Fix all files: fix URLs, figures, pyBigWig citation

Author: SarojaSomu

.zenodo.json

@@ -1,18 +1,29 @@
 {
-  "title": "PyPeakRanker: Reproducible Peak-Level Feature Extraction for Regulatory Element Ranking",
+  "title": "PyPeakRankR: Reproducible Peak-Level Feature Extraction for Regulatory Element Ranking",
   "description": "A Python package for extracting quantitative features from genomic peaks and assembling them into a reproducible, analysis-ready feature table.",
   "upload_type": "software",
   "license": "MIT",
+  "doi": "10.5281/zenodo.15238527",
   "creators": [
     {
       "name": "Somasundaram, Saroja",
       "affiliation": "Allen Institute for Brain Science",
       "orcid": "0000-0002-3729-9849"
+    },
+    {
+      "name": "Johansen, Nelson J.",
+      "affiliation": "Allen Institute for Brain Science",
+      "orcid": "0000-0002-4436-969X"
     }
   ],
   "keywords": [
-    "genomics", "ATAC-seq", "BigWig", "regulatory elements",
-    "peak ranking", "bioinformatics", "enhancer"
+    "genomics",
+    "ATAC-seq",
+    "BigWig",
+    "regulatory elements",
+    "peak ranking",
+    "bioinformatics",
+    "enhancer"
   ],
   "related_identifiers": [
     {
@@ -21,4 +32,4 @@
       "scheme": "doi"
     }
   ]
-}
+}

CONTRIBUTING.md

@@ -1,10 +1,10 @@
-# Contributing to PyPeakRanker
+# Contributing to PyPeakRankR
 
 Thank you for your interest in contributing!
 
 ## Reporting issues
 
-Please use the [GitHub issue tracker](https://github.com/AllenInstitute/PyPeakRankR/issues).
+Please use the [GitHub issue tracker](https://github.com/AllenInstitute/PeakRankR/issues).
 Include:
 - A minimal reproducible example
 - Your Python version and OS
@@ -13,8 +13,8 @@ Include:
 ## Development setup
 
 ```bash
-git clone https://github.com/AllenInstitute/PyPeakRankR
-cd PyPeakRankR
+git clone -b python-package https://github.com/AllenInstitute/PeakRankR
+cd PeakRankR
 pip install -e ".[dev]"
 ```
 

README.md

@@ -1,15 +1,15 @@
-# PyPeakRanker
+# PyPeakRankR
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
 [![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
 
-**PyPeakRanker** is a Python package for extracting quantitative features from
+**PyPeakRankR** is a Python package for extracting quantitative features from
 a predefined set of genomic peaks and assembling them into a reproducible,
 analysis-ready feature table.
 
 It generates a standardized **peak × feature matrix** enabling systematic
 ranking and comparison of regulatory elements across cell types, conditions,
-or species. PyPeakRanker does **not perform peak calling** — it standardizes
+or species. PyPeakRankR does **not perform peak calling** — it standardizes
 feature extraction so that downstream prioritization can be performed
 reproducibly using any statistical or machine-learning approach.
 
@@ -38,14 +38,14 @@ reproducibly using any statistical or machine-learning approach.
 ### Install from GitHub
 
 ```bash
-pip install git+https://github.com/AllenInstitute/PyPeakRankR.git
+pip install git+https://github.com/AllenInstitute/PeakRankR.git@python-package
 ```
 
 ### Install from source
 
 ```bash
-git clone https://github.com/AllenInstitute/PyPeakRankR
-cd PyPeakRankR
+git clone -b python-package https://github.com/AllenInstitute/PeakRankR
+cd PeakRankR
 pip install -e .
 ```
 
@@ -153,7 +153,7 @@ pytest tests/
 
 ## Design Philosophy
 
-PyPeakRanker separates **feature extraction** (deterministic, standardized)
+PyPeakRankR separates **feature extraction** (deterministic, standardized)
 from **peak ranking** (user-defined, flexible). This ensures ranking logic
 remains transparent and adaptable to specific biological questions.
 
@@ -162,31 +162,33 @@ remains transparent and adaptable to specific biological questions.
 
 ## Used in
 
-PyPeakRanker was used in the following published studies:
+PyPeakRankR was used in the following published studies:
 
 - **Johansen et al. (2025)** — [Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex](https://doi.org/10.1016/j.xgen.2025.100879). *Cell Genomics.*
   PeakRankR ranked among the top 3 methods in the BICCN community challenge across 16 competing methods.
 
 - **Wirthlin et al. (2026)** — [A Cross-Species Enhancer-AAV Toolkit for Cell Type-Specific Targeting Across the Basal Ganglia](https://doi.org/10.64898/2026.02.23.706695). *bioRxiv.*
-  PyPeakRanker was used in the Cross-species Enhancer Ranking Pipeline (CERP) to compute ATAC specificity, PhyloP conservation, GC content, signal moments, and composite rankings for 514 candidate enhancers across basal ganglia cell types in mouse and macaque.
+  PyPeakRankR was used in the Cross-species Enhancer Ranking Pipeline (CERP) to compute ATAC specificity, PhyloP conservation, GC content, signal moments, and composite rankings for 514 candidate enhancers across basal ganglia cell types in mouse and macaque.
 
 ---
 
 ## Citation
 
-If you use PyPeakRanker in your research, please cite:
+If you use PyPeakRankR in your research, please cite:
 
-> Somasundaram, S. (2026). PyPeakRanker: Reproducible Peak-Level Feature
-> Extraction for Regulatory Element Ranking.
+> Somasundaram, S. and Johansen, N.J. (2026). PyPeakRankR: Reproducible Peak-Level
+> Feature Extraction for Regulatory Element Ranking.
 > *Journal of Open Source Software*.
-> https://github.com/AllenInstitute/PyPeakRankR
+> https://github.com/AllenInstitute/PeakRankR/tree/python-package
 
 ---
 
-## Author
+## Authors
 
 Saroja Somasundaram — Allen Institute for Brain Science
 
+Nelson J. Johansen — Allen Institute for Brain Science
+
 ## License
 
 MIT License. See [LICENSE](LICENSE).

paper/browser_tracks_comparison.png

@@ -142,3 +142,10 @@ @article{Wirthlin2026
   doi     = {10.64898/2026.02.23.706695},
   note    = {Preprint}
 }
+
+@misc{Ramirez2020pyBigWig,
+  author  = {Ram{\'{i}}rez, Fidel and Diehl, Simon},
+  title   = {{pyBigWig}: A Python extension for reading {BigWig} files},
+  year    = {2020},
+  url     = {https://github.com/deeptools/pyBigWig}
+}

paper/figure6_panelA.png

@@ -71,7 +71,7 @@ heterogeneous input tracks — which is precisely what PyPeakRankR addresses.
 # State of the field
 
 Several tools perform individual aspects of peak-level feature computation.
-`pyBigWig` [@Ramírez2016] provides low-level BigWig access but no peak-level
+`pyBigWig` [@Ramirez2020pyBigWig] provides low-level BigWig access but no peak-level
 aggregation framework. `deepTools` [@Ramírez2016] computes matrix summaries but
 is oriented toward visualization rather than tabular feature assembly. ArchR
 [@Corces2018] computes cell-type specificity scores within its own data model
@@ -149,13 +149,15 @@ accessibility across cell types. This divergence illustrates why fold-change
 alone is insufficient for selecting cell-type specific regulatory elements.
 
 ![Comparison of MACS2 fold-change ranking (left) versus PyPeakRankR
-specificity ranking (right) for ten ATAC-seq peaks. Peaks with the highest
-MACS2 fold-change are not necessarily the most cell-type specific. P1 (chr4,
-red border, left) ranks last by fold-change but first by specificity. P3
-(chr1) and P10 (chr10) rank highly by fold-change but show low specificity
-scores, consistent with broad activity across cell types. PyPeakRankR
-specificity scores are normalised to [0, 1]; rank 1 indicates the peak most
-exclusively active in the target
+specificity ranking (right) for ten MACS2 narrowPeak calls from a real
+ATAC-seq experiment (test.bed). Peaks with the highest MACS2 fold-change
+are not necessarily the most cell-type specific. P1 (chr4, green border)
+ranks last by fold-change (FC = 11.7) but first by specificity. P3 (chr1)
+and P10 (chr10) rank first and second by fold-change (FC = 17.0 and 17.4)
+but near the bottom by specificity, consistent with broad chromatin
+accessibility across cell types. Specificity scores are the ratio of target
+to mean background ATAC signal, min-max normalised to [0, 1]; rank 1 is
+the peak most exclusively active in the target
 group.](browser_tracks_comparison.png)
 
 # Research impact statement
@@ -186,7 +188,7 @@ regions. The software is openly available and documented for community reuse.
 
 PyPeakRankR is implemented in Python (>=3.9) with the following dependencies:
 `pandas` [@Reback2020] for tabular data handling, `numpy` [@Harris2020] for
-numerical computation, `pyBigWig` [@Ramírez2016] for BigWig signal extraction,
+numerical computation, `pyBigWig` [@Ramirez2020pyBigWig] for BigWig signal extraction,
 `pyfaidx` [@Shirley2015] for FASTA sequence access, and `scipy` [@Virtanen2020]
 for statistical distribution metrics. The package is installable via pip from
 GitHub, provides a `pypeakranker` CLI entry point, and includes unit tests

paper/paper.bib

@@ -10,7 +10,8 @@ readme = "README.md"
 requires-python = ">=3.9"
 license = {file = "LICENSE"}
 authors = [
-  {name = "Saroja Somasundaram", email = "sarojas@alleninstitute.org"}
+  {name = "Saroja Somasundaram", email = "sarojas@alleninstitute.org"},
+  {name = "Nelson J. Johansen", email = "nelsonj@alleninstitute.org"}
 ]
 keywords = [
   "genomics", "ATAC-seq", "BigWig", "regulatory elements",