Framework and algorithms for collaborative compression

We present a framework for considering the problem of compressing large collections of similar sequences. In this framework, an unknown individual sequence is modified several times independently to obtain the collection of sequences to be compressed. For certain collections generated by context-dependent bit flips of the individual sequencepsilas bits, and for those generated by simple edit operations on the individual sequence, we derive universal compression algorithms that compress the collection of sequences almost as well as an optimal compressor that has knowledge of the underlying individual sequence and the modifying processes.