• 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)

http://gkno.me/how-to/install.html

http://gkno.me/software.html

Address of the bookmark: http://gkno.me/

• Speed: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.
• Accuracy: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).
• Specificity: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.
• GC-Content Indifferent: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the Pseudomonas aeruginosa curated annotations.
• Metagenomic Version: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.
• Ease of Use: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.
• Open Source: Prodigal source code is freely available under the General Public License.

Address of the bookmark: http://prodigal.ornl.gov/

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

Suddenly, a technical error can look like a biological difference.

This is the problem Kenneth A. Barr and Yoav Gilad tackle with CrossFilt, a new computational tool introduced in Genome Biology journal.

CrossFilt acts like a careful referee. It keeps only sequencing reads that can be reliably compared across species, filtering out reads that could create alignment bias.

When tested on human, chimpanzee, and rhesus macaque data, CrossFilt reduced false discoveries and produced more reliable cross-species comparisons.

The message is simple: before asking what makes species different, we need to make sure our methods aren't creating those differences.

In genomics, sometimes the most important discovery is finding—and removing—the bias hiding in the data.

Read more at https://link.springer.com/article/10.1186/s13059-026-04082-2?

Address of the bookmark: https://sourceforge.net/projects/excavator2tool/

Input formats, automated data upload from the UCSC Genome Browser, gene annotation, annotation of repetitive elements, and progress report were previously described in the zPicture instructions and we refer the users to these materials for more details. This introduction is mainly focused on some novel features unique to the Mulan.

Address of the bookmark: https://mulan.dcode.org/mulanInstructions.php

Address of the bookmark: https://www.niehs.nih.gov/research/resources/software/biostatistics/acana/index.cfm