<![CDATA[BOL: All site blogs]]> https://bioinformaticsonline.com/blog/all?offset=20 https://bioinformaticsonline.com/blog/view/45251/aurora-a-new-detective-for-bacterial-genomes Fri, 21 Aug 2026 11:31:00 -0500 https://bioinformaticsonline.com/blog/view/45251/aurora-a-new-detective-for-bacterial-genomes <![CDATA[Aurora: A New Detective for Bacterial Genomes]]> Imagine trying to solve a mystery with thousands of clues—but some of the clues are labelled incorrectly.

That is the challenge researchers face when studying how bacteria adapt to different environments. A bacterial gene may appear to be linked to a particular habitat, when the real reason is simply that closely related bacteria happen to live there.

In their 2025 Genome Biology paper, Bujdoš, Walter, and O’Toole introduce aurora, a machine-learning tool designed to tackle this problem.

Aurora identifies potentially mislabeled or unusual bacterial strains before performing genome-wide association studies (GWAS). By cleaning up the dataset and accounting for bacterial evolutionary relationships, it can help researchers find genetic features that are more genuinely connected to habitat adaptation.

The researchers tested aurora using simulated and real bacterial datasets and found that it could recover important genetic associations even when datasets contained misleading labels.

The bigger lesson is simple: better biological discoveries often begin with better data.

Aurora gives researchers a new way to separate real genetic clues from misleading ones—and could help us better understand how bacteria adapt, survive, and evolve in the environments they call home.

Based on Bujdoš et al., “aurora: a machine learning GWAS tool for analyzing microbial habitat adaptation,” Genome Biology (2025).[Read the original paper](https://link.springer.com/article/10.1186/s13059-025-03524-7?)

]]> BioStar https://bioinformaticsonline.com/blog/view/45249/from-dna-chaos-to-a-clear-microbial-story Fri, 21 Aug 2026 11:22:54 -0500 https://bioinformaticsonline.com/blog/view/45249/from-dna-chaos-to-a-clear-microbial-story <![CDATA[From DNA Chaos to a Clear Microbial Story]]> Imagine opening a metagenomic dataset and finding millions of DNA fragments, each carrying a tiny piece of information about the microbial world inside a sample. The challenge is not generating the data — it is turning that data into a meaningful story.

This is where MeTAline comes in.

MeTAline is a reproducible workflow designed to simplify shotgun metagenomic analysis. It brings together important steps such as quality control, host-DNA removal, taxonomic profiling and functional analysis, using tools including Kraken2, MetaPhlAn4 and HUMAnN.

What makes it especially useful is its ability to handle large datasets while keeping analyses organized and reproducible. Built with Snakemake and supporting containerized environments, MeTAline can also be used on high-performance computing systems.

In simple terms, MeTAline helps researchers move from millions of confusing DNA fragments to a clearer picture of who is there, what they might be doing, and what the data could mean.

Sometimes, the biggest challenge in science isn't collecting more data — it's finding the story hidden inside it.

Read more at https://academic.oup.com/nargab/article/7/4/lqaf158/8340158?login=false

]]> BioStar https://bioinformaticsonline.com/blog/view/45244/the-ghost-ancestors-hidden-in-our-dna Tue, 18 Aug 2026 22:16:24 -0500 https://bioinformaticsonline.com/blog/view/45244/the-ghost-ancestors-hidden-in-our-dna <![CDATA[The Ghost Ancestors Hidden in Our DNA]]> Imagine standing in a museum of human evolution.

There are Neanderthal bones. Denisovan remains. Fossils from ancient relatives of our species. Each specimen gives us another piece of the story of how Homo sapiens came to be.

But now imagine that one of the most important characters in that story has left behind no fossil, no skull, no bone, and no genome.

Nothing that a paleontologist could put behind glass.

And yet, somehow, that ancient population is still speaking.

It is speaking through us.

A fascinating new study published in Science (https://www.science.org/doi/10.1126/science.aef8874?) introduces a computational method called TRACE, which allows scientists to search modern human genomes for genetic traces of ancient populations that have never been directly identified.

For years, scientists have known that our ancestors interacted with Neanderthals and Denisovans. Their DNA still survives in modern human populations. But these may not have been the only ancient humans who crossed paths with our ancestors.

Using TRACE, researchers found evidence of an unknown archaic population that contributed DNA to the ancestors of modern humans before humans spread beyond Africa.

Even more surprisingly, they found evidence of a much older lineage—possibly around 1.8 million years old—whose genetic legacy may have reached some modern humans indirectly through Denisovans.

The extraordinary part is that researchers did not need DNA from these mysterious populations to detect them. Instead, they studied the ancestry of modern genomes and looked for genetic patterns that revealed unusually ancient branches of our family history.

It is almost like finding the footprints of someone who disappeared thousands of years ago—without ever finding the person themselves.

These discoveries suggest that human evolution was far more complicated than a simple family tree. Ancient human populations repeatedly separated, migrated, met and exchanged genes. Some eventually disappeared, but pieces of their genetic legacy survived.

And that means our DNA is more than a biological instruction manual.

It is also an archive of human history.

Some of the people recorded in that archive may never have a name or a fossil. But thanks to methods like TRACE, their genetic echoes are finally becoming visible.

The next big discovery about our ancient past may not come from a fossil in the ground.

It may already be inside us.

Reference: Zhang et al., “Recovering signatures of archaic hominin introgression using ancestral recombination graphs,” Science (2026), DOI: 10.1126/science.aef8874. Detail paper @ https://www.science.org/doi/10.1126/science.aef8874?


The TRACE repository (https://github.com/YulinZhang9806/trace) contains the Python package and command-line tools used to infer archaic admixture tracts from ancestral recombination graphs.

]]> Jitendra Narayan https://bioinformaticsonline.com/blog/view/45240/pg2-making-pangenome-graphs-easier-to-understand Tue, 18 Aug 2026 04:38:23 -0500 https://bioinformaticsonline.com/blog/view/45240/pg2-making-pangenome-graphs-easier-to-understand <![CDATA[PG2: Making Pangenome Graphs Easier to Understand]]> Genomics is moving beyond the traditional approach of studying DNA using a single reference genome. Today, researchers are increasingly using pangenomes, which represent genetic information from multiple genomes and capture a much broader range of genetic diversity.

However, pangenome graphs can be highly complex, making them difficult to visualize and interpret. A recent study published in BMC Bioinformatics introduces PG2 (PanGenoGrapher), an open-source, web-based tool designed to address this challenge. The source code and user guide are openly available on GitHub at https://github.com/iVis-at-Bilkent/pangenographer. A publicly accessible sample deployment is hosted at http://pg2.cs.bilkent.edu.tr. In addition, a demonstration video illustrating the primary use cases of PG2 is available at https://www.youtube.com/watch?v=yCd7-aGY6CQ.

PG2 combines advanced graph-layout algorithms with an interactive visualization platform. It allows researchers to explore genomic paths, identify variations, and examine relationships between different parts of a pangenome graph more easily.

This is important because visualization can play a major role in bioinformatics. When complex genomic information is presented clearly, researchers can more easily identify patterns, understand genetic variation, and generate new biological insights.

The development of PG2 represents a step toward making pangenome analysis more accessible and intuitive. As genomic datasets continue to grow and graph-based representations become more common, tools like PG2 can help researchers navigate this increasing complexity.

Ultimately, PG2 demonstrates how combining genomics, graph algorithms, and interactive visualization can make sophisticated biological data easier to understand and analyze.

Reference: Solun, G. K., Dogrusoz, U., Bingöl, Z., & Alkan, C. (2026). PG2: algorithms and a web-based tool for effective layout and visual analysis of pangenome graphs. BMC Bioinformatics. DOI: 10.1186/s12859-026-06555-4.

]]> Jitendra Narayan https://bioinformaticsonline.com/blog/view/45235/the-sweet-side-of-human-evolution-did-sugar-help-build-the-human-brain Mon, 17 Aug 2026 01:47:36 -0500 https://bioinformaticsonline.com/blog/view/45235/the-sweet-side-of-human-evolution-did-sugar-help-build-the-human-brain <![CDATA[The Sweet Side of Human Evolution: Did Sugar Help Build the Human Brain?]]> For a long time, people have focused on meat when talking about human evolution. The common idea is that our ancestors ate more animal foods, which gave them lots of energy and nutrients, helping their brains grow bigger and more demanding than those of other primates.

A new study in Science offers a different and surprisingly sweet perspective. Researchers Jennie Brand-Miller, Karen Hardy, David Raubenheimer, and Les Copeland suggest that sugars from fruits and honey, and later starch from cooked plants, may have been key in helping the human brain evolve.

The brain’s carbohydrate problem

The human brain uses a lot of energy. Even though it is only about 2% of an adult’s body weight, it takes up a large share of the body’s resting energy, mainly using glucose as fuel. Over millions of years, as our ancestors’ brains grew from about 300 grams in Australopithecus afarensis to around 1,500 grams in modern humans, the need for easy-to-access glucose would have gone up a lot.

The researchers, therefore, asked a simple question: where did all that glucose come from?

Their analysis suggests that early hominins may have gotten much of their glucose from naturally sweet foods. Fruit and honey could give them easy access to sugars before they learned to digest cooked starch well. The study’s models show that sugars may have made up a big part of their diet.

From fruit and honey to cooked starch

The story did not end with fruit.

The researchers suggest that humans shifted from eating sweet foods to eating cooked starchy foods. Once people learned to control fire and process food, cooking made starchy plants much easier to digest and turned them into a key source of glucose.

From this perspective, new ways of preparing food, like pounding, processing, and cooking, were not just about making meals softer or tastier. These changes may have given our ancestors access to much more carbohydrate energy.

This gives us a more detailed view of how the human diet evolved. Animal foods were clearly important, but carbohydrates may have been just as important for meeting the high energy needs of a growing brain.

A different way to think about the human diet

The study does not claim that our ancestors ate sugar the way we do today. There is a big difference between eating whole fruits or natural honey and eating the highly processed, refined sugars common now.

Instead, the research points to something bigger. Human evolution may have depended on our ability to find, process, and get energy from many different foods.

Seasonal fruit, honey, underground plant foods, and later cooked starches could have been important sources of glucose. The researchers think that changes in how available these foods were may have shaped how our ancestors searched for food and even affected human evolution.

What does this mean today?

The findings should not be interpreted as a license to eat more refined sugar. Instead, they challenge the simple idea that carbohydrates were less important than meat in human evolution. Our ancestors survived and evolved by exploiting different nutritional opportunities—and by developing technologies that made previously difficult foods more useful.

So perhaps the history of the human brain was not written by meat alone. ISo maybe the story of the human brain is not just about meat. It could also be about fruit, honey, roots, grains, and our unique ability to turn plants into energy. sweeter than we once imagined.

Read more about it at https://www.science.org/doi/epdf/10.1126/science.aed8437

]]> Jitendra Narayan https://bioinformaticsonline.com/blog/view/45231/the-giant-who-walked-across-ancient-taiwan Sat, 15 Aug 2026 14:48:45 -0500 https://bioinformaticsonline.com/blog/view/45231/the-giant-who-walked-across-ancient-taiwan <![CDATA[The Giant Who Walked Across Ancient Taiwan]]> Thousands of years ago, long before Taiwan became the island we know today, a large-bodied human walked across the landscape. We will probably never know what this individual looked like or where they travelled. But part of their story survived—in two ancient leg bones recovered from the seabed of the Penghu Channel.

The bones sat quietly in a fossil collection for years. One was part of a femur; the other, a tibia. They looked like the remains of an unusually large ancient human. But who did they belong to?

The answer came from an unexpected source: ancient proteins.

Using palaeoproteomic analysis, researchers found a molecular signature in both bones that matches the Denisovan lineage. The discovery identifies the two Penghu fossils as Denisovan and, more importantly, gives scientists their first substantial glimpse of the Denisovans' body size.

And the picture is striking.

One individual is estimated to have been about 1.8 metres tall and weighed around 83 kilograms. The other may have reached 1.9 metres and about 91 kilograms. Their leg bones rank among the largest known from Pleistocene Homo.

These were not small or fragile people.

They were powerful, heavily built humans moving through Ice Age eastern Asia.

But the bones tell an even more intriguing story. The femur has a pronounced ridge called a femoral pilaster—a feature particularly associated with modern human hunter-gatherers and increased mechanical strength during terrestrial movement. Why would a Denisovan, with an otherwise strongly archaic skeleton, possess this modern-looking feature?

Perhaps these Denisovans travelled extensively across the landscape. Perhaps their bodies were shaped by a demanding hunting lifestyle. Or perhaps, the researchers suggest, genetic exchange with early modern humans contributed to some of these features.

Their extraordinary size raises another mystery. A common expectation in human evolution is that populations living closer to the tropics tend to be smaller. Yet these Denisovans lived around 23°N latitude and were exceptionally large. The researchers argue that cold climate alone cannot explain their size, pointing instead toward lifestyle and diet—including evidence that at least one Penghu individual relied heavily on meat.

So, piece by piece, the Denisovans are becoming less mysterious.

What was once a shadowy population known mainly from DNA is beginning to take physical form: large, robust, mobile humans who lived across eastern Asia and whose bodies carried a fascinating mixture of ancient and modern traits.

And perhaps the most remarkable part of this story is where it began—not in a spectacular cave discovery, but with two weathered bones lying among thousands of fossils.

The Denisovans may have left no written history. But their bones are beginning to tell one.

*Note: This research is currently a bioRxiv preprint and has not yet undergone peer review.  Detail at https://www.biorxiv.org/content/10.64898/2026.08.07.743438v1.full.pdf

]]> Jitendra Narayan https://bioinformaticsonline.com/blog/view/45216/the-best-free-crash-course-on-large-language-models-llms-ive-come-across Tue, 04 Aug 2026 04:57:00 -0500 https://bioinformaticsonline.com/blog/view/45216/the-best-free-crash-course-on-large-language-models-llms-ive-come-across <![CDATA[The Best Free Crash Course on Large Language Models (LLMs) I've Come Across]]>

Stanford CME 295: Transformers & Large Language Models course by Afshine Amidi and Shervine Amidi. The official course website contains the syllabus, slides, and links to all lecture recordings.

Official course

Official YouTube playlist

Course schedule

  1. Transformer – Tokenization, embeddings, attention, Transformer architecture
  2. Transformer-Based Models & Tricks – RoPE, MQA/GQA, BERT variants
  3. Large Language Models – GPT, MoE, prompting, Chain-of-Thought
  4. LLM Training – Pretraining, SFT, LoRA, optimization
  5. LLM Tuning – RLHF, DPO, preference tuning
  6. LLM Reasoning – Reasoning models, GRPO, scaling
  7. Agentic LLMs – RAG, tool calling, ReAct, agents
  8. LLM Evaluation – LLM-as-a-Judge, benchmarks, biases
  9. Current Trends & Recap – Vision Transformers, diffusion-based LLMs, future directions

If you're looking for a research-level understanding of LLMs (beyond prompt engineering), this is one of the best freely available university courses currently available. It pairs particularly well with:

]]> Neel https://bioinformaticsonline.com/blog/view/45177/installing-crossroad-on-ubuntu Fri, 29 May 2026 05:19:45 -0500 https://bioinformaticsonline.com/blog/view/45177/installing-crossroad-on-ubuntu <![CDATA[Installing croSSRoad on Ubuntu !]]> (base) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ conda
usage: conda [-h] [-v] [--no-plugins] [-V] COMMAND ...

conda is a tool for managing and deploying applications, environments and packages.

options:
-h, --help Show this help message and exit.
-v, --verbose Can be used multiple times. Once for detailed output, twice for INFO logging, thrice for DEBUG logging, four times for TRACE logging.
--no-plugins Disable all plugins that are not built into conda.
-V, --version Show the conda version number and exit.

commands:
The following built-in and plugins subcommands are available.

COMMAND
activate Activate a conda environment.
clean Remove unused packages and caches.
commands List all available conda subcommands (including those from plugins). Generally only used by tab-completion.
compare Compare packages between conda environments.
config Modify configuration values in .condarc.
create Create a new conda environment from a list of specified packages.
deactivate Deactivate the current active conda environment.
doctor Display a health report for your environment.
env Create and manage conda environments.
export Export a given environment
info Display information about current conda install.
init Initialize conda for shell interaction.
install Install a list of packages into a specified conda environment.
list List installed packages in a conda environment.
notices Retrieve latest channel notifications.
package Create low-level conda packages. (EXPERIMENTAL)
remove (uninstall) Remove a list of packages from a specified conda environment.
rename Rename an existing environment.
repoquery Advanced search for repodata.
run Run an executable in a conda environment.
search Search for packages and display associated information using the MatchSpec format.
update (upgrade) Update conda packages to the latest compatible version.
(base) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ conda create -n jitENV
Retrieving notices: done
Channels:
- ursky
- bioconda
- conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
current version: 25.7.0
latest version: 26.5.0

Please update conda by running

$ conda update -n base -c conda-forge conda

 

## Package Plan ##

environment location: /home/hp/miniforge3/envs/jitENV

 

Proceed ([y]/n)? y


Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate jitENV
#
# To deactivate an active environment, use
#
# $ conda deactivate

(base) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ conda activate jitENV
(jitENV) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ conda install conda-forge::mamba
Channels:
- ursky
- bioconda
- conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
current version: 25.7.0
latest version: 26.5.0

Please update conda by running

$ conda update -n base -c conda-forge conda

 

## Package Plan ##

environment location: /home/hp/miniforge3/envs/jitENV

added / updated specs:
- conda-forge::mamba


The following packages will be downloaded:

package | build
---------------------------|-----------------
ca-certificates-2026.5.20 | hbd8a1cb_0 127 KB conda-forge
cpp-expected-1.3.1 | h171cf75_0 24 KB conda-forge
fmt-12.1.0 | hff5e90c_0 193 KB conda-forge
libarchive-3.8.7 | gpl_hc2c16d8_101 869 KB conda-forge
libcurl-8.20.0 | hcf29cc6_0 458 KB conda-forge
libgcc-15.2.0 | he0feb66_19 1017 KB conda-forge
libgcc-ng-15.2.0 | h69a702a_19 27 KB conda-forge
libgomp-15.2.0 | he0feb66_19 590 KB conda-forge
libmamba-2.6.2 | hd28c85e_0 2.7 MB conda-forge
libmsgpack-c-6.1.0 | h54a6638_6 39 KB conda-forge
libsolv-0.7.38 | h9463b59_0 509 KB conda-forge
libstdcxx-15.2.0 | h934c35e_19 5.6 MB conda-forge
libxml2-2.15.3 | h49c6c72_0 46 KB conda-forge
libxml2-16-2.15.3 | hca6bf5a_0 547 KB conda-forge
mamba-2.6.2 | hce6dcdd_0 553 KB conda-forge
ncurses-6.6 | hdb14827_0 897 KB conda-forge
nlohmann_json-abi-3.12.0 | h0f90c79_1 4 KB conda-forge
reproc-14.2.7.post0 | hb03c661_1 35 KB conda-forge
reproc-cpp-14.2.7.post0 | hecca717_1 26 KB conda-forge
simdjson-4.6.4 | hb700be7_0 310 KB conda-forge
spdlog-1.17.0 | hab81395_1 192 KB conda-forge
------------------------------------------------------------
Total: 14.6 MB

The following NEW packages will be INSTALLED:

_openmp_mutex conda-forge/linux-64::_openmp_mutex-4.5-20_gnu
bzip2 conda-forge/linux-64::bzip2-1.0.8-hda65f42_9
c-ares conda-forge/linux-64::c-ares-1.34.6-hb03c661_0
ca-certificates conda-forge/noarch::ca-certificates-2026.5.20-hbd8a1cb_0
cpp-expected conda-forge/linux-64::cpp-expected-1.3.1-h171cf75_0
fmt conda-forge/linux-64::fmt-12.1.0-hff5e90c_0
icu conda-forge/linux-64::icu-78.3-h33c6efd_0
keyutils conda-forge/linux-64::keyutils-1.6.3-hb9d3cd8_0
krb5 conda-forge/linux-64::krb5-1.22.2-ha1258a1_0
libarchive conda-forge/linux-64::libarchive-3.8.7-gpl_hc2c16d8_101
libcurl conda-forge/linux-64::libcurl-8.20.0-hcf29cc6_0
libedit conda-forge/linux-64::libedit-3.1.20250104-pl5321h7949ede_0
libev conda-forge/linux-64::libev-4.33-hd590300_2
libgcc conda-forge/linux-64::libgcc-15.2.0-he0feb66_19
libgcc-ng conda-forge/linux-64::libgcc-ng-15.2.0-h69a702a_19
libgomp conda-forge/linux-64::libgomp-15.2.0-he0feb66_19
libiconv conda-forge/linux-64::libiconv-1.18-h3b78370_2
liblzma conda-forge/linux-64::liblzma-5.8.3-hb03c661_0
libmamba conda-forge/linux-64::libmamba-2.6.2-hd28c85e_0
libmsgpack-c conda-forge/linux-64::libmsgpack-c-6.1.0-h54a6638_6
libnghttp2 conda-forge/linux-64::libnghttp2-1.68.1-h877daf1_0
libsolv conda-forge/linux-64::libsolv-0.7.38-h9463b59_0
libssh2 conda-forge/linux-64::libssh2-1.11.1-hcf80075_0
libstdcxx conda-forge/linux-64::libstdcxx-15.2.0-h934c35e_19
libxml2 conda-forge/linux-64::libxml2-2.15.3-h49c6c72_0
libxml2-16 conda-forge/linux-64::libxml2-16-2.15.3-hca6bf5a_0
libzlib conda-forge/linux-64::libzlib-1.3.2-h25fd6f3_2
lz4-c conda-forge/linux-64::lz4-c-1.10.0-h5888daf_1
lzo conda-forge/linux-64::lzo-2.10-h280c20c_1002
mamba conda-forge/linux-64::mamba-2.6.2-hce6dcdd_0
ncurses conda-forge/linux-64::ncurses-6.6-hdb14827_0
nlohmann_json-abi conda-forge/noarch::nlohmann_json-abi-3.12.0-h0f90c79_1
openssl conda-forge/linux-64::openssl-3.6.2-h35e630c_0
reproc conda-forge/linux-64::reproc-14.2.7.post0-hb03c661_1
reproc-cpp conda-forge/linux-64::reproc-cpp-14.2.7.post0-hecca717_1
simdjson conda-forge/linux-64::simdjson-4.6.4-hb700be7_0
spdlog conda-forge/linux-64::spdlog-1.17.0-hab81395_1
yaml-cpp conda-forge/linux-64::yaml-cpp-0.8.0-h3f2d84a_0
zstd conda-forge/linux-64::zstd-1.5.7-hb78ec9c_6


Proceed ([y]/n)? y


Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(jitENV) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ mamba install -c jitendralab -c bioconda -c conda-forge crossroad -y
jitendralab/noarch ??.?MB @ ??.?MB/s 0.3s
jitendralab/linux-64 ??.?MB @ ??.?MB/s 0.4s
bioconda/linux-64 5.6MB @ 2.9MB/s 1.9s
bioconda/noarch 5.6MB @ 2.5MB/s 2.2s
conda-forge/noarch 26.4MB @ 6.0MB/s 4.5s
conda-forge/linux-64 53.8MB @ 6.7MB/s 8.2s


Transaction

Prefix: /home/hp/miniforge3/envs/jitENV

Updating specs:

- crossroad

Package Version Build Channel Size
─────────────────────────────────────────────────────────────────────────────────────────────────
Install:
─────────────────────────────────────────────────────────────────────────────────────────────────

+ annotated-doc 0.0.4 pyhcf101f3_0 conda-forge Cached
+ annotated-types 0.7.0 pyhd8ed1ab_1 conda-forge Cached
+ anyio 4.13.0 pyhcf101f3_0 conda-forge 147kB
+ argcomplete 3.6.3 pyhd8ed1ab_0 conda-forge Cached
+ aws-c-auth 0.10.3 h3aafcba_1 conda-forge 134kB
+ aws-c-cal 0.9.14 h8e43964_1 conda-forge 57kB
+ aws-c-common 0.13.1 hb03c661_0 conda-forge 242kB
+ aws-c-compression 0.3.2 h16e98cb_1 conda-forge 22kB
+ aws-c-event-stream 0.7.1 h9be7a74_1 conda-forge 59kB
+ aws-c-http 0.11.0 hcbcd92d_1 conda-forge 230kB
+ aws-c-io 0.26.3 h955231c_3 conda-forge 182kB
+ aws-c-mqtt 0.15.2 h8af55cf_3 conda-forge 222kB
+ aws-c-s3 0.12.3 h00bea6e_2 conda-forge 153kB
+ aws-c-sdkutils 0.2.4 h16e98cb_5 conda-forge 59kB
+ aws-checksums 0.2.10 h16e98cb_1 conda-forge 102kB
+ aws-crt-cpp 0.38.3 h7b0d4b4_2 conda-forge 413kB
+ aws-sdk-cpp 1.11.747 h5a171d8_5 conda-forge 4MB
+ azure-core-cpp 1.16.2 h206d751_0 conda-forge 349kB
+ azure-identity-cpp 1.13.3 hed0cdb0_1 conda-forge 251kB
+ azure-storage-blobs-cpp 12.17.0 hf824e48_1 conda-forge 587kB
+ azure-storage-common-cpp 12.13.0 ha7a2c86_0 conda-forge 159kB
+ azure-storage-files-datalake-cpp 12.15.0 h1e5b466_0 conda-forge 304kB
+ backports.zstd 1.5.0 py314h680f03e_0 conda-forge 8kB
+ bedtools 2.31.1 h13024bc_3 bioconda Cached
+ biopython 1.87 py314h5bd0f2a_0 conda-forge 3MB
+ brotli 1.2.0 hed03a55_1 conda-forge Cached
+ brotli-bin 1.2.0 hb03c661_1 conda-forge Cached
+ brotli-python 1.2.0 py314h3de4e8d_1 conda-forge 367kB
+ certifi 2026.5.20 pyhd8ed1ab_0 conda-forge 134kB
+ charset-normalizer 3.4.7 pyhd8ed1ab_0 conda-forge Cached
+ click 8.4.1 pyhc90fa1f_0 conda-forge 105kB
+ colorama 0.4.6 pyhd8ed1ab_1 conda-forge Cached
+ contourpy 1.3.3 py314h97ea11e_4 conda-forge 324kB
+ crossroad 0.3.6 pyh7e60211_0 jitendralab 2MB
+ cycler 0.12.1 pyhcf101f3_2 conda-forge Cached
+ dnspython 2.8.0 pyhcf101f3_0 conda-forge Cached
+ email-validator 2.3.0 pyhd8ed1ab_0 conda-forge 47kB
+ email_validator 2.3.0 hd8ed1ab_0 conda-forge 7kB
+ exceptiongroup 1.3.1 pyhd8ed1ab_0 conda-forge Cached
+ expat 2.8.1 hecca717_0 conda-forge 148kB
+ fastapi 0.136.3 h5ddb490_0 conda-forge 5kB
+ fastapi-cli 0.0.23 pyhcf101f3_0 conda-forge 19kB
+ fastapi-core 0.136.3 pyhcf101f3_0 conda-forge 96kB
+ fastar 0.11.0 py314h0b738fb_0 conda-forge 423kB
+ font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge Cached
+ font-ttf-inconsolata 3.000 h77eed37_0 conda-forge Cached
+ font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge Cached
+ font-ttf-ubuntu 0.83 h77eed37_3 conda-forge Cached
+ fontconfig 2.18.0 h27c8c51_0 conda-forge 281kB
+ fonts-conda-forge 1 hc364b38_1 conda-forge Cached
+ fonttools 4.63.0 pyh7db6752_0 conda-forge 846kB
+ freetype 2.14.3 ha770c72_0 conda-forge Cached
+ gflags 2.2.2 h5888daf_1005 conda-forge 120kB
+ glog 0.7.1 hbabe93e_0 conda-forge 143kB
+ h11 0.16.0 pyhcf101f3_1 conda-forge 39kB
+ h2 4.3.0 pyhcf101f3_0 conda-forge Cached
+ hpack 4.1.0 pyhd8ed1ab_0 conda-forge Cached
+ httpcore 1.0.9 pyh29332c3_0 conda-forge Cached
+ httptools 0.7.1 py314h5bd0f2a_1 conda-forge 99kB
+ httpx 0.28.1 pyhd8ed1ab_0 conda-forge Cached
+ hyperframe 6.1.0 pyhd8ed1ab_0 conda-forge Cached
+ idna 3.17 pyhcf101f3_0 conda-forge 57kB
+ jinja2 3.1.6 pyhcf101f3_1 conda-forge Cached
+ kaleido-core 0.2.1 h3644ca4_0 conda-forge Cached
+ kiwisolver 1.5.0 py314h97ea11e_0 conda-forge 77kB
+ lcms2 2.19.1 h0c24ade_0 conda-forge 251kB
+ ld_impl_linux-64 2.45.1 default_hbd61a6d_102 conda-forge Cached
+ lerc 4.1.0 hdb68285_0 conda-forge Cached
+ libabseil 20260107.1 cxx17_h7b12aa8_0 conda-forge 1MB
+ libarrow 24.0.0 h6f10b76_3_cpu conda-forge 7MB
+ libarrow-acero 24.0.0 h635bf11_3_cpu conda-forge 592kB
+ libarrow-compute 24.0.0 h53684a4_3_cpu conda-forge 3MB
+ libarrow-dataset 24.0.0 h635bf11_3_cpu conda-forge 592kB
+ libarrow-substrait 24.0.0 hb4dd7c2_3_cpu conda-forge 502kB
+ libblas 3.11.0 8_h4a7cf45_openblas conda-forge 19kB
+ libbrotlicommon 1.2.0 hb03c661_1 conda-forge Cached
+ libbrotlidec 1.2.0 hb03c661_1 conda-forge Cached
+ libbrotlienc 1.2.0 hb03c661_1 conda-forge Cached
+ libcblas 3.11.0 8_h0358290_openblas conda-forge 19kB
+ libcrc32c 1.1.2 h9c3ff4c_0 conda-forge Cached
+ libdeflate 1.25 h17f619e_0 conda-forge Cached
+ libevent 2.1.12 hf998b51_1 conda-forge Cached
+ libexpat 2.8.1 hecca717_0 conda-forge 77kB
+ libffi 3.5.2 h3435931_0 conda-forge Cached
+ libfreetype 2.14.3 ha770c72_0 conda-forge Cached
+ libfreetype6 2.14.3 h73754d4_0 conda-forge Cached
+ libgfortran 15.2.0 h69a702a_19 conda-forge 28kB
+ libgfortran5 15.2.0 h68bc16d_19 conda-forge 2MB
+ libgoogle-cloud 3.5.0 h25dbb67_0 conda-forge 3MB
+ libgoogle-cloud-storage 3.5.0 hdbdcf42_0 conda-forge 780kB
+ libgrpc 1.78.1 h1d1128b_0 conda-forge 7MB
+ libjpeg-turbo 3.1.4.1 hb03c661_0 conda-forge Cached
+ liblapack 3.11.0 8_h47877c9_openblas conda-forge 19kB
+ libmpdec 4.0.0 hb03c661_1 conda-forge 92kB
+ libopenblas 0.3.33 pthreads_h94d23a6_0 conda-forge 6MB
+ libopentelemetry-cpp 1.26.0 h9692893_0 conda-forge 934kB
+ libopentelemetry-cpp-headers 1.26.0 ha770c72_0 conda-forge 396kB
+ libparquet 24.0.0 h7376487_3_cpu conda-forge 1MB
+ libpng 1.6.58 h421ea60_0 conda-forge 318kB
+ libprotobuf 6.33.5 h6eeba95_1 conda-forge 4MB
+ libre2-11 2025.11.05 h0dc7533_1 conda-forge 213kB
+ libsqlite 3.53.1 h0c1763c_0 conda-forge 955kB
+ libstdcxx-ng 15.2.0 hdf11a46_19 conda-forge 28kB
+ libthrift 0.22.0 h7d032f7_2 conda-forge 424kB
+ libtiff 4.7.1 h9d88235_1 conda-forge Cached
+ libutf8proc 2.11.3 hfe17d71_0 conda-forge 86kB
+ libuuid 2.42.1 h5347b49_0 conda-forge 40kB
+ libuv 1.52.1 h280c20c_0 conda-forge 420kB
+ libwebp-base 1.6.0 hd42ef1d_0 conda-forge Cached
+ libxcb 1.17.0 h8a09558_0 conda-forge Cached
+ markdown-it-py 4.2.0 pyhd8ed1ab_0 conda-forge 69kB
+ markupsafe 3.0.3 py314h67df5f8_1 conda-forge 27kB
+ mathjax 2.7.7 ha770c72_3 conda-forge Cached
+ matplotlib-base 3.10.9 py314h1194b4b_0 conda-forge 9MB
+ mdurl 0.1.2 pyhd8ed1ab_1 conda-forge Cached
+ munkres 1.0.7 py_1 bioconda Cached
+ narwhals 2.21.2 pyhcf101f3_0 conda-forge 284kB
+ nlohmann_json 3.12.0 h54a6638_1 conda-forge 136kB
+ nspr 4.38 h29cc59b_0 conda-forge Cached
+ nss 3.118 h445c969_0 conda-forge Cached
+ numpy 2.4.6 py314h2b28147_0 conda-forge 9MB
+ openjpeg 2.5.4 h55fea9a_0 conda-forge Cached
+ orc 2.3.0 h21090e2_0 conda-forge 1MB
+ packaging 26.2 pyhc364b38_0 conda-forge 92kB
+ pandas 3.0.3 py314hb4ffadd_0 conda-forge 15MB
+ perf_ssr 0.4.8 py_0 jitendralab 720kB
+ pillow 12.2.0 py314h8ec4b1a_0 conda-forge 1MB
+ pip 26.1.1 pyh145f28c_0 conda-forge 1MB
+ plotly 6.6.0 pyhd8ed1ab_0 conda-forge Cached
+ plotly-upset-hd 0.0.2 py_0 jitendralab 356kB
+ prometheus-cpp 1.3.0 ha5d0236_0 conda-forge 200kB
+ pthread-stubs 0.4 hb9d3cd8_1002 conda-forge Cached
+ pyarrow 24.0.0 py314hdafbbf9_0 conda-forge 27kB
+ pyarrow-core 24.0.0 py314h969be7f_0_cpu conda-forge 5MB
+ pydantic 2.13.4 pyhcf101f3_0 conda-forge 347kB
+ pydantic-core 2.46.4 py314h2e6c369_0 conda-forge 2MB
+ pydantic-extra-types 2.11.2 pyhcf101f3_0 conda-forge 74kB
+ pydantic-settings 2.14.1 pyhcf101f3_0 conda-forge 52kB
+ pygments 2.20.0 pyhd8ed1ab_0 conda-forge Cached
+ pyparsing 3.3.2 pyhcf101f3_0 conda-forge Cached
+ pysocks 1.7.1 pyha55dd90_7 conda-forge Cached
+ python 3.14.5 habeac84_100_cp314 conda-forge 37MB
+ python-dateutil 2.9.0.post0 pyhe01879c_2 conda-forge Cached
+ python-dotenv 1.2.2 pyhcf101f3_0 conda-forge Cached
+ python-kaleido 0.2.1 pyhd8ed1ab_0 conda-forge Cached
+ python-multipart 0.0.29 pyhcf101f3_0 conda-forge 38kB
+ python_abi 3.14 8_cp314 conda-forge 7kB
+ pyyaml 6.0.3 py314h67df5f8_1 conda-forge 202kB
+ qhull 2020.2 h434a139_5 conda-forge Cached
+ re2 2025.11.05 h5301d42_1 conda-forge 27kB
+ readline 8.3 h853b02a_0 conda-forge Cached
+ requests 2.34.2 pyhcf101f3_0 conda-forge 69kB
+ rich 15.0.0 pyhcf101f3_0 conda-forge Cached
+ rich-argparse 1.8.0 pyhd8ed1ab_0 conda-forge 27kB
+ rich-click 1.9.8 pyh8f84b5b_0 conda-forge 64kB
+ rich-toolkit 0.19.10 pyhcf101f3_0 conda-forge 33kB
+ s2n 1.7.3 hc5a330e_0 conda-forge 388kB
+ seqkit 2.13.0 he881be0_0 bioconda Cached
+ seqtk 1.5 h577a1d6_1 bioconda 142kB
+ shellingham 1.5.4 pyhd8ed1ab_2 conda-forge Cached
+ six 1.17.0 pyhe01879c_1 conda-forge Cached
+ snappy 1.2.2 h03e3b7b_1 conda-forge Cached
+ sniffio 1.3.1 pyhd8ed1ab_2 conda-forge Cached
+ sqlite 3.53.1 hbc0de68_0 conda-forge 205kB
+ starlette 1.1.0 pyhcf101f3_0 conda-forge 64kB
+ tk 8.6.13 noxft_h366c992_103 conda-forge Cached
+ tomli 2.4.1 pyhcf101f3_0 conda-forge 22kB
+ tqdm 4.67.3 pyh8f84b5b_0 conda-forge Cached
+ typer 0.26.3 pyhcf101f3_0 conda-forge 184kB
+ typing-extensions 4.15.0 h396c80c_0 conda-forge Cached
+ typing-inspection 0.4.2 pyhcf101f3_2 conda-forge 21kB
+ typing_extensions 4.15.0 pyhcf101f3_0 conda-forge Cached
+ tzdata 2025c hc9c84f9_1 conda-forge Cached
+ unicodedata2 17.0.1 py314h5bd0f2a_0 conda-forge 410kB
+ upsetplot 0.9.0 pyhd8ed1ab_1 conda-forge 28kB
+ urllib3 2.7.0 pyhd8ed1ab_0 conda-forge 104kB
+ uvicorn 0.48.0 pyhc90fa1f_0 conda-forge 56kB
+ uvicorn-standard 0.48.0 he364bde_0 conda-forge 4kB
+ uvloop 0.22.1 py314h5bd0f2a_1 conda-forge 593kB
+ watchfiles 1.2.0 py314ha5689aa_0 conda-forge 416kB
+ websockets 16.0 py314h0f05182_1 conda-forge 383kB
+ xorg-libxau 1.0.12 hb03c661_1 conda-forge Cached
+ xorg-libxdmcp 1.1.5 hb03c661_1 conda-forge Cached
+ yaml 0.2.5 h280c20c_3 conda-forge Cached
+ zlib 1.3.2 h25fd6f3_2 conda-forge Cached
+ zlib-ng 2.3.3 hceb46e0_1 conda-forge Cached

Summary:

Install: 186 packages

Total download: 142MB

─────────────────────────────────────────────────────────────────────────────────────────────────

 

Transaction starting
libgrpc 7.0MB @ 2.3MB/s 3.0s
numpy 8.9MB @ 2.3MB/s 3.8s
matplotlib-base 8.5MB @ 2.0MB/s 4.2s
libarrow 6.5MB @ 2.3MB/s 2.8s
pandas 15.3MB @ 2.5MB/s 6.2s
libopenblas 5.9MB @ 2.3MB/s 2.5s
pyarrow-core 4.8MB @ 1.6MB/s 3.0s
libprotobuf 3.7MB @ 2.4MB/s 1.6s
aws-sdk-cpp 3.6MB @ 3.1MB/s 1.2s
biopython 3.4MB @ 2.0MB/s 1.7s
libgfortran5 2.5MB @ 2.6MB/s 1.0s
libgoogle-cloud 2.6MB @ 2.4MB/s 1.1s
pydantic-core 1.9MB @ 2.7MB/s 0.7s
libarrow-compute 3.0MB @ 1.9MB/s 1.6s
orc 1.5MB @ 2.8MB/s 0.5s
libparquet 1.4MB @ 3.1MB/s 0.5s
pip 1.2MB @ 2.9MB/s 0.4s
libabseil 1.4MB @ 2.2MB/s 0.6s
pillow 1.1MB @ 2.7MB/s 0.4s
libsqlite 955.0kB @ 2.9MB/s 0.3s
libgoogle-cloud-storage 779.6kB @ 2.7MB/s 0.3s
fonttools 846.0kB @ 2.1MB/s 0.4s
libopentelemetry-cpp 934.3kB @ 1.8MB/s 0.5s
libarrow-acero 592.3kB @ 2.2MB/s 0.2s
uvloop 593.4kB @ 1.3MB/s 0.4s
libarrow-dataset 592.2kB @ 2.7MB/s 0.2s
libarrow-substrait 501.9kB @ 1.8MB/s 0.2s
azure-storage-blobs-cpp 587.1kB @ 1.6MB/s 0.3s
libthrift 423.9kB @ 2.8MB/s 0.2s
crossroad 1.8MB @ 663.3kB/s 2.6s
libuv 419.9kB @ 2.3MB/s 0.2s
fastar 423.4kB @ 966.7kB/s 0.3s
aws-crt-cpp 412.5kB @ 2.9MB/s 0.1s
watchfiles 415.6kB @ 1.6MB/s 0.3s
unicodedata2 409.6kB @ 1.8MB/s 0.2s
libopentelemetry-cpp-headers 396.4kB @ 2.2MB/s 0.2s
s2n 388.1kB @ 2.5MB/s 0.1s
brotli-python 367.4kB @ 1.7MB/s 0.1s
websockets 383.0kB @ 1.3MB/s 0.3s
azure-core-cpp 348.7kB @ 2.7MB/s 0.1s
pydantic 346.5kB @ 1.9MB/s 0.2s
contourpy 324.0kB @ 2.3MB/s 0.1s
libpng 317.7kB @ 1.8MB/s 0.2s
azure-storage-files-datalake-cpp 303.8kB @ 1.9MB/s 0.1s
narwhals 284.3kB @ 1.8MB/s 0.2s
fontconfig 280.9kB @ 866.6kB/s 0.2s
python 36.7MB @ 3.0MB/s 12.0s
azure-identity-cpp 250.5kB @ 1.5MB/s 0.1s
lcms2 251.1kB @ 2.0MB/s 0.1s
aws-c-common 242.3kB @ 2.8MB/s 0.1s
libre2-11 213.1kB @ 66.4kB/s 0.1s
aws-c-http 230.3kB @ 1.7MB/s 0.1s
aws-c-mqtt 221.7kB @ 307.2kB/s 0.1s
sqlite 205.4kB @ ??.?MB/s 0.1s
perf_ssr 720.0kB @ 247.3kB/s 2.3s
prometheus-cpp 199.5kB @ 962.8kB/s 0.1s
pyyaml 202.4kB @ 1.6MB/s 0.1s
typer 184.4kB @ 1.9MB/s 0.1s
aws-c-io 181.6kB @ 1.9MB/s 0.1s
aws-c-s3 153.0kB @ 2.2MB/s 0.1s
azure-storage-common-cpp 159.1kB @ 1.8MB/s 0.1s
expat 148.2kB @ ??.?MB/s 0.0s
anyio 146.8kB @ 2.2MB/s 0.1s
glog 143.5kB @ 2.6MB/s 0.1s
seqtk 141.8kB @ 1.8MB/s 0.1s
nlohmann_json 136.2kB @ 2.1MB/s 0.1s
aws-c-auth 134.4kB @ 1.5MB/s 0.1s
certifi 134.2kB @ 1.8MB/s 0.1s
click 105.0kB @ 1.5MB/s 0.1s
gflags 119.7kB @ 148.2kB/s 0.1s
urllib3 103.6kB @ ??.?MB/s 0.0s
aws-checksums 101.6kB @ ??.?MB/s 0.0s
fastapi-core 95.5kB @ ??.?MB/s 0.0s
libmpdec 92.4kB @ ??.?MB/s 0.0s
packaging 91.6kB @ ??.?MB/s 0.0s
libutf8proc 86.0kB @ ??.?MB/s 0.0s
kiwisolver 77.4kB @ ??.?MB/s 0.0s
libexpat 77.3kB @ 885.4kB/s 0.1s
pydantic-extra-types 73.9kB @ ??.?MB/s 0.0s
markdown-it-py 69.0kB @ ??.?MB/s 0.0s
requests 68.7kB @ ??.?MB/s 0.0s
rich-click 64.4kB @ ??.?MB/s 0.0s
aws-c-event-stream 59.3kB @ ??.?MB/s 0.0s
starlette 63.7kB @ ??.?MB/s 0.0s
aws-c-sdkutils 59.1kB @ ??.?MB/s 0.0s
aws-c-cal 56.9kB @ ??.?MB/s 0.0s
idna 56.9kB @ ??.?MB/s 0.0s
uvicorn 56.3kB @ ??.?MB/s 0.0s
pydantic-settings 52.3kB @ ??.?MB/s 0.0s
email-validator 46.8kB @ ??.?MB/s 0.0s
libuuid 40.2kB @ ??.?MB/s 0.0s
h11 39.1kB @ ??.?MB/s 0.0s
python-multipart 37.8kB @ ??.?MB/s 0.0s
rich-toolkit 32.9kB @ ??.?MB/s 0.0s
upsetplot 28.0kB @ ??.?MB/s 0.0s
libstdcxx-ng 27.8kB @ ??.?MB/s 0.0s
libgfortran 27.7kB @ ??.?MB/s 0.0s
re2 27.5kB @ ??.?MB/s 0.0s
markupsafe 27.4kB @ ??.?MB/s 0.0s
pyarrow 26.8kB @ ??.?MB/s 0.0s
aws-c-compression 22.0kB @ ??.?MB/s 0.0s
tomli 21.6kB @ ??.?MB/s 0.0s
typing-inspection 20.9kB @ ??.?MB/s 0.0s
fastapi-cli 18.9kB @ ??.?MB/s 0.0s
libblas 18.8kB @ ??.?MB/s 0.0s
httptools 99.0kB @ ??.?MB/s 0.4s
liblapack 18.8kB @ ??.?MB/s 0.0s
libcblas 18.8kB @ ??.?MB/s 0.0s
email_validator 7.1kB @ ??.?MB/s 0.0s
backports.zstd 7.5kB @ ??.?MB/s 0.0s
python_abi 7.0kB @ ??.?MB/s 0.0s
fastapi 4.8kB @ ??.?MB/s 0.0s
uvicorn-standard 4.1kB @ ??.?MB/s 0.0s
rich-argparse 26.8kB @ ??.?MB/s 0.2s
plotly-upset-hd 356.0kB @ 181.5kB/s 1.8s
Linking seqkit-2.13.0-he881be0_0
Linking bedtools-2.31.1-h13024bc_3
Linking seqtk-1.5-h577a1d6_1
Linking libuuid-2.42.1-h5347b49_0
Linking readline-8.3-h853b02a_0
Linking libexpat-2.8.1-hecca717_0
Linking nspr-4.38-h29cc59b_0
Linking mathjax-2.7.7-ha770c72_3
Linking libuv-1.52.1-h280c20c_0
Linking yaml-0.2.5-h280c20c_3
Linking ld_impl_linux-64-2.45.1-default_hbd61a6d_102
Linking libmpdec-4.0.0-hb03c661_1
Linking libwebp-base-1.6.0-hd42ef1d_0
Linking zlib-ng-2.3.3-hceb46e0_1
Linking libstdcxx-ng-15.2.0-hdf11a46_19
Linking pthread-stubs-0.4-hb9d3cd8_1002
Linking xorg-libxau-1.0.12-hb03c661_1
Linking xorg-libxdmcp-1.1.5-hb03c661_1
Linking libgfortran5-15.2.0-h68bc16d_19
Linking libpng-1.6.58-h421ea60_0
Linking libbrotlicommon-1.2.0-hb03c661_1
Linking libjpeg-turbo-3.1.4.1-hb03c661_0
Linking libdeflate-1.25-h17f619e_0
Linking lerc-4.1.0-hdb68285_0
Linking libsqlite-3.53.1-h0c1763c_0
Linking libffi-3.5.2-h3435931_0
Linking tk-8.6.13-noxft_h366c992_103
Linking azure-core-cpp-1.16.2-h206d751_0
Linking libabseil-20260107.1-cxx17_h7b12aa8_0
Linking libutf8proc-2.11.3-hfe17d71_0
Linking libopentelemetry-cpp-headers-1.26.0-ha770c72_0
Linking zlib-1.3.2-h25fd6f3_2
Linking snappy-1.2.2-h03e3b7b_1
Linking nlohmann_json-3.12.0-h54a6638_1
Linking aws-c-common-0.13.1-hb03c661_0
Linking s2n-1.7.3-hc5a330e_0
Linking gflags-2.2.2-h5888daf_1005
Linking libevent-2.1.12-hf998b51_1
Linking expat-2.8.1-hecca717_0
Linking libcrc32c-1.1.2-h9c3ff4c_0
Linking qhull-2020.2-h434a139_5
Linking libxcb-1.17.0-h8a09558_0
Linking libgfortran-15.2.0-h69a702a_19
Linking libfreetype6-2.14.3-h73754d4_0
Linking libbrotlienc-1.2.0-hb03c661_1
Linking libbrotlidec-1.2.0-hb03c661_1
Linking libtiff-4.7.1-h9d88235_1
Linking sqlite-3.53.1-hbc0de68_0
Linking nss-3.118-h445c969_0
Linking azure-identity-cpp-1.13.3-hed0cdb0_1
Linking azure-storage-common-cpp-12.13.0-ha7a2c86_0
Linking libprotobuf-6.33.5-h6eeba95_1
Linking libre2-11-2025.11.05-h0dc7533_1
Linking prometheus-cpp-1.3.0-ha5d0236_0
Linking aws-c-compression-0.3.2-h16e98cb_1
Linking aws-checksums-0.2.10-h16e98cb_1
Linking aws-c-sdkutils-0.2.4-h16e98cb_5
Linking aws-c-cal-0.9.14-h8e43964_1
Linking glog-0.7.1-hbabe93e_0
Linking libthrift-0.22.0-h7d032f7_2
Linking libopenblas-0.3.33-pthreads_h94d23a6_0
Linking libfreetype-2.14.3-ha770c72_0
Linking brotli-bin-1.2.0-hb03c661_1
Linking lcms2-2.19.1-h0c24ade_0
Linking openjpeg-2.5.4-h55fea9a_0
Linking azure-storage-blobs-cpp-12.17.0-hf824e48_1
Linking re2-2025.11.05-h5301d42_1
Linking aws-c-io-0.26.3-h955231c_3
Linking libblas-3.11.0-8_h4a7cf45_openblas
Linking fontconfig-2.18.0-h27c8c51_0
Linking freetype-2.14.3-ha770c72_0
Linking brotli-1.2.0-hed03a55_1
Linking azure-storage-files-datalake-cpp-12.15.0-h1e5b466_0
Linking libgrpc-1.78.1-h1d1128b_0
Linking aws-c-event-stream-0.7.1-h9be7a74_1
Linking aws-c-http-0.11.0-hcbcd92d_1
Linking libcblas-3.11.0-8_h0358290_openblas
Linking liblapack-3.11.0-8_h47877c9_openblas
Linking libopentelemetry-cpp-1.26.0-h9692893_0
Linking aws-c-auth-0.10.3-h3aafcba_1
Linking aws-c-mqtt-0.15.2-h8af55cf_3
Linking libgoogle-cloud-3.5.0-h25dbb67_0
Linking aws-c-s3-0.12.3-h00bea6e_2
Linking libgoogle-cloud-storage-3.5.0-hdbdcf42_0
Linking aws-crt-cpp-0.38.3-h7b0d4b4_2
Linking aws-sdk-cpp-1.11.747-h5a171d8_5
Linking python_abi-3.14-8_cp314
Linking font-ttf-dejavu-sans-mono-2.37-hab24e00_0
Linking tzdata-2025c-hc9c84f9_1
Linking font-ttf-ubuntu-0.83-h77eed37_3
Linking font-ttf-inconsolata-3.000-h77eed37_0
Linking font-ttf-source-code-pro-2.038-h77eed37_0
Linking fonts-conda-forge-1-hc364b38_1
Linking orc-2.3.0-h21090e2_0
Linking python-3.14.5-habeac84_100_cp314
Linking kaleido-core-0.2.1-h3644ca4_0
Linking libarrow-24.0.0-h6f10b76_3_cpu
Linking libparquet-24.0.0-h7376487_3_cpu
Linking libarrow-compute-24.0.0-h53684a4_3_cpu
Linking libarrow-acero-24.0.0-h635bf11_3_cpu
Linking libarrow-dataset-24.0.0-h635bf11_3_cpu
Linking libarrow-substrait-24.0.0-hb4dd7c2_3_cpu
Linking pip-26.1.1-pyh145f28c_0
Linking tomli-2.4.1-pyhcf101f3_0
Linking six-1.17.0-pyhe01879c_1
Linking pysocks-1.7.1-pyha55dd90_7
Linking hyperframe-6.1.0-pyhd8ed1ab_0
Linking hpack-4.1.0-pyhd8ed1ab_0
Linking backports.zstd-1.5.0-py314h680f03e_0
Linking pyparsing-3.3.2-pyhcf101f3_0
Linking cycler-0.12.1-pyhcf101f3_2
Linking sniffio-1.3.1-pyhd8ed1ab_2
Linking mdurl-0.1.2-pyhd8ed1ab_1
Linking narwhals-2.21.2-pyhcf101f3_0
Linking packaging-26.2-pyhc364b38_0
Linking charset-normalizer-3.4.7-pyhd8ed1ab_0
Linking certifi-2026.5.20-pyhd8ed1ab_0
Linking idna-3.17-pyhcf101f3_0
Linking pygments-2.20.0-pyhd8ed1ab_0
Linking shellingham-1.5.4-pyhd8ed1ab_2
Linking annotated-doc-0.0.4-pyhcf101f3_0
Linking colorama-0.4.6-pyhd8ed1ab_1
Linking typing_extensions-4.15.0-pyhcf101f3_0
Linking click-8.4.1-pyhc90fa1f_0
Linking tqdm-4.67.3-pyh8f84b5b_0
Linking python-kaleido-0.2.1-pyhd8ed1ab_0
Linking python-multipart-0.0.29-pyhcf101f3_0
Linking python-dotenv-1.2.2-pyhcf101f3_0
Linking argcomplete-3.6.3-pyhd8ed1ab_0
Linking python-dateutil-2.9.0.post0-pyhe01879c_2
Linking h2-4.3.0-pyhcf101f3_0
Linking dnspython-2.8.0-pyhcf101f3_0
Linking markdown-it-py-4.2.0-pyhd8ed1ab_0
Linking plotly-6.6.0-pyhd8ed1ab_0
Linking exceptiongroup-1.3.1-pyhd8ed1ab_0
Linking typing-inspection-0.4.2-pyhcf101f3_2
Linking typing-extensions-4.15.0-h396c80c_0
Linking h11-0.16.0-pyhcf101f3_1
Linking email-validator-2.3.0-pyhd8ed1ab_0
Linking rich-15.0.0-pyhcf101f3_0
Linking anyio-4.13.0-pyhcf101f3_0
Linking annotated-types-0.7.0-pyhd8ed1ab_1
Linking uvicorn-0.48.0-pyhc90fa1f_0
Linking email_validator-2.3.0-hd8ed1ab_0
Linking rich-toolkit-0.19.10-pyhcf101f3_0
Linking typer-0.26.3-pyhcf101f3_0
Linking rich-click-1.9.8-pyh8f84b5b_0
Linking rich-argparse-1.8.0-pyhd8ed1ab_0
Linking httpcore-1.0.9-pyh29332c3_0
Linking starlette-1.1.0-pyhcf101f3_0
Linking httpx-0.28.1-pyhd8ed1ab_0
Linking pyarrow-core-24.0.0-py314h969be7f_0_cpu
Linking unicodedata2-17.0.1-py314h5bd0f2a_0
Linking brotli-python-1.2.0-py314h3de4e8d_1
Linking pillow-12.2.0-py314h8ec4b1a_0
Linking kiwisolver-1.5.0-py314h97ea11e_0
Linking fastar-0.11.0-py314h0b738fb_0
Linking markupsafe-3.0.3-py314h67df5f8_1
Linking websockets-16.0-py314h0f05182_1
Linking uvloop-0.22.1-py314h5bd0f2a_1
Linking pyyaml-6.0.3-py314h67df5f8_1
Linking httptools-0.7.1-py314h5bd0f2a_1
Linking numpy-2.4.6-py314h2b28147_0
Linking pydantic-core-2.46.4-py314h2e6c369_0
Linking watchfiles-1.2.0-py314ha5689aa_0
Linking pyarrow-24.0.0-py314hdafbbf9_0
Linking contourpy-1.3.3-py314h97ea11e_4
Linking biopython-1.87-py314h5bd0f2a_0
Linking pandas-3.0.3-py314hb4ffadd_0
Linking munkres-1.0.7-py_1
Linking urllib3-2.7.0-pyhd8ed1ab_0
Linking jinja2-3.1.6-pyhcf101f3_1
Linking pydantic-2.13.4-pyhcf101f3_0
Linking uvicorn-standard-0.48.0-he364bde_0
Linking fonttools-4.63.0-pyh7db6752_0
Linking requests-2.34.2-pyhcf101f3_0
Linking pydantic-settings-2.14.1-pyhcf101f3_0
Linking pydantic-extra-types-2.11.2-pyhcf101f3_0
Linking fastapi-core-0.136.3-pyhcf101f3_0
Linking fastapi-cli-0.0.23-pyhcf101f3_0
Linking fastapi-0.136.3-h5ddb490_0
Linking plotly-upset-hd-0.0.2-py_0
Linking matplotlib-base-3.10.9-py314h1194b4b_0
Linking upsetplot-0.9.0-pyhd8ed1ab_1
Linking perf_ssr-0.4.8-py_0
Linking crossroad-0.3.6-pyh7e60211_0

Transaction finished

(jitENV) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$ crossroad -h

Usage: crossroad [OPTIONS]

Run the main croSSRoad analysis pipeline, or manage jobs.

╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --version -v Show version, logo, citation, and links. │
│ --install-completion Install completion for the current shell. │
│ --show-completion Show completion for the current shell, to copy it or customize the installation. │
│ --help -h Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Mode Selection ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --api -a Run the Crossroad web API server. │
│ --slurm -s Submit the analysis job to a Slurm cluster. │
│ --job-status JOB_ID Query the status of a specific job ID. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Input Files (provide --input-dir OR --fasta) ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --input-dir -i PATH Directory containing: `all_genome.fa`, ``, ``. Exclusive with `--fasta`. │
│ --fasta -fa PATH Input FASTA file (e.g., `all_genome.fa`). Alternative to `--input-dir`. │
│ --categories -c PATH Genome categories TSV file. Optional if using `--fasta`. Ignored if `--input-dir` is used (looks for `genome_categories.tsv` inside). │
│ --gene-bed -b PATH Gene BED file for SSR-gene analysis. Optional. If `--input-dir` is used, looks for `gene.bed` inside. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Analysis Parameters ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --reference-id -ref TEXT Reference genome ID for comparative analysis. Optional parameter for reference-based comparisons. │
│ --output-dir -o DIRECTORY Base output directory for jobs. Overrides CROSSROAD_JOB_DIR env var. │
│ --flanks -f Process flanking regions. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ PERF SSR Detection Parameters ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --mono INTEGER Mononucleotide repeat threshold. [default: 12] │
│ --di INTEGER Dinucleotide repeat threshold. [default: 6] │
│ --tri INTEGER Trinucleotide repeat threshold. [default: 4] │
│ --tetra INTEGER Tetranucleotide repeat threshold. [default: 3] │
│ --penta INTEGER Pentanucleotide repeat threshold. [default: 3] │
│ --hexa INTEGER Hexanucleotide repeat threshold. [default: 2] │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Filtering Parameters ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --min-len -l INTEGER Minimum genome length for filtering. [default: 1000] │
│ --max-len -L INTEGER Maximum genome length for filtering. [default: 10000000] │
│ --unfair -u INTEGER Maximum number of N's allowed per genome for Crossroad analysis. [default: 0] │
│ --repeat-threshold -rc INTEGER Repeat count Threshold for hotspot filtering (keeps records > this value). [default: 1] │
│ --genome-threshold -g INTEGER Genome count Threshold for hotspot filtering (keeps records > this value). [default: 2] │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Performance & Output ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --threads -t INTEGER Number of threads for Crossroad analysis. [default: 50] │
│ --plots -p Enable plot generation. │
│ --intrim-dir TEXT Name for the intermediate files directory (within the main job output dir). [default: intrim] │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

(jitENV) hp@hp-HP-Z2-Tower-G9-Workstation-Desktop-PC:~/jitendraTEST$

 

]]> ComBioX https://bioinformaticsonline.com/blog/view/45116/recommended-reading-list Sat, 18 Apr 2026 19:25:24 -0500 https://bioinformaticsonline.com/blog/view/45116/recommended-reading-list <![CDATA[Recommended reading list]]> Some of the following titles might be available as ebooks•

Population genetics: A concise guide. John Gillespie.The Johns Hopkins University Press (1997)•

Population genetics. J. S. Gale. Wiley (1980)•

Evolutionary genetics. John Maynard-Smith. Oxford University Press (1998)•

The growth of biological thought. Ernst Mayr. Harvard University Press (1985)•

Guns, germs and steel. Jared Diamond. W. W. Norton (2007)•

Evolutionary theory: Mathematical and conceptual foundations. Sean Rice. Oxford University Press (2004)

]]> Jitendra Narayan https://bioinformaticsonline.com/blog/view/44930/bioinformatics-the-bridge-between-curiosity-and-discovery Mon, 24 Nov 2025 05:16:49 -0600 https://bioinformaticsonline.com/blog/view/44930/bioinformatics-the-bridge-between-curiosity-and-discovery <![CDATA[Bioinformatics: The Bridge Between Curiosity and Discovery]]> In the sprawling universe of modern science, bioinformatics stands as one of the most transformative and empowering fields of our time. It is where biology meets computation, where data becomes meaning, and where curiosity becomes discovery. If you’ve stepped into this world—or are considering it—here’s your reminder: you’re part of a revolution.

Why Bioinformatics Matters More Than Ever

Every day, our world generates massive amounts of biological data—from genome sequences to microbiome profiles to real-time pathogen surveillance. Hidden within these datasets are the answers to some of the greatest challenges humanity faces: emerging diseases, antimicrobial resistance, environmental stress, genetic disorders, sustainable agriculture, and more.

Bioinformatics isn’t just a skill.
It’s the language of the future of biology.

By mastering it, you give yourself the power to:

Decode genomes and understand life at its most fundamental level

Identify patterns no microscope could ever reveal

Predict disease outbreaks before they occur

Accelerate drug discovery with computational precision

Contribute to open-source tools that empower scientists worldwide

You don’t just follow science—you drive it.

Every Expert Was Once a Beginner

Many newcomers feel intimidated. Command-line interfaces. R scripts. Python packages. Next-generation sequencing data. Complex machine learning models.

But here’s the truth: every bioinformatician started exactly where you are now—curious, unsure, but excited.

No one writes perfect code on day one.

No one understands genomics pipelines immediately.

What makes you a bioinformatician is not perfection, but perseverance.

When your script throws a cryptic error…
When your data refuses to format…
When your pipeline runs for 6 hours only to crash…

Remember: this is part of the journey.
Every error teaches you. Every retry strengthens you. Every breakthrough energizes you.

Bioinformatics Is Not Just a Career—It’s a Mindset

It’s the mindset of:

Problem-solving.

Continuous learning.

Turning chaos into clarity.

Seeing what others can’t.

Bioinformaticians are detectives of biological complexity. You sit at the intersection of innovation, using tools that can shape public health, medicine, agriculture, and ecology. Few fields give you such direct impact on the world.

Your Contribution Matters

As you work on your script, pipeline, genome, or model, remember:

Somewhere, your analysis might contribute to:

A new therapy

A faster diagnostic test

A better understanding of a pathogen

A more resilient crop

An open-source dataset that helps thousands

A discovery that rewrites textbooks

Your code may be small, but its ripple effect is powerful.

The Future Is Bioinformatics—And You Are Part of It

The world is shifting. Wet labs are integrating AI. Hospitals rely on genomic insights. Farmers use gene-level predictions. Governments monitor disease in real time. Students launch pipelines that become global tools.

This is a golden era—and you are not late.
You are exactly where you need to be.

Keep Pushing. Keep Learning. Keep Discovering.

Bioinformatics is a journey filled with challenges, but also with unmatched rewards.

So the next time you feel stuck, frustrated, or overwhelmed, remember:
You’re building the science of tomorrow.

Be proud. Stay curious. Keep going.
Your work matters more than you think.

]]> BioStar