• 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

A recent study published in PLOS Computational Biology introduces REvolutionH-tl 2.0 (https://pypi.org/project/revolutionhtl/), a computational tool designed to help solve this mystery. The software analyzes protein sequences and identifies evolutionary relationships between genes. It can help researchers detect important events such as gene duplication, gene loss, and speciation, allowing them to reconstruct how genes evolved across different organisms.

What makes the tool especially interesting is its focus on both speed and interpretation. The researchers compared REvolutionH-tl 2.0 with several existing computational tools and reported that it achieved competitive accuracy while requiring less computational time in their tests. The tool also includes visualization features, helping researchers turn complicated genetic data into evolutionary stories that are easier to explore.

Genes are more than simple sequences of DNA; they are records of life’s long history. Each surviving gene carries traces of the changes that shaped it. By bringing together computational analysis and visualization, REvolutionH-tl 2.0 acts like a detective, helping scientists uncover these hidden stories. Ultimately, tools like this can improve our understanding of how genomes evolved and how genes acquired the roles they perform today.

More at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013017

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

Background

The identification of chromosomal homologous segments (CHS) within and between genomes is essential for comparative genomics. Various processes including insertion/deletion and inversion could cause the degeneration of CHSs.

Address of the bookmark: https://almob.biomedcentral.com/articles/10.1186/1748-7188-4-2

Address of the bookmark: https://github.com/pratas/smash