splicekit 0.4.3

splicekit: comprehensive toolkit for splicing analysis from short-read RNA-seq

A comprehensive platform for splicing analysis of RNA-seq short-read sequencing data. splicekit input are read alignments in BAM format (look at datasets for details on how to run examples).

What is splicekit?
Installation and quick start
Running splicekit
  Annotation and comparisons
  Preparing feature (genes, exon, junction, anchor) data tables
  Running edgeR gene context analysis on junction features
Motif analysis
Scanning for RNA-protein binding with scanRBP
juDGE plots
JBrowse2 visualizations
Documentation and specifications
Changelog

splicekit is a modular platform for splicing analysis of RNA-seq datasets. The platform also integrates an JBrowse2 instance together with pybio for genomic operations and scanRBP for RNA-protein binding studies. The whole analysis is self-contained (one single folder) and the platform is written in Python, in a modular way.

What is splicekit?

From an initial config file (splicekit.config), sample annotation (samples.tab) and aligned reads in BAM format, splicekit first defines comparisons (which test samples to compare to which controls). Next, feature count tables are generated (exons, anchors, junctions, genes) based on defined comparisons. Analysis incude edgeR alt-splice (differentially used features), motif analysis with DonJuAn (junction-anchor) and DREME, RNA-protein binding enrichment analysis with scanRBP and clustering analysis on expression of features. To facilitate result and data interpretation, splicekit also provides an instance of JBrowse2.

Installation and quick start

The easiest way to install splicekit is to simply run:

$ pip install splicekit

Note that on some systems, pip is installing the executable scripts under ~/.local/bin. However this folder is not in the PATH which will result in command not found if you try to run $ splicekit on the command line. To fix this, please execute export PATH="$PATH:~/.local/bin" (and add this to your .profile). Another suggestion is to install inside a virtual environment (using virtualenv).

If you would like to install splicekit directly from this repository, clone the repository into a folder, for example ~/software/splicekit. Add the ~/software/splicekit folder to $PYTHONPATH (export PYTHONPATH=$PYTHONPATH:~/software/splicekit).

Software dependencies

splicekit uses several third-party open-source software. If you don't have the software installed on your system, we prepared a singularity definition file, where you can also directly see all dependencies. Using the singularity image, you don't need to install the dependencies yourself, you just need to install singularity.

Example runs and datasets

Example runs can be found in the datasets folder.

Configuration file documentation

Description of splicekit.config file parameters (click to show)

Core parameters that need to be set for the analyses to be run:

Parameter	Description	Default / Example
study_name	descriptive short study name	"My study"
library_type	"single-end", "paired-end"	"paired-end"
library_strand	["SECOND_READ_TRANSCRIPTION_STRAND", "FIRST_READ_TRANSCRIPTION_STRAND", "SINGLE_STRAND", "SINGLE_REVERSE", "NONE"] For unstranded data, we enter "NONE". For paired-end stranded data, the most common is "SECOND_READ_TRANSCRIPTION_STRAND", which means the second read of the pair maps in the transcript direction, and the first read maps in the reverse direction. For stranded single-end sequencing, "SINGLE_STRAND" means the reads map in the transcript direction, and "SINGLE_REVERSE" means the reads map on the opposite strand of the transcripts. Also for single-end unstranded we specify "NONE".	"NONE"

Sample annotation and bam file location:

Parameter	Description	Default / Example
sample_column	where are sample ids stored; we expect bam names: sample_id1.bam, sample_id2.bam, ...	"sample_id"
treatment_column	which column defines treatment (test and control labels)	"treatment_id"
control_name	name of controls in the treatment_column, other samples are compared to these marked controls	"DMSO"
separate_column	separate comparisons by grouping samples on separate_column? if "", do not separate	""
group_column	only include controls in the same range as other samples (sample replicates on plate1,plate2,plate3? only include controls from plate1 plate2,plate3)	""

Genome specific parameters, connected with pybio:

Parameter	Description	Default / Example
species	genome species, this is connected with pybio and Ensembl	"homo_sapiens"
genome_version	Ensembl genome version or custom genome version, leave None to take latest Ensembl version	None
bam_path	folder where bam files are stored	"bam"

scanRBP parameters:

Parameter	Description	Default / Example
scanRBP	would you like to run scanRBP? (False / True)	True
protein	name of the protein for scanning sequences with the protein PWM	"K562.TARDBP.0"
protein_label	short label for the used protein	"TDP43"

Processing parameters:

Parameter	Description	Default / Example
platform	"desktop" or "cluster" (HPC with SLURM)	"desktop"
container	leave empty or for singularity use: "singularity"; this will download and run "singularity/splicekit_version.sif"	"desktop"

Visualization, labelling and other parameters:

Parameter	Description	Default / Example
short_names	List of triples for shortening names in results, if last string is "complete", only entire strings will be considered (no partial replacement)	[], example: [("cell_line_A", "A", "complete")], this would only replace cell_line_A with A if cell_line_A is the whole string, while if "partial" is provided, also strings like ...cell_line_A... would be replaced with ...A...

After setting the basic parameters in your splicekit.config file, simply run splicekit process inside the folder with your config file. This will run all available analysis on your dataset (see details of inidividual commands in Running Splicekit)

Running splicekit

For example files (splicekit.config and samples.tab) please look in the datasets folder.

If you have a folder with splicekit.config and samples.tab, a comprehensive run of all analysis is incorporated in a single splicekit command: splicekit process. Each analysis step can be run separately by a single splicekit command.

Click to show splicekit commands

splicekit process        # runs all analysis
splicekit setup          # initializes folder structure and downloads Fontawesome for html reports
splicekit annotation     # downloads Moose annotation and creates comparisons
splicekit features       # prepares count tables in data/* folders for junctions, exons, anchors
splicekit edgeR          # runs edgeR analysis on the cluster
splicekit motifs         # runs motif analysis together with scanRBP
splicekit promisc        # runs promisc analysis from edgeR results
splicekit judge          # generates juDGE plots
splicekit clusterlogfc   # generates cluster of pair-wise logFC comaprisons of samples at the exon, junction and gene levels
splicekit version        # prints out current version
splicekit jbrowse2       # starts JBrowse2 visualization with local web server

Let's shortly describe and comment individual steps with the required inputs and resulting outputs.

Annotation and comparisons

This first step of the analysis (splicekit annotation) loads samples from the file samples.tab. It also uses the treatment_column (where is the treatment stored), control_name (name of the controls in the treatment column), group_column (group experiments by this property, e.g. plate) and separate_column (generate comparisons inside groups, e.g. cell_type) to create comparisons. Each treatment (can have several replicates / samples / readouts) is compared to the controls.

An example `sample.tab` would look like this (click to show)

An example sample.tab would look like this:

sample_id	treatment_id
1	control
2	control
3	control
4	test1
5	test1
6	test1
7	test2
8	test2
9	test2

Once we have loaded and process the sample annotation, splicekit creates "comparisons", by default this will compare treated samples to control samples. The comparisons are also stored in a simple tab delimited file, annotation/comparisons.tab:

comparison	compound_samples	DMSO_samples
test1_control	4_test1,5_test1,6_test1	1_control,2_control,3_control
test2_control	7_test2,8_test2,9_test2	1_control,2_control,3_control

In addition, this step will also create processing shell scripts and cluster job files (jobs/*). An example cluster job file:

#!/bin/bash
#BSUB -J edgeR_junctions_sample1                  # Job name
#BSUB -n 4                                        # number of tasks
#BSUB -R "span[hosts=1]"                          # Allocate all tasks in 1 host
#BSUB -q short                                    # Select queue
#BSUB -o logs_edgeR_junctions/sample1_control.out # Output file
#BSUB -e logs_edgeR_junctions/sample1_control.err # Error file

ml R
R --no-save --args splicekit comparison_junctions_data junctions control test ... < comps_edgeR.R

Preparing features (junctions, anchors, exons, genes) count tables

Running splicekit features will create count tables for junctions, anchors, exons and genes.

What are features?

We operate with 4 types of features (extendable in the future): junctions, anchors, exons and genes. All feature ids have the same format: chrstrand_start_stop. An example would be chrX-_154371360_154374505. Please check genomic coordinates for an explanation of how we report genomic coordinates accross splicekit.

Here a short description of the feature types (feature_type) we use:

• genes are annotated genes from refseq/ensembl
• exons are annotated exons from refseq/ensembl
• junctions are features connecting exons
• anchors are of two types: donor_anchors and acceptor_anchors. Donor anchors are 15nt regions upstream (inside the exon) of the donor site. Acceptor anchors are 15nt regions downstream from the acceptor site (inside the exon). Each junction has 2 anchors: donor anchor and acceptor anchor. We use anchors to evaluate the effect of junction changes, because we do not have exon information for non-annotated junctions and because taking a fixed 15nt region standardizes this kind of analysis (JUAN, junction-anchor).

In the folders data/sample_{feature_type}_data, each individual file (table) contains the list of all features and the count for the individual sample. Example file would be data/sample_exons_data/sample_99.tab:

exon_id	count
1+_1471764_1472088	13
1+_1477273_1477349	4
1+_1478643_1478744	0
1+_1479048_1479107	0
1+_1480866_1480935	0
1+_1482137_1482302	12

The second type of count tables are then the individual comparison tables. Each of these comparisons tables contain all the count information (all samples involved in the comparison). An example for a junction comparison file named data/comparison_junctions_data/test_control.tab would be:

GeneID	Start	End	Length	Symbol	1_test	2_test	3_test	4_control	5_control	6_control
1	58347029	58347353	325	A1BG	42	31	109	75	86	33
1	58347640	58350370	2731	A1BG	0	0	3	1	0	0
1	58350651	58351391	741	A1BG	0	0	10	1	0	0

Running edgeR gene context analysis on junction features

Running edgeR analysis on features (either genes, exons, junctions or anchors) consist of simply running the process script or submitting all matching job files to the cluster. This is done by the splicekit edgeR command.

Results are stored in the files results/results_edgeR_{feature_type} where feature_type is one of ["genes", "exons", "junctions", "donor_anochors", "acceptor_anchors"]. Results with FDR < splicekit.config.edgeR_FDR_thr are reported (sorted by FDR), linked to JBrowse with URL links.

Motif analysis

Motif analysis on donor site patterns (9nt sequences) is performed on the top 100 hits for each comparison. The analysis is run by splicekit motifs. Html reports are generated at results/motifs. In addition to computing motif logos, we run DREME on regulated sequences vs. controls.

scanRBP

To identify potential enrichment of RNA-protein binding at regulated sites (donor sites, acceptor sites, other areas), we developed and integrated scanRBP with splicekit.

Once we identify sets of control and regulated sequences, we compute log-odds of the binding signal for a specific protein of interest from it's PWMs. Performing bootstraps on the sequence labels, we can estimate the probability the binding at regulated sequences is different from the control binding (log-fc of the binding signal).

scanRPB is integrated with splicekit and can also be used separately as an individual package (pip install scanRBP).

juDGE plots

To determine the effect of a treatment (more involved in gene expression changes in general or more involved in splicing changes) we can generate juDGE plots (junction logFC vs. gene logFC). These plots are stored in results/judge/* and contain PNG images and also html interactive plotly reports (mouse over shows data and gene name).

On the plot above, the left panel represents a comparison where the compound causes mostly changes in junctions of several genes (y axis) and junctions are much more perturbed compared to gene expression in general (activity on the x axis). This compound would be characterized as a "splicing modifier".

In contrast, the comparison on the right panel shows more activity on changing gene expression in general (x axis). The compound involved would be labeled as "expression modifier".

JBrowse2 visualizations

To graphically explore results, we use JBrowse. We incorporate a lightweight pybio web server to explore JBrowse2 results locally, by running splicekit jbrowse2.

Documentation and specifications

Global specifications and documentation on file formats, analysis results and other objects produced by splicekit.

Genomic Coordinates (click to show details)

All genomic coordinates we operate with inside splicekit are 0-based left+right inclusive. This means, when we say for example 100-103, this would include coordinates 100, 101, 102 and 103. The first coordinate is 0. More specific details:

• feature specific documentation

  All feature coordinates (junctions, anchors, exons) are given in a numeric sort order regardless of strand (feature_start is always < feature_stop). Example chr1+_100_102 would represent a junction spanning coordinates [100,101,102]). Example chr1-_100_102 would represent a junction spanning coordinates [102,101,100]).

• junction specific documentation

  Junction coordinates cover/overlap 1-nucleotide of adjoining exons. Example 1: chr1+_100_200 represents a junction on chromosome 1 (+ strand) from [100..200]. 100 is the last nucleotide of the upstream exon and 200 is the first nucleotide of the downstream exon. Example 2: chr1-_100_200 represents a junction on chromosome 1 (- strand) from [100..200]. 200 is the last nucleotide of the upstream exon and 100 is the first nucleotide of the downstream exon.

Important

Refseq and Ensembl GTF files are 1-indexed. When we read files from refseq/ensembl, we perform -1 on all coordinates to keep this in line with other coordinate structures inside splicekit (which are all 0-indexed).

File reference/junctions.tab (click to show details)

This file contains all the junctions detected from all the samples in the project. Only junctions that could be annotated to genes are reported. However, "novel" junctions (that do not touch refseq/ensembl annotated exons) are also reported, as long as the start and stop of the junction falls inside an annotated gene (see annotated column).

column	description
junction_id	unique id of the junction, chr+strand_start_stop, example chr1-_17741_17839
donor_anchor_id	matching donor_anchor_id, usually 15nt region upstream of junction begin
acceptor_anchor_id	matching acceptor_anchor_id, usually 15nt region downstream of junction end
gene_id	refseq gene_id; note that a junction can be non-annotated (column annotated=0) but still assigned to a gene, meaning its start and stop are inside the gene
gene_name	corresponding to gene_id
chr	chromosome of the junction and anchor, also stored in junction_id and anchor_id
strand	strand of the junction and anchor, also stored in junction_id and anchor_id
annotated	1 if junction touches annotated exons, 0 otherwise
count	integer raw count of all reads across all samples in the project that support the junction

Files reference/donor_anchors.gtf and reference/acceptor_anchors.gtf (click to show details)

GTF files generated from all the donor/acceptor anchors in the reference/junctions.tab file. These GTF files are then used by featureCounts to create tables of counts for anchors across project samples.

Files results/results_edgeR_{feature_type}.tab for exons, junctions and anchors (click to show details)

The general structure of the edgeR results files for all features is the same, with specific columns added for specific features (see below the table for further details).

Below the common columns for all features. Results are filtered by FDR <= splicekit.config.edgeR_FDR_thr.

column	description
result_id	r_int identifier of result, starts with 1
comparison	name of the comparison, from annotation/comparisons.tab
compound	name of the treatment/compound tested
feature_id	gene_id / exon_id / junction_id / [donor, acceptor]_anchor_id
chr	chromosome of the feature
strand	strand of the feature
feature_start	start of feature (numerically, start always < stop), also see genomic coordinates
feature_stop	stop of feature (numerically, stop always > start), also see genomic coordinates
feature_len	length of feature
gene_id	refseq gene_id
gene_name	refseq corresponding gene_name
sum_feature_test	sum of counts for this feature across all test samples
sum_feature_control	sum of counts for this feature across all control samples
jbrowse_loc	region displayed in JBrowse
jbrowse_url	link to JBrowse view
test_pfi	test percentage feature inclusion, computed as: feature_count_test / all_features_of_gene_test
control_pfi	control percentage feature inclusion, computed as: feature_count_control / all_features_of_gene_control
delta_pfi	test_pfi - control_pfi
logFC	log fold change, reported from edgeR
exon.F	exon.F, reported from edgeR
p_value	p_value, reported from edgeR
fdr	false discovery rate, reported from edgeR

Adding to the above column table, there are additional columns present, depending on the feature.

Junction specific (additional) columns:

column	description
annotated	1 if junction is touching refseq annotated exons, 0 otherwise
donor_anchor_id	id of the donor_anchor, associated / linked to this junction
acceptor_anchor_id	id of the acceptor_anchor, associated / linked to this junction
UTR	contains text first_exon_{start_pos}, if the junction touches any first exon of any transcript of the gene

Exon specific (additional) columns:

column	description
delta_PSI	test_PSI-control_PSI, percentage spliced-in

Changelog

v0.4: released in May 2023

• added singularity container with all dependencies
• added local integrated JBrowse2
• cluster or desktop runs
• scanRBP and bootstrap analysis of RNA-protein binding
• further development and integration with pybio
• extended documentation of concepts, analysis and results

v0.3: released in January 2023 (click to show details)

• re-coded junction analysis
  • independent junctions parsing from provided bam files
  • master table of all junctions in the samples of the analyzed project, including novel junctions (refseq/ensembl non-annotated)
• clustering by logFC of pairwise-comparisons with dendrogram: junction, exon and gene levels (clusterlogfc module)
• added first_exon annotation for junctions touching annotated first exons of transcripts
• extended documentation of concepts, analysis and results

v0.2: released in October 2022 (click to show details)

• software architecture restructure with python modules
• filtering of lowly expressed features by edgeR
• DonJuan analysis (junction-anchor analysis)
• more advanced motif analysis with DREME
• filtering regulated junctions with regulated donors

v0.1: released in July 2022 (click to show details)

• initial version of splicekit
• parsing of junction and exon counts
• computing edgeR analysis from count tables and producing a results file with direct links to JBrowse2
• basic motif analysis