FIREcaller: Detecting frequently interacting regions from Hi-C data

Hi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of cis-regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller

Curvature-coupling dependence of membrane protein diffusion coefficients

We consider the lateral diffusion of a protein interacting with the curvature of the membrane. The interaction energy is minimized if the particle is at a membrane position with a certain curvature that agrees with the spontaneous curvature of the particle. We employ stochastic simulations that take into account both the thermal fluctuations of the membrane and the diffusive behavior of the particle. In this study we neglect the influence of the particle on the membrane dynamics, thus the membrane dynamics agrees with that of a freely fluctuating membrane. Overall, we find that this curvature-coupling substantially enhances the diffusion coefficient. We compare the ratio of the projected or measured diffusion coefficient and the free intramembrane diffusion coefficient, which is a parameter of the simulations, with analytical results that rely on several approximations. We find that the simulations always lead to a somewhat smaller diffusion coefficient than our analytical approach. A detailed study of the correlations of the forces acting on the particle indicates that the diffusing inclusion tries to follow favorable positions on the membrane, such that forces along the trajectory are on average smaller than they would be for random particle positions.Comment: 16 pages, 8 figure

PhosTryp: a phosphorylation site predictor specific for parasitic protozoa of the family trypanosomatidae

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation modulates protein function in organisms at all levels of complexity. Parasites of the <it>Leishmania </it>genus undergo various developmental transitions in their life cycle triggered by changes in the environment. The molecular mechanisms that these organisms use to process and integrate these external cues are largely unknown. However <it>Leishmania </it>lacks transcription factors, therefore most regulatory processes may occur at a post-translational level and phosphorylation has recently been demonstrated to be an important player in this process. Experimental identification of phosphorylation sites is a time-consuming task. Moreover some sites could be missed due to the highly dynamic nature of this process or to difficulties in phospho-peptide enrichment.</p> <p>Results</p> <p>Here we present PhosTryp, a phosphorylation site predictor specific for trypansomatids. This method uses an SVM-based approach and has been trained with recent <it>Leishmania </it>phosphosproteomics data. PhosTryp achieved a 17% improvement in prediction performance compared with Netphos, a non organism-specific predictor. The analysis of the peptides correctly predicted by our method but missed by Netphos demonstrates that PhosTryp captures <it>Leishmania</it>-specific phosphorylation features. More specifically our results show that <it>Leishmania </it>kinases have sequence specificities which are different from their counterparts in higher eukaryotes. Consequently we were able to propose two possible <it>Leishmania</it>-specific phosphorylation motifs.</p> <p>We further demonstrate that this improvement in performance extends to the related trypanosomatids <it>Trypanosoma brucei </it>and <it>Trypanosoma cruzi</it>. Finally, in order to maximize the usefulness of PhosTryp, we trained a predictor combining all the peptides from <it>L. infantum, T. brucei and T. cruzi</it>.</p> <p>Conclusions</p> <p>Our work demonstrates that training on organism-specific data results in an improvement that extends to related species. PhosTryp is freely available at <url>http://phostryp.bio.uniroma2.it</url></p

Diffusion on curved, periodic surfaces

We present a simulation algorithm for a diffusion on a curved surface given by the equation (r)ϭ0. The algorithm is tested against analytical results known for diffusion on a cylinder and a sphere, and applied to the diffusion on the P, D, and G periodic nodal surfaces. It should find application in an interpretation of twodimensional exchange NMR spectroscopy data of diffusion on biological membranes