Computational Biology Research Research Articles

Improving bioinformatics software quality through teamwork.

Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers' work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians. Additional information and guidance on how to get started is available at https://ferenckata.github.io/ImprovingSoftwareTogether.github.io/.

New Schiff Base Derived Organotin (IV) Complexes: Synthesis, Characterization, In vitro and In silico Biological Studies

Aims: The creation and testing of new Schiff base-based antibacterial organotin (IV) complexes were the objectives of this investigation. Background: Due to developed resistance, antibiotics that were once often used to treat microor-ganisms are no longer effective against them. It is thought that organotin compounds synthesized from Schiff bases have significant pharmacological effectiveness and work well as antibacterial agents. Methods: Thiocarbohydrazide and dehydroacetic acid were condensed to create the Schiff base, followed by processing with dialkyltin(IV)dichloride to synthesize the final product. Modern an-alytical techniques were used to clarify the compounds' probable structural details. The crystalline nature of the produced compounds was tested using PXRD. Results: All of the compounds were thermally stable up to 300°C. All of the synthesized com-plexes showed potent antibacterial activity in the range of 250 to 400 μg/ml. Furthermore, the computational biology research showed that, in contrast to ligands, which had a binding energy of -7.3 to -7.4 kcal/mol, complexes interacted well with dihydropteroate synthase and DNA gy-rase. Conclusion: The current study offered a unique technique for synthesizing diorganotin(IV) de-rivatives of N-substituted Schiff bases that are physiologically active. The results show that the chemicals created are promising antibacterial mediators against diseases that affect humans in the modern world. It might also open the door to future studies on drug-resistant microorganisms that could have biological uses.

Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.

Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems. We also highlight open questions, especially in the era of large language models, and call for collaboration between IML and computational biology researchers.

Privacy-Preserving Fingerprinting Against Collusion and Correlation Threats in Genomic Data

Sharing genomic databases is critical to the collaborative research in computational biology. A shared database is more informative than specific genome-wide association studies (GWAS) statistics as it enables do-it-yourself calculations. Genomic databases involve intellectual efforts from the curator and sensitive information of participants, thus in the course of data sharing, the curator (database owner) should be able to prevent unauthorized redistributions and protect genomic data privacy. As it becomes increasingly common for a single database be shared with multiple recipients, the shared genomic database should also be robust against collusion attack, where multiple malicious recipients combine their individual copies to forge a pirated one with the hope that none of them can be traced back. The strong correlation among genomic entries also make the shared database vulnerable to attacks that leverage the public correlation models. In this paper, we assess the robustness of shared genomic database under both collusion and correlation threats. To this end, we first develop a novel genomic database fingerprinting scheme, called Gen-Scope. It achieves both copyright protection (by enabling traceability) and privacy preservation (via local differential privacy) for the shared genomic databases. To defend against collusion attacks, we augment Gen-Scope with a powerful traitor tracing technique, i.e., the Tardos codes. Via experiments using a real-world genomic database, we show that Gen-Scope achieves strong fingerprint robustness, e.g., the fingerprint cannot be compromised even if the attacker changes 45% of the entries in its received fingerprinted copy and colluders will be detected with high probability. Additionally, Gen-Scope outperforms the considered baseline methods. Under the same privacy and copyright guarantees, the accuracy of the fingerprinted genomic database obtained by Gen-Scope is around 10% higher than that achieved by the baseline, and in terms of preservations of GWAS statistics, the consistency of variant-phenotype associations can be about 20% higher. Notably, we also empirically show that Gen-Scope can identify at least one of the colluders even if malicious receipts collude after independent correlation attacks.

How has the AI boom impacted algorithmic biology?

This Voices piece will highlight the impact of artificial intelligence on algorithm development among computational biologists. How has worldwide focus on AI changed the path of research in computational biology? What is the impact on the algorithmic biology research community?

Advanced computational approaches to understand protein aggregation.

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles.

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures poses a significant challenge for computational biology in leveraging structural information and accurate representation of protein surface properties. Recently, AlphaFold2 released the comprehensive proteomes of various species, and protein surface property representation plays a crucial role in protein-molecule interaction predictions, including those involving proteins, nucleic acids and compounds. Here, we proposed the first extensive database, namely ProNet DB, that integrates multiple protein surface representations and RNA-binding landscape for 326 175 protein structures. This collection encompasses the 16 model organism proteomes from the AlphaFold Protein Structure Database and experimentally validated structures from the Protein Data Bank. For each protein, ProNet DB provides access to the original protein structures along with the detailed surface property representations encompassing hydrophobicity, charge distribution and hydrogen bonding potential as well as interactive features such as the interacting face and RNA-binding sites and preferences. To facilitate an intuitive interpretation of these properties and the RNA-binding landscape, ProNet DB incorporates visualization tools like Mol* and an Online 3D Viewer, allowing for the direct observation and analysis of these representations on protein surfaces. The availability of pre-computed features enables instantaneous access for users, significantly advancing computational biology research in areas such as molecular mechanism elucidation, geometry-based drug discovery and the development of novel therapeutic approaches. Database URL: https://proj.cse.cuhk.edu.hk/aihlab/pronet/.

Diffusion models in protein structure and docking

AbstractGenerative AI is rapidly transforming the frontier of research in computational structural biology. Indeed, recent successes have substantially advanced protein design and drug discovery. One of the key methodologies underlying these advances is diffusion models (DM). Diffusion models originated in computer vision, rapidly taking over image generation and offering superior quality and performance. These models were subsequently extended and modified for uses in other areas including computational structural biology. DMs are well equipped to model high dimensional, geometric data while exploiting key strengths of deep learning. In structural biology, for example, they have achieved state‐of‐the‐art results on protein 3D structure generation and small molecule docking. This review covers the basics of diffusion models, associated modeling choices regarding molecular representations, generation capabilities, prevailing heuristics, as well as key limitations and forthcoming refinements. We also provide best practices around evaluation procedures to help establish rigorous benchmarking and evaluation. The review is intended to provide a fresh view into the state‐of‐the‐art as well as highlight its potentials and current challenges of recent generative techniques in computational structural biology.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Structure and Mechanism > Molecular Structures Software > Molecular Modeling

A tutorial hub for realising the potential of tools within the SPARC ecosystem

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) data portal is a platform that allows users to access large sets of curated data and tools. Data on the portal is sourced from researchers working in a range of disciplines applied to peripheral nerve activity, including imaging, computational biology, electrophysiology and clinical research. However, many existing tutorials and examples for SPARC do not document examples of interfaces between tools within the SPARC ecosystem. Thus, we see an opportunity to lower the barrier for entry into the SPARC ecosystem, by creating accessible guides to create workflows using multiple SPARC tools. To do this, we have developed an online hub for hosting tutorials that can guide new users through the entire workflow of acquiring data, processing it, and visualizing it, using common, open-source scientific computing tools and crucially, spanning the repertoire of currently available SPARC tools. Our approach encapsulates the workflow within a Jupyter Notebook, which is designed to be readily accessible to new users of SPARC. Having a hub of tutorials that can be easily understood will not only accelerate the growth of the SPARC community but also increase the uptake and usage of tools by members of the community.

An Evolutionary-Based Mutation With Functional Annotation to Identify Protein Complexes Within PPI Networks

The research deals with an evolutionary-based mutation with functional annotation to identify protein complexes within PPI networks. An important field of research in computational biology is the difficult and fundamental challenge of revealing complexes in protein interaction networks. The complex detection models that have been developed to tackle challenges are mostly dependent on topological properties and rarely use the biological properties of PPI networks. This research aims to push the evolutionary algorithm to its maximum by employing gene ontology (GO) to communicate across proteins based on biological information similarity for direct genes. The outcomes show that the suggested method can be utilized to improve the predictability of the complexes identified. The GO functional annotation of proteins as a heuristic guide is injected into the framework of single-objective evolutionary algorithms (EAs), while the complex detection community score (CS) model works as a fitness function in EAs. In the experiments, we analyzed the performance of our proposed algorithm when applied to the publicly accessible yeast protein networks. The results show a considerable improvement in the detection ability of complexes in the PPI network.

Nanoparticle miR-362-5p targets CDK2 and inhibits tumorigenesis in renal cell carcinoma

The malignant tumour known as renal cell carcinoma (RCC) is relatively prevalent. Finding new biomarkers is crucial for predicting RCC’s course and prognosis. MicroRNA (miRNA) production profiles in severe cancers have been the subject of numerous investigations. miR-362-5p activity in pure cell kidney cancer and any putative RCC-related processes were the main goals of this investigation. This research evaluated 4 clusters of renal disease and close miRNA Chips for interpretation characterization using the Database. Six miRNAs were produced at low levels on four pairs of expressed spectrum chips, with miR-362-5p exhibiting the biggest deregulation. Impacts of miR-362-5p seen using genuine optical quantified Cell cycle tests using stream imaging, Sequencing, Colorimetric research, and cell implantation trials. To validate the genetic variants of miR-362-5p, both a Western blot analysis and a luciferase reporter test were performed. After data analysis, the target of the inquiry was miR-362-5p. Cellular development graphs and microscopy stream analyses revealed that miR-362-5p overexpression slowed the development of RCC cells and caused a cell cycle arrest there at the G0 stage. Computational biology research suggests the miR-362-5p gene may target cyclin-dependent kinase 2 (CDK2). Mechanistic investigations show that miR-362-5p significantly inhibits CDK2 synthesis inside ccRCC cell cultures. Finally, this research demonstrated that miR-362-5p is significantly dysregulated in tissues with renal disease. miR-362-5p controls Prototype, preventing evident cell renal cell cancer from spreading. The cell phase is stopped at the G1/S phase due to CDK2 command to advance the cell phase and develop tumour cells.

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs.

Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253-28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12-17% more reads, and 21-28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265-19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58-73.)] obtain an accuracy of <60% on this setting. GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.

Utility of Machine Learning Technology in Microbial Identification: A Critical Review

Abstract Microorganisms are ubiquitous and have far-reaching effects on human life. Since their discovery in the 19th century, microorganisms have fascinated biologists. Microbes play a crucial role in the material and elemental cycles of the natural world. Growing own microbes for research purposes requires a significant time and financial investment. On the other hand, high-throughput sequencing technology cannot advance at the same clip as the culture method. The area of microbiology has made substantial use of machine learning (ML) methods to tackle this problem. Classification and prediction have emerged as key avenues for advancing microbial community research in computational biology. This research compares the advantages and disadvantages of using different algorithmic approaches in four subfields of microbiology (pathogen and epidemiology; microbial ecology; drug development; microbiome and taxonomy).

Harnessing Deep Learning for Omics in an Era of COVID-19

Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.

Hyperbolic matrix factorization improves prediction of drug-target associations

Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks.

Connecting genomic islands across prokaryotic and phage genomes via protein families

Prokaryotic genomes evolve via horizontal gene transfer (HGT), mutations, and rearrangements. A noteworthy part of the HGT process is facilitated by genomic islands (GIs). While previous computational biology research has focused on developing tools to detect GIs in prokaryotic genomes, there has been little research investigating GI patterns and biological connections across species. We have pursued the novel idea of connecting GIs across prokaryotic and phage genomes via patterns of protein families. Such patterns are sequences of protein families frequently present in the genomes of multiple species. We combined the large data set from the IslandViewer4 database with protein families from Pfam while implementing a comprehensive strategy to identify patterns making use of HMMER, BLAST, and MUSCLE. we also implemented Python programs that link the analysis into a single pipeline. Research results demonstrated that related GIs often exist in species that are evolutionarily unrelated and in multiple bacterial phyla. Analysis of the discovered patterns led to the identification of biological connections among prokaryotes and phages. These connections suggest broad HGT connections across the bacterial kingdom and its associated phages. The discovered patterns and connections could provide the basis for additional analysis on HGT breadth and the patterns in pathogenic GIs.

The in silico lab: Improving academic code using lessons from biology

"Good code" is often regarded as a nebulous, impractical ideal. Common best practices toward improving code quality can be inaccessible to those without a rigorous computer science or software engineering background, contributing to a gap between advancing scientific research and FAIR practices. We seek to equip researchers with the necessary background and context to tackle the challenge of improving code quality in computational biology research using analogies from biology to synthesize why certain best practices are critical for advancing computational research. Improving code quality requires active stewardship; we encourage researchers to deliberately adopt and share practices that ensure reusability, repeatability, and reproducibility.

POInT: Modeling Polyploidy in the Era of Ubiquitous Genomics.

Thirteen years ago, we described an evolutionary modeling tool that could resolve the orthology relationships among the homologous genomic regions created by a whole-genome duplication. This tool, which we subsequently named POInT (the Polyploid Orthology Inference Tool), was originally only useful for studying a genome duplication known from bakers' yeast and its relatives. Now, with hundreds of genome sequences that contain the relicts of ancient polyploidy available, POInT can be used to study dozens of different polyploidies, asking both questions about the history of individual events and about the commonalities and differences seen between those events. In this chapter, I give a brief history of the development of POInT as an illustration of the interconnected nature of computational biology research. I then further describe how POInT operates and some of the strengths and drawbacks of its structure. I close with a few examples of discoveries we have made using it.

GAMB-GNN: Graph Neural Networks learning from gene structure relations and Markov Blanket ranking for cancer classification in microarray data

Microarray data plays a significant role in the classification and prediction of cancer, which has become an essential area of research in computational biology and bioinformatics. Several machine learning methods have been applied to help classify and predict cancer using microarray data. However, previous methods have mainly leveraged the expression level of a subset of genes as a sample feature. These methods lack the utilization of the structure relation information among genes. At the same time, there is a lack of analysis of redundancy among features in the existing gene selection algorithms. This paper proposes a Graph Neural Networks model with the Markov Blanket ranking method for cancer classification (GAMB-GNN) in microarray data to combat these challenges. The problem of cancer classification in the microarray data is considered by gene attributes and multi-type relation networks among genes in our model. The proposed GAMB-GNN obtains the gene scores and ranking according to the relevance of genes and cancer by the gene ranking algorithm based on Markov Blanket and then selects a top fraction of the ranking list to construct the multi-type gene relations graph. Further, GAMB-GNN employs a graph classifier with a relation attention mechanism and node aggregation pooling to learn the multiple relations structure and weight to obtain a better feature representation of the sample. The proposed model is validated through experiments in six public microarray datasets. The experiment results show that the accuracy and f1-score of our method on the cancer classification task on all tested microarray datasets improved by 3.98%–24.36% and 4.22%–31.93% on average over the baseline and state-of-the-art methods.

Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics

BackgroundSingle-cell RNA sequencing (scRNA-seq) technology has contributed significantly to diverse research areas in biology, from cancer to development. Since scRNA-seq data is high-dimensional, a common strategy is to learn low-dimensional latent representations better to understand overall structure in the data. In this work, we build upon scVI, a powerful deep generative model which can learn biologically meaningful latent representations, but which has limited explicit control of batch effects. Rather than prioritizing batch effect removal over conservation of biological variation, or vice versa, our goal is to provide a bird’s eye view of the trade-offs between these two conflicting objectives. Specifically, using the well established concept of Pareto front from economics and engineering, we seek to learn the entire trade-off curve between conservation of biological variation and removal of batch effects.ResultsA multi-objective optimisation technique known as Pareto multi-task learning (Pareto MTL) is used to obtain the Pareto front between conservation of biological variation and batch effect removal. Our results indicate Pareto MTL can obtain a better Pareto front than the naive scalarization approach typically encountered in the literature. In addition, we propose to measure batch effect by applying a neural-network based estimator called Mutual Information Neural Estimation (MINE) and show benefits over the more standard maximum mean discrepancy measure.ConclusionThe Pareto front between conservation of biological variation and batch effect removal is a valuable tool for researchers in computational biology. Our results demonstrate the efficacy of applying Pareto MTL to estimate the Pareto front in conjunction with applying MINE to measure the batch effect.