bp-quant 0.5.5

Quantification of reads at defined positions to verify custom input sequences. Given a gene fusion or splicing junction of interest, this tool can quantify RNA-seq reads supporting the breakpoint (or splice junction) by quantifying reads that map to the breakpoint (junction reads) and read pairs that span the breakpoint (spanning pairs).

bp-quant

Quantification of reads at defined positions to verify custom input sequences.

Given a gene fusion or splicing junction of interest, this tool can quantify RNA-seq reads supporting the breakpoint (or splice junction) by quantifying reads that map to the breakpoint (junction reads) and read pairs that span the breakpoint (spanning pairs).

Workflow

• Input:
  • Table with target sequences and breakpoints position (CSV/TSV format)
  • fastq files / BAM file (BAM input only works in combination with STAR as aligner)
• Convert target sequences to FASTA format (bp_quant csv2fasta)
• Map reads against sequences using STAR/Bowtie2/BWA
  • Generate Index of sequences as reference (bp_quant index)
  • Map reads (bp_quant align)
• Count reads using bp_quant count
• Output:
  • Table with read counts per input sequence
  • Table with info on each read (read_info.tsv)

Dependencies

Look at this environment.yml for the individual dependencies (Hint: File can also be used to build conda environment).

Installation

# Create conda environment from above or add the dependencies to your path
pip install bp-quant

Alternatively, bp-quant can be installed from github repository:

git clone https://github.com/TRON-Bioinformatics/easyquant.git

cd easyquant

# If you have conda installed you can simply install the environment like this
conda env create -f environment.yml --prefix conda_env/
conda activate conda_env/

python -m build
pip install dist/*.whl

Usage

usage: bp_quant pipeline [-h] [-1 FQ1] [-2 FQ2] [-b BAM] -s SEQ_TAB -o OUTPUT_FOLDER [-d BP_DISTANCE] [--allow_mismatches]
                         [--interval_mode] [-m {star,bowtie2}] [-t NUM_THREADS] [--alignment_params ALIGN_PARAMS]
                         [--keep_aln | --keep_all]

Runs the complete bpquant pipeline

optional arguments:
  -h, --help            show this help message and exit
  -1 FQ1, --fq1 FQ1     Specify path to Read 1 (R1) FASTQ file
  -2 FQ2, --fq2 FQ2     Specify path to Read 2 (R2) FASTQ file
  -b BAM, --bam_file BAM
                        Specify path to input BAM file as alternative to FASTQ input
  -s SEQ_TAB, --sequence_tab SEQ_TAB
                        Specify the reference sequences as table with colums name, sequence, and position
  -o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER
                        Specify the folder to save the results into.
  -d BP_DISTANCE, --bp_distance BP_DISTANCE
                        Threshold in base pairs for the required overlap size of reads on both sides of the breakpoint for
                        junction/spanning read counting
  --allow_mismatches    Allow mismatches within the region around the breakpoint determined by the bp_distance parameter
  --interval_mode       Specify if interval mode shall be used
  -m {star,bowtie2}, --method {star,bowtie2}
                        Specify alignment software to generate the index
  -t NUM_THREADS, --threads NUM_THREADS
                        Specify number of threads to use for the alignment
  --alignment_params ALIGN_PARAMS
                        Specify custom commandline parameters to use for the alignment
  --keep_aln            Do not delete alignment files during clean up step
  --keep_all            Do not perform clean up step after re-quantification

Copyright (c) 2024 TRON gGmbH (See LICENSE for licensing details)

Use case with example data

We use toy example data from the folder example_data. It consists of a table with input sequences and positions, as well as two fastq files / one BAM file.

Fastqs as input:

bp_quant pipeline \
  -1 example_data/example_rna-seq_R1_001.fastq.gz \
  -2 example_data/example_rna-seq_R1_001.fastq.gz \
  -s example_data/CLDN18_Context_seq.csv \
  -d 10 \
  -o example_out \
  -m star \
  -t 6
  [--interval_mode]

BAM as input:

bp_quant pipeline \
  -b example_data/example_rna-seq.bam \
  -s example_data/CLDN18_Context_seq.csv \
  -d 10 \
  -o example_out \
  -m star \
  -t 6
  [--interval_mode]

Input

Table with input sequences

As input a CSV/TSV table should be given holding the target sequence with unique names and the relative position(s) of the breakpoint(s)/intervals or fusion junction.

Example format of the input table:

name	sequence	position
seq1	AACCGCCACCG	5
seq2	GTCCGTTGGCG	5
seq3	AACCGCCCTGT	5
seq4	CGGCATCATCG	0,5,10

Fastq files / BAM file

Paired fastq files or an unsorted BAM file (no multimappers) as input is required to successfully determine read classes as described below.

Config file

Output format

The main output consists of the file:

• <OUTPUT_FOLDER>/quantification.tsv contains raw read counts for each sequence

The output of the example data <OUTPUT_FOLDER>/quantification.tsv using a mismatch ratio of 0.05 (default) should look like this:

name	pos	junc	span	anch	a	b
CLDN18_1	400	570	684	25	1109	3932
CLDN18_2	361	0	1	0	1	2968
CLDN18_total	400	593	688	25	4315	4990
CLDN18_1_fake	400	0	3	0	3	3946
CLDN18_2_fake	361	0	0	0	0	3943
HPRT1	400	107	215	25	1259	974

Using the interval mode the output will look slightly different:

name	interval	overlap_interval_end_reads	span_interval_end_pairs	within_interval	coverage_perc	coverage_mean	coverage_median
CLDN18_1	0_400	570	684	1109	0.89	182.71	137.5
CLDN18_1	400_786	0	0	3932	1.0	519.51	563.5
CLDN18_2	0_361	0	1	1	0.14	0.14	0.0
CLDN18_2	361_747	0	0	2968	1.0	392.15	425.5
CLDN18_total	0_400	593	688	4315	1.0	586.16	682.0
CLDN18_total	400_786	0	0	4990	1.0	659.15	757.5
CLDN18_1_fake	0_400	0	3	3	0.16	0.38	0.0
CLDN18_1_fake	400_786	0	0	3946	1.0	521.23	551.5
CLDN18_2_fake	0_361	0	0	0	0.0	0.0	0.0
CLDN18_2_fake	361_747	0	0	3943	1.0	520.83	551.0
HPRT1	0_400	107	215	1259	1.0	167.77	175.0
HPRT1	400_793	0	0	974	0.98	126.40	101.0

Hint: This is just an example to illustrate the design of the table. Results may differ.

Columns in output file

While not using interval mode

• name name of the input sequence
• pos position of interest relative to input sequence
• junc reads overlapping the position of interest
• span read pairs spanning the position of interest
• anch maximal number of bases next to position of interest that are overlaped by a single read
• a reads mapping to sequence left of the position of interest
• b reads mapping to sequence right of the position of interest

While using interval mode

• name name of the input sequence
• interval interval of interest relative to input sequence
• overlap_interval_end_reads reads overlapping the end of the interval by at least BP_DISTANCE bases
• span_interval_end_pairs read pairs spanning the end of the interval
• within_interval reads mapping fully onto the interval
• coverage_perc percentual coverage of the interval by aligned reads
• coverage_mean average coverage per base for the interval (fold coverage)
• coverage_median median coverage per base for the interval

Things to consider

When choosing the aligner you have to take into account that there are several differences among them:

• STAR:
  • end-to-end alignment with no soft-clipping
  • very slow for small reference sequences
  • several available alignment parameters to optimize your results, which can be used while starting the pipeline
• bowtie2:
  • end-to-end alignment might lead to insertions where the context sequence starts/ends
  • faster than STAR for short reference sequences (index creation parameters are calculated automatically)