RiboKast: Reference-Free Translation Prediction from Ribo-Seq Data Using K-mers

🧬 Overview

RiboKast is a reference-free computational pipeline for identifying translated ORFs from RNA sequences using ribosome profiling (ribo-seq) data. It queries k-mers from input RNA sequences against a ribo-seq k-mer index and analyzes frame-specific coverage to determine translation activity. It is especially useful for novel transcript discovery and non-model organisms, requiring no reference genome or annotation.

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⚙️ Prerequisites

Software

• KaMRaT: used via a Singularity or Docker image.
• SeqKit: for FASTA/FASTQ processing.

Install SeqKit with Conda:

conda install -c bioconda seqkit

Python Libraries

Install with pip:

pip install pandas matplotlib pillow

Required libraries:

• pandas
• matplotlib
• argparse
• re
• sys
• Pillow

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🧪 Protocol Workflow

A. Contig-Level Analysis

1. K-mer Generation: Input RNA sequences are split into overlapping k-mers.
2. K-mer Querying: Each k-mer is queried against a ribo-seq index using KaMRaT.
3. Coverage Summing: Ribo-seq signal is summed per reading frame (P1, P2, P3).
4. Frame Prediction: The frame with the highest signal is considered dominant.
5. Statistical Validation: A binomial test is performed (p < 0.05).
6. Contig Classification:
   • RS+P+: Signal present, dominant frame validated.
   • RS+P-: Signal present, but frame not validated.
   • RS-: No ribo-seq signal detected.

Figure – RiboKast strategy. The central part (Ribo-seq k-mers) represents an index of k-mers extracted from multiple ribo-seq experiments. In this index, all k-mers have the same size and codon position. K-mers from the query sequence are queried against the index to produce a coverage vector. The major phase (predicted phase) is inferred from the coverage vector. A binomial test evaluates the significance of the major phase compared to alternative phases. Optionally, the reading phase can be corrected (adding +1 or +2) based on the offset of codons in ribo-seq k-mers.

B. ORF-Level Peptide Translation

• RS+P+: Translate only in predicted frame.
• RS+P-: Translate in 3 frames(stranded).
• RS-: Translate in 3 frames(stranded).

Peptide Selection Rules

• For RS+P+: keep the peptide before the first stop.
• For RS+P- / RS-:
  • Take the peptide before the stop.
  • After the first stop, keep peptides starting with M until the next stop.

RS State Assignment

Each peptide's nucleotide region is re-queried in the ribo-seq index:

• A peptide is RS+ if it overlaps an RS+ region of the contig.
• A single contig can yield multiple peptides with different RS states.

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📁 Input Format

🧬 Example Input FASTA

>cont_7
AAAAAAAAAAAAAAAAAAAAAGCAGCAGCACCTCATCCATTCAAGTTTGATCACGAGATTGAAGCAATTCAGTGACATCTTTAGGCTACACTTCTAAATCTAGCTCTCTTGCTATTTTCACCACATCTATAGTTGCTTCCTCCACTGAAGTCTCAAACCCCTCAAAGATTCATGAGGGCTGGAATCAGGTTTTTCCAAAATAC
>cont_40
AAAAAAAAAAAAAAAACCATGCATTGCTGCTTTTCCTACCACTTCCAGTAAGAAAATGGG

Each sequence in the FASTA file represents a contig with a unique identifier (>cont_X). These RNA sequences serve as the input for k-mer generation and ribo-seq signal querying throughout the pipeline.

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📝 Optional: Annotation File

Predicted peptides can optionally be merged with external annotations to enrich biological interpretation. This is done by providing an annotation file (TSV format), typically generated using the Annotate-contigs tool.

If provided, the annotation file is automatically integrated during the final merge step, linking peptide-level predictions with known biological features.

📤 Output

• RS+P+: High ribo-seq signal and validated dominant frame.
• RS+P-: Signal present, frame not statistically validated.
• RS-: No ribo-seq signal.

📤 Output Example

The RiboKast pipeline produces results at both the contig level and the ORF (peptide) level.

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🧬 Contig-Level Output

File: KmersFromContigsQuerySumPhaseSeqTranslatedPvalueRSState
This file summarizes k-mer coverage and translation frame predictions for each contig.

Columns:

• contig: Nucleotide sequence of the contig
• ID_contig: Contig identifier
• P1, P2, P3: Read counts per frame
• Dominant_Phase: Most expressed reading frame (p1/p2/p3)
• Functional_dominant_phase: Frame used for translation (1/2/3)
• translated_seq: Protein sequence obtained from the selected frame
• p_value: Significance of the dominant frame (binomial test)
• RSState: Ribosome state classification

Example:

contig	ID_contig	P1	P2	P3	Dominant_Phase	Functional_dominant_phase	translated_seq	p_value	RSState
AAAAAAAAAA...	AAAAAAAAAAAA...CATAACC_unsSeq	2	0	0	p1	1	KKKKKKKKHNQVWQDQT...	0.1111	RS+P-
AAAAAAAAAA...	AAAAAAAAAAAA...CCCAGTG_unsSeq	2	0	0	p1	1	KKKKKKKPSEVQIYVHQ...	0.1111	RS+P-
TACTGATCAG...	AAAAAAAAAAAA...CTCCCG_unsRev	2	0	1	p1	1	Y*SAREFFFFFFFF	0.2593	RS+P-
GTTTTCCAGC...	AAAAAAAAAAAA...CATAACC_unsRev	1	1	1	p1	1	VFQQGSQTLMPSAAR...	1.0000	RS+P-
AAAAAAAA...	AAAAAAAAAAAA...CCCAGTG_unsSeq7	NA	NA	NA	NA	NA	NA	NA	RS-

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🧬 ORF-Level Output

File: ORFs_RSState.tsv
This table contains peptides extracted from contigs and their associated ribo-seq phasing information.

Columns:

• Contig_id: Identifier of the original contig
• Peptide: Translated peptide sequence
• P1, P2, P3: Read counts associated with the peptide region
• Dominant_Phase: Most expressed reading frame in that region
• Functional_dominant_phase: Predicted frame used for translation
• p_value: Binomial test result for the phase
• RSState: RS classification of the peptide (RS+P+, RS+P-, RS-)

Example:

Contig_id	Peptide	P1	P2	P3	Dominant_Phase	Functional_dominant_phase	p_value	RSState
AAAAAAAAAAAA...CATAACC_unsSeq	KKKKKKKKHNQVWQDQT	2	0	0	p1	1	0.1111	RS+P-
AAAAAAAAAAAA...CCCAGTG_unsSeq	KKKKKKKPSEVQIYVHQSGSN	2	0	0	p1	1	0.1111	RS+P-
AAAAAAAAAAAA...CATAACC_unsRev	FPAGVANSNAFSSQASNSL...	0	1	1	p2	2	1.0000	RS+P-

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🔎 RS+P+: Signal present and frame validated
🔎 RS+P-: Signal present but frame not validated
🔎 RS-: No ribo-seq signal detected

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📈 Visual Output

The pipeline generates plots showing:

• Translation frame periodicity
• Frame dominance per contig

Example:

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📜 Scripts Summary

The RiboKast pipeline is organized into modular scripts, with all configuration centralized in the config.sh file.

• RiboKast_cont_orf.sh: The main script that coordinates the full pipeline, including both contig-level and ORF-level analysis. It internally calls:

  • run_RiboKast.sh: Handles k-mer generation, ribo-seq querying, phasing analysis, and contig translation.
  • ORFpred.sh: Performs ORF prediction and extracts peptides based on RS state and translation logic.

• post_process_RiboKast.sh: Generates summary plots and visualizations to interpret and explore the results.

To run the pipeline, make sure the main scripts and the config.sh file are located in the same directory.
Then launch the main workflow with:

bash RiboKast_cont_orf.sh

• Additional scripts: Located in the SCRIPTS/ directory. These include:
  • K-mer generation tools
  • Statistical analysis (e.g., binomial testing)
  • Translation scripts
  • Peptide RS-state reassignment

🔧 Note: All paths, parameters, and environment variables are defined in the config.sh file. Make sure to update it before running the pipeline.

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🧠 Notes

• The default k-mer length is 20, which should match the k-mer length used to build the ribo-seq index.
• The default phase shift is 0 and is configurable.
  This value represents the reading frame offset and can be adjusted (e.g., +1 or +2) depending on the codon alignment within the ribo-seq k-mers.

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📂 Repository

All scripts and examples are available at:

https://github.com/Transipedia/RiboKast