More at https://github.com/esolares/HapSolo

Address of the bookmark: https://github.com/esolares/HapSolo

• New ProSplign tool integrated with Genome Workbench (Tutorial, Video)
• New export function for BAM/cSRA coverage graphs (Tutorial)
• New export function for alignments GFF3 format ((Tutorial))
• Tree View: implemented new export mode based on selections (tutorial coming)
• Tree View: added support for distance based circular trees
• Tree View: new rooting mode (Midpoint Root) results in more balanced trees (Tutorial)
• Tree View: added possibility to right-click on an edge between two nodes and "Place Root at Middle of Branch" – to re-root at mid-branch (Tutorial)

Mauve has been developed with the idea that a multiple genome aligner should require only modest computational resources. It employs algorithmic techniques that scale well in the lengths of sequences being aligned. For example, a pair of Y. pestis genomes can be aligned in under a minute, while a group of 9 divergent Enterobacterial genomes can be aligned in a few hours. However, the current algorithm’s compute time (progressiveMauve) scales cubically in the number of genomes to align, making it unsuitable for datasets containing more than 50-100 bacterial genomes.

Address of the bookmark: http://darlinglab.org/mauve/mauve.html

Tool at https://github.com/kbseah/genome-bin-tools

Address of the bookmark: http://journal.frontiersin.org/article/10.3389/fmicb.2015.01451/full

BBSplit is a tool that bins reads by mapping to multiple references simultaneously, using BBMap. The reads go to the bin of the reference they map to best. There are also disambiguation options, such that reads that map to multiple references can be binned with all of them, none of them, one of them, or put in a special "ambiguous" file for each of them. Paired reads will always be kept together.

For example, if you had a library of something that was contaminated with e.coli and salmonella, you could do this:

bbsplit.sh in=reads.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu=clean.fq int=t

This will produce 3 output files:
out_ecoli.fq (ecoli reads)
out_salmonella.fq (salmonella reads)
clean.fq (unmapped reads)

In this case, "int=t" means that the input file is paired and interleaved. For single-end reads you would leave that out. For paired reads in 2 files, you would do this:
bbsplit.sh in1=reads1.fq in2=reads2.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu1=clean1.fq outu2=clean2.fq

BBSplit is available here:
https://sourceforge.net/projects/bbmap/

The sensitivity can be raised to be equivalent to BBMap with these flags: "minratio=0.56 minhits=1 maxindel=16000"

Address of the bookmark: https://github.com/andrewgull/MGERT

panacus supports the following calculations:

• coverage histogram
• pangenome growth statistics
• path-/group-resolved coverage table

Address of the bookmark: https://github.com/marschall-lab/panacus

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

To tackle this problem, researchers developed a new bioinformatics tool called ntSynt. It was first introduced in a 2025 study in BMC Biology titled “ntSynt: multi-genome synteny detection using minimizer graph mappings”. This tool (https://github.com/BirolLab/ntSynt) helps researcher find conserved genomic regions across several species without relying on traditional whole-genome alignments or gene annotations. Instead, ntSynt uses short DNA sequences called minimizers as landmarks. It finds minimizers shared by different genomes and maps their relationships in a graph, making it possible to spot large genomic regions that have kept a similar structure over time. The tool uses Bloom filters and graph-based mapping, which lets scientists analyze many genomes at once while saving memory and processing time.

The team tested ntSynt using both simulated data and real genomic sequences from groups like humans, chimpanzees, bonobos, gorillas, rodents, and bees. Their results showed that ntSynt can find conserved genomic regions and spot structural differences between species. In simulated tests, ntSynt identified syntenic blocks with over 97% accuracy. One standout example involved eleven bee genomes from the genus Andrena, which have a lot of variation in chromosome organization. Despite these differences, ntSynt found conserved regions covering about 85% of the smallest bee genome, completing the analysis in just 14.7 minutes and using less than 4 GB of memory. These findings highlight ntSynt’s potential for large-scale comparative genomics, where efficiency is crucial as more genomes become available.

An interesting result came up when the researchers compared primate genomes. When they looked at regions conserved between humans and other great apes, ntSynt flagged what looked like a large deletion in one gorilla genome assembly. After checking with long-read sequencing data, they found that the missing section was likely a mistake in the assembly, not a real evolutionary change. This means ntSynt can help not only with studying evolutionary relationships but also with spotting possible errors in genome assemblies. By highlighting unexpected breaks in regions that are usually conserved, the tool helps scientists tell apart true evolutionary changes from technical mistakes in reference sequences.

The importance of ntSynt becomes even clearer when we look at global projects to sequence and study Earth’s biodiversity. Projects like the Earth BioGenome Project aim to create genomic resources for many different organisms, opening up new ways to explore the history of life. But generating genomic data is just the beginning; making sense of these relationships requires powerful and scalable tools. ntSynt helps by letting researchers study chromosome evolution, conserved genome structures, and rearrangements across many species. While the tool has some limits, especially with detecting duplicated regions or analyzing very different genomes, its speed and scalability make it a valuable tool for modern comparative genomics.

The creation of ntSynt marks a significant advancement in our understanding of how genomes have evolved and become diverse over Earth's history. Since each conserved region in the genome is evidence of common ancestry and each rearrangement gives hinThe development of ntSynt is a big step forward in understanding how genomes have changed and diversified over time. Each conserved region in a genome points to shared ancestry, and each rearrangement offers clues about evolutionary events in different lineages. ntSynt helps scientists spot these patterns more easily, opening up new ways to study genome evolution across the tree of life. Evolution’s story isn’t just in the DNA sequence, but also in how those sequences are arranged and preserved over millions of years. Tools like ntSynt are helping scientists read this hidden story more clearly and reveal the deep genomic connections that link all life on Earth.

More at https://link.springer.com/article/10.1186/s12915-025-02455-w?

Common uses of the tool include:

• Displaying the sequence and/or genes in a GenBank flat file.
• Highlighting differences and/or similarities in gene content between related organisms.
• Comparing SNPs and indels between closely-related strains or serovars.
• Comparing gene expression values across multiple samples or timepoints.
• Visualizing coverage plots of RNA-Seq read alignments.

Key Features

Circleator...

• Builds on BioPerl and the input file formats that it supports, including:
  • GenBank flat files, GFF, FASTA
• Accepts a number of other commonly-used datatypes and file formats:
  • BSR and TRF output, SAM/BAM files, VCF-encoded SNPs, tab-delimited files
• Outputs publication-ready figures in the SVG (Scalable Vector Graphics) format.
• Requires only a single configuration file whose layout mirrors that of the figure itself.
  • Predefined configuration files and "track" types are supplied for common datasets.
  • Advanced features allow limited analyses to be performed as a figure is drawn.
• Includes an extensive set of regression tests.
• Offers a prototype web-based GUI (under the "Ringmaster" project.)

https://github.com/jonathancrabtree/Circleator

Address of the bookmark: https://github.com/jonathancrabtree/Circleator