Biological Sequences Research Articles

Oxidative stress-mediated epidermal growth factor receptor activation by cigarette smoke or heated tobacco aerosol in human primary bronchial epithelial cells from multiple donors.

The epidermal growth factor receptor (EGFR) signaling pathway has essential roles in maintaining homeostasis of various tissues by regulating cell proliferation and differentiation. Deregulation of the EGFR signaling pathway is associated with various chronic diseases including chronic obstructive pulmonary disease. Cigarette smoke (CS) is known to activate EGFR, which is linked to chronic obstructive pulmonary disease. The biological sequence from CS exposure to EGFR activation is initiated by oxidative stress caused by intracellular reactive oxygen species (ROS) and the depletion of glutathione, which led to EGFR ligand secretion and EGFR activation. We hypothesized that reducing exposure to CS constituents contributes to preventing CS-inducible EGFR activation. Therefore, we examined the aerosol from heated tobacco products (HTPs) because the aerosol contains fewer chemical constituents at lower levels than CS. We exposed primary human bronchial epithelial cells from four donors to the extracts of CS from a 1R6F reference cigarette or HTP aerosol from three in-market products, including our DT3.0a. The biological sequence from ROS to EGFR activation was assessed. CS induced all the tested endpoints although inter-donor differences were observed, whereas HTPs elicited most of the biological events at higher concentrations; however, EGFR phosphorylation was not observed even at fivefold higher concentration than CS. Overall, our results indicate that HTPs are less effective than CS to elicit ROS-induced EGFR activation. The reduced-risk potential of HTPs on EGFR-related diseases should be investigated further. In addition, testing with multiple donors is warranted when considering the individual differences in responses of primary cells to stimuli.

A Python-based educational software tool for visualizing bioinformatics alignment algorithms.

Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-bystep processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms.

Align-gram: Rethinking the Skip-gram Model for Protein Sequence Analysis.

The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the 'language of life', has been analyzed for a multitude of applications and inferences. Owing to the rapid development of deep learning, in recent years there have been a number of breakthroughs in the domain of Natural Language Processing. Since these methods are capable of performing different tasks when trained with a sufficient amount of data, off-the-shelf models are used to perform various biological applications. In this study, we investigated the applicability of the popular Skip-gram model for protein sequence analysis and made an attempt to incorporate some biological insights into it. We propose a novel k-mer embedding scheme, Align-gram, which is capable of mapping the similar k-mers close to each other in a vector space. Furthermore, we experiment with other sequence-based protein representations and observe that the embeddings derived from Align-gram aids modeling and training deep learning models better. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis.

Physicochemical Technique in Municipal Solid Waste (MSW) Landfill Leachate Remediation: A Review

Leachate generation is among the main challenging issues that landfill operators must handle. Leachate is created when decomposed materials and rainwater pass through the waste. Leachate carries many harmful pollutants, with high concentrations of BOD, COD, colour, heavy metals, ammoniacal nitrogen (NH3-N), and other organic and inorganic pollutants. Among them, COD, colour, and NH3-N are difficult to be completely eliminated, especially with a single treatment. They should be handled by appropriate treatment facilities before being safely released into the environment. Leachate remediation varies based on its properties, the costs of operation and capital expenditures, as well as the rules and regulations. Up until now, much scientific and engineering attention was given to the development of comprehensive solutions to leachate-related issues. The solutions normally demand a multi-stage treatment, commonly in the form of biological, chemical, and physical sequences. This review paper discussed the use of contemporary techniques to remediate landfill leachate with an emphasis on concentrated COD, colour, and NH3-N levels with low biodegradability that is normally present in old landfill or dumping grounds in developing countries. A semi-aerobic type of landfill design was also discussed, as this concept is potentially sustainable compared to others. Some of the challenges and future prospects were also recommended, especially for the case of Malaysia. This may represent landfills or dumpsites in other developing countries with the same characteristics.

Benchmarking machine learning robustness in Covid-19 genome sequence classification

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome—millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics.

Oncoguía de leucemia linfoblástica aguda 2023

Acute lymphoblastic leukemia (ALL) is a malignant proliferation of lymphoid cells (lymphoblasts) that can be precursors of B or T cells. The most frequent age of onset is 1-4 years and in adolescents and young adults of 15-39 years old. The disease response is regulated by heterogeneity, disease biology, risk factors, chromosomal rearrangements, structural variations, and contact sequences that alter the lymphoid lineage's maturation; that causes cell activation and alterations in its regulation. The introduction of schemes based on L-asparaginase, the use of tyrosine kinase inhibitors, as well as immunotherapy with blinatumomab have significantly improved the response and survival of patients between 60 % and 70 %. The objective of these guidelines of acute lymphoblastic leukemia is to show the current panorama of this pathology, guide the use of diagnostic tools, risk staging and, finally, offer the best available treatment options.

A deep learning based two-layer predictor to identify enhancers and their strength.

The enhancer is a DNA sequence that can increase the activity of promoters and thus speed up the frequency of gene transcription. The enhancer plays an essential role in activating gene expression. Currently, gene sequencing technology has been developed for 30years from the first generation to the third generation, and a variety of biological sequence data have increased significantly every year. Due to the importance of enhancer functions, it is very expensive to identify enhancers through biochemical experiments. Therefore, we need to study new methods for the identification and classification of enhancers. Based on the K-mer principle this study proposed a feature extraction method that others have not used in convolutional neural networks. Then, we combined it with one-hot encoding to build an efficient one-dimensional convolutional neural network ensemble model for predicting enhancers and their strengths. Finally, we used five commonly used classification problem evaluation indicators to compare with the models proposed by other researchers. The model proposed in this paper has a better performance by using the same independent test dataset as other models.

A Method for Bio-Sequence Analysis Algorithm Development Based on the PAR Platform

The problems of biological sequence analysis have great theoretical and practical value in modern bioinformatics. Numerous solving algorithms are used for these problems, and complex similarities and differences exist among these algorithms for the same problem, causing difficulty for researchers to select the appropriate one. To address this situation, combined with the formal partition-and-recur method, component technology, domain engineering, and generic programming, the paper presents a method for the development of a family of biological sequence analysis algorithms. It designs highly trustworthy reusable domain algorithm components and further assembles them to generate specifific biological sequence analysis algorithms. The experiment of the development of a dynamic programming based LCS algorithm family shows the proposed method enables the improvement of the reliability, understandability, and development efficiency of particular algorithms.

SALON ontology for the formal description of sequence alignments

BackgroundInformation provided by high-throughput sequencing platforms allows the collection of content-rich data about biological sequences and their context. Sequence alignment is a bioinformatics approach to identifying regions of similarity in DNA, RNA, or protein sequences. However, there is no consensus about the specific common terminology and representation for sequence alignments. Thus, automatically linking the wide existing knowledge about the sequences with the alignments is challenging.ResultsThe Sequence Alignment Ontology (SALON) defines a helpful vocabulary for representing and semantically annotating pairwise and multiple sequence alignments. SALON is an OWL 2 ontology that supports automated reasoning for alignments validation and retrieving complementary information from public databases under the Open Linked Data approach. This will reduce the effort needed by scientists to interpret the sequence alignment results.ConclusionsSALON defines a full range of controlled terminology in the domain of sequence alignments. It can be used as a mediated schema to integrate data from different sources and validate acquired knowledge.

Review on DNA Cryptography

Cryptography is the science that secures data and communication over the network by applying mathematics and logic to design strong encryption methods. In the modern era of e-business and e-commerce the protection of confidentiality, integrity and availability (CIA triad) of stored information as well as of transmitted data is very crucial. Deoxyribonucleic acid (DNA) is a genetic molecule consisting of two linked strands that wind around each other to form a double helical structure. The backbone of each strand is made of alternating deoxyribose sugar and phosphate groups. To each sugar one of four bases are attached i.e., adenine (A), cytosine (C), guanine (G) and thymine (T). DNA molecules, having the capacity to store, process and transmit information, inspires the idea of DNA cryptography. It is the rapid emerging unconventional techniques which combines the chemical characteristics of biological DNA sequences with classical cryptography to ensure non-vulnerable transmission of data. This innovative method is based on the notion of DNA computing. The methodologies of DNA cryptography are not coded mathematically; thus, it could be too secure to be cracked easily.

DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis.

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question. Specifically, given any biological sequence data, DeepBIO supports a total of 42 state-of-the-art deep-learning algorithms for model training, comparison, optimization and evaluation in a fully automated pipeline. DeepBIO provides a comprehensive result visualization analysis for predictive models covering several aspects, such as model interpretability, feature analysis and functional sequential region discovery. Additionally, DeepBIO supports nine base-level functional annotation tasks using deep-learning architectures, with comprehensive interpretations and graphical visualizations to validate the reliability of annotated sites. Empowered by high-performance computers, DeepBIO allows ultra-fast prediction with up to million-scale sequence data in a few hours, demonstrating its usability in real application scenarios. Case study results show that DeepBIO provides an accurate, robust and interpretable prediction, demonstrating the power of deep learning in biological sequence functional analysis. Overall, we expect DeepBIO to ensure the reproducibility of deep-learning biological sequence analysis, lessen the programming and hardware burden for biologistsand provide meaningful functional insights at both the sequence level and base level from biological sequences alone. DeepBIO is publicly available at https://inner.wei-group.net/DeepBIO.

An Introduction to Nanopore Sequencing: Past, Present, and Future Considerations.

There has been significant progress made in the field of nanopore biosensor development and sequencing applications, which address previous limitations that restricted widespread nanopore use. These innovations, paired with the large-scale commercialization of biological nanopore sequencing by Oxford Nanopore Technologies, are making the platforms a mainstay in contemporary research laboratories. Equipped with the ability to provide long- and short read sequencing information, with quick turn-around times and simple sample preparation, nanopore sequencers are rapidly improving our understanding of unsolved genetic, transcriptomic, and epigenetic problems. However, there remain some key obstacles that have yet to be improved. In this review, we provide a general introduction to nanopore sequencing principles, discussing biological and solid-state nanopore developments, obstacles to single-base detection, and library preparation considerations. We present examples of important clinical applications to give perspective on the potential future of nanopore sequencing in the field of molecular diagnostics.

Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons.

Profile hidden Markov models (HMMs) are a powerful way of modeling biological sequence diversity and constitute a very sensitive approach to detecting divergent sequences. Here, we report the development of protocols for the rational design of profile HMMs. These methods were implemented on TABAJARA, a program that can be used to either detect all biological sequences of a group or discriminate specific groups of sequences. By calculating position-specific information scores along a multiple sequence alignment, TABAJARA automatically identifies the most informative sequence motifs and uses them to construct profile HMMs. As a proof-of-principle, we applied TABAJARA to generate profile HMMs for the detection and classification of two viral groups presenting different evolutionary rates: bacteriophages of the Microviridae family and viruses of the Flavivirus genus. We obtained conserved models for the generic detection of any Microviridae or Flavivirus sequence, and profile HMMs that can specifically discriminate Microviridae subfamilies or Flavivirus species. In another application, we constructed Cas1 endonuclease-derived profile HMMs that can discriminate CRISPRs and casposons, two evolutionarily related transposable elements. We believe that the protocols described here, and implemented on TABAJARA, constitute a generic toolbox for generating profile HMMs for the highly sensitive and specific detection of sequence classes.

P739 Sequential treatment is effective to achieve remission and avoid colectomy in moderate-to-severe Ulcerative Colitis refractory to anti-TNF therapy

Abstract Background The development of anti-TNF agents has brought advances in the treatment of ulcerative colitis (UC). However, many patients require biological treatment sequencing. We aimed to evaluate clinical outcomes, the rate of treatment persistence and colectomy-free survival at second-, third-, and fourth-line therapy after switching from TNF-inhibitor to another treatment and to identify factors predicting the need for colectomy. Methods In this retrospective study, we included active UC patients refractory to at least one anti-TNF agent and needed treatment sequencing. The efficacy of therapies of the different lines was assessed by endoscopic or clinical scoring systems and laboratory markers. Kaplan-Meier analysis was performed to investigate the colectomy-free survival. Possible predictive factors were examined by logistic regression. Results Data of 90 UC patients were analyzed. The most frequently used first-line biologic therapy was infliximab (83.3%), which was discontinued after an average of 21.3 months. In the second-line vedolizumab (VDZ) was administered in 48.9% of cases, adalimumab in 36.7%. Average time between initiation and discontinuation was 15.7 months. In the third line, VDZ was used in 50%, tofacitinib in 29% of the patients, until an average of 8.7 months. In the fourth line, 53.1% of patients received tofacitinib and it was terminated after an average of 8.9 months. Loss of response, as a reason for treatment discontinuation was more common in the first-, and second-line therapies (58.9% and 46.2%), while primary non-response was more often observed in third-, and fourth-line therapies (52.6% and 63.6%). Fifteen patients underwent colectomy (16.7%). There was a significant increase in the probability of having surgery within 2 years after therapy initiation (p=0.0239) independently from treatment lines. Presence of deep ulcer showed inverse correlation with colectomy-free survival (p=0.0189; OR: 10.414 (95% CI: (1.472; 73.669)). Patients with high partial Mayo score at therapy initiation were more likely to undergo colectomy (p=0.0436, OR: 1.463 (95% CI: (1.011; 2.118)). In contrast, gender, age and disease extent at the time of the diagnosis, and the use of concomitant immunmodulators were not shown to be significant in predicting the subsequent surgical need. Conclusion Our results suggest that the remission rate does not decrease considerably as the sequential treatment progresses, more than half of the patients receiving fourth-line therapy were still in sustained remission. The more severe disease activity and deep ulcerations proved to be predictive for colectomy. Further studies in a larger patient population would be required to confirm our data and reveal predictive markers.

MFR-DTA: a multi-functional and robust model for predicting drug-target binding affinity and region.

Recently, deep learning has become the mainstream methodology for drug-target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug-target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers. We propose a novel Multi-Functional and Robust Drug-Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements. Then, we propose a new Elem-feature fusion block to refine the extracted features. After that, we construct a Mix-Decoder block that extracts drug-target interaction information and predicts their binding regions simultaneously. Last, we evaluate MFR-DTA on two benchmarks consistently with the existing methods and propose a new dataset, sc-PDB, to better measure the accuracy of binding region prediction. We also visualize some samples to demonstrate the locations of their binding sites and the predicted multi-scale interaction regions. The proposed method achieves excellent performance on these datasets, demonstrating its merits and superiority over the state-of-the-art methods. https://github.com/JU-HuaY/MFR.

Evaluation of IoT-Enabled hybrid model for genome sequence analysis of patients in healthcare 4.0

Genome sequence matching is vital for health analytics and treatment in healthcare 4.0. It focuses on finding whether a given sequence resembles other sequences that can help identify disease outbreaks faster. The healthcare 4.0 primarily require more efficient systems that can easily connect and interact with big data. The Internet of things (IoT) can potentially realize smart devices that can perform this role in the health sector. This research presents an IoT-enabled hybrid model for Genome sequence analysis of patients in healthcare 4.0. The proposed model utilizes an improved pattern-matching technique that uses Hadoop's ideas, OpenCL, and APAR (API). The primary local alignment search tool (BLAST) is the core algorithm extensively used to compare and find preliminary biological sequence information and calculate the matches' statistical importance. BLAST enables many strings to be distributed in the form of blocks; after that, an instance of the Mapper is mapped to process it after the output of all the mappers combined through the reducer. This mapper and reducer process has inter-node parallelism to speed up the process and efficiently utilize the shared resources. In this work, fine-grained parallelism has been introduced in Hadoop-based BLAST. The proposed work also provided inter-node and intra-node parallelism. The Mapper and reducer accelerated the conventional BLAST algorithm. The proposed model achieved better results than other traditional algorithms for different datasets in all cases.

Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach.

The dramatic increase in the number of microbe descriptions in databases, reports, and papers presents a two-fold challenge for accessing the information: integration of heterogeneous data in a standard ontology-based representation and normalization of the textual descriptions by semantic analysis. Recent text mining methods offer powerful ways to extract textual information and generate ontology-based representation. This paper describes the design of the Omnicrobe application that gathers comprehensive information on habitats, phenotypes, and usages of microbes from scientific sources of high interest to the microbiology community. The Omnicrobe database contains around 1 million descriptions of microbe properties. These descriptions are created by analyzing and combining six information sources of various kinds, i.e. biological resource catalogs, sequence databases and scientific literature. The microbe properties are indexed by the Ontobiotope ontology and their taxa are indexed by an extended version of the taxonomy maintained by the National Center for Biotechnology Information. The Omnicrobe application covers all domains of microbiology. With simple or rich ontology-based queries, it provides easy-to-use support in the resolution of scientific questions related to the habitats, phenotypes, and uses of microbes. We illustrate the potential of Omnicrobe with a use case from the food innovation domain.

Applications of transformer-based language models in bioinformatics: a survey.

The transformer-based language models, including vanilla transformer, BERT and GPT-3, have achieved revolutionary breakthroughs in the field of natural language processing (NLP). Since there are inherent similarities between various biological sequences and natural languages, the remarkable interpretability and adaptability of these models have prompted a new wave of their application in bioinformatics research. To provide a timely and comprehensive review, we introduce key developments of transformer-based language models by describing the detailed structure of transformers and summarize their contribution to a wide range of bioinformatics research from basic sequence analysis to drug discovery. While transformer-based applications in bioinformatics are diverse and multifaceted, we identify and discuss the common challenges, including heterogeneity of training data, computational expense and model interpretability, and opportunities in the context of bioinformatics research. We hope that the broader community of NLP researchers, bioinformaticians and biologists will be brought together to foster future research and development in transformer-based language models, and inspire novel bioinformatics applications that are unattainable by traditional methods. Supplementary data are available at Bioinformatics Advances online.

A Novel Image Cryptosystem Inspired by the Generation of Biological Protein Sequences

Image encryption is essential to keep images secure either in personal devices or over the cloud drives. Various robust image cryptosystems have been implemented during the past decades to keep the encrypted images far from illegal decryption by eavesdroppers. Biological sequences contain high density information that introduce new challenges and opportunities to traditional cryptography. This article presents a novel image cryptosystem, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Image-to-Protein (I2P)</i> , that is based on successively encoding the image pixels into sequences of symbols analogous biological <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DNA</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Protein</i> sequences, and then scrambling these sequences into frequent and non-frequent patterns which in turn are binary encoded into the encrypted file. The proposed cryptosystem has been applied on experimental grayscale and color images. The large key space and the complicated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Protein</i> encoding operations guaranteed resistance to many known cryptographic attacks. Furthermore, the biological encoding of the plain image pixels resulted in high similarity that achieved noticeable lossless compression when compressed using known text compression techniques.

I4mC-CPXG: A Computational Model for Identifying DNA N4- methylcytosine Sites in Rosaceae Genome Using Novel Encoding Strategy

Background: N4-methylcytosine (4mC) is one of the most widespread DNA methylation modifications, which plays an important role in DNA replication and repair, epigenetic inheritance, gene expression levels and regulation of transcription. Although biological experiments can identify potential 4mC modification sites, they are limited due to the experimental environment and labor intensive. Therefore, it is crucial to construct a computational model to identify the 4mC sites. background: N4-methylcytosine (4mC) is one of the most widespread DNA methylation modifications, which plays an important role in DNA replication and repair, epigenetic inheritance, gene expression levels and regulation of transcription. Although biological experiment can identify potential 4mC modification sites, it’s limited due to the experimental environment and labor intensive. Therefore, it is crucial to construct a computational model to identify the 4mC sites. Objective: Although some computational methods have been proposed to identify the 4mC sites, some problems should not be ignored, such as: (1) a large number of unknown nucleotides exist in the biological sequence; (2) a large number of zeros exist in the previous encoding technologies; (3) sequence distribution information is important to identify 4mC sites. Considering these aspects, we propose a computational model based on a novel encoding strategy with position specific information to identify 4mC sites. Methods: We constructed an accurate computational model i4mC-CPXG based on extreme gradient boosting. Two aspects of feature vectors are extracted according to nucleotide information and position specific information. From the aspect of nucleotide information, we used prior information to identify the base type of unknown nucleotide and decrease the influence of invalid information caused by lots of zeros. From the aspect of position specific information, the vector was designed carefully to express the base distribution and arrangement. Then the feature vector fused by nucleotide information and position specific information was input into extreme gradient boosting to construct the model. method: We constructed an accurate computational model i4mC-CPXG based on extreme gradient boosting. Two aspects feature vectors are extracted according to nucleotide information and position specific information. From the aspect of nucleotide information, we used prior information to identify the base type of unknown nucleotide and decrease the influence of invalid information caused by lots of zeros. From the aspect of position specific information, the vector was designed carefully to express the base distribution and arrangement. Then the feature vector fused by nucleotide information and position specific information was input into extreme gradient boosting to construct model. Results: The accuracy of i4mC-CPXG is 82.49% on independent dataset. The result was better than model i4mC-w2vec which was the best model in the imbalanced dataset with the ratio of 1:15. Meanwhile, our model achieved good performance on other species. These results validated the effectiveness of i4mC-CPXG. Conclusion: Our method is effective to identify potential 4mC modification sites due to the proposed new encoding strategy fused position specific information. The satisfactory prediction results of balanced datasets, imbalanced datasets and other species datasets indicate that i4mC-CPXG is valuable to provide a reasonable supplement for biology research. other: The satisfactory prediction results of balanced datasets, imbalanced datasets and other species datasets indicate that i4mC-CPXG is valuable to provide a reasonable supplement for biology research.