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K-tools links sequence enrichment to candidate post-transcriptional regulators. Select RNA populations from your experiment, count overlapping k-mers, extract a signature, and compare that signature with RBP-associated sequence preferences.

Start here

1. Install the package and its dependencies.
2. Read the parameter guide: exact thresholds, numerical examples, background construction and output interpretation.
3. Run the simulated tutorial and compare the included demo output.
4. Use the API reference for complete call signatures and methodology for the statistical definitions.

Modules

KEA: comparisons between RNA populations

Use KEA when you have a reference group (for example, RNAs sharing an experimental property) and a meaningful control group. At k=7 there are 4⁷ = 16,384 sequence types. Two complementary analyses compare pooled group frequencies and per-transcript frequency distributions. Their intersection defines the integrated signature.

The code also exposes a rank-difference approach and a continuous score-correlation analysis. These are distinct selections, not interchangeable statistical significance estimates. Methodology describes their definitions.

KRS: one RNA against a population

KRS ranks the target's normalized frequency separately for each k-mer among transcripts containing that k-mer. A k-mer at percentile ≥0.95 enters the default signature. The target participates in the ranking. This is not a selection of the top 5% of k-mer types within the target.

K-RBP: candidate RNA-binding proteins

K-RBP compares signature and background scores for each RBP–cell-line profile. A one-sided Mann–Whitney U test asks whether signature scores are higher; multiple-testing corrections and Cliff's delta support interpretation. When working from 7-mers against a 5-mer matrix, overlapping 5-mers are deduplicated before analysis.

RBP association depends on the reference matrix and experimental context. It is not a calibrated interaction probability, proof of direct binding, or evidence that other RBPs are absent.

Inputs and resources

Input	Format	Needed for
Reference/control sequences	FASTA, uppercase DNA alphabet A/C/G/T, transcript orientation	KEA
Target plus background sequences	FASTA, unique identifiers; target included exactly once	KRS
RBP reference matrix	Numeric TSV, k-mers as rows, RBP profiles as columns	Biological K-RBP
Transcript scores	Two-column DataFrame: transcript identifier and numeric score	Correlation analysis
Transcript annotation or FASTA	Annotation with ID or ensembl_transcript_id and cdna, or FASTA	Positional profiling

FASTA identifiers are the first whitespace-delimited token after >. Convert U to T for counting, resolve ambiguous nucleotides, and keep identifiers disjoint across reference/control files. The current counter does not automatically standardize the alphabet. Non-ACGT windows do not match the enumerated k-mer universe. Use sequences at least k nucleotides long.

Use matching annotation releases for sequences, region boundaries and mapped experimental observations.

Outputs

KEA writes all_kmers.tsv, enriched.txt and depleted.txt for each requested selection. KRS writes signature.txt and kmer_ranks.tsv; the full rank matrix is optional and can be large. K-RBP returns a DataFrame; save it explicitly with to_csv. The API reference records parameters, return values and output locations.

Scope of this distribution

The supported tutorial covers FASTA counting, KEA, KRS and continuous-mode K-RBP. Advanced genomic mapping and BEDTools helpers require separately configured tools and resources. See Known limitations before using them.

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K-map and CDS domain analysis

• K-map: similarity metrics, parameters and Sankey visualization
• K-CDS: inputs, frame, backgrounds and Fisher tests

K-map: compare individual RNA profiles

Rank candidate transcripts against a query using complete normalized k-mer profiles. Visualize selected pairs against a background using binned Sankey-style rank alignment. See K-map.

K-CDS

Test selected CDS occurrence overlaps with UniProt genomic features using control transcripts or a shuffled-position null. Inputs, frame filtering, QC and statistical interpretation are described in K-CDS.

K-miR

miRNA family annotation: orientation, parameters, scores and plots · CDR1as worked example