SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networks.

Silencers are repressive cis-regulatory elements that play crucial roles in transcriptional regulation. Experimental methods for identifying silencers are always costly and time-consuming. Computational methods, which relies on genomic sequence features, have been introduced as alternative approaches. However, silencers do not have significant epigenomic signature. Therefore, we explore a new way to computationally identify silencers, by incorporating chromatin structural information. We propose the SilenceREIN method, which focuses on finding silencers on anchors of chromatin loops. By using graph neural networks, we extracted chromatin structural information from a regulatory element interaction network. SilenceREIN integrated the chromatin structural information with linear genomic signatures to find silencers. The predictive performance of SilenceREIN is comparable or better than other states-of-the-art methods. We performed a genome-wide scanning to systematically find silencers in human genome. Results suggest that silencers are widespread on anchors of chromatin loops. In addition, enrichment analysis of transcription factor binding motif support our prediction results. As far as we can tell, this is the first attempt to incorporate chromatin structural information in finding silencers. All datasets and source codes of SilenceREIN have been deposited in a GitHub repository (https://github.com/JianHPan/SilenceREIN).

iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network.

Gene essentiality is defined as the extent to which a gene is required for the survival and reproductive success of a living system. It can vary between genetic backgrounds and environments. Essential protein coding genes have been well studied. However, the essentiality of non-coding regions is rarely reported. Most regions of human genome do not encode proteins. Determining essentialities of non-coding genes is demanded. We developed iEssLnc models, which can assign essentiality scores to lncRNA genes. As far as we know, this is the first direct quantitative estimation to the essentiality of lncRNA genes. By taking the advantage of graph neural network with meta-path-guided random walks on the lncRNA-protein interaction network, iEssLnc models can perform genome-wide screenings for essential lncRNA genes in a quantitative manner. We carried out validations and whole genome screening in the context of human cancer cell-lines and mouse genome. In comparisons to other methods, which are transferred from protein-coding genes, iEssLnc achieved better performances. Enrichment analysis indicated that iEssLnc essentiality scores clustered essential lncRNA genes with high ranks. With the screening results of iEssLnc models, we estimated the number of essential lncRNA genes in human and mouse. We performed functional analysis to find that essential lncRNA genes interact with microRNAs and cytoskeletal proteins significantly, which may be of interest in experimental life sciences. All datasets and codes of iEssLnc models have been deposited in GitHub (https://github.com/yyZhang14/iEssLnc).

NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative ways. In this work, we collected five benchmarking datasets for predicting NPIs. Based on these datasets, we evaluated and compared the prediction performances of existing machine-learning based methods. Graph neural network (GNN) is a recently developed deep learning algorithm for link predictions on complex networks, which has never been applied in predicting NPIs. We constructed a GNN-based method, which is called Noncoding RNA-Protein Interaction prediction using Graph Neural Networks (NPI-GNN), to predict NPIs. The NPI-GNN method achieved comparable performance with state-of-the-art methods in a 5-fold cross-validation. In addition, it is capable of predicting novel interactions based on network information and sequence information. We also found that insufficient sequence information does not affect the NPI-GNN prediction performance much, which makes NPI-GNN more robust than other methods. As far as we can tell, NPI-GNN is the first end-to-end GNN predictor for predicting NPIs. All benchmarking datasets in this work and all source codes of the NPI-GNN method have been deposited with documents in a GitHub repo (https://github.com/AshuiRUA/NPI-GNN).

KNIndex: a comprehensive database of physicochemical properties for k-tuple nucleotides.

With the development of high-throughput sequencing technology, the genomic sequences increased exponentially over the last decade. In order to decode these new genomic data, machine learning methods were introduced for genome annotation and analysis. Due to the requirement of most machines learning methods, the biological sequences must be represented as fixed-length digital vectors. In this representation procedure, the physicochemical properties of k-tuple nucleotides are important information. However, the values of the physicochemical properties of k-tuple nucleotides are scattered in different resources. To facilitate the studies on genomic sequences, we developed the first comprehensive database, namely KNIndex (https://knindex.pufengdu.org), for depositing and visualizing physicochemical properties of k-tuple nucleotides. Currently, the KNIndex database contains 182 properties including one for mononucleotide (DNA), 169 for dinucleotide (147 for DNA and 22 for RNA) and 12 for trinucleotide (DNA). KNIndex database also provides a user-friendly web-based interface for the users to browse, query, visualize and download the physicochemical properties of k-tuple nucleotides. With the built-in conversion and visualization functions, users are allowed to display DNA/RNA sequences as curves of multiple physicochemical properties. We wish that the KNIndex will facilitate the related studies in computational biology.

Focus on Radiation Protection Improves Both Correct Behavior and Procedural Performance During Simulation-Based Training - A Randomized Comparison.

BACKGROUND: To explore whether simulation-based endovascular training with focus on radiation safety could improve correct behavior without jeopardizing the learning of procedural skills. METHODS: Twenty-four residents without previous endovascular experience completed 10 clinical scenarios on a virtual-reality endovascular simulator with software for peripheral endovascular interventions. Participants were randomized to receive feedback (n = 12) or not (n = 12) on radiation protection (RP) performance after each case. Expert assessments were done at the first, second, fourth, seventh, and 10th case on RP and endovascular skills (ES). Automatic simulator metrics on procedure time, contrast dose, handling errors, and estimated radiation exposure to patient and operator were registered. Outcome metrics were analyzed by two-way mixed analysis of variance pairwise comparisons with independent t-tests. Correlations were explored using Pearson's r for internal consistency reliability. RESULTS: The RP performance was similar in both groups at their first attempt (P = 0.61), but the feedback group significantly outperformed the control group over time (P < 0.001 for all comparisons). The feedback group was however slower to learn the ES at start (P = 0.047 at second performance), but after 7 attempts no difference was shown (P = 0.59). The feedback group used more time (19.5 vs. 15.3 min; P = 0.007) but less contrast (60 vs. 100 mL; P < 0.001). The number of errors was the same in both groups, but all metrics regarding radiation exposure favored the feedback group (P-values from 0.001 to 0.008). CONCLUSIONS: Simulation-based training (SBT) is effective to acquire basic endovascular intervention skills and concurrently learn RP behavior when feedback on radiation culture is provided.

Tunable and switchable multi-wavelength pulsed fiber laser based on a nonlinear optical loop mirror and an improved Sagnac filter.

We propose and experimentally demonstrate a tunable and switchable multi-wavelength erbium-doped fiber ring pulsed laser based on a nonlinear optical loop mirror (NOLM) and an improved Sagnac filter. To achieve multi-wavelength pulsed laser output, we adopt a NOLM as a quasi-saturable absorber and an improved Sagnac loop as a wavelength selected filter. The constructed laser has a maximum output wavelength number of five with a pulse repetition frequency of 40.45 kHz and pulse duration of 108 ns. The laser can output single-wavelength and dual-wavelength pulsed lasers within a certain wavelength tuning range and a five-wavelength pulsed laser with a constant wavelength interval of 3 nm by adjusting the polarization controller. Dual-wavelength, three-wavelength, and four-wavelength pulsed lasers with various wavelength intervals are also obtained. The output performance of the constructed laser is tested with a maximum average output power of 127.45 µW and minimum pulse duration of 52 ns, and the stability of the laser output is also tested with a maximum power fluctuation of 0.62 dB and minimum wavelength drift of 0.51 nm with pump power of 350 mW.

CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning.

Cell-penetrating peptides (CPPs) have been shown to be a transport vehicle for delivering cargoes into live cells, offering great potential as future therapeutics. It is essential to identify CPPs for better understanding of their functional mechanisms. Machine learning-based methods have recently emerged as a main approach for computational identification of CPPs. However, one of the main challenges and difficulties is to propose an effective feature representation model that sufficiently exploits the inner difference and relevance between CPPs and non-CPPs, in order to improve the predictive performance. In this paper, we have developed CPPred-FL, a powerful bioinformatics tool for fast, accurate and large-scale identification of CPPs. In our predictor, we introduce a new feature representation learning scheme that enables one to learn feature representations from totally 45 well-trained random forest models with multiple feature descriptors from different perspectives, such as compositional information, position-specific information and physicochemical properties, etc. We integrate class and probabilistic information into our feature representations. To improve the feature representation ability, we further remove redundant and irrelevant features by feature space optimization. Benchmarking experiments showed that CPPred-FL, using 19 informative features only, is able to achieve better performance than the state-of-the-art predictors. We anticipate that CPPred-FL will be a powerful tool for large-scale identification of CPPs, facilitating the characterization of their functional mechanisms and accelerating their applications in clinical therapy.

DeepSE: Detecting super-enhancers among typical enhancers using only sequence feature embeddings.

Super-enhancer (SE) is a cluster of active typical enhancers (TE) with high levels of the Mediator complex, master transcriptional factors, and chromatin regulators. SEs play a key role in the control of cell identity and disease. Traditionally, scientists used a variety of high-throughput data of different transcriptional factors or chromatin marks to distinguish SEs from TEs. This kind of experimental methods are usually costly and time-consuming. In this paper, we proposed a model DeepSE, which is based on a deep convolutional neural network model, to distinguish the SEs from TEs. DeepSE represent the DNA sequences using the dna2vec feature embeddings. With only the DNA sequence information, DeepSE outperformed all state-of-the-art methods. In addition, DeepSE can be generalized well across different cell lines, which implied that cell-type specific SEs may share hidden sequence patterns across different cell lines. The source code and data are stored in GitHub (https://github.com/QiaoyingJi/DeepSE).

VisFeature: a stand-alone program for visualizing and analyzing statistical features of biological sequences.

SUMMARY: Many efforts have been made in developing bioinformatics algorithms to predict functional attributes of genes and proteins from their primary sequences. One challenge in this process is to intuitively analyze and to understand the statistical features that have been selected by heuristic or iterative methods. In this paper, we developed VisFeature, which aims to be a helpful software tool that allows the users to intuitively visualize and analyze statistical features of all types of biological sequence, including DNA, RNA and proteins. VisFeature also integrates sequence data retrieval, multiple sequence alignments and statistical feature generation functions. AVAILABILITY AND IMPLEMENTATION: VisFeature is a desktop application that is implemented using JavaScript/Electron and R. The source codes of VisFeature are freely accessible from the GitHub repository (https://github.com/wangjun1996/VisFeature). The binary release, which includes an example dataset, can be freely downloaded from the same GitHub repository (https://github.com/wangjun1996/VisFeature/releases). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions.

Golgi apparatus is an important subcellular organelle that participates the secretion pathway. The role of Golgi apparatus in cellular process is related with Golgi-resident proteins. Knowing the sub-Golgi locations of Golgi-resident proteins is helpful in understanding their molecular functions. In this work, we proposed a computational method to predict the sub-Golgi locations for the Golgi-resident proteins. We take three sub-Golgi locations into consideration: the cis-Golgi network (CGN), the Golgi stack and the trans-Golgi network (TGN). By combining Pseudo-Amino Acid Compositions (Type-II PseAAC) and the Functional Domain Enrichment Score (FunDES), our method not only achieved better performances than existing methods, but also capable of recognizing proteins of the Golgi stack location, which is never considered in other state-of-the-art works.

Towards a coarse-grained model of the peptoid backbone: the case of N,N-dimethylacetamide.

In this study, a coarse-grained (CG) model for N,N-dimethylacetamide (DMA), which represents the polypeptoid backbone, is developed as a step towards establishing a CG model of the complex polypeptoid system. Polypeptoids or poly N-substituted glycines are a type of peptidomimetic polymers that are highly tunable, and hence an ideal model system to study self-assembly as a function of chemical groups in aqueous soft matter systems. The DMA CG model is parameterized to reproduce the structural properties of DMA liquid as well as a dilute aqueous solution of DMA using a reference all atom model, namely the OPLS-AA force-field. The intermolecular forces are represented by the Stillinger-Weber potential, that consists of both two- and three-body terms that are very short-ranged. The model is validated on thermodynamic properties of liquid and aqueous DMA, as well as the vapor-liquid interface of liquid DMA and the structure of a concentrated aqueous solution of DMA in water as well as a simple peptoid in water. Without long-ranged interactions and the absence of interaction sites on hydrogen atoms, the CG DMA model is an order of magnitude faster than the higher resolution all-atom (AA) model.

Enantioselective Functionalization of Enamides at the ß-Carbon Center with Indoles.

We report an enantioconvergent approach for the functionalization of enamides at the ß-carbon atom, which involves a chiral Brønsted acid induced tautomerization of 2-amidoallyl into 1-amidoallyl cations. These putative reactive intermediates were produced by ionization of racemic α-hydroxy enamides with a chiral Brønsted acid and captured with substituted indoles in a highly regio- and enantioselective manner.

Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou's general pseudo-amino acid compositions.

Predicting protein submitochondrial locations has been studied for about ten years. A dozen of methods were developed in this regard. Although a mitochondrion has four submitochondrial compartments, all existing studies considered only three of them. The mitochondrial intermembrane space proteins were always excluded in these studies. However, there are over 50 mitochondrial intermembrane space proteins in the recent release of UniProt database. We think it is time to incorporate these proteins in predicting protein submitochondrial locations. We proposed the functional domain enrichment score, which can be used as an enhancement to our positional-specific physicochemical properties method. We constructed a high-quality working dataset from the UniProt database. This dataset contains proteins from all four submitochondrial locations. Proteins with multiple submitochondrial locations are also included. Our method achieved over 70% prediction accuracy for proteins with single location on this dataset. On the M3-317 benchmarking dataset, our method achieved comparable prediction performance to other state-of-the-art methods. Our results indicate that the intermembrane space proteins can be incorporated in predicting protein submitochondrial locations. By evaluating our method with the proteins that have multiple submitochondrial locations, we conclude that our method is capable of predicting multiple submitochondrial locations. This is the first report of ab initio methods that can identify intermembrane space proteins. This is also the first attempt to incorporate proteins with multiple submitochondrial locations. The benchmarking dataset can be obtained by emails to the corresponding author.

Aggregation of cyclic polypeptoids bearing zwitterionic end-groups with attractive dipole-dipole and solvophobic interactions: a study by small-angle neutron scattering and molecular dynamics simulation.

Aggregation behavior of cyclic polypeptoids bearing zwitterionic end-groups in methanol has been studied using a combination of experimental and simulation techniques. The data from SANS and cryo-TEM indicate that the solution contains small clusters of these cyclic polypeptoids, ranging from a single polypeptoid chain to small oligomers, while the linear counterpart shows no cluster formation. Atomistic molecular dynamics simulations reveal that the driving force for this clustering behavior is due to the interplay between the effective repulsion due to the solvation of the dipoles formed by the charged end-groups in each polypeptoid chain and the attractive forces due to dipole-dipole interactions and the solvophobic effect.

Predicting Protein Submitochondrial Locations: The 10th Anniversary.

Predicting protein submitochondrial location has been studied for about ten years. A number of methods have been developed. The prediction performances have been improved to an almost perfect level. In this review, we introduce the background of this research topic. We also compare the methods, the performances and the datasets that have been used by these studies. Towards the end, we provide hints for the future directions of this research topic.

UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences.

With the avalanche of biological sequences in public databases, one of the most challenging problems in computational biology is to predict their biological functions and cellular attributes. Most of the existing prediction algorithms can only handle fixed-length numerical vectors. Therefore, it is important to be able to represent biological sequences with various lengths using fixed-length numerical vectors. Although several algorithms, as well as software implementations, have been developed to address this problem, these existing programs can only provide a fixed number of representation modes. Every time a new sequence representation mode is developed, a new program will be needed. In this paper, we propose the UltraPse as a universal software platform for this problem. The function of the UltraPse is not only to generate various existing sequence representation modes, but also to simplify all future programming works in developing novel representation modes. The extensibility of UltraPse is particularly enhanced. It allows the users to define their own representation mode, their own physicochemical properties, or even their own types of biological sequences. Moreover, UltraPse is also the fastest software of its kind. The source code package, as well as the executables for both Linux and Windows platforms, can be downloaded from the GitHub repository.

Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection.

Recently, several efforts have been made in predicting Golgi-resident proteins. However, it is still a challenging task to identify the type of a Golgi-resident protein. Precise prediction of the type of a Golgi-resident protein plays a key role in understanding its molecular functions in various biological processes. In this paper, we proposed to use a mutual information based feature selection scheme with the general form Chou's pseudo-amino acid compositions to predict the Golgi-resident protein types. The positional specific physicochemical properties were applied in the Chou's pseudo-amino acid compositions. We achieved 91.24% prediction accuracy in a jackknife test with 49 selected features. It has the best performance among all the present predictors. This result indicates that our computational model can be useful in identifying Golgi-resident protein types.

Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical properties.

Knowing the type of a Golgi-resident protein is an important step in understanding its molecular functions as well as its role in biological processes. In this paper, we developed a novel computational method to predict Golgi-resident protein types using positional specific physicochemical properties and analysis of variance based feature selection methods. Our method achieved 86.9% prediction accuracy in leave-one-out cross-validations with only 59 features. Our method has the potential to be applied in predicting a wide range of protein attributes.

Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images.

Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).

Super-Eddington accreting massive black holes as long-lived cosmological standards.

Super-Eddington accreting massive black holes (SEAMBHs) reach saturated luminosities above a certain accretion rate due to photon trapping and advection in slim accretion disks. We show that these SEAMBHs could provide a new tool for estimating cosmological distances if they are properly identified by hard x-ray observations, in particular by the slope of their 2-10 keV continuum. To verify this idea we obtained black hole mass estimates and x-ray data for a sample of 60 narrow line Seyfert 1 galaxies that we consider to be the most promising SEAMBH candidates. We demonstrate that the distances derived by the new method for the objects in the sample get closer to the standard luminosity distances as the hard x-ray continuum gets steeper. The results allow us to analyze the requirements for using the method in future samples of active black holes and to demonstrate that the expected uncertainty, given large enough samples, can make them into a useful, new cosmological ruler.