isolens 0.5.3

Isoform-resolution epitranscriptome analysis

Isoform-aware RNA modification and poly(A) tail analysis for direct RNA sequencing data.

IsoLens

IsoLens is a Python toolkit for isoform-aware analysis of RNA modifications and poly(A) tail lengths from Oxford Nanopore direct RNA sequencing data, explicitly accounting for transcript assignment uncertainty to enable accurate transcript-level profiling.

---

Why IsoLens?

Most long-read RNA analysis tools either analyze RNA modifications or poly(A) tails without discrimination of transcript isoforms or only use reads uniquely mappp to a single isoform. IsoLens propagates transcript assignment probabilities from Oarfish throughout both modification and poly(A) analyses, enabling more accurate transcript-level estimates for genes with complex isoform structure.

Key capabilities:

• Efficient HDF5 and Parquet outputs for large-scale studies
• Isoform-aware RNA modification profiling at single-nucleotide resolution
• Modification site co-occurrence and correlation analysis
• Differential modification testing between conditions, isoforms, and genes
• Gene-level aggregation of modification sites across isoforms
• Transcript-level poly(A) tail length estimation with uncertainty propagation
• Differential poly(A) testing between conditions and isoforms
• Gene-level aggregation of transcript-level modification and poly(A) data
• Detection of bimodal distribution of poly(A) tail lengths

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Quick Start

Install:

pip install isolens

IsoLens requires sorted transcriptome alignments and Oarfish read assignment probabilities as input:

# index transcriptome
minimap2 -x map-ont -d transcriptome.mmi transcriptome.fa.gz

# mapping (minimap2/rammap)
samtools fastq -T MM,ML,pt reads.bam \
    | minimap2 --eqx -N 100 -ax map-ont -y -t8 transcriptome.mmi - \
    | samtools view -@8 -b -o alignments.bam

# run oarfish
oarfish -j 16 -a alignments.bam -o oarfish_out --filter-group no-filters \
    --model-coverage --write-assignment-probs=compressed

# sort and index alignments
samtools sort -@ 8 -o alignments.sorted.bam alignments.bam
samtools index alignments.sorted.bam

Build transcript-level modification matrices:

python -m isolens.mod_scan -b alignments.sorted.bam -a oarfish_out.prob.lz4 -o mod_scan.h5

Summarize modification sites per transcript:

python -m isolens.mod_sites -i mod_scan.h5 -o sites.parquet

Estimate transcript-level poly(A) lengths:

python -m isolens.polya_calc -a oarfish_out.prob.lz4 -b reads.bam -o polya.tsv.gz -z

---

Contents

• Pipeline Overview
• Installation
• Modules
• Python API
• Input Data Requirements
• Development
• Example Data
• License

---

Pipeline Overview

Oarfish assignment ──┐
                      ├── mod_scan (HDF5) ──┬── mod_sites ──► transcript-level site summary (.parquet/.tsv)
    BAM (alignment) ─┘       │                     │
                             │                     ├─► mod_gene ──► gene-level site summary (.parquet/.tsv)
                             │                     │
                             │                     │      └── mod_dmcg ──► gene-level differential
                             │                     │                       modification across conditions
                             │                     │
                             │─────────────────────├─► mod_corr ──► intra-transcript site correlations (.parquet/.tsv)
                             │                     │                + PDF heatmaps (-d)
                             │                     │
                             │─────────────────────├─► mod_dmc ───► differential modification
                             │                     │                across conditions
                             │                     │
                             │─────────────────────├─► mod_dmt ───► differential modification
                                                                    across isoforms of the same gene

Oarfish assignment ────┐ 
                        ├── polya_calc (.parquet/.tsv) ──┬─► polya_merge ───► pool replicates (.tsv/.parquet)
BAM (reads/alignment) ─┘                                 ├─► polya_dpc ───► condition diff (.tsv/.parquet)
                                                         ├─► polya_dpt ───► isoform diff (.tsv/.parquet)
                                                         ├─► polya_gene ───► gene-level (.tsv/.parquet)
                                                         └─► polya_bimodal ─► bimodality calls (.tsv/.parquet)

Outputs

Command	Output
mod_scan	HDF5 read × transcript position modification matrices
mod_sites	Transcript-level per-site modification summaries
mod_gene	Gene-level per-site modification summaries
mod_corr	Pairwise modification site correlations within the same transcript
mod_dmc	Transcript-level differentially modified sites between conditions)
mod_dmt	Differential modification of sites between isoforms
mod_dmcg	Gene-level differentially modified sites between conditions
polya_calc	Transcript-level poly(A) estimates
polya_merge	Merged replicate poly(A) estimates
polya_dpc	Genome-wide differential poly(A) between conditions
polya_dpt	Pairwise differential poly(A) between isoforms
polya_gene	Gene-level poly(A) summaries
polya_bimodal	Transcript-level bimodal poly(A) tail length detection

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Installation

pip install isolens

---

Modules

mod_scan — HDF5 read × position matrices

Generates a single HDF5 file containing transcript-specific read-by-position modification matrices. For each transcript, IsoLens constructs an (n_reads × transcript_length) uint8 matrix that encodes the nucleotide state at every position for every aligned read. Nucleotide states are parsed using logic consistent with that implemented in modkit, ensuring compatibility with standard Oxford Nanopore modification annotations.

Encoding: 0 = uncovered, 1 = canonical match, 2 = mismatch, 3 = deletion, 4+ = tracked modification types, 254 = untracked modification, 255 = failed (all states below probability threshold).

python -m isolens.mod_scan \
  -b alignments.bam \
  -a oarfish.lz4 \
  -o mod_scan.h5 \
  -c 0.95 \
  -t 4 -v

Key options:

Flag	Description	Default
-b, --bam	Transcriptome BAM alignment	(required)
-a, --oarfish	Oarfish assignment probability file (.lz4 or plain text)	(required)
-o, --output	Output HDF5 path	(required)
-c, --mod-cutoff	Modification probability threshold	0.95
-m, --mod-type	Modification types to scan for (SAM code suffixes)	a,m,17596,17802,19228,69426,19229,19227
-p, --min-asp	Minimum assignment probability filter	0.0
-d, --max-depth	Max reads per transcript	5000
-t, --threads	Worker threads for parallel processing	1
-v, --verbose	Print progress to stderr	off

HDF5 structure: /transcripts/<tx_name>/matrix (uint8), read_ids (string), read_weights (float32); /modification_codes (attrs); /metadata (attrs).

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mod_sites — Per-position modification summaries

Reads the HDF5 output generated by mod_scan and summarizes modification information into a Parquet or TSV file, with one row per (transcript, position, modification_type). For each site, IsoLens reports modification levels, read coverage, and modification counts, while tracking mismatches, deletions, other-modifications, and failed calls as separate categories.

When the same modification probability threshold is used (--mod-cutoff in mod_scan and --filter-threshold in modkit), the combination of mod_scan and mod_sites produces unweighted modification counts that match those generated by modkit pileup.

Multiple HDF5 files can be provided — reads for the same transcript are pooled across all files before computing statistics.

python -m isolens.mod_sites \
  -i mod_scan.h5 \
  -o sites.parquet

# With genomic coordinate mapping
python -m isolens.mod_sites \
  -i mod_scan.h5 \
  -o sites.parquet \
  -g annotations.gtf

Key options:

Flag	Description	Default
-i, --h5	Input HDF5 file(s) from mod_scan (accepts multiple)	(required)
-o, --output	Output file	(required)
-f, --format	Output format: parquet or tsv	parquet
-z, --gzip	Gzip-compress TSV output	off
-s, --sites	Predefined modification sites TSV (tx_name, posn)	all sites
-p, --min-asp	Minimum assignment probability filter	0.0
-x, --transcripts	Only process specified transcript IDs	all
-g, --gtf	GTF annotation for genomic coordinate mapping	off
-v, --verbose	Print progress	off

Output columns (23): transcript_id, position, mod_type, n_modified, wt_modified, n_unmodified, wt_unmodified, n_canonical, wt_canonical, n_othermod, wt_othermod, n_mismatch, wt_mismatch, n_deletion, wt_deletion, n_failed, wt_failed, mod_level, wt_mod_level, gene_id*, chrom*, strand*, gpos* (*requires --gtf).

---

mod_corr — Pairwise modification site correlation

Identifies cooperative or antagonistic relationships between modification sites within the same transcript. Computes both within-type and cross-type correlations using weighted 2×2 contingency tables. Multiple HDF5 files can be provided — reads for the same transcript are pooled across all files.

Metrics: Phi coefficient (Pearson's r for binary variables), odds ratio with Haldane-Anscombe correction, p-value via t-distribution, Benjamini-Hochberg FDR q-value (per-transcript), and mutual information. Both unweighted and assignment-probability-weighted variants are computed for every metric.

python -m isolens.mod_corr \
  -i mod_scan.h5 \
  -s sites.parquet \
  -o correlations.parquet \
  -m 10 -l 0.05 -c 10

Key options:

Flag	Description	Default
-i, --h5	Input HDF5 file(s) from mod_scan (accepts multiple)	(required)
-s, --sites	Site summary from mod_sites (Parquet or TSV/TSV.GZ)	(required)
-o, --output	Output file	(required)
-m, --min-mod-reads	Minimum n_modified for a site to be considered	2
-l, --min-mod-level	Minimum mod_level for a site to be considered	0.05
-c, --min-coverage	Minimum total depth for a site to be considered	10
-p, --min-asp	Minimum assignment probability filter	0.0
-f, --format	Output format: parquet or tsv	parquet
-z, --gzip	Gzip-compress TSV output	off
-d, --plot-dir	Generate pyramid heatmap PDFs per transcript in this directory	off
-t, --metric	Statistic to visualize in heatmaps (corr, wcorr, mi, wmi, or, wor)	wcorr
-x, --transcripts	Only process specified transcript IDs	all
-v, --verbose	Print progress	off

Output columns (23): transcript_id, site1, site2, mod_type1, mod_type2, n11, n10, n01, n00 (2×2 contingency counts), w11, w10, w01, w00 (weighted), corr, pvalue, qvalue (unweighted Pearson + BH FDR), wcorr, wpvalue, wqvalue (weighted Pearson + BH FDR), mi, wmi (mutual information), or, wor (log2 odds ratio).

When -d is used, generates rotated triangular heatmap PDFs per transcript showing the correlation matrix and site positions along the transcript body.

---

mod_gene — Gene-level modification aggregation

Aggregates transcript-level modification site summaries to the gene level by summing per-position counts grouped by (gene_id, chrom, strand, gpos, mod_type). Requires the site summary to have been generated with --gtf so that genomic coordinate columns are present.

python -m isolens.mod_gene \
  -i sites.parquet \
  -o gene_sites.parquet

Key options:

Flag	Description	Default
-i, --input	Site summary from mod_sites (must have GTF columns)	(required)
-o, --output	Output file	(required)
-f, --format	Output format: parquet or tsv	parquet
-z, --gzip	Gzip-compress TSV output	off
-v, --verbose	Print progress	off

Output columns: gene_id, chrom, strand, gpos, mod_type, and all per-position count/weight columns summed across transcripts. mod_level and wt_mod_level are recomputed from the summed counts.

---

mod_dmc — Differential modification between conditions

Compares modification levels between two experimental conditions at each (transcript, position, mod_type) site using read-level weighted logistic regression. Reads from multiple HDF5 files are pooled within each condition before testing.

python -m isolens.mod_dmc \
  --h5-1 cond1_rep1.h5 cond1_rep2.h5 \
  --h5-2 cond2_rep1.h5 cond2_rep2.h5 \
  --sites-1 cond1_sites.parquet \
  --sites-2 cond2_sites.parquet \
  -o dmc_results.parquet -v

Key options:

Flag	Description	Default
--h5-1	HDF5 file(s) for condition 1	(required)
--h5-2	HDF5 file(s) for condition 2	(required)
--sites-1	Site summary for condition 1	(required)
--sites-2	Site summary for condition 2	(required)
-o, --output	Output file	(required)
-f, --format	Output format: parquet or tsv	parquet
-z, --gzip	Gzip-compress TSV output	off
-p, --min-asp	Minimum assignment probability filter	0.0
-x, --transcripts	Only process specified transcript IDs	all
-v, --verbose	Print progress	off

Output columns (25): transcript_id, position, mod_type, gene_id, chrom, strand, gpos, n_modified_1, n_unmodified_1, n_modified_2, n_unmodified_2, wt_modified_1, wt_unmodified_1, wt_modified_2, wt_unmodified_2, mod_level_1, mod_level_2, wt_mod_level_1, wt_mod_level_2, delta_mod_level, delta_wt_mod_level, log2_or, p_value, q_value (BH FDR).

Method: Weighted logistic regression with Haldane-Anscombe correction for zero counts. Wald test p-values with global Benjamini-Hochberg FDR correction.

---

mod_dmt — Differential modification between isoforms

Compares modification levels between transcript isoforms that share a genomic locus, using read-level weighted logistic regression. Transcripts are grouped by (gene_id, chrom, gpos, strand, mod_type) and all isoform pairs within each group are tested.

python -m isolens.mod_dmt \
  -i pooled.h5 \
  -s sites_with_gtf.parquet \
  -o dmt_results.parquet -v

Key options:

Flag	Description	Default
-i, --h5	Input HDF5 file(s) from mod_scan	(required)
-s, --sites	Site summary from mod_sites (must have --gtf columns)	(required)
-o, --output	Output file	(required)
-f, --format	Output format: parquet or tsv	parquet
-z, --gzip	Gzip-compress TSV output	off
-p, --min-asp	Minimum assignment probability filter	0.0
-x, --transcripts	Only consider specified transcript IDs	all
-v, --verbose	Print progress	off

Output columns (25): gene_id, chrom, gpos, strand, mod_type, transcript_id_1, transcript_id_2, position_1, position_2, mod_level_1, mod_level_2, wt_mod_level_1, wt_mod_level_2, delta_mod_level, delta_wt_mod_level, n_modified_1, n_unmodified_1, n_modified_2, n_unmodified_2, wt_modified_1, wt_unmodified_1, wt_modified_2, wt_unmodified_2, log2_or, p_value, q_value (BH FDR).

Method: Same weighted logistic regression backend as mod_dmc. Transcripts are pre-loaded from HDF5 for efficient paired testing. Global BH FDR correction.

---

mod_dmcg — Gene-level differential modification

Compares modification levels between two conditions at the gene level using Fisher's exact test. Takes gene-level site summaries from mod_gene as input — no HDF5 or read-level data required.

python -m isolens.mod_dmcg \
  --sites-1 cond1_genes.parquet \
  --sites-2 cond2_genes.parquet \
  -o dmcg_results.parquet -v

Key options:

Flag	Description	Default
--sites-1	Gene-level summary for condition 1 (from mod_gene)	(required)
--sites-2	Gene-level summary for condition 2 (from mod_gene)	(required)
-o, --output	Output file	(required)
-f, --format	Output format: parquet or tsv	parquet
-z, --gzip	Gzip-compress TSV output	off
-v, --verbose	Print progress	off

Output columns (25): gene_id, chrom, strand, gpos, mod_type, n_modified_1, n_unmodified_1, n_modified_2, n_unmodified_2, wt_modified_1, wt_unmodified_1, wt_modified_2, wt_unmodified_2, mod_level_1, mod_level_2, wt_mod_level_1, wt_mod_level_2, delta_mod_level, delta_wt_mod_level, log2_or, p_value, q_value (unweighted Fisher + BH FDR), w_log2_or, w_p_value, w_q_value (weighted Fisher with rounded counts + BH FDR).

Method: Two Fisher's exact tests per matched gene-position — one on raw integer counts, one on wt_modified / wt_unmodified rounded to the nearest integer. Global BH FDR correction applied separately to each set of p-values.

---

polya_calc — Poly(A) tail length estimation

Extracts poly(A) tail lengths from Dorado's pt:i BAM tags, weighted by Oarfish assignment probabilities.

python -m isolens.polya_calc \
  -a oarfish.lz4 \
  -b reads.bam \
  -o polya.tsv.gz -z

Key options:

Flag	Description	Default
-a, --oarfish	Oarfish assignment probability file (.lz4 or plain text)	(required)
-b, --bam	BAM file with pt:i poly(A) tags (from Dorado)	(required)
-o, --output	Output TSV file	(required)
-z, --gzip	Gzip-compress output	off
-l, --log	Log-transform lengths to compute weighted geometric mean	off

Output columns (6): transcript_id, n_reads, total_wt (sum of assignment probabilities), wmlen (weighted mean poly(A) length), weights (comma-separated assignment probabilities), lengths (comma-separated raw poly(A) lengths).

---

polya_merge — Merge poly(A) replicates

Combines two poly(A) TSV files from separate replicates, recomputing weighted average tail lengths from the pooled per-read data.

python -m isolens.polya_merge \
  -i1 rep1.tsv.gz \
  -i2 rep2.tsv.gz \
  -o merged.tsv.gz -z

Key options:

Flag	Description	Default
-i1, --input1	First input TSV file (gzipped or raw)	(required)
-i2, --input2	Second input TSV file (gzipped or raw)	(required)
-o, --output	Output TSV file	(required)
-z, --gzip	Gzip-compress output	off
-l, --log	Log-transform lengths to compute weighted geometric mean	off

Output columns (6): Same schema as polya_calc — transcript_id, n_reads, total_wt, wmlen, weights, lengths. Per-transcript probability and length lists from both files are concatenated before recalculating wmlen.

---

polya_dpc — Genome-wide differential poly(A) by condition

Compares poly(A) length distributions between two experimental conditions at each shared feature (transcript or gene) using three weighted two-sample tests: Kolmogorov-Smirnov, Welch's t-test, and rank-sum (Mann-Whitney U). P-values are computed with Kish's effective sample size correction, and global Benjamini-Hochberg FDR correction is applied separately per test type.

Accepts both transcript-level (from polya_calc) and gene-level (from polya_gene) input; the feature ID column is auto-detected from the input header.

python -m isolens.polya_dpc \
  -c1 control.tsv.gz \
  -c2 treatment.tsv.gz \
  -o dpc.tsv.gz -z

Key options:

Flag	Description	Default
-c1, --condition1	Condition 1 TSV/TSV.GZ file	(required)
-c2, --condition2	Condition 2 TSV/TSV.GZ file	(required)
-o, --output	Output TSV file	(required)
-f, --format	Output format: parquet or tsv	tsv
-z, --gzip	Gzip-compress TSV output	off
-p, --min-asp	Minimum assignment probability threshold	0.0
-n, --min-pareads	Minimum reads with effective poly(A) length	5
-l, --log	Log-transform lengths for geometric means/medians and hypothesis tests on log-scale	off

Output columns (20): feature_id, n_reads_1, total_wt_1, wmlen_1, wmedlen_1, n_reads_2, total_wt_2, wmlen_2, wmedlen_2, ks_stat, ks_p_value, ks_q_value, wmlen_diff, t_stat, t_p_value, t_q_value, wmedlen_diff, u_stat, u_p_value, u_q_value. The feature ID column header is transcript_id for transcript-level input, gene_id for gene-level input.

Method: Weighted KS test, weighted Welch's t-test, and weighted rank-sum test, each with Kish's effective sample size. Global BH FDR correction applied separately to each p-value column.

---

polya_dpt — Pairwise differential poly(A) between isoforms

Compares poly(A) length distributions between all pairs of transcript isoforms within the same gene using the same three weighted two-sample tests as polya_dpc. Transcripts are grouped into genes using either a GTF annotation file (-g) or an existing gene_id column in the input.

python -m isolens.polya_dpt \
  -i polya.tsv.gz \
  -g annotation.gtf \
  -o dpt.tsv.gz -z

Key options:

Flag	Description	Default
-i, --input	Transcript-level poly(A) TSV file (gzipped or raw)	(required)
-g, --gtf	GTF annotation for transcript-to-gene mapping (gzipped or raw)	none
-o, --output	Output TSV file	(required)
-f, --format	Output format: parquet or tsv	tsv
-z, --gzip	Gzip-compress TSV output	off
-p, --min-asp	Minimum assignment probability threshold	0.0
-n, --min-pareads	Minimum reads with effective poly(A) length	5
-l, --log	Log-transform lengths for geometric means/medians and hypothesis tests on log-scale	off

Output columns (21): gene_id, transcript_1, transcript_2, n_reads_1, total_wt_1, wmlen_1, wmedlen_1, n_reads_2, total_wt_2, wmlen_2, wmedlen_2, ks_stat, ks_p_value, ks_q_value, wmlen_diff, t_stat, t_p_value, t_q_value, wmedlen_diff, u_stat, u_p_value, u_q_value.

Method: Same weighted two-sample test backend as polya_dpc. All isoform pairs within each gene are tested. Global BH FDR correction applied separately per test type.

---

polya_gene — Transcript-to-gene aggregation

Aggregates transcript-level poly(A) estimates to the gene level using a user-provided tx_name → gene_id mapping file. Per-transcript probability and length lists are pooled before recalculating the weighted average.

python -m isolens.polya_gene \
  -i polya.tsv.gz \
  -m tx2gene.tsv \
  -o gene_polya.tsv.gz -z

Key options:

Flag	Description	Default
-i, --input	Input transcript poly(A) TSV file (gzipped or raw)	(required)
-g, --gtf	GTF/annotation file for transcript-to-gene mapping (gzipped or raw)	none
-o, --output	Output gene-level TSV file	(required)
-f, --format	Output format: parquet or tsv	tsv
-z, --gzip	Gzip-compress output	off
-l, --log	Log-transform lengths to compute weighted geometric mean	off

Output columns (6): gene_id, n_reads, total_wt (sum of pooled weights), wmlen (recalculated weighted mean), weights (comma-separated pooled probabilities), lengths (comma-separated pooled lengths).

---

polya_bimodal — Bimodal poly(A) tail length detection

Detects transcripts with bimodal poly(A) tail length distributions using a consensus of two independent methods applied to a variance-stabilising log-transform (log(length + 1)):

1. Weighted 1-D Gaussian Mixture Model — fits both 1-component and 2-component models via EM, then compares them via delta-BIC (ΔBIC > 10 is evidence for two modes).
2. Weighted KDE peak detection — counts density peaks via scipy.signal.find_peaks with a configurable prominence threshold.

A transcript is called bimodal only when both methods agree.

⚠️ Important: polya_bimodal should be run on transcript-level input (from polya_calc). While gene-level input (from polya_gene) is technically accepted, the results are likely misleading — pooling reads across all isoforms of a gene ignores transcript-specific poly(A) length variation. Different isoforms of the same gene can have genuinely distinct poly(A) length distributions; aggregating them at the gene level can create artefactual bimodality or mask true bimodality present in individual isoforms.

python -m isolens.polya_bimodal \
  -i polya.tsv.gz \
  -o bimodal.tsv.gz -z

Key options:

Flag	Description	Default
-i, --input	Input poly(A) TSV from polya_calc (transcript-level recommended)	(required)
-o, --output	Output bimodality results file	(required)
-f, --format	Output format: parquet or tsv	tsv
-z, --gzip	Gzip-compress TSV output	off
-l, --min-length	Drop reads with poly(A) length below this threshold	0.0
-p, --min-asp	Drop reads with assignment probability below this threshold	0.1
-e, --min-ess	Skip feature if effective sample size below this threshold	30.0
-k, --kde-prominence	Prominence threshold for KDE peak detection	0.05

Output columns (15): feature_id, id_type (transcript_id or gene_id), n_reads_raw, n_reads_filtered, total_wt_raw, total_wt_filtered, delta_bic, bic_k1, bic_k2, ll_k1, ll_k2, n_kde_peaks, bimodal_gmm, bimodal_kde, bimodal_call (consensus: True only if both GMM and KDE agree).

---

Python API

The core data structures and parsing functions are available for programmatic use:

from isolens._parsing import parse_oarfish

tx_names, prob_map, name_to_id = parse_oarfish("assignments.lz4")

# tx_names: list[str]
# prob_map: dict[int, list[TargetAssignment]]
# name_to_id: dict[str, int]

---

Input Data Requirements

File	Source	Required tags / format
Transcriptome BAM	minimap2 + Dorado	MM/ML (base modifications), pt:i (poly(A) tail length)
Oarfish assignments	Oarfish	Read-to-transcript probability map (.lz4 compressed or plain text)

The BAM should be coordinate-sorted and aligned to a transcriptome reference.

---

Development

git clone https://github.com/gxelab/isolens.git
cd isolens
pip install -e ".[dev]"

Run without installing:

uv run python -m isolens.mod_scan \
  -b ... \
  -a ... \
  -o ...

Run tests:

pytest

Lint and format:

ruff check src tests
ruff format src tests

---

Example Data

The examples/ directory contains a small test dataset (subset of two Drosophila transcripts) suitable for verifying changes.

python -m isolens.mod_scan \
  -b examples/example.txmap.bam \
  -a examples/example.lz4 \
  -o example.mod_scan.h5 \
  -c 0.95 -v

python -m isolens.mod_sites \
  -i example.mod_scan.h5 \
  -o example.sites.parquet

python -m isolens.polya_calc \
  -a examples/example.lz4 \
  -b examples/example.txmap.bam \
  -o example.polya.tsv.gz -z

---

License

Distributed under the MIT License.