Sequencing Data Processing Research Articles

Unravelling T-cell dynamics and immune responses in initial and recurrent uveitis.

This study aimed to identify novel serological targets and investigate immune responses in patients with non-infectious uveitis, focusing on differences between initial onset and recurrent episodes. Differential gene expression analysis, immunocyte typing and T-cell receptor (TCR) gene analysis were conducted on RNA-sequenced peripheral blood samples from healthy individuals (n = 6) and non-infectious uveitis patients (n = 12), divided into 6 patients each at initial onset and recurrent stages. Peripheral blood T-cell types were analysed using flow cytometry. Bioinformatics methods included tools for RNA sequencing data processing, CIBERSORT for immune cell type prediction and specialized software for TCR repertoire analysis. Findings indicated that individuals with recurrent uveitis demonstrated a stronger adaptive immune response and a more pronounced immune imbalance compared to those with initial onset. Memory T cells were predominant in recurrent episodes, suggesting their potential role as biomarkers for disease progression. Significant differences in TCR diversity and V(D)J gene usage were observed between the various uveitis groups and healthy controls. Importantly, 38 uveitis-specific TCR sequences showed substantial expansion in the uveitis patients compared to controls. An elevated expansion of these specific TCR sequences was associated with an increased risk of uveitis development. The study highlights the critical role of adaptive immune responses and specific immune cell types in the pathogenesis of recurrent uveitis. Identification of the uveitis-specific TCR repertoire set could provide deeper insights into the disease and facilitate the development of targeted therapies for uveitis patients.

Challenges and prospects of cell-free DNA in precision oncology

Cell-free DNA (cfDNA), predominantly derived from apoptotic or necrotic cells, is emerging as a potential biomarker due to non-invasiveness and cost-effectiveness1. Additionally, another type of cfDNA such as cell-free mitochondrial DNA (cf-mtDNA) that is released from cells under stress or damage condition, may play a key role in the immune system. Thus, the presence of cfDNA in the circulation of cancer patients with differential observations between the cancer and the healthy individuals may be used to improve diagnosis by liquid biopsies2. In the past decades, liquid biopsy has been significantly advanced, particularly in detecting cfDNA, which has opened a new research era in cancer diagnostics worldwide. However, there is a big hindrance in integrating cfDNA analysis with clinical diagnosis, For example, a high rate of false positives is caused by insensitive assays, which may be improved by refining the processing of sequencing data. That is due to the technical differences of feature analysis (e.g., fragmentomics and methylation patterns) and their heterogeneity limited the application of cfDNA in the clinics3–5. Currently, there are many efforts on the way to solve the problems. Especially, the top priority is to enhance assay sensitivity and specificity and to adapt analytical techniques in oncology.Nowadays, liquid biopsy technology based on circulating free DNA (cfDNA) brings new hope for future clinical applications such as minimally invasive early screening for tumors, with the key to effectiveness lying in the integration of next-generation sequencing (NGS) with advanced separation technologies. A comparison between the RNA expression datasets and the cfDNA fragment frequencies has revealed distinct genomic characteristics between cancer patients and healthy individuals. Research has found that there are specific fragmentation patterns of cfDNA in the promoter regions of some highly expressed genes6,7. Notably, cfSort is the first supervised tissue deconvolution approach powered by deep learning, leveraging a high-resolution methylation atlas from 521 non-cancer tissue samples across 29 major human tissue types. This model accurately identifies the origin of cfDNA, demonstrating the clinical value of sequencing data in disease diagnosis and treatment monitoring8. In this perspective, we outline the current trend of cfDNA research and highlight its potential limitations in oncology (Fig. 1). It provides valuable insights for cancer diagnosis and management which may help pave the way for the future adoption of cfDNA in oncology.

Hyperuricemia suppresses lumican, exacerbating adverse remodeling after myocardial infarction by promoting fibroblast phenotype transition

BackgroundHyperuricemia is independently associated with a poor prognosis in patients with myocardial infarction (MI). Furthermore, MI induces activation of the repair response in local fibroblasts, resulting in extracellular matrix accumulation that generates a stable fibrotic scar in the infarcted area. However, researchers have not determined whether hyperuricemia affects fibroblast activation and its involvement in postinfarction cardiac remodeling.ObjectivesWe aimed to trigger hyperuricemia by administering potassium oxonate in a mouse model of MI to evaluate the role of hyperuricemia in MI pathogenesis.MethodsMicroarray datasets and single-cell sequencing data from gout patients, heart failure patients, and model mice were used to identify the underlying mechanisms responsible for the effect of hyperuricemia on MI progression. A hyperuricemia-related MI mouse model was established. Cardiac function was assessed, followed by sample collection and a uric acid assay. We conducted an enzyme-linked immunosorbent assay, histological detection, immunofluorescence, sequencing data processing, single-cell RNA-seq, and functional enrichment analysis. We then isolated and cultured cardiac fibroblasts and performed Western blotting, quantitative real-time polymerase chain reaction, and shRNA-mediated lumican knockdown assays.ResultsHyperuricemia decreased cardiac function, increased mortality, and aggravated adverse fibrosis remodeling in mice after MI. These outcomes were closely related to reduced levels of fibroblast-derived lumican. This reduction activated the TGF-β/SMAD signaling pathway to induce aberrant myofibroblast activation and extracellular matrix deposition in the infarcted area. Furthermore, lumican supplementation or uric acid-lowering therapy with allopurinol alleviated hyperuricemia-mediated abnormal cardiac remodeling.ConclusionHyperuricemia aggravates postinfarction cardiac remodeling by reducing lumican expression and promoting fibroblast phenotype transition. We highlight the clinical importance of lowering uric acid levels in hyperuricemia-related MI to prevent adverse ventricular remodeling.

Cyclic shift short-distance attention for defect detection of fine etching mesh

Abstract Based on the deficiency of the existing Swin Transformer in the detection of surface defects in metal etched grids, i.e. the isolation of information transmission between different windows, and the limitations of image matrix size and network structure,which make it not suitable for long sequence data processing and dense prediction scenarios, a defect detection method for metal etching nets based on Cyclic shift Long Short Distance Attention (C-LSDA) is proposed. In the proposed method, cross-scale features are used to capture long and short distance relationships,and enhance the feature transfer between windows. Tracking position bias (TPB) is also used to improve relative position bias (RPB), in order to help the model to better understand the spatial relationships in the input data. In addition, the image is downsampled at various scales, so that the proposed method can be suitable for long sequence data processing and dense prediction scenarios. In order to improve the detection accuracy of small targets. a multi-feature fusion pyramid network (MFFPN) for feature fusion is also proposed. The experimental results show that the proposed method is significantly superior to the traditional CNN recognition method and the transformer recognition method. The proposed methodhas good identification accuracy and low computational cost.

Low-Cost Purpose-Built Ultra-Low-Field NMR Spectrometer.

Low-field NMR has emerged as a new analytical technique for the investigation of molecular structure and dynamics. Here, we introduce a highly integrated ultralow-frequency NMR spectrometer designed for the purpose of ultralow-field NMR polarimetry of hyperpolarized contrast media. The device measures 10 cm × 10 cm × 2.0 cm and weighs only 370 g. The spectrometer's aluminum enclosure contains all components, including an RF amplifier. The device has four ports for connecting to a high-impedance RF transmit-receive coil, a trigger input, a USB port for connectivity to a PC computer, and an auxiliary RS-485/24VDC port for system integration with other devices. The NMR spectrometer is configured for a pulse-wait-acquire-recover pulse sequence, and key sequence parameters are readily controlled by a graphical user interface (GUI) of a Windows-based PC computer. The GUI also displays the time-domain and Fourier-transformed NMR signal and allows autosaving of NMR data as a CSV file. Alternatively, the RS485 communication line allows for operating the device with sequence parameter control and data processing directly on the spectrometer board in a fully automated and integrated manner. The NMR spectrometer, equipped with a 250 ksamples/s 17-bit analog-to-digital signal converter, can perform acquisition in the 1-125 kHz frequency range. The utility of the device is demonstrated for NMR polarimetry of hyperpolarized 129Xe gas and [1-13C]pyruvate contrast media (which was compared to the 13C polarimetry using a more established technology of benchtop 13C NMR spectroscopy, and yielded similar results), allowing reproducible quantification of polarization values and relaxation dynamics. The cost of the device components is only ∼$200, offering a low-cost integrated NMR spectrometer that can be deployed as a plug-and-play device for a wide range of applications in hyperpolarized contrast media production─and beyond.

Using Machine Learning and Natural Language Processing for Unveiling Similarities between Microbial Data

Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.

High throughput long-read sequencing of circulating lymphocytes of the evolutionarily distant sea lamprey reveals diversity and common elements of the variable lymphocyte receptor B (VLRB) repertoire.

The leucine-rich repeat-based variable lymphocyte receptor B (VLRB) antibody system of jawless vertebrates is capable of generating an antibody repertoire equal to or exceeding the diversity of antibody repertoires of jawed vertebrates. Unlike immunoglobulin-based immune repertoires, the VLRB repertoire diversity is characterized by variable lengths of VLRB encoding transcripts, rendering conventional immunoreceptor repertoire sequencing approaches unsuitable for VLRB repertoire sequencing. Here we demonstrate that long-read single-molecule real-time (SMRT) sequencing (PacBio) approaches permit the efficient large-scale assessment of the VLRB repertoire. We present a computational pipeline for sequence data processing and provide the first repertoire-based analysis of VLRB protein characteristics including properties of its subunits and regions of diversity within each structural leucine-rich repeat subunit. Our study provides a template to explore changes in the VLRB repertoire during immune responses and to establish large scale VLRB repertoire databases for computational approaches aimed at isolating monoclonal VLRB reagents for biomedical research and clinical applications.

P-328 Uterine Fluid microbiome composition in fertile controls and infertile women

Abstract Study question Is the microbiome of the Uterine Fluid (UF) related to outcomes of frozen-thawed blastocyst transfer? Summary answer Microbiome of UF collected by double-sheated catheter shows Lactobacilli as the most abundant species but does not relate to outcomes of frozen-thawed blastocyst transfer. What is known already The microbiome of UF has been described to be largely dominated (>90%) by Lactobacilli and has been shown to be markedly associated with the outcomes of embryo transfer. Nonetheless, previous reports have not collected UF by avoiding vaginal/cervical contamination through a double-sheated catheter. Study design, size, duration Observational study conducted at Fondazione IRCCS Ca’Granda Ospedale Maggiore Policlinico and IRCCS Ospedale San Raffaele, Milan, Italy. n = 13 fertile controls underwent UF sampling in the pre-receptive (LH + 2) and receptive (LH + 7) phase of a monitored natural cycle and n = 48 infertile women underwent UF sampling on day LH + 7 of the monitored natural cycle preceding that of their first frozen-thawed blastocyst transfer. UF was sampled by aspiration with a double-sheated catheter (commonly used for embryo transfer. Participants/materials, setting, methods Microbiome analysis was conducted following the Illumina protocol for V3-V5 regions of the 16S rRNA and using the Illumina MiSeq instrument. Raw sequencing data processing encompassed quality controls, and QIIME 2 scripts were employed to obtain taxonomic abundances (taxonomic levels L2-L7). Alpha (within sample), and beta (among samples) diversity and differential expression at taxonomic level (using limma, Bioconductor library) were calculated. Main results and the role of chance UF microbiome analysis showed lack of separation between samples from the two Centres, confirming the reproducibility of the technique. UF microbiome was also found to be stable throughout the menstrual cycle, with no diversity observed between LH + 2 and LH + 7 samples in the population of fertile controls (p = 0.9, Adonis test on beta diversity; confirmed by differential abundance tests based on linear regression models at taxonomic levels). UF of fertile controls showed Lactobacillus Helveticus as the most abundant species (16.2%), followed by other Lactobacilli (unspecified) (12.1 %), Lactobacillus Iners (11.8%), Streptococcus (unspecified) (6.8%), Bifidobacterium (4.7%) and Gardnerella (1.9 %). In women who achieved successful implantation after FET, the most abundant species was L. Helveticus (58.1%) followed by L. Iners (9.8%), Acinetobacter lwoffii (5.9%) and other Lactobacilli (unspecified) (4.0%). No statistically significant differences were found in linear regression comparisons at taxonomic level with UF of women with failed implantation, in whom most abundant species were L. Helveticus (50.4%, p = 0.11), L. Iners (15.8%, p = 0.95), other Lactobacilli (unspecified) (13.7%, p = 0.17) and Garnderella (6.5%, p = 0.19). The proportion of Lactobacillus- dominated (>90%) samples was 50% (n = 10/20) in infertile women achieving successful implantation and 46% (n = 13/28) in infertile women with failed implantation (p = 0.8). Limitations, reasons for caution These results are preliminary and only include the first n = 48 patients of the total n = 249 patients that will be enrolled. A larger sample is required to further investigate possible relations between UF microbiome composition and implantation outcomes. Wider implications of the findings As UF microbiome was found to be stable between cycle phases and it is non-invasively collected, it could be analysed in the same cycle in which ET is scheduled. This might increase accuracy compared to current tests for endometrial receptivity, that have to be performed in the cycle before ET. Trial registration number RF-2019-12369460

First report of Fusarium solani causing pink root disease on garlic (Allium sativum L.) in Lombok, Indonesia

Abstract Garlic (Allium sativum L.) is one of the important agricultural commodities in Indonesia, with Nusa Tenggara Barat (NTB) Province among the main producing regions. Several incidences of pink root disease symptoms have been reported in garlic plantations in the Sembalun highlands of Eastern Lombok District, NTB Province, Indonesia. This study aimed to isolate and identify the fungal pathogen responsible for pink root disease in garlic plants in this region. Fungal pathogens isolated from diseased garlic plants were morphologically investigated under a microscope and molecularly identified which includes DNA isolation, PCR, sequencing, processing of sequencing data, and NCBI blasts. The symptoms of the diseased plants in the fields were leaf wilting, stunted growth, and shortened roots which first turn light pink, then darken through red and purple, later turn black, and die. The mycelial colour of the pathogen on Potato Dextrose Agar (PDA) media was growth white. Observation of the pathogen under a microscope indicated that the pathogen produced oval micro-conidia and crescent-shaped macro-conidia. Based on the BLAST results which are based on the NCBI database, it can be concluded that the species of Fusarium solani was responsible for invading the garlic plant. This is the first report of pink root disease on Lombok Island in Indonesia.

IsolateR: an R package for generating microbial libraries from Sanger sequencing data.

Sanger sequencing of taxonomic marker genes (e.g. 16S/18S/ITS/rpoB/cpn60) represents the leading method for identifying a wide range of microorganisms including bacteria, archaea, and fungi. However, the manual processing of sequence data and limitations associated with conventional BLAST searches impede the efficient generation of strain libraries essential for cataloging microbial diversity and discovering novel species. isolateR addresses these challenges by implementing a standardized and scalable three-step pipeline that includes: (1) automated batch processing of Sanger sequence files, (2) taxonomic classification via global alignment to type strain databases in accordance with the latest international nomenclature standards, and (3) straightforward creation of strain libraries and handling of clonal isolates, with the ability to set customizable sequence dereplication thresholds and combine data from multiple sequencing runs into a single library. The tool's user-friendly design also features interactive HTML outputs that simplify data exploration and analysis. Additionally, in silico benchmarking done on two comprehensive human gut genome catalogues (IMGG and Hadza hunter-gather populations) showcase the proficiency of isolateR in uncovering and cataloging the nuanced spectrum of microbial diversity, advocating for a more targeted and granular exploration within individual hosts to achieve the highest strain-level resolution possible when generating culture collections. isolateR is available at: https://github.com/bdaisley/isolateR.

Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges.

Large language models (LLMs) offer transformative opportunities in bioinformatics education for medical students by creating interactive experiences. The integration of LLMs enhances educational outcomes through providing accessible code templates, clarifying the function of coding elements, and assisting in error troubleshooting. Here, we demonstrate the practical applications of LLMs with a case study on transcriptome sequencing data processing, a vital component of medical research. However, the reliability of the content that LLMs generate requires rigorous validation. Ensuring the accuracy and appropriateness of the LLM-generated information requires integrating innovative LLMs with traditional educational methods to prepare medical students effectively for future challenges in bioinformatics.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

BackgroundSingle-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.ResultsWe show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.ConclusionOur comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

Interpretable intelligent fault diagnosis strategy for fixed-wing UAV elevator fault diagnosis based on improved cross entropy loss

The current popular machine learning-based fault diagnosis methods make it difficult to explain the diagnostic results, leading to low user trust in such diagnostic techniques. In this regard, this paper explores the study of the interpretability of intelligent fault diagnosis algorithms using the elevator of a fixed-wing unmanned aerial vehicle (UAV) as a diagnostic object. The Transformer model combines excellent modeling capability and efficient sequence data processing, is chosen to mine fault signal features to guarantee accurate diagnosis. Among the proposed interpretable fault diagnosis models, the local interpretable model-agnostic explanations (LIME) model is used to provide explicit interpretability for the decision-making process of the diagnosis model. In addition, a loss function called RDCE (reinforced diagnostic cross-entropy) Loss is designed to minimize the negative impact of different sample sizes for different fault types on the diagnostic performance. This loss function is designed to weigh the various types of faults to speed up the convergence of the model and improve the diagnostic accuracy. By comparing the proposed diagnostic strategy with other commonly used machine learning models, including long short term memory and recurrent neural network (RNN), the average diagnostic accuracy of the proposed diagnostic strategy is 99.97%, significantly better than that of the comparison algorithms. At the same time, this paper provides an in-depth interpretable analysis of the diagnostic process of the Transformer. The diagnostic process of the Transformer model gives the reasons for the diagnostic results from the point of view of the kind of features processed by the model. Based on this, the diagnostic model is simplified. After streamlining the number of features from 40 to 24 according to their importance, the diagnostic accuracy of the model is improved by 0.26%, and the diagnostic efficiency is improved. In addition, the proposed diagnostic strategy also shows significant advantages in terms of noise robustness.

Universal approximation property of stochastic configuration networks for time series

For the purpose of processing sequential data, such as time series, and addressing the challenge of manually tuning the architecture of traditional recurrent neural networks (RNNs), this paper introduces a novel approach-the Recurrent Stochastic Configuration Network (RSCN). This network is constructed based on the random incremental algorithm of stochastic configuration networks. Leveraging the foundational structure of recurrent neural networks, our learning model commences with a modest-scale recurrent neural network featuring a single hidden layer and a solitary hidden node. Subsequently, the node parameters of the hidden layer undergo incremental augmentation through a random configuration process, with corresponding weights assigned structurally. This iterative expansion continues until the network satisfies predefined termination criteria. Noteworthy is the adaptability of this algorithm to handle time series data, exhibiting superior performance compared to traditional recurrent neural networks with similar architectures. The experimental results presented in this paper underscore the efficacy of the proposed RSCN for sequence data processing, showcasing its advantages over conventional recurrent neural networks in the context of the performed experiments.

Variational Bayesian Variable Selection for High-Dimensional Hidden Markov Models

The Hidden Markov Model (HMM) is a crucial probabilistic modeling technique for sequence data processing and statistical learning that has been extensively utilized in various engineering applications. Traditionally, the EM algorithm is employed to fit HMMs, but currently, academics and professionals exhibit augmenting enthusiasm in Bayesian inference. In the Bayesian context, Markov Chain Monte Carlo (MCMC) methods are commonly used for inferring HMMs, but they can be computationally demanding for high-dimensional covariate data. As a rapid substitute, variational approximation has become a noteworthy and effective approximate inference approach, particularly in recent years, for representation learning in deep generative models. However, there has been limited exploration of variational inference for HMMs with high-dimensional covariates. In this article, we develop a mean-field Variational Bayesian method with the double-exponential shrinkage prior to fit high-dimensional HMMs whose hidden states are of discrete types. The proposed method offers the advantage of fitting the model and investigating specific factors that impact the response variable changes simultaneously. In addition, since the proposed method is based on the Variational Bayesian framework, the proposed method can avoid huge memory and intensive computational cost typical of traditional Bayesian methods. In the simulation studies, we demonstrate that the proposed method can quickly and accurately estimate the posterior distributions of the parameters with good performance. We analyzed the Beijing Multi-Site Air-Quality data and predicted the PM2.5 values via the fitted HMMs.

Viral and host small RNA transcriptome analysis of SARS-CoV-1 and SARS-CoV-2-infected human cells reveals novel viral short RNAs

RNA viruses have been shown to express various short RNAs, some of which have regulatory roles during replication, transcription, and translation of viral genomes. However, short viral RNAs generated from SARS-CoV-1 and SARS-CoV-2 genomic RNAs remained largely unexplored, possibly due limitations of the widely used library preparation methods for small RNA deep sequencing and corresponding data processing. By analyzing publicly available small RNA sequencing datasets, we observed that human Calu-3 cells infected by SARS-CoV-1 or SARS-CoV-2 accumulate multiple previously unreported short viral RNAs. In addition, we verified the presence of the five most abundant SARS-CoV-2 short viral RNAs in SARS-CoV-2-infected human lung adenocarcinoma cells by quantitative PCR. Interestingly, the copy number of the observed SARS-CoV-2 short viral RNAs dramatically exceeded the expression of previously reported viral microRNAs in the same cells. We hypothesize that the reported SARS-CoV-2 short viral RNAs could serve as biomarkers for early infection stages due to their high abundance. Furthermore, unlike SARS-CoV-1, the SARS-CoV-2 infection induced significant (Benjamini-Hochberg-corrected p-value <0.05) deregulation of Y-RNA, transfer RNA, vault RNA, as well as more than 300 endogenous short RNAs that aligned predominantly to human protein-coding and long noncoding RNA transcripts. In particular, more than 20-fold upregulation of reads derived from Y-RNA (and several transfer RNAs) have been documented in RNA-seq datasets from SARS-CoV-2 infected cells. Finally, a significant proportion of short RNAs derived from full-length viral genomes also aligned to various human genome (hg38) sequences, suggesting opportunities to investigate regulatory roles of short viral RNAs during infection. Further characterization of the small RNA landscape of both viral and host genomes is clearly warranted to improve our understanding of molecular events related to infection and to design more efficient strategies for therapeutic interventions as well as early diagnosis.

LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome.

Linked-read sequencing technologies generate high-base quality short reads that contain extrapolative information on long-range DNA connectedness. These advantages of linked-read technologies are well known and have been demonstrated in many human genomic and metagenomic studies. However, existing linked-read analysis pipelines (e.g., Long Ranger) were primarily developed to process sequencing data from the human genome and are not suited for analyzing metagenomic sequencing data. Moreover, linked-read analysis pipelines are typically limited to 1 specific sequencing platform. To address these limitations, we present the Linked-Read ToolKit (LRTK), a unified and versatile toolkit for platform agnostic processing of linked-read sequencing data from both human genome and metagenome. LRTK provides functions to perform linked-read simulation, barcode sequencing error correction, barcode-aware read alignment and metagenome assembly, reconstruction of long DNA fragments, taxonomic classification and quantification, and barcode-assisted genomic variant calling and phasing. LRTK has the ability to process multiple samples automatically and provides users with the option to generate reproducible reports during processing of raw sequencing data and at multiple checkpoints throughout downstream analysis. We applied LRTK on linked reads from simulation, mock community, and real datasets for both human genome and metagenome. We showcased LRTK's ability to generate comparative performance results from preceding benchmark studies and to report these results in publication-ready HTML document plots. LRTK provides comprehensive and flexible modules along with an easy-to-use Python-based workflow for processing linked-read sequencing datasets, thereby filling the current gap in the field caused by platform-centric genome-specific linked-read data analysis tools.

MISSH: Fast Hashing of Multiple Spaced Seeds.

Alignment-free analysis of sequences has revolutionized the high-throughput processing of sequencing data within numerous bioinformatics pipelines. Hashing k-mers represents a common function across various alignment-free applications, serving as a crucial tool for indexing, querying, and rapid similarity searching. More recently, spaced seeds, a specialized pattern that accommodates errors or mutations, have become a standard choice over traditional k-mers. Spaced seeds offer enhanced sensitivity in many applications when compared to k-mers. However, it's important to note that hashing spaced seeds significantly increases computational time. Furthermore, if multiple spaced seeds are employed, accuracy can be further improved, albeit at the expense of longer processing times. This paper addresses the challenge of efficiently hashing multiple spaced seeds. The proposed algorithms leverage the similarity of adjacent spaced seed hash values within an input sequence, allowing for the swift computation of subsequent hashes. Our experimental results, conducted across various tests, demonstrate a remarkable performance improvement over previously suggested algorithms, with potential speedups of up to 20 times. Additionally, we apply these efficient spaced seed hashing algorithms to a metagenomic application, specifically the classification of reads using Clark-S [Ounit and Lonardi, 2016]. Our findings reveal a substantial speedup, effectively mitigating the slowdown caused by the utilization of multiple spaced seeds.

Research on the purification mechanism of heavy metal pollution by biochar composites driven by degree learning

This paper proposes an innovative approach by integrating deep learning technology, specifically employing the GRU recurrent neural network model based on the Seagull optimization algorithm, to enhance the accuracy of predicting biochar performance. The Seagull optimization algorithm, inspired by seagull predatory behavior, is adept at efficiently identifying optimal model parameters, thereby improving the model’s generalization ability and robustness. The GRU recurrent neural network, designed for sequence data processing, proves to be instrumental in capturing dynamic and nonlinear interactions between biochar and heavy metals. This, in turn, contributes to heightened prediction accuracy and model interpretability. The article unfolds in a structured manner, beginning with an introduction to the biochar preparation method and its characteristics. It then delves into an analysis of the sources and hazards of heavy metal pollution. Following this, the paper explains the principles and advantages of deep learning technology, providing a comprehensive foundation for the subsequent discussion. The construction and verification process of the proposed model is then detailed, concluding with the presentation of experimental results and in-depth analysis. In essence, this research introduces a pioneering idea and methodology for optimizing biochar design and effectively controlling heavy metal pollution, presenting a fresh perspective on addressing these environmental challenges.