Editing Lossless compression (section)

===Genetics and genomics ===
[[Compression of genomic sequencing data|Genetics compression algorithms]] (not to be confused with [[genetic algorithm]]s) are the latest generation of lossless algorithms that compress data (typically sequences of nucleotides) using both conventional compression algorithms and specific algorithms adapted to genetic data. In 2012, a team of scientists from Johns Hopkins University published the first genetic compression algorithm that does not rely on external genetic databases for compression. HAPZIPPER was tailored for [[International_HapMap_Project|HapMap]] data and achieves over 20-fold compression (95% reduction in file size), providing 2- to 4-fold better compression much faster than leading general-purpose compression utilities.<ref>{{cite journal |author=Chanda, P. |author2=Elhaik, E. |author3=Bader, J.S. | title=HapZipper: sharing HapMap populations just got easier | journal=Nucleic Acids Res | pages=1–7 | year=2012 | pmid=22844100 | doi=10.1093/nar/gks709 | volume=40 | issue=20 | pmc=3488212}}</ref>

Genomic sequence compression algorithms, also known as DNA sequence compressors, explore the fact that DNA sequences have characteristic properties, such as inverted repeats. The most successful compressors are XM and GeCo.<ref name=Pratas>{{cite book |last1=Pratas |first1=D. |last2=Pinho |first2=A. J. |last3=Ferreira |first3=P. J. S. G. |date=2016 |chapter=Efficient compression of genomic sequences |title=Data Compression Conference |location=Snowbird, Utah |url=http://sweet.ua.pt/pratas/papers/Pratas-2016b.pdf}}</ref> For [[eukaryotes]] XM is slightly better in compression ratio, though for sequences larger than 100 MB its computational requirements are impractical.