This readme covers all necessary instructions for the impatient to get andi up and running. For extensive instructions please consult the manual.

More at https://github.com/evolbioinf/andi/

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full

• January 20th, 2016 - New! Bpipe 0.9.9 released!
• Download latest, all
• Documentation
• Mailing List (Google Group)

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).

Instructions: set the read length/configuration and genome size, then select what you want to calculate.

Written by Stephen Turner, based on the Lander-Waterman formula, inspired by a similar calculator written by James Hadfield. Coverage is calculated as C=LN/G and reads as N=CG/L where C = Coverage (X),L = Read length (bp), G = Haploid genome size (bp), and N = Number of reads. Source code on GitHub.

Address of the bookmark: http://apps.bioconnector.virginia.edu/covcalc/

This tool is a standalone version of Ragout module [http://fenderglass.github./Ragout]

Address of the bookmark: https://github.com/fenderglass/maf2synteny

(i) can be used for HGT detection in both prokaryotes and eukaryotes,

(ii) can report a statistical P value for each gene to indicate how likely it is to be horizontally transferred, and

(iii) is fully automated (requires minimal human intervention), as well as very easy to install and run. 

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626719/

https://www.hindawi.com/journals/ijpg/2011/373875/

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

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html

Key Features of BLAST:
1. Sequence Comparison: BLAST searches for local alignments between the query sequence and sequences in a database. It identifies regions of similarity, which can help infer functional and evolutionary relationships.

2. Speed and Efficiency: BLAST uses heuristic algorithms, making it faster than exhaustive search methods, suitable for large-scale database searches.

3. Versatility: There are several versions of BLAST for different types of sequence comparisons:
- blastn: Compares a nucleotide query sequence against a nucleotide sequence database.
- blastp: Compares a protein query sequence against a protein sequence database.
- blastx: Compares a nucleotide query sequence translated in all reading frames against a protein sequence database.
- tblastn: Compares a protein query sequence against a nucleotide sequence database translated in all reading frames.
- tblastx: Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

4. Scoring and E-value: BLAST results are scored based on the quality and length of the alignments. The E-value (expect value) indicates the number of alignments one can expect to find by chance, with lower E-values representing more significant matches.

5. Output Formats: BLAST provides results in various formats, including plain text, HTML, XML, and JSON, making it adaptable for different types of analyses and integrations with other tools.

Applications of BLAST:
- Genomic Research: Identifying genes, understanding genetic diversity, and mapping genome sequences.
- Protein Function Prediction: Inferring the function of unknown proteins by comparing them to known protein sequences.
- Evolutionary Studies: Exploring evolutionary relationships between organisms by comparing their genetic material.
- Medical Research: Identifying pathogens, understanding disease mechanisms, and developing treatments by comparing sequences of interest.

Overall, BLAST is an essential tool in bioinformatics, offering a reliable and efficient way to analyze and interpret biological sequence data.

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: http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html