Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM
Brief Bioinform. 2021 Oct 20:bbab422. doi: 10.1093/bib/bbab422. Online ahead of print.ABSTRACTFungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot ...
Source: Briefings in Bioinformatics - October 20, 2021 Category: Bioinformatics Authors: Ritesh Sharma Sameer Shrivastava Sanjay Kumar Singh Abhinav Kumar Sonal Saxena Raj Kumar Singh Source Type: research

MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network
Brief Bioinform. 2021 Oct 19:bbab445. doi: 10.1093/bib/bbab445. Online ahead of print.ABSTRACTAccurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for pre...
Source: Briefings in Bioinformatics - October 19, 2021 Category: Bioinformatics Authors: Ke Han Long-Chen Shen Yi-Heng Zhu Jian Xu Jiangning Song Dong-Jun Yu Source Type: research

Locus-specific expression analysis of transposable elements
Brief Bioinform. 2021 Oct 19:bbab417. doi: 10.1093/bib/bbab417. Online ahead of print.ABSTRACTTransposable elements (TEs) have been associated with many, frequently detrimental, biological roles. Consequently, the regulations of TEs, e.g. via DNA-methylation and histone modifications, are considered critical for maintaining genomic integrity and other functions. Still, the high-throughput study of TEs is usually limited to the family or consensus-sequence level because of alignment problems prompted by high-sequence similarities and short read lengths. To entirely comprehend the effects and reasons of TE expression, howeve...
Source: Briefings in Bioinformatics - October 19, 2021 Category: Bioinformatics Authors: Robert Schwarz Philipp Koch Jeanne Wilbrandt Steve Hoffmann Source Type: research

MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network
Brief Bioinform. 2021 Oct 19:bbab445. doi: 10.1093/bib/bbab445. Online ahead of print.ABSTRACTAccurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have been developed to predict transcription factor binding sites in DNA sequences, there is room for further improvement in prediction performance. In addition, effective interpretation of deep-learning models is greatly desirable. Here we present MAResNet, a new deep-learning method, for pre...
Source: Briefings in Bioinformatics - October 19, 2021 Category: Bioinformatics Authors: Ke Han Long-Chen Shen Yi-Heng Zhu Jian Xu Jiangning Song Dong-Jun Yu Source Type: research

Locus-specific expression analysis of transposable elements
Brief Bioinform. 2021 Oct 19:bbab417. doi: 10.1093/bib/bbab417. Online ahead of print.ABSTRACTTransposable elements (TEs) have been associated with many, frequently detrimental, biological roles. Consequently, the regulations of TEs, e.g. via DNA-methylation and histone modifications, are considered critical for maintaining genomic integrity and other functions. Still, the high-throughput study of TEs is usually limited to the family or consensus-sequence level because of alignment problems prompted by high-sequence similarities and short read lengths. To entirely comprehend the effects and reasons of TE expression, howeve...
Source: Briefings in Bioinformatics - October 19, 2021 Category: Bioinformatics Authors: Robert Schwarz Philipp Koch Jeanne Wilbrandt Steve Hoffmann Source Type: research

Drug-target interaction predication via multi-channel graph neural networks
Brief Bioinform. 2021 Sep 3:bbab346. doi: 10.1093/bib/bbab346. Online ahead of print.ABSTRACTDrug-target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP networ...
Source: Briefings in Bioinformatics - October 18, 2021 Category: Bioinformatics Authors: Yang Li Guanyu Qiao Keqi Wang Guohua Wang Source Type: research

MOAI: a multi-outcome interaction identification approach reveals an interaction between vaspin and carcinoembryonic antigen on colorectal cancer prognosis
Brief Bioinform. 2021 Oct 17:bbab427. doi: 10.1093/bib/bbab427. Online ahead of print.ABSTRACTIdentifying and characterizing the interaction between risk factors for multiple outcomes (multi-outcome interaction) has been one of the greatest challenges faced by complex multifactorial diseases. However, the existing approaches have several limitations in identifying the multi-outcome interaction. To address this issue, we proposed a multi-outcome interaction identification approach called MOAI. MOAI was motivated by the limitations of estimating the interaction simultaneously occurring in multi-outcomes and by the success of...
Source: Briefings in Bioinformatics - October 18, 2021 Category: Bioinformatics Authors: Yu-Da Lin Yi-Chen Lee Chih-Po Chiang Sin-Hua Moi Jung-Yu Kan Source Type: research

PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis
In this study, a new method Predicting Kinase of Specific Phosphorylation Sites (PKSPS) is developed to predict kinases of specific phosphorylation sites in human proteins by combining PKSPS-Net with PKSPS-Seq, which considers protein-protein interaction (PPI) network information and sequence information. For PKSPS-Net, kinase-kinase and substrate-substrate similarity are quantified based on the topological similarity of proteins in the PPI network, and maximum weighted bipartite matching algorithm is proposed to predict kinase-substrate relationship. In PKSPS-Seq, phosphorylation sequence enrichment analysis is used to an...
Source: Briefings in Bioinformatics - October 18, 2021 Category: Bioinformatics Authors: Xinyun Guo Huan He Jialin Yu Shaoping Shi Source Type: research

Mining plant endogenous target mimics from miRNA-lncRNA interactions based on dual-path parallel ensemble pruning method
This study proposes a novel ensemble pruning protocol for predicting plant miRNA-lncRNA interactions at first. It adaptively prunes the base models based on dual-path parallel ensemble method to meet the challenge of cross-species prediction. Then potential eTMs are mined from predicted results. The expression levels of RNAs are identified through biological experiment to construct the lncRNA-miRNA-mRNA regulatory network, and the functions of potential eTMs are inferred through enrichment analysis. Experiment results show that the proposed protocol outperforms existing methods and state-of-the-art predictors on various pl...
Source: Briefings in Bioinformatics - October 18, 2021 Category: Bioinformatics Authors: Qiang Kang Jun Meng Chenglin Su Yushi Luan Source Type: research

T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model
In this study, we apply a sequence embedding strategy from a pre-trained language model of protein sequences (TAPE) to the classification task of T4SEs. The training dataset is mainly derived from our updated type IV secretion system database SecReT4 with newly experimentally verified T4SEs. An online web server termed T4SEfinder is developed using TAPE and a multi-layer perceptron (MLP) for T4SE prediction after a comprehensive performance comparison with several candidate models, which achieves a slightly higher level of accuracy than the existing prediction tools. It only takes about 3 minutes to make a classification f...
Source: Briefings in Bioinformatics - October 17, 2021 Category: Bioinformatics Authors: Yumeng Zhang Yangming Zhang Yi Xiong Hui Wang Zixin Deng Jiangning Song Hong-Yu Ou Source Type: research

T4SEfinder: a bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model
In this study, we apply a sequence embedding strategy from a pre-trained language model of protein sequences (TAPE) to the classification task of T4SEs. The training dataset is mainly derived from our updated type IV secretion system database SecReT4 with newly experimentally verified T4SEs. An online web server termed T4SEfinder is developed using TAPE and a multi-layer perceptron (MLP) for T4SE prediction after a comprehensive performance comparison with several candidate models, which achieves a slightly higher level of accuracy than the existing prediction tools. It only takes about 3 minutes to make a classification f...
Source: Briefings in Bioinformatics - October 17, 2021 Category: Bioinformatics Authors: Yumeng Zhang Yangming Zhang Yi Xiong Hui Wang Zixin Deng Jiangning Song Hong-Yu Ou Source Type: research

Artificial intelligence for drug response prediction in disease models
Brief Bioinform. 2021 Oct 15:bbab450. doi: 10.1093/bib/bbab450. Online ahead of print.NO ABSTRACTPMID:34655289 | DOI:10.1093/bib/bbab450 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - October 16, 2021 Category: Bioinformatics Authors: Pedro J Ballester Rick Stevens Benjamin Haibe-Kains R Stephanie Huang Tero Aittokallio Source Type: research

sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution
In conclusion, we provide new tools for scRNA-seq that enable paired profiling of the CNVs and transcriptomes of single cells, facilitating rapid and accurate deconstruction of the effects of cancer CNVs on cellular programming.PMID:34655292 | DOI:10.1093/bib/bbab413 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - October 16, 2021 Category: Bioinformatics Authors: Ali Mahdipour-Shirayeh Natalie Erdmann Chungyee Leung-Hagesteijn Rodger E Tiedemann Source Type: research

Artificial intelligence for drug response prediction in disease models
Brief Bioinform. 2021 Oct 15:bbab450. doi: 10.1093/bib/bbab450. Online ahead of print.NO ABSTRACTPMID:34655289 | DOI:10.1093/bib/bbab450 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - October 16, 2021 Category: Bioinformatics Authors: Pedro J Ballester Rick Stevens Benjamin Haibe-Kains R Stephanie Huang Tero Aittokallio Source Type: research

sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution
In conclusion, we provide new tools for scRNA-seq that enable paired profiling of the CNVs and transcriptomes of single cells, facilitating rapid and accurate deconstruction of the effects of cancer CNVs on cellular programming.PMID:34655292 | DOI:10.1093/bib/bbab413 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - October 16, 2021 Category: Bioinformatics Authors: Ali Mahdipour-Shirayeh Natalie Erdmann Chungyee Leung-Hagesteijn Rodger E Tiedemann Source Type: research

Estimating cell type-specific differential expression using deconvolution
Brief Bioinform. 2021 Oct 14:bbab433. doi: 10.1093/bib/bbab433. Online ahead of print.NO ABSTRACTPMID:34651640 | DOI:10.1093/bib/bbab433 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - October 15, 2021 Category: Bioinformatics Authors: Maria K Jaakkola Laura L Elo Source Type: research

Identifying multi-functional bioactive peptide functions using multi-label deep learning
In this study, we develop a method MLBP (Multi-Label deep learning approach for determining the multi-functionalities of Bioactive Peptides), which can predict multiple functions including anti-cancer, anti-diabetic, anti-hypertensive, anti-inflammatory and anti-microbial simultaneously. MLBP model takes the peptide sequence vector as input to replace the biological and physiochemical features used in other peptides predictors. Using the embedding layer, the dense continuous feature vector is learnt from the sequence vector. Then, we extract convolution features from the feature vector through the convolutional neural netw...
Source: Briefings in Bioinformatics - October 15, 2021 Category: Bioinformatics Authors: Wending Tang Ruyu Dai Wenhui Yan Wei Zhang Yannan Bin Enhua Xia Junfeng Xia Source Type: research

Estimating cell type-specific differential expression using deconvolution
Brief Bioinform. 2021 Oct 14:bbab433. doi: 10.1093/bib/bbab433. Online ahead of print.NO ABSTRACTPMID:34651640 | DOI:10.1093/bib/bbab433 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - October 15, 2021 Category: Bioinformatics Authors: Maria K Jaakkola Laura L Elo Source Type: research

Identifying multi-functional bioactive peptide functions using multi-label deep learning
In this study, we develop a method MLBP (Multi-Label deep learning approach for determining the multi-functionalities of Bioactive Peptides), which can predict multiple functions including anti-cancer, anti-diabetic, anti-hypertensive, anti-inflammatory and anti-microbial simultaneously. MLBP model takes the peptide sequence vector as input to replace the biological and physiochemical features used in other peptides predictors. Using the embedding layer, the dense continuous feature vector is learnt from the sequence vector. Then, we extract convolution features from the feature vector through the convolutional neural netw...
Source: Briefings in Bioinformatics - October 15, 2021 Category: Bioinformatics Authors: Wending Tang Ruyu Dai Wenhui Yan Wei Zhang Yannan Bin Enhua Xia Junfeng Xia Source Type: research

Development of interactive biological web applications with R/Shiny
Brief Bioinform. 2021 Oct 12:bbab415. doi: 10.1093/bib/bbab415. Online ahead of print.ABSTRACTDevelopment of interactive web applications to deposit, visualize and analyze biological datasets is a major subject of bioinformatics. R is a programming language for data science, which is also one of the most popular languages used in biological data analysis and bioinformatics. However, building interactive web applications was a great challenge for R users before the Shiny package was developed by the RStudio company in 2012. By compiling R code into HTML, CSS and JavaScript code, Shiny has made it incredibly easy to build we...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Lihua Jia Wen Yao Yingru Jiang Yang Li Zhizhan Wang Haoran Li Fangfang Huang Jiaming Li Tiantian Chen Huiyong Zhang Source Type: research

Disease category-specific annotation of variants using an ensemble learning framework
We present a novel ensemble learning framework-CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example,...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Zhen Cao Yanting Huang Ran Duan Peng Jin Zhaohui S Qin Shihua Zhang Source Type: research

Optimizing genomic control in mixed model associations with binary diseases
Brief Bioinform. 2021 Oct 13:bbab426. doi: 10.1093/bib/bbab426. Online ahead of print.ABSTRACTComplex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then reduced polygenic effects by regulating downward genomic heritability to control false negative errors produced in the association tests. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygeni...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Yuxin Song Li'ang Yang Li Jiang Zhiyu Hao Runqing Yang Pao Xu Source Type: research

Improving cancer driver gene identification using multi-task learning on graph convolutional network
Brief Bioinform. 2021 Oct 13:bbab432. doi: 10.1093/bib/bbab432. Online ahead of print.ABSTRACTCancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Wei Peng Qi Tang Wei Dai Tielin Chen Source Type: research

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics
Brief Bioinform. 2021 Oct 13:bbab424. doi: 10.1093/bib/bbab424. Online ahead of print.ABSTRACTProtein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Xing-Xing Shi Zhi-Zheng Wang Yu-Liang Wang Guang-Yi Huang Jing-Fang Yang Fan Wang Ge-Fei Hao Guang-Fu Yang Source Type: research

Development of interactive biological web applications with R/Shiny
Brief Bioinform. 2021 Oct 12:bbab415. doi: 10.1093/bib/bbab415. Online ahead of print.ABSTRACTDevelopment of interactive web applications to deposit, visualize and analyze biological datasets is a major subject of bioinformatics. R is a programming language for data science, which is also one of the most popular languages used in biological data analysis and bioinformatics. However, building interactive web applications was a great challenge for R users before the Shiny package was developed by the RStudio company in 2012. By compiling R code into HTML, CSS and JavaScript code, Shiny has made it incredibly easy to build we...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Lihua Jia Wen Yao Yingru Jiang Yang Li Zhizhan Wang Haoran Li Fangfang Huang Jiaming Li Tiantian Chen Huiyong Zhang Source Type: research

Disease category-specific annotation of variants using an ensemble learning framework
We present a novel ensemble learning framework-CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example,...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Zhen Cao Yanting Huang Ran Duan Peng Jin Zhaohui S Qin Shihua Zhang Source Type: research

Optimizing genomic control in mixed model associations with binary diseases
Brief Bioinform. 2021 Oct 13:bbab426. doi: 10.1093/bib/bbab426. Online ahead of print.ABSTRACTComplex computation and approximate solution hinder the application of generalized linear mixed models (GLMM) into genome-wide association studies. We extended GRAMMAR to handle binary diseases by considering genomic breeding values (GBVs) estimated in advance as a known predictor in genomic logit regression, and then reduced polygenic effects by regulating downward genomic heritability to control false negative errors produced in the association tests. Using simulations and case analyses, we showed in optimizing GRAMMAR, polygeni...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Yuxin Song Li'ang Yang Li Jiang Zhiyu Hao Runqing Yang Pao Xu Source Type: research

Improving cancer driver gene identification using multi-task learning on graph convolutional network
Brief Bioinform. 2021 Oct 13:bbab432. doi: 10.1093/bib/bbab432. Online ahead of print.ABSTRACTCancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Wei Peng Qi Tang Wei Dai Tielin Chen Source Type: research

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics
Brief Bioinform. 2021 Oct 13:bbab424. doi: 10.1093/bib/bbab424. Online ahead of print.ABSTRACTProtein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-...
Source: Briefings in Bioinformatics - October 13, 2021 Category: Bioinformatics Authors: Xing-Xing Shi Zhi-Zheng Wang Yu-Liang Wang Guang-Yi Huang Jing-Fang Yang Fan Wang Ge-Fei Hao Guang-Fu Yang Source Type: research

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction
We present a disease-related miRNA prediction method by encoding and integrating multiple representations of miRNA and disease nodes learnt from the generative and adversarial perspective. We firstly construct a bilayer heterogeneous network of miRNA and disease nodes, and it contains multiple types of connections among these nodes, which reflect neighbor topology of miRNA-disease pairs, and the attributes of miRNA nodes, especially miRNA-related families and clusters. To learn enhanced pairwise neighbor topology, we propose a generative and adversarial model with a convolutional autoencoder-based generator to encode the l...
Source: Briefings in Bioinformatics - October 11, 2021 Category: Bioinformatics Authors: Ping Xuan Dong Wang Hui Cui Tiangang Zhang Toshiya Nakaguchi Source Type: research

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction
We present a disease-related miRNA prediction method by encoding and integrating multiple representations of miRNA and disease nodes learnt from the generative and adversarial perspective. We firstly construct a bilayer heterogeneous network of miRNA and disease nodes, and it contains multiple types of connections among these nodes, which reflect neighbor topology of miRNA-disease pairs, and the attributes of miRNA nodes, especially miRNA-related families and clusters. To learn enhanced pairwise neighbor topology, we propose a generative and adversarial model with a convolutional autoencoder-based generator to encode the l...
Source: Briefings in Bioinformatics - October 11, 2021 Category: Bioinformatics Authors: Ping Xuan Dong Wang Hui Cui Tiangang Zhang Toshiya Nakaguchi Source Type: research

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2
Brief Bioinform. 2021 Oct 8:bbab416. doi: 10.1093/bib/bbab416. Online ahead of print.ABSTRACTThe outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1- 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to...
Source: Briefings in Bioinformatics - October 8, 2021 Category: Bioinformatics Authors: Qiaoming Liu Jun Wan Guohua Wang Source Type: research

Dissecting and predicting different types of binding sites in nucleic acids based on structural information
Brief Bioinform. 2021 Oct 9:bbab411. doi: 10.1093/bib/bbab411. Online ahead of print.ABSTRACTThe biological functions of DNA and RNA generally depend on their interactions with other molecules, such as small ligands, proteins and nucleic acids. However, our knowledge of the nucleic acid binding sites for different interaction partners is very limited, and identification of these critical binding regions is not a trivial work. Herein, we performed a comprehensive comparison between binding and nonbinding sites and among different categories of binding sites in these two nucleic acid classes. From the structural perspective,...
Source: Briefings in Bioinformatics - October 8, 2021 Category: Bioinformatics Authors: Zheng Jiang Si-Rui Xiao Rong Liu Source Type: research

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2
Brief Bioinform. 2021 Oct 8:bbab416. doi: 10.1093/bib/bbab416. Online ahead of print.ABSTRACTThe outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1- 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to...
Source: Briefings in Bioinformatics - October 8, 2021 Category: Bioinformatics Authors: Qiaoming Liu Jun Wan Guohua Wang Source Type: research

Dissecting and predicting different types of binding sites in nucleic acids based on structural information
Brief Bioinform. 2021 Oct 9:bbab411. doi: 10.1093/bib/bbab411. Online ahead of print.ABSTRACTThe biological functions of DNA and RNA generally depend on their interactions with other molecules, such as small ligands, proteins and nucleic acids. However, our knowledge of the nucleic acid binding sites for different interaction partners is very limited, and identification of these critical binding regions is not a trivial work. Herein, we performed a comprehensive comparison between binding and nonbinding sites and among different categories of binding sites in these two nucleic acid classes. From the structural perspective,...
Source: Briefings in Bioinformatics - October 8, 2021 Category: Bioinformatics Authors: Zheng Jiang Si-Rui Xiao Rong Liu Source Type: research

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2
Brief Bioinform. 2021 Oct 8:bbab416. doi: 10.1093/bib/bbab416. Online ahead of print.ABSTRACTThe outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1- 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to...
Source: Briefings in Bioinformatics - October 8, 2021 Category: Bioinformatics Authors: Qiaoming Liu Jun Wan Guohua Wang Source Type: research

Dissecting and predicting different types of binding sites in nucleic acids based on structural information
Brief Bioinform. 2021 Oct 9:bbab411. doi: 10.1093/bib/bbab411. Online ahead of print.ABSTRACTThe biological functions of DNA and RNA generally depend on their interactions with other molecules, such as small ligands, proteins and nucleic acids. However, our knowledge of the nucleic acid binding sites for different interaction partners is very limited, and identification of these critical binding regions is not a trivial work. Herein, we performed a comprehensive comparison between binding and nonbinding sites and among different categories of binding sites in these two nucleic acid classes. From the structural perspective,...
Source: Briefings in Bioinformatics - October 8, 2021 Category: Bioinformatics Authors: Zheng Jiang Si-Rui Xiao Rong Liu Source Type: research

An overview of machine learning methods for monotherapy drug response prediction
Brief Bioinform. 2021 Oct 8:bbab408. doi: 10.1093/bib/bbab408. Online ahead of print.ABSTRACTFor an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classificatio...
Source: Briefings in Bioinformatics - October 7, 2021 Category: Bioinformatics Authors: Farzaneh Firoozbakht Behnam Yousefi Benno Schwikowski Source Type: research

BlockPolish: accurate polishing of long-read assembly via block divide-and-conquer
Brief Bioinform. 2021 Oct 8:bbab405. doi: 10.1093/bib/bbab405. Online ahead of print.ABSTRACTLong-read sequencing technology enables significant progress in de novo genome assembly. However, the high error rate and the wide error distribution of raw reads result in a large number of errors in the assembly. Polishing is a procedure to fix errors in the draft assembly and improve the reliability of genomic analysis. However, existing methods treat all the regions of the assembly equally while there are fundamental differences between the error distributions of these regions. How to achieve very high accuracy in genome assemb...
Source: Briefings in Bioinformatics - October 7, 2021 Category: Bioinformatics Authors: Neng Huang Fan Nie Peng Ni Xin Gao Feng Luo Jianxin Wang Source Type: research

A protocol for dynamic model calibration
Brief Bioinform. 2021 Oct 8:bbab387. doi: 10.1093/bib/bbab387. Online ahead of print.ABSTRACTOrdinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the o...
Source: Briefings in Bioinformatics - October 7, 2021 Category: Bioinformatics Authors: Alejandro F Villaverde Dilan Pathirana Fabian Fr öhlich Jan Hasenauer Julio R Banga Source Type: research

An overview of machine learning methods for monotherapy drug response prediction
Brief Bioinform. 2021 Oct 8:bbab408. doi: 10.1093/bib/bbab408. Online ahead of print.ABSTRACTFor an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classificatio...
Source: Briefings in Bioinformatics - October 7, 2021 Category: Bioinformatics Authors: Farzaneh Firoozbakht Behnam Yousefi Benno Schwikowski Source Type: research

BlockPolish: accurate polishing of long-read assembly via block divide-and-conquer
Brief Bioinform. 2021 Oct 8:bbab405. doi: 10.1093/bib/bbab405. Online ahead of print.ABSTRACTLong-read sequencing technology enables significant progress in de novo genome assembly. However, the high error rate and the wide error distribution of raw reads result in a large number of errors in the assembly. Polishing is a procedure to fix errors in the draft assembly and improve the reliability of genomic analysis. However, existing methods treat all the regions of the assembly equally while there are fundamental differences between the error distributions of these regions. How to achieve very high accuracy in genome assemb...
Source: Briefings in Bioinformatics - October 7, 2021 Category: Bioinformatics Authors: Neng Huang Fan Nie Peng Ni Xin Gao Feng Luo Jianxin Wang Source Type: research

A protocol for dynamic model calibration
Brief Bioinform. 2021 Oct 8:bbab387. doi: 10.1093/bib/bbab387. Online ahead of print.ABSTRACTOrdinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the o...
Source: Briefings in Bioinformatics - October 7, 2021 Category: Bioinformatics Authors: Alejandro F Villaverde Dilan Pathirana Fabian Fr öhlich Jan Hasenauer Julio R Banga Source Type: research

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data
Brief Bioinform. 2021 Oct 5:bbab374. doi: 10.1093/bib/bbab374. Online ahead of print.ABSTRACTIdentifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear ...
Source: Briefings in Bioinformatics - October 4, 2021 Category: Bioinformatics Authors: Shuangquan Zhang Anjun Ma Jing Zhao Dong Xu Qin Ma Yan Wang Source Type: research

A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets
Brief Bioinform. 2021 Oct 5:bbab403. doi: 10.1093/bib/bbab403. Online ahead of print.ABSTRACTThe COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and compu...
Source: Briefings in Bioinformatics - October 4, 2021 Category: Bioinformatics Authors: Giulia Russo Valentina Di Salvatore Giuseppe Sgroi Giuseppe Alessandro Parasiliti Palumbo Pedro A Reche Francesco Pappalardo Source Type: research

Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data
Brief Bioinform. 2021 Oct 5:bbab398. doi: 10.1093/bib/bbab398. Online ahead of print.ABSTRACTThe discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have c...
Source: Briefings in Bioinformatics - October 4, 2021 Category: Bioinformatics Authors: Wenjing Song Weiwen Wang Dao-Qing Dai Source Type: research

Learning representation for multiple biological networks via a robust graph regularized integration approach
Brief Bioinform. 2021 Oct 5:bbab409. doi: 10.1093/bib/bbab409. Online ahead of print.ABSTRACTLearning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node rep...
Source: Briefings in Bioinformatics - October 4, 2021 Category: Bioinformatics Authors: Xiwen Zhang Weiwen Wang Chuan-Xian Ren Dao-Qing Dai Source Type: research

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data
Brief Bioinform. 2021 Oct 5:bbab374. doi: 10.1093/bib/bbab374. Online ahead of print.ABSTRACTIdentifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear ...
Source: Briefings in Bioinformatics - October 4, 2021 Category: Bioinformatics Authors: Shuangquan Zhang Anjun Ma Jing Zhao Dong Xu Qin Ma Yan Wang Source Type: research

A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets
Brief Bioinform. 2021 Oct 5:bbab403. doi: 10.1093/bib/bbab403. Online ahead of print.ABSTRACTThe COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and compu...
Source: Briefings in Bioinformatics - October 4, 2021 Category: Bioinformatics Authors: Giulia Russo Valentina Di Salvatore Giuseppe Sgroi Giuseppe Alessandro Parasiliti Palumbo Pedro A Reche Francesco Pappalardo Source Type: research

Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data
Brief Bioinform. 2021 Oct 5:bbab398. doi: 10.1093/bib/bbab398. Online ahead of print.ABSTRACTThe discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have c...
Source: Briefings in Bioinformatics - October 4, 2021 Category: Bioinformatics Authors: Wenjing Song Weiwen Wang Dao-Qing Dai Source Type: research