Prokaryote adaptive immunity (CRISPR-Cas systems) can be a threat to its carriers. We analyze the risks of autoimmune reactions related to adaptive immunity in prokaryotes by computational methods. We found important differences between bacteria and archaea with respect to autoimmunity potential. According to the results of our analysis, CRISPR-Cas systems in bacteria are more prone to self-targeting

Membrane proteins are a major focus for new drug discovery. Transmembrane beta-barrel (TMB) proteins play key roles in the translocation machinery, pore formation, membrane anchoring and ion exchange. Given their key roles and the difficulty in membrane protein structure determination, the use of computational modeling is essential. This paper focuses on the topology prediction of TMB proteins. In

As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene–gene interactions can

Biomarkers are used for clinical diagnostic purposes, but existing indexes exhibit limitations in terms of the resolving power of biomarkers. This paper proposes a new index, the magnitude-standardized index (MSI), to describe the quantitative variations and resolving powers of different biomarkers. In MSI analysis models, variation scales for ratios and differences are considered simultaneously, and

The amount of sequencing data is growing at a fast pace due to a rapid revolution in sequencing technologies. Quality scores, which indicate the reliability of each of the called nucleotides, take a significant portion of the sequencing data. In addition, quality scores are more challenging to compress than nucleotides, and they are often noisy. Hence, a natural solution to further decrease the size

Gleason score (GS) is a powerful prognostic factor in prostate cancer (PCa). A GS-7 tumor typically has the primary Gleason (architectural) pattern and secondary prevalent one being graded with 3 and 4 (or 4 and 3), respectively. Due to the well-known intratumoral multifocal occurrence of different patterns, a biological sample from a GS-7 tumor used in a molecular experiment will be uncertain regarding

Clustering analysis has been widely applied to single-cell RNA-sequencing (scRNA-seq) data to discover cell types and cell states. Algorithms developed in recent years have greatly helped the understanding of cellular heterogeneity and the underlying mechanisms of biological processes. However, these algorithms often use different techniques, were evaluated on different datasets and compared with some

Many biological and biomedical research areas such as drug design require analyzing the Gene Regulatory Networks (GRNs) to provide clear insight and understanding of the cellular processes in live cells. Under normality assumption for the genes, GRNs can be constructed by assessing the nonzero elements of the inverse covariance matrix. Nevertheless, such techniques are unable to deal with non-normality

Predicting structural properties of proteins plays a key role in predicting the 3D structure of proteins. In this study, new structural profile matrices (SPM) are developed for protein secondary structure, solvent accessibility and torsion angle class predictions, which could be used as input to 3D prediction algorithms. The structural templates employed in computing SPMs are detected by eight alignment

Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address

Protein complexes are the cornerstones of most of the biological processes. Identifying protein complexes is crucial in understanding the principles of cellular organization with several important applications, including in disease diagnosis. Several computational techniques have been developed to identify protein complexes from protein–protein interaction (PPI) data (equivalently, from PPI networks)

Conformational plasticity of the functionally important regions and binding sites in protein/enzyme structures is one of the key factors affecting their function and interaction with substrates/ligands. Molecular dynamics (MD) can address the challenge of accounting for protein flexibility by predicting the time-dependent behavior of a molecular system. It has a potential of becoming a particularly

In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune cells play an important role in tumor progression and patient outcome. We improved the relative proportion estimation algorithm of immune cells based on RNA-seq gene expression profiling and solved the multiple linear regression model by support vector

Cell survival requires the presence of essential proteins. Detection of essential proteins is relevant not only because of the critical biological functions they perform but also the role played by them as a drug target against pathogens. Several computational techniques are in place to identify essential proteins based on protein–protein interaction (PPI) network. Essential protein detection using

Background: Tumor purity is of great significance for the study of tumor genotyping and the prediction of recurrence, which is significantly affected by tumor heterogeneity. Tumor heterogeneity is the basis of drug resistance in various cancer treatments, and DNA methylation plays a core role in the generation of tumor heterogeneity. Almost all types of cancer cells are associated with abnormal DNA

The secondary and tertiary structure of a protein has a primary role in determining its function. Even though many folding prediction algorithms have been developed in the past decades — mainly based on the assumption that folding instructions are encoded within the protein sequence — experimental techniques remain the most reliable to establish protein structures. In this paper, we searched for signals

Current high-throughput experimental techniques make it feasible to infer gene regulatory interactions at the whole-genome level with reasonably good accuracy. Such experimentally inferred regulatory networks have become available for a number of simpler model organisms such as S. cerevisiae, and others. The availability of such networks provides an opportunity to compare gene regulatory processes

In this work, we study period control of the mammalian cell cycle via coupling with the cellular clock. For this, we make use of the oscillators’ synchronization dynamics and investigate methods of slowing down the cell cycle with the use of clock inputs. Clock control of the cell cycle is well established via identified molecular mechanisms, such as the CLOCK:BMAL1-mediated induction of the wee1 gene

Gene regulatory network inference is one of the central problems in computational biology. We need models that integrate the variety of data available in order to use their complementarity information to overcome the issues of noisy and limited data. BENIN: Biologically Enhanced Network INference is our proposal to integrate data and infer more accurate networks. BENIN is a general framework that jointly

Identifying protein complexes is an important issue in computational biology, as it benefits the understanding of cellular functions and the design of drugs. In the past decades, many computational methods have been proposed by mining dense subgraphs in Protein–Protein Interaction Networks (PINs). However, the high rate of false positive/negative interactions in PINs prevents accurately detecting complexes

Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity

A new method has been introduced which allows us to determine the stability of protein complexes with point changes of amino acid residues that also take into account the three-dimensional structure of the complex. This formulated and proven theorem is aimed at determining the criterion for the stability of protein molecules. The algorithm and software package were developed for analyzing protein interactions

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual’s life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of

Membrane proteins play essential roles in modern medicine. In recent studies, some membrane proteins involved in ectodomain shedding events have been reported as the potential drug targets and biomarkers of some serious diseases. However, there are few effective tools for identifying the shedding event of membrane proteins. So, it is necessary to design an effective tool for predicting shedding event

MicroRNA (miRNA) sponges’ regulatory mechanisms play an important role in developing human cancer. Herein, we develop a new method to explore potential miRNA sponge interactions (EPMSIs) for breast cancer. Based on some known interactions, and a matching gene expression profile, EPMSIs explored other potential miRNA sponge interactions for breast cancer. Every interaction is inferred with a value representing

Background: Phosphorylation of histidine residues plays crucial roles in signaling pathways and cell metabolism in prokaryotes such as bacteria. While evidence has emerged that protein histidine phosphorylation also occurs in more complex organisms, its role in mammalian cells has remained largely uncharted. Thus, it is highly desirable to develop computational tools that are able to identify histidine

The automatic extraction of disease named entity is a challenging research problem that has attracted attention from the biomedical text mining community. Handcrafted feature methods were employed for this task given a little success since they are limited by the scope of the expert. Lately, deep learning-based methods have been employed to solve this issue. However, most architectures used for this

Our protein block (PB) sequence database PDB-2-PBv1.0 provides PB sequences and dihedral angles for 74,297 protein structures comprising of 103,252 protein chains of Protein Data Bank (PDB) as on 2011. Since there are a lot of practical applications of PB and also as the size of PDB database increases, it becomes necessary to provide the PB sequences for all PDB protein structures. The current updated

RNA polymerase/promoter recognition represents a basic problem of molecular biology. Decades-long efforts were made in the area, and yet certain challenges persist. The usage of certain most suitable model subjects is pivotal for the research. System of T7 bacteriophage RNA-polymerase/T7 native promoter represents an exceptional example for the purpose. Moreover, it has been studied the most and successfully

T lymphocytes are white blood cells that play a central role in cell-mediated immunity. Ca2+ has its major signaling function when it is elevated in the cytosolic compartment. The free cytosolic Ca2+ dynamics plays a very important role in the activation, and fate decision process in the T lymphocytes. Here, we develop a quantitative spatio-temporal Ca2+ dynamic model which includes, the Ca2+ releasing

Taxon addition order and branch lengths are optimized by genetic algorithms (GAS) within the fastDNAml algorithm for constructing phylogenetic trees of high likelihood. Results suggest that optimizing the order in which taxa are added improves the likelihood of the resulting trees.

Aligning more than two biological sequences is termed multiple sequence alignment (MSA). To analyze biological sequences, MSA is one of the primary activities with potential applications in phylogenetics, homology markers, protein structure prediction, gene regulation, and drug discovery. MSA problem is considered as NP-complete. Moreover, with the advancement of Next-Generation Sequencing techniques

Presynaptic and postsynaptic neurotoxins are two types of neurotoxins from venomous animals and functionally important molecules in the neurosciences; however, their experimental characterization is difficult, time-consuming, and costly. Therefore, bioinformatics tools that can identify presynaptic and postsynaptic neurotoxins would be very useful for understanding their functions and mechanisms. In

One of the main problems in Computational Biology is to find the evolutionary distance among species. In most approaches, such distance only involves rearrangements, which are mutations that alter large pieces of the species’ genome. When we represent genomes as permutations, the problem of transforming one genome into another is equivalent to the problem of Sorting Permutations by Rearrangement Operations

As a rule, receptor-ligand assay data are fitted by logistic functions (4PL model, 5PL model, Feldman’s model). The preparation of the initial estimates for parameters of these functions is an important problem for processing receptor-ligand interaction data. This study represents a new mathematical approach to calculate the initial estimates more closely to the true values of parameters. The main

The circular chromosome conformation capture technique followed by sequencing (4C-seq) has been used in a number of studies to investigate chromosomal interactions between DNA fragments. Computational pipelines have been developed and published that offer various possibilities of 4C-seq data processing and statistical analysis. Here, we present an overview of four of such pipelines (fourSig, FourCSeq

Readhead et al. recently reported in Neuron the detection and association of human herpesviruses 6A (HHV6A) and 7 (HHV7) with Alzheimer’s disease by shotgun sequencing. I was skeptical of the specificity of their modified Viromescan bioinformatics method and subsequent analysis for numerous reasons. Using their supplementary data, the prevalence of variola virus, the etiological agent of the eradicated

Background: Patterns on proteins and genomic sequences are vastly analyzed, extracted and collected in databases. Although protein patterns originate from genomic coding regions, very few works have directly or indirectly dealt with coding region patterns induced from protein patterns. Results: In this paper, we have defined a new genomic pattern structure suitable for representing induced patterns

The number of genes belonging to a multi-gene family usually varies substantially over their evolutionary history as a consequence of gene duplications and losses. A first step toward analyzing these histories in detail is the inference of the changes in copy number that take place along the individual edges of the underlying phylogenetic tree. The corresponding maximum parsimony minimizes the total

Gene set analysis aims to identify differentially expressed or co-expressed genes within a biological pathway between two experimental conditions, so that it can eventually reveal biological processes and pathways involved in disease development. In the last few decades, various statistical and computational methods have been proposed to improve statistical power of gene set analysis. In recent years

Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection

Assays for transposase-accessible chromatin sequencing (ATAC-seq) provides an innovative approach to study chromatin status in multiple cell types. Moreover, it is also possible to efficiently extract differentially accessible chromatin (DACs) regions by using state-of-the-art algorithms (e.g. DESeq2) to predict gene activity in specific samples. Furthermore, it has recently been shown that small dips

Motivation: In single-cell RNA-sequencing (scRNA-seq) experiments, RNA transcripts are extracted and measured from isolated cells to understand gene expression at the cellular level. Measurements from this technology are affected by many technical artifacts, including batch effects. In analogous bulk gene expression experiments, external references, e.g. synthetic gene spike-ins often from the External

Understanding disease comorbidity contributes to improved quality of life in patients who are suffering from multiple diseases. Therefore, to better explore comorbid diseases, the clarification of associations between diseases based on biological functions is essential. In our study, we propose a method for identifying disease comorbidity in a module-based network, named the module-module interaction

There are many short-read aligners that can map short reads to a reference genome/sequence, and most of them can directly accept a FASTQ file as the input query file. However, the raw data usually need to be pre-processed. Few software programs specialize in pre-processing raw data generated by a variety of next-generation sequencing (NGS) technologies. Here, we present AUSPP, a Perl script-based pipeline

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through

We previously developed Windows-based Biochemical Engineering System analyzing Tool-KIT (WinBEST-KIT), a biochemical reaction simulator for analyzing large-scale and complicated biochemical reaction networks. One particularly notable feature is the ability for users to define original mathematical equations for representing unknown kinetic mechanisms and customize them as GUI components for representing

This study aimed to introduce an algorithm and identify intein motif and blocks involved in protein splicing, and explore the underlying methods in the development of detection of protein motifs. Inteins are mobile protein splicing elements capable of self-splicing post-translationally. They exist in viruses and bacteriophage, notwithstanding this broad phylogenetic distribution, all inteins apportion

Mapping short reads to a reference genome is an essential step in many next-generation sequencing (NGS) analyses. In plants with large genomes, a large fraction of the reads can align to multiple locations of the genome with equally good alignment scores. How to map these ambiguous reads to the genome is a challenging problem with big impacts on the downstream analysis. Traditionally, the default method

The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related

Plant exclusive virus-derived small interfering RNAs (vsiRNAs) regulate various biological processes, especially important in antiviral immunity. The identification of plant vsiRNAs is important for understanding the biogenesis and function mechanisms of vsiRNAs and further developing anti-viral plants. In this study, we extracted plant vsiRNA sequences from the PVsiRNAdb database. We then utilized

Molecular recognition features (MoRFs) usually act as “hub” sites in the interaction networks of intrinsically disordered proteins (IDPs). Because an increasing number of serious diseases have been found to be associated with disordered proteins, identifying MoRFs has become increasingly important. In this study, we propose an ensemble learning strategy, named MoRFPred_en, to predict MoRFs from protein

Molecular dynamics (MD) simulation software allows probing the equilibrium structural dynamics of a molecule of interest, revealing how a molecule navigates its structure space one structure at a time. To obtain a broader view of dynamics, typically one needs to launch many such simulations, obtaining many trajectories. A summarization of the equilibrium dynamics requires integrating the information

An important goal in template-free protein structure prediction is how to control the quality of computed tertiary structures of a target amino-acid sequence. Despite great advances in algorithmic research, given the size, dimensionality, and inherent characteristics of the protein structure space, this task remains exceptionally challenging. It is current practice to aim to generate as many structures

MicroRNAs are single-stranded noncoding RNAs known to down-regulate target genes at the protein or mRNA level. Computational prediction of targets is essential for elucidating the detailed functions of microRNA. However, prediction specificity and sensitivity of the existing algorithms still need to be improved to generate useful hypotheses for subsequent experimental testing. A new microRNA binding-site

At present, most of the researches on protein classification are based on graph kernels. The essence of graph kernels is to extract the substructure and use the similarity of substructures as the kernel values. In this paper, we propose a novel graph kernel named vertex-edge similarity kernel (VES kernel) based on mixed matrix, the innovation point of which is taking the adjacency matrix of the graph

The prediction of the optimal secondary structure for a given RNA sequence represents a challenging computational problem in bioinformatics. This challenge becomes harder especially with the discovery of different pseudoknot classes, which is a complex topology that plays diverse roles in biological processes. Many recent studies have been proposed to predict RNA secondary structure with some pseudoknot