resolveS: An ultra-fast, memory-efficient and reference-free tool for RNA-seq Strandedness Detection

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The goal of this tool is "Rapid RNA-Seq Strand Specificity Detection".

Accurate determination of strand specificity (stranded vs. non-stranded) is a critical prerequisite for transcriptomic analysis. It is a necessary parameter for configuring essential bioinformatics tools like featureCounts and Trinity. However, this information is often missing or incorrectly annotated in public datasets, which can lead to reproducibility issues and misinterpretation of results.

resolveS is a high-performance tool designed to solve this problem instantly. It is super-fast, memory-efficient, and user-friendly, making it the perfect addition to any RNA-Seq Quality Control (QC) pipeline. Whether you are exploring public data or validating your own libraries, resolveS provides the necessary metadata to ensure your downstream analysis is accurate and reproducible.

In addition to being faster and more memory-efficient, resolveS introduces a new feature: it can infer strandedness for species without a reference genome and report a confidence level.

Installation & Usage Guide

First, please download the archive file from the releases section. Follow the instructions below based on your existing environment to proceed with the software installation.

Please refer to $ resolveS -h for more information on the version and usage.

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1. Out-of-the-Box: One-Stop Solution

If you prefer a one-step solution, don't want to install any dependencies, and want to run directly in any environment.

Then download resolveS_singularity_v0.2.x.sif or resolveS_apptainer_v0.2.x.sif. This is a ready-to-use and time-saving solution. No need to install anything!

If you want software that works out of the box without installing any complex dependencies:

# Single-end FASTQ
singularity exec /path/to/resolveS_singularity_v0.2.x.sif resolveS -1 sample.fastq.gz

# Paired-end FASTQ
singularity exec /path/to/resolveS_singularity_v0.2.x.sif resolveS -1 sample_R1.fq.gz -2 sample_R2.fq.gz

# Pre-aligned single-end SAM
singularity exec /path/to/resolveS_singularity_v0.2.x.sif resolveS -a aligned.sam -m 1

2. Portable Program Version

If you don't want to learn about containers, want to use the software directly, and don't want to install any dependencies, you can use the portable version.

Then download portable_program_v0.2.x.tar.gz, and extract it with tar -xvf ...

You will get the following program structure after extraction:

resolveS
├── LICENSE
├── README.md
├── README_zh.md
├── bin
│   ├── resolveS                       # Single-end/paired-end FASTQ or explicit-mode SAM
│   ├── default_align_by_bowtie2.sh
│   ├── default_align_single_by_bowtie2.sh
│   ├── auto_counting_withChrom.pl     # Mode-aware single/pair counting
│   └── default_counting_withChrom.pl  # Progressive per-rRNA-sequence detection (Perl)
├── bowtie2
├── examples
├── ref_default

Usage:

# Single-end FASTQ
./resolveS/bin/resolveS -1 sample.fastq.gz

# Paired-end FASTQ
./resolveS/bin/resolveS -1 sample_R1.fq.gz -2 sample_R2.fq.gz

Save the results to a text file:

# Use -o to write results
./resolveS/bin/resolveS -1 sample_R1.fq.gz -2 sample_R2.fq.gz -o results.tsv

Finally, the Strand_Type column is the inferred result.

The -b parameter allows batch processing with FASTQ or SAM metadata.

Script Variants

resolveS provides multiple script variants for different use cases:

Script	Description	Input Mode	Default -u	Core Scripts
resolveS	Single-end/paired-end FASTQ or explicit-mode SAM	-1, -1/-2, or -a ... -m	5M reads or pairs	default_align*_by_bowtie2.sh + auto_counting_withChrom.pl

3. If you already have Bowtie 2 and Perl installed

Simply extract the downloaded archive. Then, you can directly run the executable file named resolveS. If you wish to execute it from any directory, you may add this file to your system's PATH environment variable.

The release archive usually includes the default bowtie2 index at ref_default/. If not, download it from https://github.com/yudalang3/resolveS/releases.

The final program structure should be as follows:

resolveS/
├── bin/
│   ├── resolveS
│   ├── default_align_by_bowtie2.sh
│   ├── default_align_single_by_bowtie2.sh
│   ├── auto_counting_withChrom.pl
│   └── default_counting_withChrom.pl
└── ref_default/
    ├── default.1.bt2
    ├── default.2.bt2
    ├── default.3.bt2
    ├── default.4.bt2
    ├── default.rev.1.bt2
    └── default.rev.2.bt2

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4. If you prefer using Conda / Mamba

You are already an advanced user. You can check the bin directory yourself and modify the BOWTIE2_BIN variable in default_align_by_bowtie2.sh to configure bowtie2.

You also need to download the bowtie2 index files

Then follow the general steps:

Method 1: Create and Activate Environment (Recommended)

conda/mamba create -n resolveS bowtie2 perl
conda/mamba activate resolveS

Method 2: Create Environment, then Install via Bioconda

conda/mamba create -n resolveS
conda/mamba activate resolveS
mamba install bioconda::bowtie2 perl

After activating the environment, proceed with the installation steps as described in the section above ("If you already have Bowtie 2 and Perl installed").

Usage and Output Demonstration

For the end-user, the most convenient usage is:

• Single-end FASTQ: resolveS -1 sample.fastq.gz
• Paired-end FASTQ: resolveS -1 R1.fq.gz -2 R2.fq.gz
• Single-end SAM: resolveS -a aligned.sam -m 1
• Paired-end SAM: resolveS -a aligned.sam -m 2

FASTQ mode is inferred automatically from -1/-2. SAM mode is never inferred: -m 1 means single-end SAM and -m 2 means paired-end SAM. -p and -u are ignored for SAM input and emit warnings when supplied.

resolveS outputs: File, Strand_Type, MAPQ_Filter, Detection_Level, Overall_fallback_Fwd, Overall_fallback_Rev, Overall_fallback_Fwd_Ratio, Overall_fallback_Rev_Ratio, Overall_fallback_Rel_Diff

Notes for resolveS output columns:

• File: input identifier (absolute path of R1 or SAM)
• MAPQ_Filter: final MAPQ cutoff used (MAPQ-20/10/3/1)
• Detection_Level: progressive detection stage (e.g. 3of3, 4of5, 6of7, 7of8) or *-fallback
• Overall_fallback_Fwd/Overall_fallback_Rev: number of rRNA sequences where forward/reverse read counts dominate (ties excluded)
• Overall_fallback_Fwd_Ratio/Overall_fallback_Rev_Ratio: proportion of fwd/rev rRNA sequences (e.g. 0.538 means 53.8%)
• Overall_fallback_Rel_Diff: relative difference = (Fwd - Rev) / mean(Fwd, Rev); positive = forward-biased

Interpreting Results

The Detection_Level column in resolveS output indicates the confidence of strand detection. Higher levels mean more agreement among top rRNA sequences.

Confidence Level Table (from highest to lowest)

MAPQ_Filter	Detection_Level	Confidence	Description
MAPQ-20	3of3	Highest	Top 3 rRNA sequences all agree
MAPQ-20	4of5	High	4 of top 5 rRNA sequences agree
MAPQ-20	6of7	High	6 of top 7 rRNA sequences agree
MAPQ-20	7of8	Moderate	7 of top 8 rRNA sequences agree
MAPQ-10	3of3 ~ 7of8	Moderate	Same as above but required lower MAPQ threshold
MAPQ-3	3of3 ~ 7of8	Low	Required very low MAPQ threshold
MAPQ-1	3of3 ~ 7of8	Low	Most permissive threshold (still excludes MAPQ=0 multi-mappers)
Any	*-fallback	Lowest	Progressive detection failed; used global Rel_Diff as fallback

Key points:

• MAPQ-20 results are most reliable (high-quality alignments only)
• Lower MAPQ thresholds (10/3/1) are tried progressively only when higher thresholds yield all-insufficient-fallback
• *-fallback suffix indicates the progressive per-rRNA-sequence detection failed and the final result is based on global statistics
• Common fallback types: only-N-rRNAs-fallback, 4of8-split-fallback, multi-of8-fallback, all-insufficient-fallback

Technical Details

Pipeline overview (Default: resolveS)

The default resolveS uses single-end or paired-end alignment (or an explicitly typed pre-aligned SAM) and performs progressive per-rRNA-sequence detection:

flowchart TD
    A["Input: single FASTQ, paired FASTQ, or SAM via -a -m"] --> B["(optional) bowtie2 alignment"]
    B --> C["SAM"]
    C --> D["auto_counting_withChrom.pl"]
    D --> E["Progressive rRNA sequence voting + adaptive MAPQ"]
    E --> F["Strand_Type + Detection_Level"]

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Key points:

• Uses single-end alignment (-1 sample.fq) or paired-end alignment (-1 R1.fq -2 R2.fq)
• Accepts a pre-aligned SAM file only with explicit mode (-a aligned.sam -m 1 or -a aligned.sam -m 2)
• Progressive detection based on top rRNA sequences (3/3 → 4/5 → 6/7 → 7/8), with fallback when needed
• Adaptive MAPQ thresholds: 20 → 10 → 3 → 1 (only when necessary)
• Default: 5M reads for single-end FASTQ or 5M read pairs for paired-end FASTQ (-u 5)

Decision logic (current implementation)

MAPQ Progressive Strategy (resolveS only)

The resolveS script uses an adaptive MAPQ strategy to maximize detection success:

flowchart TD
    A["Start with MAPQ >= 20"] --> B["Run per-rRNA-sequence detection"]
    B --> C{"Result = all-insufficient-fallback?"}
    C -->|No| D["Return result"]
    C -->|Yes| E{"More MAPQ levels?"}
    E -->|Yes| F["Try lower MAPQ: 10 → 3 → 1"]
    F --> B
    E -->|No| G["Return best available result"]

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This ensures high-quality results when possible, but falls back to lower MAPQ thresholds when necessary. The lowest tier is MAPQ >= 1 (not 0), so MAPQ=0 pure multi-mappers are excluded even in the most permissive fallback.

Multi-mapping reads

rRNA sequences are highly repetitive, so a single read can align equally well to multiple reference copies. resolveS handles such multi-mapping reads as follows:

• Bowtie2 runs in default mode (no -k/-a), so each read produces exactly one best alignment — multiple alignments per read are never reported (no secondary 0x100 records).
• For a multi-mapping read, Bowtie2 places it pseudo-randomly at one location and assigns a low MAPQ (0/1).
• Counting therefore filters by MAPQ (>= 20 by default; see ladder above). This removes the pseudo-randomly placed multi-mappers, so the strand-bias signal is contributed only by uniquely mapped reads.
• In the paired-end pipeline, only R1 is counted and a proper pair (0x2) is required; filtering R1 by MAPQ effectively discards the whole multi-mapping pair.

Strand Type Determination

flowchart TD
    A["Rel_Diff = (Fwd-Rev) / mean(Fwd,Rev)<br/>Binomial two-tailed p: X~Binomial(total, 0.5), statistic=fwd"] --> B{"low coverage?"}
    B -->|yes| C["insufficient-data"]
    B -->|no| D{"abs(Rel_Diff) <= 0.6?"}
    D -->|yes| E["fr-unstranded"]
    D -->|no| F{"p >= 0.01?"}
    F -->|yes| E
    F -->|no| G{"Rel_Diff > 0?"}
    G -->|yes| H["fr-secondstrand"]
    G -->|no| I["fr-firststrand"]

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Full Program Documentation

Parameters Explanation

resolveS (single-end/paired-end FASTQ or pre-aligned SAM)

Single sample mode:

• -1 <file>: FASTQ file. With only -1, runs single-end FASTQ mode; with -1 and -2, runs paired-end FASTQ mode.
• -2 <file>: R2 fastq file for paired-end FASTQ mode.
• -a <file>: Pre-aligned SAM file mode: skip alignment and use an existing SAM file.
• -m <1|2>: SAM input only. 1 = single-end SAM, 2 = paired-end SAM. Required with -a or SAM batch; invalid for FASTQ input.
• -p <int>: Number of alignment threads (default: 8). Ignored for SAM input.
• -u <number>: FASTQ alignment limit, in millions (default: 5). In single-end mode this means reads; in paired-end mode this means read pairs. Ignored for SAM input.
• -r <path>: Reference genome database path, can be any bowtie2 index (default: ../ref_default/default).
• -o <file>: Output the inference results to the file (default: stdout).
• -d: Debug mode - keep intermediate files and print per-rRNA-sequence summary to stderr.
• -h: Show help message and exit.

Batch mode:

• -b <meta_data_file>: Metadata file (auto-detected):
  • Single-end FASTQ batch: 1 FASTQ path per line
  • Paired-end FASTQ batch: 2 columns (tab-separated R1_path<TAB>R2_path)
  • SAM batch: 1 SAM path per line and requires -m 1 or -m 2

Intermediate Files

When using -d (debug mode), the following intermediate files are preserved:

• resolveS.sam: The alignment output from bowtie2 for non-batch FASTQ runs.
• resolveS.sample_0001.sam, resolveS.sample_0002.sam, ...: Per-sample alignment output for FASTQ batch runs.
• stderr output: When -d is enabled, auto_counting_withChrom.pl prints per-rRNA-sequence distribution tables to stderr, including rRNA sequence name, forward/reverse counts, total, major strand direction, and strand type for each rRNA sequence.

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What's New (v0.2.0)

• resolveS supports single-end FASTQ input (-1 sample.fastq.gz) and keeps paired-end FASTQ input (-1 R1 -2 R2).
• Pre-aligned SAM input now requires explicit mode: -m 1 for single-end SAM and -m 2 for paired-end SAM.
• Batch metadata supports homogeneous single-end FASTQ, paired-end FASTQ, or SAM batches.
• Default pipeline is align → auto_counting_withChrom.pl (mode-aware progressive per-rRNA-sequence voting + adaptive MAPQ).
• Output defaults to stdout; use resolveS -o to write results to a file.
• Strand calls now require both abs(Rel_Diff) > 0.6 and binomial two-tailed p < 0.01; otherwise the rRNA sequence is treated as fr-unstranded.