Cloud Computing for Detecting High-Order Genome-Wide Epistatic Interaction via Dynamic Clustering

Backgroud: Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. Results: In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Conclusions: Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS

Predicting Protein Interactions by Brownian Dynamics Simulations

We present a newly adapted Brownian-Dynamics (BD)-based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. In order to reduce the computational costs for energy evaluations, a shell-based grid force field was developed to represent the receptor protein and solvation effects. The performance of this BD protein docking approach has been evaluated on a test set of 24 crystal protein complexes. Reproduction of experimental structures in the test set indicates the adequate conformational sampling and accurate scoring of this BD protein docking approach. Furthermore, we have developed an approach to account for the flexibility of proteins, which has been successfully applied to reproduce the experimental complex structure from the structure of two unbounded proteins. These results indicate that this adapted BD protein docking approach can be useful for the prediction of protein-protein interactions

The Molecular Mechanism by which PIP2 Opens the Intracellular G-Loop Gate of a Kir3.1 Channel

Abstract Inwardly rectifying potassium (Kir) channels are characterized by a long pore comprised of continuous transmembrane and cytosolic portions. A high-resolution structure of a Kir3.1 chimera revealed the presence of the cytosolic (G-loop) gate captured in the closed or open conformations. Here, we conducted molecular-dynamics simulations of these two channel states in the presence and absence of phosphatidylinositol bisphosphate (PIP2), a phospholipid that is known to gate Kir channels. Simulations of the closed state with PIP2 revealed an intermediate state between the closed and open conformations involving direct transient interactions with PIP2, as well as a network of transitional inter- and intrasubunit interactions. Key elements in the G-loop gating transition involved a PIP2-driven movement of the N-terminus and C-linker that removed constraining intermolecular interactions and led to CD-loop stabilization of the G-loop gate in the open state. To our knowledge, this is the first dynamic molecular view of PIP2-induced channel gating that is consistent with existing experimental data

Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds

Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater to users' particular interests due to their inability of leveraging user guidance. Although there exist seed-guided topic discovery approaches that leverage user-provided seeds to discover topic-representative terms, they are less concerned with two factors: (1) the existence of out-of-vocabulary seeds and (2) the power of pre-trained language models (PLMs). In this paper, we generalize the task of seed-guided topic discovery to allow out-of-vocabulary seeds. We propose a novel framework, named SeeTopic, wherein the general knowledge of PLMs and the local semantics learned from the input corpus can mutually benefit each other. Experiments on three real datasets from different domains demonstrate the effectiveness of SeeTopic in terms of topic coherence, accuracy, and diversity.Comment: 12 pages; Accepted to NAACL 202

Building KCNQ1/KCNE1 Channel Models and Probing their Interactions by Molecular-Dynamics Simulations

The slow delayed rectifier (IKs) channel is composed of KCNQ1 (pore-forming) and KCNE1 (auxiliary) subunits, and functions as a repolarization reserve in the human heart. Design of IKs-targeting anti-arrhythmic drugs requires detailed three-dimensional structures of the KCNQ1/KCNE1 complex, a task made possible by Kv channel crystal structures (templates for KCNQ1 homology-modeling) and KCNE1 NMR structures. Our goal was to build KCNQ1/KCNE1 models and extract mechanistic information about their interactions by molecular-dynamics simulations in an explicit lipid/solvent environment. We validated our models by confirming two sets of model-generated predictions that were independent from the spatial restraints used in model-building. Detailed analysis of the molecular-dynamics trajectories revealed previously unrecognized KCNQ1/KCNE1 interactions, whose relevance in IKs channel function was confirmed by voltage-clamp experiments. Our models and analyses suggest three mechanisms by which KCNE1 slows KCNQ1 activation: by promoting S6 bending at the Pro hinge that closes the activation gate; by promoting a downward movement of gating charge on S4; and by establishing a network of electrostatic interactions with KCNQ1 on the extracellular surface that stabilizes the channel in a pre-open activated state. Our data also suggest how KCNE1 may affect the KCNQ1 pore conductance

Identification of a Novel PIP2 Interaction Site and its Allosteric Regulation by the RCK1 Site Associated with Ca2+ Coordination in Slo1 Channels

We consider the Schr\"odinger operator on a combinatorial graph consisting of a finite graph and a finite number of discrete half-lines, all jointed together, and compute an asymptotic expansion of its resolvent around the threshold $0$. Precise expressions are obtained for the first few coefficients of the expansion in terms of the generalized eigenfunctions. This result justifies the classification of threshold types solely by growth properties of the generalized eigenfunctions. By choosing an appropriate free operator a priori possessing no zero eigenvalue or zero resonance we can simplify the expansion procedure as much as that on the single discrete half-line.Comment: 55 pages, minor revisions, final versio

A check-valved silicone diaphragm pump

Two generations of check-valved silicone rubber diaphragm pumps are presented. Significant improvements have been made from pump to pump including the design and fabrication of a double-sided check valve, a bossed silicone membrane, and silicone gaskets. Water flow rates of up to 13 ml/min and a maximum back pressure of 5.9 kPa were achieved through pneumatic operation with an external compressed air source. Using a custom designed solenoid actuator, flow rates of up to 4.5 ml/min and a maximum back pressure of 2.1 kPa have been demonstrated

HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories

GitHub has become an important platform for code sharing and scientific exchange. With the massive number of repositories available, there is a pressing need for topic-based search. Even though the topic label functionality has been introduced, the majority of GitHub repositories do not have any labels, impeding the utility of search and topic-based analysis. This work targets the automatic repository classification problem as keyword-driven hierarchical classification. Specifically, users only need to provide a label hierarchy with keywords to supply as supervision. This setting is flexible, adaptive to the users' needs, accounts for the different granularity of topic labels and requires minimal human effort. We identify three key challenges of this problem, namely (1) the presence of multi-modal signals; (2) supervision scarcity and bias; (3) supervision format mismatch. In recognition of these challenges, we propose the HiGitClass framework, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation. Experimental results on two GitHub repository collections confirm that HiGitClass is superior to existing weakly-supervised and dataless hierarchical classification methods, especially in its ability to integrate both structured and unstructured data for repository classification.Comment: 10 pages; Accepted to ICDM 2019; Some typos fixe