# Nextflow Groovy vs Snakemake Python: How to Choose

Nextflow is a bioinformatics workflow manager whose pipelines are written in Groovy and built around a streaming dataflow model, while Snakemake is a bioinformatics workflow manager whose pipelines are written in Python and built around rules that describe how to produce output files from input files. Both solve the same problem. They take a multistep analysis, run it reliably on a laptop, a cluster, or the cloud, and record what ran so it can be repeated.

The rule of thumb: if your group wants to plug into the nf-core ecosystem of shared modules and community pipelines, or you need robust task-level resume across an HPC scheduler and cloud environments, choose Nextflow. If your team already writes Python every day, wants analysis logic expressed directly in Python, or is converting an existing Python script into a dependable pipeline, choose Snakemake. And before you commit to either, run a small prototype of your real analysis in your candidate tool on your own hardware, because a short prototype exposes incompatibilities that no feature table will [1].

This article explains what each tool is, compares them on the criteria that decide real projects, walks through worked scenarios, and lists the mistakes people make when choosing between them.

## What a Bioinformatics Workflow Manager Actually Does

![Decision path asking about nf-core needs, Python team skills and dynamic branching, ending in a Nextflow or Snakemake recommendation.](/article-images/illustrations/nextflow-groovy-vs-snakemake-python-tool-choice-triage.webp)

A [workflow management](/knowledge/bioinformatics/snakemake-vs-nextflow-workflow-architectures) system represents, manages, and executes multistep computational analyses [1]. In bioinformatics, those steps are things like quality control, trimming, alignment, variant calling, and summary reporting, each typically a command-line tool with its own inputs, outputs, and resource needs.

Doing this with hand-edited shell scripts breaks quickly. You lose track of which samples finished, rerunning one sample reruns everything, and the script that worked on your laptop fails on the cluster. A workflow manager fixes these problems in specific, concrete ways [1]:

1. It gives the analysis a common, machine-readable description, which supports reproducibility and lets teams share components.
2. It supports incremental build, meaning it executes only the parts of the workflow affected by new inputs or changed configuration.
3. It supports re-entrancy, meaning you can resume execution from where a previous run stopped instead of starting over.
4. It improves portability by integrating with containers, high-performance computing (HPC) schedulers, and cloud services.
5. Most importantly, it lets you delegate how the workflow runs to the tool and its developers, so you can focus on what the workflow should do and on the science itself [1].

Nextflow and Snakemake are two of the most widely adopted command-line workflow managers in bioinformatics, and both are mature, actively developed open-source projects [2]. GUI platforms such as Galaxy serve a complementary role for interactive, browser-based analysis, while Nextflow and Snakemake dominate for scripted, automatable pipelines [2].

The choice between them is not about which is better in the abstract. It is about which execution model, language, and community fit your team and your infrastructure. One well-documented way to decide is to take a real analysis in your domain, prototype it in each candidate system, and evaluate the experience directly rather than relying on feature checklists [1].

## Nextflow in Brief: Groovy as the Pipeline Language

### What Groovy is and why Nextflow uses it

Groovy is a dynamic language that runs on the Java Virtual Machine (JVM). Its syntax is Java-compatible, it supports scripting without boilerplate, and it treats functions as values you can pass around. Nextflow is itself written in Groovy, and pipeline scripts are Groovy scripts interpreted by the Nextflow runtime.

That design choice has consequences. A Nextflow pipeline can use the full expressiveness of a real programming language inside the workflow definition: loops, conditionals, string interpolation, closures, and access to the JVM ecosystem. The cost is that pipeline authors who only know Python or R must learn a second language, even if the workflow-specific parts of the syntax are simple.

### The core model: processes and channels

A Nextflow pipeline is a set of `process` blocks connected by channels. A process describes one compute step. A channel is an asynchronous stream of data items flowing between processes. When a process finishes with one item, the runtime feeds the next item without waiting for other items to finish, which means independent samples flow through the pipeline in parallel by default.

A minimal example that aligns paired-end reads with BWA:

```groovy
process ALIGN {
    tag "${meta.id}"
    cpus 4

    input:
    tuple val(meta), path(reads)
    path index

    output:
    tuple val(meta), path("${meta.id}.bam")

    script:
    """
    bwa mem -t ${task.cpus} index/${reads} ${reads[2]} > ${meta.id}.sam
    samtools view -b ${meta.id}.sam > ${meta.id}.bam
    """
}

read_pairs = Channel.fromFilePairs("data/*_R{1,2}.fastq.gz")
read_pairs.view { sample -> "pair ready: ${sample}" }
```

The `val`, `path`, and `tuple` declarations define typed inputs. The `script` block is a shell script with Groovy string interpolation, so values from the workflow are substituted directly into the command. The runtime handles staging files into a per-task work directory, running the command, capturing exit status, and staging outputs onward.

### DSL2 and modular pipelines

Nextflow's current authoring style is called DSL2, the Domain-Specific Language version 2 [2]. Under DSL2, every process lives in its own module file, instances of that process can be invoked multiple times with different inputs inside one pipeline, and pipelines can import modules and compose them into subworkflows. This fixed the central pain of the older DSL1, where you could not reuse the same process twice in one script without fragile renaming tricks.

DSL2 is the foundation of nf-core, a community framework that maintains a large library of standardized modules and subworkflows plus a growing collection of community-built analysis pipelines, and it has seen adoption by research consortia such as the six EuroFAANG farmed [animal research](/blog/guides/animal-research) consortia [2]. The nf-core model matters for the Nextflow versus Snakemake decision because it changes what you write. With nf-core, a small team can run a production-grade RNA-seq or variant-calling pipeline by configuring an existing community pipeline rather than writing one from scratch [2].

### Resume and caching

Nextflow caches every executed task in a `work` directory keyed by a hash of the task's inputs, command, container, and other attributes. Running the pipeline again with the resume option reuses any task whose hash is unchanged and reruns only the affected tasks. In practice this means a crashed run of 500 samples at step 40 resumes at step 40, not from the beginning.

### The honest trade-off

The dataflow model is powerful for irregular workloads: variable numbers of outputs per input, dynamic fan-out, and streaming patterns. It is also the part of Nextflow that takes longest to internalize. Operations like joining two channels, splitting a tuple, or debugging why a channel emitted nothing at all are the classic stumbling blocks for newcomers coming from scripting backgrounds.

## Snakemake in Brief: Python as the Pipeline Language

### What the workflow language looks like

Snakemake pipelines live in files called Snakefiles. A Snakefile defines rules, and each rule declares input files, output files, optional parameters, and the shell command, script, notebook, or Python code that produces the outputs. Snakemake scans your target files, matches them against rule output patterns containing wildcards, and derives the directed acyclic graph (DAG) of jobs automatically.

The same alignment step as a Snakemake rule:

```python
rule bwa_mem:
    input:
        reads=["data/{sample}_R1.fastq.gz", "data/{sample}_R2.fastq.gz"],
        ref="refs/genome.fa"
    output:
        "results/aligned/{sample}.bam"
    threads: 4
    shell:
        "bwa mem -t {threads} {input.ref} {input.reads} > {output}"
```

The `{sample}` wildcard is the pattern-matching heart of Snakemake. Request `results/aligned/tumorA.bam` and Snakemake works out that `sample` is `tumorA`, resolves which rules must run to create the inputs, and schedules everything. You describe what each output means and the system infers the plan, which is the opposite mental model from Nextflow's explicit streams.

Because the workflow language is Python, you can drop directly into Python inside a rule's `run` block, call `script:` to invoke a standalone Python script with a `snakemake` object injected into it, or import your own Python modules. For labs whose analysis code is already Python, this is the single biggest draw.

### Environments, containers, and wrappers

Each rule can declare a Conda environment file, so different steps of one pipeline can run under different pinned software environments, and Snakemake creates those environments from the rule definitions. Rules can also declare a container image. The Snakemake-Wrappers repository provides a community-maintained collection of ready-made, reusable rule wrappers for common bioinformatics tools, which alongside community workflows gives Snakemake a modular ecosystem comparable in spirit to nf-core modules [3].

### Rerun logic and re-entrancy

Snakemake decides what to rerun using output file timestamps plus recorded metadata about the rule, code, parameters, and inputs that produced each file. When you edit a rule's shell command or change a parameter, Snakemake detects the mismatch and marks affected jobs for rerun on the next invocation. Like Nextflow, this delivers the incremental build and re-entrancy that workflow managers promise: rerun a partially completed workflow and only the unfinished or invalidated jobs execute [1].

### The honest trade-off

Snakemake's DAG inference is elegant for file-driven projects but has sharp edges. Two rules whose output patterns can both match the same filename create ambiguity, and wildcard constraints must sometimes be written explicitly. Very dynamic workflows, where the number of outputs is unknown until runtime, require extra machinery compared with Nextflow's native streaming. Python is also an unforgiving language for indentation errors in mixed code-and-YAML files, and a Snakefile that grows organically can turn into a monolith without deliberate modularity.

## Side-by-Side Comparison

The table below is the core of the decision. Read it as a set of tendencies, not laws, because both tools are flexible enough to violate almost any row.

| Criterion | Nextflow (Groovy) | Snakemake (Python) |
|--|--|--|
| Workflow language | Groovy on the JVM, a Java-family scripting language | Python, with the full standard library available in the workflow |
| Mental model | Explicit dataflow: processes consume and emit channels, items flow independently | Rule-based DAG: outputs with wildcards are declared, and the dependency graph is inferred |
| Dynamic workloads | Native strength, channels handle unknown item counts and branching streams naturally | Possible but needs more scaffolding, dynamic outputs require extra rules or helper logic |
| Resume behavior | Task-level cache keyed on inputs, code, container, and environment, invoked with the resume option | Timestamp and metadata-based rerun triggers that detect changed code, params, and inputs |
| Modular ecosystem | DSL2 modules and subworkflows, nf-core module and subworkflow library with shared standards [2] | The module system with rule inheritance, plus the Snakemake-Wrappers community collection [3] |
| Community pipelines | nf-core, a large framework of peer-reviewed pipelines with strict standards [2] | Community workflows and wrappers, generally lighter central governance |
| Container support | Docker, Singularity or Apptainer, Podman, plus Conda | Docker, Singularity or Apptainer, plus per-rule Conda environments |
| Execution targets | Local, SLURM, SGE, LSF, PBS, Kubernetes, AWS, Google Cloud, Azure | Local, SLURM and generic cluster schedulers, Kubernetes, cloud options |
| Language overlap with bioinformatics | Low, Groovy is rare outside the JVM world | Very high, Python is the dominant analysis language in the field |
| Code-in-pipeline style | Shell script fragments inside process blocks, Groovy logic outside them | Python logic directly inside the workflow, run blocks and script directives |
| Best documented fit | Consortia, core facilities, teams adopting shared community standards [2] | Method-development labs and Python-heavy teams converting scripts to pipelines [1] |

## Where the Two Really Differ

Feature tables list similarities. The criteria below are the ones that actually settle projects.

### Language and team skills

Python is the lingua franca of bioinformatics analysis, so the difference between nextflow groovy and snakemake python starts here. A team of Python-fluent scientists can read a Snakefile on day one and write one within a week. The same team facing a Nextflow pipeline must learn Groovy syntax, JVM quirks, and channel operators before the workflow logic itself makes sense.

Two mitigating facts cut the other way. First, the workflow-specific DSL of Nextflow is a thin layer, and many productive Nextflow authors never write sophisticated Groovy, they copy patterns from nf-core pipelines. Second, Groovy's full programming power is available when you do need it, which helps for complex [preprocessing](/blog/data-analysis/what-is-data-preprocessing) logic, parsing sample sheets, and building unusual channel structures. The practical question is not which language is better, it is which language your specific team can debug at 6 p.m. before a grant deadline.

A related point from the software-engineering literature: whatever language you pick, the pipeline quality depends on well-defined task interfaces, explicit input and output specifications, and a clean separation between compute tasks and glue or adaptation tasks [3]. Both languages can express that discipline, and neither enforces it.

### Execution model: streaming channels versus inferred DAG

This is the deepest technical difference in the nextflow vs snakemake question.

In Nextflow, you construct the flow of data yourself. You create channels, transform them with operators such as `map`, `filter`, `join`, and `flatMap`, and feed them into processes. Execution is asynchronous by default, so with 200 samples the runtime keeps every available core or node busy without you writing any scheduler logic. The model shines when sample processing is irregular, for example when per-sample read counts change downstream step counts.

In Snakemake, you declare rules and targets, and the DAG emerges. This declarative style is often easier to reason about for classic file-transformation pipelines: you can print the DAG, inspect why a file will or will not be produced, and reason about dependencies the way `make` users always have. The trade-off appears with dynamism. If the number of outputs depends on runtime results, Snakemake needs checkpoint rules or helper scripts, while Nextflow handles the same situation with operators that emit variable numbers of items.

A useful heuristic: if your pipeline looks like a fixed recipe applied uniformly to a sample sheet, both tools handle it equally well and the DAG model may feel more transparent. If your pipeline has complex branching, aggregations over unknown numbers of items, or scatter-gather patterns that mutate mid-run, the dataflow model pays for its learning curve.

### Resuming and caching

![Comparison of Nextflow and Snakemake resume behaviour across trigger, granularity, disk cost and failure risk.](/article-images/illustrations/nextflow-groovy-vs-snakemake-python-resume-mechanisms.webp)

Both systems honor re-entrancy, the ability to selectively re-execute parts of a workflow and resume from where a previous run stopped [1], but their mechanisms differ in ways that matter during failures.

Nextflow's resume is hash-based and task-level. A task is considered unchanged only if its inputs, code, container, and relevant settings all hash identically, so a resumed run reuses exactly the tasks that cannot have changed. The known cost is the size of the `work` directory, which can grow to many terabytes for large cohorts, and the fact that anything that invalidates the hash reruns the task even when the change was cosmetic.

Snakemake's rerun triggers watch timestamps, code changes, parameter changes, and recorded metadata. This is generally more frugal with disk and picks up code edits reliably, but time-based logic can surprise you if inputs are touched without changing content or if timestamps are unreliable on shared filesystems.

For a busy production pipeline rerun daily against arriving data, either behavior works, but test both on a realistic interrupted run before deciding.

### Reuse, modules, and community standards

Community-driven modularity is where Nextflow currently has the most visible institutional momentum. The nf-core framework, built on DSL2, provides an extensive library of modules and subworkflows with shared conventions, enabling research communities to adopt common standards progressively as resources allow [2]. For a consortium or core facility, adopting nf-core means inheriting peer review, standardized documentation, consistent parameter handling, and a large user base reporting bugs.

Snakemake's equivalent assets are the modular ecosystems of nf-core modules and Snakemake wrappers, both explicitly recognized as community-driven components that support reproducible pipeline development through well-defined task interfaces [3]. Snakemake wrappers give you a curated, tested rule for hundreds of common tools, and the module system lets you compose workflows from other workflows with rule inheritance. The governance is looser than nf-core, which cuts both ways: lower ceremony to contribute, less uniformity across components.

### Portability and execution targets

Both tools integrate with containers, HPC schedulers, and cloud platforms, which is one of the standard benefits that make workflow managers attractive in the first place [1]. The practical difference lies in deployment maturity and defaults rather than capability. Nextflow's wave of cloud-native integrations and its configuration model were designed for identical behavior across a laptop, an SLURM cluster, and a cloud batch service, and the nf-core ecosystem leans hard on that portability for its CI testing. Snakemake's cluster support is mature and its per-rule environment and container model makes each job self-describing, which simplifies some portability problems while adding configuration surface in others.

If your compute is a single institutional SLURM cluster, both tools are equally at home and this criterion should carry little weight. If your roadmap includes bursting to AWS Batch or running the same pipeline across multiple institutions with heterogeneous schedulers, weight Nextflow's portability story more heavily.

### Testing, validation, and software engineering

A workflow manager automates execution, it does not guarantee a correct or reproducible pipeline. The software-engineering literature on reproducible pipeline development makes this explicit: robust pipelines need systematic validation checks for the environment, inputs, and runtime, standardized project organization, and comprehensive testing with both real and synthetic data inside continuous integration [3]. The same work reinterprets the SOLID principles for workflow systems and pipeline code, advocating well-defined task interfaces and explicit input-output contracts at both the framework level and the pipeline level [3].

Both Nextflow and Snakemake support this discipline well. Nextflow pairs naturally with nf-core's standardized testing conventions, including automated pipeline tests and component tests within the nf-core framework [2] [3]. Snakemake projects can implement the same validation and CI structure with standard Python tooling, and synthetic-data test fixtures slot cleanly into its rule model. When comparing candidate tools, evaluate how easy each makes it to run a miniature end-to-end test with a tiny dataset, because that single capability drives most long-term maintenance quality.

### Debugging and day-two experience

Most pipeline time is spent after the first successful run, on failures, updates, and extensions. Nextflow gives you a per-task work directory with the exact script, environment, and logs for every executed job, plus trace and timeline reports, which makes post-mortem debugging methodical. Snakemake gives you per-job logs, a printable DAG, and the ability to rerun a single failed job interactively in the same shell environment, which many developers find faster for tight debug loops.

Both benefit enormously from console logging of the underlying commands. Teams that enable command echoing from day one resolve the majority of runtime failures by reading the executed command line rather than the tool's silent default output.

## Worked Scenarios

### Scenario 1: A genomics core facility serving many research groups

A sequencing core processes hundreds of samples per month across RNA-seq, whole-genome, and targeted panels, on an SLURM cluster, with plans to offer cloud runs. The core has two full-time bioinformaticians and a rotation of students.

Choose Nextflow. The nf-core pipelines cover the standard assay classes with peer-reviewed implementations the core does not have to maintain alone [2], DSL2 modules let the core add internal steps as custom modules that follow community conventions [2], and hash-based resume keeps long cohort runs survivable when a storage hiccup kills the run at hour 30. The Groovy learning curve is absorbed once by the two staff bioinformaticians, and the students consume pipelines rather than author them. The multi-executor portability protects the cloud roadmap.

### Scenario 2: A method-development lab writing new algorithms in Python

A [computational biology](/knowledge/bioinformatics/vaccinomics-and-the-future-of-personalized-vaccines) lab develops a new single-cell analysis method. The codebase is Python, the scientists prototype in Jupyter, and the deliverable is a pipeline others can run on their own data. The pipeline shape is still evolving week to week.

Choose Snakemake. The rules map directly onto the existing Python scripts, the `script:` directive executes the lab's own code with inputs and outputs wired in, and per-rule Conda environments pin the fast-moving dependencies of a method under development. The declarative DAG makes it obvious to a new student which steps exist and in what order. This mirrors the RiboViz experience, where a workflow implemented as a Python script was reimplemented within a workflow management system to gain automated execution, re-entrancy, and adherence to best practices for scientific computing, with the team prototyping candidate systems to find the right fit [1].

### Scenario 3: A research consortium standardizing analysis across institutions

Six institutions across several countries agree to run identical animal-genomics analyses and publish comparable results. Institutions differ in schedulers, container runtimes, and staff expertise.

Choose Nextflow with nf-core. The nf-core framework exists precisely to let communities adopt common standards progressively as resources and needs allow, and its adoption by the six EuroFAANG farmed animal consortia demonstrates the model working at exactly this scale [2]. FAIR compliance, meaning findability, accessibility, interoperability, and reusability of the analysis, is a stated driver for standardized community pipelines [2]. A Snakemake-based consortium is achievable, but you would be assembling the shared governance yourself.

### Scenario 4: A solo analyst with a folder of working Python scripts

A postdoc has a 600-line analysis script that works but reruns everything on every change and has failed silently in ways discovered weeks later. Next quarter brings 40 more samples.

Choose Snakemake. Converting the script into ten rules with explicit inputs, outputs, and a couple of conda environment files is typically a day or two of work, and the payoff is immediate, incremental reruns and honest dependency tracking. This is the incremental-build benefit workflow managers exist to provide [1]. Starting a Groovy-based pipeline from scratch to wrap the same scripts would spend the postdoc's week on language plumbing rather than analysis.

### Scenario 5: A hybrid where both tools coexist

Some organizations legitimately run both. A common pattern is nf-core pipelines in Nextflow for standardized production assays, with Snakemake for exploratory method development on the same cluster. This is defensible when the two activities have different staff and different change tempos, but it doubles configuration surface, and small teams should resist the temptation rather than embrace it. One tool, learned deeply, beats two learned shallowly.

## Common Mistakes and Limitations

**Choosing on language familiarity alone.** Teams pick Snakemake because they know Python, then discover their real pain was cloud deployment or shared community standards, which is where the nf-core ecosystem's momentum lives [2]. Conversely, teams pick Nextflow for its ecosystem and then stall for weeks on channel operations that a Python-native tool would have made trivial. Run the prototype on your own data before deciding [1].

**Assuming resume means "unchanged results."** Both caching systems rerun tasks when inputs, code, or environments change, and neither guarantees bit-identical outputs from different tool versions. Pin your containers and environment files, and record them with your results. A pipeline that runs without containers is not reproducible in any meaningful sense, regardless of which manager wrapped it.

**Skipping validation and testing.** The most common reproducibility failure is not the workflow language, it is the absence of environment checks, input validation, and CI tests with real and synthetic data [3]. A beautiful DAG or an elegant channel graph with no tests will still ship wrong results confidently.

**Building a monolith.** In Snakemake, everything crammed into one Snakefile with inline Python blocks resists reuse. In Nextflow, ad hoc channel gymnastics inside the main script do the same damage. Both problems have the same cure, which the software-engineering guidance makes explicit: well-defined task interfaces, explicit input and output specifications, and separation of compute tasks from glue tasks [3]. Use modules or separate processes accordingly.

**Underestimating wildcard ambiguity and channel confusion.** The two most frequent new-user failures are, in Snakemake, two rules whose output patterns both match a requested file, and in Nextflow, a channel that silently emits nothing because an upstream filter emptied it. Both fail quietly by design. Build a tiny two-rule or two-process toy whenever you are unsure how matching or streaming will behave.

**Migrating mid-project for cosmetic reasons.** Rewriting a functioning pipeline in the other tool is rarely worth it unless a decisive criterion, such as consortium adoption or cloud portability, actually applies [2] [1]. The migration cost is paid in re-validation of every step.

**Expecting the tool to fix project chaos.** Neither manager compensates for an unversioned sample sheet, results scattered across personal folders, or undocumented parameters. Standardized project organization and comprehensive testing are your work, not the framework's [3].

One more limitation worth stating plainly: the right tool is project-specific, and the only reliable way to know is to prototype your actual workload in both on your actual hardware, as the prototyping approach to choosing a workflow management system recommends [1].

## Frequently Asked Questions

### Is Groovy harder to learn than Python for writing pipelines?

For most bioinformaticians, yes, because Python is usually a language they already know while Groovy usually is not. The workflow-specific parts of Nextflow are a thin layer over Groovy, so motivated authors become productive quickly, but debugging channel logic still requires Groovy fluency that a Python-native tool does not demand.

### Can both tools run on my university HPC cluster and in the cloud?

Yes. Both integrate with containers, common HPC schedulers such as SLURM, and cloud platforms, and delegating execution details to the workflow manager is one of their core benefits [1]. If multi-cloud or multi-institution portability is your dominant requirement, Nextflow's tooling for that scenario is more developed, but a single-cluster lab will be equally productive with either.

### Which tool gives better reproducibility?

Neither tool is inherently more reproducible. Reproducibility comes from pinned environments, containers, validated inputs, and automated testing, all of which both systems support and all of which you must actually implement [3]. Standardized community pipelines help by shipping those practices pre-built [2].

### Can I call my existing Python and R scripts from both tools?

Yes. Nextflow runs your scripts inside process blocks as ordinary shell commands with interpolated values. Snakemake offers a `script:` directive that executes external Python or R scripts with a `snakemake` object providing inputs, outputs, and parameters. Both approaches keep your analysis code independent of the workflow tool.

### Do I have to use nf-core if I choose Nextflow?

No. You can write a standalone Nextflow pipeline with no nf-core involvement. You would be giving up the community module and subworkflow library, shared standards, and peer-reviewed pipelines, which for many teams are the strongest reasons to pick Nextflow in the first place [2].

### Is one of the tools faster than the other?

For real bioinformatics workloads, execution time is dominated by the tools inside the pipeline, such as aligners and callers, not by the workflow manager. Both schedule tasks in parallel and both scale across cluster nodes, so end-to-end runtime differences between them are negligible for typical pipelines. Prototype on your data if scheduling overhead on your specific cluster worries you.

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## Sources

1. [Using prototyping to choose a bioinformatics workflow management system.](https://pubmed.ncbi.nlm.nih.gov/33630841/)
2. [Empowering bioinformatics communities with Nextflow and nf-core.](https://pubmed.ncbi.nlm.nih.gov/40731283/)
3. [Software engineering for reproducible pipeline development in bioinformatics.](https://pubmed.ncbi.nlm.nih.gov/42756310/)