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1. Domain prediction with probabilistic directional context

   https://doi.org/10.1093/bioinformatics/btx221  

   Ochoa, Alejandro ; Singh, Mona ; Valencia, ed., Alfonso ( April 2017 , Bioinformatics)

   Protein domain prediction is one of the most powerful approaches for sequence-based function prediction. Although domain instances are typically predicted independently of each other, newer approaches have demonstrated improved performance by rewarding domain pairs that frequently co-occur within sequences. However, most of these approaches have ignored the order in which domains preferentially co-occur and have also not modeled domain co-occurrence probabilistically.

   We introduce a probabilistic approach for domain prediction that models ‘directional’ domain context. Our method is the first to score all domain pairs within a sequence while taking their order into account, even for non-sequential domains. We show that our approach extends a previous Markov model-based approach to additionally score all pairwise terms, and that it can be interpreted within the context of Markov random fields. We formulate our underlying combinatorial optimization problem as an integer linear program, and demonstrate that it can be solved quickly in practice. Finally, we perform extensive evaluation of domain context methods and demonstrate that incorporating context increases the number of domain predictions by ∼15%, with our approach dPUC2 (Domain Prediction Using Context) outperforming all competing approaches.

   dPUC2 is available at http://github.com/alexviiia/dpuc2.

   Supplementary data are available at Bioinformatics online.

    

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2. DaTeR: error-correcting phylogenetic chronograms using relative time constraints

   https://doi.org/10.1093/bioinformatics/btad084  

   Mondal, Abhijit ; Rangel, L. Thiberio ; Payette, Jack G. ; Fournier, Gregory P. ; Bansal, Mukul S. ; Schwartz, ed., Russell ( February 2023 , Bioinformatics)

   A chronogram is a dated phylogenetic tree whose branch lengths have been scaled to represent time. Such chronograms are computed based on available date estimates (e.g. from dated fossils), which provide absolute time constraints for one or more nodes of an input undated phylogeny, coupled with an appropriate underlying model for evolutionary rates variation along the branches of the phylogeny. However, traditional methods for phylogenetic dating cannot take into account relative time constraints, such as those provided by inferred horizontal transfer events. In many cases, chronograms computed using only absolute time constraints are inconsistent with known relative time constraints.

   In this work, we introduce a new approach, Dating Trees using Relative constraints (DaTeR), for phylogenetic dating that can take into account both absolute and relative time constraints. The key idea is to use existing Bayesian approaches for phylogenetic dating to sample posterior chronograms satisfying desired absolute time constraints, minimally adjust or ‘error-correct’ these sampled chronograms to satisfy all given relative time constraints, and aggregate across all error-corrected chronograms. DaTeR uses a constrained optimization framework for the error-correction step, finding minimal deviations from previously assigned dates or branch lengths. We applied DaTeR to a biological dataset of 170 Cyanobacterial taxa and a reliable set of 24 transfer-based relative constraints, under six different molecular dating models. Our extensive analysis of this dataset demonstrates that DaTeR is both highly effective and scalable and that its application can significantly improve estimated chronograms.

   Freely available from https://compbio.engr.uconn.edu/software/dater/

   Supplementary data are available at Bioinformatics online.

    

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3. Estimation of speciation times under the multispecies coalescent

   https://doi.org/10.1093/bioinformatics/btac679  

   Peng, Jing ; Swofford, David L. ; Kubatko, Laura ; Martelli, ed., Pier Luigi ( October 2022 , Bioinformatics)

   The multispecies coalescent model is now widely accepted as an effective model for incorporating variation in the evolutionary histories of individual genes into methods for phylogenetic inference from genome-scale data. However, because model-based analysis under the coalescent can be computationally expensive for large datasets, a variety of inferential frameworks and corresponding algorithms have been proposed for estimation of species-level phylogenies and associated parameters, including speciation times and effective population sizes.

   We consider the problem of estimating the timing of speciation events along a phylogeny in a coalescent framework. We propose a maximum a posteriori estimator based on composite likelihood (MAPCL) for inferring these speciation times under a model of DNA sequence evolution for which exact site-pattern probabilities can be computed under the assumption of a constant θ throughout the species tree. We demonstrate that the MAPCL estimates are statistically consistent and asymptotically normally distributed, and we show how this result can be used to estimate their asymptotic variance. We also provide a more computationally efficient estimator of the asymptotic variance based on the non-parametric bootstrap. We evaluate the performance of our method using simulation and by application to an empirical dataset for gibbons.

   The method has been implemented in the PAUP* program, freely available at https://paup.phylosolutions.com for Macintosh, Windows and Linux operating systems.

   Supplementary data are available at Bioinformatics online.

    

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4. Cryo-shift: reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization

   https://doi.org/10.1093/bioinformatics/btab794  

   Bandyopadhyay, Hmrishav ; Deng, Zihao ; Ding, Leiting ; Liu, Sinuo ; Uddin, Mostofa Rafid ; Zeng, Xiangrui ; Behpour, Sima ; Xu, Min ; Xu, ed., Jinbo ( November 2021 , Bioinformatics)

   Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution. Cellular cryo-ET images help in resolving the structures of macromolecules and determining their spatial relationship in a single cell, which has broad significance in cell and structural biology. Subtomogram classification and recognition constitute a primary step in the systematic recovery of these macromolecular structures. Supervised deep learning methods have been proven to be highly accurate and efficient for subtomogram classification, but suffer from limited applicability due to scarcity of annotated data. While generating simulated data for training supervised models is a potential solution, a sizeable difference in the image intensity distribution in generated data as compared with real experimental data will cause the trained models to perform poorly in predicting classes on real subtomograms.

   In this work, we present Cryo-Shift, a fully unsupervised domain adaptation and randomization framework for deep learning-based cross-domain subtomogram classification. We use unsupervised multi-adversarial domain adaption to reduce the domain shift between features of simulated and experimental data. We develop a network-driven domain randomization procedure with ‘warp’ modules to alter the simulated data and help the classifier generalize better on experimental data. We do not use any labeled experimental data to train our model, whereas some of the existing alternative approaches require labeled experimental samples for cross-domain classification. Nevertheless, Cryo-Shift outperforms the existing alternative approaches in cross-domain subtomogram classification in extensive evaluation studies demonstrated herein using both simulated and experimental data.

   https://github.com/xulabs/aitom.

   Supplementary data are available at Bioinformatics online.

    

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5. AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields

   https://doi.org/10.1093/bioinformatics/btw446  

   Wang, Sheng ; Ma, Jianzhu ; Xu, Jinbo ( August 2016 , Bioinformatics)

   Protein intrinsically disordered regions (IDRs) play an important role in many biological processes. Two key properties of IDRs are (i) the occurrence is proteome-wide and (ii) the ratio of disordered residues is about 6%, which makes it challenging to accurately predict IDRs. Most IDR prediction methods use sequence profile to improve accuracy, which prevents its application to proteome-wide prediction since it is time-consuming to generate sequence profiles. On the other hand, the methods without using sequence profile fare much worse than using sequence profile.

   This article formulates IDR prediction as a sequence labeling problem and employs a new machine learning method called Deep Convolutional Neural Fields (DeepCNF) to solve it. DeepCNF is an integration of deep convolutional neural networks (DCNN) and conditional random fields (CRF); it can model not only complex sequence–structure relationship in a hierarchical manner, but also correlation among adjacent residues. To deal with highly imbalanced order/disorder ratio, instead of training DeepCNF by widely used maximum-likelihood, we develop a novel approach to train it by maximizing area under the ROC curve (AUC), which is an unbiased measure for class-imbalanced data.

   Our experimental results show that our IDR prediction method AUCpreD outperforms existing popular disorder predictors. More importantly, AUCpreD works very well even without sequence profile, comparing favorably to or even outperforming many methods using sequence profile. Therefore, our method works for proteome-wide disorder prediction while yielding similar or better accuracy than the others.

   http://raptorx2.uchicago.edu/StructurePropertyPred/predict/

   wangsheng@uchicago.edu, jinboxu@gmail.com

   Supplementary data are available at Bioinformatics online.

    

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