High-throughput Sequencing Data Research Articles

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data.

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analysing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated datasets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared to database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, datasets, and various classification software can be found at: http://lab.malab.cn/∼tqz/project/taxonomic/.

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences

BackgroundEmergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing.ResultsIn this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30–50 aa; 100–150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG.ConclusionsARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet, with an online service provided at https://ARGNet.hku.hk.7nR91jX7ySRMRPZrE5tsTyVideo

Biofiltration of toluene in the presence of ethyl acetate or n-hexane: Performance and microbial community.

This study describes the operation of two independent parallel laboratory-scale biotrickling filters (BTFs) to degrade different types of binary volatile organic compound (VOC) mixtures. Comparison experiments were conducted to evaluate the effects of two typical VOCs, i.e., ethyl acetate (a hydrophilic VOC) and n-hexane (a hydrophobic VOC) on the removal performance of toluene (a moderately hydrophobic VOC) in BTFs ''A" and ''B", respectively. Experiments were carried out by stabilizing the toluene concentration at 1.64 g m-3 and varying the concentrations of gas-phase ethyl acetate (0.85-2.8 g m-3) and n-hexane (0.85-2.8 g m-3) at an empty bed residence time (EBRT) of 30 s. In the presence of ethyl acetate (850 ± 55 mg m-3), toluene exhibited the highest removal efficiency (95.4 ± 2.2%) in BTF "A". However, the removal rate of toluene varied from 48.1 ± 6.9% to 70.1 ± 6.8% when 850 ± 123 mg m-3 to 2800 ± 136 mg m-3 of n-hexane was introduced into BTF "B". The high-throughput sequencing data revealed that the genera Pseudomonas and Comamonadaceae_unclassified are the core microorganisms responsible for the degradation of toluene. The intensity of the inhibitory or synergistic effects on toluene removal was influenced by the type and concentration of the introduced VOC, as well as the number and activity of the genera Pseudomonas and Comamonadaceae_unclassified. It provides insights into the interaction between binary VOCs during biofiltration from a microscopic perspective.

Auxiliary diagnosis and prognosis evaluation of KIF4A, RAD51AP1 and CDKN3 in esophageal cancer

To investigate the expression of mRNA in esophageal cancer (ESCA) tissues and its potential and diagnostic and prognostic value by high-throughput sequencing data. Using the Cancer Genome Atlas Program (TCGA) database in USA by integrative bioinformatics analysis methods, the gene expression profiles and clinical data of 173 patients with ECSA were collected. The mRNA expression levels in ESCA tissue and para-cancerous tissue samples were analyzed using DESeq2, edgeR and limma to screen the differentially expressed genes (DEGs). DEGs-related protein network diagrams were drawn. GO and KEGG function enrichment analysis were performed and the hub genes were screened and the survival analysis of hub genes was analyzed. Genes related to the prognosis of ESCA were selected and their prognostic value in ESCA was analyzed. Finally, the receiver operating characteristic curve was drawn to evaluate its diagnostic value. The results showed that using TCGA cancer data, a total of 620 up-regulated DEGs and 668 down-regulated DEGs with significant differential expression between ESCA and para-cancerous tissues were screened. DEGs were mainly involved in receptor complexes, ubiquitin ligase complexes, etc., playing GTPase activity, phospholipid binding, and other molecular functions, and participating in the regulation of intracellular substance transport, small molecule metabolism, and other biological processes. Protein functional enrichment analysis showed that these proteins were mainly enriched in the IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, Epstein-Barr virus infection, neutrophil extracellular trap formation, and other pathways involved in the formation and development process of ESCA. Survival analysis showed that the overall survival rate of ESCA patients with high expression of KIF4A, RAD51AP1, and CDKN3 was significantly shortened, and the difference was statistically significant (P<0.05). Furthermore, the areas under the curve (AUC) of KIF4A, RAD51AP1, and CDKN3 for diagnosing esophageal cancer were 0.956, 0.951 and 0.979, respectively, with sensitivities and specificities both exceeding 80%. Additionally, ROC results of the combined diagnostic model of these three genes showed an AUC of 0.979, with sensitivities and specificities of 0.914 and 1, respectively. This indicates that KIF4A, RAD51AP1 and CDKN3 have individual or combined auxiliary diagnostic value for ESCA. In conclusion, KIF4A, RAD51AP1 and CDKN3 have high diagnostic efficiency for ESCA, and their increased expression is closely related to the prognosis, suggesting that these three genes could be used as auxiliary diagnostic and prognostic factors for ESCA.

Impact of root canal disinfection on the bacteriome present in primary endodontic infection: A next generation sequencing study.

To investigate the bacteriome present in teeth with primary endodontic infection (PEI) and apical periodontitis (AP) and to determine quantitatively and qualitatively the impact of chemomechanical preparation (CMP) using 2.5% sodium hypochlorite NAOCl on the bacteriome found in PEI with AP using the Illumina MiSeq platform. Thirty-six paired samples from 18 patients were successfully sequenced and analysed. Samples were collected at two sampling times: before (s1) and after (s2) CMP using 2.5% NaOCl. The DNA was extracted from s1 and s2 samples and quantified using quantitative PCR (qPCR). All 36 samples were sequenced using the Illumina MiSeq platform. Raw V3-V4 amplicon sequencing data were processed with the DADA2 pipeline to generate amplicon sequence variants (ASVs). Alpha diversity metrics representing abundance (Chao1) and diversity and evenness (Shannon, Simpson) were computed. The paired-sample Wilcoxon's test was used to compare alpha diversity metrics and qPCR counts between s1 and s2. The PERMANOVA method (with 999 permutations) was applied to compare community composition between sample types (s1 versus s2) and between patient IDs. ALDEx2 (ANOVA-like differential expression tool for high-throughput sequencing data) to investigate differentially abundant taxa between s1 and s2. A paired-sample Wilcoxon's test was used to compare alpha diversity metrics and qPCR counts between s1 and s2. The qPCR counts were significantly higher in s1 compared to s2 (p = .0007). The Chao1 index indicated no difference in alpha diversity (p < .7019); whereas Shannon (p = .0056) and Simpson (p = .02685) indexes showed higher values in s2. The PERMANOVA test using Adonis2 showed a significant effect of sample time on community composition (R2 = .0630, p = .012). Patient ID also showed a significant effect on community composition (R2 = .6961, p = .001). At the genus level, Dialister, Mogibacterium, Prevotella, and Olsenella were differentially enriched at s1, while Actinomyces, Stenotrophomonas_unclassified, Enterococcus_unclassified, and Actinomyces_unclassified were differentially enriched in s2. The bacteriome present in teeth with PEI with AP is complex and diverse. CMP using 2.5% NaOCl showed a high quantitatively and qualitatively disinfectant impact on the bacteriome present in PEI with AP.

Depletion of protective microbiota promotes the incidence of fruit disease.

Plant-associated microbiomes play important roles in plant health and productivity. However, despite fruits being directly linked to plant productivity, little is known about the microbiomes of fruits and their potential association with fruit health. Here, by integrating 16S rRNA gene, ITS high-throughput sequencing data and microbiological culturable approaches, we reported that roots and fruits (pods) of peanut, a typical plant that bears fruits underground, recruit different bacterial and fungal communities independently of cropping conditions, and that the incidence of pod disease under monocropping conditions is attributed to the depletion of Bacillus genus and enrichment of Aspergillus genus in geocarposphere. On this basis, we constructed a synthetic community (SynCom) consisting of three Bacillus strains from geocarposphere soil under rotation conditions with high culturable abundance. Comparative transcriptome, microbiome profiling and plant phytohormone signaling analysis reveal that the SynCom exhibited more effective Aspergillus growth inhibition and pod disease control than individual strain, which was underpinned by a combination of molecular mechanisms related to fungal cell proliferation interference, mycotoxins biosynthesis impairment and jasmonic acid-mediated plant immunity activation. Overall, our results reveal the filter effect of plant organs on the microbiome, and that depletion of key protective microbial community promotes the fruit disease incidence.

Simulation of nanopore sequencing signal data with tunable parameters.

In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, transcriptome or read sequences and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols; noise-free 'ideal' data; or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.

Performance and microbial community composition of full-scale high-rate cascade sludge digestion system via pie-shaped reactor configuration

A full-scale high-rate cascade anaerobic digestion (CAD) system was evaluated for its ability to enhance enzymatic sludge hydrolysis. The system included a newly built digester, innovatively divided into three pie-shaped compartments (500 m3 each), followed by an existing, larger digester (1500 m3). The system treated a mixture of waste activated sludge and primary sludge, achieving a stable total chemical oxygen demand reduction efficiency (56.1 ± 6.8 %), and enhanced sludge hydrolytic enzyme activities at a 14.5-day total solids retention time (SRT). High-throughput sequencing data revealed a consistent microbial community across reactors, dominated by consortia that govern hydrolysis and acidogenesis. Despite relatively short SRTs in the initial reactors of the CAD system, acetoclastic methanogens belonging to Methanosaeta became the most abundant archaea. ‬‬‬‬‬‬‬‬‬‬‬‬‬ This study proves that the CAD system achieves stable sludge reduction, accelerates enzymatic hydrolysis at full-scale, and paves the way for its industrialization in municipal waste sewage sludge treatment.

Virus discovery in cultured portunid crabs: Genomic, phylogenetic, histopathological and microscopic characterization of a reovirus and a new bunyavirus

Portunid crabs are distributed worldwide and highly valued in aquaculture. Viral infections are the main limiting factor for the survival of these animals and, consequently, for the success of commercial-scale cultivation. However, there is still a lack of knowledge about the viruses that infect cultured portunid crabs worldwide. Herein, the genome sequence and phylogeny of Callinectes sapidus reovirus 2 (CsRV2) are described, and the discovery of a new bunyavirus in Callinectes danae cultured in southern Brazil is reported. The CsRV2 genome sequence consists of 12 dsRNA segments (20,909 nt) encode 13 proteins. The predicted RNA-dependent RNA polymerase (RdRp) shows a high level of similarity with that of Eriocheir sinensis reovirus 905, suggesting that CsRV2 belongs to the genus Cardoreovirus. The CsRV2 particles are icosahedral, measuring approximately 65 nm in diameter, and exhibit typical non-turreted reovirus morphology. High throughput sequencing data revealed the presence of an additional putative virus genome similar to bunyavirus, called Callinectes danae Portunibunyavirus 1 (CdPBV1). The CdPBV1 genome is tripartite, consisting of 6,654 nt, 3,120 nt and 1,656 nt single-stranded RNA segments that each encode a single protein. Each segment has a high identity with European shore crab virus 1, suggesting that CdPBV1 is a new representative of the family Cruliviridae. The putative spherical particles of CdPBV1 measure ∼120 nm in diameter and present a typical bunyavirus morphology. The results of the histopathological analysis suggest that these new viruses can affect the health and, consequently, the survival of C. danae in captivity. Therefore, the findings reported here should be used to improve prophylactic and pathogen control practices and contribute to the development and optimization of the production of soft-shell crabs on a commercial scale in Brazil.

PAX1 represses canonical Wnt signaling pathway and plays dual roles during endoderm differentiation

BackgroundPaired box 1 (PAX1) is a transcription factor and essential for the development of pharyngeal pouches-derived tissues, including thymus. PAX1 mutations are identified in Severe Combined Immunodeficiency (SCID) patients with Otofaciocervical Syndrome Type 2 (OTFCS2). However, despite the critical roles of PAX1 in embryonic development and diseases, detailed insights into its molecular mode of action are critically missing.MethodsThe repressing roles of PAX1 and SCID associated mutants on Wnt signaling pathway were investigated by luciferase reporter assays, qRT-PCR and in situ hybridization in HEK293FT, HCT116 cells and zebrafish embryos, respectively. Co-immunoprecipitation (co-IP) and western blotting assays were carried out to identify the molecular mechanisms underlying PAX1’s role on Wnt signaling pathway. hESC based endoderm differentiation, flow cytometry, high-throughput sequencing data analysis, and qRT-PCR assays were utilized to determine the roles of PAX1 during endoderm differentiation.ResultsHere, we show that PAX1 represses canonical Wnt signaling pathway in vertebrate cells. Mechanically, PAX1 competes with SUMO E3 ligase PIASy to bind to TCF7L2, thus perturbing TCF7L2 SUMOylation level, further reducing its transcriptional activity and protein stability. Moreover, we reveal that PAX1 plays dual roles in hESC-derived definitive and foregut/pharyngeal endoderm cells, which give rise to the thymus epithelium, by inhibiting Wnt signaling. Importantly, our data show PAX1 mutations found in SCID patients significantly compromise the suppressing ability of PAX1 on Wnt signaling.ConclusionsOur study presents a novel molecular mode of action of PAX1 in regulation of canonical Wnt signaling and endoderm differentiation, thus providing insights for the molecular basis of PAX1 associated SCID, offering better understanding of the behavior of PAX1 in embryogenesis.

First report of the Horse nettle virus infecting tomato (Solanum lycopersicum L.) in the United States.

Tomato (Solanum lycopersicum L.), a member of the Solanaceae family, represents one of the most extensively cultivated vegetable species worldwide and traces its origin to western South America (Caruso et al. 2022). In a field survey conducted in 2023 in Bixby, Tulsa County, Oklahoma, distinct symptoms were noted in two plants: one exhibited mottling and cupping of leaves and brown discoloration on leaves, petioles, and stems, while the other displayed a downward curling of leaves. Leaf samples from both symptomatic tomato plants (labelled as K4 and K5) were collected, and total RNA was extracted individually via the TRI Reagent® method (Molecular Research Center Inc., Cincinnati, OH, USA). Subsequently, the RNA samples were pooled and subjected to high-throughput sequencing (HTS) on the NextSeq 500/550 high-output kit v2.5 (Illumina, U.S.A.) at the genomic facility, Oklahoma State University (Stillwater, OK). Total read count of 8,227,020 (average length =150.5 bp) was obtained, trimmed, and de novo assembled using CLC Genomics Workbench v22.0.1 (QIAGEN) and used for BLASTn and BLASTx analysis. Two contigs: 6,375 bp (average coverage 2,915.92, read count 142,538) and 3,564 bp (average coverage 3,035.91, read count 82,370) from the pooled sample showed 88.6% and 96.7% nucleotide identities with RNA 1 (OP292294) and RNA 2 (OP292295) of Horse nettle virus A (HNA-A) isolate MD-1, respectively. Sequences of both partial contigs (RNA 1, accession no. PP063196) and RNA 2, accession no. PP063197) were submitted to GenBank. The HTS data did not reveal any other viral or viroid sequences in these two tomato samples. To further confirm the presence of HNV-A, total RNA from K4 and K5 samples was tested individually by RT-PCR using HNV specific primers (Supplementary Table 1) based on the two partial contig sequences. The expected PCR products (491 bp and 451 bp) were obtained only from the K4 sample and none from the K5 sample. PCR products were extracted from an agarose gel, cloned into the pGEM®-T Easy vector (Promega), and transformed into Escherichia coli DH5α cells (New England Bio Labs). Two clones for each PCR product were sequenced by Sanger sequencing. Nucleotide sequence comparisons and BLASTn analysis of 491 bp and 451 bp showed 86% and 97% nucleotide identity with RNA 1 and RNA 2 of HNV-A isolate MD-1 (OP292294 and OP292295), respectively. Additionally, eight more leaf samples from eight different symptomatic tomato plants were collected in the same field and tested by RT-PCR as described above. All eight samples were positive by RT-PCR, but no PCR band was obtained in the total RNA from a healthy tomato leaf used as a control. Sequences from the PCR products were identical to the obtained HTS sequences. Our results confirmed for the first time that HNV-A can infect tomatoes. Currently, HNV-A has been reported to only infect a single weed (Horse nettle, Solanum carolinense) (Zhou et al. 2023). The identification of HNV-A in tomatoes in Oklahoma suggests a potential host shift is of concern for local growers as well as tomato growers worldwide. This shift underscores the urgency for an in-depth investigation into the transmission and host specificity of HNV-A. This is the first report in the United States and the world that HNV-A could infect tomatoes naturally in a grower field.

Diversity of fungal communities on Cabernet and Aglianico grapes from vineyards located in Southern Italy.

Grape-associated microbial community is influenced by a combination of viticultural, climatic, pedological and anthropological factors, collectively known as terroir. Therefore, grapes of the same cultivar grown in different areas can be appreciated for their distinctive biogeographic characteristics. In our previous study, we showed that the phenotypic response of Aglianico and Cabernet grapevines from Molise and Sicily regions is significantly influenced by the prevailing pedoclimatic conditions, particularly soil physical properties. However, the scale at which microbial communities differ could be important in clarifying the concept of terroir, including whether it is linked to the grape variety present in a particular vineyard. To explore this further, in the research presented here, a comparative study on the fungal communities inhabiting the berry surfaces of Cabernet and Aglianico cultivars was conducted on different vineyards located in Southern Italy (Molise, Sicily and Campania regions, the first two of which had been involved in our previous study) by using high-throughput sequencing (HTS) and multivariate data analysis. The descriptive approach through relative abundance analysis showed the most abundant phyla (Ascomycota, Basidiomycota, and Chytridiomycota), families (Cladosporiaceae, Saccotheciaceae, Pleosporaceae, Saccharomycodaceae, Sporidiobolaceae, Didymellaceae, Filobasidiaceae, Bulleribasidiaceae, and Saccharomycetaceae) and genera (Cladosporium, Aureobasidium, Alternaria, Stemphylium and Filobasidium) detected on grape berries. The multivariate data analysis performed by using different packages (phyloseq, Vegan, mixOmics, microbiomeMarker and ggplot2) highlighted that the variable "vineyard location" significantly affect the fungal community, while the variable "grape variety" has no significant effect. Thus, some taxa are found to be part of specific vineyard ecosystems rather than specific grape varieties, giving additional information on the microbial contribution to wine quality, thanks to the presence of fermentative yeasts or, conversely, to the involvement in negative or detrimental roles, due to the presence of grape-deriving fungi implied in the spoilage of wine or in grapevine pathogenesis. In this connection, the main functions of core taxa fungi, whose role in the vineyard environment is still poorly understood, are also described.

Human Breast Milk Exosomal miRNAs are Influenced by Premature Delivery and Affect Neurodevelopment.

This study investigates the exosomal microRNA (miRNA) profiles of term and preterm breast milk, including the most abundant and differentially expressed (DE) miRNAs, and their impact on neurodevelopment in infants. Mature milk is collected from the mothers of term and preterm infants. Using high-throughput sequencing and subsequent data analysis, exosomal miRNA profiles of term and preterm human breast milk (HBM) are acquired and it is found that the let-7 and miR-148 families are the most abundant miRNAs. Additionally, 23 upregulated and 15 downregulated miRNAs are identified. MiR-3168 is the most upregulated miRNA in preterm HBM exosome, exhibiting targeting activity toward multiple genes involved in the SMAD and MAPK signaling pathways and playing a crucial role in early neurodevelopment. Additionally, the effects of miR-3168 on neurodevelopment is confirmed and it is determined that it is an essential factor in the differentiation of neural stem cells (NSCs). This study demonstrates that miRNA expression in breast milk exosomes can be influenced by preterm delivery, thereby potentially impacting neurodevelopment in preterm infants.

Cost-effective evaluation of Aptamer candidates in SELEX-based Aptamer isolation

Aptamers are short, single-stranded nucleic acids with high affinity and specificity for various targets, making them valuable in diagnostics and therapeutics. Their isolation traditionally involves a time-consuming and costly process called SELEX. While SELEX methods have evolved to improve binding and amplification, the crucial step of aptamer identification from sequencing data remains expensive and often overlooked. Common identification methods require modification of aptamer candidates with labels like biotin or fluorescent dyes, which becomes costly and cumbersome for high-throughput sequencing data. This paper presents an efficient and cost-effective approach to streamline aptamer identification. It employs asymmetric polymerase chain reaction (PCR) to generate modified single-stranded DNA copies of aptamer candidates, simplifying the modification process. By using excess modified forward primers and limited reverse primers, this method reduces costs since only unmodified candidates need to be synthesized initially. The approach was demonstrated with an IgE protein aptamer and successfully applied to identify aptamers from a pool of 12 candidates against a monoclonal antibody. The validity of the results was further confirmed through the direct synthesis of fluorophore-conjugated aptamer candidates, yielding consistent outcomes while reducing the cost by threefold. This approach addresses a critical bottleneck in aptamer discovery by significantly reducing the time and cost associated with aptamer identification, facilitating aptamer-based research and making aptamers more accessible for various applications in diagnostics and therapeutics.

Systematic identification and characterization of exon-intron circRNAs.

Exon-intron circRNAs (EIciRNAs) are a circRNA subclass with retained introns. Global features of EIciRNAs remain largely unexplored, mainly owing to the lack of bioinformatic tools. The regulation of intron retention (IR) in EIciRNAs and the associated functionality also require further investigation. We developed a framework, FEICP, which efficiently detected EIciRNAs from high-throughput sequencing (HTS) data. EIciRNAs are distinct from exonic circRNAs (EcircRNAs) in aspects such as with larger length, localization in the nucleus, high tissue specificity, and enrichment mostly in the brain. Deep learning analyses revealed that compared with regular introns, the retained introns of circRNAs (CIRs) are shorter in length, have weaker splice site strength, and have higher GC content. Compared with retained introns in linear RNAs (LIRs), CIRs are more likely to form secondary structures and show greater sequence conservation. CIRs are closer to the 5'-end, whereas LIRs are closer to the 3'-end of transcripts. EIciRNA-generating genes are more actively transcribed and associated with epigenetic marks of gene activation. Computational analyses and genome-wide CRISPR screening revealed that SRSF1 binds to CIRs and inhibits the biogenesis of most EIciRNAs. SRSF1 regulates the biogenesis of EIciLIMK1, which enhances the expression of LIMK1 in cis to boost neuronal differentiation, exemplifying EIciRNA physiological function. Overall, our study has developed the FEICP pipeline to identify EIciRNAs from HTS data, and reveals multiple features of CIRs and EIciRNAs. SRSF1 has been identified to regulate EIciRNA biogenesis. EIciRNAs and the regulation of EIciRNA biogenesis play critical roles in neuronal differentiation.

Soil Nutrient, Enzyme Activity, and Microbial Community Characteristics of E. urophylla × E. grandis Plantations in a Chronosequence

The effects of continuous Eucalyptus cropping on soil properties and microbial characteristics and the specific factors influencing tree species growth remain elusive. In this study, three Eucalyptus stands of three different ages were selected, and soil nutrients, microbial biomass, enzyme activity, microbial community composition, and diversity were quantified for each. The findings indicated a significant decline in soil pH, soil cation exchange, soil organic matter, and available phosphorus content with the plantation age. Simultaneously, there was an observed increase in soil alkaline hydrolyzed nitrogen content. In addition, urease and acid phosphatase activities did not show a significant difference with age. In spite of this, catalase activity exhibited a decline corresponding to the advancement in plantation age. The carbon and nitrogen content of the soil microbial biomass increased with the progression of Eucalyptus planting time. The high-throughput sequencing data demonstrated a reduction in microbial diversity in Eucalyptus soils as the planting age increased. Interestingly, the microbial community structure exhibited minimal alterations, and did not exhibit a predominantly oligotrophic state overall. In conclusion, the study results showed that short-term successive Eucalyptus cropping exerts a significant negative impact on the soil system.

Multiple approaches in the study of ancient human microbiome

Abstract Research related to the analysis of microbial communities in ancient human samples began two decades ago. Since then, new technologies as well as new bioinformatic tools have been developed for the manipulation of massive data. All this together, with the theoretical knowledge generated from microbial ecology has expanded our understanding of the human microbiome. Despite the challenge of studying ancient samples and their microbiome, some approaches applied to the contemporary human microbiome have been successfully applied to ancient samples. Most studies in ancient samples have focused on taxonomic profiling at the community level with metagenomic or high-throughput 16S rRNA gene amplicon sequencing data. However, other approaches have emerged in order to get deeper understanding of the ancient human microbiome. Therefore, the aim of this review is to discuss all the approaches and methodologies successfully applied to ancient samples and ancient microbiome as well as the information that can be generated using them, in order to understand the evolution of human microbiome interactions throughout history.

Microbial community structures and bacteria-Cylindrospermopsis raciborskii interactions in Yilong Lake.

Cylindrospermopsis raciborskii-dominated harmful algae blooms have been reported globally in recent years. However, our understanding of the ecology of C. raciborskii in natural conditions is still poor. In this study, we collected the water samples from a C. raciborskii-blooming lake, Yilong Lake, in Yunnan province, China, and used both culture-dependent and culture-independent approaches to investigate their microbial communities and the interactions between C. raciborskii and the other bacteria. The composition and diversity of microbial communities were revealed with 16S rRNA gene high-throughput sequencing data analysis. Microbial co-occurrences analysis suggests C. raciborskii may have complex associations with other bacteria. Based on co-inoculation tests, we obtained 14 strains of bacterial strains from the water samples that exhibited either algicidal or promoting effects on a strain of C. raciborskii. Two bacterial isolates exhibited a consistent performance between co-occurrence analysis and experimental results. Effects of these bacteria-algae interspecies interactions on the bloom event are discussed. All these results may provide new insights into the C. raciborskii-dominated blooms and how its interspecies relationships with other bacteria may influence the bloom events in eutrophic waters throughout the world.

Transcriptome Analysis of Tomato Leaves Reveals Candidate Genes Responsive to Tomato Brown Rugose Fruit Virus Infection.

Tomato brown rugose fruit virus (ToBRFV) is a newly-emerging tobamovirus which was first reported on tomatoes in Israel and Jordan, and which has now spread rapidly in Asia, Europe, North America, and Africa. ToBRFV can overcome the resistance to other tobamoviruses conferred by tomato Tm-1, Tm-2, and Tm-22 genes, and it has seriously affected global crop production. The rapid and comprehensive transcription reprogramming of host plant cells is the key to resisting virus attack, but there have been no studies of the transcriptome changes induced by ToBRFV in tomatoes. Here, we made a comparative transcriptome analysis between tomato leaves infected with ToBRFV for 21 days and those mock-inoculated as controls. A total of 522 differentially expressed genes were identified after ToBRFV infection, of which 270 were up-regulated and 252 were down-regulated. Functional analysis showed that DEGs were involved in biological processes such as response to wounding, response to stress, protein folding, and defense response. Ten DEGs were selected and verified by qRT-PCR, confirming the reliability of the high-throughput sequencing data. These results provide candidate genes or signal pathways for the response of tomato leaves to ToBRFV infection.

Enricherator: A Bayesian Method for Inferring Regularized Genome-wide Enrichments from Sequencing Count Data

A pervasive question in biological research studying gene regulation, chromatin structure, or genomics is where, and to what extent, does a signal of interest arise genome-wide? This question is addressed using a variety of methods relying on high-throughput sequencing data as their final output, including ChIP-seq for protein-DNA interactions,1 GapR-seq for measuring supercoiling,2 and HBD-seq or DRIP-seq for R-loop positioning.3,4 Current computational methods to calculate genome-wide enrichment of the signal of interest usually do not properly handle the count-based nature of sequencing data, they often do not make use of the local correlation structure of sequencing data, and they do not apply any regularization of enrichment estimates. This can result in unrealistic estimates of the true underlying biological enrichment of interest, unrealistically low estimates of confidence in point estimates of enrichment (or no estimates of confidence at all), unrealistic gyrations in enrichment estimates at very close (<10 bp) genomic loci due to noise inherent in sequencing data, and in a multiple-hypothesis testing problem during interpretation of genome-wide enrichment estimates. We developed a tool called Enricherator to infer genome-wide enrichments from sequencing count data. Enricherator uses the variational Bayes algorithm to fit a generalized linear model to sequencing count data and to sample from the approximate posterior distribution of enrichment estimates (https://github.com/jwschroeder3/enricherator). Enrichments inferred by Enricherator more precisely identify known binding sites in cases where low coverage between binding sites leads to false-positive peak calls in these noisy regions of the genome; these benefits extend to published datasets.