Local Alignment Search Tool Algorithm Research Articles

Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family

PurposeIn this study, we present DeepVirusClassifier, a tool capable of accurately classifying Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral sequences among other subtypes of the coronaviridae family. This classification is achieved through a deep neural network model that relies on convolutional neural networks (CNNs). Since viruses within the same family share similar genetic and structural characteristics, the classification process becomes more challenging, necessitating more robust models. With the rapid evolution of viral genomes and the increasing need for timely classification, we aimed to provide a robust and efficient tool that could increase the accuracy of viral identification and classification processes. Contribute to advancing research in viral genomics and assist in surveilling emerging viral strains.MethodsBased on a one-dimensional deep CNN, the proposed tool is capable of training and testing on the Coronaviridae family, including SARS-CoV-2. Our model’s performance was assessed using various metrics, including F1-score and AUROC. Additionally, artificial mutation tests were conducted to evaluate the model’s generalization ability across sequence variations. We also used the BLAST algorithm and conducted comprehensive processing time analyses for comparison.ResultsDeepVirusClassifier demonstrated exceptional performance across several evaluation metrics in the training and testing phases. Indicating its robust learning capacity. Notably, during testing on more than 10,000 viral sequences, the model exhibited a more than 99% sensitivity for sequences with fewer than 2000 mutations. The tool achieves superior accuracy and significantly reduced processing times compared to the Basic Local Alignment Search Tool algorithm. Furthermore, the results appear more reliable than the work discussed in the text, indicating that the tool has great potential to revolutionize viral genomic research.ConclusionDeepVirusClassifier is a powerful tool for accurately classifying viral sequences, specifically focusing on SARS-CoV-2 and other subtypes within the Coronaviridae family. The superiority of our model becomes evident through rigorous evaluation and comparison with existing methods. Introducing artificial mutations into the sequences demonstrates the tool’s ability to identify variations and significantly contributes to viral classification and genomic research. As viral surveillance becomes increasingly critical, our model holds promise in aiding rapid and accurate identification of emerging viral strains.

Detection of Periodontal Pathogens from Dental Plaques of Dogs with and without Periodontal Disease.

Dental plaque bacteria are one of the main factors responsible for the development of a periodontal disease, which is the most common infectious disease in dogs. The aim of this study was to identify the presence of periodontal disease-related bacteria in the dental plaque of dogs. Plaque samples were taken from dogs with and without periodontal disease. Samples were analyzed for the presence of Porphyromonas gulae, Tannerella forsythia and Treponema denticola using a PCR technique amplifying 16S rRNA genes of P. gulae and T. forsythia and flaB2 genes of Treponema species, including T. denticola. The presence of T. forsythia was confirmed in all samples. P. gulae was detected in all dogs with periodontal disease and in 71.43% of dogs without periodontal disease. Treponema spp. were detected in 64.29% of the samples. Based on Sanger sequencing and Basic Local Alignment Search Tool algorithm, Treponema spp. were identified as T. denticola and Treponema putidum. T. denticola was present in 28.57% of dogs with periodontal disease, while T. putidum was present in 42.86% of dogs with periodontal disease and in 57.14% of dogs without periodontal disease. T. putidum was positively correlated with both P. gulae and T. forsythia, suggesting that it may be involved in the development of periodontal disease.

High throughput BLAST algorithm using spark and cassandra

The rise of high-resolution and high-throughput sequencing technologies has driven the emergence of such new fields of application as precision medicine. However, this has also led to an increase in the storage and processing requirements for the bioinformatics tools, which can only be provided by high-performance and massive data processing infrastructures. Such technologies allow the development of scalable, efficient and reliable bioinformatics tools. In this paper, a new implementation of the Basic Local Alignment Search Tool algorithm is presented. Our proposal, named Sparky-Blast, utilizes Cassandra database to store the different reference datasets and the Apache Spark processing framework to calculate the indexes and process the queries. This successful approach avoids the bottleneck that suffers the original BLAST version that is limited to the resources of a single machine. Sparky-Blast is capable of using the distributed resources of a Big-Data Cluster to process queries in parallel, thus, improving both the response time and the system throughput. At the same time, the use of a distributed architecture like Hadoop provides unlimited scalability from the point of view of both the hardware infrastructure and performance.

Enhanced bioremediation of toxic metals and harvesting electricity through sediment microbial fuel cell

Performance of sediment microbial fuel cells (SMFCs) with aerated (A-SMFC) and nonaerated (NA-SMFC) cathodes was evaluated at different operating conditions in toxic metal removal and power generation. The A- and NA-SMFC open-circuit voltages were respectively about 665 and 275 mV, with quite steady performances for 120 days. The cell design points of both SMFCs were calculated by implementing polarization curves, and they were at 1 kΩ (power density 8.1 mW/m2 and current density 0.0504 mA/m2 with voltage 150 mV) for NA-SMFC and 100 Ω (power density 252.81 mW/m2 and current density 0.954 mA/m2 with voltage of 275 mV) for A-SMFC, respectively. Cathode potentials were at 30 kΩ 290 mV (NA-SMFC) and 500 mV (A-SMFC). As to the anode, at 30 KΩ, it was −180 mV (NA-SMFC) and 190 mV (A-SMFC). The voltammetry profiles of A-SMFC showed maximum current (forward scan, 22.7 μA; reverse scan, −19.4 μA) followed by NA-SMFC (forward scan, 11.3 μA; reverse scan, −9.5 μA). The cell design points of A-SMFC and NA-SMFC were altered after pH and temperature amendments at 200 and 700 Ω, respectively. As to metal removal rate, the maximum arsenic cadmium and lead removal was observed in A-SMFC at pH 7.0 (77.70%, 90.86%, and 83.91%) and 45°C (66.22%, 79.03%, and 71.17%). Scanning electron microscopy confirmed, at pH 7.0 and 45°C, an optimal biofilm growth at cathode and anode graphite of both SMFCs. After 120 days of operation, genomic DNA was extracted from biofilms and analyzed for rDNA 16S sequences. Similarity search was performed by using Basic Local Alignment Search Tool algorithm against the National Center for Biotechnology Information Gen Bank showing Pseudomonas spp. dominance at both anode and cathode. The results revealed that the A-SMFC system could be employed as an effective and long-term tool for power generation as well as stimulated bioremediation of the polluted sediments.

A One-Step, Real-Time PCR Assay for Rapid Detection of Rhinovirus

One-step, real-time PCR assays for rhinovirus have been developed for a limited number of PCR amplification platforms and chemistries, and some exhibit cross-reactivity with genetically similar enteroviruses. We developed a one-step, real-time PCR assay for rhinovirus by using a sequence detection system (Applied Biosystems; Foster City, CA). The primers were designed to amplify a 120-base target in the noncoding region of picornavirus RNA, and a TaqMan (Applied Biosystems) degenerate probe was designed for the specific detection of rhinovirus amplicons. The PCR assay had no cross-reactivity with a panel of 76 nontarget nucleic acids, which included RNAs from 43 enterovirus strains. Excellent lower limits of detection relative to viral culture were observed for the PCR assay by using 38 of 40 rhinovirus reference strains representing different serotypes, which could reproducibly detect rhinovirus serotype 2 in viral transport medium containing 10 to 10,000 TCID(50) (50% tissue culture infectious dose endpoint) units/ml of the virus. However, for rhinovirus serotypes 59 and 69, the PCR assay was less sensitive than culture. Testing of 48 clinical specimens from children with cold-like illnesses for rhinovirus by the PCR and culture assays yielded detection rates of 16.7% and 6.3%, respectively. For a batch of 10 specimens, the entire assay was completed in 4.5 hours. This real-time PCR assay enables detection of many rhinovirus serotypes with the Applied Biosystems reagent-instrument platform.

Discovery of a Human Peptide Sequence Signaling Islet Neogenesis

To identify triggers for islet neogenesis in humans that may lead to new treatments that address the underlying mechanism of disease for patients with type 1 or type 2 diabetes. In an effort to identify bioactive human peptide sequences that might trigger islet neogenesis, we evaluated amino acid sequences within a variety of mammalian pancreas-specific REG genes. We evaluated GenBank, the Basic Local Alignment Search Tool algorithm, and all available proteomic databases and developed large-scale protein-to-protein interaction maps. Studies of peptides of interest were conducted in human pancreatic ductal tissue, followed by investigations in mice with streptozocin-induced diabetes. Our team has defined a 14-amino acid bioactive peptide encoded by a portion of the human REG3a gene we termed Human proIslet Peptide (HIP), which is well conserved among many mammals. Treatment of human pancreatic ductal tissue with HIP stimulated the production of insulin. In diabetic mice, administration of HIP improved glycemic control and significantly increased islet number. Bioinformatics analysis, coupled with biochemical interaction studies in a human pancreatic cell line, identified the human exostoses-like protein 3 (EXTL3) as a HIP-binding protein. HIP enhanced EXTL3 translocation from the membrane to the nucleus, in support of a model whereby EXTL3 mediates HIP signaling for islet neogenesis. Our data suggest that HIP may be a potential stimulus for islet neogenesis and that the differentiation of new islets is a process distinct from beta cell proliferation within existing islets. Human clinical trials are soon to commence to determine the effect of HIP on generating new islets from one's own pancreatic progenitor cells.

Profiling genes expressed in human fetal cartilage using 13,155 expressed sequence tags

Objective: To analyze the gene expression profile of human fetal cartilage by expressed sequence tags (ESTs).Methods: A human fetal cartilage (8–12 weeks) cDNA library was constructed using the λ ZAP Express vector. ESTs were obtained by partial sequencing of cDNA clones. The basic local alignment search tool algorithm was used to compare all generated ESTs to known sequences.Results: A total of 13,155 ESTs were analyzed, of which 8696 ESTs (66.1%) matched known genes, 53 ESTs (0.4%) were putatively novel (with no match) and the rest matched other ESTs, genomic DNA and repetitive sequences. Importantly, we identified 2448 unique known genes through non-redundancy analysis of the known gene matches, which were then functionally categorized. The tissue specificity of this library was reflected by its EST profile of the extracellular matrix (ECM) proteins. Collagens were the major transcripts, representing 68.5% of the ECM proteins. Proteoglycans were the second most abundant, constituting 9.5%. Collagen type II was the most abundant gene of all. Glypican 3, decorin and aggrecan were the major transcripts of proteoglycans. Many genes involved in cartilage development were identified, such as insulin-like growth factor-II, its receptor and binding proteins, connective tissue growth factor and fibroblast growth factors. Proteases and their regulatory factors were also identified, including matrix metalloprotease 2 and tissue inhibitor of metalloproteinase 1.Conclusions: The EST approach is an effective way of characterizing the genes expressed in cartilage. These data represent the most extensive molecular information on human fetal cartilage to date. The availability of this information will serve as a basis for further research to identify genes that are essential in cartilage development.