# DeNoFo: a file format and toolkit for standardized, comparable de novo gene annotation

Elias Dohmen, Margaux Aubel, Lars A Eicholt, Paul Roginski, Victor Luria,

Amir Karger, Anna Grandchamp

► To cite this version:

Elias Dohmen, Margaux Aubel, Lars A Eicholt, Paul Roginski, Victor Luria, et al.. DeNoFo: a file format and toolkit for standardized, comparable de novo gene annotation. Bioinformatics, 2025, 41 (10), pp.btaf539. 10.1093/bioinformatics/btaf539. hal-05500639

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# Genome analysis

# DeNoFo: a file format and toolkit for standardized, comparable *de novo* gene annotation

Elias Dohmen<fnref n="1" />\*,<fnref n="2" />, Margaux Aubel<fnref n="1" />,<fnref n="3" />, Lars A. Eicholt<fnref n="1" />,<fnref n="3" />, Paul Roginski<fnref n="2" />,<fnref n="5" />, Victor Luria<fnref n="3" />,<fnref n="4" />,<fnref n="5" />,<fnref n="6" />,<fnref n="7" />

## Abstract

**Motivation:** *De novo* genes emerge from previously non-coding regions of the genome, challenging the traditional view that new genes primarily arise through duplication and adaptation of existing ones. Characterized by their rapid evolution and their novel structural properties or functional roles, *de novo* genes represent a young area of research. Therefore, the field currently lacks established standards and methodologies, leading to inconsistent terminology and challenges in comparing and reproducing results.

**Results:** This work presents a standardized annotation format to document the methodology of *de novo* gene datasets in a reproducible way. We developed DeNoFo, a toolkit to provide easy access to this format that simplifies annotation of datasets and facilitates comparison across studies. Unifying the different protocols and methods in one standardized format, while providing integration into established file formats, such as fasta or gff, ensures comparability of studies and advances new insights in this rapidly evolving field.

**Availability and implementation:** DeNoFo is available through the official Python Package Index (PyPI) and at https://github.com/EDohmen/denofo. All tools have a graphical user interface and a command line interface. The toolkit is implemented in Python3, available for all major platforms and installable with pip and uv.

## 1 Introduction

*De novo* genes are defined as genes that emerge from previously non-genic sequences within a genome. Since the early 2000s, significant progress has been made in understanding the mechanisms underlying their emergence (Long *et al.* 2003, Begun *et al.* 2007, Cai *et al.* 2008, Knowles and McLysaght 2009, Tautz and Domazet-Lošo 2011, Carvunis *et al.* 2012, Wissler *et al.* 2013, Schmitz *et al.* 2018, Van Oss and Carvunis 2019, Heames *et al.* 2020, Parikh *et al.* 2022, Vakirlis *et al.* 2022, Zheng and Zhao 2022, Iyengar and Bornberg-Bauer 2023, Rich and Carvunis 2023, Aldrovandi *et al.* 2024, Peng and Zhao 2024, Zhao *et al.* 2024). The prevalence of *de novo* genes across all domains of life is now well-documented, and their biological roles within respective species remain an active area of research (Li *et al.* 2010, Gubala *et al.* 2017, Baalsrud *et al.* 2018, Zhang *et al.* 2019, Lange *et al.* 2021, Rivard *et al.* 2021, Broeils *et al.* 2023, Vakirlis and Kupczok 2024).

A major challenge in the study of *de novo* genes is the accurate discrimination of recently originated *de novo* genes from those that have emerged via alternative mechanisms (Schmitz

*et al.* 2018). This challenge stems from the scarcity of automated pipelines capable of automatically detecting and annotating *de novo* genes, which have only recently been developed (Arendsee *et al.* 2019, Casola *et al.* 2022, Roginski *et al.* 2024). Consequently, the majority of *de novo* genes have been identified through the implementation of non-standardized pipelines that integrate diverse software and custom scripts. Moreover, the characterization criteria of *de novo* emergence, which varies across studies (Van Oss and Carvunis 2019), is a crucial factor in the identification of *de novo* genes. Past definitions have varied, incorporating different evolutionary stages and criteria (Keeling *et al.* 2019, Weisman 2022), thresholds for how much of a gene must have arisen *de novo* to qualify as such (McLysaght and Hurst 2016), criteria for characterizing the absence of homology (Casola 2018, Vakirlis *et al.* 2020, Weisman *et al.* 2022), and different modes of emergence (Pereira *et al.* 2025). The ambiguities in defining and identifying *de novo* gene emergence have prompted the usage of diverse methodologies. While exploring various methods is essential for a relatively young field of research, the lack of consensus on definitions and the

<footnote n="1">¹Institute for Evolution and Biodiversity, University of Münster, Münster 48151, Germany</footnote>

<footnote n="2">²Institute for Integrative Biology of the Cell (I2BC), Université Paris-Saclay, CEA, CNRS, Gif-sur-Yvette 91198, France</footnote>

<footnote n="3">³Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, United States</footnote>

<footnote n="4">⁴Department of Systems Biology, Harvard Medical School, Boston, MA 02115, United States</footnote>

<footnote n="5">⁵Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States</footnote>

<footnote n="6">⁶IT-Research Computing, Harvard Medical School, Boston, MA 02115, United States</footnote>

<footnote n="7">⁷Aix Marseille University, INSERM, TAGC, UMR_S1090, Marseille 13288, France</footnote>

<footnote>\*Corresponding author. Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, Münster 48151, Germany.
E-mail: e.dohmen@uni-muenster.de.</footnote>

<footnote>Associate Editor: Macha Nikolski</footnote>
use of varying methodologies, have hindered the effective comparison of results across studies (Schmitz et al. 2018, Aubel et al. 2023, Grandchamp et al. 2025).

As a consequence, datasets comprising *de novo* genes from the same species can differ considerably, and in some cases, may not even be overlapping, depending on the methodologies and definitions used to determine the *de novo* status of a given gene. Several studies investigating *de novo* gene emergence in the same species show large discrepancies. For example, in Roginski et al. (2024), the authors detected 89 *de novo* genes in humans, while Vakirlis et al. (2022) identified 155 *de novo* Open Reading Frames (ORFs), and Dowling et al. (2020) identified 2749 human-specific *de novo* ORFs. The observed discrepancies can be attributed to methodological differences. Specifically, Roginski et al. (2024) detected *de novo* genes through the analysis of annotated genomes, Vakirlis et al. (2022) utilized ORFs identified via ribosome profiling as candidates, and Dowling et al. (2020) sought candidates within transcriptomic data.

Similarly, Heames et al. (2020) detected 66 *de novo* genes in *Drosophila melanogaster*, while Roginski et al. (2024) detected 92 and Peng and Zhao (2024) 555 *de novo* genes. In the same species Zheng and Zhao (2022) identified 993 *de novo* ORFs and a fifth study detected an average of 1548 *de novo* ORFs (Grandchamp et al. 2023). These discrepancies primarily stem from variations in methodology and underlying data sources.

These methodological discrepancies and different definitions are discussed in more detail in Grandchamp et al. (2025), along with the categorization and standardization of these methods for the annotation format presented here. The characterization and understanding of *de novo* gene emergence is a young research field, and for future advancements, the exploration of different methodologies remains crucial. However, the wide variety of methodologies complicates the comparability of datasets and hinders progress.

Consequently, there is an essential need for a standardized and comparable approach to ensure reproducibility and facilitate the comparison of results across studies without restrictions on the applied methods and definitions. To address this need, we present DeNoFo, a toolkit designed to automate and streamline the annotation process for *de novo* gene detection and validation as well as rapid and straightforward comparison of methodology across studies.

# 2 Tool description

## 2.1 The standardized annotation format

The present diversity in definitions, terms, protocols, tools and pipelines mentioned above to investigate *de novo* gene evolution impedes comparability of datasets and studies.

While it is important to enable a certain flexibility regarding the methodology to detect and describe *de novo* genes, other factors such as the meaning of terms or comparable description of approaches should be standardized to enable reproducibility and comparability. We propose the following annotation format to categorize and standardize the methods used for *de novo* gene detection while still allowing for the required flexibility to include a great variety of approaches. With this, information on used protocols and tools from both bioinformatic and wet lab approaches can be integrated into widely accepted file formats in genomics.

The here described format focuses on the methodology used for the detection of *de novo* genes in different studies, in contrast to providing information about individual genes. By omitting gene-centric properties and only covering methodological aspects, all genes of a study can be covered by a single description of their methodology and studies become comparable between each other.

Based on the standardized and categorized methods for bioinformatic detection of *de novo* genes we compiled (Grandchamp et al. 2025), the format is structured into six main sections (see Fig. 1): input data, evolutionary information, homology filter, non-coding homologs, lab verification and hyperlinks (URLs/DOIs).

Our annotation format with the here described information can be stored either in a separate file and shared as such or can be included in widely accepted bioinformatic file formats. The default DeNoFo file format is JSON-based and human-readable to a high degree (*.dngf file extension). To allow for integration into widely accepted file types and formats, such as fasta or gff, we developed additionally a short string encoding of the DeNoFo format, which can be added to the fasta header or the additional info column in gff files. This short string encoding of the DeNoFo format is not human-readable due to its compressed information content. For easy use and transfer of information into this proposed unified format as well as extraction of information from the mentioned file types annotated with this format, we developed a toolkit to facilitate the usage.

Examples, tutorials, and further details about the annotation format, the information contained in each section, and the functionality of the tools are available in the documentation at https://denofo.readthedocs.io.

## 2.2 Toolkit functionality

The tools of the DeNoFo toolkit assist in the conversion of workflows into the required format, the automated

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Structure of the *de novo* gene annotation format with six main sections and related subsections. The star indicates non-optional sections. More details about each section can be found in the documentation at https://denofo.readthedocs.io. This figure has been designed using resources from bioicons.com.</figcaption></figure>
annotation of *de novo* genes with the format, and the extraction of information in an easily accessible and interpretable manner. Additionally, a tool is provided for the comparison of two such *de novo* gene set annotations with regard to their similarities and differences in methodological approaches. All tools presented here are available with a graphical user interface (GUI) and a command-line interface (CLI).

The **DeNoFo-Questionnaire** represents the core component of the toolkit, serving as a guide that directs users through the required sections of the format via interactive queries. These queries offer either pre-populated options or the option to enter custom answers. Only knowledge about the used methodology is needed for this step without further input files. To ensure user convenience, the user is permitted to move between questions and modify previous answers as required. The resulting DeNoFo annotation (output) is saved to a user-specified file in a JSON-like *de novo* gene file format (dngf, that is also the file extension).

The **DeNoFo-Converter** is a tool designed for the purpose of converting the DeNoFo format from one file type to another. To illustrate this function, one may consider the process of annotating sequences in a fasta or gff file (first input) with an annotation file in the dngf format that has already been saved (second input) resulting in a file of the first input format with added DeNoFo annotation (output). To enhance user convenience, the tool offers various options for annotating or extracting only annotations from a selection of sequences.

The **DeNoFo-Comparator** is a tool designed for the purpose of comparing two studies or datasets that have been annotated in the DeNoFo format. This tool facilitates the loading of two different DeNoFo annotations (input) and the generation of a report that either highlights the similarities or differences between the two studies or datasets (output). The report is presented in an easily interpretable, human-readable formatting.

The help function and tooltips of the GUI and CLI versions of each tool, as well as the DeNoFo documentation (https://denofo.readthedocs.io), contain further explanations and examples with regard to the options and functionality of the toolkit and its constituent tools.

## 2.3 Implementation

The DeNoFo toolkit has been developed in modern Python (version 3.12 or higher) and is released under the GPL v3 license. All GUI versions are implemented using PyQt6. The DeNoFo format is implemented as a set of pydantic models which contain the definitions and validations for the DeNoFo format.

The pydantic models are independent of the tools with specific user interfaces. Decoupling the models from the tools allows anyone to interact with the format programmatically without using our tools, while ensuring a high level of robustness of the format. For example, any *de novo* gene detection software could implement its own routine to automatically generate a valid annotation format file, so that a user does not need to manually generate one using the DeNoFo-Questionnaire.

For easy and fast setup the DeNoFo toolkit supports installation via pip or uv and has been tested on all major platforms (Linux, Windows, MacOS).

## 3 Conclusion

The DeNoFo format and toolkit presented here provide the community with an easily accessible, unified and comparable

standard for methodology annotation for *de novo* genes. The overarching objective of this initiative is to enhance the comparability, interpretability and reproducibility of datasets and findings within this new and heterogeneous field of research.

The format has been designed to be as flexible and open as possible to accommodate the diverse methodological approaches in this field, while ensuring a unified annotation and reporting with a focus on best practice. To this end, the format has been designed to allow for changes and adaptation as this field of research moves forward. In order to facilitate this, the format has been implemented in such a way that additions and changes can be made with as few compatibility issues as possible. This approach enables the incorporation of new databases or software for future studies in the format, without requiring researchers to update their existing annotations.

The community is encouraged to contribute to the format by adding new approaches, databases, software and more, as well as requesting or contributing features and functionality that would improve the accessibility, usability or comparability of the format, toolkit and datasets.

In conclusion, the unified format and toolkit for *de novo* gene annotation, developed with and for the community, will hopefully pave the way for faster progress and deeper understanding in this field of research, while being continuously adapted and improved in conjunction with innovative methods and novel tools to advance the field of *de novo* gene evolution.

## Acknowledgements

We thank Erich Bornberg-Bauer (EBB), Nikolaos Vakirlis, Li Zhao, Anne Lopes, Bharat Ravi Iyengar and Andreas Lange for their useful feedback during the planning phase of the project. We acknowledge support from the Open Access Publication Fund of the University of Münster. For Fig. 1, the following icons were taken from https://bioicons.com/: brain-1 icon (color changed), healthy-lung icon and intestine icon by Servier https://smart.servier.com/ licensed under CC-BY 3.0 simple_DNA_backbone icon (colour changed) by Marnie-Maddock https://github.com/MarnieMaddock licensed under CC-BY 4.0 Riparian Plots icon by Chenxin-Li https://github.com/cxli233 licensed under CC-BY 4.0 Protein_monochrome icon (colour changed) by DBCLS https://togotv.dbcls.jp/en/pics.html licensed under CC-BY 4.0.

## Author contributions

Elias Dohmen (Conceptualization [lead], Project administration [equal], Software [lead], Validation [equal], Visualization [supporting], Writing—original draft [lead], Writing—review & editing [lead]), Margaux Aubel (Conceptualization [supporting], Validation [equal], Visualization [lead], Writing—original draft [supporting], Writing—review & editing [equal]), Lars A. Eicholt (Conceptualization [supporting], Validation [equal], Visualization [supporting], Writing—original draft [supporting], Writing—review & editing [equal]), Paul Roginski (Conceptualization [supporting], Validation [equal], Visualization [supporting], Writing—review & editing [equal]), Victor Luria (Conceptualization [supporting], Validation [equal], Writing—review & editing [equal]), Amir Karger (Conceptualization [supporting], Validation [equal], Writing—review & editing [equal]), and Anna Grandchamp (Conceptualization [lead], Project administration [equal],
Validation [equal], Visualization [lead], Writing—original draft [lead], Writing—review & editing [equal])

Conflict of interest: None declared.

## Funding

E.D. was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [503348080]. This grant with the additional grant number BO2544-22-1 was awarded to EBB. M.A. received funding from the Volkswagen foundation with grant code 98183 awarded to EBB. A.G. was supported by the Deutsche Forschungsgemeinschaft priority program "Genomic Basis of Evolutionary Innovations" (SPP 2349) BO 2544/20-1 to EBB, and by the (HFSP) Postdoctoral Fellowship (Grant No. 2023-981550) awarded to EBB, Anne-Ruxandra Carvunis and Christine Brun. L.A.E. has been supported by EMBO Scientific Exchange Grant 10944. V.L. was supported by NIH grant R01NS095654 (to Nenad Sestan).

## Data availability

The source code for the DeNoFo toolkit with all data underlying this Application Note are available at https://github.com/EDohmen/denofo and in the documentation with additional references and case studies at https://denofo.readthedocs.io

## References

Aldrovandi S, Fajardo Castro J, Ullrich K *et al.* Expression of random sequences and de novo evolved genes from the mouse in human cells reveals functional diversity and specificity. *Genome Biol Evol* 2024; **16**:evae175.

Arendsee Z, Li J, Singh U *et al.* Fagin: synteny-based phylostratigraphy and finer classification of young genes. *BMC Bioinformatics* 2019; **20**:440.

Aubel M, Eicholt L, Bornberg-Bauer E. Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning. *F1000Res* 2023; **12**:347.

Baalsrud HT, Tørresen OK, Solbakken MH *et al.* De novo gene evolution of antifreeze glycoproteins in codfishes revealed by whole genome sequence data. *Mol Biol Evol* 2018; **35**:593–606.

Begun DJ, Lindfors HA, Kern AD *et al.* Evidence for de novo evolution of testis-expressed genes in the *Drosophila Yakuba/Drosophila erecta* clade. *Genetics* 2007; **176**:1131–7.

Broeils LA, Ruiz-Orera J, Snel B *et al.* Evolution and implications of de novo genes in humans. *Nat Ecol Evol* 2023; **7**:804–15.

Cai J, Zhao R, Jiang H *et al.* De novo origination of a new protein-coding gene in saccharomyces cerevisiae. *Genetics* 2008; **179**:487–96.

Carvunis A-R, Rolland T, Wapinski I *et al.* Proto-genes and de novo gene birth. *Nature* 2012; **487**:370–4.

Casola C. From de novo to “de nono”: the majority of novel protein-coding genes identified with phylostratigraphy are old genes or recent duplicates. *Genome Biol Evol* 2018; **10**:2906–18.

Casola C, Owoyemi A, Pepper AE *et al.* Accurate identification of de novo genes in plant genomes using machine learning algorithms. *bioRxiv* 2022, preprint: not peer reviewed.

Dowling D, Schmitz JF, Bornberg-Bauer E. Stochastic gain and loss of novel transcribed open reading frames in the human lineage. *Genome Biol Evol* 2020; **12**:2183–95.

Grandchamp A, Kühl L, Lebherz M *et al.* Population genomics reveals mechanisms and dynamics of de novo expressed open reading frame emergence in drosophila melanogaster. *Genome Res* 2023; **33**:872–90.

Grandchamp A, Aubel M, Eicholt LA *et al.* De novo gene emergence: summary, classification, and challenges of current methods. *EcoEvoRxiv*, https://doi.org/10.32942/X2DP88, 2025, preprint: not peer reviewed.

Gubala AM, Schmitz JF, Kearns MJ *et al.* The Goddard and Saturn genes are essential for drosophila male fertility and may have arisen de novo. *Mol Biol Evol* 2017; **34**:1066–82.

Heames B, Schmitz J, Bornberg-Bauer E. A continuum of evolving de novo genes drives protein-coding novelty in drosophila. *J Mol Evol* 2020; **88**:382–98.

Iyengar BR, Bornberg-Bauer E. Neutral models of de novo gene emergence suggest that gene evolution has a preferred trajectory. *Mol Biol Evol* 2023; **40**:msad079.

Keeling DM, Garza P, Nartey CM, Carvunis A-R. The meanings of ‘function’ in biology and the problematic case of de novo gene emergence. *Elife* 2019; **8**:e47014.

Knowles DG, McLysaght A. Recent de novo origin of human protein-coding genes. *Genome Res* 2009; **19**:1752–9.

Lange A, Patel PH, Heames B *et al.* Structural and functional characterization of a putative de novo gene in drosophila. *Nat Commun* 2021; **12**:1667.

Li D, Dong Y, Jiang Y *et al.* A de novo originated gene depresses budding yeast mating pathway and is repressed by the protein encoded by its antisense strand. *Cell Res* 2010; **20**:408–20.

Long M, Betrán E, Thornton K *et al.* The origin of new genes: glimpses from the young and old. *Nat Rev Genet* 2003; **4**:865–75.

McLysaght A, Hurst LD. Open questions in the study of de novo genes: what, how and why. *Nat Rev Genet* 2016; **17**:567–78.

Parikh SB, Houghton C, Van Oss SB *et al.* Origins, evolution, and physiological implications of de novo genes in yeast. *Yeast* 2022; **39**:471–81.

Peng J, Zhao L. The origin and structural evolution of de novo genes in drosophila. *Nat Commun* 2024; **15**:810.

Pereira AB, Marano M, Bathala R *et al.* Orphan genes are not a distinct biological entity. *Bioessays* 2025; **47**:e2400146.

Rich A, Carvunis A-R. De novo gene increases brain size. *Nat Ecol Evol* 2023; **7**:180–1.

Rivard EL, Ludwig AG, Patel PH *et al.* A putative de novo evolved gene required for spermatid chromatin condensation in drosophila melanogaster. *PLoS Genet* 2021; **17**:e1009787.

Roginski P, Grandchamp A, Quignot C *et al.* De novo emerged gene search in eukaryotes with dense. *Genome Biol Evol* 2024; **16**:evae159.

Schmitz JF, Ullrich KK, Bornberg-Bauer E. Incipient de novo genes can evolve from frozen accidents that escaped rapid transcript turnover. *Nat Ecol Evol* 2018; **2**:1626–32.

Tautz D, Domazet-Lošo T. The evolutionary origin of orphan genes. *Nat Rev Genet* 2011; **12**:692–702.

Vakirlis N, Kupczok A. Large-scale investigation of species-specific orphan genes in the human gut microbiome elucidates their evolutionary origins. *Genome Res* 2024; **34**:888–903.

Vakirlis N, Carvunis A-R, McLysaght A. Synteny-based analyses indicate that sequence divergence is not the main source of orphan genes. *Elife* 2020; **9**:e53500.

Vakirlis N, Vance Z, Duggan KM *et al.* De novo birth of functional microproteins in the human lineage. *Cell Rep* 2022; **41**:111808.

Van Oss SB, Carvunis A-R. De novo gene birth. *PLoS Genet* 2019; **15**:e1008160.

Weisman CM. The origins and functions of de novo genes: against all odds? *J Mol Evol* 2022; **90**:244–57. https://doi.org/10.1007/s00239-022-10055-3

Weisman CM, Murray AW, Eddy SR. Mixing genome annotation methods in a comparative analysis inflates the apparent number of lineage-specific genes. *Curr Biol* 2022; **32**:2632–9.e2.

Wissler L, Gadau J, Simola DF *et al.* Mechanisms and dynamics of orphan gene emergence in insect genomes. *Genome Biol Evol* 2013; **5**:439–55.

Zhang L, Ren Y, Yang T *et al.* Rapid evolution of protein diversity by de novo origination in oryza. *Nat Ecol Evol* 2019; **3**:679–90.

Zhao L, Svetec N, Begun DJ. De novo genes. *Annu Rev Genet* 2024; **58**:211–32.

Zheng, E. B. and Zhao, L. Protein evidence of unannotated ORFS in drosophila reveals diversity in the evolution and properties of young proteins. *Elife* 2022; **11**:e78772.