The mammalian inner ear contains the cochlea and vestibular organs, which are responsible for hearing and balance, respectively. The epithelia of these sensory organs contain hair cells that function as mechanoreceptors to transduce sound and head motion. The molecular mechanisms underlying hair cell development and differentiation are poorly understood. Math1, a mouse homolog of the Drosophila proneural gene atonal, is expressed in inner ear sensory epithelia. Embryonic Math1-null mice failed to generate cochlear and vestibular hair cells. This gene is thus required for the genesis of hair cells.

The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.

Sequencing of multiple related species followed by comparative genomics analysis constitutes a powerful approach for the systematic understanding of any genome. Here, we use the genomes of 12 Drosophila species for the de novo discovery of functional elements in the fly. Each type of functional element shows characteristic patterns of change, or 'evolutionary signatures', dictated by its precise selective constraints. Such signatures enable recognition of new protein-coding genes and exons, spurious and incorrect gene annotations, and numerous unusual gene structures, including abundant stop-codon readthrough. Similarly, we predict non-protein-coding RNA genes and structures, and new microRNA (miRNA) genes. We provide evidence of miRNA processing and functionality from both hairpin arms and both DNA strands. We identify several classes of pre-and post-transcriptional regulatory motifs, and predict individual motif instances with high confidence. We also study how discovery power scales with the divergence and number of species compared, and we provide general guidelines for comparative studies.The sequencing of the human genome and the genomes of dozens of other metazoan species has intensified the need for systematic methods to extract biological information directly from DNA sequence. Comparative genomics has emerged as a powerful methodology for this endeavour 1,2 . Comparison of few (two-four) closely related genomes has proven successful for the discovery of protein-coding genes 3-5 , RNA genes 6,7 , miRNA genes 8-11 and catalogues of regulatory elements 3,4,12-14 . The resolution and discovery power of these studies should increase with the number of genomes [15][16][17][18][19][20] , in principle enabling the systematic discovery of all conserved functional elements.The fruitfly Drosophila melanogaster is an ideal system for developing and evaluating comparative genomics methodologies. Over the past century, Drosophila has been a pioneering model in which many of the basic principles governing animal development and population biology were established 21 . In the past decade, the genome sequence of D. melanogaster provided one of the first systematic views *These authors contributed equally to this work. {Lists of participants and affiliations appear at the end of the paper.

T-cell acute lymphoblastic leukemia (T-ALL) is caused by the cooperation of multiple oncogenic lesions1,2. We used exome sequencing on 67 T-ALLs to gain insight into the mutational spectrum in these leukemias. We detected protein-altering mutations in 508 genes, with an average of 8.2 mutations in pediatric and 21.0 mutations in adult T-ALL. Using stringent filtering, we predict seven new oncogenic driver genes in T-ALL. We identify CNOT3 as a tumor suppressor mutated in 7 of 89 (7.9%) adult T-ALLs, and its knockdown causes tumors in a sensitized Drosophila melanogaster model3. In addition, we identify mutations affecting the ribosomal proteins RPL5 and RPL10 in 12 of 122 (9.8%) pediatric T-ALLs, with recurrent alterations of Arg98 in RPL10. Yeast and lymphoid cells expressing the RPL10 Arg98Ser mutant showed a ribosome biogenesis defect. Our data provide insights into the mutational landscape of pediatric versus adult T-ALL and identify the ribosome as a potential oncogenic factor.