Illumina MiSeq 16S rRNA Gene Library Preparation for Poultry Processing Microbiome Analyses.

The standardization of the microbiome sequencing of poultry rinsates is essential for generating comparable microbial composition data among poultry processing facilities if this technology is to be adopted by the industry. Samples must first be acquired, DNA must be extracted, and libraries must be constructed. In order to proceed to library sequencing, the samples should meet quality control standards. Finally, data must be analyzed using computer bioinformatics pipelines. This data can subsequently be incorporated into more advanced computer algorithms for risk assessment. Ultimately, *a uniform sequencing pipeline will enable both the government regulatory agencies and the poultry industry to identify potential weaknesses in food safety.This chapter presents the different steps for monitoring the population dynamics of the microbiome in poultry processing using 16S rDNA sequencing.

Next Generation Sequencing: Latent applications in clinical diagnostics with the advent of bioinformatic frameworks.

For the past 3-4 decades, the discovery of Sanger's method of pyrosequencing was the only method unparalleled till 2005 being employed as a method of whole genome sequencing (WGS). Following this, a revolutionary extensive parallel sequencing method, Next Generation Sequencing (NGS), was engineered. NGS supported a substantial number of bases under a high throughput metagenomic interrogation. Bioinformatics contributed notably to this advancement. It provided alignment tools, assembly algorithms, and protocols such as Illumina and hybridization capture which have metamorphosed clinical and translational diagnostics. With the extension in precision medicine and targeted therapy under NGS sectors such as epigenetics, transcriptomics, mutation detection, prognosis, therapeutics, and patient management have been gaining progress. Using NGS in real-time clinical settings has been proven to produce positive outcomes. The most recent instrumental benefaction of NGS has been decoding the SARS-CoV-2 virus epidemiology with the assistance of multiplex PCR. So far, it had been employed to inspect different levels of viral loads from low to mid. This has been executed by amplification and phylogenetic examination of the load to raise a connective link with the evolutionary history leading up to the period of origin. The depletion in the consumed time and extensive genome size under analysis was further coupled by a cutback in the cost of sequencing while executing NGS. With the aid of this review paper, we aspire to manifest how the above-mentioned elements have boosted, tissue, microbial, and molecular data interrogation. Along with this, promoting, and stimulating an extensive evaluation and expansion in the paradigm of morphological and phenotypic study, via bioinformatics can facilitate further advancement in personalized and concise clinical research.

The impact of extraction method and pollen concentration on community composition for pollen metabarcoding.

Premise: Plants and pollinators closely interact with each other to form complex networks of species interactions. Metabarcoding of pollen collections has recently been proposed as an advantageous method for the construction of such networks, but the extent to which diversity and community analyses depend on the extraction method and pollen concentration used remains unclear. Methods: In this study, we used a dilution series of two pollen mixtures (a mock community and pooled natural pollen loads from bumblebees) to assess the effect of mechanical homogenization and two DNA extraction kits (spin column DNA extraction kit and magnetic bead DNA extraction kit) on the detected pollen richness and community composition. Results: All species were successfully detected using the three methods, even in the most dilute samples. However, the extraction method had a significant effect on the detected pollen richness and community composition, with simple mechanical homogenization introducing an extraction bias. Discussion: Our findings suggest that all three methods are effective for detecting plant species in the pollen loads on insects, even in cases of very low pollen loads. However, our results also indicate that extraction methods can have a profound impact on the ability to correctly assess the community composition of the pollen loads on insects. The choice of extraction methodology should therefore be carefully considered to ensure reliable and unbiased results in pollen diversity and community analyses.

Complete genome sequence of a vampire bat-related rabies virus obtained by metagenomics from a patient with encephalitis of unknown etiology, French Guiana.

We report the complete genome sequence of a rabies virus obtained by direct metagenomics from the cerebellum of a gold panner who died of unknown encephalitis in French Guiana. Phylogenetic analysis exhibited a close genetic relationship with vampire bat-related isolates, confirming the second case of human rabies identified in this territory.

Metagenome assembly and annotation of data from the rhizosphere soil of drought-stressed CRN-3505 maize cultivar.

This data article reports shotgun metagenomic data obtained from drought-stressed maize rhizosphere through the Illumina Novaseq platform, utilizing the KBase online platform. 428,339,852 high-quality post-sequences were obtained, showcasing an average GC content of 65.45 %. The investigation, conducted at Molelwane farm in Mafikeng, South Africa, identified 13 metagenome-assembled genomes (MAGs). Functional annotation of these MAGs revealed their involvement in essential plant growth and development functions, such as sulfur and nitrogen metabolism. The dataset was deposited into the NCBI database, and MAGs accessions are available at DDBJ/ENA/GenBank under the accession number PRJNA101755.

Responses of soil enzymes, bacterial communities and soil nitrification to the pre-emergence herbicide pyroxasulfone.

Pyroxasulfone is a relatively new herbicide that is sprayed on soils to control grassy weeds and some broadleaf weeds during the cultivation of agronomic crops. However, information regarding its environmental risks to soil ecosystems is currently limited. Here, the response of soil characteristics and soil bacterial communities to pyroxasulfone exposure were evaluated. The rate of pyroxasulfone degradation in soil decreased with increasing herbicide concentration, and its half-life at doses of 0.12 (the recommended field rate), 1.2 and 12 mg kg-1 was estimated to be 15.75 d, 39.46 d and 78.08 d, respectively. Soil pH markedly increased after pyroxasulfone treatment. Pyroxasulfone significantly inhibited urease activity but had a small effect on soil sucrase activity. In the late stages of degradation, the abundance of bacteria clearly decreased in soils treated with pyroxasulfone at doses of 1.2 and 12 mg kg-1. Compared with the control group, a distinct decrease in bacterial network complexity was observed at a pyroxasulfone dose of 0.12 mg kg-1, while the opposite phenomenon was observed at a pyroxasulfone dose of 12 mg kg-1. The copy numbers of the AOA amoA and AOB amoA genes exposed to 10- and 100-fold the recommended rates of pyroxasulfone were significantly lower than those in soils without pyroxasulfone residue at 25 and 60 days after treatment. In summary, pyroxasulfone at the recommended rate had a slight effect on soil enzymes, the bacterial community and soil nitrification; however, the potential adverse impacts of pyroxasulfone at higher concentrations on these soil factors deserve further attention.

Combining Short- and Long-Read Sequencing Technologies to Identify SARS-CoV-2 Variants in Wastewater.

During the COVID-19 pandemic, the monitoring of SARS-CoV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare the sequencing technology from Illumina and from Oxford Nanopore Technologies, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities. Our study used two analytical approaches to identify the main variants in the samples: the presence of signature and marker mutations and the co-occurrence of signature mutations within the same amplicon. We observed that each sequencing method detected certain variants at different frequencies as each method preferentially detects mutations of distinct variants. Illumina sequencing detected more mutations with a predominant lineage that is in low abundance across the population or unknown for that time period, while Nanopore sequencing had a higher detection rate of mutations that are predominantly found in the high abundance B.1.1.7 (Alpha) lineage as well as a higher sequencing rate of co-occurring mutations in the same amplicon. We present a workflow that integrates short-read and long-read sequencing to improve the detection of SARS-CoV-2 variant lineages in mixed population samples, such as wastewater.

Improved draft genome of Ignatzschineria larvae DSM 13226 via hybrid assembly.

Ignatzschineria larvae is studied for its role in decomposition and disease ecology; however, the type strain reference genome remains fragmented. The current reference genome consists of 61 contigs calculated at 82.18% complete with 10.98% contamination. Here, we announce the hybrid genome assembly as an improved single contig.

CRISPR-mediated megabase-scale transgene de-duplication to generate a functional single-copy full-length humanized DMD mouse model.

BACKGROUND: The development of sequence-specific precision treatments like CRISPR gene editing therapies for Duchenne muscular dystrophy (DMD) requires sequence humanized animal models to enable the direct clinical translation of tested strategies. The current available integrated transgenic mouse model containing the full-length human DMD gene, Tg(DMD)72Thoen/J (hDMDTg), has been found to have two copies of the transgene per locus in a tail-to-tail orientation, which does not accurately simulate the true (single) copy number of the DMD gene. This duplication also complicates analysis when testing CRISPR therapy editing outcomes, as large genetic alterations and rearrangements can occur between the cut sites on the two transgenes. RESULTS: To address this, we performed long read nanopore sequencing on hDMDTg mice to better understand the structure of the duplicated transgenes. Following that, we performed a megabase-scale deletion of one of the transgenes by CRISPR zygotic microinjection to generate a single-copy, full-length, humanized DMD transgenic mouse model (hDMDTgSc). Functional, molecular, and histological characterisation shows that the single remaining human transgene retains its function and rescues the dystrophic phenotype caused by endogenous murine Dmd knockout. CONCLUSIONS: Our unique hDMDTgSc mouse model simulates the true copy number of the DMD gene, and can potentially be used for the further generation of DMD disease models that would be better suited for the pre-clinical assessment and development of sequence specific CRISPR therapies.

Transcriptome datasets of maize plant cultures treated with humic- and amino acids.

There has been a global surge in the need for commercially accessible plant conditioners that are derived from natural ingredients and are therefore environmentally benign. Currently, sustainable agriculture and minimizing the ecological impact are of great importance. Preparations that contain commonly used humic acids and/or natural amino acids are ideal for meeting these criteria. An investigation was conducted to examine the impact of three plant foliar fertilizers containing humic acid and one fertilizer containing a combination of humic and amino acids on maize crops. By employing the shallow mRNA sequencing technique, we acquired datasets that, once processed, are ideal for investigating the impacts of the foliar fertilizers examined in the study. Five SRA datasets were uploaded to NCBI. These datasets include the TSA (Transcriptome Shotgun Assembly), the contigs that were blasted, mapped, and annotated from the pre-processed datasets, as well as the count table obtained from the RNA-seq read quantification. All of these data are included in the Mendeley database. In the future, the databases will enable the investigation of alterations in plant biochemical processes at the gene expression level.

Comparative Analysis of Microbial Communities in Diseased and Healthy Sweet Cherry Trees (Prunus avium L.).

The European sweet cherry Prunus avium (L.), a member of the Rosaceae family, is one of the most popular and economically valuable fruits. However, the rapid spread of gummosis and poor management practices have become the major obstacles to their production. To identify pathogenic microorganisms responsible for gummosis disease, we conducted observations comparing the garden of Bailuyuan, which heavily suffered from gummosis disease and horn beetle damage, with the orchard of Mayuhe, which only suffered from gummosis disease, both from Xi'an, Shaanxi, China. Samples were obtained from the healthy tissues and gummosis disease tissues that used the Illumina sequence of 16S rRNA and the internal transcribed spacer region (ITS) to identify bacterial and fungal communities in these samples. An alpha diversity analysis revealed a significantly higher fungal diversity of disease than in healthy tissue in the gummosis period. The results suggested that an imbalance in the fungal genera may be associated with gummosis disease. Species relative analyses showed some bacterial genera (Pelagibacterium, Halomonas, Azospirillum, Aquabacterium and Alistipes) and fungal genera (Penicillium, Alternaria and Rhodotorula) in the diseased tissues of gummosis. Among these, the increased relative abundance of the bacteria genes Halomonas, Pelagibacterium, Chelativorans, Pantoea, Aquabacterium, Alternaria and fungi genes Penicillium, Cystobasidium, Rhodotorula may be associated with gummosis of P. avium. The bacterial genera Methylobacterium, Psychroglaciecola, Aeromonas, Conexibacter and fungal genera Didymella, Aureobasidium, Mycosphaerella, Meyerozyma are probably antagonists of the pathogen of gummosis. These findings are an initial step in the identification of potential candidates for the biological control of the disease.

Illumina-based transcriptomic analysis of the fast-growing leguminous tree Acacia crassicarpa: functional gene annotation and identification of novel SSR-markers.

Acacia crassicarpa is a fast-growing leguminous tree that is widely cultivated in tropical areas such as Indonesia, Malaysia, Australia, and southern China. This tree has versatile utility in timber, furniture, and pulp production. Illumina sequencing of A. crassicarpa was conducted, and the raw data of 124,410,892 reads were filtered and assembled de novo into 93,317 unigenes, with a total of 84,411,793 bases. Blast2GO annotation, Benchmark Universal Single-Copy Ortholog evaluation, and GO-term classification produced a catalogue of unigenes for studying primary metabolism, phytohormone signaling, and transcription factors. Massive transcriptomic analysis has identified microsatellites composed of simple sequence repeat (SSR) loci representing di-, tri-, and tetranucleotide repeat units in the predicted open reading frames. Polymorphism was induced by PCR amplification of microsatellite loci located in several genes encoding auxin response factors and other transcription factors, which successfully distinguished 16 local trees of A. crassicarpa tested, representing potentially exploitable molecular markers for efficient tree breeding for plantation and biomass exploitation.

Deciphering the differences of bacterial communities between high- and low-productive wheat fields using high-throughput sequencing.

Microbial communities have been demonstrated to be essential for healthy and productive soil ecosystems. However, an understanding of the relationship between soil microbial community and soil productivity levels is remarkably limited. In this study, bulk soil (BS), rhizosphere soil (RS), and root (R) samples from the historical high-productive (H) and low-productive (L) soil types of wheat in Hebei province of China were collected and analyzed by high-throughput sequencing. The study highlighted the richness, diversity, and structure of bacterial communities, along with the correlation networks among different bacterial genera. Significant differences in the bacterial community structure between samples of different soil types were observed. Compared with the low-productive soil type, the bacterial communities of samples from the high-productive soil type possessed high species richness, low species diversity, complex and stable networks, and a higher relative abundance of beneficial microbes, such as Pseudoxanthomonas, unclassified Vicinamibacteraceae, Lysobacter, Massilia, Pseudomonas, and Bacillus. Further analysis indicated that the differences were mainly driven by soil organic matter (SOM), available nitrogen (AN), and electrical conductivity (EC). Overall, the soil bacterial community is an important factor affecting soil health and crop production, which provides a theoretical basis for the targeted regulation of microbes in low-productivity soil types.

A novel HLA-C*07 variant allele, HLA-C*07:01:01:141, identified by next-generation sequencing.

Characterisation of the novel HLA-C*07:01:01:141 allele in a 23-year-old Greek bone marrow donor.

The novel HLA-A*32 allele, HLA-A*32:01:56, identified by next generation sequencing.

HLA-A*32:01:56 differs from HLA-A-32:01:01 by a single nucleotide variation in Exon 5, codon 313.3.

Primed and ready: nanopore metabarcoding can now recover highly accurate consensus barcodes that are generally indel-free.

BACKGROUND: DNA metabarcoding applies high-throughput sequencing approaches to generate numerous DNA barcodes from mixed sample pools for mass species identification and community characterisation. To date, however, most metabarcoding studies employ second-generation sequencing platforms like Illumina, which are limited by short read lengths and longer turnaround times. While third-generation platforms such as the MinION (Oxford Nanopore Technologies) can sequence longer reads and even in real-time, application of these platforms for metabarcoding has remained limited possibly due to the relatively high read error rates as well as the paucity of specialised software for processing such reads. RESULTS: We show that this is no longer the case by performing nanopore-based, cytochrome c oxidase subunit I (COI) metabarcoding on 34 zooplankton bulk samples, and benchmarking the results against conventional Illumina MiSeq sequencing. Nanopore R10.3 sequencing chemistry and super accurate (SUP) basecalling model reduced raw read error rates to ~ 4%, and consensus calling with amplicon_sorter (without further error correction) generated metabarcodes that were ≤ 1% erroneous. Although Illumina recovered a higher number of molecular operational taxonomic units (MOTUs) than nanopore sequencing (589 vs. 471), we found no significant differences in the zooplankton communities inferred between the sequencing platforms. Importantly, 406 of 444 (91.4%) shared MOTUs between Illumina and nanopore were also found to be free of indel errors, and 85% of the zooplankton richness could be recovered after just 12-15 h of sequencing. CONCLUSION: Our results demonstrate that nanopore sequencing can generate metabarcodes with Illumina-like accuracy, and we are the first study to show that nanopore metabarcodes are almost always indel-free. We also show that nanopore metabarcoding is viable for characterising species-rich communities rapidly, and that the same ecological conclusions can be obtained regardless of the sequencing platform used. Collectively, our study inspires confidence in nanopore sequencing and paves the way for greater utilisation of nanopore technology in various metabarcoding applications.

A deep learning approach to prediction of blood group antigens from genomic data.

BACKGROUND: Deep learning methods are revolutionizing natural science. In this study, we aim to apply such techniques to develop blood type prediction models based on cheap to analyze and easily scalable screening array genotyping platforms. METHODS: Combining existing blood types from blood banks and imputed screening array genotypes for ~111,000 Danish and 1168 Finnish blood donors, we used deep learning techniques to train and validate blood type prediction models for 36 antigens in 15 blood group systems. To account for missing genotypes a denoising autoencoder initial step was utilized, followed by a convolutional neural network blood type classifier. RESULTS: Two thirds of the trained blood type prediction models demonstrated an F1-accuracy above 99%. Models for antigens with low or high frequencies like, for example, Cw, low training cohorts like, for example, Cob, or very complicated genetic underpinning like, for example, RhD, proved to be more challenging for high accuracy (>99%) DL modeling. However, in the Danish cohort only 4 out of 36 models (Cob, Cw, D-weak, Kpa) failed to achieve a prediction F1-accuracy above 97%. This high predictive performance was replicated in the Finnish cohort. DISCUSSION: High accuracy in a variety of blood groups proves viability of deep learning-based blood type prediction using array chip genotypes, even in blood groups with nontrivial genetic underpinnings. These techniques are suitable for aiding in identifying blood donors with rare blood types by greatly narrowing down the potential pool of candidate donors before clinical grade confirmation.

Exosomal transcript cargo and functional correlation with HNSCC patients' survival.

BACKGROUND: HPV status in a subset of HNSCC is linked with distinct treatment outcomes. Present investigation aims to elucidate the distinct clinicopathological features of HPV-positive and HPV-negative HNSCC and investigate their association with the HNSCC patient survival. MATERIALS AND METHODS: The total RNA of exosomes from HPV-positive (93VU147T) and HPV-negative (OCT-1) HNSCC cells was isolated, and the transcripts were estimated using Illumina HiSeq X. The expression of altered transcripts and their clinical relevance were further analyzed using publicly available cancer transcriptome data from The Cancer Genome Atlas (TCGA). RESULTS: Transcriptomic analyses identified 3785 differentially exported transcripts (DETs) in HPV-positive exosomes compared to HPV-negative exosomes. DETs that regulate the protein machinery, cellular redox potential, and various neurological disorder-related pathways were over-represented in HPV-positive exosomes. TCGA database revealed the clinical relevance of altered transcripts. Among commonly exported abundant transcripts, SGK1 and MAD1L1 showed high expression, which has been correlated with poor survival in HNSCC patients. In the top 20 DETs of HPV-negative exosomes, high expression of FADS3, SGK3, and TESK2 correlated with poor survival of the HNSCC patients in the TCGA database. CONCLUSION: Overall, our study demonstrates that HPV-positive and HPV-negative cells' exosomes carried differential transcripts cargo that may be related to pathways associated with neurological disorders. Additionally, the altered transcripts identified have clinical relevance, correlating with patient survival in HNSCC, thereby highlighting their potential as biomarkers and as therapeutic targets.

Beyond the base pairs: comparative genome-wide DNA methylation profiling across sequencing technologies.

Deoxyribonucleic acid (DNA) methylation plays a key role in gene regulation and is critical for development and human disease. Techniques such as whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) allow DNA methylation analysis at the genome scale, with Illumina NovaSeq 6000 and MGI Tech DNBSEQ-T7 being popular due to their efficiency and affordability. However, detailed comparative studies of their performance are not available. In this study, we constructed 60 WGBS and RRBS libraries for two platforms using different types of clinical samples and generated approximately 2.8 terabases of sequencing data. We systematically compared quality control metrics, genomic coverage, CpG methylation levels, intra- and interplatform correlations, and performance in detecting differentially methylated positions. Our results revealed that the DNBSEQ platform exhibited better raw read quality, although base quality recalibration indicated potential overestimation of base quality. The DNBSEQ platform also showed lower sequencing depth and less coverage uniformity in GC-rich regions than did the NovaSeq platform and tended to enrich methylated regions. Overall, both platforms demonstrated robust intra- and interplatform reproducibility for RRBS and WGBS, with NovaSeq performing better for WGBS, highlighting the importance of considering these factors when selecting a platform for bisulfite sequencing.

The novel HLA-A*23:140 allele was identified in a Brazilian bone marrow volunteer donor.

The HLA-A*23:140 allele differs from HLA-A*23:01:01 by one nucleotide substitution (G > A), position 1968 in exon 5.