Biological Datasets Research Articles

LearnMSA: learning and aligning large protein families.

The alignment of large numbers of protein sequences is a challenging task and its importance grows rapidly along with the size of biological datasets. State-of-the-art algorithms have a tendency to produce less accurate alignments with an increasing number of sequences. This is a fundamental problem since many downstream tasks rely on accurate alignments. We present learnMSA, a novel statistical learning approach of profile hidden Markov models (pHMMs) based on batch gradient descent. Fundamentally different from popular aligners, we fit a custom recurrent neural network architecture for (p)HMMs to potentially millions of sequences with respect to a maximum a posteriori objective and decode an alignment. We rely on automatic differentiation of the log-likelihood, and thus, our approach is different from existing HMM training algorithms like Baum-Welch. Our method does not involve progressive, regressive, or divide-and-conquer heuristics. We use uniform batch sampling to adapt to large datasets in linear time without the requirement of a tree. When tested on ultra-large protein families with up to 3.5 million sequences, learnMSA is both more accurate and faster than state-of-the-art tools. On the established benchmarks HomFam and BaliFam with smaller sequence sets, it matches state-of-the-art performance. All experiments were done on a standard workstation with a GPU. Our results show that learnMSA does not share the counterintuitive drawback of many popular heuristic aligners, which can substantially lose accuracy when many additional homologs are input. LearnMSA is a future-proof framework for large alignments with many opportunities for further improvements.

Mapping the impacts of multiple stressors on the decline in kelps along the coast of Victoria, Australia

AbstractAimKelp forests throughout temperate regions of the world serve as foundation species that play a critical role in sustaining the health and function of marine ecosystems but are experiencing declines in abundance due to a loss in resilience as the ocean climate changes. Ocean warming along southeast Australia has already been linked to dramatic losses of kelp species and is contributing to the range expansion and population increases of two species of sea urchin. This research attempts to understand the impact of multiple stressors on the decline in kelps in this region.LocationCoastal Waters of Victoria, Southeast Australia.MethodsIn this study, we use long‐term (>20 years) datasets on biological observations across Victorian waters to determine trends in coverage and the impact of multiple environmental variables (oceanography, habitat, and urchin abundances) on two important kelps that serve as foundation species (Phyllospora comosaandEcklonia radiata)using boosted regression trees. These models were then used to develop predictive distribution models for each species and also to project abundance distributions into the future.ResultsWe found that both kelp species are decreasing in percent coverage over time with multiple environmental variables contributing to these declines, including increasing temperatures, intensifying wave energy, changes in currents and recruitment patterns, and increases in urchin populations. Additionally, future projections of temperature, wave energy, and urchin populations show that both species will continue to decrease across 62%–94% of their range by 2090.Main conclusionsLong‐term biological datasets allowed us to develop maps of the past, current, and future distributions of these important foundation species, providing valuable information to managers for prioritization of areas for targeted urchin management and restoration of kelps. Understanding the environmental factors affecting their distribution helps guide manager restoration investments in regions where kelp populations are most likely to persist in the future.

Computational Prediction of Drug-Disease Association Based on Graph-Regularized One Bit Matrix Completion.

Investigation of existing drugs is an effective alternative to the discovery of new drugs for treating diseases. This task of drug re-positioning can be assisted by various kinds of computational methods to predict the best indication for a drug given the open-source biological datasets. Owing to the fact that similar drugs tend to have common pathways and disease indications, the association matrix is assumed to be of low-rank structure. Hence, the problem of drug-disease association prediction can be modeled as a low-rank matrix completion problem. In this work, we propose a novel matrix completion framework that makes use of the side-information associated with drugs/diseases for the prediction of drug-disease indications modeled as neighborhood graph: Graph regularized 1-bit matrix completion (GR1BMC). The algorithm is specially designed for binary data and uses parallel proximal algorithm to solve the aforesaid minimization problem taking into account all the constraints including the neighborhood graph incorporation and restricting predicted scores within the specified range. The results have been validated on two standard databases by evaluating the AUC across the 10-fold cross-validation splits. The usage of the method is also evaluated through a case study where top 5 indications are predicted for novel drugs, which then are verified with the CTD database.

Predicting miRNA-Disease Association Based on Improved Graph Regression.

Recently, as a growing number of associations between microRNAs (miRNAs) and diseases are discovered, researchers gradually realize that miRNAs are closely related to several complicated biological processes and human diseases. Hence, it is especially important to construct availably models to infer associations between miRNAs and diseases. In this study, we presented Improved Graph Regression for miRNA-Disease Association Prediction (IGRMDA) to observe potential relationship between miRNAs and diseases. In order to reduce the inherent noise existing in the acquired biological datasets, we utilized matrix decomposition algorithm to process miRNA functional similarity and disease semantic similarity and then combining them with existing similarity information to obtain final miRNA similarity data and disease similarity data. Then, we applied miRNA-disease association data, miRNA similarity data and disease similarity data to form corresponding latent spaces. Furthermore, we performed improved graph regression algorithm in latent spaces, which included miRNA-disease association space, miRNA similarity space and disease similarity space. Non-negative matrix factorization and partial least squares were used in the graph regression process to obtain important related attributes. The cross validation experiments and case studies were also implemented to prove the effectiveness of IGRMDA, which showed that IGRMDA could predict potential associations between miRNAs and diseases.

Integrating research infrastructures into infectious diseases surveillance operations: Focus on biobanks

Technological advances in the first two decades of the 21st century have profoundly impacted medical research in many ways, with large population cohorts, biological sample collections and datasets through biobanks becoming valued global resources to guide biomedical research, drug development, and medical practice. However, in order for biobanks to maximize their impact and scientific reach of their resources, they would need to act within a complex network of infrastructures and activities. Therefore, different ways have emerged in which biobanks, including those for infectious diseases, can emerge as (part of) infrastructures, integrate within existing ones, or become an independent, yet an interoperable component of the existing infrastructural landscape. However, there has been a limited understanding and study of such mechanisms to date. This perspective aims to address this knowledge gap and illustrates these three high-level ways in which such infrastructures could integrate their activities and identifies the necessary key pre-conditions for doing so, while drawing from specific examples.

Interpretable machine learning methods for predictions in systems biology from omics data.

Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by "omics" experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design "interpretable" models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: "What is interpretability?" We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.

Obtaining genetics insights from deep learning via explainable artificial intelligence.

Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory information would frequently be of greater value than the predictions themselves, as it can enable new insights into genetic processes. We review progress in the emerging area of explainable AI (xAI), a field with the potential to empower life science researchers to gain mechanistic insights into complex deep learning models. We discuss and categorize approaches for model interpretation, including an intuitive understanding of how each approach works and their underlying assumptions and limitations in the context of typical high-throughput biological datasets.

Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study.

SummaryBackgroundArterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter.MethodsThis study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score.FindingsComplete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers.InterpretationWe have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment.FundingEuropean Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).

Distributed Big Data Storage Infrastructure for Biomedical Research Featuring High-Performance and Rich-Features

The biomedical field entered the era of “big data” years ago, and a lot of software is being developed to tackle the analysis problems brought on by big data. However, very few programs focus on providing a solid foundation for file systems of biomedical big data. Since file systems are a key prerequisite for efficient big data utilization, the absence of specialized biomedical big data file systems makes it difficult to optimize storage, accelerate analysis, and enrich functionality, resulting in inefficiency. Here we present F3BFS, a functional, fundamental, and future-oriented distributed file system, specially designed for various kinds of biomedical data. F3BFS makes it possible to boost existing software’s performance without modifying its main algorithms by transmitting raw datasets from generic file systems. Further, F3BFS has various built-in features to help researchers manage biology datasets more efficiently and productively, including metadata management, fuzzy search, automatic backup, transparent compression, etc.

P440 General protocol applied to the identification and production of new biomarkers with potential use for the diagnosis of histoplasmosis

Poster session 3, September 23, 2022, 12:30 PM - 1:30 PMObjective: To identify and produce novel biomarkers with potential use for the specific diagnosis of H. capsulatum infection.MethodsHere, we design a novel strategy to search and select new Candidate genes for biomarkers that integrates the use of a computational analysis model that includes the application of bioinformatic tools such as OrthoMCL, BLASTp, TargetP, and SignalP, applied on a local collection of proteome database obtained manually from GenBank-NCBI, and the analysis of previously published biological and experimental data sets, including a secreted proteome database obtained from pathogenic yeast-phase H. capsulatum culture filtrates, a Histoplasma yeast and mycelial transcriptomes database, and a urine-peptides database from Histoplasma-immunoassay-positive patients.For the synthesis of the Candidates, an internal protocol for the production of recombinant proteins in prokaryotic and eukaryotic systems was applied. Obtaining polyclonal antibodies (PAb) specific for each biomarker was carried out by adapting a rapid immunization protocol for BALB/c mice.Finally, the computational model was experimentally validated, evaluating the reactivity and specificity of PAb anti-Histoplasma with fungus culture extracts and samples from patients with histoplasmosis.ResultsUsing the computational analysis model, 2 Candidate genes for diagnostic biomarkers were identified. Subsequently, the construction of expression vector for each Candidate and the production of these genes were achieved using a standardized protocol for the production of recombinant proteins.Polyclonal antibodies (PAb) anti-histoplasma were obtained and shown to be reactive against purified H. capsulatum-antigens. Finally, we confirmed the presence of these antigens in yeast culture extracts of H. capsulatum and demonstrated the immunoreactivity of anti-Histoplasma PAb with urine samples from patients previously diagnosed with histoplasmosis.ConclusionThe generation of novel strategies that combine data analysis, computational tools, and transcriptomic and proteomic techniques could be very useful for the identification of new biomarker genes and the development of microbiological diagnostic tests for important pathogens.

Tracking mutational semantics of SARS-CoV-2 genomes

Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.

RNA velocity unraveled.

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

A model and cooperative co-evolution algorithm for identifying driver pathways based on the integrated data and PPI network

Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways.

Analysis of the Contribution of Intrinsic Disorder in Shaping Potyvirus Genetic Diversity.

Intrinsically disordered regions (IDRs) are abundant in the proteome of RNA viruses. The multifunctional properties of these regions are widely documented and their structural flexibility is associated with the low constraint in their amino acid positions. Therefore, from an evolutionary stand point, these regions could have a greater propensity to accumulate non-synonymous mutations (NS) than highly structured regions (ORs, or ‘ordered regions’). To address this hypothesis, we compared the distribution of non-synonymous mutations (NS), which we relate here to mutational robustness, in IDRs and ORs in the genome of potyviruses, a major genus of plant viruses. For this purpose, a simulation model was built and used to distinguish a possible selection phenomenon in the biological datasets from randomly generated mutations. We analyzed several short-term experimental evolution datasets. An analysis was also performed on the natural diversity of three different species of potyviruses reflecting their long-term evolution. We observed that the mutational robustness of IDRs is significantly higher than that of ORs. Moreover, the substitutions in the ORs are very constrained by the conservation of the physico-chemical properties of the amino acids. This feature is not found in the IDRs where the substitutions tend to be more random. This reflects the weak structural constraints in these regions, wherein an amino acid polymorphism is naturally conserved. In the course of evolution, potyvirus IDRs and ORs follow different evolutive paths with respect to their mutational robustness. These results have forced the authors to consider the hypothesis that IDRs and their associated amino acid polymorphism could constitute a potential adaptive reservoir.

A Multimodal Framework for Improving in Silico Drug Repositioning With the Prior Knowledge From Knowledge Graphs.

Drug repositioning/repurposing is a very important approach towards identifying novel treatments for diseases in drug discovery. Recently, large-scale biological datasets are increasingly available for pharmaceutical research and promote the development of drug repositioning, but efficiently utilizing these datasets remains challenging. In this paper, we develop a novel multimodal framework, termed GraphPK (Graph-based Prior Knowledge) for improving in silico drug repositioning via using the prior knowledge from a drug knowledge graph. First, we construct a knowledge graph by integrating relevant bio-entities (drugs, diseases, etc.) and associations/interactions among them, and apply the knowledge graph embedding technique to extract prior knowledge of drugs and diseases. Moreover, we make use of the known drug-disease association, and obtain known association-based features from an association bipartite graph through graph embedding, and also take into account biological domain features, i.e., drug chemical structures and disease semantic similarity. Finally, we design a multimodal neural network to combine three types of features from the knowledge graph, the known associations and the biological domain, and build the prediction model for predicting drug-disease associations. Massive experiments show that our method outperforms other state-of-the-art methods in terms of most metrics, and the ablation analysis regarding the three types of features reveals that prior knowledge from knowledge graphs can not only lift the predictive power of in silico drug repositioning, but also enhance the model's robustness to different scenarios. The results of case studies offer support that GraphPK has the potential for actual use.

Competence of medicinal plant database using data mining algorithms for large biological databases

This article covers how to use algorithms to extract the most important information from huge biological data sets for various data mining needs. Bioinformatics newline refers to the application of computer techniques to analyse and interpret massive data sets. Bioinformatics has advanced dramatically in recent years as a result of the massive increase of biological data created by the scientific community. Data mining is an interdisciplinary field that aids in comprehending and analysing large amounts of data in order to generate useful information. There are various data mining activities that may be applied on big biological data sets, such as association rule mining, classification prediction clustering, and so on. Pattern matching and the use of fuzzy logic in data mining are two examples of related data mining jobs. The major goal of this study is to determine how well different data mining methods perform on huge biological data sets. The author's goal is to determine the efficacy of these algorithms on biological data, as well as to propose appropriate algorithms for a variety of biological data sets relevant to various disorders Newline.

The diagnostic potential and barriers of microbiome based therapeutics.

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with thepromotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.

Information Theory as an Experimental Tool for Integrating Disparate Biophysical Signaling Modules.

There is a growing appreciation in the fields of cell biology and developmental biology that cells collectively process information in time and space. While many powerful molecular tools exist to observe biophysical dynamics, biologists must find ways to quantitatively understand these phenomena at the systems level. Here, we present a guide for the application of well-established information theory metrics to biological datasets and explain these metrics using examples from cell, developmental and regenerative biology. We introduce a novel computational tool named after its intended purpose, calcium imaging, (CAIM) for simple, rigorous application of these metrics to time series datasets. Finally, we use CAIM to study calcium and cytoskeletal actin information flow patterns between Xenopus laevis embryonic animal cap stem cells. The tools that we present here should enable biologists to apply information theory to develop a systems-level understanding of information processing across a diverse array of experimental systems.