Library construction for next-generation sequencing: overviews and challenges

Chaperonin containing TCP1 subunit 6A may activate Notch and Wnt pathways to facilitate the malignant behaviors and cancer stemness in oral squamous cell carcinoma

Chaperonin containing TCP1 subunit 6A (CCT6A) was recently discovered to be involved in cancer pathogenesis and stemness; however, its role in oral squamous cell carcinoma (OSCC) has not been reported. The current study aimed to investigate the impact of CCT6A on OSCC cell malignant behaviors and stemness and to explore its potentially interreacted pathways. SCC-15 and HSC-3 cells were transfected with the plasmid loading control overexpression, CCT6A overexpression, control knockout, or CCT6A knockout. Wnt4 overexpression or Notch1 overexpression plasmids were transfected into CCT6A-knockout SCC-15 cells. Cell proliferation, apoptosis, invasion, stemness, Notch, and Wnt pathways were detected in both cell lines, whereas RNA sequencing was only performed in SCC-15 cells. CCT6A was upregulated in five OSCC cell lines, including SCC-15, HSC-3, SAT, SCC-9, and KON, compared to that in the control cell line. In SCC-15 and HSC-3 cells, CCT6A overexpression increased cell proliferation, invasion, sphere formation, CD133, and Sox2 expression, but decreased cell apoptosis; on the contrary, CCT6A knockout exhibited an opposite effect on the above indexes. RNA-sequencing data revealed that the Wnt and Notch pathways were involved in the CCT6A'effect on SCC-15 cell functions. CCT6A positively regulates the Wnt and Notch pathways in SCC-15 and HSC-3 cells. Importantly, it was shown that activation of the Wnt or Notch pathways attenuated the effect of CCT6A knockout on SCC-15 cell survival, invasion, and stemness. CCT6A may promote OSCC malignant behavior and stemness by activating the Wnt and Notch pathways.

Next-Generation Sequencing Methods for Near-Real-Time Molecular Epidemiology of HIV and HCV

The World Health Organisation has set targets of reducing the transmission of new hepatitis C (HCV) infections by 90%, and ending human immunodeficiency virus-1 (HIV) as a public health threat, by 2030. To achieve this, efficient and timely viral surveillance, and effective public health interventions, are required. Traditional epidemiological methods are largely dependent on the recognition of incident cases with symptomatic illness; acute HIV and HCV infections are commonly asymptomatic, which may lead to delays in the recognition of such new infections. Instead, for these viruses, molecular epidemiology may improve the detection of, and response to, clusters of viral transmission. Molecular epidemiology using historical datasets has highlighted key populations that may have benefitted from a timely intervention. Similar analyses performed on contemporary samples are needed to underpin the 2030 targets, but this requires the generation of a cohesive dataset of viral genome sequences in near-real-time. To generate such data, methodologies harnessing next-generation sequencing (NGS) should be utilised. Here we discuss the opportunity presented by NGS for public health surveillance of HIV and HCV, and discuss three methods that can generate sequences for such analysis. These include full-length genome amplification, utilised for analysis of HCV in the research space; tiling PCR, which was the method of choice for many diagnostic laboratories in the SARS-CoV-2 pandemic; and bait-capture hybridisation, which has been utilised in local HIV outbreaks. These techniques could be applied for near-real-time HIV and HCV surveillance, informing public health strategies that will be key to achieving 2030 targets.

Application of the Agilent 2100 Bioanalyzer instrument as quality control for next-generation sequencing

Next-generation sequencing (NGS) technologies have expanded the spectrum of forensic DNA analysis by facilitating efficient and precise genotyping of a large number of genetic markers. Yet, challenges persist regarding complex sample processing and assurance of equal molar concentrations across pooled samples. Since optimal cluster density is crucial for sequencing performance, the determination of both quantity and quality is indispensable for library preparation. In this study, we investigated the application of the Agilent 2100 Bioanalyzer for library quality control, as studies for forensic approaches, particularly for highly degraded postmortem samples, are rare. Our analysis encompassed assessing total DNA concentrations, fluorescence unit (FU) values, and adapter dimer concentrations in purified DNA libraries derived from buccal swabs and tissue samples of decomposed corpses. The sensitivity study tested a serial dilution derived from buccal swabs and revealed a decrease in FU values and an increase in adapter dimers with declining DNA input concentrations. Deviations in total DNA concentrations and average peak heights between the Agilent 2100 Bioanalyzer runs indicated a lack of repeatability in data and presented challenges in accurate quantification, which was also observed in previous studies. Yet, the analysis of degraded samples from decomposed human remains has shown the ability to detect adapter dimer concentrations, which can be crucial for the quality of subsequent NGS library preparation and sequencing success. Therefore, the Agilent 2100 Bioanalyzer proves to be a valuable tool for NGS quality control.

Peripheral blood to next-generation sequencing ready DNA library: a novel engineering design for automation

Whole genome sequencing (WGS) has become a gold standard for diagnosing genomic variation. Peripheral blood is a common sample source for the extraction of nucleic acids for Next-Generation Sequencing (NGS) applications. Here, we present an integrated and fully automated device design that uses new concepts of fluid mechanics, heat-mass transfer, and thermodynamics of enzymatic reactions to extract nucleic acids from the blood and perform DNA library preparation from a pre-filled plate. We demonstrate that the presented device effectively extracts dsDNA with an average of 25.03 µg/mL and 25.91 µg/mL yield from citrate-stabilized human peripheral blood stored in Fresh (4 °C) and Frozen (-20 °C) conditions, respectively. Furthermore, our method automatically extracts nucleic acids and creates a high-quality sequence-ready DNA library from blood stabilized with citrate and EDTA for 8 samples simultaneously in a single run with a total operation time of ~ 7 h. Our results show the required coverage and depth of the genome, highlighting an essential application of this device in processing blood samples for genome sequencing.

Sequence-matching adapter trimmers generate consistent quality and assembly metrics for Illumina sequencing of RNA viruses

Trimming adapters and low-quality bases from next-generation sequencing (NGS) data is crucial for optimal analysis. We evaluated six trimming programs, implementing five different algorithms, for their effectiveness in trimming adapters and improving quality, contig assembly, and single-nucleotide polymorphism (SNP) quality and concordance for poliovirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and norovirus paired data sequenced on Illumina iSeq and MiSeq platforms. Trimmomatic and BBDuk effectively removed adapters from all datasets, unlike FastP, AdapterRemoval, SeqPurge, and Skewer. All trimmers improved read quality (Q ≥ 30, 87.8 - 96.1%) compared to raw reads (83.6 - 93.2%). Trimmers implementing traditional sequence-matching (Trimmomatic and AdapterRemoval) and overlapping algorithm (FastP) retained the highest-quality reads. While all trimmers improved the maximum contig length and genome coverage for iSeq and MiSeq viral assemblies, BBDuk-trimmed reads assembled the shortest contigs. SNP concordance was consistently high (> 97.7 - 100%) across trimmers. However, BBDuk-trimmed reads had the lowest quality SNPs. Overall, the two adapter trimmers that utilized the traditional sequence-matching algorithm performed consistently across the viral datasets analyzed. Our findings guide software selection and inform future versatile trimmer development for viral genome analysis.

Automation of customizable library preparation for next-generation sequencing into an open microfluidic platform

Next-generation sequencing (NGS) is becoming more relevant for medical diagnostics, especially for using cell-free DNA to monitor response to therapy in cancer management, as high sensitivity of NGS enables detection of rare events. Sequencing Library preparation is a time-consuming and complex process, and large-scale liquid handlers are often used for automation. However, for smaller labs and low-to-medium throughput samples, these liquid handlers are expensive and need experts for handling. This work presents a proof-of-concept for library preparation on a commercially available and open lab-on-a-chip platform, which provides an alternative automation for low-to-medium throughput requirements. It covers common library preparation steps optimized to a microfluidic environment that include customizable PCR for target enrichment, end-repair, adapter ligation, nucleic acid purification via magnetic beads, and an integrated quantification step. The functionality of the cartridge is demonstrated with reference cfDNA containing different allelic frequencies of seven known mutations. Processing the samples in the cartridge reveals highly comparable results to manual processing (Pearson r = 0.94) based on amplicon sequencing. Summarized, the proposed automated lab-on-a-chip workflow for customizable library preparation could further pave the way for NGS to evolve from a technology used for research purposes to one that is applied in routine cancer management.

A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length

Ribosomes are the molecular machinery that catalyse all the fundamental steps involved in the translation of mRNAs into proteins. Given the complexity of this process, the efficiency of protein synthesis depends on a large number of factors among which ribosome drop-off (i.e. the premature detachment of the ribosome from the mRNA template) plays an important role. However, an in vitro quantification of the extent to which ribosome drop-off occurs is not trivial due to difficulties in obtaining the needed experimental evidence. In this work we focus on the study of ribosome drop-off in Saccharomyces cerevisiae by using 'Ribofilio', a novel software tool that relies on a high sensitive strategy to estimate the ribosome drop-off rate from ribosome profiling data. Our results show that ribosome drop-off events occur at a significant rate also when S. cerevisiae is cultured in standard conditions. In this context, we also identified a correlation between the ribosome drop-off rate and the genes length: the longer the gene, the lower the drop-off rate.

Leveraging insect viruses and genetic manipulation for sustainable agricultural pest control

The potential of insect viruses in the biological control of agricultural pests is well-recognized, yet their practical application faces obstacles such as host specificity, variable virulence, and resource scarcity. High-throughput sequencing (HTS) technologies have significantly advanced our capabilities in discovering and identifying new insect viruses, thereby enriching the arsenal for pest management. Concurrently, progress in reverse genetics has facilitated the development of versatile viral expression vectors. These vectors have enhanced the specificity and effectiveness of insect viruses in targeting specific pests, offering a more precise approach to pest control. This review provides a comprehensive examination of the methodologies employed in the identification of insect viruses using HTS. Additionally, it explores the domain of genetically modified insect viruses and their associated challenges in pest management. The adoption of these cutting-edge approaches holds great promise for developing environmentally sustainable and effective pest control solutions. © 2023 Society of Chemical Industry.

Employing emerging technologies such as motion capture to study the complex interplay between genotype and power-related performance traits

Exercise genomics has progressed alongside advancements in molecular genetic technologies that have enhanced our understanding of associations between genes and performance traits. This novel field of research incorporates techniques and tools from epidemiology, molecular genetics, exercise physiology and biostatistics to investigate the complex interplay between genotype and specific quantitative performance traits, such as muscle power output. Here I aimed to illustrate how interdisciplinary training can ensure the effective use of new emerging technologies, such as motion capture, to examine the influence of genetic and epigenetic factors on power-related quantitative performance traits. Furthermore, this study raises awareness about the present research trends in this field, and highlights current gaps and potential future developments. The acquired knowledge will likely have important future implications in the biotech industry, with a focus on gene therapy to combat age-related muscle power decline, personalized medicine and will drive advancements in exercise program design.

Untacking small RNA profiling and RNA fragment footprinting: Approaches and challenges in library construction

Small RNAs (sRNAs) with sizes ranging from 15 to 50 nucleotides (nt) are critical regulators of gene expression control. Prior studies have shown that sRNAs are involved in a broad range of biological processes, such as organ development, tumorigenesis, and epigenomic regulation; however, emerging evidence unveils a hidden layer of diversity and complexity of endogenously encoded sRNAs profile in eukaryotic organisms, including novel types of sRNAs and the previously unknown post-transcriptional RNA modifications. This underscores the importance for accurate, unbiased detection of sRNAs in various cellular contexts. A multitude of high-throughput methods based on next-generation sequencing (NGS) are developed to decipher the sRNA expression and their modifications. Nonetheless, distinct from mRNA sequencing, the data from sRNA sequencing suffer frequent inconsistencies and high variations emanating from the adapter contaminations and RNA modifications, which overall skew the sRNA libraries. Here, we summarize the sRNA-sequencing approaches, and discuss the considerations and challenges for the strategies and methods of sRNA library construction. The pros and cons of sRNA sequencing have significant implications for implementing RNA fragment footprinting approaches, including CLIP-seq and Ribo-seq. We envision that this review can inspire novel improvements in small RNA sequencing and RNA fragment footprinting in future. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Processing > Processing of Small RNAs Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs.

ViTAL: Vision TrAnsformer based Low coverage SARS-CoV-2 lineage assignment

MOTIVATION

Rapid spread of viral diseases such as Coronavirus disease 2019 (COVID-19) highlights an urgent need for efficient surveillance of virus mutation and transmission dynamics, which requires fast, inexpensive and accurate viral lineage assignment. The first two goals might be achieved through low-coverage whole-genome sequencing (LC-WGS) which enables rapid genome sequencing at scale and at reduced costs. Unfortunately, LC-WGS significantly diminishes the genomic details, rendering accurate lineage assignment very challenging.

RESULTS

We present ViTAL, a novel deep learning algorithm specifically designed to perform lineage assignment of low coverage-sequenced genomes. ViTAL utilizes a combination of MinHash for genomic feature extraction and Vision Transformer for fine-grain genome classification and lineage assignment. We show that ViTAL outperforms state-of-the-art tools across diverse coverage levels, reaching up to 87.7% lineage assignment accuracy at 1× coverage where state-of-the-art tools such as UShER and Kraken2 achieve the accuracy of 5.4% and 27.4% respectively. ViTAL achieves comparable accuracy results with up to 8× lower coverage than state-of-the-art tools. We explore ViTAL's ability to identify the lineages of novel genomes, i.e. genomes the Vision Transformer was not trained on. We show how ViTAL can be applied to preliminary phylogenetic placement of novel variants.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in https://github.com/zuherJahshan/vital and can be accessed with 10.5281/zenodo.10688110.

Exploring Cereal Metagenomics: Unravelling Microbial Communities for Improved Food Security

Food security is an urgent global challenge, with cereals playing a crucial role in meeting the nutritional requirements of populations worldwide. In recent years, the field of metagenomics has emerged as a powerful tool for studying the microbial communities associated with cereal crops and their impact on plant health and growth. This chapter aims to provide a comprehensive overview of cereal metagenomics and its role in enhancing food security through the exploration of beneficial and pathogenic microbial interactions. Furthermore, we will examine how the integration of metagenomics with other tools can effectively address the adverse effects on food security. For this purpose, we discuss the integration of metagenomic data and machine learning in providing novel insights into the dynamic interactions shaping plant-microbe relationships. We also shed light on the potential applications of leveraging microbial diversity and epigenetic modifications in improving crop resilience and yield sustainability. Ultimately, cereal metagenomics has revolutionized the field of food security by harnessing the potential of beneficial interactions between cereals and their microbiota, paving the way for sustainable agricultural practices.

Characterization and mitigation of artifacts derived from NGS library preparation due to structure-specific sequences in the human genome

BACKGROUND

Hybridization capture-based targeted next generation sequencing (NGS) is gaining importance in routine cancer clinical practice. DNA library preparation is a fundamental step to produce high-quality sequencing data. Numerous unexpected, low variant allele frequency calls were observed in libraries using sonication fragmentation and enzymatic fragmentation. In this study, we investigated the characteristics of the artifact reads induced by sonication and enzymatic fragmentation. We also developed a bioinformatic algorithm to filter these sequencing errors.

RESULTS

We used pairwise comparisons of somatic single nucleotide variants (SNVs) and insertions and deletions (indels) of the same tumor DNA samples prepared using both ultrasonic and enzymatic fragmentation protocols. Our analysis revealed that the number of artifact variants was significantly greater in the samples generated using enzymatic fragmentation than using sonication. Most of the artifacts derived from the sonication-treated libraries were chimeric artifact reads containing both cis- and trans-inverted repeat sequences of the genomic DNA. In contrast, chimeric artifact reads of endonuclease-treated libraries contained palindromic sequences with mismatched bases. Based on these distinctive features, we proposed a mechanistic hypothesis model, PDSM (pairing of partial single strands derived from a similar molecule), by which these sequencing errors derive from ultrasonication and enzymatic fragmentation library preparation. We developed a bioinformatic algorithm to generate a custom mutation "blacklist" in the BED region to reduce errors in downstream analyses.

CONCLUSIONS

We first proposed a mechanistic hypothesis model (PDSM) of sequencing errors caused by specific structures of inverted repeat sequences and palindromic sequences in the natural genome. This new hypothesis predicts the existence of chimeric reads that could not be explained by previous models, and provides a new direction for further improving NGS analysis accuracy. A bioinformatic algorithm, ArtifactsFinder, was developed and used to reduce the sequencing errors in libraries produced using sonication and enzymatic fragmentation.

A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Simultaneous identification of DNA and RNA pathogens using metagenomic sequencing in cases of severe acute respiratory infection

Metagenomic next-generation sequencing (mNGS) is a valuable technique for identifying pathogens. However, conventional mNGS requires the separate processing of DNA and RNA genomes, which can be resource- and time-intensive. To mitigate these impediments, we propose a novel method called DNA/RNA cosequencing that aims to enhance the efficiency of pathogen detection. DNA/RNA cosequencing uses reverse transcription of total nucleic acids extracted from samples by using random primers, without removing DNA, and then employs mNGS. We applied this method to 85 cases of severe acute respiratory infections (SARI). Influenza virus was identified in 13 cases (H1N1: seven cases, H3N2: three cases, unclassified influenza type: three cases) and was not detected in the remaining 72 samples. Bacteria were present in all samples. Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii were detected in four influenza-positive samples, suggesting coinfections. The sensitivity and specificity for detecting influenza A virus were 73.33% and 95.92%, respectively. A κ value of 0.726 indicated a high level of concordance between the results of DNA/RNA cosequencing and SARI influenza virus monitoring. DNA/RNA cosequencing enhanced the efficiency of pathogen detection, providing a novel capability to strengthen surveillance and thereby prevent and control infectious disease outbreaks.

Deqformer: high-definition and scalable deep learning probe design method

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing depth among each probe. To accurately predict probe coverage depth, a model called Deqformer is proposed in this study. Deqformer utilizes the oligonucleotides sequence of each probe, drawing inspiration from Watson-Crick base pairing and incorporating two BERT encoders to capture the underlying information from the forward and reverse probe strands, respectively. The encoded data are combined with a feed-forward network to make precise predictions of sequencing depth. The performance of Deqformer is evaluated on four different datasets: SNP panel with 38 200 probes, lncRNA panel with 2000 probes, synthetic panel with 5899 probes and HD-Marker panel for Yesso scallop with 11 000 probes. The SNP and synthetic panels achieve impressive factor 3 of accuracy (F3acc) of 96.24% and 99.66% in 5-fold cross-validation. F3acc rates of over 87.33% and 72.56% are obtained when training on the SNP panel and evaluating performance on the lncRNA and HD-Marker datasets, respectively. Our analysis reveals that Deqformer effectively captures hybridization patterns, making it robust for accurate predictions in various scenarios. Deqformer leads to a novel perspective for probe design pipeline, aiming to enhance efficiency and effectiveness in probe design tasks.

Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin? A systematic review

Food authentication using molecular techniques is of great importance to fight food fraud. Metabarcoding, based on the next-generation sequencing (NGS) technologies, allowing large-scale taxonomic identification of complex samples via massive parallel sequencing of fragments (called DNA barcodes) simultaneously, has become increasingly popular in many scientific fields. A systematic review to answer the question "Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin?" is presented. The inclusion criteria were focused on the selection of scientific papers (SPs) only applying metabarcoding to foodstuff of animal origin collected on the market. The 23 included SPs were first analyzed with respect to the metabarcoding phases: library preparation (target genes, primer pairs, and fragment length), sequencing (NGS platforms), and final data analysis (bioinformatic pipelines). Given the importance of primer selection, the taxonomic coverage of the used primers was also evaluated. In addition, the SPs were scored based on the use of quality control measures (procedural blanks, positive controls, replicates, curated databases, and thresholds to filter the data). A lack of standardized protocols, especially with respect to the target barcode/s and the universal primer/s, and the infrequent application of the quality control measures, leads to answer that metabarcoding is not ripe enough for authenticating foodstuff of animal origin. However, the observed trend of the SP quality improvement over the years is encouraging. Concluding, a proper protocol standardization would allow a wider use of metabarcoding by both official and private laboratories, enabling this method to become the primary for the authentication of foodstuffs of animal origin.

Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

Analysis approaches for the identification and prediction of N6-methyladenosine sites

The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m⁶A sites, in particular full-transcriptome m⁶A. And individual m⁶A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m⁶A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m⁶A methylation, including the experimental detection of m⁶A methylation sites, techniques of data analysis, the way of predicting m⁶A methylation sites, m⁶A methylation databases, and detection of m⁶A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.

Whole blood RNA extraction efficiency contributes to variability in RNA sequencing data sets

Numerous methodologies are used for blood RNA extraction, and large quantitative differences in recovered RNA content are reported. We evaluated three archived data sets to determine how extraction methodologies might influence mRNA and lncRNA sequencing results. The total quantity of RNA recovered /ml of blood affects RNA sequencing by impacting the recovery of weakly expressed mRNA, and lncRNA transcripts. Transcript expression (TPM counts) plotted in relation to transcript size (base pairs, bp) revealed a 30% loss of short to midsized transcripts in some data sets. Quantitative recovery of RNA is of considerable importance, and it should be viewed more judiciously. Transcripts common to the three data sets were subsequently normalized and transcript mean TPM counts and TPM count coefficient of variation (CV) were plotted in relation to increasing transcript size. Regression analysis of mean TPM counts versus transcript size revealed negative slopes in two of the three data sets suggesting a reduction of TPM transcript counts with increasing transcript size. In the third data set, the regression slope line of mRNA transcript TPM counts approximates zero and TPM counts increased in proportion to transcript size over a range of 200 to 30,000 bp. Similarly, transcript TPM count CV values also were uniformly distributed over the range of transcript sizes. In the other data sets, the regression CV slopes increased in relation to transcript size. The recovery of weakly expressed and /or short to midsized mRNA and lncRNA transcripts varies with different RNA extraction methodologies thereby altering the fundamental sequencing relationship between transcript size and TPM counts. Our analysis identifies differences in RNA sequencing results that are dependent upon the quantity of total RNA recovery from whole blood. We propose that incomplete RNA extraction directly impacts the recovery of mRNA and lncRNA transcripts from human blood and speculate these differences contribute to the "batch" effects commonly identified between sequencing results from different archived data sets.

Natural genetic variation quantitatively regulates heart rate and dimension

The polygenic contribution to heart development and function along the health-disease continuum remains unresolved. To gain insight into the genetic basis of quantitative cardiac phenotypes, we utilize highly inbred Japanese rice fish models, Oryzias latipes, and Oryzias sakaizumii. Employing automated quantification of embryonic heart rates as core metric, we profiled phenotype variability across five inbred strains. We observed maximal phenotypic contrast between individuals of the HO5 and the HdrR strain. HO5 showed elevated heart rates associated with embryonic ventricular hypoplasia and impaired adult cardiac function. This contrast served as the basis for genome-wide mapping. In a segregation population of 1192 HO5 x HdrR F2 embryos, we mapped 59 loci (173 genes) associated with heart rate. Experimental validation of the top 12 candidate genes in loss-of-function models revealed their causal and distinct impact on heart rate, development, ventricle size, and arrhythmia. Our study uncovers new diagnostic and therapeutic targets for developmental and electrophysiological cardiac diseases and provides a novel scalable approach to investigate the intricate genetic architecture of the vertebrate heart.

Processing DNA Storage through Programmable Assembly in a Droplet-Based Fluidics System

DNA can be used to store digital data, and synthetic short-sequence DNA pools are developed to store high quantities of digital data. However, synthetic DNA data cannot be actively processed in DNA pools. An active DNA data editing process is developed using splint ligation in a droplet-controlled fluidics (DCF) system. DNA fragments of discrete sizes (100-500 bps) are synthesized for droplet assembly, and programmed sequence information exchange occurred. The encoded DNA sequences are processed in series and parallel to synthesize the determined DNA pools, enabling random access using polymerase chain reaction amplification. The sequencing results of the assembled DNA data pools can be orderly aligned for decoding and have high fidelity through address primer scanning. Furthermore, eight 90 bps DNA pools with pixel information (png: 0.27-0.28 kB), encoded by codons, are synthesized to create eight 270 bps DNA pools with an animation movie chip file (mp4: 12 kB) in the DCF system.

Application of single-cell sequencing to the research of tumor microenvironment

Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

PerFSeeB: designing long high-weight single spaced seeds for full sensitivity alignment with a given number of mismatches

BACKGROUND

Technical progress in computational hardware allows researchers to use new approaches for sequence alignment problems. For a given sequence, we usually use smaller subsequences (anchors) to find possible candidate positions within a reference sequence. We may create pairs ("position", "subsequence") for the reference sequence and keep all such records without compression, even on a budget computer. As sequences for new and reference genomes differ, the goal is to find anchors, so we tolerate differences and keep the number of candidate positions with the same anchors to a minimum. Spaced seeds (masks ignoring symbols at specific locations) are a way to approach the task. An ideal (full sensitivity) spaced seed should enable us to find all such positions subject to a given maximum number of mismatches permitted.

RESULTS

Several algorithms to assist seed generation are presented. The first one finds all permitted spaced seeds iteratively. We observe specific patterns for the seeds of the highest weight. There are often periodic seeds with a simple relation between block size, length of the seed and read. The second algorithm produces blocks for periodic seeds for blocks of up to 50 symbols and up to nine mismatches. The third algorithm uses those lists to find spaced seeds for reads of an arbitrary length. Finally, we apply seeds to a real dataset and compare results for other popular seeds.

CONCLUSIONS

PerFSeeB approach helps to significantly reduce the number of reads' possible alignment positions for a known number of mismatches. Lists of long, high-weight spaced seeds are available in Additional file 1. The seeds are best in weight compared to seeds from other papers and can usually be applied to shorter reads. Codes for all algorithms and periodic blocks can be found at https://github.com/vtman/PerFSeeB .

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

Epiphytic proliferation of Zymoseptoria tritici isolates on resistant wheat leaves

The wheat pathogen Zymoseptoria tritici is capable of a long period of pre-invasive epiphytic growth. Studies have shown that virulent isolates vary in the extent, duration and growth form of this epiphytic growth, and the fungus has been observed to undergo behaviours such as asexual reproduction by budding and vegetative fusion of hyphae on the leaf surface. This epiphytic colonisation has been investigated very little during interactions in which an isolate of Z. tritici is unable to colonise the apoplast, as occurs during avirulence. However, avirulent isolates have been seen to undergo sexual crosses in the absense of leaf penetration, and it is widely accepted that the main point of distinction between virulent and avirulent isolates occurs at the point of attempted leaf penetration or attempted apoplastic growth, which fails in the avirulent case. In this work, we describe extensive epiphytic growth in three isolates which are unable or have very limited ability to invade the leaf, and show that growth form is as variable as for fully virulent isolates. We demonstrate that during certain interactions, Z. tritici isolates rarely invade the leaf and form pycnidia, but induce necrosis. These isolates are able to achieve higher epiphytic biomass than fully virulent isolates during asymptomatic growth, and may undergo very extensive asexual reproduction on the leaf surface. These findings have implications for open questions such as whether and how Z. tritici obtains nutrients on the leaf surface and the nature of its interaction with wheat defences.

'Longing' for the Next Generation of Liquid Biopsy: The Diagnostic Potential of Long Cell-Free DNA in Oncology and Prenatal Testing

Liquid biopsy using cell-free DNA (cfDNA) has gained global interest as a molecular diagnostic tool. However, the analysis of cfDNA in cancer patients and pregnant women has been focused on short DNA molecules (e.g., ≤ 600 bp). With the detection of long cfDNA in the plasma of pregnant women and cancer patients in two recent studies, a new avenue of long cfDNA-based liquid biopsy has been opened. In this review, we summarize our current knowledge in this nascent field of long cfDNA analysis, focusing on the fragmentomic and epigenetic features of long cfDNA. In particular, long-read sequencing enabled single-molecule methylation analysis and subsequent determination of the tissue-of-origin of long cfDNA, which has promising clinical potential in prenatal and cancer testing. We also examine some of the limitations that may hinder the immediate clinical applications of long cfDNA analysis and the current efforts involved in addressing them. With concerted efforts in this area, it is hoped that long cfDNA analysis will add to the expanding armamentarium of liquid biopsy.

A Customized Human Mitochondrial DNA Database (hMITO DB v1.0) for Rapid Sequence Analysis, Haplotyping and Geo-Mapping

The field of mitochondrial genomics has advanced rapidly and has revolutionized disciplines such as molecular anthropology, population genetics, and medical genetics/oncogenetics. However, mtDNA next-generation sequencing (NGS) analysis for matrilineal haplotyping and phylogeographic inference remains hindered by the lack of a consolidated mitogenome database and an efficient bioinformatics pipeline. To address this, we developed a customized human mitogenome database (hMITO DB) embedded in a CLC Genomics workflow for read mapping, variant analysis, haplotyping, and geo-mapping. The database was constructed from 4286 mitogenomes. The macro-haplogroup (A to Z) distribution and representative phylogenetic tree were found to be consistent with published literature. The hMITO DB automated workflow was tested using mtDNA-NGS sequences derived from Pap smears and cervical cancer cell lines. The auto-generated read mapping, variants track, and table of haplotypes and geo-origins were completed in 15 min for 47 samples. The mtDNA workflow proved to be a rapid, efficient, and accurate means of sequence analysis for translational mitogenomics.

Mapinsights: deep exploration of quality issues and error profiles in high-throughput sequence data

High-throughput sequencing (HTS) has revolutionized science by enabling super-fast detection of genomic variants at base-pair resolution. Consequently, it poses the challenging problem of identification of technical artifacts, i.e. hidden non-random error patterns. Understanding the properties of sequencing artifacts holds the key in separating true variants from false positives. Here, we develop Mapinsights, a toolkit that performs quality control (QC) analysis of sequence alignment files, capable of detecting outliers based on sequencing artifacts of HTS data at a deeper resolution compared with existing methods. Mapinsights performs a cluster analysis based on novel and existing QC features derived from the sequence alignment for outlier detection. We applied Mapinsights on community standard open-source datasets and identified various quality issues including technical errors related to sequencing cycles, sequencing chemistry, sequencing libraries and across various orthogonal sequencing platforms. Mapinsights also enables identification of anomalies related to sequencing depth. A logistic regression-based model built on the features of Mapinsights shows high accuracy in detecting 'low-confidence' variant sites. Quantitative estimates and probabilistic arguments provided by Mapinsights can be utilized in identifying errors, bias and outlier samples, and also aid in improving the authenticity of variant calls.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

Transcriptional Profiling of Skeletal Muscle Stem Cells After In Vivo Engraftment into a Heterologous Niche Environment

Adult stem cells play a critical role in the maintenance and repair of the organs in which they reside. However, their function is highly dependent on the crosstalk with their niche environment that changes during development and in disease states. The niche provides signals to stem cells to activate, proliferate, self-renew, or remain in quiescence. In skeletal muscle, the niche is perturbed in disease contexts such as aging, muscular dystrophies, and cachexia. Therefore, it is important to develop methods that permit the decoupling of niche-mediated from cell-intrinsic changes that occur in muscle stem cells (MuSCs) in development and disease contexts. With the purpose of determining the effect of the niche environment on the MuSC transcriptome, function, or health, we have coupled an allogeneic stem cell transplantation system, meaning the transplantation of MuSCs from a donor mouse into a recipient host mouse, with Switching Mechanism at 5' End of RNA Template (SMART-Seq) to quantify the effects of the niche on the MuSC transcriptome in vivo. Briefly, MuSCs are isolated from a GFP reporter donor mouse (Pax7-nGFP) and transplanted into the irradiated muscles of immunocompromised allogeneic hosts. The MuSCs are re-isolated by fluorescence-activated cell sorting (FACS) after three weeks of inhabiting the heterologous niche, defined as a niche that is different from their originating niche, and sequencing-ready libraries are created. This method allows for the direct comparison of the transcriptome of stem cells before and after transplantation into a host of a different age, disease status, or genetic background. This method can be used to accurately quantify the direct effect of the niche environment on the stem cell gene expression profile and to decouple cell-intrinsic versus niche-mediated alterations in the stem cell transcriptome. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Allogeneic muscle stem cell transplantation.

Nanopore sequencing technology and its applications

Since the development of Sanger sequencing in 1977, sequencing technology has played a pivotal role in molecular biology research by enabling the interpretation of biological genetic codes. Today, nanopore sequencing is one of the leading third-generation sequencing technologies. With its long reads, portability, and low cost, nanopore sequencing is widely used in various scientific fields including epidemic prevention and control, disease diagnosis, and animal and plant breeding. Despite initial concerns about high error rates, continuous innovation in sequencing platforms and algorithm analysis technology has effectively addressed its accuracy. During the coronavirus disease (COVID-19) pandemic, nanopore sequencing played a critical role in detecting the severe acute respiratory syndrome coronavirus-2 virus genome and containing the pandemic. However, a lack of understanding of this technology may limit its popularization and application. Nanopore sequencing is poised to become the mainstream choice for preventing and controlling COVID-19 and future epidemics while creating value in other fields such as oncology and botany. This work introduces the contributions of nanopore sequencing during the COVID-19 pandemic to promote public understanding and its use in emerging outbreaks worldwide. We discuss its application in microbial detection, cancer genomes, and plant genomes and summarize strategies to improve its accuracy.

Clinical validation of a single NGS targeted panel pipeline using the KAPA HyperChoice system for detection of germline, somatic and mitochondrial sequence and copy number variants

BACKGROUND

Comprehensive molecular diagnostics are highly dependent on the technical performance of next-generation sequencing (NGS) pipelines, which are assessed by data quality, cost, turnaround time, and accuracy of detecting a range of sequence and copy number variants.

METHODS

A dataset of 285 clinically validated cases (205 retrospective and 80 prospective), carrying complex sequence and copy number variants and thousands of genetic polymorphisms underwent a clinical validation of the KAPA HyperChoice target enrichment system with parallel sample fidelity assessment across a number of NGS panels. The analysis included assessment of peripheral blood, urine, muscle and FFPE tissues.

RESULTS

High-quality and exceptionally uniform data with 100% coverage of all targeted panels were obtained, resulting in complete sensitivity and specificity for all variant types across nearly all panels and tissue types. Overall reduction in cost and turnaround times was obtained with the implementation of a parallel genotyping sample fidelity system.

CONCLUSION

Results of the laboratory quality improvement study focused on a single NGS pipeline that includes both nuclear and mitochondrial genomes demonstrated utility in the clinical setting to assess a range of referral reasons, necessary due to the complex molecular etiology of human genetic disorders, while reducing costs and turnaround times.

Potential unreliability of ALK variant allele frequency in the efficacy prediction of targeted therapy in NSCLC

Anaplastic lymphoma kinase (ALK) is the most common fusion gene involved in non-small cell lung cancer (NSCLC), and remarkable response has been achieved with the use of ALK tyrosine kinase inhibitors (ALK-TKIs). However, the clinical efficacy is highly variable. Pre-existing intratumoral heterogeneity (ITH) has been proven to contribute to the poor treatment response and the resistance to targeted therapies. In this work, we investigated whether the variant allele frequencies (VAFs) of ALK fusions can help assess ITH and predict targeted therapy efficacy. Through the application of next-generation sequencing (NGS), 7.2% (326/4548) of patients were detected to be ALK positive. On the basis of the adjusted VAF (adjVAF, VAF normalization for tumor purity) of four different threshold values (adjVAF < 50%, 40%, 30%, or 20%), the association of ALK subclonality with crizotinib efficacy was assessed. Nonetheless, no statistical association was observed between median progression-free survival (PFS) and ALK subclonality assessed by adjVAF, and a poor correlation of adjVAF with PFS was found among the 85 patients who received first-line crizotinib. Results suggest that the ALK VAF determined by hybrid capture-based NGS is probably unreliable for ITH assessment and targeted therapy efficacy prediction in NSCLC.

Adapter dimer contamination in sRNA-sequencing datasets predicts sequencing failure and batch effects and hampers extracellular vesicle-sRNA analysis

Small RNA (sRNA) profiling of Extracellular Vesicles (EVs) by Next-Generation Sequencing (NGS) often delivers poor outcomes, independently of reagents, platforms or pipelines used, which contributes to poor reproducibility of studies. Here we analysed pre/post-sequencing quality controls (QC) to predict issues potentially biasing biological sRNA-sequencing results from purified human milk EVs, human and mouse EV-enriched plasma and human paraffin-embedded tissues. Although different RNA isolation protocols and NGS platforms were used in these experiments, all datasets had samples characterized by a marked removal of reads after pre-processing. The extent of read loss between individual samples within a dataset did not correlate with isolated RNA quantity or sequenced base quality. Rather, cDNA electropherograms revealed the presence of a constant peak whose intensity correlated with the degree of read loss and, remarkably, with the percentage of adapter dimers, which were found to be overrepresented sequences in high read-loss samples. The analysis through a QC pipeline, which allowed us to monitor quality parameters in a step-by-step manner, provided compelling evidence that adapter dimer contamination was the main factor causing batch effects. We concluded this study by summarising peer-reviewed published workflows that perform consistently well in avoiding adapter dimer contamination towards a greater likelihood of sequencing success.

Exploring the crop epigenome: a comparison of DNA methylation profiling techniques

Epigenetic modifications play a vital role in the preservation of genome integrity and in the regulation of gene expression. DNA methylation, one of the key mechanisms of epigenetic control, impacts growth, development, stress response and adaptability of all organisms, including plants. The detection of DNA methylation marks is crucial for understanding the mechanisms underlying these processes and for developing strategies to improve productivity and stress resistance of crop plants. There are different methods for detecting plant DNA methylation, such as bisulfite sequencing, methylation-sensitive amplified polymorphism, genome-wide DNA methylation analysis, methylated DNA immunoprecipitation sequencing, reduced representation bisulfite sequencing, MS and immuno-based techniques. These profiling approaches vary in many aspects, including DNA input, resolution, genomic region coverage, and bioinformatics analysis. Selecting an appropriate methylation screening approach requires an understanding of all these techniques. This review provides an overview of DNA methylation profiling methods in crop plants, along with comparisons of the efficacy of these techniques between model and crop plants. The strengths and limitations of each methodological approach are outlined, and the importance of considering both technical and biological factors are highlighted. Additionally, methods for modulating DNA methylation in model and crop species are presented. Overall, this review will assist scientists in making informed decisions when selecting an appropriate DNA methylation profiling method.

Intra-Host Co-Existing Strains of SARS-CoV-2 Reference Genome Uncovered by Exhaustive Computational Search

The COVID-19 pandemic caused by SARS-CoV-2 has had a severe impact on people worldwide. The reference genome of the virus has been widely used as a template for designing mRNA vaccines to combat the disease. In this study, we present a computational method aimed at identifying co-existing intra-host strains of the virus from RNA-sequencing data of short reads that were used to assemble the original reference genome. Our method consisted of five key steps: extraction of relevant reads, error correction for the reads, identification of within-host diversity, phylogenetic study, and protein binding affinity analysis. Our study revealed that multiple strains of SARS-CoV-2 can coexist in both the viral sample used to produce the reference sequence and a wastewater sample from California. Additionally, our workflow demonstrated its capability to identify within-host diversity in foot-and-mouth disease virus (FMDV). Through our research, we were able to shed light on the binding affinity and phylogenetic relationships of these strains with the published SARS-CoV-2 reference genome, SARS-CoV, variants of concern (VOC) of SARS-CoV-2, and some closely related coronaviruses. These insights have important implications for future research efforts aimed at identifying within-host diversity, understanding the evolution and spread of these viruses, as well as the development of effective treatments and vaccines against them.

Cas9-mediated replacement of expanded CAG repeats in a pig model of Huntington's disease

The monogenic nature of Huntington's disease (HD) and other neurodegenerative diseases caused by the expansion of glutamine-encoding CAG repeats makes them particularly amenable to gene therapy. Here we show the feasibility of replacing expanded CAG repeats in the mutant HTT allele with a normal CAG repeat in genetically engineered pigs mimicking the selective neurodegeneration seen in patients with HD. A single intracranial or intravenous injection of adeno-associated virus encoding for Cas9, a single-guide RNA targeting the HTT gene, and donor DNA containing the normal CAG repeat led to the depletion of mutant HTT in the animals and to substantial reductions in the dysregulated expression and neurotoxicity of mutant HTT and in neurological symptoms. Our findings support the further translational development of virally delivered Cas9-based gene therapies for the treatment of genetic neurodegenerative diseases.

Oligodendrocyte lineage cells: Advances in development, disease, and heterogeneity

Oligodendrocyte progenitor cells (OPCs) originate in the ventricular zone (VZ) of the brain and spinal cord, and their primary function is to differentiate into oligodendrocytes (OLs). Studies have shown that OPCs and OLs are pathologically and physiologically heterogeneous. Previous transcriptome analyses used Bulk RNA-seq, which compares average gene expression in cells and does not allow for heterogeneity. In recent years, the development of single-cell sequencing (scRNA-seq) and single-cell nuclear sequencing (snRNA-seq) has allowed us to study an individual cell. In this review, sc/snRNA-seq was used to study the different subpopulations of OL lineage cells, their developmental trajectories, and their applications in related diseases. These techniques can distinguish different subpopulations of cells, and identify differentially expressed genes in particular cell types under certain conditions, such as treatment or disease. It is of great significance to the study of the occurrence, prevention, and treatment of various diseases.

A Clinician's Guide to Bioinformatics for Next-Generation Sequencing

Next-generation sequencing (NGS) technologies are high-throughput methods for DNA sequencing and have become a widely adopted tool in cancer research. The sheer amount and variety of data generated by NGS assays require sophisticated computational methods and bioinformatics expertise. In this review, we provide background details of NGS technology and basic bioinformatics concepts for the clinician investigator interested in cancer research applications, with a focus on DNA-based approaches. We introduce the general principles of presequencing library preparation, postsequencing alignment, and variant calling. We also highlight the common variant annotations and NGS applications for other molecular data types. Finally, we briefly discuss the revealed utility of NGS methods in NSCLC research and study design considerations for research studies that aim to leverage NGS technologies for clinical care.

A Polyketide Synthetase Gene Cluster Is Responsible for Antibacterial Activity of Burkholderia contaminans MS14

Burkholderia contaminans MS14, isolated from a soil sample in Mississippi, is known for producing the novel antifungal compound occidiofungin. In addition, MS14 exhibits a broad range of antibacterial activities against common plant pathogens. Random mutagenesis and gene complementation indicate that four genes are required for antibacterial activity of strain MS14 against the fire blight pathogen Erwinia amylovora. With the aim of finding the biosynthetic gene cluster for the unknown antibacterial compound, we used RNA-seq to analyze the transcriptome of MS14 wild type and mutants lacking antibacterial activity. The twofold lower expressed genes in all mutants were studied, and a polyketide synthase (PKS) gene cluster was predicted to be directly involved in MS14 antibacterial activities. The nptII-resistance cassette and CRISPR-Cas9 systems were used to mutate the PKS gene cluster. Plate bioassays showed that either insertion or frame-shifting one of the PKS genes resulted in a loss of antibacterial activity. Considering that the antibacterial-defective mutants maintain the same antifungal activities as the wild-type strain, the results suggest that this PKS gene cluster is highly likely to be involved in or directly responsible for the production of MS14 antibacterial activity. Purification efforts revealed that the antibacterial activity of the compound synthesized by the gene cluster is sensitive to UV radiation. Nevertheless, these findings have provided more insights to understand the antibacterial activity of strain MS14.

RNA Sequencing Analysis of Saliva exRNA

Next-generation sequencing (NGS) methodologies are rapidly developing. However, RNA Sequencing of saliva is challenging due to low abundance and integrity of extracellular RNA, as well as large amounts of bacterial RNAs that may be encountered in saliva. In addition, the literature about human salivary extracellular RNA is very scarce. Therefore, in our chapter, we present the most appropriate protocols for saliva collection, pre- and post-processing, including bioinformatic analysis of salivary RNA Sequencing data. However, the choice of the proper method for RNA extraction, cDNA library preparation, and computational pipeline can make a significant impact on the final quality of data and their interpretation.

Decoding the microbial universe with metagenomics: a brief insight

A major part of any biological system on earth involves microorganisms, of which the majority are yet to be cultured. The conventional methods of culturing microbes have given fruitful outcomes yet have limitations. The curiosity for better understanding has led to the development of culture-independent molecular methods that help push aside the roadblocks of earlier methods. Metagenomics unifies the scientific community in search of a better understanding of the functioning of the ecosystem and its component organisms. This approach has opened a new paradigm in advanced research. It has brought to light the vast diversity and novelty among microbial communities and their genomes. This review focuses on the development of this field over time, the techniques and analysis of data generated through sequencing platforms, and its prominent interpretation and representation.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

Single-Cell Sequencing in Rheumatic Diseases: New Insights from the Perspective of the Cell Type

Rheumatic diseases are a group of highly heterogeneous autoimmune and inflammatory disorders involving multiple systems. Dysfunction of immune and non-immune cells participates in the complex pathogenesis of rheumatic diseases. Therefore, studies on the abnormal activation of cell subtypes provided a specific basis for understanding the pathogenesis of rheumatic diseases, which promoted the accuracy of disease diagnosis and the effectiveness of various treatments. However, there was still a far way to achieve individualized precision medicine as the result of heterogeneity among cell subtypes. To obtain the biological information of cell subtypes, single-cell sequencing, a cutting-edge technology, is used for analyzing their genomes, transcriptomes, epigenetics, and proteomics. Novel results identified multiple cell subtypes in tissues of patients with rheumatic diseases by single-cell sequencing. Consequently, we provide an overview of recent applications of single-cell sequencing in rheumatic disease and cross-tissue to understand the cell subtypes and functions.

Treasures from trash in cancer research

INTRODUCTION

Cancer research has significantly improved in recent years, primarily due to next-generation sequencing (NGS) technology. Consequently, an enormous amount of genomic and transcriptomic data has been generated. In most cases, the data needed for research goals are used, and unwanted reads are discarded. However, these eliminated data contain relevant information. Aiming to test this hypothesis, genomic and transcriptomic data were acquired from public datasets.

MATERIALS AND METHODS

Metagenomic tools were used to explore genomic cancer data; additional annotations were used to explore differentially expressed ncRNAs from miRNA experiments, and variants in adjacent to tumor samples from RNA-seq experiments were also investigated.

RESULTS

In all analyses, new data were obtained: from DNA-seq data, microbiome taxonomies were characterized with a similar performance of dedicated metagenomic research; from miRNA-seq data, additional differentially expressed sncRNAs were found; and in tumor and adjacent to tumor tissue data, somatic variants were found.

CONCLUSIONS

These findings indicate that unexplored data from NGS experiments could help elucidate carcinogenesis and discover putative biomarkers with clinical applications. Further investigations should be considered for experimental design, providing opportunities to optimize data, saving time and resources while granting access to multiple genomic perspectives from the same sample and experimental run.

Exosome RNA Sequencing as a Tool in the Search for Cancer Biomarkers

Numerous noninvasive methods are currently being used to determine biomarkers for diseases such as cancer. However, these methods are not always precise and reliable. Thus, there is an unmet need for better diagnostic and prognostic biomarkers that will be used to diagnose cancer in early, more treatable stages of the disease. Exosomes are extracellular vesicles of endocytic origin released by the majority of cells. Exosomes contain and transport nucleic acids, proteins, growth factors, and cytokines from their parent cells to surrounding or even distant cells via circulation in biofluids. Exosomes have attracted the interest of researchers, as recent data indicate that exosome content may be indicative of disease stages and may contribute to disease progression via exosome-mediated extracellular communication. Therefore, the contents of these vesicles are being investigated as possible biomarkers for disease diagnosis and prognosis. The functions of exosomes and their contents in disease development are becoming clearer as isolation and analytical methods, such as RNA sequencing, advance. In this review, we discuss current advances and challenges in exosomal content analyses with emphasis on information that can be generated using RNA sequencing. We also discuss how the RNA sequencing of exosomes may be used to discover novel biomarkers for the detection of different stages for various cancers using specific microRNAs that were found to be differentially expressed between healthy controls and cancer-diagnosed subjects.

Polydimethylsiloxane microstructure-induced acoustic streaming for enhanced ultrasonic DNA fragmentation on a microfluidic chip

Next-generation sequencing (NGS) is an essential technology for DNA identification in genomic research. DNA fragmentation is a critical step for NGS and doing this on-chip is of great interest for future integrated genomic solutions. Here we demonstrate fast acoustofluidic DNA fragmentation via ultrasound-actuated elastic polydimethylsiloxane (PDMS) microstructures that induce acoustic streaming and associated shear forces when placed in the field of an ultrasonic transducer. Indeed, acoustic streaming locally generates high tensile stresses that can mechanically stretch and break DNA molecule chains. The improvement in efficiency of the on-chip DNA fragmentation is due to the synergistic effect of these tensile stresses and ultrasonic cavitation phenomena. We tested these microstructure-induced effects in a DNA-containing microfluidic channel both experimentally and by simulation. The DNA fragmentation process was evaluated by measuring the change in the DNA fragment size over time. The chip works well with both long and short DNA chains; in particular, purified lambda (λ) DNA was cut from 48.5 kbp to 3 kbp in one minute with selected microstructures and further down to 300 bp within two and a half minutes. The fragment size of mouse genomic DNA was reduced from 1.4 kbp to 400 bp in one minute and then to 200 bp in two and a half minutes. The DNA fragmentation efficiency of the chip equipped with the PDMS microstructures was twice that of the chip without the microstructures. Exhaustive comparison shows that the on-chip fragmentation performance reaches the level of high-end professional standards. Recently, DNA fragmentation was shown to be enhanced using vibrating air microbubbles when the chip was placed in an acoustic field. We think the microbubble-free microstructure-based device we present is easier to operate and more reliable, as it avoids microbubble preparation and maintenance, while showing high DNA fragmentation performance.

Prokaryotic ncRNAs: Master regulators of gene expression

ncRNA plays a very pivotal role in various biological activities ranging from gene regulation to controlling important developmental networks. It is imperative to note that this small molecule is not only present in all three domains of cellular life, but is an important modulator of gene regulation too in all these domains. In this review, we discussed various aspects of ncRNA biology, especially their role in bacteria. The last two decades of scientific research have proved that this molecule plays an important role in the modulation of various regulatory pathways in bacteria including the adaptive immune system and gene regulation. It is also very surprising to note that this small molecule is also employed in various processes related to the pathogenicity of virulent microorganisms.

Next-generation sequencing in the biodiversity conservation of endangered medicinal plants

Medicinal plants have been used as traditional herbal medicines in the treatment of various types of diseases. However, the increased demand for these plants highlights the importance of conservation specifically for endangered species. Significant advancements in next-generation sequencing (NGS) technologies have accelerated medicinal plant research while reducing costs and time demands. NGS systems enable high-throughput whole genome sequencing as well as direct RNA sequencing and transcriptome analysis. The sequence data sets created can be used in a variety of areas of study, including biodiversity conservation, comparative genomics, transcriptomic analysis, single cell mining, metagenomics, epigenetics, molecular marker discovery, multi genome sequencing, and so on. Commercial sequencing service providers are constantly working to improve technologies to address bioinformatics problems in NGS data analysis. Several genome sequencing projects on medicinal plants have been completed recently and a few more are in the works. In some medicinal plants, massive NGS-based data has been developed. In the present review, we have attempted to briefly discuss advancements in NGS technology on medicinally essential plants in India. The review will also provide ideas for applying NGS technologies for exploring genomes of various endangered medicinal plants whose genome sequences are not normally available and thus provides valuable insights for the conservation of these vulnerable species.