title: An efficient and extensible approach for compressing phylogenetic trees.
abstract: BACKGROUND
  Biologists require new algorithms to efficiently compress and store their large collections of phylogenetic trees. Our previous work showed that TreeZip is a promising approach for compressing phylogenetic trees. In this paper, we extend our TreeZip algorithm by handling trees with weighted branches. Furthermore, by using the compressed TreeZip file as input, we have designed an extensible decompressor that can extract subcollections of trees, compute majority and strict consensus trees, and merge tree collections using set operations such as union, intersection, and set difference.
  
  
  RESULTS
  On unweighted phylogenetic trees, TreeZip is able to compress Newick files in excess of 98%. On weighted phylogenetic trees, TreeZip is able to compress a Newick file by at least 73%. TreeZip can be combined with 7zip with little overhead, allowing space savings in excess of 99% (unweighted) and 92%(weighted). Unlike TreeZip, 7zip is not immune to branch rotations, and performs worse as the level of variability in the Newick string representation increases. Finally, since the TreeZip compressed text (TRZ) file contains all the semantic information in a collection of trees, we can easily filter and decompress a subset of trees of interest (such as the set of unique trees), or build the resulting consensus tree in a matter of seconds. We also show the ease of which set operations can be performed on TRZ files, at speeds quicker than those performed on Newick or 7zip compressed Newick files, and without loss of space savings.
  
  
  CONCLUSIONS
  TreeZip is an efficient approach for compressing large collections of phylogenetic trees. The semantic and compact nature of the TRZ file allow it to be operated upon directly and quickly, without a need to decompress the original Newick file. We believe that TreeZip will be vital for compressing and archiving trees in the biological community.
authors.affiliation: Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. sjm@cse.tamu.edu
authors.forename: Suzanne J
authors.initials: SJ
authors.lastname: Matthews
authors.forename: Tiffani L
authors.initials: TL
authors.lastname: Williams
mesh_terms: D000465:Algorithms; D000818:Animals; D002965:Classification / Q000379:methods*; D006801:Humans; D010802:Phylogeny; D012984:Software