A multi-species functional embedding integrating sequence and network structure

A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment. In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HANDL (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space and the vectors themselves capture functional similarity across species, and are useful for a variety of functional tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HANDL embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are significantly separated in HANDL space, and the direction of separation is conserved across species. Software for the HANDL algorithm is available at http://bit.ly/lrgr-handl.Published versio

On cycles in the transcription network of Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>We investigate the cycles in the transcription network of <it>Saccharomyces cerevisiae</it>. Unlike a similar network of <it>Escherichia coli</it>, it contains many cycles. We characterize properties of these cycles and their place in the regulatory mechanism of the cell.</p> <p>Results</p> <p>Almost all cycles in the transcription network of <it>Saccharomyces cerevisiae </it>are contained in a single <it>strongly connected component</it>, which we call LSCC (L for "largest"), except for a single cycle of two transcription factors. The fact that LSCC includes almost all cycles is well explained by the properties of a random graph with the same in- and out-degrees of the nodes.</p> <p>Among different physiological conditions, cell cycle has the most significant relationship with LSCC, as the set of 64 transcription interactions that are active in all phases of the cell cycle has overlap of 27 with the interactions of LSCC (of which there are 49).</p> <p>Conversely, if we remove the interactions that are active in all phases of the cell cycle (25% of interactions to transcription factors), the LSCC would have only three nodes and 5 edges, many fewer than expected. This subgraph of the transcription network consists mostly of interactions that are active only in the stress response subnetwork.</p> <p>We also characterize the role of LSCC in the topology of the network. We show that LSCC can be used to define a natural hierarchy in the network and that in every physiological subnetwork LSCC plays a pivotal role.</p> <p>Conclusion</p> <p>Apart from those well-defined conditions, the transcription network of <it>Saccharomyces cerevisiae </it>is devoid of cycles. It was observed that two conditions that were studied and that have no cycles of their own are <it>exogenous</it>: diauxic shift and DNA repair, while cell cycle and sporulation are <it>endogenous</it>. We claim that in a certain sense (slow recovery) stress response is <it>endogenous </it>as well.</p

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Top of the Pods - In search of a podcasting “podagogy” for language learning

The popularization of portable media players such as the iPod, and the delivery of audio and video content through content management software such as iTunes mean that there is a wealth of language learning resources freely available to users who may download them and use them anywhere at any time. These resources vary greatly in quality and follow different approaches to learning. This paper provides a taxonomy of podcast resources, reviews materials in the light of Second Language Acquisition theories, argues for better design, and outlines directions for future research

The Integrated Genomic Landscape of Thymic Epithelial Tumors

Thymic epithelial tumors (TETs) are one of the rarest adult malignancies. Among TETs, thymoma is the most predominant, characterized by a unique association with autoimmune diseases, followed by thymic carcinoma, which is less common but more clinically aggressive. Using multi-platform omics analyses on 117 TETs, we define four subtypes of these tumors defined by genomic hallmarks and an association with survival and World Health Organization histological subtype. We further demonstrate a marked prevalence of a thymoma-specific mutated oncogene, GTF2I, and explore its biological effects on multi-platform analysis. We further observe enrichment of mutations in HRAS, NRAS, and TP53. Last, we identify a molecular link between thymoma and the autoimmune disease myasthenia gravis, characterized by tumoral overexpression of muscle autoantigens, and increased aneuploidy

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

Elasticity and Petri nets

Digital electronic systems typically use synchronous clocks and primarily assume fixed duration of their operations to simplify the design process. Time elastic systems can be constructed either by replacing the clock with communication handshakes (asynchronous version) or by augmenting the clock with a synchronous version of a handshake (synchronous version). Time elastic systems can tolerate static and dynamic changes in delays (asynchronous case) or latencies (synchronous case) of operations that can be used for modularity, ease of reuse and better power-delay trade-off. This paper describes methods for the modeling, performance analysis and optimization of elastic systems using Marked Graphs and their extensions capable of describing behavior with early evaluation. The paper uses synchronous elastic systems (aka latency-tolerant systems) for illustrating the use of Petri nets, however, most of the methods can be applied without changes (except changing the delay model associated with events of the system) to asynchronous elastic systems.Peer ReviewedPostprint (author's final draft

Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma

BACKGROUND Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. METHODS We performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis. RESULTS Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2'antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH). CONCLUSIONS Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renalcell carcinoma consisted of at least three subtypes based on molecular and phenotypic features