Detection Of Sequences Research Articles

The Effect of Methodological Considerations on the Construction of Gene-Based Plant Pan-genomes.

Pan-genomics is an emerging approach for studying the genetic diversity within plant populations. In contrast to common resequencing studies that compare whole genome sequencing data with a single reference genome, the construction of a pan-genome (PG) involves the direct comparison of multiple genomes to one another, thereby enabling the detection of genomic sequences and genes not present in the reference, as well as the analysis of gene content diversity. Although multiple studies describing PGs of various plant species have been published in recent years, a better understanding regarding the effect of the computational procedures used for PG construction could guide researchers in making more informed methodological decisions. Here, we examine the effect of several key methodological factors on the obtained gene pool and on gene presence-absence detections by constructing and comparing multiple PGs of Arabidopsis thaliana and cultivated soybean, as well as conducting a meta-analysis on published PGs. These factors include the construction method, the sequencing depth, and the extent of input data used for gene annotation. We observe substantial differences between PGs constructed using three common procedures (de novo assembly and annotation, map-to-pan, and iterative assembly) and that results are dependent on the extent of the input data. Specifically, we report low agreement between the gene content inferred using different procedures and input data. Our results should increase the awareness of the community to the consequences of methodological decisions made during the process of PG construction and emphasize the need for further investigation of commonly applied methodologies.

Collusion Detection and Trust Management for Indoor Navigation System With Crowdsourcing

The indoor navigation system supported by spatial crowdsourcing emerges as a promising application to provide customized location service for requesters. An important stage of crowdsourcing is to select trustworthy workers. Workers' reputation, as an essential criterion of this selection, is usually evaluated by feedback ratings from requesters. However, the reputation in the crowdsourcing-based indoor navigation system is vulnerable to the collusion attack, that is malicious workers (i.e., attackers) collude with requesters to illegally increase reputation. In this paper, we propose a collusion detection scheme to distinguish attackers and provide a secure reputation mechanism. Specifically, we first identify collusive requesters categorized into three different levels according to their feedback rating behaviors. Then, the weighted logistic regression (WLR) is developed to distinguish the collusive requesters who provide exorbitant feedback ratings. Furthermore, we employ an outlying sequence detection based on the maximum mean discrepancy (MMD), to resist the multiple location queries initiated by the same collusive requester through analyzing the distribution distance. In addition, we propose a community detection algorithm, named Fastgreedy, to identify the collusion from many requesters. Finally, the extensive simulation results demonstrate that the proposed scheme can effectively detect collusive requesters and significantly outperform other methods.

Ultrasensitive and Specific Clustered Regularly Interspaced Short Palindromic Repeats Empowered a Plasmonic Fiber Tip System for Amplification-Free Monkeypox Virus Detection and Genotyping.

The urgent necessity for highly sensitive diagnostic tools has been accentuated by the ongoing mpox (monkeypox) virus pandemic due to the complexity in identifying asymptomatic and presymptomatic carriers. Traditional polymerase chain reaction-based tests, despite their effectiveness, are hampered by limited specificity, expensive and bulky equipment, labor-intensive operations, and time-consuming procedures. In this study, we present a clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a-based diagnostic platform with a surface plasmon resonance-based fiber tip (CRISPR-SPR-FT) biosensor. The compact CRISPR-SPR-FT biosensor, with a 125 μm diameter, offers high stability and portability, enabling exceptional specificity for mpox diagnosis and precise identification of samples with a fatal mutation site (L108F) in the F8L gene. The CRISPR-SPR-FT system can analyze viral double-stranded DNA from mpox virus without amplification in under 1.5 h with a limit of detection below 5 aM in plasmids and about 59.5 copies/μL when in pseudovirus-spiked blood samples. Our CRISPR-SPR-FT biosensor thus offers fast, sensitive, portable, and accurate target nucleic acid sequence detection.

Quantum Algorithm for Anomaly Detection of Sequences

AbstractAnomaly detection of sequences is a hot topic in data mining. Anomaly detection using piecewise aggregate approximation in the amplitude domain (called ADPAAD) is one of the widely used methods in anomaly detection of sequences. The core step in the classical algorithm for performing ADPAAD is to construct an approximate representation of the subsequence, where the elements of each subsequence are divided into several subsections according to the amplitude domain, and then the average of the subsections is computed. It is computationally expensive when processing large‐scale sequences. In this paper, a quantum algorithm for ADPAAD is proposed, which can divide the subsequence elements and compute the average in parallel. The quantum algorithm can achieve polynomial speedups on the number of subsequences and the length of subsequences over its classical counterpart.

Progress and Perspective of CRISPR-Cas9 Technology in Translational Medicine.

Translational medicine aims to improve human health by exploring potential treatment methods developed during basic scientific research and applying them to the treatment of patients in clinical settings. The advanced perceptions of gene functions have remarkably revolutionized clinical treatment strategies for target agents. However, the progress in gene editing therapy has been hindered due to the severe off-target effects and limited editing sites. Fortunately, the development in the clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR-Cas9) system has renewed hope for gene therapy field. The CRISPR-Cas9 system can fulfill various simple or complex purposes, including gene knockout, knock-in, activation, interference, base editing, and sequence detection. Accordingly, the CRISPR-Cas9 system is adaptable to translational medicine, which calls for the alteration of genomic sequences. This review aims to present the latest CRISPR-Cas9 technology achievements and prospect to translational medicine advances. The principle and characterization of the CRISPR-Cas9 system are firstly introduced. The authors then focus on recent pre-clinical and clinical research directions, including the construction of disease models, disease-related gene screening and regulation, and disease treatment and diagnosis for multiple refractory diseases. Finally, some clinical challenges including off-target effects, in vivo vectors, and ethical problems, and future perspective are also discussed.

Pandemic potential of poxviruses: From an ancient killer causing smallpox to the surge of monkeypox.

Smallpox caused by the variola virus (VARV) was one of the greatest infectious killers of mankind. Historical records trace back smallpox for at least a millennium while phylogenetic analysis dated the ancestor of VARV circulating in the 20th century into the 19th century. The discrepancy was solved by the detection of distinct VARV sequences first in 17th-century mummies and then in human skeletons dated to the 7th century. The historical records noted marked variability in VARV virulence which scientists tentatively associated with gene losses occurring when broad-host poxviruses narrow their host range to a single host. VARV split from camel and gerbil poxviruses and had no animal reservoir, a prerequisite for its eradication led by WHO. The search for residual pockets of VARV led to the discovery of the monkeypox virus (MPXV); followed by the detection of endemic smallpox-like monkeypox (mpox) disease in Africa. Mpox is caused by less virulent clade 2 MPXV in West Africa and more virulent clade 1 MPXV in Central Africa. Exported clade 2 mpox cases associated with the pet animal trade were observed in 2003 in the USA. In 2022 a world-wide mpox epidemic infecting more than 80,000 people was noted, peaking in August 2022 although waning rapidly. The cases displayed particular epidemiological characteristics affecting nearly exclusively young men having sex with men (MSM). In contrast, mpox in Africa mostly affects children by non-sexual transmission routes possibly from uncharacterized animal reservoirs. While African children show a classical smallpox picture, MSM mpox cases show few mostly anogenital lesions, low-hospitalization rates and 140 fatal cases worldwide. MPXV strains from North America and Europe are closely related, derived from clade 2 African MPXV. Distinct transmission mechanisms are more likely causes for the epidemiological and clinical differences between endemic African cases and the 2022 epidemic cases than viral traits.

Ferroptosis in the Lacrimal Gland Is Involved in Dry Eye Syndrome Induced by Corneal Nerve Severing.

Dry eye syndrome (DES) is a prevalent postoperative complication after myopic corneal refractive surgeries and the main cause of postoperative dissatisfaction. Although great efforts have been made in recent decades, the molecular mechanism of postoperative DES remains poorly understood. Here, we used a series of bioinformatics approaches and experimental methods to investigate the potential mechanism involved in postoperative DES. BALB/c mice were randomly divided into sham, unilateral corneal nerve cutting (UCNV) + saline, UCNV + vasoactive intestinal peptide (VIP), and UCNV + ferrostatin-1 (Fer-1, inhibitor of ferroptosis) groups. Corneal lissamine green dye and tear volume were measured before and two weeks after the surgery in all groups. Lacrimal glands were collected for secretory function testing, RNA sequencing, ferroptosis verification, and inflammatory factor detection. UCNV significantly induced bilateral decreases in tear secretion. Inhibition of the maturation and release of secretory vesicles was observed in bilateral lacrimal glands. More importantly, UCNV induced ferroptosis in bilateral lacrimal glands. Furthermore, UCNV significantly decreased VIP, a neural transmitter, in bilateral lacrimal glands, which increased Hif1a, the dominant transcription factor of transferrin receptor protein 1 (TfR1). Supplementary VIP inhibited ferroptosis, which decreased the inflammatory reaction and promoted the maturation and release of secretory vesicles. Supplementary VIP and Fer-1 improved tear secretion. Our data suggest a novel mechanism by which UCNV induces bilateral ferroptosis through the VIP/Hif1a/TfR1 pathway, which might be a promising therapeutic target for DES-induced by corneal refractive surgeries.

Unique Peptides of Cathelicidin-1 in the Early Detection of Mastitis-In Silico Analysis.

Based on the results of previously performed clinical studies, cathelicidin-1 has been proposed as a potential biomarker for the early diagnosis of mastitis in ewes. It has been hypothesized that the detection of unique peptides (defined as a peptide, irrespective of its length, that exists in only one protein of a proteome of interest) and core unique peptides (CUPs) (representing the shortest peptide that is unique) of cathelicidin-1 may potentially improve its identification and consequently the diagnosis of sheep mastitis. Peptides of sizes larger than those of the size of CUPs, which include consecutive or over-lapping CUPs, have been defined as 'composite core unique peptides' (CCUPs). The primary objective of the present study was the investigation of the sequence of cathelicidin-1 detected in ewes' milk in order to identify its unique peptides and core unique peptides, which would reveal potential targets for accurate detection of the protein. An additional objective was the detection of unique sequences among the tryptic digest peptides of cathelicidin-1, which would improve accuracy of identification of the protein when performing targeted MS-based proteomics. The potential uniqueness of each peptide of cathelicidin-1 was investigated using a bioinformatics tool built on a big data algorithm. A set of CUPs was created and CCUPs were also searched. Further, the unique sequences in the tryptic digest peptides of cathelicidin-1 were also detected. Finally, the 3D structure of the protein was analyzed from predicted models of proteins. In total, 59 CUPs and four CCUPs were detected in cathelicidin-1 of sheep origin. Among tryptic digest peptides, there were six peptides that were unique in that protein. After 3D structure analysis of the protein, 35 CUPs were found on the core of cathelicidin-1 of sheep origin and among them, 29 were located on amino acids in regions of the protein with 'very high' or 'confident' estimates of confidence of the structure. Ultimately, the following six CUPs: QLNEQ, NEQS, EQSSE, QSSEP, EDPD, DPDS, are proposed as potential antigenic targets for cathelicidin-1 of sheep. Moreover, another six unique peptides were detected in tryptic digests and offer novel mass tags to facilitate the detection of cathelicidin-1 during MS-based diagnostics.

On the Performance Limits of High-Speed Transmission Using a Single Wideband Coherent Receiver

The performance of a wideband coherent receiver was investigated. The relative impact of digital pre-distortion, geometric constellation shaping and pilot sequence detection, as well as the number of sub-channels in the super-channel, on the receiver performance was explored. The detection of a net data rate of 2.36 Tb/s after 75 km transmission of 8 × 26 GBd DP-GS-256-QAM super-channels was demonstrated using a single 110-GHz electrical bandwidth receiver. The overall improvement due to the digital pre-distortion and tailored geometric constellation shaping was 1.2 bit/4D-sym in the achievable information rate.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

BackgroundAlthough digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.MethodsTwenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.ResultsWe identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture).Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.ConclusionsOverall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances.Trial registration ISRCTN – 12246987.

Smartphone-assisted point-of-care testing of nucleic acids based on hybridization chain reaction, magnetic beads, and gold nanorods etching

Colorimetric biosensors are simple but effective tools that are gaining popularity due to their ability to provide low-cost, rapid, and accurate detection for viruses like the Novel coronavirus, Influenza A, and Dengue virus, especially in point-of-care testing (POCT) and visual detection. In this study, a smartphone-assisted nucleic acid POCT was built using hybridization chain reaction (HCR), magnetic beads (MBs), and oxidized 3,3′,5,5′-tetramethylbenzidine (TMB2+)-mediated etching of gold nanorods (GNRs). The application of HCR without enzyme isothermal characteristics and MBs with easy separation, can quickly amplify nucleic acid signal and remove other reaction components. The blue shift of longitudinal localized surface plasmon resonance (LSPR) based on GNRs showed significant differences in etching color for different concentrations of target nucleic acid, which convert the signal into a visually semi-quantitative colorimetric result, achieving quantitative analysis with the color recognition software built into smartphones. This strategy, which only takes 40 min to detect and is two-thirds less time than the PCR, was successfully applied for the detection of the Dengue target sequence with a detection limit of 1.25 nM and exhibited excellent specificity for distinguishing single-base mutations, indicating broad application prospects in clinical laboratory diagnosis and enriching the research of nucleic acid POCT.

The Predictive Role of the Posterior Cerebellum in the Processing of Dynamic Emotions.

Recent studies have bolstered the important role of the cerebellum in high-level socio-affective functions. In particular, neuroscientific evidence shows that the posterior cerebellum is involved in social cognition and emotion processing, presumably through its involvement in temporal processing and in predicting the outcomes of social sequences. We used cerebellar transcranial random noise stimulation (ctRNS) targeting the posterior cerebellum to affect the performance of 32 healthy participants during an emotion discrimination task, including both static and dynamic facial expressions (i.e., transitioning from a static neutral image to a happy/sad emotion). ctRNS, compared to the sham condition, significantly reduced the participants' accuracy to discriminate static sad facial expressions, but it increased participants' accuracy to discriminate dynamic sad facial expressions. No effects emerged with happy faces. These findings may suggest the existence of two different circuits in the posterior cerebellum for the processing of negative emotional stimuli: a first-time-independent mechanism which can be selectively disrupted by ctRNS, and a second time-dependent mechanism of predictive "sequence detection"which can be selectively enhanced by ctRNS. This latter mechanism might be included among the cerebellar operational models constantly engaged in the rapid adjustment of social predictions based on dynamic behavioral information inherent to others' actions. We speculate that it might be one of the basic principles underlying the understanding of other individuals' social and emotional behaviors during interactions.

Massively multiplex blocker displacement amplification (BDA) for the detection of measurable residual disease in acute myeloid leukemia.

e19045 Background: Next-generation sequencing (NGS) technologies, used to accomplish high throughput characterization of DNA, have an inherent error rate of between 0.1-1%. However, many important DNA sequences possess variant allele frequencies (VAFs) below this rate, potentially precluding their detection and identification by NGS. The previously published blocker displacement amplification (BDA) research technology has enabled selective amplification of sequences with variants, facilitating detection by NGS of these sequences with low VAFs at low read depths. Methods: Here we present the massively multiplex BDA library preparation method for the NGS-based detection of low VAF sequences. By optimizing our previously published Simulated Annealing Design using Dimer Likelihood Estimation (SADDLE) algorithm, we designed a 4 tube assay, comprising up to ~2400 amplicons in each tube, and ~8300 total amplicons. Tube 1 was designed with the highest likelihood of on-target amplification and the lowest likelihood of dimerization or non-specific amplification. While Tube 1 targeted ~1000 regions covering the most-frequently observed mutations in current public databases of patients with acute myeloid leukemia (AML) across more than 200 genes, Tubes 2-4 were designed for enrichment of the full coding sequence of 63 genes. To highlight the performances of SADDLE, coverage uniformity, primer dimerization, and non-specific amplification of tube 1 were compared to those of a naively designed tube. The panel was further tested with contrived and reference samples to demonstrate its analytical performance. Results: The SADDLE algorithm design decreased the first pass amplicon dropout rate more than 10-fold, from 30% to ~2%, and further improved the overall on-target rate compared to the naively designed panel. Through SADDLE, we were able to achieve an 80% median coverage of 63 genes. The resulting panel has a bulk limit of detection (LoD) of below 0.1% VAF. Conclusions: Massively multiplex BDA enabled the NGS characterization of DNA targets with VAFs suitable for detection of AML measurable residual disease (MRD). This technology represents a 100-fold increase in current plexity for the detection of low VAF alleles over our previously published multiplex BDA technology, demonstrating the ability to perform high throughput sequencing of rare mutations.

Learning and Fusing Multi-View Code Representations for Function Vulnerability Detection

The explosive growth of vulnerabilities poses a significant threat to the security of software systems. While various deep-learning-based vulnerability detection methods have emerged, they primarily rely on semantic features extracted from a single code representation structure, which limits their ability to detect vulnerabilities hidden deep within the code. To address this limitation, we propose S2FVD, short for Sequence and Structure Fusion-based Vulnerability Detector, which fuses vulnerability-indicative features learned from the multiple views of the code for more accurate vulnerability detection. Specifically, S2FVD employs either well-matched or carefully extended neural network models to extract vulnerability-indicative semantic features from the token sequence, attributed control flow graph (ACFG) and abstract syntax tree (AST) representations of a function, respectively. These features capture different perspectives of the code, which are then fused to enable S2FVD to accurately detect vulnerabilities that are well-hidden within a function. The experiments conducted on two large vulnerability datasets demonstrated the superior performance of S2FVD against state-of-the-art approaches, with its accuracy and F1 scores reaching 98.07% and 98.14% respectively in detecting the presence of vulnerabilities, and 97.93% and 97.94%, respectively, in pinpointing specific vulnerability types. Furthermore, with regard to the real-world dataset D2A, S2FVD achieved average performance gains of 6.86% and 14.84% in terms of accuracy and F1 metrics, respectively, over the state-of-the-art baselines. This ablation study also confirms the superiority of fusing the semantics implied in multiple distinct code views to further enhance vulnerability detection performance.

De Novo Structural Variations of Escherichia coli Detected by Nanopore Long-Read Sequencing.

Spontaneous mutations power evolution, whereas large-scale structural variations (SVs) remain poorly studied, primarily because of the lack of long-read sequencing techniques and powerful analytical tools. Here, we explore the SVs of Escherichia coli by running 67 wild-type (WT) and 37 mismatch repair (MMR)-deficient (ΔmutS) mutation accumulation lines, each experiencing more than 4,000 cell divisions, by applying Nanopore long-read sequencing and Illumina PE150 sequencing and verifying the results by Sanger sequencing. In addition to precisely repeating previous mutation rates of base-pair substitutions and insertion and deletion (indel) mutation rates, we do find significant improvement in insertion and deletion detection using long-read sequencing. The long-read sequencing and corresponding software can particularly detect bacterial SVs in both simulated and real data sets with high accuracy. These lead to SV rates of 2.77 × 10-4 (WT) and 5.26 × 10-4 (MMR-deficient) per cell division per genome, which is comparable with previous reports. This study provides the SV rates of E. coli by applying long-read sequencing and SV detection programs, revealing a broader and more accurate picture of spontaneous mutations in bacteria.

On the Origin of the Split Main Sequences of the Young Massive Cluster NGC 1856

The detection of split main sequences (MSs) associated with young clusters (≲600 Myr) has attracted a lot of attention. A prevailing scenario is that a bimodality of stellar rotation distribution drives the bifurcation of the MS. Nevertheless, the origin of the stellar rotation dichotomy remains unclear. Hypotheses involving tidally locked binaries or blue straggler stars (BSSs) have been proposed to explain the observed split MSs. This work examines if the long-term dynamical evolution of star clusters can produce the observed split MSs, through high-performance N-body simulation. For example, the young massive cluster NGC 1856 exhibits an apparent MS bifurcation. Our simulation reports that at the age of NGC 1856, tidally locked binaries are fully mixed with single stars. This is consistent with the observation that there is no significant spatial difference between blue MS and red MS stars. However, we find that only high-mass-ratio binaries can evolve to the tidally locked phase at the age of the NGC 1856. These tidally locked binaries will populate a much redder sequence than the MS of single stars rather than a blue MS, which is inconsistent with the hypothesis. The number of tidally locked binaries cannot account for the observation. Our simulation shows that BSSs produced by binary interactions do populate in the blue periphery as shown in a color–magnitude diagram, and their spatial distribution shows a similar pattern of single stars. However, the number of BSSs does not fit the observation.

Detection of Potential Viral Sequence from Next Generation Sequencing Data Using Convolutional Neural Network

Next Generation Sequencing (NGS) is a modern sequencing technology that can determine the sequences of RNA and DNA faster and at lower cost. The availability of NGS data has sparked numerous efforts in bioinformatics, especially in the study of genetic variation and viral sequence detection. Viral sequence detection has been one of the important processes in studying virus-induced diseases. Common methods in detecting viral sequences involve alignment of the sequence with existing databases, which remains limited as these databases might be incomplete and difficult to detect highly divergent viruses. Thus, machine learning and deep learning have been used in this regard, to unveil the patterns that distinguish viral sequences through learning from the NGS data. This study focuses on viral sequence detection using convolutional neural network (CNN). This study intended to investigate how CNN model can be used for analysis of NGS data and develop a CNN model for detecting potential viral sequences from NGS data. The CNN architecture used for this study is based on an existing design that divided into two branches namely pattern and frequency branch that cater for extracting different aspects of information from the data and lastly combined into a full model. This study further implemented slightly modified architecture that includes additional convolution layer and pooling layer. Then, parameter tuning is implemented to identify near optimal parameters for the CNN to elucidate the performance impact. The evaluation of the optimized CNN model is done using a dataset with 18,445 DNA sequences. The results show that the CNN model in this study achieved a better performance compared with existing in terms of area under receiver operating characteristics curve (AUROC) for full model (+0.1434).

Programmable polymer materials empowered by DNA nanotechnology

AbstractOver the past century, synthetic polymers have had a transformative impact on human life, replacing nature‐derived materials in many areas. Yet, despite their many advantages, the structure and function of synthetic polymers still appear rudimentary compared to biological matter: cells use dynamic self‐assembly to construct complex materials and operate sophisticated macromolecular devices. The field of DNA nanotechnology has demonstrated that synthetic DNA molecules can be programmed to undergo predictable self‐assembly, offering unparalleled control over the formation and dynamic properties of artificial nanostructures. Intriguingly, the principles of DNA nanotechnology can be applied to the engineering of soft programmable materials, bringing the abilities of synthetic polymers closer to their biological counterparts. In this perspective, we discuss the unique features of DNA‐functionalized polymer materials. We describe design principles that allow researchers to build complex supramolecular architectures with predictable and dynamically adjustable material properties. Finally, we highlight two key application areas where this biologically inspired material class offers particularly promising opportunities: (1) as dynamic matrices for 3D cell and organoid culture and (2) as smart materials for nucleic acid sequencing and pathogen detection.

Occurrence and genetic diversity of prophage sequences identified in the genomes of L. casei group bacteria

It is widely believed that microorganisms belonging to L. casei group can have positive effects on the human body. Therefore, these bacteria are used in many industrial processes, including the production of dietary supplements and probiotic preparations. When using live microorganisms in technological processes, it is important to use those without phage sequences within their genomes that can ultimately lead to lysis of the bacteria. It has been shown that many prophages have a benign nature, meaning that they don’t directly lead to lysis or inhibit microbial growth. Moreover, the presence of phage sequences in the genomes of these bacteria increases their genetic diversity, which may contribute to easier colonization of new ecological niches. In the 439 analyzed genomes of the L. casei group, 1509 sequences of prophage origin were detected. The average length of intact prophage sequences analyzed was just under 36 kb. GC content of tested sequences was similar for all analyzed species (44.6 ± 0.9%). Analyzing the protein coding sequences collectively, it was found that there was an average of 44 putative ORFs per genome, while the ORF density of all phage genomes varied from 0.5 to 2.1. The average nucleotide identity calculated on sequence alignments for analyzed sequences was 32.7%. Of the 56 L. casei strains used in the next part of the study, 32 did not show culture growth above the OD600 value of 0.5, even at a mitomycin C concentration of 0.25 μg/ml. Primers used for this study allowed for the detection of prophage sequences for over 90% of tested bacterial strains. Finally, prophages of selected strains were induced using mitomycin C, phage particles were isolated and then genomes of viruses obtained were sequenced and analyzed.

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data.

Early detection of breast cancer can be achieved through mutation detection in DNA sequences, which can be acquired through patient blood samples. Mutation detection can be performed using alignment and machine learning techniques. However, alignment techniques require reference sequences, and machine learning techniques still cannot predict index mutation and require supporting tools. Therefore, in this research, a Temporal Convolutional Network (TCN) model was proposed to detect the type and index mutation faster and without reference sequences and supporting tools. The architecture of the proposed TCN model is specifically designed for sequential labeling tasks on DNA sequence data. This allows for the detection of the mutation type of each nucleotide in the sequence, and if the nucleotide has a mutation, the index mutation can be obtained. The proposed model also uses 2-mers and 3-mers mapping techniques to improve detection performance. Based on the tests that have been carried out, the proposed TCN model can achieve the highest F1-score of 0.9443 for COSMIC dataset and 0.9629 for RSCM dataset, Additionally, the proposed TCN model can detect index mutation six times faster than BiLSTM model. Furthermore, the proposed model can detect type and index mutations based on the patient's DNA sequence, without the need for reference sequences or other additional tools.