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/

Address of the bookmark: https://www.nature.com/articles/srep20802

SynVisio requires two files to run:

• The simplified gff file that was used as an input for a McScanX query.
• The collinearity file generated as an output by McScanX for the same input query.
• Optional track file in bedgraph format to annotate the generated charts.

SynVisio offers different types of visualizations such as Linear Parallel plots, Hive plots, Stacked Parallel Plots and Dot plots. Users can configure the type of plots required and then choose the source and the target chromosomes that need to be mapped. Users also have option to download the generated visualizations in publication ready SVG or PNG formats.

Address of the bookmark: https://synvisio.github.io/#/

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.

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/dentearl/evolverSimControl

Main features:

1. works with any number of scaffold assemblies in de-novo non-progressive fashion
2. allows to simultaneously work with scaffold assemblies obtained from any in silico and in vitro techniques, supporting multiple existing formats via built-in converters
3. creates an extensive report with several comparative quality metrics (both on assembly level and on the level of individual assembly points)
4. constructs a merged combined scaffold assembly
5. provides an interactive framework for a visual comparative analysis of the given assemblies

Address of the bookmark: https://cblab.org/camsa/