Introduction

LINDTIE is a tool designed to identify aberrant transcripts in cancer using long-read RNA-seq data generated by platforms such as Oxford Nanopore Technologies (ONT) and PacBio. It extends beyond canonical gene fusions to capture the full spectrum of cancer transcriptome rearrangements, including fusion, transcribed structural variant (TSV), and novel splice variant (NSV). The pipeline accepts raw transcriptome (RNA-seq) FASTQ files from case and control samples and produces TSV files containing the novel variants it identifies.

LINDTIE uses a hybrid strategy that integrates reference-free de novo assembly with reference-guided assembly, combined with differential transcript expression analysis, to uncover previously uncharacterised transcripts. The workflow consists of four core procedures: assembly, quantification, differential expression analysis, and annotation.

LINDTIE is implemented in Nextflow, providing users with enhanced control over pipeline execution, including the ability to interrupt runs, adjust parameters from the command line, and resume analyses from previous checkpoints.

Pipeline Overview

LINDTIE employs a one-to-N case–control design and a four-stage analysis workflow to identify aberrant transcripts from long-read RNA-seq data. Understanding this workflow helps you interpret results and troubleshoot issues.

Workflow Diagram

Installation + Configuration

Prerequisites

LINDTIE is built using Nextflow.

Before running the pipeline, ensure that the following are installed or available on your Linux-based system:

• Nextflow
• A container engine: Singularity / Apptainer (recommended for HPCs) or Docker

Installing from GitHub

Clone the LINDTIE repository:

git clone https://github.com/jiawei-tan/LINDTIE.git

Configuration

Navigate to the LINDTIE base directory to begin configuring the pipeline:

cd LINDTIE

Edit the Nextflow configuration file (nextflow.config), located in the LINDTIE base directory:

i. Process Configuration (Executor, Queue, and Resource Profiles)

Under the process block in nextflow.config, you can specify the executor used by your HPC system, the queue to submit jobs to, and resource profiles for different types of tasks.

Select an HPC executor and queue:

Choose an appropriate Nextflow executor (e.g., slurm, pbs, sge, lsf, local, etc.) supported by your compute environment. The default configuration supplied with LINDTIE is optimized for WEHI's Milton HPC, which uses the SLURM workload manager. Refer to the Nextflow documentation for the full list of available executors.

Example (default SLURM configuration):

// Process execution configuration – modify as required
process {
  executor = 'slurm'
  queue  = 'regular'           // default SLURM queue
  cache  = 'lenient'
  errorStrategy = 'retry'      // default retry failed tasks
}

For further details on customizing Nextflow configuration files, see the official documentation.

Resource Profiles with Labels:

LINDTIE assigns resource requirements to tasks using Nextflow labels. The default settings allocate resources appropriate for typical HPC environments, but you may reduce or increase these values depending on your system’s available resources.

Example (default label-specific resource settings):

// Configuration for short-running, lightweight tasks
withLabel: 'process_short' {
  cpus   = 1
  memory = 4.GB              // 4 GB RAM
  time   = 1.h               // 1-hour time limit
}

// Configuration for moderately intensive tasks
withLabel: 'process_medium' {
  cpus   = 8
  memory = 16.GB             // 16 GB RAM
  time   = 8.h               // 8-hour time limit
}

// Configuration for long-running, resource-heavy tasks
withLabel: 'process_long' {
  cpus   = 16
  memory = 64.GB             // 64 GB RAM
  time   = 16.h              // 16-hour time limit
}

ii. Environment Configuration (JVM Memory Limits)

Under the env block, you can adjust the Java Virtual Machine (JVM) heap size for Nextflow:

Limit the JVM heap size:

The default setting allocates 100 GB of heap memory, which you may reduce or increase depending on your system’s available resources.

Adjusting this value ensures that Nextflow does not exceed the memory limits enforced by your HPC scheduler.

Example (default JVM heap size):

env {
  // JVM heap size (-Xmx sets the maximum heap memory) - default 100GB
  NXF_JVM_ARGS = '-Xmx100g'
}

Setting Up References

Download the compressed pre-built reference package from Zenodo (1.06GB):

curl -O https://zenodo.org/records/18531809/files/LINDTIE_ref.tar.gz

# decompress the tar.gz file and remove the tar.gz file                        
tar xzf LINDTIE_ref.tar.gz && rm LINDTIE_ref.tar.gz

This will generate a ref directory containing the seven required reference files. Ensure that the ref directory is placed inside the LINDTIE base directory:

LINDTIE/
└── ref/
    ├── chess3.0_with_HTLV1_HPV_HBV_HIV1_HIV2_EBV.fa
    ├── chess3.0_with_HTLV1_HPV_HBV_HIV1_HIV2_EBV.gtf
    ├── chess3.0_with_HTLV1_HPV_HBV_HIV1_HIV2_EBV.info
    ├── Cosmic_CancerGeneCensus_v103_GRCh38_tier_fusion.tsv
    ├── hg38_splice_junctions.bed
    ├── hg38_with_HTLV1_HPV_HBV_HIV1_HIV2_EBV.fa
    └── tx2gene.txt

The reference comprises both human and viral sequences, including viruses known to integrate into the host genome, such as HTLV-1 (NC_001436.1), HPV (NC_027779.1), HBV (NC_003977.2), HIV-1 (NC_001802.1), HIV-2 (NC_001722.1), and EBV (NC_009334.1).

Running LINDTIE

In the directory where you will run LINDTIE, create the required cases and controls subdirectories:

mkdir -p cases
mkdir -p controls

Input Files

Allocate the long-read RNA-seq data in FASTQ or FASTA format (can be gzipped) into the appropriate directories for your case and control samples.

Cases refer to the cancer samples in which you want to identify variants, while controls are used as the reference for comparison. Ideally, control samples should be benign tissue of the same type as the primary tumour. If this is not feasible, such as in blood cancers, acceptable alternatives include remission samples or samples from other individuals, ideally of the same cancer type.

Including more controls increases statistical power; aim for a minimum of 2, with 10 to 15 being optimal.

Run LINDTIE with Nextflow

Run LINDTIE with one of the following commands:

nextflow run LINDTIE/main.nf -params-file LINDTIE/params.yaml -profile singularity

nextflow run LINDTIE/main.nf -params-file LINDTIE/params.yaml -profile docker

You can also run nextflow run LINDTIE/main.nf --help to see all available options and parameters.

Submitting the Nextflow Script as a Job

A more effective approach than launching the Nextflow driver job from the login node is to wrap the Nextflow run command in a script and submit the workflow as a job.

A run_LINDTIE.sh template bash script is provided in the LINDTIE base directory:

#!/bin/bash

#SBATCH --job-name=run_LINDTIE
#SBATCH --partition=regular
#SBATCH --ntasks=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=8G
#SBATCH --time=24:00:00
#SBATCH --mail-type=BEGIN,END,FAIL
#SBATCH --mail-user=your-email@example.com
#SBATCH --output=script_output/%x_%J.out
#SBATCH --error=script_output/%x_%J.err

module load nextflow/25.04.2 singularity/4.1.5

# modify the path to the LINDTIE base directory
LINDTIE_dir=/path/to/your/LINDTIE

nextflow run $LINDTIE_dir/main.nf -params-file $LINDTIE_dir/params.yaml -profile singularity

Fine Tuning Parameters

Adjust these parameters based on your sample characteristics and analysis requirements. Parameters marked with ⭐ are commonly modified.

Configuration Methods

There are two ways to configure the parameters for LINDTIE:

Option 1: Edit the params.yaml file before running

• Open the params.yaml file located in the LINDTIE base directory.
• Modify any parameters as needed.
• Save the file and then run LINDTIE.

Option 2: Override parameters at runtime using command line arguments

• Run LINDTIE with the desired parameters using the command line.

# Default parameters for the workflow
# Assembly Mode: 'hybrid', 'denovo', 'denovo_subset', or 'ref_guided'
assembly_mode: 'hybrid'

# Tool Presets
# minimap2 presets (passed to -ax):
#   'map-ont' : Oxford Nanopore genomic reads (default)
#   'map-pb'  : PacBio CLR genomic reads
#   'map-hifi': PacBio HiFi/CCS genomic reads (v2.19+)
#   'lr:hq'   : Nanopore Q20 genomic reads (v2.27+)
minimap2_preset: 'map-ont'

# rnabloom2 presets:
#   ''       : Leave empty for ONT (default)
#   '-lrpb'  : For PacBio
rnabloom2_preset: ''

subset_count: null               # NULL for no subsetting, otherwise the number of reads to subset to
detect_viral_integration: false  # true or false (default: false)
RUN_DE: true                     # true or false (default: true)
fdr: 0.05                        # default 0.05
min_cpm: 0.5                     # default 0.5
min_logfc: 2                     # default 2
min_clip: 20                     # default 20
min_gap: 7                       # default 7
min_match: '30,0.3'              # default '30,0.3'
splice_motif_mismatch: 1         # default 1
oarfish_num_bootstraps: 10       # default 10
gene_filter: NULL                # default NULL (e.g. "TP53,BRCA2")
var_filter: NULL                 # default NULL (e.g. "DEL,INS")
single_sample_min_vaf: 0.1       # default 0.1
                            

nextflow run LINDTIE/main.nf \
    -params-file LINDTIE/params.yaml \
    -profile singularity \
    --rnabloom2_preset "lrpb" \
    --minimap2_preset "map-pb" \
    --assembly_mode "denovo"

Output

After running LINDTIE, a results directory (<caseName>_output) will be created. This directory is organized by analysis steps, with the final results for the sample stored in the FinalOutput folder:

<caseName>_output/
├── 01-Assembly
├── 02-Quantification
├── 03-DifferentialExpression
├── 04-Annotation
├── FinalOutput
└── run_parameters.log

FinalOutput Results

The final results produced by LINDTIE are located in <caseName>_output/FinalOutput/, which contains the following files:

<caseName>_output/FinalOutput/
├── log
├── refined_annotated_contigs.bam
├── refined_annotated_contigs.bam.bai
├── refined_annotated_contigs.fasta
├── refined_annotated_contigs.vcf
├── <caseName>_all_variants_ranked_results.tsv
├── <caseName>_discarded_results.tsv
└── <caseName>_results.tsv

Primary Results: <caseName>_results.tsv

This file is the primary result table. Variants with multiple annotations are collapsed, meaning each contig appears as a single row with consolidated information.

Output File Column Descriptions

<caseName>_all_variants_ranked_results.tsv

This file is an expanded version of the <caseName>_results.tsv file. This table lists all variant annotations individually, without collapsing (i.e., a contig may appear in multiple rows if it has multiple annotations). It includes all columns present in <caseName>_results.tsv, plus three additional columns:

<caseName>_discarded_results.tsv

This file contains variants filtered out due to low-complexity sequences. Specifically, variants are placed in this table if the seq1 or seq2 columns contain:

• a polyA or polyT stretch of ≥ 10 bp, or
• a perfect dinucleotide repeat of ≥ 10 repeats (i.e., 20 bp total).

Visualization Files

The following files are useful for inspection in IGV to visualize alignments and examine the refined contig sequences:

• refined_annotated_contigs.bam / .bam.bai: BAM and BAM index files that contain the aligned refined transcript sequences for visualization.
• refined_annotated_contigs.fasta: A FASTA file that contains the refined transcript sequences used in the analysis.
• refined_annotated_contigs.vcf: A VCF file that lists the refined variant calls.

Intermediate Files Generated at Each Step

LINDTIE produces several intermediate files throughout the pipeline. These files can be useful for troubleshooting, quality checks, or deeper inspection of specific steps.

<caseName>_output/
├── run_parameters.log
├── 01-Assembly
│   ├── denovo_read_counts.log (assembly_mode = hybrid or denovo or denovo_subset)
│   ├── rnabloom.transcripts.fa (assembly_mode = hybrid or denovo or denovo_subset)
│   ├── read_counts_summary.log (assembly_mode = hybrid or ref_guided or denovo_subset)
│   ├── confident_mapped.bam & confident_mapped.bam.bai (assembly_mode = hybrid or ref_guided or denovo_subset)
│   ├── reads_all_sorted.bam & reads_all_sorted.bam.bai (assembly_mode = hybrid or ref_guided or denovo_subset)
│   ├── stringtie2_assembly.fa (assembly_mode = hybrid or ref_guided or denovo_subset)
│   └── stringtie2_assembly.gtf (assembly_mode = hybrid or ref_guided or denovo_subset)
├── 02-Quantification
│   ├── cases
│   │   ├── <caseName>.infreps.pq
│   │   ├── <caseName>.meta_info.json
│   │   └── <caseName>.quant
│   └── controls
│       ├── <controlName>.infreps.pq
│       ├── <controlName>.meta_info.json
│       └── <controlName>.quant
├── 03-DifferentialExpression
│   ├── DE_contigs.fasta
│   ├── DE_contigs_mapped_to_hg38.bam
│   ├── DE_contigs_mapped_to_hg38.bam.bai
│   ├── DE.log
│   ├── DE_MD_plot.png
│   ├── DE_MDS_plot.png
│   ├── DE_QLDisp_plot.png
│   ├── DE_transcript_full_results.txt
│   └── DE_transcript_significant.txt
└── 04-Annotation
    ├── annotated_contigs.bam
    ├── annotated_contigs.bam.bai
    ├── annotated_contigs_info.tsv
    ├── annotated_contigs.vcf
    └── annotation.log

run_parameters.log

A log file that contains the run parameters used to run LINDTIE.

01-Assembly

• denovo_read_counts.log: A log file that contains the read counts for the de novo assembly. Only present when assembly_mode = denovo or denovo_subset.
• rnabloom.transcripts.fa: A FASTA file that contains the assembled transcript sequences produced by RNA-Bloom2. Only present when assembly_mode = hybrid or denovo or denovo_subset.
• read_counts_summary.log: A log file that contains the read counts summary. Only present when assembly_mode = hybrid or ref_guided or denovo_subset.
• confident_mapped.bam: A BAM file that contains the confident mapped reads. Only present when assembly_mode = hybrid or ref_guided or denovo_subset.
• reads_all_sorted.bam: A BAM file that contains all the reads mapped to the reference genome. Only present when assembly_mode = hybrid or ref_guided or denovo_subset.
• stringtie2_assembly.fa: A FASTA file that contains the assembled transcript sequences produced by StringTie2. Only present when assembly_mode = hybrid or ref_guided or denovo_subset.
• stringtie2_assembly.gtf: A GTF file that contains the assembled transcript annotations produced by StringTie2. Only present when assembly_mode = hybrid or ref_guided or denovo_subset.
• read_counts_summary.log: A log file that contains the read counts summary. Only present when assembly_mode = hybrid or ref_guided or denovo_subset.

02-Quantification

Files generated by Oarfish for both the case and control samples:

• <sampleName>.quant: A tab-separated file that contains the quantified transcripts along with their lengths, metadata, and the estimated number of reads originating from each transcript.
• <sampleName>.meta_info.json: A JSON file that contains the parameters used to run Oarfish and other sample-level metadata excluding transcript quantifications.
• <sampleName>.infreps.pq: A Parquet file that contains estimated transcript counts, with each row representing a transcript and each column representing an inferential replicate.

03-DifferentialExpression

Files containing results from differential expression analysis of assembled transcripts:

• DE_contigs.fasta: A FASTA file that contains the transcripts sequences identified as significantly differentially expressed.
• DE_contigs_mapped_to_hg38.bam / DE_contigs_mapped_to_hg38.bam.bai: BAM and BAM index files that contain the alignments of differentially expressed transcripts to the hg38 reference genome for visualization.
• DE.log: A log file that contains the log messages from the differential expression analysis.
• DE_MD_plot.png: A PNG file that contains the mean–difference (MD) plot of expression changes.
• DE_MDS_plot.png: A PNG file that contains the multidimensional scaling (MDS) plot showing sample relationships.
• DE_QLDisp_plot.png: A PNG file that contains the quasi-likelihood dispersion diagnostic plot.
• DE_transcript_full_results.txt: A text file that contains the complete statistical results for all transcripts tested.
• DE_transcript_significant.txt: A text file that contains the subset of transcripts identified as significantly differentially expressed.

04-Annotation

Files containing structural and functional annotations of transcripts:

• annotated_contigs.bam / annotated_contigs.bam.bai: BAM and BAM index files that contain the annotated transcript alignments generated from alignment-based analysis.
• annotated_contigs_info.tsv: A tab-separated file that contains metadata and functional annotations for each transcript.
• annotated_contigs.vcf: A VCF file that contains variant calls identified during the annotation process.
• annotation.log: A log file that contains log messages from the annotation workflow.

LINDTIE's Variant Classification

The following are the variant types identified and classified by LINDTIE:

LINDTIE's variant classification rules and filtering logic are based on the following criteria:

LINDTIE's Variant-Specific Criteria

The following are the variant-specific criteria used by LINDTIE to classify the variants:

[1] Spliced Contig: Any alignment containing a splice (≥1 gap)

[2] Spliced Exon: EE/NE variants that have adjacent supporting junctions, and for selected junction variants themselves.

[3] Valid Motif: True when the 2-bp splice motifs at the relevant boundaries match canonical GT-AG (or CT-AC on the opposite strand) within the allowed mismatch tolerance

LINDTIE’s Scoring System

LINDTIE's scoring system is based on the following criteria:

Resume Your Run

You can easily resume your run in case of changes to the parameters or inputs using -resume. Nextflow will try to not recalculate steps that are already done:

nextflow run LINDTIE/main.nf -params-file LINDTIE/params.yaml -resume

Nextflow will need access to the working directory where temporary calculations are stored. Per default, this is set to work but can be adjusted via -w /path/to/any/workdir. In addition, the .nextflow.log file is needed to resume a run, thus, this will only work if you resume the run from the same folder where you started it.

Testing LINDTIE

Example test data is included to help you quickly verify that the pipeline is running correctly. The test set contains one case sample and two control samples, located in the test_case directory under the LINDTIE base directory:

LINDTIE/
└──test_case/
		├── cases
		│   └── test-case.fastq.gz
		├── controls
		│   ├── test-control0.fastq.gz
		│   └── test-control1.fastq.gz
		└── run_LINDTIE.sh
                    

You can test LINDTIE either by running the following command directly in the terminal or by executing the provided run_LINDTIE.sh script (make sure to modify the path):

# modify the path to the LINDTIE base directory
LINDTIE_dir=/path/to/your/LINDTIE

nextflow run $LINDTIE_dir/main.nf -params-file $LINDTIE_dir/params.yaml -profile singularity
cases/*.fastq.gz controls/*.fastq.gz

Approximate run time: 8-10 minutes

Once LINDTIE has finished running, you should see output on the terminal similar to the following:

View the collapsed results at: test-case_output/FinalOutput/test-case_results.tsv

The table below summarizes the number of variants detected for each variant type in the test case:

Examples of Use

Best Practices

Full List of Tools Used in LINDTIE

Listed below are all software tools and version numbers packaged within the Nextflow container used by LINDTIE:

bioconda::rnabloom=2.0.1
bioconda::gffread=0.12.7
bioconda::stringtie=2.2.3
bioconda::minimap2=2.30
bioconda::samtools=1.22
bioconda::bbmap=39.52
bioconda::oarfish=0.8.1
bioconda::bio=1.8.0
bioconda::pysam=0.23.3
bioconda::pybedtools=0.12.0
bioconda::bioconductor-edger=4.4.0
bioconda::bioconductor-tximport=1.34.0
conda-forge::pandas=2.3.0
conda-forge::numpy=2.3.0
conda-forge::intervaltree=3.1.0
conda-forge::r-dplyr=1.1.4
conda-forge::r-data.table=1.17.6
conda-forge::r-jsonlite=2.0.0 
conda-forge::r-readr=2.1.5 
conda-forge::r-arrow=19.0.1

Glossary of Terms

This glossary defines key terms used throughout the LINDTIE documentation and output files.

General Terms

Aberrant Transcript

A transcript that differs from the normal reference transcriptome, potentially caused by genomic rearrangements, novel splicing, or other alterations. In cancer, aberrant transcripts may drive tumor growth or serve as biomarkers.

Contig

A contiguous sequence assembled from overlapping reads. In LINDTIE, contigs represent assembled transcript sequences that are then analyzed for variants.

De Novo Assembly

The process of assembling reads into contigs without using a reference genome. This approach can detect novel sequences not present in the reference.

Fusion Transcript

A chimeric RNA molecule containing sequences from two different genes, typically resulting from chromosomal rearrangements. Examples include BCR-ABL in CML and EML4-ALK in lung cancer.

Long-read RNA-seq (lrRNA-seq)

RNA sequencing using technologies that produce reads thousands of bases long (ONT, PacBio), enabling full-length transcript sequencing and better detection of structural variants.

Splice Motif

The conserved sequence at splice junctions. The canonical splice motif is GT-AG (GT at the 5' donor site, AG at the 3' acceptor site). LINDTIE validates splice junctions against known motifs.

Statistical Terms

CPM (Counts Per Million)

A normalization method that scales raw read counts to per-million reads, allowing comparison between samples with different sequencing depths. Formula: CPM = (read count / total reads) × 1,000,000

FDR (False Discovery Rate)

The expected proportion of false positives among all significant results. An FDR of 0.05 means 5% of significant results are expected to be false positives. Lower FDR = higher confidence.

logFC (Log Fold Change)

The log2-transformed ratio of expression between case and control samples. A logFC of 2 means 4x higher expression in cases; logFC of -2 means 4x lower expression in cases.

P-value

The probability of observing the data (or more extreme) if there is no true difference between groups. Lower p-values indicate stronger evidence against the null hypothesis.

TPM (Transcripts Per Million)

A normalization method that accounts for both sequencing depth and transcript length, making values comparable across samples and genes. More appropriate than CPM for comparing expression levels of different transcripts.

VAF (Variant Allele Frequency)

The proportion of reads supporting the variant allele versus the total reads at that position. Higher VAF suggests the variant is present in more cells (clonal) rather than a subclonal event.

Output Field Terms

CIGAR String

A compact representation of how a sequence aligns to a reference. Characters include: M (match/mismatch), I (insertion), D (deletion), N (skipped region/intron), S (soft clip). Example: "100M50N100M" = 100 bases match, 50 base intron, 100 bases match.

Benchmarking

Performance comparisons

Output

Understanding results

Benchmarking & Performance

Troubleshooting

Frequently Asked Questions

Changelog

This page documents all notable changes to LINDTIE. The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

v0.1.0 - Initial Release

Released: 2025

Added

• Initial release of LINDTIE pipeline
• De novo assembly using RNA-Bloom for long-read data
• Quantification with Oarfish and minimap2 alignment
• Differential expression analysis between case and control samples
• Comprehensive variant annotation system
• Support for Oxford Nanopore Technologies (ONT) data
• Support for PacBio long-read data
• Nextflow-based workflow for HPC environments
• Docker and Singularity container support
• Configurable resource profiles (short, medium, long processes)
• TSV output with ranked variant results

Tool Versions

• RNA-Bloom2: 2.0.1
• minimap2: 2.30
• samtools: 1.22
• Oarfish: 0.8.1
• pandas: 2.3.0
• bio: 1.8.0
• pysam: 0.23.3
• pybedtools: 0.12.0
• edgeR: 4.4.0
• tximport: 1.34.0
• numpy: 2.3.0
• intervaltree: 3.1.0
• dplyr: 1.1.4
• data.table: 1.17.6
• jsonlite: 2.0.0
• readr: 2.1.5
• arrow: 19.0.1

Version Policy

LINDTIE follows semantic versioning (MAJOR.MINOR.PATCH):

• MAJOR: Incompatible changes to input/output format or parameters
• MINOR: New features added in a backward-compatible manner
• PATCH: Bug fixes and minor improvements

Migration Guides

Citing LINDTIE

If you use LINDTIE in your research, please cite: