Number Of Sequences Research Articles

KEGG orthology prediction of bacterial proteins using natural language processing

BackgroundThe advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, making it necessary to use auto annotation tools. These tools are now indispensable in the biological research landscape, bridging the gap between the vastness of unannotated sequences and meaningful biological insights.ResultsIn this work, we propose a novel pipeline for KEGG orthology annotation of bacterial protein sequences that uses natural language processing and deep learning. To assess the effectiveness of our pipeline, we conducted evaluations using the genomes of two randomly selected species from the KEGG database. In our evaluation, we obtain competitive results on precision, recall, and F1 score, with values of 0.948, 0.947, and 0.947, respectively.ConclusionsOur experimental results suggest that our pipeline demonstrates performance comparable to traditional methods and excels in identifying distant relatives with low sequence identity. This demonstrates the potential of our pipeline to significantly improve the accuracy and comprehensiveness of KEGG orthology annotation, thereby advancing our understanding of functional relationships within biological systems.

Forecasting SARS-CoV-2 spike protein evolution from small data by deep learning and regression

The emergence of SARS-CoV-2 variants during the COVID-19 pandemic caused frequent global outbreaks that confounded public health efforts across many jurisdictions, highlighting the need for better understanding and prediction of viral evolution. Predictive models have been shown to support disease prevention efforts, such as with the seasonal influenza vaccine, but they require abundant data. For emerging viruses of concern, such models should ideally function with relatively sparse data typically encountered at the early stages of a viral outbreak. Conventional discrete approaches have proven difficult to develop due to the spurious and reversible nature of amino acid mutations and the overwhelming number of possible protein sequences adding computational complexity. We hypothesized that these challenges could be addressed by encoding discrete protein sequences into continuous numbers, effectively reducing the data size while enhancing the resolution of evolutionarily relevant differences. To this end, we developed a viral protein evolution prediction model (VPRE), which reduces amino acid sequences into continuous numbers by using an artificial neural network called a variational autoencoder (VAE) and models their most statistically likely evolutionary trajectories over time using Gaussian process (GP) regression. To demonstrate VPRE, we used a small amount of early SARS-CoV-2 spike protein sequences. We show that the VAE can be trained on a synthetic dataset based on this data. To recapitulate evolution along a phylogenetic path, we used only 104 spike protein sequences and trained the GP regression with the numerical variables to project evolution up to 5 months into the future. Our predictions contained novel variants and the most frequent prediction mapped primarily to a sequence that differed by only a single amino acid from the most reported spike protein within the prediction timeframe. Novel variants in the spike receptor binding domain (RBD) were capable of binding human angiotensin-converting enzyme 2 (ACE2) in silico, with comparable or better binding than previously resolved RBD-ACE2 complexes. Together, these results indicate the utility and tractability of combining deep learning and regression to model viral protein evolution with relatively sparse datasets, toward developing more effective medical interventions.

Innovative inbuilt moving bed biofilm reactor for nitrogen removal applied in household aquarium.

An innovative inbuilt moving bed biofilm reactor (MBBR) was created to protect fish from nitrogen in a household aquarium. During the 90 experimental days, the ammonia nitrogen (NH4+-N) concentration in the aquarium with the inbuilt MBBR was always below 0.5 mg/L, which would not threaten the fish. Concurrently, nitrite and nitrate nitrogen concentrations were always below 0.05 mg/L and 4.5 mg/L, respectively. However, the blank contrast aquarium accumulated 1.985 mg/L NH4+-N on the 16th day, which caused the fish to die. The suspended biofilms could achieve the specific NH4+-N removal rate of 45.43 g/m3/d. Biofilms presented sparsely with filamentous structures and showed certain degrees of roughness. The bacterial communities of the suspended biofilms and the sediment were statistically different (p < 0.05), reflected in denitrifying and nitrifying bacteria. In particular, the relative abundance of Nitrospira reached 1.4%, while the genus was barely found in sediments. The suspended biofilms showed potentials for nitrification function with the predicted sequence numbers of ammonia monooxygenase [1.14.99.39] and hydroxylamine dehydrogenase [EC:1.7.2.6] of 220 and 221, while the values of the sediment were only 5 and 1. This study created an efficient NH4+-N removal inbuilt MBBR for household aquariums and explored its mechanism to afford a basis for its utilization.

Accurately clustering biological sequences in linear time by relatedness sorting

Clustering biological sequences into similar groups is an increasingly important task as the number of available sequences continues to grow exponentially. Search-based approaches to clustering scale super-linearly with the number of input sequences, making it impractical to cluster very large sets of sequences. Approaches to clustering sequences in linear time currently lack the accuracy of super-linear approaches. Here, I set out to develop and characterize a strategy for clustering with linear time complexity that retains the accuracy of less scalable approaches. The resulting algorithm, named Clusterize, sorts sequences by relatedness to linearize the clustering problem. Clusterize produces clusters with accuracy rivaling popular programs (CD-HIT, MMseqs2, and UCLUST) but exhibits linear asymptotic scalability. Clusterize generates higher accuracy and oftentimes much larger clusters than Linclust, a fast linear time clustering algorithm. I demonstrate the utility of Clusterize by accurately solving different clustering problems involving millions of nucleotide or protein sequences.

Tumor-associated antigen-specific cell imaging based on upconversion luminescence and nucleic acid rolling circle amplification.

Tumor-associated antigen (TAA)-based diagnosis has gained prominence for early tumor screening, treatment monitoring, prognostic assessment, and minimal residual disease detection. However, limitations such as low sensitivity and difficulty in extracting non-specific binding membrane proteins still exist in traditional detection methods. Upconversion luminescence (UCL) exhibits unique physical and chemical properties under wavelength near-infrared light excitation. Rolling circle amplification (RCA) is an efficient DNA amplification technique with amplification factorsas high as 105. Therefore, the above two excellent techniques can be employed for highly accurate imaging analysis of tumor cells. Herein, we developed a novel nanoplatform for TAA-specific cell imaging based on UCL and RCA technology. An aptamer-primer complex selectively binds to Mucin 1 (MUC1), one of TAA on cell surface, to trigger RCA reaction, generating a large number of repetitive sequences. These sequences provide lots of binding sites for complementary signal probes, producing UCL from lanthanide-doped upconversion nanoparticles (UCNPs) after releasing quencher group. The experimental results demonstrate the specific attachment of upconversion nanomaterials to cancer cells which express a high level of MUC1, indicating the potential of UCNPs and RCA in tumor imaging.

Two-way ranging with carrier frequency and phase offset compensation based on software defined radio

In the context of Two-Way Ranging (TWR)measurements, distinct transmitter and receiver devices lead to discrepancies in clock timing, resulting in a Carrier Frequency Offset (CFO) and Carrier Phase Offset (CPO) that diminish the accuracy of distance measurements. Furthermore, moving objects also exacerbate these issues, increasing measurement errors. The effect of the CFO leads to variations and bit value inversion in the code in one frame when measuring two-way ranging distances with N random number sequence. In order to resolve this issue, the Fast Fourier Transform (FFT) is used in this work to determine the CFO and peak cross-correlation for the searching CPO. In outdoor experiments using Software Defined Radio (SDR) conducted over a maximum distance of 80 m, it was found that the precision of measurements was accurate within 1.25 m, and the overall accuracy was within 0.82 m when the Signal-to-Noise Ratio (SNR) was greater than 5 dB.

Focused Exome Sequencing Gives a High Diagnostic Yield in the Indian Subcontinent

The genetically isolated yet heterogeneous and highly consanguineous Indian population has shown a higher prevalence of rare genetic disorders. However, there is a significant socioeconomic burden for genetic testing to be accessible to the general population. In the current study, we analyzed next-generation sequencing data generated through focused exome sequencing from individuals with different phenotypic manifestations referred for genetic testing to achieve a molecular diagnosis. We reported pathogenic or likely pathogenic variants in 280 out of 833 cases with a diagnostic yield of 33.6%. Homozygous sequence and copy number variants were found as positive diagnostic findings in 131 (15.7%) cases owing to the high consanguinity in the Indian population. No relevant findings related to reported phenotype were identified in 6.2% of the cases. Patients referred for testing due to metabolic disorder and neuromuscular disorder had higher diagnostic yields. Carrier testing of asymptomatic individuals with a family history of the disease, through focused exome sequencing, achieved positive diagnosis in 54 of 118 cases tested. We also reported copy number variants in trans with single nucleotide variants and mitochondrial variants in a few of the cases. The diagnostic yield and the findings from this study signify that a focused exome test is a good lower-cost alternative for whole exome and whole genome sequencing and as a first-tier approach to genetic testing.

Splice site recognition - deciphering Exon-Intron transitions for genetic insights using Enhanced integrated Block-Level gated LSTM model

Bioinformatics is a contemporary interdisciplinary area focused on analyzing the growing number of genome sequences. Gene variants are differences in DNA sequences among individuals within a population. Splice site recognition is a crucial step in the process of gene expression, where the coding sequences of genes are joined together to form mature messenger RNA (mRNA). These genetic variants that disrupt genes are believed to be the primary reason for neuro-developmental disorders like ASD (Autism Spectrum Disorder) is a neuro-developmental disorder that is diagnosed in individuals, families, and society and occurs as the developmental delay in one among the hundred genes that are associated with these disorders. Missense variants, premature stop codons, or deletions alter both the quality and quantity of encoded proteins. Predicting genes within exons and introns presents main challenges, such as dealing with sequencing errors, short reads, incomplete genes, overlapping, and more. Although many traditional techniques have been utilized in creating an exon prediction system, the primary challenge lies in accurately identifying the length and spliced strand location classification of exons in conjunction with introns. From now on, the suggested approach utilizes a Deep Learning algorithm to analyze intricate and extensive genomic datasets. M−LSTM is utilized to categorize three binary combinations (EI as 1, IE as 2, and none as 3) using spliced DNA strands. The M−LSTM system is able to sequence extensive datasets, ensuring that long information can be stored without any impact on the current input or output. This enables it to recognize and address long-term connections and problems with rapidly increasing gradients. The proposed model is compared internally with Naïve Bayes and Random Forest to assess its efficacy. Additionally, the proposed model's performance is forecasted by utilizing probabilistic parameters like recall, F1-score, precision, and accuracy to assess the effectiveness of the proposed system.

Mitochondrial genome data provide insights into the phylogenetic relationships within Triplophysadalaica (Kessler, 1876) (Cypriniformes, Nemacheilidae).

Due to the detrimental effect of formaldehyde on DNA, ethanol has replaced formalin as the primary preservative for animal specimens. However, short-term formalin fixation of specimens might be applied during field collection. In an increasing number of studies, DNA extraction and sequencing have been successfully conducted from formalin-fixed specimens. Here the DNA from five specimens of Triplophysadalaica (Kessler, 1876) were extracted and performed high-throughput sequencing. Four of the specimens underwent short-term fixation with formalin and were subsequently transferred to ethanol. One was continuously stored in ethanol. No significant difference of DNA quality and amount were observed among these samples. Followed by assembly and annotation, five mitochondrial genomes ranging in length from 16,569 to 16,572 bp were obtained. Additionally, previously published data of other individuals or species were included to perform phylogenetic analyses. In the reconstructed trees, all eight individuals of T.dalaica form a monophyletic group within the Triplophysa branch. The group is divided into three clades: (1) samples from the Yellow River, (2) those from the Yangtze River, and (3) those from the Haihe River, and the Lake Dali Nur. This study sheds initial light on the phylogeographic relationships among different populations of T.dalaica, and will support the research about its evolutionary history in the future.

Annotation of 2,507 Saccharomyces cerevisiae genomes.

Saccharomyces cerevisiae (baker's yeast, budding yeast) is one of the most important model organisms for biological research and is a crucial microorganism in industry. Currently, a huge number of Saccharomyces cerevisiae genome sequences are available at the public domain. However, these genomes are distributed at different websites and a large number of them are released without annotation information. To provide one complete annotated genome data resource, we collected 2,507 Saccharomyces cerevisiae genome assemblies and re-annotated 2,506 assemblies using a custom annotation pipeline, producing a total of 15,407,164 protein-coding gene models. With a custom pipeline, all these gene sequences were clustered into families. A total of 1,506 single-copy genes were selected as marker genes, which were then used to evaluate the genome completeness and base qualities of all assemblies. Pangenomic analyses were performed based on a selected subset of 847 medium-high-quality genomes. Statistical comparisons revealed a number of gene families showing copy number variations among different organism sources. To the authors' knowledge, this study represents the largest genome annotation project of S. cerevisiae so far, providing rich genomic resources for the future studies of the model organism S. cerevisiae and its relatives.IMPORTANCESaccharomyces cerevisiae (baker's yeast, budding yeast) is one of the most important model organisms for biological research and is a crucial microorganism in industry. Though a huge number of Saccharomyces cerevisiae genome sequences are available at the public domain, these genomes are distributed at different websites and most are released without annotation, hindering the efficient reuse of these genome resources. Here, we collected 2,507 genomes for Saccharomyces cerevisiae, performed genome annotation, and evaluated the genome qualities. All the obtained data have been deposited at public repositories and are freely accessible to the community. This study represents the largest genome annotation project of S. cerevisiae so far, providing one complete annotated genome data set for S. cerevisiae, an important workhorse for fundamental biology, biotechnology, and industry.

Reducing Airway Occlusion Time Without Losing Accuracy to Predict Successful Mechanical Ventilator Liberation During the Measurement of the Timed Inspiratory Effort Index.

In 2013, a new predictor of successful mechanical ventilation liberation named timed inspiratory effort (TIE) index was devised with the normalization of the maximum inspiratory pressure (obtained within 60 s of unidirectional airway occlusion) with the time at which the value was reached. The aim of this study was to verify whether the presence of a sequence of a certain number of inspiratory effort values between 30-60 s > 1.0 cm H2O/s could predict weaning success in a performance comparable to the TIE index. This was a retrospective observational study using 4 databases of previous studies on the TIE index. All patients receiving mechanical ventilation for ≤ 24 h were eligible. Liberation from mechanical ventilation-extubation decisions was made based on performance with spontaneous breathing trials. P < .05 was considered significant. The performance of the TIE index was evaluated by calculating the area under the receiver operating characteristics (AUROC) curve. From 349 eligible patients, 165 subjects were selected for analysis. The AUROC for the TIE index in the studied sample was 0.92 (95% CI 0.87-0.97, P < .001). A sequence of ≤ 4 inspiratory efforts > 1.0 cm H2O/s was found in 51.5% of the subjects, with successful ventilatory liberation occurring in 95.3%. The highest specificity values belonged to the sequence of ≤ 4 and ≤ 5 inspiratory efforts > 1.0 cm H2O/s; the highest positive predictive value and positive likelihood ratio belonged to the sequence of ≤ 4 inspiratory efforts > 1.0 cm H2O/s. The mean time that could have been spared if the procedure were interrupted after the first sequence of 4 inspiratory efforts > 1.0 cm H2O/s was 23 ± 3 s. The presence of a sequence of ≤ 4 inspiratory efforts > 1.0 cm H2O/s during the TIE index measurement was a reliable predictor of weaning success, which could allow timely interruption of the procedure and entail a substantial reduction in airway occlusion time.

Analyses and Insights into Genetic Reassortment and Natural Selection as Key Drivers of Piscine orthoreovirus Evolution.

Piscine orthoreovirus (PRV) is a pathogen that causes heart and skeletal muscle inflammation in Salmo salar and has also been linked to circulatory disorders in other farmed salmonids, such as Oncorhynchus kisutch and Oncorhynchus mykiss. The virus has a segmented, double-stranded RNA genome, which makes it possible to undergo genetic reassortment and increase its genomic diversity through point mutations. In this study, genetic reassortment in PRV was assessed using the full genome sequences available in public databases. This study used full genome sequences that were concatenated and genome-wide reassortment events, and phylogenetic analyses were performed using the recombination/reassortment detection program version 5 (RDP5 V 5.5) software. Additionally, each segment was aligned codon by codon, and overall mean distance and selection was tested using the Molecular Evolutionary Genetics Analysis X software, version 10.2 (MEGA X version 10.2). The results showed that there were 17 significant reassortment events in 12 reassortant sequences, involving genome exchange between low and highly virulent genotypes. PRV sequences from different salmonid host species did not appear to limit the reassortment. This study found that PRV frequently undergoes reassortment events to increase the diversity of its segmented genome, leading to antigenic variation and increased virulence. This study also noted that to date, no reassortment events have been described between PRV-1 and PRV-3 genotypes. However, the number of complete genomic sequences within each genotype is uneven. This is important because PRV-3 induces cross-protection against PRV-1, making it a potential vaccine candidate.

Clinical phenotype of a Kallmann syndrome patient with IL17RD and CPEB4 variants.

This study aimed to characterize the clinical phenotype and genetic variations in patients with Kallmann syndrome (KS). This study involved the collection and analysis of clinical data from an individual with sporadic KS. Following this, peripheral blood samples were obtained from the patient and his parents. Genomic deoxyribonucleic acid was extracted and subjected to whole-exome sequencing and genomic copy number variation (CNV) detection. Finally, Sanger sequencing was performed to validate the suspected pathogenic variants. Whole-exome sequencing confirmed that the child carried both the IL17RD variant (c.2101G>A, p.Gly701Ser) inherited from the mother and the new CPEB4 variant (c.1414C>T, p.Arg472*). No pathogenic CNVs were identified in CNV testing. Bioinformatics analysis shows that the IL17RD protein undergoing Gly701Ser mutation and is speculated to be phosphorylated and modified, thereby disrupting fibroblast growth factor signaling. This study also suggested that the CPEB4 might play a crucial role in the key signaling process affecting olfactory bulb morphogenesis. Overall, the findings of this study broaden the gene expression profile of KS-related pathogenic genes. This offers a new avenue for exploring the pathogenic mechanism of KS and provides valuable insights for precise clinical diagnosis and treatment strategies for this condition.

Assembly and comparative analysis of the initial complete mitochondrial genome of Primulina hunanensis (Gesneriaceae): a cave-dwelling endangered plant

BackgroundPrimulina hunanensis, a troglobitic plant within the Primulina genus of Gesneriaceae family, exhibits robust resilience to arid conditions and holds great horticultural potential as an ornamental plant. The work of chloroplast genome (cpDNA) has been recently accomplished, however, the mitochondrial genome (mtDNA) that is crucial for plant evolution has not been reported.ResultsIn this study, we sequenced and assembled the P. hunanensis complete mtDNA, and elucidated its evolutionary and phylogenetic relationships. The assembled mtDNA spans 575,242 bp with 43.54% GC content, encompassing 60 genes, including 37 protein-coding genes (PCGs), 20 tRNA genes, and 3 rRNA genes. Notably, high number of repetitive sequences in the mtDNA and substantial sequence translocation from chloroplasts to mitochondria were observed. To determine the evolutionary and taxonomic positioning of P. hunanensis, a phylogenetic tree was constructed using mitochondrial PCGs from P. hunanensis and 32 other taxa. Furthermore, an exploration of PCGs relative synonymous codon usage, identification of RNA editing events, and an investigation of collinearity with closely related species were conducted.ConclusionsThis study reports the initial assembly and annotation of P. hunanensis mtDNA, contributing to the limited mtDNA repository for Gesneriaceae plants and advancing our understanding of their evolution for improved utilization and conservation.

(1232) - Characterizing the Impact of Donor Sequence Number on Outcomes Following Heart Transplantation

(1232) - Characterizing the Impact of Donor Sequence Number on Outcomes Following Heart Transplantation

(941) - High Donor Sequence Number and Outcomes Following Lung Transplantation in the United States

(941) - High Donor Sequence Number and Outcomes Following Lung Transplantation in the United States

(351) - Higher Donor Sequence Numbers are Not Associated with Worse Lung Transplant Outcomes

(351) - Higher Donor Sequence Numbers are Not Associated with Worse Lung Transplant Outcomes

ROUGH LACUNARY STATISTICAL ANALYSIS OF ORDER OF η OF TRIPLE DIFFERENCE SEQUENCE SPACES

We generalized the concepts in the probability of rough lacunary statistical analysis of order η by introducing the difference operator Δ_γ^α of fractional order, where α is a proper fraction and γ=(γ_mnk ) is any fixed sequence of nonzero real or complex numbers. We study some properties of this operator involving lacunary sequence θ and arbitrary sequence p=(p_rst ) of strictly positive real numbers and investigate the topological structures of related with triple difference sequence spaces. The main focus of the present paper is to generalize rough lacunary statistical analysis of order η of triple difference sequence spaces and give the relations between statistical analysis and lacunary statistical analysis of order η.

Performance of tri-tubular conical energy absorber under axial compression

Quasi static axial compression loading on tri-tubular cone (TC) has been carried out using LS-DYNA finite element analysis method. Tri-tubular cones of three arrangements; the first arrangement (model TC-1) consists of cone heights of 50 mm, 75 mm and 100 mm where the inner cone is the maximum height. The second arrangement (model TC-2) consists of cone heights of 100 mm, 75 mm, and 50 mm where the outer cone is the maximum height. The third arrangement (model TC-3) consists of three cones of the same height of 100 mm. Cone semi vertex angle of 20o was maintained for all tri-tubular cones tested. Materials used for this research are glass, jute and jute-glass/epoxy. Crashworthiness analyses were performed to investigate the effect of material used, and tri-tubular cone arrangement on peak load. Crush efficiency, and absorbed energy were drawn and discussed. Failure mechanism of the fractured specimens was also discussed. Effect of number of layers and fiber stacking sequence were also investigated. Results show that the cone arrangement TC-3 gives better performance than the cone arrangement TC-2 followed by the cone arrangement TC-1. Maximum load obtained by tri-tubular cone type TC-3 was found higher 7.09% and 14.96% than TC-2 and TC-1 respectively for glass/epoxy. Material saving was achieved by using tri-tubular cones of different heights under compression. Material used has significant influence on the absorbed energy. Failure mode of tri-tubular conical energy absorber was presented and discussed.

Blood-inspired random bit generation using microfluidics system

The development of random number generators (RNGs) using speckle patterns is pivotal for secure encryption key generation, drawing from the recent statistical properties identified in speckle-based imaging. Speckle-based RNG systems generate a sequence of random numbers through the unpredictable and reproducible nature of speckle patterns, ensuring a source of randomness that is independent of algorithms. However, to guarantee their effectiveness and reliability, these systems demand a meticulous and rigorous approach. In this study, we present a blood-inspired RNG system with a microfluidics device, designed to generate random numbers at a rate of 5.5 MHz and a high-speed of 1250 fps. This process is achieved by directing a laser beam through a volumetric scattering medium to procure speckle patterns. Additionally, designed microfluidic device requires only a minimal blood sample of 5 µl to capture these speckle patterns effectively. After implementing the two-pass tuple-output von Neumann debiasing algorithm to counteract statistical biases, we utilized the randomness statistical test suite from the National Institute of Standards and Technology for validation. The generated numbers successfully passed these tests, ensuring their randomness and unpredictability. Our blood-inspired RNG, utilizing whole blood, offers a pathway for affordable, high-output applications in fields like encryption, computer security, and data protection.