Biological Sequences Research Articles

Innovation in Bioinformatics: Recent tools, Database and Involvement of Artificial Intelligence

Bioinformatics has evolved in recent years into a crucial subject and a well-liked research area that is interconnected with many approaches and disciplines. The capacity of bioinformatics and its approaches to tackle challenging biological problems and promote research and development. There are various tools and database which are used in bioinformatics. AI is the capacity of a computational system to carry out various activities associated with intellectual beings and as a computer system's imitation of human intelligence processes. The bioinformatics applications with artificial intelligence have the capacity to annotate the data in the direction of logical conclusions. By combining AI and bioinformatics molecular dynamic simulations, molecular docking studies, annotations of biological sequences, computational drug design, and gene prediction can be analyzed effectively. The structural bioinformatics tools with artificial intelligence (AI) are effective approaches for designing novel active chemicals to treat neurological diseases and cancer. Immunoinformatics, vaccinology, health informatics, medical informatics, medical science, and pharmaceutical sciences are just a few of the health sciences that have benefited greatly from advances in AI and bioinformatics. Future developments in omics and other fields are predicted to generate large amounts of data quickly, and bioinformatics will be essential in managing, analyzing, and discovering new uses for this data. Bioinformatics will be crucial in saving time and costs by applying AI to examine the massive data sets. Additionally, it will hasten biological discoveries, particularly those related to health, biomedical research, and robotic surgery.

PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins

BackgroundNumerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition.ResultsWe defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch’s code and benchmark datasets are publicly available.ConclusionsPEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org, and the source code can be found at https://github.com/IEDB/PEPMatch.

Biologic Therapy for Inflammatory Bowel Disease: Real-World Comparative Effectiveness and Impact of Drug Sequencing in 13 222 Patients within the UK IBD BioResource.

This study compares the effectiveness of different biologic therapies and sequences in patients with inflammatory bowel disease [IBD] using real-world data from a large cohort with long exposure. Demographic, disease, treatment, and outcome data were retrieved for patients in the UK IBD BioResource. Effectiveness of treatment was based on persistence free of discontinuation or failure, analysed by Kaplan-Meier survival analysis with inverse probability of treatment weighting to adjust for differences between groups. In total, 13 222 evaluable patients received at least one biologic. In ulcerative colitis [UC] first-line vedolizumab [VDZ] demonstrated superior effectiveness over 5 years compared to anti-tumour necrosis factor [anti-TNF] agents [p = 0.006]. VDZ was superior to both infliximab [IFX] and adalimumab [ADA] after ADA and IFX failure respectively [p < 0.001 and p < 0.001]. Anti-TNF therapy showed similar effectiveness when used as first-line treatment, or after failure of VDZ. In Crohn's disease [CD] we found significant differences between first-line treatments over 10 years [p = 0.045], with superior effectiveness of IFX compared to ADA in perianal CD. Non-anti-TNF biologics were superior to a second anti-TNF after first-line anti-TNF failure in CD [p = 0.035]. Patients with UC or CD experiencing TNF failure due to delayed loss of response or intolerance had superior outcomes when switching to a non-anti-TNF biologic, rather than a second anti-TNF. We provide real-world evidence to guide biologic selection and sequencing in a range of common scenarios. Our findings challenge current guidelines regarding drug selection after loss of response to first anti-TNF treatment.

S151 Biologic Therapy Sequencing in Ulcerative Colitis: A Real-World Observational Study of Second-Line Therapy After Vedolizumab

S151 Biologic Therapy Sequencing in Ulcerative Colitis: A Real-World Observational Study of Second-Line Therapy After Vedolizumab

Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners.

This chapter proposes a prototype-based classification approach for analyzing DNA barcodes that uses a spectral representation of DNA sequences and a non-gradient neural network. Biological sequences can be viewed as data components with higher non-fixed dimensions, which correspond to the length of the sequences. Through computational procedures such as one-hot encoding, numerical encoding plays an important role in DNA sequence evaluation (OHE). However, the OHE method has some disadvantages: (1) It does not add any details that could result in an additional predictive variable, and (2) if the variable has many classes, OHE significantly expands the feature space. To address these shortcomings, this chapter proposes a computationally efficient framework for classifying DNA sequences of living organisms in the image domain. A multilayer perceptron trained by a pseudoinverse learning autoencoder (PILAE) algorithm is used in the proposed strategy. The learning control parameters and the number of hidden layers do not have to be specified during the PILAE training process. As a result, the PILAE classifier outperforms other deep neural network (DNN) strategies such as the VGG-16 and Xception models.

An advanced course sequence in eukaryotic cell and molecular biology: A year-long course where active learning and peer-led groups resulted in higher learning gains and significant course passing scores.

We developed an advanced, year-long course sequence in eukaryotic cell and molecular biology in order to increase conceptual understanding. Three years of historical data from a one semester, traditional-lecture, senior cell and molecular biology course (n = 237) were compared with 3 years of data collected from the year-long course sequence (n = 176). There were significant content gains for the students who enrolled in the course sequence when pre- and post-assessments were compared (p < 0.0001). There was an association between earning a C or better in the course sequence and 70% or higher in the post-assessment instrument (p < 0.05). Final course grades for Bio 135A were calculated from three open ended exams and the percentage of correct answers on the clicker questions. For Bio135B, final grades were calculated from three open ended exams, clicker responses, a seven-page literature review on an environmental carcinogen and its effects on signal transduction pathways, and a formal presentation of one of the research articles they used in the literature review. The students who took the second semester of the course passed at higher rates than the students who enrolled in the traditional-lecture course (p < 0.05). Clicker answers to the research problem sets and the final course grades correlated significantly for both semesters of the course sequence (p < 0.01). We conclude that conceptually-connected learning gains can be obtained when the content is taught in a format that includes short lectures and group work to solve research questions.

An Improved Hashing Approach for Biological Sequence to Solve Exact Pattern Matching Problems

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.

Sequence-Tunable Phase Behavior and Intrinsic Fluorescence in Dynamically Interacting Peptides.

A conceptual framework towards understanding biological condensed phases is emerging, derived from biological, biomimetic, and synthetic sequences. However, de novo peptide condensate design remains a challenge due to an incomplete understanding of the structural and interactive complexity. We designed peptide modules based on a simple repeat motif composed of tripeptide spacers (GSG, SGS, GLG) interspersed with adhesive amino acids (R/H and Y). We show, using sequence editing and a combination of computation and experiment, that n→π* interactions in GLG backbones are a dominant factor in providing sufficient backbone structure, which in turn regulates the water interface, collectively promoting liquid droplet formation. Moreover, these R(GLG)Y and H(GLG)Y condensates unexpectedly display sequence-dependent emission that is a consequence of their non-covalent network interactions, and readily observable by confocal microscopy.

Sequence‐Tunable Phase Behavior and Intrinsic Fluorescence in Dynamically Interacting Peptides

AbstractA conceptual framework towards understanding biological condensed phases is emerging, derived from biological, biomimetic, and synthetic sequences. However, de novo peptide condensate design remains a challenge due to an incomplete understanding of the structural and interactive complexity. We designed peptide modules based on a simple repeat motif composed of tripeptide spacers (GSG, SGS, GLG) interspersed with adhesive amino acids (R/H and Y). We show, using sequence editing and a combination of computation and experiment, that n→π* interactions in GLG backbones are a dominant factor in providing sufficient backbone structure, which in turn regulates the water interface, collectively promoting liquid droplet formation. Moreover, these R(GLG)Y and H(GLG)Y condensates unexpectedly display sequence‐dependent emission that is a consequence of their non‐covalent network interactions, and readily observable by confocal microscopy.

Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study.

Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-principle models, machine learning techniques are easier to implement, and can identify correlations that would be hard to describe at a mechanistic level, but strongly rely on high-quality input training data. Here, we evaluate the potential of the "chaos game" representation to provide input data for machine learning models. The chaos game is an algorithm originally developed for the production of fractal structures, and later on applied also to the representation of biological sequences, such as genes and proteins. Our results show that the combination of the chaos game representation with convolutional neural networks results in comparable accuracy to other machine learning approaches, thus indicating that chaos game representations could be a valid alternative to existing featurization strategies for machine learning models of biological sequences. We implement the chaos game in Python 3.8.10, and use it to produce fractal as well as novel expanding representations of protein sequences. We then feed the resulting images to a convolutional neural network, built in Python 3.8.10, using TensorFlow 2.9.1, Keras 2.9.0, and the scikit-learn 1.1.1 packages. We select as case study a recently published dataset for the antibody emibetuzumab, with the objective of co-optimizing antibodies variants with both high affinity and low non-specific binding.

Enhanced Genetic Method for Optimizing Multiple Sequence Alignment

In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences. Consequently, this paper proposes an enhanced evolutionary-based approach that simplifies the sequence alignment problem without considering the sequences in the non-dominated solution. Our method employs a multi-objective optimization technique that uniquely excludes non-dominated solution sets, effectively mitigating computational complexities. Utilizing the Sum of Pairs and the Total Conserved Column as primary objective functions, our approach offers a novel perspective. We adopt an integer coding approach to enhance the computational efficiency, representing chromosomes with sets of integers during the alignment process. Using the SABmark and BAliBASE datasets, extensive experimentation is conducted to compare our method with existing ones. The results affirm the superior solution quality achieved by our approach compared to its predecessors. Furthermore, via the Wilcoxon signed-rank test, a statistical analysis underscores the statistical significance of our model’s improvement (p &lt; 0.05). This comprehensive approach holds promise for advancing Multiple Sequence Alignment in bioinformatics.

Mutation Interest in the Receptor-Binding Domain of Spike Protein of Severe Acute Respiratory Syndrome Coronavirus 2 in the Northwest of Iran

Background: The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been undergoing variation. Most of the variants cause no concern for human health. Some others have had worse outcomes in terms of transmissibility, vaccination resistance, and, generally, the survival of patients infected with SARS-CoV-2. Objectives: This study investigated the mutation of interest in the receptor-binding domain (RBD) of SARS-CoV-2. Methods: Ribonucleic acid (RNA) was extracted from 40 swap samples. Next, the polymerase chain reaction (PCR) assay was carried out to detect the RBD. Investigation of SARS-COV-2 RBD was performed completely by phylogenetic and tree alignment. The multiple sequence alignment (MSA) of the biological sequence of RBD was created by BioEdit, Snap Gene, and MEGA software and was then compared to sequences of different variants of SARS-COV-2 in the GenBank (National Center for Biotechnology Information). The Influenza Surveillance and Response System (GSAID) was used to detect mutations in the RBD sequence. Results: Multiple sequence alignment (MSA) RBD domain showed that the RBD domain sequence obtained from the Iranian patients' highest identity by severe acute respiratory syndrome coronavirus 2 isolate MZ907347. Several mutations of interest, including A475V, L452R, V483A, and F490L, were detected in the RBD region. However, the dN/dS analysis detected positive selection in RBD regions. Conclusions: Amino acid changes in the surface protein can significantly alter the viral function and/or interactions with neutralizing antibodies. Most of the nucleotide changes in the spike gene reduce infectivity. The V503W and P521Q mutations reduce infectivity, the A522Q mutation increases sensitivity to neutralizing antibodies, and the H519T mutation decreases susceptibility to convalescent sera.

Long-acting interferon: pioneering disease modification of myeloproliferative neoplasms.

Myeloproliferative neoplasms (MPNs) are clonal disorders of hematopoietic stem cells. The malignant clones produce cytokines that drive self-perpetuating inflammatory responses and tend to transform into more aggressive clones, leading to disease progression. The progression of MPNs follows a biological sequence from the early phases of malignancy, polycythemia vera, and essential thrombocythemia, to advanced myelofibrosis and leukemic transformation. To date, the treatment of MPNs has focused on preventing thrombosis by decreasing blood cell counts and relieving disease-related symptoms. However, interferon (IFN) has been used to treat MPNs because of its ability to attack cancer cells directly and modulate the immune system. IFN also has the potential to modulate diseases by inhibiting JAK2 mutations, and recent studies have demonstrated clinical and molecular improvements. Long-acting IFN is administered less frequently and has fewer adverse effects than conventional IFN. The current state of research on long-acting IFN in patients with MPNs is discussed, along with future directions.

Real-World Persistence of Successive Biologics in Patients With Inflammatory Bowel Disease: Findings From ROTARY.

Patients with inflammatory bowel disease (IBD) may receive multiple successive biologic treatments in clinical practice; however, data are limited on the comparative effectiveness of biologics and the impact of treatment sequence on outcomes. The ROTARY (Real wOrld ouTcomes Across tReatment sequences in inflammatorY bowel disease patients) study was a retrospective, observational cohort study conducted using data from the Optum Clinical Database between January 1, 2012, and February 29, 2020. Adult patients with Crohn's disease (CD) or ulcerative colitis (UC) who received 2 biologics successively were included. Biologic treatment sequences were analyzed descriptively. Cox proportional hazards models, adjusted for baseline demographics and clinical characteristics, were used to estimate the hazard ratio of switching or discontinuation for each first- and second-line biologic compared with first- and second-line adalimumab, respectively. In total, 4648 patients with IBD (CD, n = 3008; UC, n = 1640) were identified. Most patients received tumor necrosis factor α antagonist (anti-TNFα) treatment followed by another anti-TNFα treatment or vedolizumab. Vedolizumab and infliximab had 39.4% and 34.6% lower rates of switching or discontinuation than adalimumab, respectively, as first-line biologics in patients with CD and 30.8% and 34.3% lower rates as first-line biologics in patients with UC, respectively. Vedolizumab, infliximab, and ustekinumab had 47.2%, 40.0%, and 43.5% lower rates of switching or discontinuation than adalimumab, respectively, as second-line biologics in CD and 56.5%, 43.0%, and 45.6% lower rates as second-line biologics in patients with UC, respectively. Although anti-TNFα treatments were most commonly prescribed, the adjusted rates of discontinuation for adalimumab as both a first- and second-line biologic were higher than for vedolizumab, infliximab, or ustekinumab.

Tunable Sulfated Alginate-based Hydrogel Platform with enhanced anti-inflammatory and antioxidant capacity for promoting burn wound repair

Amidst progressive advancements in tissue engineering, there has been a significant enhancement in the efficacy of anti-inflammatory hydrogel dressings, addressing a myriad of clinical challenges on wound healing. A frequent complication during the initial stages of deep second-degree burn wound healing is the onset of an inflammatory storm, typically occurring without effective intervention. This event disrupts normal biological healing sequences, leading to undesirable regression. In response, we have customized a tunable, multidimensional anti-inflammatory hydrogel platform based on sulfated alginates (Algs), loaded with Prussian blue (PB) nanozymes. This platform competently eliminates surplus reactive oxygen species (ROS) present in the wound bed. Algs, functioning as a mimic of sulfated glycosaminoglycans (including heparin, heparan sulfate, and chondroitin sulfate) in the extracellular matrices (ECM), demonstrate a high affinity towards inflammatory chemokines such as interleukin-8 (IL-8) and monocyte chemotactic protein-1 (MCP-1). This affinity effectively impedes the infiltration of inflammatory cells into the wound. Concurrently, Algs markedly modulate the macrophage phenotype transition from M1 to M2. Ultimately, our potent anti-inflammatory hydrogels, which strategically target inflammatory chemokines, M1 macrophages, and ROS, successfully attenuate dysregulated hyperinflammation in wound sites. Precise immunomodulation administered to deep second-degree burn wounds in mice has demonstrated promotion of neovascular maturation, granulation tissue formation, collagen deposition, and wound closure. Our biomimetic hydrogels, therefore, represent a significant expansion in the repertoire of anti-inflammatory strategies available for clinical practice.

GraphPart: homology partitioning for biological sequence analysis.

When splitting biological sequence data for the development and testing of predictive models, it is necessary to avoid too-closely related pairs of sequences ending up in different partitions. If this is ignored, performance of prediction methods will tend to be overestimated. Several algorithms have been proposed for homology reduction, where sequences are removed until no too-closely related pairs remain. We present GraphPart, an algorithm for homology partitioning that divides the data such that closely related sequences always end up in the same partition, while keeping as many sequences as possible in the dataset. Evaluation of GraphPart on Protein, DNA and RNA datasets shows that it is capable of retaining a larger number of sequences per dataset, while providing homology separation on a par with reduction approaches.

Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data

Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring the decision process and preventing scientist to fully conceptualize predicted outcomes. Furthermore, domain experts like healthcare providers need explainable predictions to assess whether a predicted outcome can be trusted in high stakes scenarios and to help them integrating a model into their own routine. Therefore, interpretable models play a crucial role for the incorporation of machine learning into high stakes scenarios like healthcare. In this paper we introduce Convolutional Motif Kernel Networks, a neural network architecture that involves learning a feature representation within a subspace of the reproducing kernel Hilbert space of the position-aware motif kernel function. The resulting model enables to directly interpret and evaluate prediction outcomes by providing a biologically and medically meaningful explanation without the need for additional post-hoc analysis. We show that our model is able to robustly learn on small datasets and reaches state-of-the-art performance on relevant healthcare prediction tasks. Our proposed method can be utilized on DNA and protein sequences. Furthermore, we show that the proposed method learns biologically meaningful concepts directly from data using an end-to-end learning scheme.

Investigation of the BERT model on nucleotide sequences with non-standard pre-training and evaluation of different k-mer embeddings.

In recent years, pre-training with the transformer architecture has gained significant attention. While this approach has led to notable performance improvements across a variety of downstream tasks, the underlying mechanisms by which pre-training models influence these tasks, particularly in the context of biological data, are not yet fully elucidated. In this study, focusing on the pre-training on nucleotide sequences, we decompose a pre-training model of Bidirectional Encoder Representations from Transformers (BERT) into its embedding and encoding modules to analyze what a pre-trained model learns from nucleotide sequences. Through a comparative study of non-standard pre-training at both the data and model levels, we find that a typical BERT model learns to capture overlapping-consistent k-mer embeddings for its token representation within its embedding module. Interestingly, using the k-mer embeddings pre-trained on random data can yield similar performance in downstream tasks, when compared with those using the k-mer embeddings pre-trained on real biological sequences. We further compare the learned k-mer embeddings with other established k-mer representations in downstream tasks of sequence-based functional prediction. Our experimental results demonstrate that the dense representation of k-mers learned from pre-training can be used as a viable alternative to one-hot encoding for representing nucleotide sequences. Furthermore, integrating the pre-trained k-mer embeddings with simpler models can achieve competitive performance in two typical downstream tasks. The source code and associated data can be accessed at https://github.com/yaozhong/bert_investigation.

Tweedie Distributions for Biological Sequences Alignments

Tweedie Distributions for Biological Sequences Alignments

Polymorphism and mutational diversity of virulence (vcgCPI/vcgCPE) and resistance determinants (aac(3)-IIa, (aacC2, strA, Sul 1, and 11) among human pathogenic Vibrio species recovered from surface waters in South-Western districts of Uganda

BackgroundVibrio species are among the autochthonous bacterial populations found in surface waters and associated with various life-threatening extraintestinal diseases, especially in human populations with underlying illnesses and wound infections. Presently, very diminutive information exists regarding these species’ mutational diversity of virulence and resistance genes. This study evaluated variations in endonucleases and mutational diversity of the virulence and resistance genes of Vibrio isolates, harboring virulence-correlated gene (vcgCPI), dihydropteroate synthase type 1 and type II genes (Sul 1 and 11), (aadA) aminoglycoside (3′′) (9) adenylyltransferase gene, (aac(3)-IIa, (aacC2)a, aminoglycoside N(3)-acetyltransferase III, and (strA) aminoglycoside 3′-phosphotransferase resistance genes. MethodsUsing combinations of molecular biology techniques, bioinformatics tools, and sequence analysis. ResultsOur result revealed various nucleotide variations in virulence determinants of V. vulnificus (vcgCPI) at nucleotide positions (codon) 73–75 (A → G) and 300–302 (N → S). The aminoglycosides resistance gene (aadA) of Vibrio species depicts a nucleotide difference at position 482 (A → G), while the aminoglycosides resistance gene (sul 1 and 11) showed two variable regions of nucleotide polymorphism (102 and 140). The amino acid differences exist with the nucleotide polymorphism at position 140 (A → E). The banding patterns produced by the restriction enzymes HinP1I, MwoI, and StyD4I showed significant variations. Also, the restriction enzyme digestion of protein dihydropteroate synthase type 1 and type II genes (Sul 1 and 11) differed significantly, while enzymes DpnI and Hinf1 indicate no significant differences. The restriction enzyme NlaIV showed no band compared to reference isolates from the GenBank. However, the resistant determinants show significant point nucleotide mutation, which does not produce any amino acid change with diverse polymorphic regions, as revealed in the restriction digest profile. ConclusionThe described virulence and resistance determinants possess specific polymorphic locus relevant to pathogenomics studies, pharmacogenomic, and control of such water-associated strains.