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1. Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize

   https://doi.org/10.1186/s13059-022-02747-2  

   Ramstein, Guillaume P. ; Buckler, Edward S. ( September 2022 , Genome Biology)

   Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations.

   Using only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants.

   Our results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (https://doi.org/10.25739/hybz-2957).

    

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2. HiCARN: resolution enhancement of Hi-C data using cascading residual networks

   https://doi.org/10.1093/bioinformatics/btac156  

   Hicks, Parker ; Oluwadare, Oluwatosin ; Robinson, ed., Peter ( March 2022 , Bioinformatics)

   High throughput chromosome conformation capture (Hi-C) contact matrices are used to predict 3D chromatin structures in eukaryotic cells. High-resolution Hi-C data are less available than low-resolution Hi-C data due to sequencing costs but provide greater insight into the intricate details of 3D chromatin structures such as enhancer–promoter interactions and sub-domains. To provide a cost-effective solution to high-resolution Hi-C data collection, deep learning models are used to predict high-resolution Hi-C matrices from existing low-resolution matrices across multiple cell types.

   Here, we present two Cascading Residual Networks called HiCARN-1 and HiCARN-2, a convolutional neural network and a generative adversarial network, that use a novel framework of cascading connections throughout the network for Hi-C contact matrix prediction from low-resolution data. Shown by image evaluation and Hi-C reproducibility metrics, both HiCARN models, overall, outperform state-of-the-art Hi-C resolution enhancement algorithms in predictive accuracy for both human and mouse 1/16, 1/32, 1/64 and 1/100 downsampled high-resolution Hi-C data. Also, validation by extracting topologically associating domains, chromosome 3D structure and chromatin loop predictions from the enhanced data shows that HiCARN can proficiently reconstruct biologically significant regions.

   HiCARN can be accessed and utilized as an open-sourced software at: https://github.com/OluwadareLab/HiCARN and is also available as a containerized application that can be run on any platform.

   Supplementary data are available at Bioinformatics online.

    

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3. A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images

   https://doi.org/10.1093/bioinformatics/btac558  

   Duanmu, Hongyi ; Bhattarai, Shristi ; Li, Hongxiao ; Shi, Zhan ; Wang, Fusheng ; Teodoro, George ; Gogineni, Keerthi ; Subhedar, Preeti ; Kiraz, Umay ; Janssen, Emiel A. M. ; et al ( August 2022 , Bioinformatics)

   Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge-based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge-derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions.

   The experimental dataset consists of 26 419 pathology image patches of 1000×1000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making.

   The codes, the documentation and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers

   Supplementary data are available at Bioinformatics online.

    

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4. DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning

   https://doi.org/10.1093/bioinformatics/btz367  

   Chen, Hao ; Shaw, Dipan ; Zeng, Jianyang ; Bu, Dongbo ; Jiang, Tao ( July 2019 , Bioinformatics)

   Alternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gene functions have been well studied, little is known about the specific functions of isoforms, making accurate prediction of isoform functions highly desirable. However, the existing approaches to predicting isoform functions are far from satisfactory due to at least two reasons: (i) unlike genes, isoform-level functional annotations are scarce. (ii) The information of isoform functions is concealed in various types of data including isoform sequences, co-expression relationship among isoforms, etc.

   In this study, we present a novel approach, DIFFUSE (Deep learning-based prediction of IsoForm FUnctions from Sequences and Expression), to predict isoform functions. To integrate various types of data, our approach adopts a hybrid framework by first using a deep neural network (DNN) to predict the functions of isoforms from their genomic sequences and then refining the prediction using a conditional random field (CRF) based on co-expression relationship. To overcome the lack of isoform-level ground truth labels, we further propose an iterative semi-supervised learning algorithm to train both the DNN and CRF together. Our extensive computational experiments demonstrate that DIFFUSE could effectively predict the functions of isoforms and genes. It achieves an average area under the receiver operating characteristics curve of 0.840 and area under the precision–recall curve of 0.581 over 4184 GO functional categories, which are significantly higher than the state-of-the-art methods. We further validate the prediction results by analyzing the correlation between functional similarity, sequence similarity, expression similarity and structural similarity, as well as the consistency between the predicted functions and some well-studied functional features of isoform sequences.

   https://github.com/haochenucr/DIFFUSE.

   Supplementary data are available at Bioinformatics online.

    

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5. Patterns of amino acid conservation in human and animal immunodeficiency viruses

   https://doi.org/10.1093/bioinformatics/btw441  

   Voitenko, Olga S. ; Dhroso, Andi ; Feldmann, Anna ; Korkin, Dmitry ; Kalinina, Olga V. ( August 2016 , Bioinformatics)

   Due to their high genomic variability, RNA viruses and retroviruses present a unique opportunity for detailed study of molecular evolution. Lentiviruses, with HIV being a notable example, are one of the best studied viral groups: hundreds of thousands of sequences are available together with experimentally resolved three-dimensional structures for most viral proteins. In this work, we use these data to study specific patterns of evolution of the viral proteins, and their relationship to protein interactions and immunogenicity.

   We propose a method for identification of two types of surface residues clusters with abnormal conservation: extremely conserved and extremely variable clusters. We identify them on the surface of proteins from HIV and other animal immunodeficiency viruses. Both types of clusters are overrepresented on the interaction interfaces of viral proteins with other proteins, nucleic acids or low molecular-weight ligands, both in the viral particle and between the virus and its host. In the immunodeficiency viruses, the interaction interfaces are not more conserved than the corresponding proteins on an average, and we show that extremely conserved clusters coincide with protein–protein interaction hotspots, predicted as the residues with the largest energetic contribution to the interaction. Extremely variable clusters have been identified here for the first time. In the HIV-1 envelope protein gp120, they overlap with known antigenic sites. These antigenic sites also contain many residues from extremely conserved clusters, hence representing a unique interacting interface enriched both in extremely conserved and in extremely variable clusters of residues. This observation may have important implication for antiretroviral vaccine development.

   A Python package is available at https://bioinf.mpi-inf.mpg.de/publications/viral-ppi-pred/

   voitenko@mpi-inf.mpg.de or kalinina@mpi-inf.mpg.de

   Supplementary data are available at Bioinformatics online.

    

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