Ground-state properties, vortices, and collective excitations in a two-dimensional Bose-Einstein condensate with gravitylike interatomic attraction

We study the ground-state properties of a Bose-Einstein condensate with short-range repulsion and gravitylike 1/r interatomic attraction in two-dimensions (2D). Using the variational approach we obtain the ground-state energy and analyze the stability of the condensate for a range of interaction strengths in 2D. We also determine the collective excitations at zero temperature using the time-dependent variational method. We analyze the properties of the Thomas-Fermi-gravity and gravity regimes, and we examine the vortex states, finding the coherence length and monopole mode frequency for these regimes. Our results are compared and contrasted with those in 3D condensates. © 2008 The American Physical Society

Ground-State properties and collective excitations in a 2D Bose-Einstein condensate with gravity-like interatomic attraction

We study the ground-state properties of a Bose-Einstein condensate (BEC) with the short-range repulsion and gravitylike 1/r interatomic attraction in two-dimensions (2D). Using the variational approach, we obtain the ground-state energy and show that the condensate is stable for all interaction strenghts in 2D. We also determine the collective excitations at zero temperature using the time-dependent variational method. We analyze the properties of the Thomas-Fermi-gravity (TF-G) and gravity (G) regimes. © Springer Science+Business Media, LLC 2007

GLANET: Genomic loci annotation and enrichment tool

Motivation: Genomic studies identify genomic loci representing genetic variations, transcription factor (TF) occupancy, or histone modification through next generation sequencing (NGS) technologies. Interpreting these loci requires evaluating them with known genomic and epigenomic annotations. Results: We present GLANET as a comprehensive annotation and enrichment analysis tool which implements a sampling-based enrichment test that accounts for GC content and/or mappability biases, jointly or separately. GLANET annotates and performs enrichment analysis on these loci with a rich library. We introduce and perform novel data-driven computational experiments for assessing the power and Type-I error of its enrichment procedure which show that GLANET has attained high statistical power and well-controlled Type-I error rate. As a key feature, users can easily extend its library with new gene sets and genomic intervals. Other key features include assessment of impact of single nucleotide variants (SNPs) on TF binding sites and regulation based pathway enrichment analysis. Availability and implementation: GLANET can be run using its GUI or on command line. GLANET's source code is available at https://github.com/burcakotlu/GLANET. Tutorials are provided at https://glanet.readthedocs.org. © 2017 The Author

A specialized learner for inferring structured cis-regulatory modules

BACKGROUND: The process of transcription is controlled by systems of transcription factors, which bind to specific patterns of binding sites in the transcriptional control regions of genes, called cis-regulatory modules (CRMs). We present an expressive and easily comprehensible CRM representation which is capable of capturing several aspects of a CRM's structure and distinguishing between DNA sequences which do or do not contain it. We also present a learning algorithm tailored for this domain, and a novel method to avoid overfitting by controlling the expressivity of the model. RESULTS: We are able to find statistically significant CRMs more often then a current state-of-the-art approach on the same data sets. We also show experimentally that each aspect of our expressive CRM model space makes a positive contribution to the learned models on yeast and fly data. CONCLUSION: Structural aspects are an important part of CRMs, both in terms of interpreting them biologically and learning them accurately. Source code for our algorithm is available at

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants.

Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA\u27s superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/

Regulatory architecture of the RCA gene cluster captures an intragenic TAD boundary, CTCF-Mediated chromatin looping and a long-range intergenic enhancer

The Regulators of Complement Activation (RCA) gene cluster comprises several tandemly arranged genes with shared functions within the immune system. RCA members, such as complement receptor 2 (CR2), are well-established susceptibility genes in complex autoimmune diseases. Altered expression of RCA genes has been demonstrated at both the functional and genetic level, but the mechanisms underlying their regulation are not fully characterised. We aimed to investigate the structural organisation of the RCA gene cluster to identify key regulatory elements that influence the expression of CR2 and other genes in this immunomodulatory region. Using 4C, we captured extensive CTCF-mediated chromatin looping across the RCA gene cluster in B cells and showed these were organised into two topologically associated domains (TADs). Interestingly, an inter-TAD boundary was located within the CR1 gene at a well-characterised segmental duplication. Additionally, we mapped numerous gene-gene and gene-enhancer interactions across the region, revealing extensive co-regulation. Importantly, we identified an intergenic enhancer and functionally demonstrated this element upregulates two RCA members (CR2 and CD55) in B cells. We have uncovered novel, long-range mechanisms whereby autoimmune disease susceptibility may be influenced by genetic variants, thus highlighting the important contribution of chromatin topology to gene regulation and complex genetic disease.This work was supported by the National Institutes of Health [R01 AI24717 to JH], the Australian Government Research Training Program Scholarship at the University of Western Australia [to JC and JSC], the Spanish Government [BFU2016-74961-P to JG-S] and an institutional grant Unidad de Excelencia María de Maeztu [MDM-206-0687 to the Department of Gene Regulation and Morphogenesis, Centro Andaluz de Biología del Desarrol]

Parameter estimation for robust HMM analysis of ChIP-chip data

Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Conclusion: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.13 page(s

Analyzing ChIP-chip Data Using Bioconductor

Analyzing ChIP-chip Data Using Bioconducto