• Their relative orientation is correct
• Their relative order is correct
• The gap estimate is similar to the true distance on the reference

The problem of scaffolding validation is also a challenging one. One of the main issues which hinders from an adequate scaffolding evaluation are genome repeats. The previous standard for evaluation (Hunt et al., Genome Biology, 2014) did not take into account repeats. In this evaluation framework, repeats are taken into account.

The new evaluation framework considers the optimal assignment of contigs in the output scaffolding to contigs in the reference scaffolding in the sense of the number of correct links.

https://github.com/mandricigor/repeat-aware

Address of the bookmark: https://github.com/mandricigor/repeat-aware

For more background see also the publication at BMC Bioinformatics.

https://github.com/clwgg/nQuire

Address of the bookmark: https://github.com/clwgg/nQuire

The framework is based on rigorous bioinformatic analysis and rooted very much on our recent clustering of antibody CDR regions. It uses the North/Dunbrack CDR definition as outlined in the North/Dunbrack clustering paper.

More at

https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign

https://bio-jade.readthedocs.io/en/latest/installation.html

Address of the bookmark: https://www.rosettacommons.org/docs/latest/application_documentation/antibody/RosettaAntibodyDesign

It is enormous, billions of letters long. Some of those letters are easy to understand because they contain instructions for building proteins, the tiny machines that keep our cells alive.

But what if some of the most important instructions aren't in those obvious chapters?

Scientists have long suspected that the mysterious stretches of DNA between protein-coding genes may contain some of life's most interesting secrets. These regions can act like switches, telling genes when, where, and how strongly to turn on. Among them are conserved non-coding elements, or CNEs DNA sequences that have remained remarkably similar across different species.

Think of a CNE as a sentence that nature has copied into several editions of the same book.

A mouse has it.

A bird has it.

A human has it.

And despite millions of years of evolution, much of the sentence remains almost unchanged.

That conservation is a clue: this piece of DNA probably matters.

But there was a problem. Finding these hidden sentences across enormous genomes is difficult. Researchers needed to compare many genomes, identify conserved regions, and then determine which of those regions might have evolved unusually quickly in particular branches of the evolutionary tree.

Enter CNEwrap. https://github.com/YanCCscu/CNEwrap

The researchers developed CNEwrap as a streamlined toolkit capable of handling large-scale genome comparisons, conserved-element detection, and evolutionary analysis. Inside it is a new algorithm called EvoAcc, designed to identify CNEs that show accelerated evolution in specific species or lineages.

Why does that matter?

Because evolution isn't just about what stays the same.

Sometimes, the most interesting story is hidden in what changed.

Imagine that nearly every species carries the same regulatory sentence, but one lineage suddenly has several unusual edits. Perhaps those changes helped shape a new body structure, a different adaptation, or another distinctive trait.

EvoAcc is designed to help researchers find these evolutionary clues. In the study's tests, it performed particularly well in scenarios involving two or three accelerated lineages and showed improved sensitivity to insertion and deletion mutations compared with several existing approaches.

The researchers also tested CNEwrap using functional genomic regions from mammals and found that EvoAcc could recover human-specific accelerated regions while identifying some signals that other methods missed.

So the story of CNEwrap is really a story about looking beyond the obvious.

For decades, scientists have often focused on the DNA that directly encodes proteins. But the genome is more like a vast library: the protein-coding genes are the main text, while non-coding regions may contain punctuation, instructions, switches, annotations, and editing notes that tell the cell how to read the book.

CNEwrap gives researchers a new way to search that hidden material.

And perhaps somewhere inside those billions of DNA letters is an evolutionary story we haven't read yet.

The exciting part is that we now have better tools to find it.

Read more about it https://academic.oup.com/nar/article/54/14/gkag709/8738333?

Shell history search command

Type history at a shell prompt:
$ history

It will display the list of all used commandline history with an serial number.

To search particular command, enter:
$ history | grep command-name
$ history | egrep -i 'scp|ssh|ftp'
Emacs Line-Edit Mode Command History Searching

To get previous command containing string, hit [CTRL]+[r] followed by search string:

(reverse-i-search):

To get previous command, hit [CTRL]+[p]. You can also use up arrow key.

CTRL-p

To get next command, hit [CTRL]+[n]. You can also use down arrow key.

CTRL-n

fc command

Apart from hostory command there are fc command to extract the command from history. The fc stands for either "find command" or "fix command.

For example list last 10 command, enter:
$ fc -l 10
To list commands 130 through 150, enter:
$ fc -l 130 150
To list all commands since the last command beginning with ssh, enter:
$ fc -l ssh
You can edit commands 1 through 5 using vi text editor, enter:
$ fc -e vi 1 5

Delete command history

The -c option causes the history list to be cleared by deleting all of the entries:
$ history -c

Check out the changes they made.

They added the cleanup-blastdb-volumes.py script to remove unused BLAST database volumes. Read the documentation here.

They also switched the protocol from ftp to https to access BLAST databases for increased performance and reliability when downloading data from the NCBI with the update_blastdb.pl script.

And fixed a few bugs related to downloading data from the NCBI, and mt_mode crashing blastn and blastx.

Check out the release notes.

Download BLAST+ 2.14.1

Questions or comments? Please write the BLAST help desk.

More info and download: https://blast.ncbi.nlm.nih.gov/doc/blast-news/2023-BLAST-News.html

GSP4PDB is part of the services provided by the Bioinformatic Group of the University of Talca

http://gdblab.com/gsp4pdb/gsp4pdb2/

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3352-x

Address of the bookmark: http://gdblab.com/gsp4pdb/gsp4pdb2/

Address of the bookmark: https://github.com/pachterlab/gget

More at https://www.biorxiv.org/content/10.1101/2024.06.03.597269v1

Address of the bookmark: https://github.com/TravisWheelerLab/ULTRA