The discoveries of microRNAs and riboswitches, among others, have shown functional RNAs to be biologically more important and genomically more prevalent than previously anticipated. We have developed a general comparative genomics method based on phylogenetic stochastic context-free grammars for identifying functional RNAs encoded in the human genome and used it to survey an eight-way genome-wide alignment of the human, chimpanzee, mouse, rat, dog, chicken, zebra-fish, and puffer-fish genomes for deeply conserved functional RNAs. At a loose threshold for acceptance, this search resulted in a set of 48,479 candidate RNA structures. This screen finds a large number of known functional RNAs, including 195 miRNAs, 62 histone 3'UTR stem loops, and various types of known genetic recoding elements. Among the highest-scoring new predictions are 169 new miRNA candidates, as well as new candidate selenocysteine insertion sites, RNA editing hairpins, RNAs involved in transcript auto regulation, and many folds that form singletons or small functional RNA families of completely unknown function. While the rate of false positives in the overall set is difficult to estimate and is likely to be substantial, the results nevertheless provide evidence for many new human functional RNAs and present specific predictions to facilitate their further characterization.

Background. Transcriptional regulation in eukaryotes is often multifactorial, involving multiple transcription factors binding to the same transcription control region (e.g., upstream activating sequences and enhancers), and to understand the regulatory content of eukaryotic genomes it is necessary to consider the co-occurrence and spatial relationships of individual binding sites. The identification of sequences conserved among related species (often known as phylogenetic footprinting) has been successfully used to identify individual transcription factor binding sites. Here, we extend this concept of functional conservation to higher-order features of transcription control regions involved in the multifactorial control of gene expression.Results. We used the genome sequences of four yeast species of the genus Saccharomyces to identify sequences potentially involved in multifactorial control of gene expression. We found 1,117 potential regulatory "templates": pairs of hexameric sequences that are jointly conserved in transcription regulatory regions and also exhibit non-random relative spacing. Many of the individual sequences in these templates correspond to known transcription factor binding sites, and the sets of genes containing a particular template in their transcription control regions tend to be differentially expressed in conditions where the corresponding transcription factors are known to be active.Conclusions. The incorporation of both joint conservation and spacing constraints of sequence pairs predicts groups of target genes that were specific for common patterns of gene expression. Our work suggests that positional information, especially the relative spacing between transcription factor binding sites, may represent a common organizing principle of transcription control regions.

In [13] we reported the genome sequences of S. paradoxus, S. mikatae and S. bayanus and compared these three yeast species to their close relative, S. cerevisiae. Genome-wide comparative analysis allowed the identification of functionally important sequences, both coding and non-coding. In this companion paper we describe the mathematical and algorithmic results underpinning the analysis of these genomes.We developed statistical methods for the systematic de-novo identification of regulatory motifs. Without making use of co-regulated gene sets, we discovered virtually all previously known DNA regulatory motifs as well as several noteworthy novel motifs. With the additional use of gene ontology information, expression clusters and transcription factor binding profiles, we assigned candidate functions to the novel motifs discovered.Our results demonstrate that entirely automatic genome-wide annotation, gene validation, and discovery of regulatory motifs is possible. Our findings are validated by the extensive experimental knowledge in yeast, confirming their applicability to other genomes.

One important approach to sequencing a large genome is (i) to sequence a collection of non-overlapping `seed' chosen from a genomic library of large-insert clones (such as bacterial artificial chromosome (BACs)) and then (ii) to take successive `walking' steps by selecting and sequencing minimally overlapping clones, using information such as clone-end sequences to identify the overlaps. We analyze the strategic issues involved in using this approach. We derive formulas showing how two key factors, the initial density of seed clones and the depth of the genomic library used for walking, affect the cost and time of a sequencing project—that is, the amount of redundant sequencing and the number of steps to cover the vast majority of the genome. We also discuss a variant strategy in which a second genomic library with clones having a somewhat smaller insert size is used to close gaps. This approach can dramatically decrease the amount of redundant sequencing, without affecting the rate at which the genome is covered.

We describe a novel analytical approach to gene recognition based on cross-species comparison We first undertook a comparison of orthologous genomic look from human and mouse, studying the extent of similarity in the number, size and sequence of exons and introns We then developed an approach for recognizing genes within such orthologous regions, by first aligning the regions using an iterative global alignment system and then identifying genes based on conservation of exonic features at aligned positions in both species The alignment and gene recognition are performed by new programs called GLASS and ROSETTA, respectively ROSETTA performed well at exact identification of coding exons in 117 orthologous pairs tested.

Classification of patient samples is a crucial aspect of cancer diagnosis and treatment. We present a method for classifying samples by computational analysis of gene expression data. We consider the classification problem in two parts: class discovery and class prediction. Class discovery refers to the process of dividing samples into reproducible classes that have similar behavior or properties, while class prediction places new samples into already known classes. We describe a method for performing class prediction and illustrate its strength by correctly classifying bone marrow and blood samples from acute leukemia patients. We also describe how to use our predictor to validate newly discovered classes, and we demonstrate how this technique could have discovered the key distinctions among leukemias if they were not already known. This proof-of-concept experiment paves the way for a wealth of future work on the molecular classification and understanding of disease.

This paper describes a fast and fully automated dictionary based approach to gene annotation and exon prediction. Two dictionaries are constructed, one from the nonredundant protein OWL database and the other from the dbEST database. These dictionaries are used to obtain O(1) time lookups of tuples in the dictionaries (4 tuples for the OWL database and 11 tuples for the \ndbEST database). These tuples can be used to rapidly find the longest matches at every position in an input sequence to the database sequences. Such matches provide very useful information pertaining to locating common segments between exons, alternative splice sites, and frequency data of long tuples for statistical purposes. These dictionaries also provide the basis for both homology determination, and statistical approaches to exon prediction. For instance, using the OWL protein database on a benchmark test set of 130 genes, and after removing sequences from the database with exact amino acid homology to genes in our test set, we find 88% of coding nucleotides, and 99% of our predictions of coding nucleotides are correct. Also, 81% of coding exons are predicted exactly, while 82% of our predictions of exons agree exactly with the published annotation of their genes.

Genome maps are crucial tools in human genetic research, providing known landmarks for locating disease genes and frameworks for large-scale sequencing.Radiation hybrid mapping is one technique for building genome maps.In this paper, we describe the methods used to build radiation hybrid maps of the entire human genome.We present the hidden Markov model that we employ to estimate the likelihood of a map despite uncertainty about the data, and we discuss the problem of searching for maximum-likelihood maps.We describe the graph algorithms used to find sparse but reliable init,ial maps and our methods of extending them.Finally, we show results validating our software on simulated data, and we describe our genome-wide human radiat,ion hybrid maps and the evidence supporting them.