Fi-NeMo

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Identifying transcription factor binding sites from neural network contribution scores

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# Fi-NeMo: Finding Neural Network Motifs

**Fi-NeMo** (**Fi**nding **Ne**ural Network **Mo**tifs) is a GPU-accelerated motif instance calling tool for identifying transcription factor binding sites from neural network contribution scores.

## Overview

Fi-NeMo implements a competitive optimization approach using proximal gradient descent to identify motif instances by solving a sparse linear reconstruction problem. Unlike traditional sequence-based methods, Fi-NeMo leverages context-aware importance scores from deep neural networks, enabling the identification of both high-confidence canonical motif instances and low-prevalence cofactor motif instances that are often missed by conventional approaches.

### Features

- **GPU-accelerated optimization**: Fast processing of large contribution score datasets using PyTorch
- **Competitive motif assignment**: Biologically-motivated algorithm that resolves similar motifs
- **Context-aware analysis**: Leverages neural network importance scores for improved sensitivity and specificity
- **Comprehensive evaluation**: Built-in tools for assessing and visualizing motif discovery quality and hit calling performance
- **Multiple input formats**: Support for bigWig, HDF5, and TF-MoDISco output formats

## Method

Fi-NeMo solves motif instance calling as an optimization problem that reconstructs contribution score tracks as sparse linear combinations of motif CWMs, formulated as an L1-regularized linear model. This competitive assignment encourages overlapping motif instances to be resolved in a meaningful way, with stronger matches receiving higher coefficients while weaker or redundant matches are suppressed.

## References

Fi-NeMo is described in:
> Wang, Ramalingam, Tseng, Schreiber, et al. "Decoding predictive motif lexicons and syntax from deep learning models of transcription factor binding profiles." (manuscript in preparation)

Related tools:
- [TF-MoDISco](https://github.com/kundajelab/tfmodisco): *De novo* motif discovery from importance scores
- [BPNet](https://github.com/kundajelab/bpnet): Deep learning models for TF binding prediction
- [ChromBPNet](https://github.com/kundajelab/chrombpnet): Deep learning models for chromatin accessibility prediction

## Installation

### Installing from PyPI

```sh
pip install finemo
```

### Installing from Source

#### Clone the GitHub Repository

```sh
git clone https://github.com/kundajelab/Fi-NeMo
cd Fi-NeMo
```

#### Create a Conda Environment with Dependencies

This step is optional but recommended for conda users.

```sh
conda env create -f environment.yml -n $ENV_NAME
conda activate $ENV_NAME
```

#### Install the Python Package

```sh
pip install --editable .
```

#### Update an Existing Installation

To update, simply fetch the latest changes from the GitHub repository.

```sh
git pull
```

If needed, update the conda environment with the latest dependencies and reinstall the package.

```sh
conda env update -f environment.yml -n $ENV_NAME --prune
pip install --editable .
```

## Data Inputs

Required:

- Contribution scores for peak sequences in bigWig format, [ChromBPNet H5](https://github.com/kundajelab/chrombpnet/wiki/Generate-contribution-score-bigwigs#output-format) format, [BPNet H5](https://github.com/kundajelab/bpnet-refactor?tab=readme-ov-file#3-compute-importance-scores) format, or [tfmodisco-lite](https://github.com/jmschrei/tfmodisco-lite/tree/main#running-tfmodisco-lite) input format.
- Motif CWMs in [tfmodisco-lite](https://github.com/jmschrei/tfmodisco-lite/tree/main#running-tfmodisco-lite) H5 output format.

Recommended:

- Peak region coordinates in uncompressed [ENCODE NarrowPeak](https://genome.ucsc.edu/FAQ/FAQformat.html#format12) format.

## API Documentation

For Fi-NeMo's Python API documentation, see: https://kundajelab.github.io/Fi-NeMo/finemo.html

## Command-Line Usage

Fi-NeMo provides a command-line utility named `finemo` for motif instance calling and analysis. The typical workflow involves three main steps:

1. **Preprocessing**: Transform input contributions and sequences into a unified format
2. **Hit Calling**: Identify motif instances using the Fi-NeMo algorithm  
3. **Reporting and Analysis**: Generate visualizations and perform post-processing

For detailed options for any subcommand, run `finemo <subcommand> -h`.

### Preprocessing

The following commands transform input contributions and sequences into a compressed `.npz` file for quick loading. This file contains:

- `sequences`: A one-hot-encoded sequence array (`np.int8`) with dimensions `(n, 4, w)`, where `n` is the number of regions, and `w` is the width of each region. Bases are ordered as ACGT.
- `contributions`: A contribution score array (`np.float16`) with dimensions `(n, 4, w)` for hypothetical scores or `(n, w)` for projected scores only.

If peak region coordinates are provided, the output also includes:

- `chr`: An array of chromosome names (`np.dtype('U')`) with dimensions `(n)`.
- `chr_id`: An array of numerical chromosome IDs (`np.uint32`) with dimensions `(n)`.
- `peak_region_start`: An array of peak region start coordinates (`np.int32`) with dimensions `(n)`.
- `peak_id`: An array of numerical peak region IDs (`np.uint32`) with dimensions `(n)`.
- `peak_name`: An array of peak region names (`np.dtype('U')`) with dimensions `(n)`.

Preprocessing commands do not require GPU.

#### Shared arguments

- `-o/--out-path`: The path to the output `.npz` file. Required.
- `-w/--region-width`: The width of the input region centered around each peak summit. Default is 1000.
- `-p/--peaks`: A peak regions file in ENCODE NarrowPeak format. This is required for `finemo extract-regions-bw` and optional for all other preprocessing commands.

#### `finemo extract-regions-bw`

Extract sequences and contributions from FASTA and bigWig files.

> **Note** BigWig files only provide projected contribution scores.
Thus, the output only supports analyses based solely on projected contributions.

Usage: `finemo extract-regions-bw -p <peaks> -f <fasta> -b <bigwigs> -o <out_path> [-w <region_width>]`

- `-f/--fasta`: A genome FASTA file. If an `.fai` index file doesn't exist in the same directory, it will be created.
- `-b/--bigwigs`: One or more bigWig files of contribution scores, with paths delimited by whitespace. Scores are averaged across files.

#### `finemo extract-regions-chrombpnet-h5`

Extract sequences and contributions from ChromBPNet H5 files.

Usage: `finemo extract-regions-chrombpnet-h5 -c <h5s> -o <out_path> [-p <peaks>] [-w <region_width>]`

- `-c/--h5s`: One or more H5 files of contribution scores, with paths delimited by whitespace. Scores are averaged across files.

#### `finemo extract-regions-bpnet-h5`

Extract sequences and contributions from BPNet H5 files.

Usage: `finemo extract-regions-bpnet-h5 -c <h5s> -o <out_path> [-p <peaks>] [-w <region_width>]`

- `-c/--h5s`: One or more H5 files of contribution scores, with paths delimited by whitespace. Scores are averaged across files.

#### `finemo extract-regions-modisco-fmt`

Extract sequences and contributions from tfmodisco-lite input `.npy`/`.npz` files.

Usage: `finemo extract-regions-modisco-fmt -s <sequences> -a <attributions> -o <out_path> [-p <peaks>] [-w <region_width>]`

- `-s/--sequences`: A `.npy` or `.npz` file containing one-hot encoded sequences.
- `-a/--attributions`: One or more `.npy` or `.npz` files of hypothetical contribution scores, with paths delimited by whitespace. Scores are averaged across files.

### Hit Calling

#### `finemo call-hits`

Identify motif instances in input regions using the Fi-NeMo competitive optimization algorithm. This is the core functionality that leverages TF-MoDISco CWMs to find motif occurrences in contribution score data.

Usage: `finemo call-hits -r <regions> -m <modisco_h5> -o <out_dir> [-p <peaks>] [-t <cwm_trim_threshold>] [-l <global_lambda>] [-b <batch_size>] [-J]`

- `-r/--regions`: A `.npz` file of input sequences, contributions, and coordinates. Created with a `finemo extract-regions-*` command.
- `-m/--modisco-h5`: A tfmodisco-lite output H5 file of motif patterns.
- `-o/--out-dir`: The path to the output directory.
- `-t/--cwm-trim-threshold`: The threshold to determine motif start and end positions within the full CWMs. Default is 0.3. If you need finer control over motif trimming, check out the `-T/--cwm-trim-thresholds` and `-R/--cwm-trim-coords` options.
- `-l/--global-lambda`: The L1 regularization weight determining the sparsity of hits. Default is 0.7.
- `-b/--batch-size`: The batch size used for optimization. Default is 2000.
- `-J/--compile`: Enable JIT compilation for faster execution. This option may not work on older GPUs.

#### Outputs

`hits.tsv`: The full list of coordinate-sorted hits with the following fields:

- `chr`: Chromosome name. `NA` if peak coordinates are not provided.
- `start`: Hit start coordinate from trimmed CWM, zero-indexed. Absolute if peak coordinates are provided, otherwise relative to the input region.
- `end`: Hit end coordinate from trimmed CWM, zero-indexed, exclusive. Absolute if peak coordinates are provided, otherwise relative to the input region.
- `start_untrimmed`: Hit start coordinate from trimmed CWM, zero-indexed. Absolute if peak coordinates are provided, otherwise relative to the input region.
- `end_untrimmed`: Hit end coordinate from trimmed CWM, zero-indexed, exclusive. Absolute if peak coordinates are provided, otherwise relative to the input region.
- `motif_name`: The hit motif name as specified in the provided tfmodisco H5 file.
- `hit_coefficient`: The regression coefficient for the hit, normalized per peak region.
- `hit_coefficient_global`: The regression coefficient for the hit, scaled by the overall importance of the region. **This is the primary hit score.**
- `hit_similarity`: The cosine similarity between the untrimmed CWM and the contribution score of the motif hit.
- `hit_importance`: The total absolute contribution score within the motif hit.
- `strand`: The orientation of the hit (`+` or `-`).
- `peak_name`: The name of the peak region containing the hit, taken from the `name` field of the input peak data. `NA` if peak coordinates are not provided.
- `peak_id`: The numerical index of the peak region containing the hit.

`hits_unique.tsv`: A deduplicated list of hits in the same format as `hits.tsv`. In cases where peak regions overlap, `hits.tsv` may list multiple instances of a hit, each linked to a different peak. `hits_unique.tsv` arbitrarily selects one instance per duplicated hit. **This file is empty if peak coordinates are not provided.**

`hits.bed`: A coordinate-sorted BED file of unique hits. It includes:

- `chr`: Chromosome name. `NA` if peak coordinates are not provided.
- `start`: Hit start coordinate from trimmed CWM, zero-indexed.  Absolute if peak coordinates are provided, otherwise relative to the input region.
- `end`: Hit end coordinate from trimmed CWM, zero-indexed, exclusive.  Absolute if peak coordinates are provided, otherwise relative to the input region.
- `motif_name`: Hit motif name, taken from the provided tfmodisco H5 file.
- `score`: (Left blank)
- `strand`: The orientation of the hit (`+` or `-`).

`peaks_qc.tsv`: Per-peak statistics. It includes:

- `peak_id`: The numerical index of the peak region.
- `nll`: The final regression negative log likelihood, proportional to the mean squared error (MSE).
- `dual_gap`: The final duality gap.
- `num_steps`: The number of optimization steps taken.
- `step_size`: The optimization step size.
- `global_scale`: The peak-level scaling factor, used to normalize by overall importance.
- `peak_id`: The numerical index of the peak region containing the hit.
- `chr`: The chromosome name. `NA` if peak coordinates are not provided.
- `peak_region_start`: The start coordinate of the peak region, zero-indexed. All zero if peak coordinates are not provided.
- `peak_name`: The name of the peak region, derived from the input peak data's `name` field. `NA` if peak coordinates are not provided.

`motif_data.tsv`: Per-motif metadata. It includes:

- `motif_id`: The numerical index of the motif. Note that the forward and reverse complement of a motif are treated separately.
- `motif_name`: The motif name used.
- `motif_name_orig`: The motif name as specified in the provided tfmodisco H5 file.
- `strand`: The orientation of the motif (`+` or `-`).
- `motif_start`: The start coordinate of the trimmed motif in the untrimmed motif, zero-indexed.
- `motif_end`: The end coordinate of the trimmed motif in the untrimmed motif, zero-indexed, exclusive.
- `motif_scale`: The motif scaling factor, used to normalize by motif importance.
- `lambda`: The motif-specific L1 regularization weight.

`motif_cwms.npy`: A NumPy array of motif CWMs, with dimensions `(n, 4, w)`, where `n` is the number of motifs, and `w` is the width of each motif. Bases are ordered as ACGT. The ordering of motifs corresponds to the `motif_id` field in `motif_data.tsv`.

`params.json`: The parameters used for hit calling.

#### Parameter Guidelines

**Sensitivity Control (`-l/--global-lambda`)**
- Controls sparsity and sensitivity of hit calling
- Higher values (e.g., 0.8-0.9) → fewer, higher-confidence hits
- Lower values (e.g., 0.5-0.6) → more sensitive, may include weaker hits
- Default of 0.7 works well for chromatin accessibility data
- ChIP-seq data may benefit from lower values (0.6)

**Motif Trimming (`-t/--cwm-trim-threshold`)**  
- Determines where motif boundaries are set within full CWMs
- Lower values → more conservative trimming, longer motifs
- Higher values → more aggressive trimming, shorter core motifs
- Affects resolution of closely-spaced motif instances

**Performance Optimization (`-b/--batch-size`, `-J`)**
- Set batch size to utilize available GPU memory efficiently
- Reduce batch size if you encounter out-of-memory errors
- Enable JIT compilation (`-J`) for faster execution on newer GPUs

#### Important Notes

- **Scale Invariance**: Hit calling depends on motif and contribution score shapes, not absolute magnitudes. Use `hit_coefficient_global` or `hit_importance` for importance-based thresholding.
- **Legacy Format Support**: Convert older TF-MoDISco files using `modisco convert` from [tfmodisco-lite](https://github.com/jmschrei/tfmodisco-lite).

### Output reporting and post-processing

#### `finemo report`

Generate an HTML report (`report.html`) visualizing TF-MoDISco seqlet recall and hit distributions.
If `-n/--no-recall` is not set, the regions used for hit calling must exactly match those used during the TF-MoDISco motif discovery process.
This command does not utilize the GPU.

Usage: `finemo report -r <regions> -H <hits> -o <out_dir> [-m <modisco_h5>] [-W <modisco_region_width>] [-n]`

- `-r/--regions`: A `.npz` file containing input sequences, contributions, and coordinates. Must be the same as that used for `finemo call-hits`.
- `-H/--hits`: The output directory generated by the `finemo call-hits` command on the regions specified in `--regions`.
- `-o/--out-dir`: The path to the report output directory.
- `-m/--modisco-h5`: The tfmodisco-lite output H5 file of motif patterns. Must be the same as that used for hit calling unless `--no-recall` is set. If omitted, seqlet-derived metrics will not be computed.
- `-W/--modisco-region-width`: The width of the region around each peak summit used by tfmodisco-lite. Default is 400.
- `-n/--no-recall`: Do not compute motif recall metrics. Default is False.

Additional report outputs:

- `motif_report.tsv`: Statistics on the distribution of hits per motif. The columns and values correspond to those in the HTML report's table.
- `motif_occurrences.tsv`: The number of hits of each motif in each input region. Also includes the total number of hits per region.
- `CWMs`: A directory containing visualizations of motif CWMs, as well as corresponding tables with numerical CWM values.
- `seqlets.tsv`: tf-modisco seqlet coordinates for each motif in each region. Only generated if `-m/--modisco-h5` is provided.

#### `finemo collapse-hits`

Identify the best hits by motif similarity within groups of overlapping hits. Adds a 0/1 `is_primary` column to the `hits.tsv` file, indicating whether a hit is the best hit in its group. This command does not utilize the GPU.

Usage: `usage: finemo collapse-hits -i <hits> -o <out_path> [-O <overlap>]`

- `-i/--hits`: The path to the input hits file. This should be the `hits.tsv` or `hits_unique.tsv` file generated by the `finemo call-hits` command.
- `-o/--out-path`: The path to the output file. This will be a copy of the input file with an additional `is_primary` column.
- `-O/--overlap-frac`: The minimum fraction overlap required for two hits to be considered overlapping. Precisely, given two hits of lengths `x` and `y`, the minimum number of overlapping bases is `overlap_frac * (x + y) / 2`. Default is 0.2.

#### `finemo intersect-hits`

Find the intersection of hits across multiple runs. This command does not utilize the GPU.

Usage: `finemo intersect-hits -i <hits> -o <out_path> [-r]`

- `-i/--hits`: The path to one or more input hits file. This should be the `hits.tsv` or `hits_unique.tsv` file generated by the `finemo call-hits` command.
- `-o/--out-path`: The path to the output file. Reoccuring columns are suffixed with the positional index of the input file (e.g. `hit_importance_1`), with the exception of index 0.
- `-r/--relaxed`: By default, the intersection assumes consistent input region definitions (name and coordinates) and motif trimming across runs. In contrast, this relaxed intersection criteria uses only motif names and untrimmed hit coordinates. However, this is not suitable when hit genomic coordinates are unknown. Default is False.

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