenome)

Authors

pantas is developed by Simone Ciccolella, Davide Cozzi, and Luca Denti.

For inquiries on this software please open an issue.