Search

Background: Global estimates for cholera annually approximate 4 million cases worldwide with 95,000 deaths. Recent outbreaks, including Haiti and Yemen, are reminders that cholera is still a global health concern. Cholera outbreaks can rapidly induce high death tolls by overwhelming the capacity of health facilities, especially in remote areas or areas of civil unrest. Recent studies demonstrated that stool specimens preserved on filter paper facilitate molecular analysis of Vibrio cholerae in resource limited settings. Specimens preserved in a rapid, low-cost, safe and sustainable manner for sequencing provides previously unavailable data about circulating cholera strains. This may ultimately provide new information to shape public policy response on cholera control and elimination. Methodology/Principal findings: Whole genome sequencing (WGS) recovered close to a complete sequence of the V. cholerae O1 genome with satisfactory genome coverage from stool specimens enriched in alkaline peptone water (APW) and V. cholerae culture isolates, both spotted on filter paper. The minimum concentration of V. cholerae DNA sufficient to produce quality genomic information was 0.02 ng/μL. The genomic data confirmed the presence or absence of genes of epidemiological interest, including cholera toxin and pilus loci. WGS identified a variety of diarrheal pathogens from APW-enriched specimen spotted filter paper, highlighting the potential for this technique to explore the gut microbiome, potentially identifying co-infections, which may impact the severity of disease. WGS demonstrated that these specimens fit within the current global cholera phylogenetic tree, identifying the strains as the 7th pandemic El Tor. Conclusions: WGS results allowed for mapping of short reads from APW-enriched specimen and culture isolate spotted filter papers this provided valuable molecular epidemiological sequence information on V. cholerae strains from remote, low-resource settings. These results identified the presence of co-infecting pathogens while providing rare insight into the specific V. cholerae strains causing outbreaks in cholera-endemic areas. [ABSTRACT FROM AUTHOR]

This comment addresses the work of Wang and Li (DOI: 10.1007/s12293-009-0012-0, hereafter referred to as WL) published online on September 01, 2009 in a (special) issue of Memetic Computing. We respectfully beg to differ in opinion with WL and argue that the concept of multi-strategy evolutionary search used by WL is not particularly novel. Similar ideas have been presented elsewhere, and these publications date back to at least early 2007. For instance, in a series of papers published in PNAS (2007), SSSAJ (2008), WRR (2008), IEEE-TEVC (2009), JH (2010), Vrugt and coworkers have introduced AMALGAM, a multi-method (or ensemble) search approach to solve emerging single and multiple objective search and optimization problems. In the past few years, MATLAB, C++, Python, and R implementations of AMALGAM have been distributed extensively among researchers and practitioners in various fields of study.

Extracellular vesicles (EVs) are the collective term for the various vesicles that are released by cells into the extracellular space. Such vesicles include exosomes and microvesicles, which vary by their size and/or protein and genetic cargo. With the discovery that EVs contain genetic material in the form of RNA (evRNA) has come the increased interest in these vesicles for their potential use as sources of disease biomarkers and potential therapeutic agents. Rapid developments in the availability of deep sequencing technologies have enabled the study of EV-related RNA in detail. In October 2012, the International Society for Extracellular Vesicles (ISEV) held a workshop on “evRNA analysis and bioinformatics.” Here, we report the conclusions of one of the roundtable discussions where we discussed evRNA analysis technologies and provide some guidelines to researchers in the field to consider when performing such analysis.

Motivation: To analyze the relative proportion of bioinformatics papers and their nonbioinformatics counterparts in the top 20 most cited papers annually for the past two decades. Results: When defining bioinformatics papers as encompassing both those that provide software for data analysis or methods underlying data analysis software, we find that over the past two decades, more than a third (34%) of the most cited papers in science were bioinformatics papers, which is approximately a 31-fold enrichment relative to the total number of bioinformatics papers published. More than half of the most cited papers during this span were bioinformatics papers. Yet, the average 5-year JIF of top 20 bioinformatics papers was 7.7, whereas the average JIF for top 20 non-bioinformatics papers was 25.8, significantly higher (P<4.5 x 10-29). The 20-year trend in the average JIF between the two groups suggests the gap does not appear to be significantly narrowing. For a sampling of the journals producing top papers, bioinformatics journals tended to have higher Gini coefficients, suggesting that development of novel bioinformatics resources may be somewhat 'hit or miss'. That is, relative to other fields, bioinformatics produces some programs that are extremely widely adopted and cited, yet there are fewer of intermediate success. [ABSTRACT FROM AUTHOR]

Despite advances in myocardial reperfusion therapies, acute myocardial ischaemia/reperfusion injury and consequent ischaemic heart failure represent the number one cause of morbidity and mortality in industrialized societies. Although different therapeutic interventions have been shown beneficial in preclinical settings, an effective cardio-protective or regenerative therapy has yet to be successfully introduced in the clinical arena. Given the complex pathophysiology of the ischaemic heart, large scale, unbiased, global approaches capable of identifying multiple branches of the signalling networks activated in the ischaemic/reperfused heart might be more successful in the search for novel diagnostic or therapeutic targets. High-throughput techniques allow high-resolution, genome-wide investigation of genetic variants, epigenetic modifications, and associated gene expression profiles. Platforms such as proteomics and metabolomics (not described here in detail) also offer simultaneous readouts of hundreds of proteins and metabolites. Isolated omics analyses usually provide Big Data requiring large data storage, advanced computational resources and complex bioinformatics tools. The possibility of integrating different omics approaches gives new hope to better understand the molecular circuitry activated by myocardial ischaemia, putting it in the context of the human 'diseasome'. Since modifications of cardiac gene expression have been consistently linked to pathophysiology of the ischaemic heart, the integration of epigenomic and transcriptomic data seems a promising approach to identify crucial disease networks. Thus, the scope of this Position Paper will be to highlight potentials and limitations of these approaches, and to provide recommendations to optimize the search for novel diagnostic or therapeutic targets for acute ischaemia/reperfusion injury and ischaemic heart failure in the post-genomic era. [ABSTRACT FROM AUTHOR]

Aging is associated with substantial physiological changes and constitutes a major risk factor for neurological disorders including dementia. Alterations in gene expression upon aging have been extensively studied; however, an in-depth characterization of post-transcriptional regulatory events remains elusive. Here, we profiled the age-related changes of the transcriptome and translatome in the female mouse hippocampus by RNA sequencing of total RNA and polysome preparations at four ages (3-, 6-, 12-, 20-month-old); and we implemented a variety of bioinformatics approaches to unravel alterations in transcript abundance, alternative splicing, and polyadenylation site selection. We observed mostly wellcoordinated transcriptome and translatome expression signatures across age including upregulation of transcripts related to immune system processes and neuroinflammation, though transcripts encoding ribonucleoproteins or associated with mitochondrial functions, calcium signaling and the cell-cycle displayed substantial discordant profiles, suggesting translational control associated with age-related deficits in hippocampal-dependent behavior. By contrast, alternative splicing was less preserved, increased with age and was associated with distinct functionally-related transcripts encoding proteins acting at synapses/dendrites, RNA-binding proteins; thereby predicting regulatory roles for RBM3 and CIRBP. Only minor changes in polyadenylation site selection were identified, indicating pivotal 3'-end selection in young adults compared to older groups. Overall, our study provides a comprehensive resource of age-associated post-transcriptional regulatory events in the mouse hippocampus, enabling further examination of the molecular features underlying age-associated neurological diseases. [ABSTRACT FROM AUTHOR]

Computational intelligence methods for bioinformatics and biostatistics: 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6-8, 2018, Revised Selected Papers. CIBB 2012 was organized in Houston (TX), then in Nice (France) in 2013, Cambridge (UK) in 2014, Naples (Italy) in 2015, Stirling (UK) in 2016, Cagliari (Italy) in 2017, Lisbon (Portugal) in 2018, and Bergamo (Italy) in 2019. This supplement contains seven revised and extended papers selected from CIBB 2018 and CIBB 2019, the 15th and 16th editions of the international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics. [Extracted from the article]

The article discusses the most-cited researches in history. Topics include topics of papers and citations that has allowed them to reach the top the most cited research like Bioinformatics, Phylogenetics and Statistics, a chart showing the top 10 most cited researches and information on the first systematic effort to track citations the Science Citation Index (SCI).

Information about several papers discussed in the 7th International Young Scientists School "Systems Biology and Bioinformatics" held in Novosibirsk, Russia on June 22-25, 2015, is presented. Topics include next generation sequencing (NGS), evolutionary bioinformatics, and systems biology and gene network modeling. The conference featured several research papers which include from A. I. Klimenko, A. V. Bryanskaya and T. M. Khlebodarova.

The second ISCB-Asia conference of the International Society for Computational Biology took place December 17-19, 2012, in Shenzhen, China. The conference was co-hosted by BGI as the first Shenzhen Conference on Computational Genomics (SCCG). 45 talks were presented at ISCB-Asia/SCCG 2012. The topics covered included software tools, reproducible computing, next-generation sequencing data analysis, transcription and mRNA regulation, protein structure and function, cancer genomics and personalized medicine. Nine of the proceedings track talks are included as full papers in this supplement. In this report we first give a short overview of the conference by listing some statistics and visualizing the talk abstracts as word clouds. Then we group the talks by topic and briefly summarize each one, providing references to related publications whenever possible. Finally, we close with a few comments on the success of this conference [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are the collective term for the various vesicles that are released by cells into the extracellular space. Such vesicles include exosomes and microvesicles, which vary by their size and/or protein and genetic cargo. With the discovery that EVs contain genetic material in the form of RNA (evRNA) has come the increased interest in these vesicles for their potential use as sources of disease biomarkers and potential therapeutic agents. Rapid developments in the availability of deep sequencing technologies have enabled the study of EV-related RNA in detail. In October 2012, the International Society for Extracellular Vesicles (ISEV) held a workshop on "evRNA analysis and bioinformatics." Here, we report the conclusions of one of the roundtable discussions where we discussed evRNA analysis technologies and provide some guidelines to researchers in the field to consider when performing such analysis. [ABSTRACT FROM AUTHOR]

In budding yeast, Cdc13, Stn1, and Ten1 form a heterotrimeric complex (CST) that is essential for telomere protection and maintenance. Previous bioinformatics analysis revealed a putative oligonucleotide/oligosaccharide-binding (OB) fold at the N terminus of Stn1 (Stn1N) that shows limited sequence similarity to the OB fold of Rpa2, a subunit of the eukaryotic ssDNA-binding protein complex replication protein A (RPA). Here we present functional and structural analyses of Stn1 and Ten1 from multiple budding and fission yeast. The crystal structure of the Candida tropicalis Stn1N complexed with Ten1 demonstrates an Rpa2N-Rpa3-like complex. In both structures, the OB folds of the two components pack against each other through interactions between two C-terminal helices. The structure of the C-terminal domain of Saccharomyces cerevisiae Stn1 (Stn1C) was found to comprise two related winged helix-turn-helix (WH) motifs, one of which is most similar to the WH motif at the C terminus of Rpa2, again supporting the notion that Stn1 resembles Rpa2. The crystal structure of the fission yeast Schizosaccharomyces pombe Stn1N-Ten1 complex exhibits a virtually identical architecture as the C. tropicalis Stn1N-Ten1. Functional analyses of the Candida albicans Stn1 and Ten1 proteins revealed critical roles for these proteins in suppressing aberrant telomerase and recombination activities at telomeres. Mutations that disrupt the Stn1-Ten1 interaction induce telomere uncapping and abolish the telomere localization of Ten1. Collectively, our structural and functional studies illustrate that, instead of being confined to budding yeast telomeres, the CST complex may represent an evolutionarily conserved RPA-like telomeric complex at the 3′ overhangs that works in parallel with or instead of the well-characterized POT1-TPP1/TEBPα-β complex. [ABSTRACT FROM AUTHOR]

Motivation: Nowadays, publishers of scientific journals face the tough task of selecting high-quality articles that will attract as many readers as possible from a pool of articles. This is due to the growth of scientific output and literature. The possibility of a journal having a tool capable of predicting the citation count of an article within the first few years after publication would pave the way for new assessment systems. [ABSTRACT FROM PUBLISHER]

Background: The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. MutationFinder is a widely used program for finding mentions of specific mutations in texts. We report on augmenting the positive attributes of MutationFinder with a more inclusive regular expression list to create ResidueFinder, which finds mentions of native amino acids as well as mutations. We also consider parameter options for both ResidueFinder and MutationFinder to explore trade-offs between precision, recall, and computational efficiency. We test our methods and software in full text as well as abstracts. Results: We find there is much more variety of formats for mentioning residues in the entire text of papers than in abstracts alone. Failure to take these multiple formats into account results in many false negatives in the program. Since MutationFinder, like several other programs, was primarily tested on abstracts, we found it necessary to build an expanded regular expression list to achieve acceptable recall in full text searches. We also discovered a number of artifacts arising from PDF to text conversion, which we wrote elements in the regular expression library to address. Taking into account those factors resulted in high recall on randomly selected primary research articles. We also developed a streamlined regular expression (called "cut") which enables a several hundredfold speedup in both MutationFinder and ResidueFinder with only a modest compromise of recall. All regular expressions were tested using expanded F-measure statistics, i.e., we compute Fβ for various values of where the larger the value of β the more recall is weighted, the smaller the value of β the more precision is weighted. Conclusions: ResidueFinder is a simple, effective, and efficient program for finding individual residue mentions in primary literature starting with text files, implemented in Python, and available in SourceForge.net. The most computationally efficient versions of ResidueFinder could enable creation and maintenance of a database of residue mentions encompassing all articles in PubMed. [ABSTRACT FROM AUTHOR]

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein–protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation. [ABSTRACT FROM AUTHOR]

Background. Network pharmacology (NP) has become an increasingly important focus in the drug research field over the past decade. However, no study to date has mapped the current status of NP. Therefore, we performed a bibliometric study to evaluate the top 100 cited papers on NP. Methods. We searched the Web of Science Core Collection from its inception to February 25, 2019, using the terms "network pharmacology" and "systems pharmacology." Key data, including title, publication year, number of citations, authors, countries/regions, organizations, and journals, were retrieved and analyzed using Excel 2016 and VOSviewer 1.6.10. Results. The total number of citations for the 100 cited papers ranged from 21 to 1,238, published in 53 journals, from 2005 to 2017. The top three journals with the most publications on NP were Clinical Pharmacology & Therapeutics (n = 8, IF2017 = 6.544), Journal of Ethnopharmacology (n = 8, IF2017 = 3.115), and PLoS One (n = 7, IF2017 = 2.766). Most published articles were from the USA (n = 41) and China (n = 35). The most active author was Wang Yonghua from the Northwest A&F University, and of the 100 publications, 14 listing his name. The most frequently used substantive terms included "drug discovery," "traditional Chinese medicine (TCM)," "in-vitro," "cancer," and "cardiovascular disease." Conclusions. The USA and China made the greatest contribution to NP research. The current NP research mainly focused on NP methods (including experimental validation) and using them to explore the molecular mechanisms of TCM for some critical diseases such as cardiovascular diseases and cancers. Furthermore, we believe some guidelines should be developed to regulate NP studies. [ABSTRACT FROM AUTHOR]

Bioinformatics allows and encourages the application of many different parallel computing approaches. This special issue brings together high-quality state-of-the-art contributions about parallel computing in bioinformatics, from different points of view or perspectives, that is, from high-performance, heterogeneous, and cloud computing. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2018 (6th International Workshop on Parallelism in Bioinformatics, and part of EuroMPI 2018). The domains and topics covered in these five papers are timely and important, and the authors have done an excellent job of presenting the material. [ABSTRACT FROM AUTHOR]

Succinic acid, also known as dicarboxylic acid, is one of the biochemical products chemically produced from Escherichia coli (E. coli) metabolism. However, by using conventional methods succinic acid cannot be produced sufficiently and it is costly. Hence, there is a lot of ongoing research on E. coli by using in silico methods. Researchers build computational models of E. coli to analyze and modify their metabolic network. This paper proposes a hybrid of ant colony optimization–genetic algorithm–flux balance analysis (ACOGAFBA) in enhancing the succinic acid production of E. coli by identifying genes to be knocked out. Ant colony optimization (ACO) is a swarm intelligent optimization that is inspired based on the natural foraging behavior of ant colony. Local search technique like genetic algorithm (GA) is applied to solve optimization and search problem by approximation. Flux balance analysis (FBA) is used for fitness calculation after gene knockout. FBA identifies a point (fitness) in flux space by using quadratic programming, which is closest to the wild type point. ACOGAFBA produced three sets of gene knockout lists. The dataset i JR904 is used in this paper. The results show that ACOGAFBA can identify the set of knockout genes to improve succinic acid production in E. coli. [ABSTRACT FROM AUTHOR]

Papers from the 2010 American Medical Informatics Association (AMIA) Summit on Translational Bioinformatics held from March 10-12, 2010 in San Francisco, California are presented including "Mapping Transcription Mechanisms from Multimodal Genomic Data," "Using Gene Co-Expression Network Analysis to Predict Biomarkers for Chronic Lymphocytic Leukemia," and "Mining Multi-Item Drug Adverse Effect Associations in Spontaneous Reporting Systems."

Papers from the 9th International Conference on Bioinformatics, held September 26-28, 2010 in Tokyo, Japan are presented including "Robust and Accurate Prediction of Noncoding RNAs from Aligned Sequences," "Integrating Diverse Biological and Computational sources for Reliable Protein-Protein Interactions," and "DiScRIBinATE: A Rapid Method for Accurate Taxonomic Classification of Metagenomic Sequences."

Papers from the 2010 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference, held February 19-20, 2010 in Jonesboro, Arkansas are presented including "Analysis of Bovine Viral Diarrhea Viruses-Infected Monocytes: Identification of Cytopathic and Noncytopathic Biotype Differences," "Enzyme Structure Dynamics of Xylanase I from Trichoderma Longibrachiatum," and "Comparative Genome Analysis of PHB Gene Family Reveals Deep Evolutionary Origins and Diverse Gene Function."

The article presents papers from the Asia Pacific Bioinformatics Network (APBioNet) Eighth International Conference on Bioinformatics (InCoB2009) in Singapore from September 7-11, 2009 including "Genome-Wide Analysis of Alternative Splicing in Cow: Implications in Bovine as a Model for Human Diseases," "MapNext: A Software Tool for Spliced and Unspliced Alignments and SNP Detection of Short Sequence Reads," and "Measuring Similarity Between Gene Expression Profiles: A Bayesian Approach."

Bioinformatics allows and encourages the application of many different parallel programming approaches. This special issue brings together high-quality state-of-the-art contributions about parallel programming in bioinformatics, from some interesting points of view or perspectives. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2017 (5th International Workshop on Parallelism in Bioinformatics, and part of ICA3PP 2017). The domains and topics covered in these 2 papers are timely and important, and the authors have done an excellent job of presenting the material. [ABSTRACT FROM AUTHOR]

Simple Summary: This review discusses the enzymatic processes governing the initial stages of the synthesis of glycerophospholipids (phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine) and triacylglycerol. The key enzymes analyzed include glycerol-3-phosphate acyltransferases (GPAT) and 1-acylglycerol-3-phosphate acyltransferases (AGPAT). Additionally, because most AGPATs have lysophospholipid acyltransferase (LPLAT) activity, enzymes involved in the Lands cycle with similar functions were also included. The review further explores the potential therapeutic implications of inhibiting these enzymes in the treatment of metabolic diseases and cancer. By elucidating the enzymatic pathways involved in lipid synthesis and their impact on various pathological conditions, the article contributes to the understanding of these processes and their potential as therapeutic targets. This review delves into the enzymatic processes governing the initial stages of glycerophospholipid (phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine) and triacylglycerol synthesis. The key enzymes under scrutiny include GPAT and AGPAT. Additionally, as most AGPATs exhibit LPLAT activity, enzymes participating in the Lands cycle with similar functions are also covered. The review begins by discussing the properties of these enzymes, emphasizing their specificity in enzymatic reactions, notably the incorporation of polyunsaturated fatty acids (PUFAs) such as arachidonic acid and docosahexaenoic acid (DHA) into phospholipids. The paper sheds light on the intricate involvement of these enzymes in various diseases, including obesity, insulin resistance, and cancer. To underscore the relevance of these enzymes in cancer processes, a bioinformatics analysis was conducted. The expression levels of the described enzymes were correlated with the overall survival of patients across 33 different types of cancer using the GEPIA portal. This review further explores the potential therapeutic implications of inhibiting these enzymes in the treatment of metabolic diseases and cancer. By elucidating the intricate enzymatic pathways involved in lipid synthesis and their impact on various pathological conditions, this paper contributes to a comprehensive understanding of these processes and their potential as therapeutic targets. [ABSTRACT FROM AUTHOR]

Recent data have revealed that genetic variation could be attributed to overdominance, or heterozygote advantage. However, genomic survey showed that only a small number of genes that have polymorphisms maintained by overdominance which is consistent with many published papers. Google Web, Google scholar, NCBI Databases and OMIC Tools were used to obtain data for this review paper. Different key words were used to retrieve the required research articles and bioinformatics-based information, such as “overdominance’’ and “overdominance in animals’’. Research papers used for this review were published over the last 10 to 15 years and information regarding overdominance in livestock was considered for current review. It is hoped that in the future, more loci with overdominance will be discovered. In this review, we will illustrate eight examples of overdominance in livestock. We also want to emphasize that given a low number of reported cases in overdominance, it does not reflect the unimportance of heterozygote advantage in adaptive functions. [ABSTRACT FROM AUTHOR]

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Yu et al. constructed a weighted four-layer disease-disease similarity network to characterize the associations at different levels between diseases. He et al. proposed an NRLMFMDA (neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimental validation of disease-miRNA association. Liu et al. classified muscle-invasive bladder cancer into two conservative subtypes using miRNA, mRNA, and lncRNA expression data; investigated subtype-related biological pathways; and evaluated the subtype classification performance using machine learning methods. [Extracted from the article]

This manuscript discusses the critical need for advancing genetic testing capabilities for neurological disorders (NDs) in Tanzania, emphasizing the importance, challenges, and strategies for implementation. Neurological disorders, often caused by a complex interplay of genetic and environmental factors, disproportionately affect low and middle-income countries like Tanzania. Patients with neurogenetic diagnoses in Tanzania face substantial obstacles, including lack of trust in medical professionals, stigmatization, and limited access to proper care. The manuscript underscores the infancy of genetic testing implementation for NDs in Tanzania, citing challenges such as high costs, limited availability, and the absence of routine testing in diagnostic procedures. The paper recommends establishing well-characterized cohorts, conducting a nationwide emergency census survey, and enhancing diagnostic services and healthcare financing. The paper stresses on the importance of collaboration, research, public awareness, and advocacy to address the challenges posed by NDs in Tanzania. The manuscript proposes a comprehensive plan, including training and capacity building in molecular diagnosis and bioinformatics, to establish genetic testing capabilities and reduce the burden of NDs in the country. Overall, the manuscript calls for a collaborative effort involving government support, stakeholder engagement, and international partnerships to advance genetic testing and improve healthcare outcomes for individuals with neurological disorders in Tanzania. [ABSTRACT FROM AUTHOR]

Multiple Sequence Alignment (MSA) plays a pivotal role in bioinformatics, facilitating various critical biological analyses, including the prediction of unknown protein structures and functions. While numerous methods are available for MSA, bioinspired algorithms stand out for their efficiency. Despite the growing research interest in addressing the MSA challenge, only a handful of comprehensive reviews have been undertaken in this domain. To bridge this gap, this study conducts a thorough analysis of bioinspired-based methods for MSA through a systematic literature review (SLR). By focusing on publications from 2010 to 2024, we aim to offer the most current insights into this field. Through rigorous eligibility criteria and quality standards, we identified 45 relevant papers for review. Our analysis predominantly concentrates on bioinspired-based techniques within the context of MSA. Notably, our findings highlight Genetic Algorithm and Memetic Optimization as the most commonly utilized algorithms for MSA. Furthermore, benchmark datasets such as BAliBASE and SABmark are frequently employed in evaluating MSA solutions. Structural-based methods emerge as the preferred approach for assessing MSA solutions, as revealed by our systematic literature review. Additionally, this study explores current trends, challenges, and unresolved issues in the realm of bioinspired algorithms for MSA, offering practitioners and researchers valuable insights and comprehensive understanding of the field. [ABSTRACT FROM AUTHOR]

There is an ongoing process in which mitochondrial sequences are being integrated into the nuclear genome. The importance of these sequences has already been revealed in cancer biology, forensic, phylogenetic studies and in the evolution of the eukaryotic genetic information. Human and numerous model organisms' genomes were described from those sequences point of view. Furthermore, recent studies were published on the patterns of these nuclear localised mitochondrial sequences in different taxa. However, the results of the previously released studies are difficult to compare due to the lack of standardised methods and/or using few numbers of genomes. Therefore, in this paper our primary goal is to establish a uniform mining pipeline to explore these nuclear localised mitochondrial sequences. Our results show that the frequency of several repetitive elements is higher in the flanking regions of these sequences than expected. A machine learning model reveals that the flanking regions' repetitive elements and different structural characteristics are highly influential during the integration process. In this paper, we introduce a general mining pipeline for all mammalian genomes. The workflow is publicly available and is believed to serve as a validated baseline for future research in this field. We confirm the widespread opinion, on - as to our current knowledge - the largest dataset, that structural circumstances and events corresponding to repetitive elements are highly significant. An accurate model has also been trained to predict these sequences and their corresponding flanking regions. [ABSTRACT FROM AUTHOR]

Bioinformatics is a rapidly developing field enabling scientific experiments via computer models and simulations. In recent years, there has been an extraordinary growth in biological databases. Therefore, it is extremely important to propose effective methods and algorithms for the fast and accurate processing of biological data. Sequence comparisons are the best way to investigate and understand the biological functions and evolutionary relationships between genes on the basis of the alignment of two or more DNA sequences in order to maximize the identity level and degree of similarity. This paper presents a new version of the pairwise DNA sequences alignment algorithm, based on a new method called CAT, where a dependency with a previous match and the closest neighbor are taken into consideration to increase the uniqueness of the CAT profile and to reduce possible collisions, i.e., two or more sequence with the same CAT profiles. This makes the proposed algorithm suitable for finding the exact match of a concrete DNA sequence in a large set of DNA data faster. In order to enable the usage of the profiles as sequence metadata, CAT profiles are generated once prior to data uploading to the database. The proposed algorithm consists of two main stages: CAT profile calculation depending on the chosen benchmark sequences and sequence comparison by using the calculated CAT profiles. Improvements in the generation of the CAT profiles are detailed and described in this paper. Block schemes, pseudo code tables, and figures were updated according to the proposed new version and experimental results. Experiments were carried out using the new version of the CAT method for DNA sequence alignment and different datasets. New experimental results regarding collisions, speed, and efficiency of the suggested new implementation are presented. Experiments related to the performance comparison with Needleman–Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. A performance analysis of the proposed algorithm based on the CAT method against the Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is widely used for biological data searching, was performed. The impact of prior matching dependencies on uniqueness for generated CAT profiles is investigated. The experimental results from sequence alignment demonstrate that the proposed CAT method-based algorithm exhibits minimal deviation, which can be deemed negligible if such deviation is considered permissible in favor of enhanced performance. It should be noted that the performance of the CAT algorithm in terms of execution time remains stable, unaffected by the length of the analyzed sequences. Hence, the primary benefit of the suggested approach lies in its rapid processing capabilities in large-scale sequence alignment, a task that traditional exact algorithms would require significantly more time to perform. [ABSTRACT FROM AUTHOR]

Based on bioinformatics, this paper constructs an agricultural business management platform to enhance agricultural efficiency and optimize the agricultural business environment. Firstly, the correspondence between expression behavior and physiochemical functions of crop genomes was established by local sampling method to reveal the important characteristics of crop bioinformatics. Then, comparative genomics is used to analyze crop genomes to understand the evolutionary aspects of biological phylogeny and to reveal differences in genetic variation, gene family evolution and gene function. Finally, new digital technologies are used to monitor crop growth parameters from macroscopic to microscopic levels to analyze ecological information about crops, such as soil moisture, climatic conditions, plant morphology and physiological characteristics. The results show that after the implementation of digital agriculture, the productivity of Chinese regions has increased by 38%, the average score of the business environment is around 75.8, and the GDP per capita has increased by 28%. The application of the platform has optimized the production efficiency and business environment of agriculture and provided strong support for sustainable agricultural development. [ABSTRACT FROM AUTHOR]

In this paper, an alignment-free bioinformatics technique, termed the 20D-Dynamic Representation of Protein Sequences, is utilized to investigate the similarity/dissimilarity between Baculovirus and Echinococcus multilocularis genome sequences. In this method, amino acid sequences are depicted as 20D-dynamic graphs, comprising sets of "material points" in a 20-dimensional space. The spatial distribution of these material points is indicative of the sequence characteristics and is quantitatively described by sequence descriptors akin to those employed in dynamics, such as coordinates of the center of mass of the 20D-dynamic graph and the tensor of the moment of inertia of the graph (defined as a symmetric matrix). Each descriptor unveils distinct features of similarity and is employed to establish similarity relations among the examined sequences, manifested either as a symmetric distance matrix ("similarity matrix"), a classification map, or a phylogenetic tree. The classification maps are introduced as a new way of visualizing the similarity relations obtained using the 20D-Dynamic Representation of Protein Sequences. Some classification maps are obtained using the Principal Component Analysis (PCA) for the center of mass coordinates and normalized moments of inertia of 20D-dynamic graphs as input data. Although the method operates in a multidimensional space, we also apply some visualization techniques, including the projection of 20D-dynamic graphs onto a 2D plane. Studies on model sequences indicate that the method is of high quality, both graphically and numerically. Despite the high similarity observed among the sequences of E. multilocularis, subtle discrepancies can be discerned on the 2D graphs. Employing this approach has led to the discovery of numerous new similarity relations compared to our prior study conducted at the DNA level, using the 4D-Dynamic Representation of DNA/RNA Sequences, another alignment-free bioinformatics method also introduced by us. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF–target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF–target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF–target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species. [ABSTRACT FROM AUTHOR]

Nucleotide sequence reagents are verifiable experimental reagents in biomedical publications, because their sequence identities can be independently verified and compared with associated text descriptors. We have previously reported that incorrectly identified nucleotide sequence reagents are characteristic of highly similar human gene knockdown studies, some of which have been retracted from the literature on account of possible research fraud. Because of the throughput limitations of manual verification of nucleotide sequences, we developed a semi-automated fact checking tool, Seek & Blastn, to verify the targeting or non-targeting status of published nucleotide sequence reagents. From previously described and unknown corpora of 48 and 155 publications, respectively, Seek & Blastn correctly extracted 304/342 (88.9%) and 1066/1522 (70.0%) nucleotide sequences and a predicted targeting/ non-targeting status. Seek & Blastn correctly predicted the targeting/ non-targeting status of 293/304 (96.4%) and 988/1066 (92.7%) of the correctly extracted nucleotide sequences. A total of 38/39 (97.4%) or 31/79 (39.2%) Seek & Blastn predictions of incorrect nucleotide sequence reagent use were correct in the two literature corpora. Combined Seek & Blastn and manual analyses identified a list of 91 misidentified nucleotide sequence reagents, which could be built upon through future studies. In summary, incorrect nucleotide sequence reagents represent an under-recognized source of error within the biomedical literature, and fact checking tools such as Seek & Blastn may help to identify papers and manuscripts affected by these errors. [ABSTRACT FROM AUTHOR]

Reproducible research is a concept of providing access to data and software along with published scientific findings. By means of some case studies from different disciplines, we will illustrate reasons why readers should be given the possibility to look at the data and software independently from the authors of the original publication. We report results of a survey comprising 100 papers recently published in Bioinformatics. The main finding is that authors of this journal share a culture of making data available. However, the number of papers where source code for simulation studies or analyzes is available is still rather limited. [ABSTRACT FROM PUBLISHER]

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section. [ABSTRACT FROM AUTHOR]

Background: Recent evidences indicated that shear stress is critical in orchestrating gene expression in cardiovascular disease. It is necessary to identify the mechanism of shear stress influencing gene expression in physiology and pathophysiology conditions. This paper aimed to identify candidate hub genes and its transcription factors with bioinformatics. Methods: We analyzed microarray expression profile of GSE16706 to identify differentially expressed genes (DEGs) in low shear stress (1 dyne/cm2) treated human umbilical vein endothelial cells (HUVECs) compared with static condition for 24 h. Results: 652 DEGs, including 333 up-regulated and 319 down-regulated DEGs, were screen out. Functional enrichment analysis indicated enrichment items mainly included cytokine-cytokine receptor interaction and cell cycle. Five hub genes (CDC20, CCNA2, KIF11, KIF2C and PLK1) and one significant module (score = 17.39) were identified through protein–protein interaction (PPI) analysis. Key transcriptional factor FOXC1 displayed close interaction with all the hub genes via gene-transcriptional factor network. Single-gene GSEA analysis indicated that CDC20 was linked to the G2M_CHECKPOINT pathway and cell cycle pathway. Conclusions: By using integrated bioinformatic analysis, a new transcriptional factor and hub-genes network related to HUVECs treated with low shear stress were identified. The new regulation mechanism we discovered may be a promising potential therapeutic target for cardiovascular disease. [ABSTRACT FROM AUTHOR]

The article presents an overview of papers that appeared in the November 2003 issue of the journal "The International Journal of High Performance Computer Applications." The papers included are from the Third International Workshop on Grid Computing. The first paper by researcher Craig A. Lee investigates distributed query processing on the Grid using a prototype query language and a bioinformatics case study. The next paper by researcher Jim Smith et al looks at the throughput behavior for Monte Carlo simulations on the Grid. By contrast, the paper by researcher Marcio Faerman et al. looks at guided searches through parameter spaces. The paper by researcher William H. Bell et al. evaluates data replication algorithms by simulating the EU DataGrid TestBed 1 sites including their network topology. Coupling existing, parallel numerical codes on the Grid is an issue addressed by researcher Christian Péerez et al. The paper by researcher Gregor von Laszewski et al. investigates computational thermo-chemistry where enthalpy diagrams and thermochemical reaction tables are used to manage the associated computations through a Grid framework. Last, but not least, researcher Gabrielle Allen et al. report on GridLab and the Grid Application Toolkit that will define a set of generic, high-level APIs for building Grid applications.