Assessing structural variation in a personal genome-towards a human reference diploid genome.

BACKGROUND: Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods. RESULTS: We demonstrate Parliament's efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus. CONCLUSIONS: HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, the HS1011 data constitute a public resource for novel SV discovery, software calibration, and personal genome structural variation analysis.

ChildSeq-RNA: A next-generation sequencing-based diagnostic assay to identify known fusion transcripts in childhood sarcomas.

Childhood sarcomas can be extremely difficult to accurately diagnose on the basis of morphological characteristics alone. Ancillary methods, such as RT-PCR or fluorescence in situ hybridization, to detect pathognomonic gene fusions can help to distinguish these tumors. Two major deficiencies of these assays are their inability to identify gene fusions at nucleotide resolution or to detect multiple gene fusions simultaneously. We developed a next-generation sequencing-based assay designated ChildSeq-RNA that uses the Ion Torrent platform to screen for EWSR1-FLI1 and EWSR1-ERG, PAX3-FOXO1 and PAX7-FOXO1, EWSR1-WT1, and ETV6-NTRK3 fusions of Ewing sarcoma (ES), alveolar rhabdomyosarcoma, desmoplastic small round cell tumor, and congenital fibrosarcoma, respectively. To rapidly analyze resulting data, we codeveloped a bioinformatics tool, termed ChildDecode, that operates on a scalable, cloud-computing platform. Total RNA from four ES cell lines plus 33 clinical samples representing ES, alveolar rhabdomyosarcoma, desmoplastic small round cell tumor, and congenital fibrosarcoma tumors was subjected to ChildSeq-RNA. This accurately identified corresponding gene fusions in each tumor type, with no examples of false positive fusion detection in this proof-of-concept study. Comparison with previous RT-PCR findings demonstrated high sensitivity (96.4%; 95% CI, 82.3%-99.4%) and specificity (100%; 95% CI, 56.6%-100%) of ChildSeq-RNA to detect gene fusions. Herein, we propose ChildSeq-RNA as a novel tool to detect gene fusions in childhood sarcomas at single-nucleotide resolution.

Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks.

A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.

Comparison of Francisella tularensis genomes reveals evolutionary events associated with the emergence of human pathogenic strains.

BACKGROUND: Francisella tularensis subspecies tularensis and holarctica are pathogenic to humans, whereas the two other subspecies, novicida and mediasiatica, rarely cause disease. To uncover the factors that allow subspecies tularensis and holarctica to be pathogenic to humans, we compared their genome sequences with the genome sequence of Francisella tularensis subspecies novicida U112, which is nonpathogenic to humans. RESULTS: Comparison of the genomes of human pathogenic Francisella strains with the genome of U112 identifies genes specific to the human pathogenic strains and reveals pseudogenes that previously were unidentified. In addition, this analysis provides a coarse chronology of the evolutionary events that took place during the emergence of the human pathogenic strains. Genomic rearrangements at the level of insertion sequences (IS elements), point mutations, and small indels took place in the human pathogenic strains during and after differentiation from the nonpathogenic strain, resulting in gene inactivation. CONCLUSION: The chronology of events suggests a substantial role for genetic drift in the formation of pseudogenes in Francisella genomes. Mutations that occurred early in the evolution, however, might have been fixed in the population either because of evolutionary bottlenecks or because they were pathoadaptive (beneficial in the context of infection). Because the structure of Francisella genomes is similar to that of the genomes of other emerging or highly pathogenic bacteria, this evolutionary scenario may be shared by pathogens from other species.

Characterization of protein kinase CK2 from Trypanosoma brucei.

CK2 is a ubiquitous but enigmatic kinase. The difficulty in assigning a role to CK2 centers on the fact that, to date, no biologically relevant modulator of its function has been identified. One common theme revolves around a constellation of known substrates involved in growth control, compatible with its concentration in the nucleus and nucleolus. We had previously described the identification of two catalytic subunits of CK2 in Trypanosoma brucei and characterized one of them. Here we report the characterization of the second catalytic subunit, CK2alpha', and the identification and characterization of the regulatory subunit CK2beta. All three subunits are primarily localized to the nucleolus in T. brucei. We also show that CK2beta interacts with the nucleolar protein NOG1, adding to the interaction map which previously linked CK2alpha to the nucleolar protein NOPP44/46, which in turn associates with the rRNA binding protein p37. CK2 activity has four distinctive features: near equal affinity for GTP and ATP, heparin sensitivity, and stimulation by polyamines and polybasic peptides. Sequence comparison shows that the parasite orthologues have mutations in residues previously mapped as important in specifying affinity for GTP and stimulation by both polyamines and polybasic peptides. Studies of the enzymatic activity of the T. brucei CK2s show that both the affinity for GTP and stimulation by polyamines have been lost and only the features of heparin inhibition and stimulation by polybasic peptides are conserved.

Derivation of genetic interaction networks from quantitative phenotype data.

We have generalized the derivation of genetic-interaction networks from quantitative phenotype data. Familiar and unfamiliar modes of genetic interaction were identified and defined. A network was derived from agar-invasion phenotypes of mutant yeast. Mutations showed specific modes of genetic interaction with specific biological processes. Mutations formed cliques of significant mutual information in their large-scale patterns of genetic interaction. These local and global interaction patterns reflect the effects of gene perturbations on biological processes and pathways.

Identification and functional analysis of the essential and regulatory light chains of the only type II myosin Myo1p in Saccharomyces cerevisiae.

Cytokinesis in Saccharomyces cerevisiae involves coordination between actomyosin ring contraction and septum formation and/or targeted membrane deposition. We show that Mlc1p, a light chain for Myo2p (type V myosin) and Iqg1p (IQGAP), is the essential light chain for Myo1p, the only type II myosin in S. cerevisiae. However, disruption or reduction of Mlc1p-Myo1p interaction by deleting the Mlc1p binding site on Myo1p or by a point mutation in MLC1, mlc1-93, did not cause any obvious defect in cytokinesis. In contrast, a different point mutation, mlc1-11, displayed defects in cytokinesis and in interactions with Myo2p and Iqg1p. These data suggest that the major function of the Mlc1p-Myo1p interaction is not to regulate Myo1p activity but that Mlc1p may interact with Myo1p, Iqg1p, and Myo2p to coordinate actin ring formation and targeted membrane deposition during cytokinesis. We also identify Mlc2p as the regulatory light chain for Myo1p and demonstrate its role in Myo1p ring disassembly, a function likely conserved among eukaryotes.

A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules.

Peptide recognition modules mediate many protein-protein interactions critical for the assembly of macromolecular complexes. Complete genome sequences have revealed thousands of these domains, requiring improved methods for identifying their physiologically relevant binding partners. We have developed a strategy combining computational prediction of interactions from phage-display ligand consensus sequences with large-scale two-hybrid physical interaction tests. Application to yeast SH3 domains generated a phage-display network containing 394 interactions among 206 proteins and a two-hybrid network containing 233 interactions among 145 proteins. Graph theoretic analysis identified 59 highly likely interactions common to both networks. Las17 (Bee1), a member of the Wiskott-Aldrich Syndrome protein (WASP) family of actin-assembly proteins, showed multiple SH3 interactions, many of which were confirmed in vivo by coimmunoprecipitation.