From Observation to Conservation: How Digital Birding Networks Are Redefining Ecology in South Asia

A decade ago, birdwatching in South Asia was a quiet, personal pursuit — a pair of binoculars, a notebook, and long hours of patience. Today, it has become a continent-wide data movement, where every sighting, sound, and checklist contributes to understanding the state of nature itself.

From school students in Assam using Merlin to identify their first bulbuls, to seasoned birders in Sri Lanka uploading full eBird checklists, a silent revolution is unfolding. Citizen science — powered by mobile apps, open databases, and community networks — is reshaping how conservation happens in the region.

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1. A New Kind of Ecology: Networked, Open, and Real-Time

Traditional ecological research depended on slow, manual data collection — limited in time and geography. Now, thousands of birders across South Asia contribute daily data that scientists once could only dream of.

Platforms like eBird and BirdCount India act as living laboratories, continuously updating our understanding of migration, breeding patterns, and local abundance.

This data flow is no longer top-down. It’s bottom-up science, where every participant becomes both observer and data generator — an ecosystem of collective intelligence.

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2. The Digital Infrastructure Behind the Movement

At the heart of this transformation are a few key technological pillars:

• 🪶 Merlin Bird ID — AI-powered identification and accessible learning.

• 📊 eBird — standardized data collection and global database integration.

• 🌏 BirdCount India — regional verification and contextual interpretation.

• 🎧 BirdNET — crowd-sourced acoustic monitoring expanding into tropical soundscapes.

• 🧠 iNaturalist — ecosystem-level data integration linking birds to plants, insects, and habitats.

Together, these platforms form a modular workflow — each app a node in a larger web of observation, verification, and insight.

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3. The Indian Model: Community-Led Conservation

India’s citizen-science ecosystem has a distinctly local flavor. It thrives not just because of technology, but because of grassroots coordination.

Projects like:

• MigrantWatch — tracking wintering and passage migrants,

• Kerala Bird Atlas and Tamil Nadu Bird Atlas — mapping state-wide species distributions,

• State of India’s Birds (SoIB) — using eBird data for national-level policy briefs,

demonstrate how open data, when nurtured by local institutions, can directly inform habitat management and species protection.

The SoIB 2023 report, for example, used over 30 million eBird records to reveal declines in several common species — prompting public discourse and conservation action.

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4. Where Citizen Data Meets Science

This isn’t just about crowdsourcing — it’s about co-creating science.
Researchers at institutions like the National Centre for Biological Sciences (NCBS), SACON, and BNHS now routinely use eBird and iNaturalist datasets to:

• Model seasonal migration shifts under climate change,

• Identify urban “green corridors” critical for resident species,

• Study agricultural landscapes and their effects on bird diversity.

The line between hobbyist and scientist is blurring. A well-documented checklist from a village in Odisha can carry as much weight in a migration model as a satellite tag from a formal research project.

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5. A Regional Perspective: South Asia’s Shared Skies

Birds don’t respect borders — and neither should bird data.
South Asia’s flyways connect Siberia to Sri Lanka, with wetlands, deltas, and mountains acting as stopover nodes.

Recognizing this, groups across India, Nepal, Bhutan, Bangladesh, and Sri Lanka are now coordinating eBird-based surveys under shared frameworks.
This regional collaboration supports the Central Asian Flyway initiative — aligning citizen science with international conservation treaties like the Convention on Migratory Species (CMS).

For the first time, citizen-collected data is influencing transboundary conservation.

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6. Challenges Ahead: Data Quality, Inclusion, and Equity

As participation grows, so do the challenges.

• Data quality: not all checklists are equally reliable; automated vetting and expert review are key.

• Representation gaps: most data comes from urban centers — rural and forested areas remain underreported.

• Accessibility: language barriers and digital literacy still exclude many potential contributors.

Future growth will depend on training programs, regional language interfaces, and offline-first tools — ensuring that citizen science reflects the true diversity of South Asia’s landscapes and people.

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7. The Future: AI-Driven, Locally Grounded

Emerging initiatives aim to integrate machine learning and edge devices for large-scale ecological sensing:

• Autonomous recorders trained on BirdNET models deployed in forests.

• Merlin updates using regional audio datasets from local contributors.

• Real-time dashboards linking eBird and climate data for conservation alerts.

But even as technology advances, one truth remains: the best sensors are still human eyes and ears, connected by curiosity and care.

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8. Why This Matters

In an era of biodiversity loss, these digital birding networks represent more than convenience — they’re a form of citizen empowerment.
They transform passive observation into active stewardship.

When a farmer logs a pond heron, or a student identifies a hoopoe, they’re not just recording a bird — they’re helping build the region’s ecological memory.

As the data grows, so does our collective ability to protect what’s left.

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✨ The Takeaway

South Asia’s citizen science model — powered by Merlin, eBird, and BirdCount India — is quietly setting a global precedent.
It shows that conservation doesn’t have to wait for massive funding or central planning.
It can begin with a phone, a field, and a single question: what bird is that?

In that moment of curiosity lies the seed of science — and the hope of conservation.

Citizen Science in Flight: How Indian and South Asian Birders Are Powering a New Era of Data

When a birder in Bengaluru uploads a sighting of a Black Drongo, and another in Bhutan records a Himalayan Monal call, they may not realize they are part of something vast — a living data web stretching across South Asia, linking forests, farms, and smartphones.

In the past decade, citizen science in birding has gone from a niche pursuit to a movement. What began as enthusiasts noting birds in notebooks is now a coordinated regional network generating millions of verified records — data that scientists, conservationists, and policymakers rely on to track migration, habitat change, and species decline.

And at the center of this transformation? A set of well-integrated tools and workflows that make participation simple, satisfying, and scientifically powerful.

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1. The Core Workflow: Merlin → eBird → BirdCount India

If you’re birding anywhere in India, Nepal, Sri Lanka, Bangladesh, or Bhutan, this trio forms the backbone of modern citizen science:

🪶 Step 1: Identify with Merlin

Merlin’s regional packs for India and South Asia include hundreds of local species, from the Indian Roller to the Malabar Whistling Thrush. You can identify birds offline — even deep inside a sanctuary with no signal.
Once you’re confident of your ID, tap “This is my bird” to log the sighting.

📋 Step 2: Upload to eBird

That same sighting syncs to eBird, where you can add count details, location, time, and habitat notes. eBird’s India portal is localized — with hotlists, challenges, and data tailored for subcontinental birders.

🌏 Step 3: Contribute to BirdCount India

Behind the scenes, your eBird records flow into BirdCount India, a partnership between the BirdLife network, the National Centre for Biological Sciences (NCBS), and local birding clubs.
Their teams curate, verify, and analyze submissions to produce countrywide trends — like the State of India’s Birds report, which is shaping real conservation decisions.

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2. Regional Add-ons: BirdNET and iNaturalist

🎧 BirdNET for Sound Data

In sound-rich tropical environments, visual ID isn’t always possible — think dense Western Ghats forests or mangrove thickets.
BirdNET excels here. Record bird calls, get automated suggestions, and cross-check them in Merlin. Uploading verified calls to eBird enriches acoustic datasets that are vital for AI model training and species monitoring.

🌿 iNaturalist for Habitat Context

Pairing iNaturalist with eBird gives your data ecological context. You might log a White-throated Kingfisher in eBird and, in the same spot, identify its perch tree in iNaturalist. This holistic approach helps researchers understand where birds thrive — not just that they exist.

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3. Workflows That Work in the Field

🔹 The “Pocket Birder” Workflow (for rural/remote areas)

• Download Merlin’s India and Nepal pack before heading out.

• Record bird calls offline.

• When back online, cross-check using BirdNET and upload to eBird.

• Join a regional WhatsApp group (many state birding societies have one) for help confirming tricky IDs.

🔹 The “Team Survey” Workflow (for clubs or schools)

• Assign routes and hotspots using eBird’s shared checklists.

• Use standardized survey durations (e.g., 15-min stationary counts).

• Review all checklists before submitting them to ensure consistent metadata.

• Submit to BirdCount India projects like Asian Waterbird Census or Great Backyard Bird Count.

🔹 The “Data-to-Insight” Workflow (for research-minded birders)

• Export your eBird data for a region of interest.

• Use R packages like auk or visualization tools like ShinyBirds.

• Identify seasonal trends or species shifts, and contribute insights back to local clubs or BirdCount forums.

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4. How India Is Building Its Own Birding Data Culture

Citizen science in India isn’t just about using global apps — it’s about local ownership of data and stories.

Projects like:

• MigrantWatch (tracking migratory species)

• Early Bird (a school-based birdwatching program)

• Kerala Bird Atlas and Tamil Nadu Bird Atlas
  have demonstrated that consistent, community-led data collection can be as rigorous as formal surveys.

Meanwhile, universities and NGOs are beginning to analyze eBird data alongside satellite imagery and climate layers — turning citizen logs into models of habitat change, crop–bird interactions, and urban biodiversity resilience.

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5. What’s Next: AI, Policy, and Participation

South Asia’s bird data is exploding — but its power lies in how it’s used.
New initiatives aim to:

• Integrate AI-assisted monitoring using BirdNET datasets and low-cost recorders.

• Feed data directly into national biodiversity portals like India’s IndOBIS and GBIF.

• Influence policy — such as wetland protection priorities — using State of India’s Birds trend maps.

In the near future, even rural schools may run small bird observatories powered by old smartphones, collecting sounds and sightings daily. The age of community observatories is dawning.

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6. Why It Matters

Every checklist you upload, every call you record, adds a pixel to the bigger picture of South Asia’s avian life. From the rooftop kites of Delhi to the waders of Chilika, each record tells a story — of migration, adaptation, or loss.

Citizen science isn’t just about data. It’s about democratizing observation, giving everyone — farmer, student, or scientist — the means to notice and contribute.

And in noticing, we begin to protect.

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🌍 The Takeaway

Citizen science in India and South Asia is no longer a hobbyist’s pastime — it’s becoming the region’s most powerful conservation tool.

By combining Merlin for discovery, eBird for documentation, and BirdCount India for data curation, birders here are setting a global example of what collaborative, community-driven science can achieve.

So the next time you lift your phone to identify that flash of wings, remember — you’re not just spotting a bird.
You’re helping map the living pulse of an entire subcontinent.

Beyond Merlin: How Other Birding Apps Compare and Complement It

When Merlin Bird ID first arrived, it felt like magic — a free app that could listen, look, and tell you what bird you were seeing or hearing. But as any birder quickly learns, the digital field is broader than Merlin alone. Over the years, a whole ecosystem of apps has emerged — some focused on bird calls, others on data logging, community science, or detailed field guides.

So, if Merlin is the friendly wizard in your pocket, who are its companions in this modern birding fellowship? Let’s explore how Merlin stacks up — and teams up — with other birding apps.

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1. Merlin vs. Audubon Bird Guide: The Scholar and the Wizard

While Merlin charms you with quick, AI-driven IDs, the Audubon Bird Guide feels more like a classic mentor — a beautifully detailed field guide you can carry anywhere.

Audubon’s strength lies in its depth: each species profile is rich with behavioral notes, plumage variations, habitat details, and high-quality illustrations. It’s especially beloved in North America, where its coverage is strongest.

How they complement each other:
Use Merlin to identify what you saw or heard, then open Audubon to learn why that bird behaves as it does. Together, they blend speed and scholarship — the thrill of discovery with the pleasure of understanding.

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2. Merlin vs. BirdNET: The Battle of the Ears

If you’re the kind of birder who listens first and looks later, BirdNET deserves a place beside Merlin. Developed by the Cornell Lab (yes, the same team behind Merlin) and Chemnitz University of Technology, BirdNET specializes in audio recognition.

BirdNET can often identify calls that Merlin’s sound model might miss — especially in noisy habitats or regions still being added to Merlin’s coverage. But Merlin’s interface feels more polished, offering immediate playback and richer visual feedback.

Best practice: record with both. BirdNET is the audiophile’s tool; Merlin adds the context. The two share data roots, but their listening styles differ.

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3. Merlin vs. eBird: From Curiosity to Contribution

Merlin is for instant gratification; eBird is for lasting impact.

Run by the same Cornell Lab of Ornithology, eBird turns your sightings into global data. Every time you record a checklist, you contribute to one of the world’s largest biodiversity databases — the same data that trains Merlin’s models.

How to use both together:
Identify with Merlin, then log your confirmed observations in eBird. The two sync naturally — Merlin’s “This is my bird” button can export sightings into your eBird life list. It’s a seamless loop between discovery and citizen science.

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4. Merlin vs. iNaturalist (and Seek): Beyond Birds

Sometimes your curiosity won’t stop at feathers. That’s where iNaturalist (and its beginner-friendly version, Seek) come in. They’re built for identifying all living things — plants, insects, mammals, even fungi.

Merlin is specialized and data-driven; iNaturalist is communal and democratic. Every observation becomes part of a global identification process, refined by experts and enthusiasts alike.

In short:

• Merlin teaches you what you’re seeing.

• iNaturalist shows you how others see it too.

Together, they widen your ecological lens — perfect for those who want to understand entire ecosystems, not just the birds.

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5. Merlin vs. Sibley and BirdsEye: For the Serious Birder

For advanced birders, The Sibley eGuide and BirdsEye cater to finer distinctions.

• Sibley is like the authoritative textbook: detailed illustrations, calls, and subspecies differences.

• BirdsEye is more like a real-time radar, alerting you to nearby sightings or rare species in your region.

If you’re already fluent in field marks and migration charts, these apps go beyond identification — they help you predict and find your next lifer.

Tip: Use Merlin for quick checks in the field, and Sibley or BirdsEye for deeper preparation before trips or surveys.

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The Verdict: Choose the App That Matches Your Birding Style

Birder Type	Best App Combo	Why
Newcomer	Merlin + Audubon	Friendly identification plus deep learning
Sound-focused birder	Merlin + BirdNET	Dual-system listening improves accuracy
Citizen scientist	Merlin + eBird	Instant ID + long-term data contribution
Nature generalist	Merlin + iNaturalist / Seek	Go beyond birds, explore full biodiversity
Field researcher / expert	Merlin + Sibley + BirdsEye	Detailed species info + hotspot intelligence

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The Bigger Picture: A Collaborative Future

No single app can replace the joy of field birding — the patience, the listening, the notebook smudged with rain. But together, these digital tools weave a network where every observation matters.

Merlin identifies; eBird organizes; iNaturalist verifies; BirdNET listens; Audubon teaches. Each contributes a different piece to the puzzle of understanding our planet’s avian life.

In the end, technology doesn’t distance us from nature — it helps us notice it more carefully. And in a world where birds are disappearing faster than ever, noticing is the first step to caring.

The Merlin Story: From Field Guide to Smart Identifier

Merlin was launched as a free mobile app to bring the power of Cornell’s birding databases to everyday users. Over time, it’s grown far beyond a static “book in your hands.” It now supports identification by question prompts (size, color, behavior), by photo, and by sound (in many regions). 

A core strength of Merlin is that it is built on—and continuously informed by—the massive user-submitted observation database eBird (and the Macaulay Library of photos and recordings). Merlin’s algorithms (for photo ID and sound ID) are trained using compiled bird photos, recordings, and metadata contributed by birders worldwide. 

In short: Merlin is not just static—it learns and evolves as more data comes in.

Key Features & How They Help

Here are the major features that make Merlin powerful and enjoyable to use:

1. Bird ID by prompts (Wizard / descriptive)

You can answer a few simple questions (where and when you saw the bird, how large it was, what main colors you saw, and what it was doing) and get a likely candidate list. Merlin filters out species unlikely for your location or season, narrowing possibilities.

This is great when you see a bird but can’t snap a clean photo or record a song.

2. Photo ID (computer vision)

You can feed Merlin a photo (camera or gallery) and the app uses computer vision to suggest likely species matches. It doesn’t always get the top one right, but in many cases it gives you a plausible shortlist.

Even if your photo is far from perfect (through foliage, a bit blurry, partial view), Merlin often still gives useful suggestions.

Photo ID is powered by models trained on large datasets from eBird / Macaulay Library and collaborators like Caltech / Visipedia.

3. Sound ID / Bird Song Recognition

This is one of Merlin’s coolest features: you can press “Sound ID,” and the app listens in real time (or from a recording) to identify which birds are singing nearby. It displays suggestions as it hears them.

Behind the scenes, Merlin transforms audio into a spectrogram (a visual frequency-vs-time display) and then runs its trained neural network to match patterns to known species.

However, Sound ID is not fully global yet: in many regions (especially less-studied ones) its species coverage is limited.

You can help expand the system by submitting recordings tagged with background species in eBird checklists.

4. Explore Birds / Likely Birds lists

Rather than waiting to identify one by one, you can browse all species likely for your location and date. Merlin lets you set a location (even in advance) so you can work offline.

You can also sort the list by “most likely” (instead of alphabetical) to focus on species with the highest probability.

5. Bird Packs for regions / travel

If you're traveling, you can download bird packs for specific regions (e.g. India, Europe, Central America) so that Merlin’s identification system is tailored to that area.

6. Saving / Life List (“Save My Bird”)

Once you're confident in an ID, you can tap “This is My Bird” to save it to your personal life list—complete with date, location, and saved to your Merlin profile.

Because Merlin is connected to eBird, your saved birds (and their metadata) are visible in your eBird interface (My eBird → Manage Checklists).

7. Offline support

One essential feature: once you’ve preloaded the relevant bird pack or configured location filters, Merlin will function offline (for ID, Explore Birds, and using saved location data). This is immensely useful when you're in the field out of cellular reach.

8. Sound and Photo Libraries / Reference media

For each bird species, Merlin includes photos, recordings of songs/calls, range maps, and descriptive notes. You can play back calls for reference or compare your observations.

Utility & Real-World Use

Here’s why Merlin is more than just a fun gadget—it adds real value to birding (for beginners and experienced alike):

• Instant feedback in the field. When you come across a bird you don’t recognize, Merlin can often give you candidate names right then and there—especially helpful when the bird flies off too quickly.

• Learning & education. By comparing your observations with what Merlin suggests, you train your eye and ear over time.

• Encouraging citizen science. Because Merlin is connected to eBird, your observations help strengthen the larger database (if you choose to upload via eBird).

• Expanding audio birding. Some birds are cryptic or low in visibility, but singing. Sound ID helps you “see” via ear.

• Travel support. You don’t need to carry multiple field guides; just download the regional pack and go.

• Memory & documentation. Your life list preserves not just the bird names but when/where you saw them.

• Supporting research and conservation. More observations (with verified metadata) help researchers understand species distributions, phenology, and shifts in ranges over time.

However, Merlin isn’t perfect. In complicated cases—very similar species, tricky lighting, overlapping songs—its suggestions may be ambiguous or incorrect. Many birders treat Merlin as a companion, not the final authority. Also, in poorly documented regions, Merlin’s models might not have as strong a foundation yet.

Some users in birding forums estimate ~75% accuracy for Sound ID in their contexts (i.e. it misses some and occasionally mislabels).

Still, its strengths and ease of use make it a powerful tool.

Integration with eBird & Other Databases

One of Merlin’s biggest advantages is how it leverages and interfaces with other birding data systems.

eBird & Macaulay Library

Merlin is tightly interwoven with eBird, Cornell’s platform for community bird observations:

• The species lists and likelihood models in Merlin come from aggregated eBird observations (hundreds of millions of checklists).
• Photo ID and Sound ID models are trained on large volumes of images and recordings from eBird checklists and the Macaulay Library. 
• When you save a bird in Merlin and mark it via “This is My Bird,” the data becomes part of your eBird interface (checklists) in many cases.

This synergy means Merlin “stands on the shoulders” of the global birding community.

Export / Interoperability

If you use other platforms (such as iNaturalist or other personal bird-logging tools), there is some possibility to export your Merlin records, though integration is not always seamless.

• Some users report exporting their Merlin list (CSV or similar) and then importing into iNaturalist or other systems.

• But because Merlin’s primary observational backend is eBird, the best “native” integration is with eBird.

• Note: Some birders suggest caution about over-automated merging between Merlin and eBird, especially when automatic IDs are used without validation. (In forums, some say that automatic use of Merlin’s IDs in eBird might lead to low-quality data). 

Beyond Birding Databases?

Merlin’s design is quite domain-specific, so it doesn’t integrate broadly with non-birding systems (ERP systems, for instance). (Do note: there is a commercial “Merlin” enterprise software in other domains, but that’s not related.)

So in essence, Merlin is built to connect deeply with the birding / citizen science ecosystem (not general business systems).  

Tips & Best Practices

To get the most out of Merlin, here are some user tips:

1. Pre-download your regional bird packs before going into remote areas so you can use it offline.

2. Set location filters ahead (in the Explore Birds menu) so Merlin knows which species to focus on.

3. Use multiple identification modes. E.g. if Photo ID is inconclusive, switch to descriptive or Sound ID (if allowed in your region).

4. Use good photo / audio habits. Try to get clear views (good lighting, minimal obstruction) and clear recordings (less background noise).

5. Verify suggestions. Don’t accept Merlin’s top suggestion blindly—compare with what you see/hear and the reference media.

6. Contribute recordings / media. If you have good audio recordings or photos, tagging background (other species) in eBird helps train the models.

7. Backup or export your life list if you use multiple platforms (in case you ever switch tools).

8. Stay aware of coverage limits. In under-sampled regions, some species may not yet be well represented by the models, so treat the output as suggestions, not guarantees.

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A Birding Companion, Not a Replacement

The beauty of Merlin isn’t that it replaces field guides or human expertise—it’s how it augments them.

• For beginners, it accelerates learning and reduces the frustration of not knowing what you saw.

• For intermediate or advanced birders, it’s a second opinion, a reference, a fast lookup tool.

• For regions or species you’re less familiar with, it helps fill gaps in your knowledge.

Because Merlin is kept free and open (in the sense of leveraging eBird’s open data), it encourages broader participation in birding and citizen science. Through smart integration and continuous model improvement, it’s helping transform what once was a hobby for enthusiasts into a more accessible, data-driven experience.

"Don't look up" is so real and not just in the media

We talked about BBC, i.e., Black-bone chicken and how they have dispersed across Asia after sequencing the genomes of Kadaknath chicken. The focus of the earlier post was mostly on the history and what can be inferred regarding the possible history based on genetics. Yet, one of the technically challenging tasks performed by Shinde et al., 2023 was the resolution of the structural organisation of the Fm locus. When the study was published on 22nd June 2023, almost all the previous literature strongly supported the *Fm_2 as the correct arrangement of the Fm locus. Hence, it seemed trivial to establish this based on long-read sequencing. However, we were in for a big surprise.

The Surprise

The surprise came on 06th December 2023 with the publication of a "chromosome-level genome assembly for the Silkie chicken" as part of a Communications Biology paper. Communications Biology proclaims itself as an "open access journal". It is published by the Nature Portfolio to cover "high-quality research, reviews and commentary in all areas of the biological sciences". The journal has an impact factor in the range of 5 to 6 and could be comparable to some of the journals in the Frontiers portfolio, such as the following:

1. Frontiers in Bioengineering and Biotechnology
2. Frontiers in Cell and Developmental Biology
3. Frontiers in Cellular and Infection Microbiology
4. Frontiers in Cellular Neuroscience
5. Frontiers in Chemistry
6. Frontiers in Endocrinology
7. Frontiers in Immunology
8. Frontiers in Microbiology
9. Frontiers in Molecular Biosciences
10. Frontiers in Nutrition
11. Frontiers in Pharmacology
12. Frontiers in Plant Science
13. Frontiers in Public Health
14. Public Health Reviews

Now, what distinguishes Communications Biology from these journals? Established in 2018 as a sister journal to Communications Physics and Communications Chemistry, the journal is aimed at "providing a new open access option for biologists while applying less stringent criteria for impact and significance than the Nature-branded journals, including Nature Communications." This journal aims to fill the gap between the different Nature portfolio journals. 

Returning to the surprise of 6th December, the new genome assembly of the Silkie chicken presented by CAU (China Agricultural University) claims that the *FM_1 is the correct arrangement of the Fm locus. The Chinese CAU was formed in 1995 and should not be confused with the Central Agricultural University at Lamphelpat, Imphal (formed by an act of parliament in 1992). CAU is among the top 350 global universities in the world university rankings. In the agriculture and forestry ranking, CAU is one of the top universities ranked at least in the top 10. With 5 shared first authors and 3 shared corresponding authors, the study represents a large body of work of great interest to the chicken genomics community. As soon as it became clear that the results of the CAU_Silkie genome were contradictory to those of Shinde et al., 2023, the obvious questions were whether Shinde et al. got the result wrong? If they got it wrong, what step did they go wrong in and why?

The fear of being wrong

The CAU_Silkie genome manuscript was submitted to CommBio on 3rd August 2022 and Accepted only on 21 November 2023. This again suggests a very thorough review spanning more than 1 year. To be more precise, it is 475 days or 1 year, 3 months, 18 days or 15 months, 18 days. The author list consists of scientists from the UK, Australia, Canada, and multiple institutes in China. Again, this suggests a big multi-national study by serious big genomics groups. At first sight, it appeared that Shinde et al. had published a wrong result. Publishing a wrong result is always a big nightmare for any researcher, as it suggests many things and could be interpreted in many more ways.

On a side note, the first version of the Shinde et al. pre-print appeared on bioRxiv on August 01, 2022. The revised version of this pre-print was posted on March 06, 2023, and accepted at Frontiers in Genetics on June 05 2023. The review time of ~3 months at Frontiers is 1/5th the time spent by the CAU_Silkie genome manuscript in review. Could the Shinde manuscript receive a detailed enough review in 3 months? Although the names of the reviewers are not listed, the journal notes, "Communications Biology thanks the anonymous reviewers for their contribution to the peer review of this work." This suggests more than 1 person may have reviewed the CAU_Silkie genome article. While the Frontiers article lists the names of the three reviewers, the number of person-hours spent reviewing the article seems much higher for the CAU_Silkie genome manuscript.

We quickly analysed the references cited by the CAU_Silkie genome paper using the RIS file downloaded from the Communications Biology website. Of the 133 references listed, 124 have the publication year in the PY field of RIS format. 

•       1 PY  - 1979
•       1 PY  - 1994
•       1 PY  - 1995
•       1 PY  - 1996
•       1 PY  - 1998
•       5 PY  - 1999
•       1 PY  - 2000
•       2 PY  - 2001
•       1 PY  - 2003
•       6 PY  - 2004
•       3 PY  - 2005
•       1 PY  - 2007
•       4 PY  - 2008
•       4 PY  - 2009
•       4 PY  - 2010
•       4 PY  - 2011
•       4 PY  - 2012
•       3 PY  - 2013
•       9 PY  - 2014
•       6 PY  - 2015
•       7 PY  - 2016
•       9 PY  - 2017
•       8 PY  - 2018
•      14 PY  - 2019
•       9 PY  - 2020
•       8 PY  - 2021
•       5 PY  - 2022
•       1 PY  - 2023

The paper from 2023 is the PNAS paper on the "Evolutionary analysis of a complete chicken genome". Quantifying the number of papers cited based on the journal in which it is published shows most cited articles are from Bioinformatics and Nucleic Acids Res.

•       1 JO  - Agric. Gene
•       1 JO  - Am. J. Physiol.
•       1 JO  - Avian Pathol.
•       1 JO  - Biochem. Mol. Biol. Int.
•       1 JO  - BMC Biol.
•       1 JO  - BMC Biotechnol.
•       1 JO  - BMC Genet.
•       1 JO  - Cell
•       1 JO  - Cell Metab.
•       1 JO  - Cell Res.
•       1 JO  - Cell Syst.
•       1 JO  - Chromosoma
•       1 JO  - Cytogenet Genome Res.
•       1 JO  - Dev. Comp. Immunol.
•       1 JO  - Endocrine
•       1 JO  - Endocrinology
•       1 JO  - Endocr. Rev.
•       1 JO  - Front. Agr. Sci. Eng.
•       1 JO  - Front. Biosci.
•       1 JO  - Front. Physiol.
•       1 JO  - G3 (Bethesda)
•       1 JO  - Gene
•       1 JO  - Immunobiology
•       1 JO  - Immunol. Today
•       1 JO  - Int. J. Endocrinol.
•       1 JO  - J. Biol. Chem.
•       1 JO  - J. Cell Biochem.
•       1 JO  - J. Hered.
•       1 JO  - Mol. Cell Endocrinol.
•       1 JO  - NAR Genom. Bioinforma.
•       1 JO  - Nat. Genet.
•       1 JO  - Nat. Metab.
•       1 JO  - Nat. Protoc.
•       1 JO  - Nat. Rev. Rheumatol.
•       1 JO  - Paleobiology
•       1 JO  - R. Soc. Open Sci.
•       1 JO  - Sci. China Life Sci.
•       1 JO  - Trends Endocrinol. Metab.
•       2 JO  - BMC Bioinforma.
•       2 JO  - Dev. Dyn.
•       2 JO  - Front. Immunol.
•       2 JO  - Methods Mol. Biol.
•       2 JO  - Mol. Immunol.
•       2 JO  - PLoS Comput. Biol.
•       3 JO  - Gen. Comp. Endocrinol.
•       3 JO  - Immunogenetics
•       3 JO  - Nat. Biotechnol.
•       3 JO  - Nat. Commun.
•       3 JO  - Nat. Methods
•       3 JO  - PLoS ONE
•       3 JO  - Poult. Sci.
•       3 JO  - Proc. Natl. Acad. Sci. USA
•       3 JO  - Science
•       4 JO  - BMC Genomics
•       4 JO  - Mol. Biol. Evol.
•       4 JO  - PLoS Genet.
•       5 JO  - J. Immunol.
•       6 JO  - Genome Biol.
•       6 JO  - Nature
•       8 JO  - Nucleic Acids Res.
•       9 JO  - Bioinformatics

Quantifying the number of papers by the author's name reveals most papers cited are from Kaufman, J. 

•       1 AU  - Afanassieff, M.
•       1 AU  - Aharoni, T.
•       1 AU  - Akash, M. S. H.
•       1 AU  - Akiba, Y.
•       1 AU  - Ashwell, C.
•       1 AU  - Ashwell, C. M.
•       1 AU  - Baek, M.
•       1 AU  - Bagatto, B.
•       1 AU  - Bailey, T. L.
•       1 AU  - Balakrishnan, C. N.
•       1 AU  - Baldwin-Brown, J. G.
•       1 AU  - Baudouin-Gonzalez, L.
•       1 AU  - Beck, S.
•       1 AU  - Bed’hom, B.
•       1 AU  - Bennett, C.
•       1 AU  - Bennett, C. E.
•       1 AU  - Black-Pyrkosz, A.
•       1 AU  - Bolger, A. M.
•       1 AU  - Bornelov, S.
•       1 AU  - Borodovsky, M.
•       1 AU  - Borst, S. E.
•       1 AU  - Boswell, T.
•       1 AU  - Botero-Castro, F.
•       1 AU  - Brocht, D. M.
•       1 AU  - Bruna, T.
•       1 AU  - Bruneau, G.
•       1 AU  - Butter, C.
•       1 AU  - Camacho, C.
•       1 AU  - Carroll, S. B.
•       1 AU  - Carvajal, A. K.
•       1 AU  - Chakraborty, M.
•       1 AU  - Chang, S.
•       1 AU  - Chan, P. P.
•       1 AU  - Chazara, O.
•       1 AU  - Cheng, H.
•       1 AU  - Chen, L.
•       1 AU  - Chiang, C.
•       1 AU  - Cline, D. L.
•       1 AU  - Concepcion, G. T.
•       1 AU  - Courcelle, E.
•       1 AU  - Covey, S. D.
•       1 AU  - Czerwinski, S. M.
•       1 AU  - Dakovic, N.
•       1 AU  - Dalman, M. R.
•       1 AU  - Dharmayanthi, A. B.
•       1 AU  - Dong, K.
•       1 AU  - Durand, N. C.
•       1 AU  - Durbin, R.
•       1 AU  - Eddy, S. R.
•       1 AU  - Einat, P.
•       1 AU  - Emerson, J. J.
•       1 AU  - Erben, H. K.
•       1 AU  - Erickson, C. A.
•       1 AU  - Eshdat, Y.
•       1 AU  - Fakiola, M.
•       1 AU  - Fan, J.
•       1 AU  - Faraco, C. D.
•       1 AU  - Faraut, T.
•       1 AU  - Farkašová, H.
•       1 AU  - Feng, C.
•       1 AU  - Feng, X.
•       1 AU  - Figuet, E.
•       1 AU  - Firtina, C.
•       1 AU  - Flynn, J. M.
•       1 AU  - Friedman, J. M.
•       1 AU  - Galtier, N.
•       1 AU  - Ghurye, J.
•       1 AU  - Glavas, M. M.
•       1 AU  - Goel, M.
•       1 AU  - Goto, R. M.
•       1 AU  - Gouet, P.
•       1 AU  - Grabherr, M. G.
•       1 AU  - Grant, C. E.
•       1 AU  - Greiff, K.
•       1 AU  - Hansen, N. F.
•       1 AU  - Hateren, A.
•       1 AU  - Heller, D.
•       1 AU  - Henry, J.
•       1 AU  - Hincke, M. T.
•       1 AU  - Hjellnes, V.
•       1 AU  - Hoefs, J.
•       1 AU  - Hoff, K. J.
•       1 AU  - Holt, J.
•       1 AU  - Horev, G.
•       1 AU  - Hron, T.
•       1 AU  - Huang, Z.
•       1 AU  - Hu, J.
•       1 AU  - Imsland, F.
•       1 AU  - Ivashkiv, L. B.
•       1 AU  - Jain, C.
•       1 AU  - Jeffares, D. C.
•       1 AU  - Jiao, W. B.
•       1 AU  - Johnson, J.
•       1 AU  - Johnson, M.
•       1 AU  - Jones, C. D.
•       1 AU  - Kaiser, P.
•       1 AU  - Kalliolias, G. D.
•       1 AU  - Kalvari, I.
•       1 AU  - Kaspers, B.
•       1 AU  - Kieffer, T. J.
•       1 AU  - Kim, D.
•       1 AU  - Kim, J.
•       1 AU  - King, M. D.
•       1 AU  - Kolbe, D. L.
•       1 AU  - Kolmogorov, M.
•       1 AU  - Koren, S.
•       1 AU  - Lamont, S. J.
•       1 AU  - Lee, G. H.
•       1 AU  - Liaqat, A.
•       1 AU  - Lin, Y.
•       1 AU  - Liu, P.
•       1 AU  - Liu, Q.
•       1 AU  - Liu, S.
•       1 AU  - Loehlin, D. W.
•       1 AU  - Lohse, M.
•       1 AU  - Lomsadze, A.
•       1 AU  - Londraville, R. L.
•       1 AU  - Long, A. D.
•       1 AU  - Lovell, P. V.
•       1 AU  - Love, M. I.
•       1 AU  - Lowe, T. M.
•       1 AU  - Manni, M.
•       1 AU  - Mann, K.
•       1 AU  - Marcais, G.
•       1 AU  - McMillan, L.
•       1 AU  - McMurtry, J. P.
•       1 AU  - Mellouk, N.
•       1 AU  - Mesa, C. M.
•       1 AU  - Milne, S.
•       1 AU  - Monget, P.
•       1 AU  - Moreno, A.
•       1 AU  - Morgenstern, B.
•       1 AU  - Morin, V.
•       1 AU  - Nabholz, B.
•       1 AU  - Nawrocki, E. P.
•       1 AU  - Noble, W. S.
•       1 AU  - Nolan, L. K.
•       1 AU  - O’Connor, R. E.
•       1 AU  - Okimoto, R.
•       1 AU  - Pačes, J.
•       1 AU  - Paggi, J. M.
•       1 AU  - Pajer, P.
•       1 AU  - Park, C.
•       1 AU  - Parks-Dely, J. A.
•       1 AU  - Pastor, M. V.
•       1 AU  - Pereira, S.
•       1 AU  - Pevzner, P. A.
•       1 AU  - Phillippy, A. M.
•       1 AU  - Pierce, B. G.
•       1 AU  - Pitel, F.
•       1 AU  - Pontarotti, P.
•       1 AU  - Prokop, J. W.
•       1 AU  - Pyl, P. T.
•       1 AU  - Qu, F.
•       1 AU  - Rehman, K.
•       1 AU  - Reyes-Grajeda, J. P.
•       1 AU  - Rhie, A.
•       1 AU  - Robert, X.
•       1 AU  - Rohde, F.
•       1 AU  - Romero, A.
•       1 AU  - Rustad, T.
•       1 AU  - Salzberg, S. L.
•       1 AU  - Sato, K.
•       1 AU  - Schneeberger, K.
•       1 AU  - Seppey, M.
•       1 AU  - Sharp, P. J.
•       1 AU  - Shaw, I.
•       1 AU  - Sherman, B. T.
•       1 AU  - Sherman, R. M.
•       1 AU  - Shiina, T.
•       1 AU  - Siedler, F.
•       1 AU  - Slizyte, R.
•       1 AU  - Smith, J.
•       1 AU  - Solinhac, R.
•       1 AU  - Staines, K.
•       1 AU  - Sun, Z.
•       1 AU  - Takahashi, K.
•       1 AU  - Takimoto, T.
•       1 AU  - Taouis, M.
•       1 AU  - Tartaglia, L. A.
•       1 AU  - Taylor, R. L.
•       1 AU  - Thulien, K. J.
•       1 AU  - Tian, M.
•       1 AU  - Tian, X.
•       1 AU  - Tilak, M. K.
•       1 AU  - Tixier-Boichard, M.
•       1 AU  - Tregaskes, C. A.
•       1 AU  - Usadel, B.
•       1 AU  - Vaz, S. A.
•       1 AU  - Vingron, M.
•       1 AU  - Waddington, D.
•       1 AU  - Walker, B. A.
•       1 AU  - Wang, J. R.
•       1 AU  - Wang, K.
•       1 AU  - Wang, M. S.
•       1 AU  - Wang, P.
•       1 AU  - Wang, Y.
•       1 AU  - Warren, W. C.
•       1 AU  - Wedepohl, K. H.
•       1 AU  - Wong, G. K.
•       1 AU  - Xie, Q.
•       1 AU  - Yin, Z. T.
•       1 AU  - You, Z.
•       1 AU  - Yuan, J.
•       1 AU  - Yuen, Z. W.
•       1 AU  - Zdobnov, E. M.
•       1 AU  - Zhang, G.
•       1 AU  - Zhang, Y.
•       1 AU  - Zhao, S.
•       1 AU  - Zhu, F.
•       1 AU  - Zoorob, R.
•       2 AU  - Anders, S.
•       2 AU  - Dunn, I. C.
•       2 AU  - Elleder, D.
•       2 AU  - Haas, B. J.
•       2 AU  - Huber, W.
•       2 AU  - Moon, D. A.
•       2 AU  - Stanke, M.
•       2 AU  - Sun, H.
•       2 AU  - Veniamin, S. M.
•       2 AU  - Zhang, H.
•       2 AU  - Zhang, J.
•       3 AU  - Dorshorst, B.
•       3 AU  - Friedman-Einat, M.
•       3 AU  - Li, H.
•       3 AU  - Magor, K. E.
•       3 AU  - Miller, M. M.
•       3 AU  - Seroussi, E.
•       4 AU  - Kaufman, J.

Of these references, 1 to 30 are first cited in the Introduction (30). 31-83 in the Results (53), 84-91 in the Discussion (8), 92 to 132 in the Methods (41) and 133 is cited in the Code availability (1) section. The results section has the highest number of first citations. 

In what way are the results different?

The CAU_Silkie genome abstract describes the result: "We also provide whole-genome methylation and genetic variation maps, and resolve a complex genetic region that may contribute to fibromelanosis in these animals."

Figure 1b of the CAU_Silkie genome paper describes the Fm locus result: "The genomic collinearity for inverted duplication associated with fibromelanosis and the Hi-C heatmap of Chr20. Arrows of different colors represent the clip direction. The density curves at each end represent sequencing coverage." The order in the figure is DUP1-> <-DUP2 <-INT <-DUP1 - DUP2->. 

Compare this with the 3 scenarios possible:

*FM_1: DUP1-> <-DUP2 <-INT <-DUP1 - DUP2->

*FM_2: DUP1-> <-DUP2 - DUP1-> INT-> DUP2->

*FM_3: DUP1-> INT-> DUP2-> <-DUP1 - DUP2->

The Shinde et al. study claims to establish *FM_2 as the correct arrangement and is in concordance with previous studies published in Plos Genetics: Dorshorst (2015) and PLoS ONE: Dharmayanthi (2017). While the Journal of Heredity: Dorshorst (2010) paper maps the location of Fm, nothing is known about the structure of this region then. The GigaScience: Sohn (2018) paper does try to solve the structure of the Fm locus and argues that scenario 1 is potentially correct. However, this GigaScience paper is completely ignored in the CAU_Silkie study as the breed used is Yeonsan Ogye, not Silkie.

In addition to the main figure, the Supplementary Figure S11 of the CAU_Silkie genome manuscript has a cartoon depiction of "The possible rearrangement hypothesis of hyperpigmentation". 

Interestingly, here, *FM_1 is defined as DUP1-> <-DUP2 - DUP1-> INT-> DUP2->. This corresponds to the FM2 of the Dorshorst (2015) paper.

In the same figure, *FM_2 is defined as DUP1-> <-DUP2 - <-INT <-DUP1 - DUP2->. This corresponds to the *FM_1 of the Dorshorst (2015) paper.

This redefinition seems unnecessarily confusing, and the motivation for such renaming is hard to comprehend. 

Some more details about the dataset and the genome assembly:

1. The CAU_Silkie_1.0 genome available on NCBI (https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_033088195.1/) has a total size of 1080553668 compared to the 1,080,256,408 mentioned in Supplementary Table 1. What is the source of the 297260 bp difference between these two values?
2. Although the CAU manuscript states, "DNA from the same female Silkie bird was used to generate PacBio, Nanopore sequencing libraries", the genome assembly does not have a W chromosome assembled. Shouldn't the complete genomic sequence include the W chromosome?
3. Data from two projects are linked to the CAU_Silkie genome paper. The project PRJNA805080 contains the genomics data, and PRJNA827662 contains the transcriptomic data. The genomics data consists of paired-end Illumina re-sequencing data from 15 samples, data from one female individual consisting of 2 libraries of PromethION, 3 libraries of HI-C (Illumina NovaSeq 6000) and 3 libraries of Sequel II. All the fastq read headers lack library details except for SRR17968808. Why are the headers stripped of the original information? Does SRA need a way to validate the data? This is a severe flaw in the procedure of sequencing data deposition in these archives.

PacBio HiFi sequencing of Gal gallus: adult female silkie - Blood

>gnl|SRA|SRR17981950.1.11 Biological (Biological) m64066_200930_085914 483 Data size(G) 

>gnl|SRA|SRR17981951.1.11 Biological (Biological) m64061_201030_024601 494 Data size(G)

>gnl|SRA|SRR17981952.1.11 Biological (Biological) m64082_201009_073749 410 Data size(G)

Hi-C sequencing of Gal gallus: adult female silkie - Breast muscle

>gnl|SRA|SRR17968807.1.11 Biological (Biological) 200826_A00682_0423_BH5YHJDSXY 4.1 Data size(G)
>gnl|SRA|SRR17968808.1.1A00262:510:HCWVKDSXY:2:1101:1018:1031 Biological (Biological) 200911_A00869_0294_AHCW57DSXY 102 Data size(G)
>gnl|SRA|SRR17968809.1.11 Biological (Biological) 200918_A00262_0510_BHCWVKDSXY 5.1 Data size(G)

Nanopore sequencing of Gal gallus: adult female silkie - Blood

>gnl|SRA|SRR17968711.1.11 Biological (Biological) PAG02028_e86ed44e 44 Data size(G)
>gnl|SRA|SRR17968712.1.11 Biological (Biological) PAG02064_2db1aec4 31 Data size(G)

While these minor issues with the dataset and the way it is shared are common in genomics papers, the contradictory result concerning the structural organisation of the Fm locus is a major difference.

The relief of not being wrong

One of the first things we did after the 6th December surprise was re-analysing all the data from the Shinde et al., 2023 manuscript. To our great relief, our conclusions seemed to be correct and well-founded. However, our understanding may be wrong, or the CAU_Silkie genome manuscript interpretation may need to be corrected. 

The Fm locus region assembly is a challenging task. For instance, the earlier genomes of Silkie2 (GCA_024679325.10) and Silkie3 (GCA_024653025.1) reporting the De Novo Assembly of 20 Chicken Genomes in the journal Molecular Biology and Evolution (Impact Factor: 10.7, 4 out of 52 in Evolutionary Biology), 2022 did not manage to assembly the Fm locus and resolve its correct structure. The genome assembly of Yeonsan Ogye (GCA_002798355.1) generated by Sohn et al. (2018) and published in Gigascience (Impact Factor: 9.238) does not provide a complete assembly of this region and cannot identify which of the 3 possible scenarios is correct.

No wonder the CAU silkie paper published in Communications Biology (Impact Factor: 6.548) has the following bold proclamation at the end of the paper: "In our case, the high-quality Silkie chicken assembly solves FM traits which had not been investigated by large-scale chicken pan-genome assemblies, especially based on the second-generation whole-genome sequencing data, and insufficient third-generation data."

As I write this text, 39 Chicken (Gallus gallus) genomes are available on the NCBI genome page. Several were published last year (2023) and used long-read sequencing technologies. More chicken genomes are expected to be published soon as the costs decrease, and the technology becomes more widely available.

1. Four of the assemblies (GCA_016699485.1, GCA_016700215.2, GCA_027408225.1, and GCA_027557775.1) are from the VGP (Vertebrate Genome Project). 
2. Four high-quality assemblies of indigenous (Hu, Piao, Wuding and Daweishan) chicken genomes were published in a single study by Wu et al., 2024.
3. Twenty assemblies are from a single study by Li et al., 2022 and span all major breeds of chicken: Asil, Naked Neck, Fayoumi, White Plymouth Rock, Daweishan, Liyang,  Chahua, Houdan, Thailand Gamefowl, Langshan, White Leghorn (White_Leghorn_2 and White_Leghorn_3), Rhode Island Red (Rhode_Island_Red_2 and Rhode_Island_Red_3), Cornish (Cornish_2 and Cornish_3), Tibetan chicken (Tibetan_chicken_2_zangji and Tibetan_chicken_3_TB), Two Silkie (Silkies_3_SK and Silkies_2_wuji). Most of the samples used in this study are provided by China Agricultural University, proving that CAU is the undisputed leader in chicken genomics. Two samples are from Chittagong Veterinary and Animal Sciences University (CVASU), Bangladesh. 
4. Two Illumina short-read-only assemblies (one male and one female) generated using MaSuRCA v. 4.0.8 from Chattogram Veterinary and Animal Sciences University (CVASU) are not linked to any published articles.

Genome Assembly	DUP1	DUP2	INT	FLANK1	FLANK2	Breed
GCA_030914265.1	124368	178823	421602	735439	502763	Piao
GCA_024686315.1	126610	170999	410657	507381	509386	Langshan
GCA_024686285.1	126677	170938	412850	510730	502380	Thailand Gamefowl
GCA_027408255.1	126695	170999	412990	408641	502266	Ross
GCA_016700215.2	126701	170978	412877	504844	502221	Cross of Broiler mother + white leghorn layer father
GCA_016699485.1	126714	171020	413030	504992	502627	Cross of Broiler mother + white leghorn layer father
GCA_025370635.1	126725	170844	416113	503748	501855	Tibetan chicken
GCA_024653045.1	127158	170923	411693	508443	501395	Houdan
GCA_024687005.1	127320	177882	419518	542738	557884	Cornish
GCA_000002315.5	127379	170815	412533	503216	502498	Red Jungle Fowl
GCA_034509845.1	127411	171032	412747	504320	502225	Hailanhe
GCA_024686295.1	127422	181556	414953	543593	539657	Chahua
GCA_027557775.1	127446	170939	412896	504096	501984	Ross
GCA_027408205.1	127446	170936	412847	493490	502012	Cobb
GCA_024652985.1	127464	173787	412750	503891	502322	Rhode Island Red
GCA_024686355.1	127464	170955	411332	503580	501959	Asil
GCA_030979905.1	127471	170964	412882	503757	501648	Hu
GCA_024686465.1	127485	170954	412751	504173	502916	Naked Neck
GCA_030849555.1	127499	170977	412841	503785	502889	Daweishan
GCA_030914275.1	127500	170949	412831	504572	502165	Wuding
GCA_024652995.1	127503	170862	412765	503950	502148	White Leghorn
GCA_024653035.1	127510	170921	412874	507907	500932	Cornish
GCA_024206055.2	127516	171029	412362	505101	502739	Huxu
GCA_024686275.1	127516	170844	412531	504882	502030	Fayoumi
GCA_027408225.1	127538	170982	412918	499931	502231	Cobb
GCA_034769225.1	127602	170971	412375	503714	502886	GG1M (isolate)
GCA_034769275.1	127651	171017	412409	504978	503879	GG1F (isolate)
GCA_024679905.1	127802	180936	419275	532420	516919	White Leghorn
GCA_024653025.1	129169	1763	412215	504060	502166	Silkie
GCA_024679415.1	129678	170969	411404	496876	511457	White Plymouth Rock
GCA_024679395.1	131654	186473	430346	544670	564581	Tibetan chicken
GCA_024679355.1	132866	175664	438903	534081	516172	Daweishan
GCA_024679375.1	133534	180358	431227	516130	521784	Liyang
GCA_024679765.1	139881	182361	410841	540793	503976	Rhode Island Red
GCA_024679325.1	154503	189852	411884	528345	557780	Silkie
GCA_002798355.1	171519	240450	413946	507857	507596	Yeonsan Ogye
GCA_033088195.1	255007	342004	412392	504829	502201	Silkie
GCA_034509865.1	265295	341998	412333	713469	502903	Silkie
GCA_034509885.1	288497	343770	412770	503902	502371	Lueyang

Matters Arising: A potential mechanism for self-correction

It is one thing to know that "you think you are right" and entirely another when "you are actually right". Science is supposed to be self-correcting and has robust mechanisms in place to ensure such self-correction. One of the existing processes for the self-correction of science is the framework for peers to perform post-publication reviews through comments and criticism of peer-reviewed publications. Enough has been written about the limitations of the peer-review process already. However, post-publication review is one of the solutions to overcome the shortcomings of peer review. Such post-publication peer review (PP PR) can be anonymous, as it happens on websites like PubPeer and the like. Another form of PP PR is by publishing comments or "Letters to the Editor" in the same journal that published the original article or in a related journal. While such comments and letters have existed in one form or another, a more formalised peer-reviewed version of the process has been missing or at least not very effective.

One initiative by Springer that makes some positive strides in this direction is the "Matters Arising" article type. It is described as "Formal post-publication commentary on published papers can involve either challenges or clarifications of the published work and may, after peer review, be published online as Matters Arising, usually alongside a Reply from the original Nature authors." The system in place for these types of articles has matured over the years as the editors and the scientific community have become more familiar with this setup. For instance, the current version of the offering states that "Matters Arising are exceptionally interesting and timely scientific comments and clarifications on original research papers published within the past 18 months in Nature.". This hard deadline of 18 months seems to have been worked out after looking at the trend in the past few years. 

Contributions from India are becoming more common

While Matters Arising type articles are a type of "Research Article", they are rarely published by Indian authors. It may have something to do with a mindset of avoiding challenging authority, "Mai-Baap Sarkar". Speaking truth to power is not alien to Indians. After all, Mahatma Gandhi popularised satyagraha (literally, "truth-force"). Yet, we all know how that turned out not just for Gandhi but also for Martin Luther King Jr. The Matters Arising that we could identify from India are listed below:

• BMC Infectious Diseases (From Department of Community Health, Christian Medical College, Vellore, India)
• Nature Communications (From Department of Biostatistics, St John’s Medical College, Bengaluru, India)
• Communications Biology (Department of Biological Sciences, IISER Bhopal, Bhauri, Madhya Pradesh, India)

Do Matters Arising articles provide a resolution or just present both sides of the debate?

The last one we list is the result of Sharma et al., trying to claim that *Fm_2 is the correct scenario. In the journal Communications Biology, ~7000 articles have been published compared to 23 Matters Arising (including the replies). This works out to less than half a percent of articles (0.33%). So even globally, Matters Arising type articles are rare. After all, nobody wants to be a Galileo. In Scientific Reports, 62 Matters Arising, compared to 231641 articles, is a very small fraction. We compiled the list of these submissions and their replies to see how we stand compared to other Matters Arising articles. The time between the publication of the original article and the submission of the Matters Arising article (Received Days) and the time between the publication of the original article and the acceptance of the Matters Arising article were plotted. See below.

1. Matters Arising Received - Original article date = Received Days
2. Matters Arising Accepted - Original article date = Accepted Days

Two clusters can be seen. One consists of Matters Arising that were submitted within ~100 days of the original articles, and another cluster of articles submitted more than ~300 days after the original article. While every Matters Arising has a Reply published soon after, one does not. At 448 days, the Sharma et al. manuscript does have the longest review time for articles submitted within 100 days of the original article. The longer review time does give some confidence that the science behind it has been evaluated in detail. Compared to the short review time of Frontiers, the multiple rounds of review by multiple experts do ensure greater confidence. 

Possible challenges or sources of error in Shinde et al.:

In this additional scrutiny, what factors were considered in greater detail?

1) Raw read data errors: Could the HDPs, haplotype-consistent tiling paths simply result from sequencing errors.

2) Use other datasets (from other papers) and quantify junction read counts to evaluate if heterozygote/homozygote individuals are used.

3) Genome assemblies (39 available): Do the results remain the same with different genome assemblies? The following assemblies were used: GRCg6a, bGalGal1.mat.broiler.GRCg7b, CAU_Silkie_1.0.

4) Read mapper: bwa mem vs NGMLR vs minimap2. Could the HDPs be an artefact of the read mapper used?

5) Error in counting read support: javarkit vs IGV vs samtools vs custom Python scripts??

For some more aspects, we carefully parse the Reply to Sharma et al.

1) The second line of the reply is, "Our work1 has assembled a Silkie genome (CAU_Silkie) using a composite approach, resolving in one fell swoop the complex genomic variation of the fibromelanosis (Fm) trait and identifying a large number of important genes related to metabolism, immunity, and reproduction in birds."

The phrase "one fell swoop" appears in Macbeth (Act 4, Scene 3) when Macduff learns that Macbeth has murdered his wife and children. In shock and grief, he cries out:

"All my pretty ones?
Did you say all? O hell-kite! All?
What, all my pretty chickens and their dam
At one fell swoop?"

Here, "fell" means fierce, deadly, or cruel, and "swoop" refers to the way a bird of prey, like a falcon or kite, suddenly attacks. Macduff compares Macbeth to a "hell-kite" (a ruthless predator) and laments how his family was wiped out in a single, merciless attack—"one fell swoop."

Over time, the phrase evolved to mean doing something all at once, often with dramatic or decisive effect. Notably, Macduff says, "all my pretty chickens"

2) The first line of the second paragraph is, "The structural variation involved in Fm includes not only genomic duplications but also inversions, especially since the individual assembled in our work is a heterozygote (Fm/fm).". In contrast to the original article, in this reply, it is claimed that the individual assembled is a heterozygote. This is particularly important as Sharma et al. claim the individual is a homozygote. 

Having made this claim, the authors state, "The validity of our results is supported by several solid lines of evidence."

(a) First evidence is the results of read mapping to the wild-type (*N) and mutant type (*Fm_1) structures. 

(b) The second evidence is "the String Graph and contigs from haplotype assembly". 

Based on this reasoning, the authors conclude, "All the evidence suggests that our results are correct."

Once the evidence is presented, the reply turns to the problematic evidence from Sharma et al.,. "Nagarjun et al.4 mentions some ‘key pieces of evidence’ that argue there is something wrong with our assembly, but they are all quite problematic.". 

In the sentence:

mentions some ‘key pieces of evidence’ that argue there is something wrong with our assembly,

the single quotes around ‘key pieces of evidence’ are used for one of the following reasons:

1. Emphasis or distancing: The writer might be signaling that they are quoting someone else's exact words or that they are not fully endorsing the phrase. It can imply skepticism, irony, or a different interpretation than the literal meaning.

2. Scare quotes: These are quotation marks used around a word or phrase to indicate that it’s being used in a non-standard, unusual, or ironic way. So here, it might suggest that the evidence being presented as “key” might not actually be as strong or convincing as claimed.

3. Quotation within a quotation (depending on context): If the sentence is part of a larger quotation that already uses double quotes, the single quotes can show a quote within a quote. But since this sentence is isolated, this seems less likely unless there's a broader context.

So, in this case, the single quotes likely imply skepticism or quotation of someone else’s claim — as in, they're being called "key pieces of evidence", but the speaker may not entirely agree.

The next sections empasise this beginning using statements such as: "HDP is not accurate when applied to heterozygous individuals". Hence, one of the main points of contention is whether the individual used for assembly is heterozygous or homozygous.

Summary of Potentially Literary or Idiomatic Phrases:

Phrase	Type	Origin / Note
"In one fell swoop"	Direct literary allusion	Shakespeare, Macbeth
"Gold standard"	Idiomatic metaphor	20th c. journalism/lit
"Lines of evidence"	Technical phrase with rhetorical flair	Classical rhetoric
"All the evidence suggests..."	Forensic/narrative tone	Common in mystery/legal genres
"At this point, the single read can prove..."	Rhetorical flourish	Echoes legal or dramatic logic
"Another view is..."	Narrative pacing	Fiction/mystery-style argumentation

Patton Science paper and lack of evidence 

The papers by Patton et al. (2020) and Stammnitz et al. (2024) present opposing interpretations of the epidemiological trajectory of Devil Facial Tumor Disease (DFTD) in Tasmanian devils—Patton et al. argue that the disease is stabilizing into an endemic phase, while Stammnitz et al. refute this, finding no evidence for such a shift. This scientific dialogue mirrors the kind of tension seen in the Matters Arising exchange over the FM locus (fibromelanosis) in chickens, where initial claims about the genetic basis of the black-bone trait were later challenged through data reinterpretation and deeper comparative genomics. However, unlike a formal Matters Arising—where critique and rebuttal occur within the same journal—here the discourse spans across venues, illustrating a broader philosophical point: that scientific knowledge evolves not just through journal-specific dialogue, but through cross-platform, iterative analysis. Both cases reflect the self-correcting nature of science and the importance of maintaining open channels for debate beyond formalized comment formats.

Scientific Summaries

1. Patton et al. (2020) – Science

Title: A transmissible cancer shifts from emergence to endemism in Tasmanian devils

Summary: This study investigates the dynamics of Devil Facial Tumor Disease (DFTD), a transmissible cancer affecting Tasmanian devils. Utilizing an epidemiological phylodynamic approach, the researchers analyzed the disease's spread and adaptation over time. They found that in areas with lower devil densities, the disease's growth and transmission rates have slowed. This suggests that DFTD is transitioning from an emergent epidemic to an endemic state, implying a potential stabilization of the disease within the population.

2. Stammnitz et al. (2024) – Royal Society Open Science

Title: No evidence that a transmissible cancer has shifted from emergence to endemism in Tasmanian devils

Summary: Contrasting the findings of Patton et al., this study re-examines the status of DFTD in Tasmanian devils. Through comprehensive data analysis, the authors argue that there is no substantial evidence supporting the transition of DFTD from an emergent to an endemic state. They emphasize that the disease continues to pose significant threats to devil populations, and caution against assumptions of disease stabilization.

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Philosophical Perspective

Reconciling Contradictory Viewpoints

The divergence in conclusions between these two studies underscores the complexity of interpreting ecological and epidemiological data. Science often progresses through such debates, where differing methodologies, data sets, or analytical frameworks can lead to contrasting interpretations. This dynamic is not indicative of flaws but rather reflects the iterative nature of scientific inquiry, where hypotheses are continually tested and re-evaluated.

Role of Publication Venues

The venues of publication—Science and Royal Society Open Science—play a pivotal role in shaping the discourse. Science, being a high-impact journal, often publishes groundbreaking studies that can influence subsequent research directions. In contrast, Royal Society Open Science provides a platform for open peer review and encourages the publication of studies that may challenge prevailing narratives. This ecosystem allows for a balanced scientific dialogue, where initial findings can be scrutinized and refined through subsequent research.​

While Matters Arising articles are typically short critiques or follow-ups published in the same journal as the original work — often directly linked to the original article — the case here is philosophically and procedurally different:

• Same Topic, Different Journals: The original claim (DFTD shifting to endemism) was published in Science — a high-profile venue. The counterargument was published years later in Royal Society Open Science, an open-access journal that encourages broader debate and methodological transparency.

• Independent Platforms, Independent Framing: In a Matters Arising format, the critical article is tethered to the original and usually appears soon after. Here, Stammnitz et al. authored a full-length paper that challenges the entire framing of the earlier study, rather than submitting a formal comment to Science. This affords them greater space, nuance, and independence to re-analyze data and reframe conclusions.

• Philosophical Implication: This approach emphasizes that scientific truth does not reside within journals, but in rigorous process and reproducibility. It also reflects how open-access ecosystems like Royal Society Open Science serve as vital venues for long-form re-evaluation, even of claims made in elite journals.

This kind of exchange reinforces that science thrives on open, distributed critique, not just gated formats like Matters Arising. Both papers, by existing side by side in the literature, allow the community to weigh evidence, reproduce analyses, and refine our understanding — the essence of robust, evolving knowledge.