Address of the bookmark: https://github.com/martinjvickers/KAST

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.

Address of the bookmark: https://github.com/tanghaibao/quota-alignment

To cite Mugsy, use:

Angiuoli SV and Salzberg SL. Mugsy: Fast multiple alignment of closely related whole genomes.Bioinformatics 2011 27(3):334-4

Address of the bookmark: http://mugsy.sourceforge.net/

Address of the bookmark: http://molevol.cmima.csic.es/castresana/Gblocks_server.html

Mulan (http://mulan.dcode.org/), a novel method and a network server for comparing multiple draft and finished-quality sequences to identify functional elements conserved over evolutionary time. Mulan brings together several novel algorithms: the TBA multi-aligner program for rapid identification of local sequence conservation, and the multiTF program for detecting evolutionarily conserved transcription factor binding sites in multiple alignments. In addition, Mulan supports two-way communication with the GALA database; alignments of multiple species dynamically generated in GALA can be viewed in Mulan, and conserved transcription factor binding sites identified with Mulan/multiTF can be integrated and overlaid with extensive genome annotation data using GALA.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC540288/

• nodes, which are labeled by sequences and ids
• edges, which connect two nodes via either of their respective ends
• paths, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges

Address of the bookmark: https://github.com/vgteam/vg

Results: We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on ge-nome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% accuracy at supra-species level and 78% accuracy for species level.

Availability and Implementation: Cnidaria is written in C++ and Python and is available at http://www.ab.wur.nl/cnidaria.

Contact: Saulo Aflitos - sauloal@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Address of the bookmark: https://github.com/sauloal/cnidaria/wiki

RaGOO is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome. The focus of this tool is on practicality and therefore has the following features:

1. Good performance. On a MacBook Pro using Arabidopsis data, pseudochromosome construction takes less than a minute and the whole pipeline with SV calling takes ~2 minutes.
2. Intact ordering and orienting of contigs.
3. Misassembly correction
4. GFF lift-over
5. Structural variant calling with and integrated version of Assemblytics
6. Confidence scores associated with the grouping, localization, and orientation for each contig.

Address of the bookmark: https://github.com/malonge/RaGOO

Features

• Very accurate results (Martin et al., WhatsHap: fast and accurate read-based phasing)
• Works well with Illumina, PacBio, Oxford Nanopore and other types of reads
• It phases SNVs, indels and even “complex” variants (such as TCG → AGAA)
• Pedigree phasing mode uses reads from related individuals (such as trios) to improve results and to reduce coverage requirements (Garg et al., Read-Based Phasing of Related Individuals).
• WhatsHap is easy to install
• It is easy to use: Pass in a VCF and one or more BAM files, get out a phased VCF. Supports multi-sample VCFs.
• It produces standard-compliant VCF output by default
• If desired, get output that is compatible with ReadBackedPhasing
• Open Source (MIT license)

Address of the bookmark: https://whatshap.readthedocs.io/en/latest/