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1. A unifying framework for joint trait analysis under a non-infinitesimal model

   https://doi.org/10.1093/bioinformatics/bty254  

   Johnson, Ruth ; Shi, Huwenbo ; Pasaniuc, Bogdan ; Sankararaman, Sriram ( June 2018 , Bioinformatics)

   A large proportion of risk regions identified by genome-wide association studies (GWAS) are shared across multiple diseases and traits. Understanding whether this clustering is due to sharing of causal variants or chance colocalization can provide insights into shared etiology of complex traits and diseases.

   In this work, we propose a flexible, unifying framework to quantify the overlap between a pair of traits called UNITY (Unifying Non-Infinitesimal Trait analYsis). We formulate a Bayesian generative model that relates the overlap between pairs of traits to GWAS summary statistic data under a non-infinitesimal genetic architecture underlying each trait. We propose a Metropolis–Hastings sampler to compute the posterior density of the genetic overlap parameters in this model. We validate our method through comprehensive simulations and analyze summary statistics from height and body mass index GWAS to show that it produces estimates consistent with the known genetic makeup of both traits.

   The UNITY software is made freely available to the research community at: https://github.com/bogdanlab/UNITY.

   Supplementary data are available at Bioinformatics online.

    

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2. QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data

   https://doi.org/10.1093/bioinformatics/btz692  

   Xie, Juan ; Ma, Anjun ; Zhang, Yu ; Liu, Bingqiang ; Cao, Sha ; Wang, Cankun ; Xu, Jennifer ; Zhang, Chi ; Ma, Qin ; Birol, ed., Inanc ( September 2019 , Bioinformatics)

   The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed.

   We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq.

   The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2.

   czhang87@iu.edu or qin.ma@osumc.edu

   Supplementary data are available at Bioinformatics online.

    

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3. diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data

   https://doi.org/10.1093/bioinformatics/btx623  

   Lareau, Caleb A. ; Aryee, Martin J. ; Berger, ed., Bonnie ( September 2017 , Bioinformatics)

   The 3D architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in DNA looping structure are associated with variation in gene expression and cell state. To systematically assess changes in DNA looping architecture between samples, we introduce diffloop, an R/Bioconductor package that provides a suite of functions for the quality control, statistical testing, annotation, and visualization of DNA loops. We demonstrate this functionality by detecting differences between ENCODE ChIA-PET samples and relate looping to variability in epigenetic state.

   Diffloop is implemented as an R/Bioconductor package available at https://bioconductor.org/packages/release/bioc/html/diffloop.html

   Supplementary data are available at Bioinformatics online.

    

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4. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

   https://doi.org/10.1093/bioinformatics/bty451  

   Wei, Leyi ; Zhou, Chen ; Chen, Huangrong ; Song, Jiangning ; Su, Ran ; Hancock, ed., John ( June 2018 , Bioinformatics)

   Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides.

   To address this, we propose a novel predictor named Anti-Cancer peptide Predictor with Feature representation Learning (ACPred-FL) for accurate prediction of ACPs based on sequence information. More specifically, we develop an effective feature representation learning model, with which we can extract and learn a set of informative features from a pool of support vector machine-based models trained using sequence-based feature descriptors. By doing so, the class label information of data samples is fully utilized. To improve the feature representation, we further employ a two-step feature selection technique, resulting in a most informative five-dimensional feature vector for the final peptide representation. Experimental results show that such five features provide the most discriminative power for identifying ACPs than currently available feature descriptors, highlighting the effectiveness of the proposed feature representation learning approach. The developed ACPred-FL method significantly outperforms state-of-the-art methods.

   The web-server of ACPred-FL is available at http://server.malab.cn/ACPred-FL.

   Supplementary data are available at Bioinformatics online.

    

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5. Bayesian inference of distributed time delay in transcriptional and translational regulation

   https://doi.org/10.1093/bioinformatics/btz574  

   Choi, Boseung ; Cheng, Yu-Yu ; Cinar, Selahattin ; Ott, William ; Bennett, Matthew R. ; Josić, Krešimir ; Kim, Jae Kyoung ; Valencia, ed., Alfonso ( July 2019 , Bioinformatics)

   Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks.

   We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth–death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy.

   Accompanying code in R is available at https://github.com/cbskust/DDE_BD.

   Supplementary data are available at Bioinformatics online.

    

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