Address of the bookmark: https://github.com/dglemay/G-NEST

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

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852411/

Features

• Linear representation of several segments of DNA
• Comparisons represented by areas between the segments (like Artemis, for example)
• Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files
• Plot several subsegments of the same segment on the same line, separated by a //
• Automatic or manual placement of the segments on the plot
• Add annotations to all the lines
• Create smart, automatic annotations for genomes, based on gene names
• Add a user-generated tree
• Add a global scale or a scale to each line
• Use user-defined graphical functions to represent genes

Address of the bookmark: http://genoplotr.r-forge.r-project.org/

install.packages("gggenes")

If you want the development version, install it from GitHub:

devtools::install_github("wilkox/gggenes")

More at https://github.com/wilkox/gggenes

Address of the bookmark: http://wilkox.org/gggenes

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6568-2

Address of the bookmark: https://github.com/HuffordLab/GenomeQC

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Gene Set Clustering based on Functional annotation

GeneSCF serves as command line tool for clustering the list of genes given by the users based on functional annotation (Gene Ontology, KEGG, REACTOME and NCG 4.0). It requires gene list in the form of Entrez Gene ID (UIDs) or Official gene symbols as a input. GeneSCF supports more organisms from V1.1. Examples to download database as simple text file using GeneSCF "prepare_database" module, 1) https://www.biostars.org/p/197414/#197416 , 2) https://www.biostars.org/p/191532/#191540

The advantage of using GeneSCF over other enrichment tools is that, it performs enrichment analysis in real-time (v1.1 and above) by accessing source databases. With command-line versions of tools, as you know you can run multiple gene list simultaneously.

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Requirement:

GeneSCF only works on Linux system, it has been successfully tested on Ubuntu, Mint and Cent OS. Other distributions of Linux might work as well.

Documentation:

http://genescf.kandurilab.org/documentation.php

Report issues on Biostars or GitHub Project page

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The enzyme portal integrates many resources, most of them hosted by EBI and also external ones such as BioPortal. Its main goal is to provide information about enzymes in a suitable format, with a usable interface designed for intended users. Instead of reinventing the wheel, it makes use of available and reliable resources to that end.

Related Literature:

http://nar.oxfordjournals.org/content/41/D1/D773.full

http://www.biomedcentral.com/1471-2105/14/103

Address of the bookmark: http://www.ebi.ac.uk/enzymeportal/

The MMseqs2 user guide is available as Github Wiki or as PDF file (Thanks to pandoc!)

Please cite: Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, doi: 10.1038/nbt.3988 (2017).

Address of the bookmark: https://github.com/soedinglab/MMseqs2

Caretta, a multiple structure alignment suite meant for homologous but sequentially divergent protein families which consistently returns accurate alignments with a higher coverage than current state-of-the-art tools. Caretta is available as a GUI and command-line application and additionally outputs an aligned structure feature matrix for a given set of input structures, which can readily be used in downstream steps for supervised or unsupervised machine learning. 

Address of the bookmark: http://www.bioinformatics.nl/caretta/