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.

Scientists face a big challenge with the human genome. They can read its letters, but understanding their roles is much harder. Only about 2 percent of the genome codes for proteins. The rest acts like a huge control panel, deciding when and where genes turn on. With about 9 billion possible single-letter changes, testing them all in a lab just isn’t possible.

So, Google DeepMind asked a new question: what if we could predict what those changes might do?

This question led to the AlphaGenome Atlas (https://deepmind.google.com/science/alphagenome/atlas?), a detailed map of nearly every possible single-letter change in the human genome. Instead of checking each change one by one, researchers can use the Atlas to spot the ones most likely to matter. The AlphaGenome Variant Impact (AVI) score works like a trail marker, pointing scientists toward the changes worth a closer look.

This is where the Atlas gets especially useful. Much of the genome lies outside the protein-coding regions, where DNA acts as a switch or controller for genes. AlphaGenome lets researchers explore these areas and see how small changes could affect gene activity.

An atlas isn't the destination; it's a guide.

The AlphaGenome Atlas doesn’t replace experiments or solve every mystery. Instead, it helps scientists decide where to begin. From billions of possibilities, it turns the vast genetic landscape into something researchers can start to explore.

There are nine billion possibilities, a vast map, and maybe among them therWith nine billion possibilities and a huge map to explore, there may be clues hidden here to some of medicine’s toughest mysteries.

More at https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/alphagenome-atlas.pdf

This is where KiNext comes into the picture. Introduced in a 2024 study published in BMC Bioinformatics, KiNext is a computational workflow designed to identify and classify protein kinases from predicted protein sequences. Instead of relying on a single search method, it brings together several approaches, including Hidden Markov Models, sequence alignment, phylogenetic analysis, and structural comparison.

The search begins with a simple question: does a protein contain the characteristics of a kinase? Protein kinases can change considerably during evolution, but important regions of their sequences often retain recognizable patterns. KiNext uses Hidden Markov Models, or HMMs, to detect these patterns. An HMM does not require a protein to be an exact match to a known kinase. Instead, it looks for a statistical sequence signature associated with kinase proteins, making it possible to detect more distant candidates.

Once potential kinases are identified, KiNext takes the analysis further. It distinguishes conventional eukaryotic protein kinases from atypical protein kinases and then attempts to classify them into different kinase groups and families. This distinction is important because simply identifying a protein as a kinase does not tell the complete story. Different kinase families can have very different evolutionary histories and biological functions.

The next stage brings evolution into the picture. KiNext can align kinase sequences and construct phylogenetic trees, allowing researchers to examine how newly identified proteins are related to previously characterized kinases. When sequence evidence alone is difficult to interpret, structural information can provide another clue. The workflow can incorporate AlphaFold-predicted structures and Foldseek-based structural comparisons to investigate whether an unusual protein resembles known kinase structures.

The researchers tested KiNext using two very different organisms: the Pacific oyster, Crassostrea gigas, and the green alga Ostreococcus tauri. In C. gigas, KiNext recovered previously reported kinases while identifying additional candidates. Structural analysis provided further evidence for many of the newly detected proteins. In O. tauri, the workflow similarly recovered most previously reported kinases and identified additional candidates while refining some of their classifications.

What makes KiNext particularly interesting is not just its ability to find kinases, but how the entire analysis is organized. The workflow uses Nextflow, allowing the different computational steps to be connected into a reproducible pipeline. Containers can also help manage software dependencies, making it easier to run the workflow across different computing environments.

This reproducibility becomes increasingly important as the number of available genomes continues to grow. A researcher studying one organism may be able to perform an analysis manually, but repeating the same process across hundreds or thousands of genomes quickly becomes impractical. A standardized workflow provides a way to perform the analysis consistently while keeping track of how the results were generated.

At its core, KiNext demonstrates a broader change taking place in modern genomics. Sequencing a genome provides an enormous amount of information, but the real scientific challenge begins afterward: understanding what all those sequences mean. Protein kinases are only one part of this larger puzzle, yet they are particularly important because they act as molecular switches throughout the cell.

By combining sequence profiles, evolutionary analysis, and structural evidence within a reproducible computational framework, KiNext provides researchers with a systematic way to uncover these molecular switches. Its real value lies not only in finding more kinases, but in making the process scalable, repeatable, and easier to apply to new genomes.

As genome sequencing continues to expand across the tree of life, tools such as KiNext can help turn enormous collections of protein sequences into meaningful biological stories—one kinase at a time.

Read more about it @

https://link.springer.com/article/10.1186/s12859-024-05953-w

into contiguous sequences (called contigs). One can use the sequences of different sequencing technologies either in a single assembly run (a true hybrid assembly) or by mapping one type of data to an assembly of other sequencing type (a semi-hybrid assembly (or mapping)) or by mapping a data against consensus sequences of other assemblies (a simple mapping).

The MIRA acronym stands for Mimicking Intelligent Read Assembly and the program pretty well does what its acronym says (well, most of the time anyway). It is the Swiss army knife of sequence assembly that I've used and developed during the past 14 years to get assembly jobs I work on done efficiently - and especially accurately. That is, without me actually putting too much manual work into it.

More at http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html

Address of the bookmark: http://mira-assembler.sourceforge.net/docs/DefinitiveGuideToMIRA.html

Reference https://www.pacb.com/press_releases/illumina-to-acquire-pacific-biosciences-for-approximately-1-2-billion-broadening-access-to-long-read-sequencing-and-accelerating-scientific-discovery/

The BeadChip was developed in collaboration with leading ovine researchers from AgResearch, Baylor UCSC, CSIRO, and the USDA as part of the International Sheep Genomics Consortium. It features over 54,241 evenly spaced probes that target single nucleotide polymorphisms (SNPs). More than 18,000 of these markers were discovered through sequencing reduced representation libraries with the Illumina Genome Analyzer IIx. A set of 600 SNPs were identified by BAC end sequencing and validated with Illumina GoldenGate Genotyping Assays over 403 animals from 23 breeds. The remaining SNPs were derived from the draft ovine genome.

Read more @

http://res.illumina.com/documents/products/datasheets/datasheet_ovinesnp50.pdf

http://www.illumina.com/products/ovinesnp50_dna_analysis_kit.ilmn

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html

So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are PacBio E. coli sample, ERS473430, ERS544009, ERS554120, ERS605484, ERS617393, ERS646601, ERS659581, ERS670327, ERS685285, ERS743109 and a deprecated PacBio E. coli data set. ONT data are acquired from the Loman Lab.

For a C. elegans PacBio data set (only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the HGAP3produces a 104Mb assembly with N50 1.61Mb. This dotter plot gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.

Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that minimap can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as MHAP and DALIGNER. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.

Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)

We start with an all against all comparison:

minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz

Then we can assemble

miniasm -f reads.fq reads.paf.gz > reads.gfa

Convert GFA to FASTA:

awk '/^S/{print ">"$2"\n"$3}' reads.gfa | fold > reads.fa

And then count how many contigs:

grep ">" reads.fa | wc -l

# Download sample PacBio from the PBcR website
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz | tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
# Install minimap and miniasm (requiring gcc and zlib)
git clone https://github.com/lh3/minimap && (cd minimap && make)
git clone https://github.com/lh3/miniasm && (cd miniasm && make)
# Overlap
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz
# Layout
miniasm/miniasm -f reads.fq reads.paf.gz > reads.gfa

Address of the bookmark: https://github.com/lh3/miniasm

https://github.com/ewijaya/slidesort-bpr

Address of the bookmark: https://code.google.com/archive/p/slidesort-bpr/

How to use TAREAN:

• Install a local instance of the pipeline using its source code available from bitbucket repository.
• Use  public Galaxy-based server at https://repeatexplorer-elixir.cerit-sc.cz/. The server is provided in frame of the Elixir CZ project and is maintained by CESNET and CERIT-SC. Simple registration is required to use this service.

Development of TAREAN was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2015047).

References

Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) – TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads. Nucleic Acids Res., doi:10.1093/nar/gkx257

Address of the bookmark: https://bitbucket.org/petrnovak/repex_tarean