SLRanger 0.0.1.5

An integrated approach for spliced leader detection and operon prediction in eukaryotes using long RNA reads

SLRanger

An integrated approach for spliced leader detection and operon prediction in eukaryotes using long RNA reads

Workflow

(A) A schematic diagram illustrates the direct RNA sequencing workflow for species exhibiting trans-splicing. In such species, spliced leader (SL) sequences are located at the 5′ ends of RNA reads due to the trans-splicing mechanism. Mature mRNAs containing SL sequences are sequenced using Oxford Nanopore direct RNA sequencing, and the resulting long reads are subsequently aligned to the reference genome. (B) A flowchart outlines the SL detection module of SLRanger. After aligning long RNA reads to the reference genome, the unaligned 5′-end fragments are extracted and aligned against a reference set of SL and random sequences (as controls). Using SLRanger’s scoring scheme, each candidate read is assigned an “SLRanger score.” Reads will be assigned to the corresponding SL-type according to the highest “SLRanger score”. Reads that can’t be classified as SL1 will be considered as the SL2 type. If SL1 and SL2 can’t be distinguished, the read is labeled SL1_unknown. If variants of SL2 can’t be distinguished, the read is labeled SL2_unknown. A dynamic cutoff is then applied to identify high-confidence SL-containing reads, referred to as “solid SL reads”. (C) An illustration of the principle underlying operon prediction by SLRanger. Based on the presence of high-confidence SL sequences in each read and their genomic mapping positions relative to gene annotations, operon structures are inferred. Genes with a high proportion of SL1-type reads are predicted to be upstream operon genes, whereas genes with a high proportion of SL2-type reads or supported by multiple SL2-type reads are predicted to be downstream operon genes.

Installation

The pipeline is invoked using a CLI written in Python(3.9-3.11) and requires a Unix-based operating system. For conda method, we provided installation from pypi and git

i. Conda method

1. Prepare a new conda env

conda create -n SLRanger_env python=3.9
conda activate SLRanger_env
conda install -c bioconda bedtools minimap2 samtools

2a. Install from PyPI

# install from pypi
pip install SLRanger

2b. Install from Github

#  install from git
git clone https://github.com/lrslab/SLRanger.git
cd SLRanger/
python setup.py install

ii. Docker method

docker pull zhihaguo/slranger_env

Manual

SLRanger encompasses two primary functions, spliced leader (SL) detection and operon prediction, used to determine whether long RNA reads carry SL sequences and predict the operon structure based on the SL information.

1. Preprocessing

Reference and annotation selection

The long RNA reads will be mapped to the genome reference. The genome reference (fasta/fa/fna file) and annotation file (GFF file) should be determined before running SLRanger. These can be downloaded from NCBI or assembled independently.

In our sample folder,we provided C. elegans annotation file.

Long reads alignment

Additionally, we require users to provide their own alignment file (BAM file). For long reads, minimap2 is the recommended software. In the sample folder, we have provided test.bam, which was generated using the following command.

minimap2 -ax splice -uf -t 80 -k14 --MD --secondary=no $reference $basecall_file > tmp.sam
samtools view -hbS tmp.sam | samtools sort -@ 32 -F 260 -o test.bam
samtools index test.bam

2. Spliced Leader detection

SL_detect.py is designed to detect spliced leaders.

Command options

Available options can be viewed by running SL_detect.py -h in the command line.

SL_detect.py -h
usage: SL_detect.py [-h] -r REF -b BAM [-o OUTPUT] [--visualization] [-t CPU]

help to know spliced leader and distinguish SL1 and SL2

options:
  -h, --help            show this help message and exit
  -r REF, --ref REF     SL reference (fasta file recording SL sequence, required)
  -i BAM, --input BAM     input the bam file (required)
  -m , --mode           RNA or cDNA
  -o OUTPUT, --output OUTPUT
                        output file (default: SLRanger.txt)
  --visualization       Turn on the visualization mode
  -t CPU, --cpu CPU     CPU number (default: 4)
  -c CUTOFF, --cutoff CUTOFF
                        The value used to filter high confident SL reads. 
                        The higher the value, the stricter it is. 
                        The range is between 0-10. (default: 4)                      

Output description

i. result table

col name	description
query_name	Unique name of reads
strand	Mapping direction of reads
soft_length	Length of soft clipping at 5' end of reads (length of 5' unmapped region)
aligned_length	Aligned length of reads
read_end	End position of locally sequence mapped to SL sequence
query_length	Length of locally sequence mapped to SL sequence
consensus	Consensus sequence between the locally sequence mapped to SL sequence and SL sequence reference
random_sw_score	Optimal score of 5' unmapped region mapped to random sequences obtain by Smith-Waterman algorithm (SW score)
random_final_score	Final score of 5' unmapped region mapped to random sequences obtained by SLRanger scoring system
random_SL_score	Random final score normlized by the maximum possible score for the length of the locally mapped region sequence (SL_score)
sw_score	Optimal score of 5' unmapped region mapped to SL reference sequences obtain by Smith-Waterman algorithm (SW score)
final_score	Final score of 5' unmapped region mapped to SL reference sequences obtained by SLRanger scoring system
SL_score	Final score normlized by the maximum possible score for the length of the locally mapped region sequence (SL_score)
SL_type	Spliced Leader types; random if random_SL_score > SL_score

ii. visualization result

The summary table and figures, including the Data Summary Table and the pictures including Cumulative Counts (SW), Cumulative Counts (SL), Query Length Distribution, Aligned Length Distribution, SL Type Distribution. will be output in a webpage format. An example is provided here.

Example

We provided test data to run as below.

git clone https://github.com/lrslab/SLRanger.git
cd sample/
unzip data.zip
SL_detect.py --ref SL_list_cel.fa --bam RNA_test.bam -o SLRanger.txt -t 4 --visualization
SL_detect.py --ref SL_list_cel.fa --bam cDNAtest.bam -o SLRanger_cDNA.txt -t 4 --visualization

3. Operon prediction

operon_predict.py is designed to predict operons.

Command options

Available options can be viewed by running operon_predict.py -h in the command line.

operon_predict.py  -h
usage: operon_predict.py [-h] -g GFF -b BAM -i INPUT [-o OUTPUT] [-d DISTANCE]
help to know spliced leader and distinguish SL1 and SL2

options:
  -h, --help            show this help message and exit
  -g GFF, --gff GFF     GFF annotation file (required)
  -b BAM, --bam BAM     bam file (required)
  -i INPUT, --input INPUT
                        input the SL detection file (result file from SL_detect.py, required)
  -o OUTPUT, --output OUTPUT
                        output operon detection file ( default: SLRanger.gff)
  -d DISTANCE, --distance DISTANCE
                        promoter scope (default: 5000)
  -c CUTOFF, --cutoff CUTOFF
                        The value used to filter high confident SL reads. 
                        The higher the value, the stricter it is. 
                        The range is between 0-10. (default: 4)                      

Output description

A GFF file will be returned.

Example

We provided test data to run as below (should be run after SL_detect.py).

cd sample/
operon_predict.py -g cel_wormbase.gff -b test.bam -i SLRanger.txt  -o test.gff