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1. ROC++: Robust Optimization in C++

   https://doi.org/10.1287/ijoc.2022.1209  

   Vayanos, Phebe ; Jin, Qing ; Elissaios, George ( November 2022 , INFORMS Journal on Computing)

   Over the last two decades, robust optimization has emerged as a popular means to address decision-making problems affected by uncertainty. This includes single-stage and multi-stage problems involving real-valued and/or binary decisions and affected by exogenous (decision-independent) and/or endogenous (decision-dependent) uncertain parameters. Robust optimization techniques rely on duality theory potentially augmented with approximations to transform a (semi-)infinite optimization problem to a finite program, the robust counterpart. Whereas writing down the model for a robust optimization problem is usually a simple task, obtaining the robust counterpart requires expertise. To date, very few solutions are available that can facilitate the modeling and solution of such problems. This has been a major impediment to their being put to practical use. In this paper, we propose ROC++, an open-source C++ based platform for automatic robust optimization, applicable to a wide array of single-stage and multi-stage robust problems with both exogenous and endogenous uncertain parameters, that is easy to both use and extend. It also applies to certain classes of stochastic programs involving continuously distributed uncertain parameters and endogenous uncertainty. Our platform naturally extends existing off-the-shelf deterministic optimization platforms and offers ROPy, a Python interface in the form of a callable library, and the ROB file format for storing and sharing robust problems. We showcase the modeling power of ROC++ on several decision-making problems of practical interest. Our platform can help streamline the modeling and solution of stochastic and robust optimization problems for both researchers and practitioners. It comes with detailed documentation to facilitate its use and expansion. The latest version of ROC++ can be downloaded from https://sites.google.com/usc.edu/robust-opt-cpp/ . Summary of Contribution: The paper “ROC++: Robust Optimization in C++” proposes a new open-source C++ based platform for modeling, automatically reformulating, and solving robust optimization problems. ROC++ can address both single-stage and multi-stage problems involving exogenous and/or endogenous uncertain parameters and real- and/or binary-valued adaptive variables. The ROC++ modeling language is similar to the one provided for the deterministic case by state-of-the-art deterministic optimization solvers. ROC++ comes with detailed documentation to facilitate its use and expansion. It also offers ROPy, a Python interface in the form of a callable library. The latest version of ROC++ can be downloaded from https://sites.google.com/usc.edu/robust-opt-cpp/ . History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: This material is based upon work supported by the National Science Foundation under Grant No. 1763108. This support is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1209 ) or is available from the IJOC GitHub software repository ( https://github.com/INFORMSJoC ) at ( https://dx.doi.org/10.5281/zenodo.6360996 ). 

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2. RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

   https://doi.org/10.1093/bioinformatics/btab813  

   Bittrich, Sebastian ; Rose, Yana ; Segura, Joan ; Lowe, Robert ; Westbrook, John D. ; Duarte, Jose M. ; Burley, Stephen K. ; Valencia, ed., Alfonso ( December 2021 , Bioinformatics)

   Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task.

   We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D.

   Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar).

   Supplementary data are available at Bioinformatics online.

    

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3. scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data

   https://doi.org/10.1093/bioinformatics/btac684  

   Gu, Haocheng ; Cheng, Hao ; Ma, Anjun ; Li, Yang ; Wang, Juexin ; Xu, Dong ; Ma, Qin ; Birol, ed., Inanc ( October 2022 , Bioinformatics)

   Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell–cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized.

   The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell–cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms.

   scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0.

   Supplementary data are available at Bioinformatics online.

    

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4. Verdant: automated annotation, alignment and phylogenetic analysis of whole chloroplast genomes

   https://doi.org/10.1093/bioinformatics/btw583  

   McKain, Michael R. ; Hartsock, Ryan H. ; Wohl, Molly M. ; Kellogg, Elizabeth A. ; Kelso, ed., Janet ( September 2016 , Bioinformatics)

   Chloroplast genomes are now produced in the hundreds for angiosperm phylogenetics projects, but current methods for annotation, alignment and tree estimation still require some manual intervention reducing throughput and increasing analysis time for large chloroplast systematics projects.

   Verdant is a web-based software suite and database built to take advantage a novel annotation program, annoBTD. Using annoBTD, Verdant provides accurate annotation of chloroplast genomes without manual intervention. Subsequent alignment and tree estimation can incorporate newly annotated and publically available plastomes and can accommodate a large number of taxa. Verdant sharply reduces the time required for analysis of assembled chloroplast genomes and removes the need for pipelines and software on personal hardware.

   Verdant is available at: http://verdant.iplantcollaborative.org/plastidDB/. It is implemented in PHP, Perl, MySQL, Javascript, HTML and CSS with all major browsers supported.

   Supplementary data are available at Bioinformatics online.

    

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5. A generative model for the behavior of RNA polymerase

   https://doi.org/10.1093/bioinformatics/btw599  

   Azofeifa, Joseph G. ; Dowell, Robin D. ; Berger, ed., Bonnie ( September 2016 , Bioinformatics)

   Transcription by RNA polymerase is a highly dynamic process involving multiple distinct points of regulation. Nascent transcription assays are a relatively new set of high throughput techniques that measure the location of actively engaged RNA polymerase genome wide. Hence, nascent transcription is a rich source of information on the regulation of RNA polymerase activity. To fully dissect this data requires the development of stochastic models that can both deconvolve the stages of polymerase activity and identify significant changes in activity between experiments.

   We present a generative, probabilistic model of RNA polymerase that fully describes loading, initiation, elongation and termination. We fit this model genome wide and profile the enzymatic activity of RNA polymerase across various loci and following experimental perturbation. We observe striking correlation of predicted loading events and regulatory chromatin marks. We provide principled statistics that compute probabilities reminiscent of traveler’s and divergent ratios. We finish with a systematic comparison of RNA Polymerase activity at promoter versus non-promoter associated loci.

   Transcription Fit (Tfit) is a freely available, open source software package written in C/C ++ that requires GNU compilers 4.7.3 or greater. Tfit is available from GitHub (https://github.com/azofeifa/Tfit).

   Supplementary data are available at Bioinformatics online.

    

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