Address of the bookmark: https://github.com/Megagenomics/FastProNGS

USAGE:

• -query: sequence A in fasta format
• -db: sequence B in fasta format
• -out: output matrix
• -kmer Integer: k>1 (default 32) Use 32 for chromosomes and genomes and 16 for small bacteria
• -diffuse Integer: z>0 (default 4) Use 4 for everything - if using large plant genomes you can try using 1
• -dimension Size of the output matrix and plot. Integer: d>0 (default 1000) Use 1000 for everything that is not full genome size, where 2000 is recommended

Address of the bookmark: https://github.com/estebanpw/chromeister

Address of the bookmark: https://github.com/MGXlab/Jaeger

DNA:

Nucleotide Code: Base:

---------------- -----

A.................Adenine

C.................Cytosine

G.................Guanine

T (or U)..........Thymine (or Uracil)

R.................A or G

Y.................C or T

S.................G or C

W.................A or T

K.................G or T

M.................A or C

B.................C or G or T

D.................A or G or T

H.................A or C or T

V.................A or C or G

N.................any base . or -............gap

Protein:

Amino Acid Code: Three letter Code: Amino Acid:

---------------- ------------------ -----------

A.................Ala.................Alanine

B.................Asx.................Aspartic acid or Asparagine

C.................Cys.................Cysteine

D.................Asp.................Aspartic Acid

E.................Glu.................Glutamic Acid

F.................Phe.................Phenylalanine

G.................Gly.................Glycine

H.................His.................Histidine

I.................Ile.................Isoleucine

K.................Lys.................Lysine

L.................Leu.................Leucine

M.................Met.................Methionine

N.................Asn.................Asparagine

P.................Pro.................Proline

Q.................Gln.................Glutamine

R.................Arg.................Arginine

S.................Ser.................Serine

T.................Thr.................Threonine

V.................Val.................Valine

W.................Trp.................Tryptophan

X.................Xaa.................Any amino acid

Y.................Tyr.................Tyrosine

Z.................Glx.................Glutamine or Glutamic acid

http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf

http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot

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.

Cactus uses substantial resources. For primate-sized genomes (3 gigabases each), you should expect Cactus to use approximately 120 CPU-days of compute per genome, with about 120 GB of RAM used at peak. The requirements scale roughly quadratically, so aligning two 1-megabase bacterial genomes takes only 1.5 CPU-hours and 14 GB RAM. 

Address of the bookmark: https://github.com/ComparativeGenomicsToolkit/cactus

https://academic.oup.com/bioinformatics/article/35/12/2153/5162749

Address of the bookmark: https://github.com/arzwa/wgd

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

Address of the bookmark: https://www.kegg.jp/blastkoala/