Channel
@biocodeltd
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Telegram's recommendations · Cite this entry
5,766subscribers
-5 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001428142103 |
|---|---|
| Type | Channel |
| Username | @biocodeltd |
| Created | Between 1 April 2019 and 30 September 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 27 August 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 27 August 2026 |
| On Telegram | t.me/biocodeltd |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 27 Aug 2026, 10:27 | 5,766 | +6 |
| 24 Aug 2026, 13:16 | 5,760 | +2 |
| 21 Aug 2026, 04:13 | 5,758 | -1 |
| 14 Aug 2026, 12:59 | 5,759 | -7 |
| 11 Aug 2026, 10:38 | 5,766 | -5 |
| 7 Aug 2026, 20:30 | 5,771 | no change |
| 7 Aug 2026, 20:25 | 5,771 | first reading |
29 posts held, back to 11 May 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 7 pagesof Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 10 of 17 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 11 August 2026 |
|---|---|
| Posts held | 29 (11 May 2025 – 11 August 2026) |
| Views total | 5,126 |
| Reactions total | 26 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 03:54 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
47 reactions across 20 posts, in 5 distinct kinds. The most used accounts for 74.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 35 | 74.5% | |
| 👍 | 6 | 12.8% | |
| 👏 | 3 | 6.38% | |
| 🤯 | 2 | 4.26% | |
| 🔥 | 1 | 2.13% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 20 of the 29 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 47reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 29 most recent posts we hold, published 11 May 2025 to 11 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
GATK's documentation is thorough, but it can be a barrier when you need to start calling variants. If you are new to short-read variant discovery, the sheer volume of parameters and best practices can slow you down. BioCode's Practical GATK Mastery course approaches this differently. It walks you through the recommended pipeline for short-read data, step by step. You start with BAM processing, move through variant c…
Struggling to turn raw reads into biological insight? Often the bottleneck is messy data. Our 'Manipulation of Biological Datasets in R using Dplyr and TidyR' course teaches you to clean, reshape, and manipulate biological datasets efficiently with dplyr and tidyr. Ideal for machine learning and data science workflows, this course helps you get from raw data to analysis-ready formats. Priced at $29.99, it's a practic…
👍1
Long-read sequencing is changing how we assemble genomes and study structural variation, but the two main technologies take very different routes. PacBio HiFi delivers highly accurate reads (typically >99.9%) that are 10–25 kb long. That combination of length and accuracy makes it excellent for de novo assembly and for resolving complex repeats. Oxford Nanopore, by contrast, offers ultra-long reads that can exceed …
Long-read whole-genome sequencing generates enormous alignment files, and variant calling on them can take hours. The figure shows how the Clair3 pipeline is reorganised: the genome is split into chunks that are processed in parallel, and feature generation for the neural network runs concurrently on CPU while GPU inference proceeds. Read haplotagging is moved into memory to avoid repeated I/O. The result is a roughl…
🔥1
AI has designed a working virus genome for the first time. Stanford researchers used genome language models — Evo1 and Evo2, trained on DNA the way ChatGPT is trained on text — to design bacteriophages from scratch. They synthesised 302 designs; 16 worked, killing E. coli. Hold onto the scale: a phage genome is about 5,400 base pairs. The smallest living cell is around 500,000. The human genome is 3 billion. This i…
🤯2
The GATK engine is built on a simple idea: walkers. A walker defines what to do at each locus or read; the engine handles traversal. This separation enables parallel processing across genomic intervals. Each interval is processed independently in the map stage, then the engine merges partial outputs into ordered final files. The figure shows this flow: solid arrows trace the main path from raw data through interval p…
❤1
Fragile X syndrome is caused by an expansion of CGG repeats in the FMR1 gene. Traditional PCR can struggle to size these repeats accurately, especially when alleles are large or mosaic. The figure shows how targeted long-read sequencing (tLRS-FMR1) resolves this. In sample P7, tLRS-FMR1 counts 29 and 309 CGG repeats precisely, while PCR only reports a full mutation above 200. In mosaic cases, such as P11, P12 and P14…
👏3👍1
Long-read sequencing reads entire RNA molecules end to end, capturing full-length isoforms. Short reads (small bars) may cover only a few exons, making it hard to tell which exons belong together. Long reads (longer bars) span the whole transcript, revealing the exact exon combination and complete isoform. This matters because alternative splicing produces many isoforms from one gene, each with different functions. …
A bioinformatics portfolio does not require a PhD, but it does require proof that you can do the work. If your background is entirely wet-lab, you already understand biological questions and experimental design. What you need to show employers is that you can handle data and run analyses reproducibly. Start with a variant-calling pipeline on a public dataset. Download raw sequencing reads from a repository like the …
❤4
Protein structure prediction has gone from a decades-long puzzle to routine computation, but the pipeline matters. It starts with the amino acid sequence, then builds a multiple sequence alignment (MSA) to capture evolutionary info—residues mutating together are often close in 3D. The core is deep learning. AlphaFold2 and RoseTTAFold use attention architectures to turn the MSA into distance maps and torsion angles, …
❤1
Protein language models now replace the slowest step in structure prediction: searching for homologues to build a multiple sequence alignment. Traditional methods like JackHMMER scan huge databases—accurate but slow. DeepFold-PLM embeds sequences into dense vectors via pre-trained language models, then retrieves homologues by comparing embeddings in a vector database. This PLM-based alignment, plmMSA, is ~47× faster …
❤6
A study flowchart is a roadmap of scientific reasoning. This one, from Frontiers in Immunology, shows how network pharmacology, molecular docking, and wet-lab tests probed piceatannol for rheumatoid arthritis. It starts by finding piceatannol's targets, intersecting them with disease genes. Network analysis reveals key hubs and pathways. Docking predicts binding, offering a mechanism. Crucially, they validate predic…
Showing the 12 most recent of 29 posts we hold for @biocodeltd. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 27 August 2026 — this entry's latest reading, not the date you are reading this.
“BioCode: Learn Bioinformatics” (@biocodeltd), 5,766 subscribers as measured 27 August 2026. Telegram Register, tgregister.com/channel/biocodeltd.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.