Yeast Datasets Research Articles

A community effort to optimize sequence-based deep learning models of gene regulation.

A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural networks but diverged in architectures and training strategies. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide models into modular building blocks. We tested all possible combinations for the top three models, further improving their performance. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets, demonstrating the progress that can be driven by gold-standard genomics datasets.

NanoTrans: an integrated computational framework for comprehensive transcriptome analysis with nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) provides the direct access to native RNA strands with full-length information, shedding light on rich qualitative and quantitative properties of gene expression profiles. Here with NanoTrans, we present an integrated computational framework that comprehensively covers all major DRS-based application scopes, including isoform clustering and quantification, poly(A) tail length estimation, RNA modification profiling, and fusion gene detection. In addition to its merit in providing such a streamlined one-stop solution, NanoTrans also shines in its workflow-orientated modular design, batch processing capability, all-in-one tabular and graphic report output, as well as automatic installation and configuration supports. Finally, by applying NanoTrans to real DRS datasets of yeast, Arabidopsis, as well as human embryonic kidney and cancer cell lines, we further demonstrate its utility, effectiveness, and efficacy across a wide range of DRS-based application settings.

Flexible modeling of regulatory networks improves transcription factor activity estimation

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.

Reliable calibration and validation of phenomenological and hybrid models of high-cell-density fed-batch cultures subject to metabolic overflow

Fed-batch cultures are the preferred operation mode for industrial bioprocesses requiring high cellular densities. Avoids accumulation of major fermentation by-products due to metabolic overflow, increasing process productivity. Reproducible operation at high cell densities is challenging (>100 gDCW/L), which has precluded rigorous model evaluation. Here, we evaluated three phenomenological models and proposed a novel hybrid model including a neural network. For this task, we generated highly reproducible fed-batch datasets of a recombinant yeast growing under oxidative, oxygen-limited, and respiro-fermentative metabolic regimes. The models were reliably calibrated using a systematic workflow based on pre-and post-regression diagnostics. Compared to the best-performing phenomenological model, the hybrid model substantially improved performance by 3.6- and 1.7-fold in the training and test data, respectively. This study illustrates how hybrid modeling approaches can advance our description of complex bioprocesses that could support more efficient operation strategies.

Deep convolutional neural networks with genetic algorithm-based synthetic minority over-sampling technique for improved imbalanced data classification

Imbalanced data classification presents a challenge in machine learning, inducing biased model learning. Moreover, data dimensionality poses another challenge as it highly impacts classifier performance. This paper proposes a new deep-learning method that combines feature selection with oversampling to address these challenges. The proposed approach, GA-SMOTE-DCNN, integrates a genetic algorithm (GA) for feature selection, SMOTE for oversampling, and a deep 1D-convolutional neural network (DCNN) for classification. This study reveals that pre-splitting the data into training and testing sets before applying SMOTE results in higher accuracy, showing an improvement in accuracy ranging between 1.94% and 3.98% compared to post-SMOTE splitting for each dataset. This method achieved accuracy rates of 86.81% for the Balance Scale dataset, 86.15% for the Oil Spill dataset, 89.21% for the Yeast dataset, 91.32% for the Mammography dataset, 88.23% for the Australian credit dataset, and 89.53% for the German Credit dataset when compared with benchmark methods, underscoring its significance in tackling high-dimensional and imbalanced data classification problems. This method demonstrates scalability in effectively addressing challenges associated with high-dimensional and imbalanced data classification across various domains.

Dopcc: Detecting overlapping protein complexes via multi-metrics and co-core attachment method.

Identification of protein complex is an important issue in the field of system biology, which is crucial to understanding the cellular organization and inferring protein functions. Recently, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) networks. However, most of these methods only focus on local information of proteins in the PPI network, which are easily affected by the noise in the PPI network. Meanwhile, it's still challenging to detect protein complexes, especially for overlapping cases. To address these issues, we propose a new method, named Dopcc, to detect overlapping protein complexes by constructing a multi-metrics network according to different strategies. First, we adopt the Jaccard coefficient to measure the neighbor similarity between proteins and denoise the PPI network. Then, we propose a new strategy, integrating hierarchical compressing with network embedding, to capture the high-order structural similarity between proteins. Further, a new co-core attachment strategy is proposed to detect overlapping protein complexes from multi-metrics. The experimental results show that our proposed method, Dopcc, outperforms the other eight state-of-the-art methods in terms of F-measure, MMR, and Composite Score on two yeast datasets. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/Dopcc.

An Effective Computational Method for Predicting Self-Interacting Proteins Based on VGGNet Convolutional Neural Network and Gray-Level Co-occurrence Matrix.

Predicting Self-interacting proteins (SIPs) is a crucial area of research in predicting protein functions, as well as in understanding gene-disease and disease-drug associations. These interactions are integral to numerous cellular processes and play pivotal roles within cells. However, traditional methods for identifying SIPs through biological experiments are often expensive, time-consuming, and have long cycles. Therefore, the development of effective computational methods for accurately predicting SIPs is not only necessary but also presents a significant challenge. In this research, we introduce a novel computational prediction technique, VGGNGLCM, which leverages protein sequence data. This method integrates the VGGNet deep convolutional neural network (VGGN) with the Gray-Level Co-occurrence Matrix (GLCM) to detect Self-interacting proteins associations. Specifically, we initially utilized Position Specific Scoring Matrix (PSSM) to capture protein evolutionary information and integrated key features from PSSM using GLCM. We then employed VGGNet as a predictive classifier, leveraging its capabilities for powerful learning and classification prediction. Subsequently, the extracted features were input into the VGGNet deep convolutional neural network to identify Self-interacting proteins. To evaluate the performance of the VGGNGLCM model, we conducted experiments using yeast and human datasets, achieving average accuracies of 95.68% and 97.72% respectively. Additionally, we compared the prediction performance of the VGGNet classifier with that of the Convolutional Neural Network (CNN) and the state-of-the-art Support Vector Machine (SVM) using the same feature extraction method. We also compared the prediction ability of VGGNGLCM with other existing approaches. The comparison results further demonstrate the superior performance of VGGNGLCM over other prediction models in this domain. The experimental verification further strengthens the evidence that VGGNGLCM is effective and robust compared to existing methods. It also highlights the high accuracy and robustness of the VGGNGLCM model in predicting Self-interacting proteins (SIPs). Consequently, we believe that the VGGNGLCM method serves as a valuable computational tool and can catalyze extensive bioinformatics research related to SIPs prediction.

Parameterization of asymmetric sigmoid functions in weighted gene co-expression network analysis

In most the biological contexts, examining gene expressions at the genomic level gives more accurate results than examining genes individually. It can improve understanding of the molecular mechanisms that cause molecular alterations. Weighted gene co-expression network analysis (WGCNA), which has recently been widely used to cluster transcriptomic datasets, implements a soft thresholding procedure using power function. However, these functions may sometimes exaggerate minor differences in expression correlations. We have previously proposed to use asymmetric sigmoid functions in soft thresholding as an alternative solution. However, the number of variables in asymmetric sigmoid functions may vary and parameterization can be problematic. In this study, we have introduced a systematic procedure for parameterizing asymmetric sigmoid function to ease using it as an alternative soft-thresholding solution in WGCNA. The efficiency of the employment was shown on four different COVID-19 datasets, on a yeast dataset, and on an E.Coli dataset. The results indicate that this approach provides biologically plausible associations for the resulting modules.

Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.

Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.

Interlocus Gene Conversion, Natural Selection, and Paralog Homogenization.

Following a duplication, the resulting paralogs tend to diverge. While mutation and natural selection can accelerate this process, they can also slow it. Here, we quantify the paralog homogenization that is caused by point mutations and interlocus gene conversion (IGC). Among 164 duplicated teleost genes, the median percentage of postduplication codon substitutions that arise from IGC rather than point mutation is estimated to be between 7% and 8%. By differentiating between the nonsynonymous codon substitutions that homogenize the protein sequences of paralogs and the nonhomogenizing nonsynonymous substitutions, we estimate the homogenizing nonsynonymous rates to be higher for 163 of the 164 teleost data sets as well as for all 14 data sets of duplicated yeast ribosomal protein-coding genes that we consider. For all 14 yeast data sets, the estimated homogenizing nonsynonymous rates exceed the synonymous rates.

MEM-FET: Essential protein prediction using membership feature and machine learning approach.

Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day-to-day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM-FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM-FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM-FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.

A Framework for Identifying Essential Proteins with Hybridizing Deep Neural Network and Ordinary Least Squares

Essential proteins are vital for maintaining life activities and play a crucial role in biological processes. Identifying essential proteins is of utmost importance as it helps in understanding the minimal requirements for cell life, discovering pathogenic genes and drug targets, diagnosing diseases, and comprehending the mechanism of biological evolution. The latest research suggests that integrating protein–protein interaction (PPI) networks and relevant biological sequence features can enhance the accuracy and robustness of essential protein identification. In this paper, a deep neural network (DNN) method was used to identify a yeast essential protein, which was named IYEPDNN. The method combines gene expression profiles, PPI networks, and orthology as input features to improve the accuracy of DNN while reducing computational complexity. To enhance the robustness of the yeast dataset, the common least squares method is used to supplement absenting data. The correctness and effectiveness of the IYEPDNN method are verified using the DIP and GAVIN databases. Our experimental results demonstrate that IYEPDNN achieves an accuracy of 84%, and it outperforms state-of-the-art methods (WDC, PeC, OGN, ETBUPPI, RWAMVL, etc.) in terms of the number of essential proteins identified. The findings of this study demonstrate that the correlation between features plays a crucial role in enhancing the accuracy of essential protein prediction. Additionally, selecting the appropriate training data can effectively address the issue of imbalanced training data in essential protein identification.

Classification of Imbalanced Data Using SMOTE and AutoEncoder Based Deep Convolutional Neural Network

The imbalanced data classification is a challenging issue in many domains including medical intelligent diagnosis and fraudulent transaction analysis. The performance of the conventional classifier degrades due to the imbalanced class distribution of the training data set. Recently, machine learning and deep learning techniques are used for imbalanced data classification. Data preprocessing approaches are also suitable for handling class imbalance problem. Data augmentation is one of the preprocessing techniques used to handle skewed class distribution. Synthetic Minority Oversampling Technique (SMOTE) is a promising class balancing approach and it generates noise during the process of creation of synthetic samples. In this paper, AutoEncoder is used as a noise reduction technique and it reduces the noise generated by SMOTE. Further, Deep one-dimensional Convolutional Neural Network is used for classification. The performance of the proposed method is evaluated and compared with existing approaches using different metrics such as Precision, Recall, Accuracy, Area Under the Curve and Geometric Mean. Ten data sets with imbalance ratio ranging from 1.17 to 577.87 and data set size ranging from 303 to 284807 instances are used in the experiments. The different imbalanced data sets used are Heart-Disease, Mammography, Pima Indian diabetes, Adult, Oil-Spill, Phoneme, Creditcard, BankNoteAuthentication, Balance scale weight & distance database and Yeast data sets. The proposed method shows an accuracy of 96.1%, 96.5%, 87.7%, 87.3%, 95%, 92.4%, 98.4%, 86.1%, 94% and 95.9% respectively. The results suggest that this method outperforms other deep learning methods and machine learning methods with respect to G-mean and other performance metrics.

A novel hybrid CNN and BiGRU-Attention based deep learning model for protein function prediction.

Proteins are the building blocks of all living things. Protein function must be ascertained if the molecular mechanism of life is to be understood. While CNN is good at capturing short-term relationships, GRU and LSTM can capture long-term dependencies. A hybrid approach that combines the complementary benefits of these deep-learning models motivates our work. Protein Language models, which use attention networks to gather meaningful data and build representations for proteins, have seen tremendous success in recent years processing the protein sequences. In this paper, we propose a hybrid CNN + BiGRU - Attention based model with protein language model embedding that effectively combines the output of CNN with the output of BiGRU-Attention for predicting protein functions. We evaluated the performance of our proposed hybrid model on human and yeast datasets. The proposed hybrid model improves the Fmax value over the state-of-the-art model SDN2GO for the cellular component prediction task by 1.9 %, for the molecular function prediction task by 3.8 % and for the biological process prediction task by 0.6 % for human dataset and for yeast dataset the cellular component prediction task by 2.4 %, for the molecular function prediction task by 5.2 % and for the biological process prediction task by 1.2 %.

MM-StackEns: A new deep multimodal stacked generalization approach for protein–protein interaction prediction

Accurate in-silico identification of protein–protein interactions (PPIs) is a long-standing problem in biology, with important implications in protein function prediction and drug design. Current computational approaches predominantly use a single data modality for describing protein pairs, which may not fully capture the characteristics relevant for identifying PPIs. Another limitation of existing methods is their poor generalization to proteins outside the training graph. In this paper, we aim to address these shortcomings by proposing a new ensemble approach for PPI prediction, which learns information from two modalities, corresponding to pairs of sequences and to the graph formed by the training proteins and their interactions. Our approach uses a siamese neural network to process sequence information, while graph attention networks are employed for the network view. For capturing the relationships between the proteins in a pair, we design a new feature fusion module, based on computing the distance between the distributions corresponding to the two proteins. The prediction is made using a stacked generalization procedure, in which the final classifier is represented by a Logistic Regression model trained on the scores predicted by the sequence and graph models. Additionally, we show that protein sequence embeddings obtained using pretrained language models can significantly improve the generalization of PPI methods. The experimental results demonstrate the good performance of our approach, which surpasses all the related work on two Yeast data sets, while outperforming the majority of literature approaches on two Human data sets and on independent multi-species data sets.

Hypothesis Testing in High-Dimensional Instrumental Variables Regression With an Application to Genomics Data

Gene expression and phenotype association can be affected by potential unmeasured confounders from multiple sources, leading to biased estimates of the associations. Since genetic variants largely explain gene expression variations, they can be used as instruments in studying the association between gene expressions and phenotype in the framework of high dimensional instrumental variable (IV) regression. However, because the dimensions of both genetic variants and gene expressions are often larger than the sample size, statistical inferences such as hypothesis testing for such high dimensional IV models are not trivial and have not been investigated in literature. The problem is more challenging since the instrumental variables (e.g., genetic variants) have to be selected among a large set of genetic variants. This paper considers the problem of hypothesis testing for sparse IV regression models and presents methods for testing single regression coefficient and multiple testing of multiple coefficients, where the test statistic for each single coefficient is constructed based on an inverse regression. A multiple testing procedure is developed for selecting variables and is shown to control the false discovery rate. Simulations are conducted to evaluate the performance of our proposed methods. These methods are illustrated by an analysis of a yeast dataset in order to identify genes that are associated with growth in the presence of hydrogen peroxide.

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier

BackgroundSelf-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information.ResultsIn this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins.ConclusionsExtensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.

An autoencoder-based deep learning method for genotype imputation.

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a single batch loss rather than the average loss over batches. This modified AE imputation model was evaluated using a yeast dataset, the human leukocyte antigen (HLA) data from the 1,000 Genomes Project (1KGP), and our in-house genotype data from the Louisiana Osteoporosis Study (LOS). Our modified AE imputation model has achieved comparable or better performance than the existing SCDA model in terms of evaluation metrics such as the concordance rate (CR), the Hellinger score, the scaled Euclidean norm (SEN) score, and the imputation quality score (IQS) in all three datasets. Taking the imputation results from the HLA data as an example, the AE model achieved an average CR of 0.9468 and 0.9459, Hellinger score of 0.9765 and 0.9518, SEN score of 0.9977 and 0.9953, and IQS of 0.9515 and 0.9044 at missing ratios of 10% and 20%, respectively. As for the results of LOS data, it achieved an average CR of 0.9005, Hellinger score of 0.9384, SEN score of 0.9940, and IQS of 0.8681 at the missing ratio of 20%. In summary, our proposed method for genotype imputation has a great potential to increase the statistical power of GWAS and improve downstream post-GWAS analyses.

Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques.

The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.

MGEGFP: a multi-view graph embedding method for gene function prediction based on adaptive estimation with GCN.

In recent years, a number of computational approaches have been proposed to effectively integrate multiple heterogeneous biological networks, and have shown impressive performance for inferring gene function. However, the previous methods do not fully represent the critical neighborhood relationship between genes during the feature learning process. Furthermore, it is difficult to accurately estimate the contributions of different views for multi-view integration. In this paper, we propose MGEGFP, a multi-view graph embedding method based on adaptive estimation with Graph Convolutional Network (GCN), to learn high-quality gene representations among multiple interaction networks for function prediction. First, we design a dual-channel GCN encoder to disentangle the view-specific information and the consensus pattern across diverse networks. By the aid of disentangled representations, we develop a multi-gate module to adaptively estimate the contributions of different views during each reconstruction process and make full use of the multiplexity advantages, where a diversity preservation constraint is designed to prevent the over-fitting problem. To validate the effectiveness of our model, we conduct experiments on networks from the STRING database for both yeast and human datasets, and compare the performance with seven state-of-the-art methods in five evaluation metrics. Moreover, the ablation study manifests the important contribution of the designed dual-channel encoder, multi-gate module and the diversity preservation constraint in MGEGFP. The experimental results confirm the superiority of our proposed method and suggest that MGEGFP can be a useful tool for gene function prediction.