At a Snowbird conference on neural nets in 1992, David Haussler and his colleagues at UC Santa Cruz (including one of us, AK) described preliminary results on modelling protein sequence multiple alignments with probabilistic models called ‘hidden Markov models’ (HMMs). Copies of their technical report were widely circulated. Some of them found their way to the MRC Laboratory of Molecular Biology in Cambridge, where RD and GJM were just switching research interests from neural modelling to computational genome sequence analysis, and where SRE had arrived as a new postdoctoral student with a background in experimental molecular genetics and an interest in computational analysis. AK later also came to Cambridge for a year.

All of us quickly adopted the ideas of probabilistic modelling. We were persuaded that hidden Markov models and their stochastic grammar analogues are beautiful mathematical objects, well fitted to capturing the information buried in biological sequences. The Santa Cruz group and the Cambridge group independently developed two freely available HMM software packages for sequence analysis, and independently extended HMM methods to stochastic context-free grammar analysis of RNA secondary structures. Another group led by Pierre Baldi at JPL/Caltech was also inspired by the work presented at the Snowbird conference to work on HMM-based approaches at about the same time.

By late 1995, we thought that we had acquired a reasonable amount of experience in probabilistic modelling techniques. On the other hand, we also felt that relatively little of the work had been communicated effectively to the community.