Biological Sequences Research Articles

Elliptic geometry-based kernel matrix for improved biological sequence classification

Protein sequence classification plays a pivotal role in bioinformatics as it enables the comprehension of protein functions and their involvement in diverse biological processes. While numerous machine learning models have been proposed to tackle this challenge, traditional approaches face limitations in capturing the intricate relationships and hierarchical structures inherent in genomic sequences. These limitations stem from operating within high-dimensional non-Euclidean spaces. To address this issue, we introduce the application of the elliptic geometry-based approach for protein sequence classification. First, we transform the problem in elliptic geometry and integrate it with the Gaussian kernel to map the problem into the Mercer kernel. The Gaussian-Elliptic approach allows for the implicit mapping of data into a higher-dimensional feature space, enabling the capture of complex nonlinear relationships. This feature becomes particularly advantageous when dealing with hierarchical or tree-like structures commonly encountered in biological sequences. Experimental results highlight the effectiveness of the proposed model in protein sequence classification, showcasing the advantages of utilizing elliptic geometry in bioinformatics analyses. It outperforms state-of-the-art methods by achieving 76% and 84% accuracies for DNA and Protein datasets, respectively. Furthermore, we provide theoretical justifications for the proposed model. This study contributes to the burgeoning field of geometric deep learning, offering insights into the potential applications of elliptic representations in the analysis of biological data.

Density estimation for ordinal biological sequences and its applications

Density estimation for ordinal biological sequences and its applications

MMLmiRLocNet: miRNA Subcellular Localization Prediction based on Multi-view Multi-label Learning for Drug Design.

Identifying subcellular localization of microRNAs (miRNAs) is essential for comprehensive understanding of cellular function and has significant implications for drug design. In the past, several computational methods for miRNA subcellular localization is being used for uncovering multiple facets of RNA function to facilitate the biological applications. Unfortunately, most existing classification methods rely on a single sequencebased view, making the effective fusion of data from multiple heterogeneous networks a primary challenge. Inspired by multi-view multi-label learning strategy, we propose a computational method, named MMLmiRLocNet, for predicting the subcellular localizations of miRNAs. The MMLmiRLocNet predictor extracts multi-perspective sequence representations by analyzing lexical, syntactic, and semantic aspects of biological sequences. Specifically, it integrates lexical attributes derived from k-mer physicochemical profiles, syntactic characteristics obtained via word2vec embeddings, and semantic representations generated by pre-trained feature embeddings. Finally, module for extracting multi-view consensus-level features and specific-level features was constructed to capture consensus and specific features from various perspectives. The full connection networks are utilized as the output module to predict the miRNA subcellular localization. Experimental results suggest that MMLmiRLocNet outperforms existing methods in terms of F1, subACC, and Accuracy, and achieves best performance with the help of multi-view consensus features and specific features extract network.

PreMLS: The undersampling technique based on ClusterCentroids to predict multiple lysine sites.

The translated protein undergoes a specific modification process, which involves the formation of covalent bonds on lysine residues and the attachment of small chemical moieties. The protein's fundamental physicochemical properties undergo a significant alteration. The change significantly alters the proteins' 3D structure and activity, enabling them to modulate key physiological processes. The modulation encompasses inhibiting cancer cell growth, delaying ovarian aging, regulating metabolic diseases, and ameliorating depression. Consequently, the identification and comprehension of post-translational lysine modifications hold substantial value in the realms of biological research and drug development. Post-translational modifications (PTMs) at lysine (K) sites are among the most common protein modifications. However, research on K-PTMs has been largely centered on identifying individual modification types, with a relative scarcity of balanced data analysis techniques. In this study, a classification system is developed for the prediction of concurrent multiple modifications at a single lysine residue. Initially, a well-established multi-label position-specific triad amino acid propensity algorithm is utilized for feature encoding. Subsequently, PreMLS: a novel ClusterCentroids undersampling algorithm based on MiniBatchKmeans was introduced to eliminate redundant or similar major class samples, thereby mitigating the issue of class imbalance. A convolutional neural network architecture was specifically constructed for the analysis of biological sequences to predict multiple lysine modification sites. The model, evaluated through five-fold cross-validation and independent testing, was found to significantly outperform existing models such as iMul-kSite and predML-Site. The results presented here aid in prioritizing potential lysine modification sites, facilitating subsequent biological assays and advancing pharmaceutical research. To enhance accessibility, an open-access predictive script has been crafted for the multi-label predictive model developed in this study.

Efficient Storage and Analysis of Genomic Data: A k-mer Frequency Mapping and Image Representation Method.

k-mer frequencies are crucial for understanding DNA sequence patterns and structure, with applications in motif discovery, genome classification, and short read assembly. However, the exponential increase in the dimension of frequency tables with increasing k-mer length poses storage challenges. In this study, we present a novel method for compressing k-mer data without information loss, aiming to optimize storage and analysis processes. We employed Chaos Game Representation (CGR) to map k-mers to coordinates and used these components to generate raster images of k-mers. The CGR maps were partitioned and labeled based on substrings, with each substring mapped to a subframe, creating a fractal-like structure. The entire k-mer frequency set of each genomic sequence was represented as a single image, with each pixel corresponding to a specific k-mer and its occurrence. This approach reduced file size by up to 16-fold compared to plain text and 3-fold compared to binary format. Furthermore, we demonstrated the feasibility of performing alignment-free similarity analyses on images derived from k-mer frequencies of whole genome sequences from 14 plant species. Our results highlight the potential of this method as a fast and efficient tool for accessing, processing, and analyzing large biological sequence datasets, enabling the retrieval of k-mer frequencies and image reconstruction.

Viability Study of SYCL as a Unified Programming Model for Heterogeneous Systems Based on GPUs in Bioinformatics

Bioinformatics and Computational Biology are disciplines that have used GPUs for over two decades to accelerate data processing in computational applications, where CUDA has been the most utilized programming language. However, the exclusive use of CUDA presents a portability issue, as it is only compatible with NVIDIA GPUs and not with other heterogeneous architectures, such as AMD or Intel GPUs, or any other type of accelerator. To address this portability challenge, the Khronos Group recently introduced the SYCL standard, a multi-platform programming model that offers a high-level programming interface. This standard facilitates the development of portable applications that can efficiently leverage the capabilities of different hardware devices, such as NVIDIA, Intel, and AMD GPUs, without needing significant modifications to the source code. Therefore, this thesis set out as its general objective to evaluate the feasibility of SYCL as a unified, portable, and efficient heterogeneous programming model for the design and development of computationally demanding applications on heterogeneous GPU-based systems, specifically in the field of bioinformatics. Initially, a detailed investigation was conducted about heterogeneous programming models, performance metrics, and bioinformatics concepts, in order to establish the theoretical foundations of this thesis. Then, the SW# suite was chosen as the case study, as it represents a clear example of a CUDA-based bioinformatics application for biological sequence alignment. Using the SYCLomatic tool, a complete migration of the CUDA code to SYCL was carried out, which involved modifying the generated code and solving runtime errors. Additionally, the functionality was verified, optimizations were applied, and the resulting SYCL code was standardized to be compatible with other SYCL implementations. Subsequently, multiple experiments were conducted to evaluate the functionality and performance portability of the software migrated to SYCL. These experiments involved running the application on a wide variety of HPC platforms, including different CPUs and GPUs from various manufacturers. The results obtained demonstrated performance comparable to CUDA in most configurations, confirming the effectiveness of SYCL. Moreover, good performance portability across platforms was observed due to SYCL's ability to run on various hardware combinations. Furthermore, performance remained consistent when switching SYCL implementations. In conclusion, this study demonstrates that SYCL is a viable alternative as a unified, portable, and efficient programming model in the context of heterogeneous computing with GPUs for bioinformatics applications. The findings of this study lay the groundwork for the transition of legacy applications and the development of new solutions leveraging the capabilities of SYCL.

Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays.

With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks. We demonstrate the reduction in the number of experiments using data from the development of DNA competitors for use in an amplification-based diagnostic assay. We use cross-validation to compare the predictive accuracy of different transfer learning models, and then compare the performance of the models for both single objective and penalized optimization tasks.

Eight novel diagnostic markers differentiate lineages of the highly invasive myrtle rust pathogen Austropuccinia psidii.

Austropuccinia psidii is the causal agent of myrtle rust in over 480 species within the family Myrtaceae. Lineages of A. psidii are structured by their hosts in the native range, and some have success in infecting newly encountered hosts. For example, the pandemic biotype has spread beyond South America, and proliferation of other lineages is an additional risk to biodiversity and industries. Efforts to manage A. psidii incursions, including lineage differentiation, relies on variable microsatellite markers. Testing these markers is time-consuming, complex, and requires reference material that is not always readily available. We designed a novel diagnostic approach targeting eight selectively chosen loci including the fungal mating-type HD (homeodomain) transcription factor locus. The HD locus (bW1/2-HD1 and bE1/2-HD2) is highly polymorphic, facilitating clear biological predictions about its inheritance from founding populations. To be considered as potentially derived from the same lineage, all four HD alleles must be identical. If all four HD alleles are identical six additional markers can further differentiate lineage identity. Our lineage diagnostics relies on PCR amplification of eight loci in different genotypes of A. psidii followed by amplicon sequencing using Oxford Nanopore Technologies (ONT) and comparative analysis. The lineage-specific assay was validated on four isolates with existing genomes, uncharacterized isolates, and directly from infected leaf material. We reconstructed alleles from amplicons and confirmed their sequence identity relative to their reference. Genealogies of alleles confirmed the variations at the loci among lineages/isolates. Our study establishes a robust diagnostic tool for differentiating known lineages of A. psidii based on biological predictions and available nucleotide sequences. This tool is suited to detecting the origin of new pathogen incursions.

Mathematics behind the identifying CpG islands

The major objective of the paper is to review the theory for an hidden Markov model, a very general type of probabilistic model for sequences of symbols. In order for the hidden Markov model to be applicable to real-world applications, three key problems about the model must be addressed, and to do this, first we go over how to choose the best state sequence to explain an observation sequence, then we go over how to calculate the probability of an observation sequence, and finally we go over how to compute the maximization of the probability of the observation sequence. From these three angles, we review the mathematical concept behind the identification of CpG islands. The entire process and study of the outcomes have been tackled by examining both hypothetical and real DNA sequences side by side. We use well-known biological sequence analysis servers to carry out the experiment. Analytical and algorithmic approaches are compared while taking the hypothetical DNA sequence example into consideration.

DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification

BackgroundThe rapid advancements in deep neural network models have significantly enhanced the ability to extract features from microbial sequence data, which is critical for addressing biological challenges. However, the scarcity and complexity of labeled microbial data pose substantial difficulties for supervised learning approaches. To address these issues, we propose DNASimCLR, an unsupervised framework designed for efficient gene sequence data feature extraction.ResultsDNASimCLR leverages convolutional neural networks and the SimCLR framework, based on contrastive learning, to extract intricate features from diverse microbial gene sequences. Pre-training was conducted on two classic large scale unlabelled datasets encompassing metagenomes and viral gene sequences. Subsequent classification tasks were performed by fine-tuning the pretrained model using the previously acquired model. Our experiments demonstrate that DNASimCLR is at least comparable to state-of-the-art techniques for gene sequence classification. For convolutional neural network-based approaches, DNASimCLR surpasses the latest existing methods, clearly establishing its superiority over the state-of-the-art CNN-based feature extraction techniques. Furthermore, the model exhibits superior performance across diverse tasks in analyzing biological sequence data, showcasing its robust adaptability.ConclusionsDNASimCLR represents a robust and database-agnostic solution for gene sequence classification. Its versatility allows it to perform well in scenarios involving novel or previously unseen gene sequences, making it a valuable tool for diverse applications in genomics.

AnnoDUF: A Web-Based Tool for Annotating Functions of Proteins Having Domains of Unknown Function.

The rapid expansion of biological sequence databases due to high-throughput genomic and proteomic sequencing methods has left a considerable number of identified protein sequences with unclear or incomplete functional annotations. Domains of unknown function (DUFs) are protein domains that lack functional annotations but are present in numerous proteins. To address the challenge of finding functional annotations for DUFs, we have developed a computational method that efficiently identifies and annotates these enigmatic protein domains by utilizing the position-specific iterative basic local alignment search tool (PSI-BLAST) and data mining techniques. Our pipeline identifies putative potential functionalities of DUFs, thereby decreasing the gap between known sequences and functions. The tool can also take user input sequences to annotate. We executed our pipeline on 5111 unique DUF sequences obtained from Pfam, resulting in putative annotations for 2007 of these. These annotations were subsequently incorporated into a comprehensive database and interfaced with a web-based server named "AnnoDUF". AnnoDUF is freely accessible to both academic and industrial users, via the World Wide Web at the link http://bts.ibab.ac.in/annoduf.php. All scripts used in this study are uploaded to the GitHub repository, and these can be accessed from https://github.com/BioToolSuite/AnnoDUF.

SWQC: Efficient sequencing data quality control on the next-generation sunway platform

Sequencing data quality control can significantly prevent low-quality data from impacting downstream applications in bioinformatics. The enormous growth of biological sequencing data in recent years introduces new challenges to the efficiency of quality control processes and motivates the need for fast implementations on modern compute systems. The powerful next-generation heterogeneous Sunway platform holds significant potential for addressing this challenge. However, there are currently no dedicated quality control applications that can fully utilize its computational power. To bridge this gap, we introduce SWQC, a novel quality control application specifically designed for the Sunway platform. We present an efficient distributed FASTQ I/O framework for Sunway-based workstations and supercomputers to take advantage of fast SSDs and the parallel file system. In order to support both process-level and thread-level (CPE-level) parallelism to leverage the computational power, we refactor and optimize all standard quality control modules for the heterogeneous Sunway architecture. When using a single node, SWQC achieves speedups between 2 and 40 over highly optimized quality control applications executed on a high-end 48-core AMD server. Additionally, when using 16 nodes, SWQC achieves parallel efficiencies of 70% (for reading and writing a single file) and 95% (for reading one file and writing split files) compared to a single node. Overall, SWQC is able to perform quality control operations for a 140GB FASTQ file within only 70 s using a single Sunway node. It is publicly available at https://github.com/RabbitBio/SWQC.

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.

Optimized Spectral Clustering Methods For Potentially Divergent Biological Sequences

Various recent researches in bioinformatics demonstrated that clustering is a very efficient technique for sequence analysis. Spectral clustering is particularly efficient for highly divergent sequences and GMMs (Gaussian Mixture Models) are often able to cluster overlapping groups if given an adequately designed embedding. The current study used spectral embedding and Mixture Models for clustering potentially divergent biological sequences. The research approach resulted in a pipeline consisting of the following four steps. The first step consists of aligning the biological sequences. The pairwise affinity of the sequences is computed in the second step. Then the Laplacian Eigenmap embedding of the data is performed in the third step. Finally, the last step consists of a GMM-based clustering. Improving the quality of the generated clustering and the performance of this approach is directly related to the enhancement of each one of these four steps. The main contribution is proposing four GMM-based algorithms for automatically selecting the optimal number of clusters and optimizing the clustering quality. A clustering quality assessment method, based on phylogenetic trees, is also proposed. Moreover, a performance study and analysis have been conducted while testing different clustering methods and GMM implementations. Experimental results demonstrated the superiority of using the BIC (Bayesian Information Criterion) for selecting the optimal GMM configuration. Significant processing speed improvements were also recorded for the implementation of the proposed algorithms.

Artificial intelligence-guided strategies for next-generation biological sequence design

Artificial intelligence-guided strategies for next-generation biological sequence design

Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data.

Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly data-hungry, compute-intensive, and hard to interpret. Hyperdimensional computing (HDC) has recently emerged as an exciting alternative. The key idea is that random vectors of high dimensionality can represent concepts such as sequence identity or phylogeny. These vectors can then be combined using simple operators for learning, reasoning, or querying by exploiting the peculiar properties of high-dimensional spaces. Our work reviews and explores HDC's potential for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds great potential for various omics data searching, biosignal analysis, and health applications.

BAD2matrix: Phylogenomic matrix concatenation, indel coding, and more

AbstractPremiseCommon steps in phylogenomic matrix production include biological sequence concatenation, morphological data concatenation, insertion/deletion (indel) coding, gene content (presence/absence) coding, removing uninformative characters for parsimony analysis, recording with reduced amino acid alphabets, and occupancy filtering. Existing software does not accomplish these tasks on a phylogenomic scale using a single program.Methods and ResultsBAD2matrix is a Python script that performs the above‐mentioned steps in phylogenomic matrix construction for DNA or amino acid sequences as well as morphological data. The script works in UNIX‐like environments (e.g., LINUX, MacOS, Windows Subsystem for LINUX).ConclusionsBAD2matrix helps simplify phylogenomic pipelines and can be downloaded from https://github.com/dpl10/BAD2matrix/tree/master under a GNU General Public License v2.

Graph-based analysis of DNA sequence comparison in closed cotton species: A generalized method to unveil genetic connections.

Graph theory provides a systematic method for modeling and analysing complicated biological data as an effective bioinformatics tool. Based on current trends, the number of DNA sequences in the DNA database is growing quickly. To determine the origin of a species and identify homologous sequences, it is crucial to detect similarities in DNA sequences. Alignment-free techniques are required for accurate measures of sequence similarity, which has been one of the main issues facing computational biologists. The current study provides a mathematical technique for comparing DNA sequences that are constructed in graph theory. The sequences of each DNA were divided into pairs of nucleotides, from which weighted loop digraphs and corresponding weighted vectors were computed. To check the sequence similarity, distance measures like Cosine, Correlation, and Jaccard were employed. To verify the method, DNA segments from the genomes of ten species of cotton were tested. Furthermore, to evaluate the efficacy of the proposed methodology, a K-means clustering method was performed. This study proposes a proof-of-model that utilises a distance matrix approach that promises impressive outcomes with future optimisations to be made to the suggested solution to get the hundred percent accurate result. In the realm of bioinformatics, this paper highlights the use of graph theory as an effective tool for biological data study and sequence comparison. It's expected that further optimization in the proposed solution can bring remarkable results, as this paper presents a proof-of-concept implementation for a given set of data using the proposed distance matrix technique.

Distinguishing word identity and sequence context in DNA language models

Transformer-based large language models (LLMs) are very suited for biological sequence data, because of analogies to natural language. Complex relationships can be learned, because a concept of "words" can be generated through tokenization. Training the models with masked token prediction, they learn both token sequence identity and larger sequence context. We developed methodology to interrogate model learning, which is both relevant for the interpretability of the model and to evaluate its potential for specific tasks. We used DNABERT, a DNA language model trained on the human genome with overlapping k-mers as tokens. To gain insight into the model′s learning, we interrogated how the model performs predictions, extracted token embeddings, and defined a fine-tuning benchmarking task to predict the next tokens of different sizes without overlaps. This task evaluates foundation models without interrogating specific genome biology, it does not depend on tokenization strategies, vocabulary size, the dictionary, or the number of training parameters. Lastly, there is no leakage of information from token identity into the prediction task, which makes it particularly useful to evaluate the learning of sequence context. We discovered that the model with overlapping k-mers struggles to learn larger sequence context. Instead, the learned embeddings largely represent token sequence. Still, good performance is achieved for genome-biology-inspired fine-tuning tasks. Models with overlapping tokens may be used for tasks where a larger sequence context is of less relevance, but the token sequence directly represents the desired learning features. This emphasizes the need to interrogate knowledge representation in biological LLMs.

Sequencing of Targeted Therapy in Psoriasis: Does it Matter?

With the continued development of biologics for the treatment of psoriasis, some patients have achieved optimal control, but a recommended biologic sequence if a biologic fails to initially improve the skin, termed primary nonresponse, or loses efficacy after initial improvement, termed secondary nonresponse, is still lacking. Primary and secondary nonresponse can occur with any class of biologics, and the type of nonresponse can drive the choice of whether to switch within a biologic class or to a different biologic class. The choice of biologic can also be challenging when managing psoriasis and concomitant psoriatic arthritis, as treatment differs on the basis of the severity of both diseases and further classification of axial and peripheral joint involvement. When choosing a biologic, each patient's comorbidities and preferences are also taken into account to provide the optimal therapy. With this lack of anestablishedbiologic sequence after biologic failure, the objective of our review is to define a therapy sequence for the tumor necrosis factor (TNF), interleukin-17 (IL-17), and interleukin-23 (IL-23) inhibitor classes in the treatment of psoriasis and psoriatic arthritis. Our proposed biologic sequence was derived through an analysis of the efficacy of each biologic class, primary and secondary nonresponse rates from clinical trials, and clinical experience with expert opinion.