Editing FASTA format (section)

===Compression===
The compression of FASTA files requires a specific compressor to handle both channels of information: identifiers and sequence. For improved compression results, these are mainly divided into two streams where the compression is made assuming independence. For example, the algorithm MFCompress<ref name="MFCompress">{{cite journal | vauthors = Pinho AJ, Pratas D | title = MFCompress: a compression tool for FASTA and multi-FASTA data | journal = Bioinformatics | volume = 30 | issue = 1 | pages = 117–8 | date = January 2014 | pmid = 24132931 | pmc = 3866555 | doi = 10.1093/bioinformatics/btt594 }}</ref> performs lossless compression of these files using context modelling and arithmetic encoding. Genozip,<ref name="Genozip">{{Cite journal |last1=Lan |first1=Divon |last2=Tobler |first2=Ray |last3=Souilmi |first3=Yassine |last4=Llamas |first4=Bastien |date=2021-02-15 |title=Genozip: a universal extensible genomic data compressor |url=https://doi.org/10.1093/bioinformatics/btab102 |journal=Bioinformatics |volume=37 |issue=16 |pages=2225–2230 |doi=10.1093/bioinformatics/btab102 |issn=1367-4803 |pmc=8388020 |pmid=33585897}}</ref> a software package for compressing genomic files, uses an extensible context-based model. Benchmarks of FASTA file compression algorithms have been reported by Hosseini et al. in 2016,<ref name="Morteza">{{Cite journal |last1=Hosseini |first1=Morteza |last2=Pratas |first2=Diogo |last3=Pinho |first3=Armando J. |date=2016 |title=A Survey on Data Compression Methods for Biological Sequences |journal=Information |language=en |volume=7 |issue=4 |pages=56 |doi=10.3390/info7040056 |issn=2078-2489 |doi-access=free }}</ref> and Kryukov et al. in 2020.<ref name="SCB">{{cite journal | vauthors = Kryukov K, Ueda MT, Nakagawa S, Imanishi T | title = Sequence Compression Benchmark (SCB) database—A comprehensive evaluation of reference-free compressors for FASTA-formatted sequences | journal = GigaScience | volume = 9 | issue = 7 | pages = giaa072 | date = July 2020 | pmid = 32627830 | pmc = 7336184 | doi = 10.1093/gigascience/giaa072 }}</ref>