The impact of large and complex epigenomic datasets on biological insights or clinical applications is limited by the lack of accessibility by easy, intuitive, and fast tools. Here, we describe an epigenomics comparative cyber-infrastructure (EPICO), an open-access reference set of libraries to develop comparative epigenomic data portals. Using EPICO, large epigenome projects can make available their rich datasets to the community without requiring specific technical skills. As a first instance of EPICO, we implemented the BLUEPRINT Data Analysis Portal (BDAP). BDAP provides a desktop for the comparative analysis of epigenomes of hematopoietic cell types based on results, such as the position of epigenetic features, from basic analysis pipelines. The BDAP interface facilitates interactive exploration of genomic regions, genes, and pathways in the context of differentiation of hematopoietic lineages. This work represents initial steps toward broadly accessible integrative analysis of epigenomic data across international consortia. EPICO can be accessed at https://github.com/inab, and BDAP can be accessed at http://blueprint-data.bsc.es.

Cactophilic Drosophila species provide a valuable model to study gene–environment interactions and ecological adaptation. Drosophila buzzatii and Drosophila mojavensis are two cactophilic species that belong to the repleta group, but have very different geographical distributions and primary host plants. To investigate the genomic basis of ecological adaptation, we sequenced the genome and developmental transcriptome of D. buzzatii and compared its gene content with that of D. mojavensis and two other noncactophilic Drosophila species in the same subgenus. The newly sequenced D. buzzatii genome (161.5 Mb) comprises 826 scaffolds (>3 kb) and contains 13,657 annotated protein-coding genes. Using RNA sequencing data of five life-stages we found expression of 15,026 genes, 80% protein-coding genes, and 20% noncoding RNA genes. In total, we detected 1,294 genes putatively under positive selection. Interestingly, among genes under positive selection in the D. mojavensis lineage, there is an excess of genes involved in metabolism of heterocyclic compounds that are abundant in Stenocereus cacti and toxic to nonresident Drosophila species. We found 117 orphan genes in the shared D. buzzatii–D. mojavensis lineage. In addition, gene duplication analysis identified lineage-specific expanded families with functional annotations associated with proteolysis, zinc ion binding, chitin binding, sensory perception, ethanol tolerance, immunity, physiology, and reproduction. In summary, we identified genetic signatures of adaptation in the shared D. buzzatii–D. mojavensis lineage, and in the two separate D. buzzatii and D. mojavensis lineages. Many of the novel lineage-specific genomic features are promising candidates for explaining the adaptation of these species to their distinct ecological niches.

The development of high-throughput sequencing technologies has advanced our understanding of cancer. However, characterizing somatic structural variants in tumor genomes is still challenging because current strategies depend on the initial alignment of reads to a reference genome. Here, we describe SMUFIN (somatic mutation finder), a single program that directly compares sequence reads from normal and tumor genomes to accurately identify and characterize a range of somatic sequence variation, from single-nucleotide variants (SNV) to large structural variants at base pair resolution. Performance tests on modeled tumor genomes showed average sensitivity of 92% and 74% for SNVs and structural variants, with specificities of 95% and 91%, respectively. Analyses of aggressive forms of solid and hematological tumors revealed that SMUFIN identifies breakpoints associated with chromothripsis and chromoplexy with high specificity. SMUFIN provides an integrated solution for the accurate, fast and comprehensive characterization of somatic sequence variation in cancer.The recent development of high-throughput sequencing technologies has made possible the sequencing of genomes at an unprecedented speed, allowing the identification of the genetic basis of numerous diseases. These advances have been particularly important in the study of cancer, providing information on thousands of tumor genomes and a large catalog of genomic alteration associated with oncogenesis 1 .The characterization of somatic variation in tumor samples is, therefore, rapidly becoming a standard practice in biomedicine 2 . In a large fraction of biomedical studies that rely on high-throughput sequencing, the production of genome sequence data exceeds available computer resources and the capabilities of analytic protocols. This is particularly pertinent in the field of cancer genomics, where the increasing sequencing of tumor genomes calls for faster and more accurate analyses.The identification of somatic variants associated with cancer typically requires sequencing tumor and normal genome samples from the same patient, followed by multiple sequence comparisons. Normal and pathological reads are aligned to a reference genome, and the alignment is used to identify sequence changes to isolate the somatic fraction of variants (i.e., those detected only in the tumor). In principle, this simple strategy can be used to detect single-nucleotide variants (SNVs) and structural variants. Existing methods for the detection of somatic SNVs show high sensitivity and specificity 3,4 , but identifying structural variants is still challenging and remains largely unsolved. The need for a reference sequence is particularly limiting. Reads carrying variations, such as those covering somatic changes in the tumor, are more difficult to align to the reference genome 5 , and corresponding variants might become undetectable. Moreover, reference-based methods also must discriminate germline changes from somatic variants. In addition to these limitations at detection level, this a...