The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.

The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.

Comparative-based gene recognition is driven by the principle that conserved regions between related organisms are more likely than divergent regions to be coding. We describe a probabilistic framework for gene structure and alignment that can be used to simultaneously find both the gene structure and alignment of two syntenic genomic regions. A key feature of the method is the ability to enhance gene predictions by finding the best alignment between two syntenic sequences, while at the same time finding biologically meaningful alignments that preserve the correspondence between coding exons. Our probabilistic framework is the generalized pair hidden Markov model, a hybrid of (1) generalized hidden Markov models, which have been used previously for gene finding, and (2) pair hidden Markov models, which have applications to sequence alignment. We have built a gene finding and alignment program called SLAM, which aligns and identifies complete exon/intron structures of genes in two related but unannotated sequences of DNA. SLAM is able to reliably predict gene structures for any suitably related pair of organisms, most notably with fewer false-positive predictions compared to previous methods (examples are provided for Homo sapiens/Mus musculus and Plasmodium falciparum/Plasmodium vivax comparisons). Accuracy is obtained by distinguishing conserved noncoding sequence (CNS) from conserved coding sequence. CNS annotation is a novel feature of SLAM and may be useful for the annotation of UTRs, regulatory elements, and other noncoding features.

Hidden Markov models (HMMs) have been successfully applied to a variety of problems in molecular biology, ranging from alignment problems to gene nding and annotation. Alignment problems can be solved with pair HMMs, while gene nding programs rely on generalized HMMs in order to model exon lengths. In this paper, we introduce the generalized pair HMM (GPHMM), which is an extension of both pair and generalized HMMs. We show how GPHMMs, in conjunction with approximate alignments, can be used for cross-species gene nding and describe applications to DNA-cDNA and DNA-protein alignment. GPHMMs provide a unifying and probabilistically sound theory for modeling these problems.

We describe a new method for simultaneously identifying novel homologous genes with identical structure in the human, mouse, and rat genomes by combining pairwise predictions made with the SLAM gene-finding program. Using this method, we found 3698 gene triples in the human, mouse, and rat genomes which are predicted with exactly the same gene structure. We show, both computationally and experimentally, that the introns of these triples are predicted accurately as compared with the introns of other ab initio gene prediction sets. Computationally, we compared the introns of these gene triples, as well as those from other ab initio gene finders, with known intron annotations. We show that a unique property of SLAM, namely that it predicts gene structures simultaneously in two organisms, is key to producing sets of predictions that are highly accurate in intron structure when combined with other programs. Experimentally, we performed reverse transcription-polymerase chain reaction (RT-PCR) in both the human and rat to test the exon pairs flanking introns from a subset of the gene triples for which the human gene had not been previously identified. By performing RT-PCR on orthologous introns in both the human and rat genomes, we additionally explore the validity of using RT-PCR as a method for confirming gene predictions.[Supplemental material is available online at