A comparison of Illumina and Ion Torrent sequencing platforms in the context of differential gene expression

Assessing population exposure to airborne fungi in the UK over one year using high-throughput sequencing (HTS) metabarcoding methods

Airborne fungi are significant contributors to allergic and infectious disease. While microscopy remains the primary method for fungal identification, high-throughput sequencing (HTS) enables untargeted analysis of a much wider range of environmental taxa. This study used HTS to better characterise airborne fungal composition over a 12-month period in two UK locations, the city of Leicester in central England and a rural site in Chilton (Oxfordshire) approximately 115 km further south. Air samples were collected over a year. A subset of 240 samples (120 per location) were analysed by HTS with a combined internal transcribed spacer region (ITS2) and D1/D2 region of the large subunit (LSU) metabarcoding approach. With statistical imputation a representative 12-month dataset was created. Differences in fungal diversity and composition were explored, incorporating meteorological data. HTS analysis identified 272 fungal genera across locations and seasons, approximately 4-fold more than in other studies using traditional microscopy methods. Fungal diversity, richness and composition at the two locations were broadly similar with some taxa-specific differences likely reflecting land-use types (urban vs rural) and/or local meteorological variables. In particular, air temperature and precipitation significantly influenced fungal composition. This study demonstrates the value of HTS for characterising airborne fungi. While it does not provide absolute quantitation, HTS could be used as a screening tool to identify novel associations between fungal exposure and health outcomes, and should be used in combination with quantitative methods, such as microscopy and quantitative PCR (qPCR). Greater spatial and temporal understanding of the wide range of airborne fungal exposure is crucial for exploring associated health impacts and developing improved public health interventions and alert systems for susceptible individuals.

Error-corrected ultradeep next-generation sequencing for detection of clonal haematopoiesis and haematological neoplasms - sensitivity, specificity and accuracy

Clonal haematopoiesis of indeterminate potential (CHIP) is an aging-associated phenomenon that has recently been correlated with a broad spectrum of human diseases, including haematological malignancy, cytopenia, coronary heart disease, stroke, and overall mortality. CHIP is defined as a somatic variant in blood cells with an allele frequency (VAF) ≥ 0.02, however recent reports show smaller clones are associated with poorer clinical outcome. Error-corrected ultradeep next-generation sequencing (NGS) assays detecting variants < 0.02 VAF also have clinical value for monitoring measurable residual disease (MRD) for myeloid neoplasms. However, limited data are available on optimal parameters, limits of detection, and accuracy of ultra-sensitive detection. We investigated parameters to improve accuracy of Illumina sequencing-by-synthesis method, including read depth, input DNA quantity, and molecular barcoding-based data filtering, while adhering to clinical accreditation criteria. Validation data were generated from reference standards and reference samples from a clinically accredited pathology laboratory. Analytical range measurements included linearity and bias, and precision included repeatability, reproducibility and detection rate. The lower limit of detection was ≥ 0.004 (0.4%) at depth > 3,000 × . Trueness measured using reference standards demonstrated a sensitivity, specificity, positive and negative predictive values, and accuracy of 100%, including FLT3-ITD, and 100% concordance was achieved with reference samples for reported variants and absence of variants. Sequencing blood samples from 383 community-dwelling adults (mean depth 3758×) revealed 2,190 somatic variants/sample, > 99.9% were < 0.02 VAF. Our data including cost-benefit analysis enables pathology and research laboratories to make informed decisions for detection of CHIP (VAF ≥ 0.02), sub-CHIP (VAF 0.01-0.02) and MRD (VAF ≥ 0.004).

High-throughput sequencing: a breakthrough in molecular diagnosis for precision medicine

High-resolution insights into the nucleotide arrangement within an organism's genome are pivotal for deciphering its genetic composition, function, and evolutionary trajectory. Over the years, nucleic acid sequencing has been instrumental in driving significant advancements in genomics and molecular biology. The advent of high-throughput or next-generation sequencing (NGS) technologies has revolutionized whole genome sequencing, revealing novel and intriguing features of genomes, such as single nucleotide polymorphisms and lethal mutations in both coding and non-coding regions. These platforms provide a practical approach to comprehensively identifying and analyzing whole genomes with remarkable throughput, accuracy, and scalability within a short time frame. The resulting data holds immense potential for enhancing healthcare systems, developing novel and personalized therapies, and preparing for future pandemics and outbreaks. Given the wide array of available high-throughput sequencing platforms, selecting the appropriate technology based on specific needs is crucial. However, there is limited information regarding sample preparation, sequencing principles, and output data to facilitate a comparative evaluation of these platforms. This review details various NGS technologies and approaches, examining their advantages, limitations, and future potential. Despite being in their early stages and facing challenges, ongoing advancements in NGS are expected to yield significant future benefits.

Leveraging Next-Generation Sequencing Application from Identity to Purity Profiling of Nucleic Acid-Based Products

Background/Objectives: The nucleic acid-based product (NAP) portfolio is expanding continuously and provides safer curative options for many disease indications. Nucleic acid-based products offer several advantages compared to proteins and virus-based products. They represent an emerging field; thus, their quality control and regulatory landscape is evolving to ensure adequate quality and safety. Next-Generation Sequencing (NGS) is mostly recommended for NAP identity testing, and we are leveraging its application for impurity profiling. Methods: We proposed a workflow for the purity assessment of NAPs through short-read Illumina NGS followed by data analysis of mRNA vaccine and pDNA samples. We determined the sequence identity, DNA and RNA contamination, off-target RNA contamination, and poly-A count with the proposed workflow. Results: Our workflow predicted most of the critical quality controls of mRNA vaccine and plasmid DNA samples, especially focusing on the identity and the nucleotide-based impurities. Additionally, NGS data interpretation also assisted in strategic decisions for NAP manufacturing process optimizations. Conclusions: We recommend the adaptation of incremental NGS data by regulatory agencies to identify nucleotide-based impurities in NAPs. Perhaps NGS adaptation under cGMP compliance needs to be deliberated with the regulatory bodies, especially focusing on the methods qualification and validation part, starting from the sample collection, NGS library preparation, NGS run, and its data analysis pipeline.

BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in polymerase chain reaction (PCR) amplification, barcode read errors and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

Revealing the seed microbiome: Navigating sequencing tools, microbial assembly, and functions to amplify plant fitness

Microbial communities within seeds play a vital role in transmitting themselves to the next generation of plants. These microorganisms significantly impact seed vigor and early seedling growth, for successful crop establishment. Previous studies reported on seed-associated microbial communities and their influence on processes like dormancy release, germination, and disease protection. Modern sequencing and conventional methods reveal microbial community structures and environmental impacts, these information helps in microbial selection and manipulation. These studies form the foundation for using seed microbiomes to enhance crop resilience and productivity. While existing research has primarily focused on characterizing microbiota in dried mature seeds, a significant gap exists in understanding how these microbial communities assemble during seed development. The review also discusses applying seed-associated microorganisms to improve crops in the context of climate change. However, limited knowledge is available about the microbial assembly pattern on seeds, and their impact on plant growth. The review provides insight into microbial composition, functions, and significance for plant health, particularly regarding growth promotion and pest control.

An Overview of Advances in Rare Cancer Diagnosis and Treatment

Cancer stands as the leading global cause of mortality, with rare cancer comprising 230 distinct subtypes characterized by infrequent incidence. Despite the inherent challenges in addressing the diagnosis and treatment of rare cancers due to their low occurrence rates, several biomedical breakthroughs have led to significant advancement in both areas. This review provides a comprehensive overview of state-of-the-art diagnostic techniques that encompass new-generation sequencing and multi-omics, coupled with the integration of artificial intelligence and machine learning, that have revolutionized rare cancer diagnosis. In addition, this review highlights the latest innovations in rare cancer therapeutic options, comprising immunotherapy, targeted therapy, transplantation, and drug combination therapy, that have undergone clinical trials and significantly contribute to the tumor remission and overall survival of rare cancer patients. In this review, we summarize recent breakthroughs and insights in the understanding of rare cancer pathophysiology, diagnosis, and therapeutic modalities, as well as the challenges faced in the development of rare cancer diagnosis data interpretation and drug development.

DNA Sequencing Technologies and DNA Barcoding

DNA barcodes are short, standardized DNA segments that geneticists can use to identify all living taxa. On the other hand, DNA barcoding identifies species by analyzing these specific regions against a DNA barcode reference library. In its initial years, DNA barcodes sequenced by Sanger's method were extensively used by taxonomists for the characterization and identification of species. But in recent years, DNA barcoding by next-generation sequencing (NGS) has found broader applications, such as quality control, biomonitoring of protected species, and biodiversity assessment. Technological advancements have also paved the way to metabarcoding, which has enabled massive parallel sequ.encing of complex bulk samples using high-throughput sequencing techniques. In future, DNA barcoding along with high-throughput techniques will show stupendous progress in taxonomic classification with reference to available sequence data.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking, and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully-length mRNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM, or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in PCR amplification, barcode read errors, and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

Global Meta-analysis of Urine Microbiome: Colonization of Polycyclic Aromatic Hydrocarbon-degrading Bacteria Among Bladder Cancer Patients

BACKGROUND

The application of next-generation sequencing techniques has enabled characterization of urinary tract microbiome. Although many studies have demonstrated associations between the human microbiome and bladder cancer (BC), these have not always reported consistent results, thereby necessitating cross-study comparisons. Thus, the fundamental questions remain how we can utilize this knowledge.

OBJECTIVE

The aim of our study was to examine the disease-associated changes in urine microbiome communities globally utilizing a machine learning algorithm.

DESIGN, SETTING, AND PARTICIPANTS

Raw FASTQ files were downloaded for the three published studies in urinary microbiome in BC patients, in addition to our own prospectively collected cohort.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Demultiplexing and classification were performed using the QIIME 2020.8 platform. De novo operational taxonomic units were clustered using the uCLUST algorithm and defined by 97% sequence similarity and classified at the phylum level against the Silva RNA sequence database. The metadata available from the three studies included were used to evaluate the differential abundance between BC patients and controls via a random-effect meta-analysis using the metagen R function. A machine learning analysis was performed using the SIAMCAT R package.

RESULTS AND LIMITATIONS

Our study includes 129 BC urine and 60 healthy control samples across four different countries. We identified a total of 97/548 genera to be differentially abundant in the BC urine microbiome compared with that of healthy patients. Overall, while the differences in diversity metrics were clustered around the country of origin (Kruskal-Wallis, p < 0.001), collection methodology was a driver of microbiome composition. When assessing dataset from China, Hungary, and Croatia, data demonstrated no discrimination capacity to distinguish between BC patients and healthy adults (area under the curve [AUC] 0.577). However, inclusion of samples with catheterized urine improved the diagnostic accuracy of prediction for BC to AUC 0.995, with precision-recall AUC = 0.994. Through elimination of contaminants associated with the collection methodology among all cohorts, our study identified increased abundance of polycyclic aromatic hydrocarbon (PAH)-degrading bacteria Sphingomonas, Acinetobacter, Micrococcus, Pseudomonas, and Ralstonia to be consistently present in BC patients.

CONCLUSIONS

The microbiota of the BC population may be a reflection of PAH exposure from smoking, environmental pollutants, and ingestion. Presence of PAHs in the urine of BC patients may allow for a unique metabolic niche and provide necessary metabolic resources where other bacteria are not able to flourish. Furthermore, we found that while compositional differences are associated with geography more than with disease, many are driven by the collection methodology.

PATIENT SUMMARY

The goal of our study was to compare the urine microbiome of bladder cancer patients with that of healthy controls and evaluate any potential bacteria that may be more likely to be found in patients with bladder cancer. Our study is unique as it evaluates this across multiple countries, to find a common pattern. After we removed some of the contamination, we were able to localize several key bacteria that are more likely to be found in the urine of bladder cancer patients. These bacteria all share their ability to break down tobacco carcinogens.

Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis

Oral cancer (OC) has become a significant barrier to health worldwide due to its high morbidity and mortality rates. OC is among the most prevalent types of cancer that affect the head and neck region, and the overall survival rate at 5 years is still around 50%. Moreover, it is a multifactorial malignancy instigated by genetic and epigenetic variabilities, and molecular heterogeneity makes it a complex malignancy. Oral potentially malignant disorders (OPMDs) are often the first warning signs of OC, although it is challenging to predict which cases will develop into malignancies. Visual oral examination and histological examination are still the standard initial steps in diagnosing oral lesions; however, these approaches have limitations that might lead to late diagnosis of OC or missed diagnosis of OPMDs in high-risk individuals. The objective of this review is to present a comprehensive overview of the currently used novel techniques viz., liquid biopsy, next-generation sequencing (NGS), microarray, nanotechnology, lab-on-a-chip (LOC) or microfluidics, and artificial intelligence (AI) for the clinical diagnostics and management of this malignancy. The potential of these novel techniques in expanding OC diagnostics and clinical management is also reviewed.

Comparative analysis of two next-generation sequencing platforms for analysis of antimicrobial resistance genes

OBJECTIVES

The use of antibiotics in human medicine and livestock production has contributed to the widespread occurrence of Antimicrobial Resistance (AMR). Recognizing the relevance of AMR to human and livestock health, it is important to assess the occurrence of genetic determinants of resistance in medical, veterinary, and public health settings in order to understand risks of transmission and treatment failure. Advances in next-generation sequencing technologies have had a significant impact on research in microbial genetics and microbiome analyses. The aim of the present study was to compare the Illumina MiSeq and Ion Torrent S5 Plus sequencing platforms for the analysis of AMR genes in a veterinary/public health setting.

METHODS

All samples were processed in parallel for the two sequencing technologies, subsequently following a common bioinformatics workflow to define the occurrence and abundance of AMR gene sequences. The Comprehensive Antibiotic Resistance Database (CARD), QIAGEN Microbial Insight - Antimicrobial Resistance, Antimicrobial resistance database, and Comprehensive Antibiotic Resistance Database developed by CLC bio (CARD-CLC) databases were compared for analysis, with the most genes identified using CARD.

RESULTS

Drawing on these results, we described an end-to-end workflow for the analysis of AMR genes a using advances in next-generation sequencing. No statistically significant differences were observed among any other genes except the tet-(40) gene between two sequencing platforms, which may be due to the short amplicon length.

CONCLUSIONS

Irrespective of sequencing chemistry and platform used, comparative analysis of AMR genes and candidate host organism suggest that the Illumina MiSeq and Ion Torrent platforms performed almost equally. Regardless of sequencing platform, the results were closely comparable with minor differences.

Benchmarking second and third-generation sequencing platforms for microbial metagenomics

Shotgun metagenomic sequencing is a common approach for studying the taxonomic diversity and metabolic potential of complex microbial communities. Current methods primarily use second generation short read sequencing, yet advances in third generation long read technologies provide opportunities to overcome some of the limitations of short read sequencing. Here, we compared seven platforms, encompassing second generation sequencers (Illumina HiSeq 300, MGI DNBSEQ-G400 and DNBSEQ-T7, ThermoFisher Ion GeneStudio S5 and Ion Proton P1) and third generation sequencers (Oxford Nanopore Technologies MinION R9 and Pacific Biosciences Sequel II). We constructed three uneven synthetic microbial communities composed of up to 87 genomic microbial strains DNAs per mock, spanning 29 bacterial and archaeal phyla, and representing the most complex and diverse synthetic communities used for sequencing technology comparisons. Our results demonstrate that third generation sequencing have advantages over second generation platforms in analyzing complex microbial communities, but require careful sequencing library preparation for optimal quantitative metagenomic analysis. Our sequencing data also provides a valuable resource for testing and benchmarking bioinformatics software for metagenomics.

A Comprehensive Review of Performance of Next-Generation Sequencing Platforms

Background

Next-generation sequencing methods have been developed and proposed to investigate any query in genomics or clinical activity involving DNA. Technical advancement in these sequencing methods has enhanced sequencing volume to several billion nucleotides within a very short time and low cost. During the last few years, the usage of the latest DNA sequencing platforms in a large number of research projects helped to improve the sequencing methods and technologies, thus enabling a wide variety of research/review publications and applications of sequencing technologies.

Objective

The proposed study is aimed at highlighting the most fast and accurate NGS instruments developed by various companies by comparing output per hour, quality of the reads, maximum read length, reads per run, and their applications in various domains. This will help research institutions and biological/clinical laboratories to choose the sequencing instrument best suited to their environment. The end users will have a general overview about the history of the sequencing technologies, latest developments, and improvements made in the sequencing technologies till now.

Results

The proposed study, based on previous studies and manufacturers' descriptions, highlighted that in terms of output per hour, Nanopore PromethION outperformed all sequencers. BGI was on the second position, and Illumina was on the third position.

Conclusion

The proposed study investigated various sequencing instruments and highlighted that, overall, Nanopore PromethION is the fastest sequencing approach. BGI and Nanopore can beat Illumina, which is currently the most popular sequencing company. With respect to quality, Ion Torrent NGS instruments are on the top of the list, Illumina is on the second position, and BGI DNB is on the third position. Secondly, memory- and time-saving algorithms and databases need to be developed to analyze data produced by the 3rd- and 4th-generation sequencing methods. This study will help people to adopt the best suited sequencing platform for their research work, clinical or diagnostic activities.

Consideration of metabolomics and transcriptomics data in the context of using avian embryos for toxicity testing

Early-life stage (ELS) avian toxicity tests have been proposed as a more ethical alternative to traditional standardized tests with adult birds. At the same time, 'omics approaches are gaining traction in the field of avian toxicology, but little has been done to characterize the metabolome and transcriptome at different life stages. The present study uses 'omics data from toxicity tests of 8 environmental chemicals in ELS and adult Japanese quail (Coturnix japonica) to address this data gap. Previous analyses of these data focused on responses to each of the individual chemicals. Here, we consider data from all studies to describe variation in the metabolome and transcriptome between life stages and across independent experiments, irrespective of chemical treatment. Of the 230 metabolites detected in liver, 163 were shared between the two life stages. However, many of the targeted bile acids that were present in the adult liver were absent from ELS samples. For the transcriptome, >90% of the 18,364 detected transcripts were common to both life stages. Based on the 213 genes solely detected in ELS liver, the neuroactive ligand-receptor interaction pathway was significantly enriched. Multivariate and hierarchical clustering analyses revealed that variability among independent experiments was higher for the adult than the ELS studies at both the metabolomic and transcriptomic levels. Our results indicate concordance of the two approaches, with less variation between independent experiments in the ELS metabolome and transcriptome than in adults, lending support for the use of ELS as an alternative toxicity testing strategy.

Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine

Precision medicine has greatly aided in improving health outcomes using earlier diagnosis and better prognosis for chronic diseases. It makes use of clinical data associated with the patient as well as their multi-omics/genomic data to reach a conclusion regarding how a physician should proceed with a specific treatment. Compared to the symptom-driven approach in medicine, precision medicine considers the critical fact that all patients do not react to the same treatment or medication in the same way. When considering the intersection of traditionally distinct arenas of medicine, that is, artificial intelligence, healthcare, clinical genomics, and pharmacogenomics—what ties them together is their impact on the development of precision medicine as a field and how they each contribute to patient-specific, rather than symptom-specific patient outcomes. This study discusses the impact and integration of these different fields in the scope of precision medicine and how they can be used in preventing and predicting acute or chronic diseases. Additionally, this study also discusses the advantages as well as the current challenges associated with artificial intelligence, healthcare, clinical genomics, and pharmacogenomics.

Biomonitoring of Fungal and Oomycete Plant Pathogens by Using Metabarcoding

Fungal and oomycete plant pathogens are responsible for the devastation of various ecosystems such as forest and crop species worldwide. In an effort to protect such natural resources for food, lumber, etc., early detection of non-indigenous phytopathogenic fungi in new areas is a key approach in managing threats at their source of introduction. A workflow was developed using high-throughput sequencing (HTS), more specifically metabarcoding, a method for rapid and higher throughput species screening near high-risk areas, and over larger geographical spaces. Biomonitoring of fungal and oomycete entities of plant pathogens (e.g., airborne spores) regained from environmental samples and their processing by metabarcoding is thoroughly described here. The amplicon-based approach goes from DNA and sequencing library preparation using custom-designed polymerase chain reaction (PCR) fusion primers that target the internal transcribed spacer 1 (ITS1) from fungi and oomycetes and extends to multiplex HTS with the Ion Torrent platform. In addition, a brief and simplified overview of the bioinformatics analysis pipeline and other available tools required to process amplicon sequences is also included. The raw data obtained and processed enable users to select a bioinformatics pipeline in order to directly perform biodiversity, presence/absence, geographical distribution, and abundance analyses through the tools suggested, which allows for accelerated identification of phytopathogens of interest.

Improving the quality of barley transcriptome de novo assembling by using a hybrid approach for lines with varying spike and stem coloration

De novo transcriptome assembly is an important stage of RNA-seq data computational analysis. It allows the researchers to obtain the sequences of transcripts presented in the biological sample of interest. The availability of accurate and complete transcriptome sequence of the organism of interest is, in turn, an indispensable condition for further analysis of RNA-seq data. Through years of transcriptomic research, the bioinformatics community has developed a number of assembler programs for transcriptome reconstruction from short reads of RNA-seq libraries. Different assemblers makes it possible to conduct a de novo transcriptome reconstruction and a genome-guided reconstruction. The majority of the assemblers working with RNA-seq data are based on the De Bruijn graph method of sequence reconstruction. However, specif ics of their procedures can vary drastically, as do their results. A number of authors recommend a hybrid approach to transcriptome reconstruction based on combining the results of several assemblers in order to achieve a better transcriptome assembly. The advantage of this approach has been demonstrated in a number of studies, with RNA-seq experiments conducted on the Illumina platform. In this paper, we propose a hybrid approach for creating a transcriptome assembly of the barley Hordeum vulgare isogenic line Bowman and two nearly isogenic lines contrasting in spike pigmentation, based on the results of sequencing on the IonTorrent platform. This approach implements several de novo assemblers: Trinity, Trans-ABySS and rnaSPAdes. Several assembly metrics were examined: the percentage of reference transcripts observed in the assemblies, the percentage of RNA-seq reads involved, and BUSCO scores. It was shown that, based on the summation of these metrics, transcriptome meta-assembly surpasses individual transcriptome assemblies it consists of.

Review of sheep and goat pox disease: current updates on epidemiology, diagnosis, prevention and control measures in Ethiopia

Sheep pox, goat pox, and lumpy skin diseases are economically significant and contagious viral diseases of sheep, goats and cattle, respectively, caused by the genus Capripoxvirus (CaPV) of the family Poxviridae. Currently, CaPV infection of small ruminants (sheep and goats) has been distributed widely and are prevalent in Central Africa, the Middle East, Europe and Asia. This disease poses challenges to food production and distribution, affecting rural livelihoods in most African countries, including Ethiopia. Transmission occurs mainly by direct or indirect contact with infected animals. They cause high morbidity (75-100% in endemic areas) and mortality (10-85%). Additionally, the mortality rate can approach 100% in susceptible animals. Diagnosis largely relies on clinical symptoms, confirmed by laboratory testing using real-time PCR, electron microscopy, virus isolation, serology and histology. Control and eradication of sheep pox virus (SPPV), goat pox virus (GTPV), and lumpy skin disease (LSDV) depend on timely recognition of disease eruption, vector control, and movement restriction. To date, attenuated vaccines originating from KSGPV O-180 strains are effective and widely used in Ethiopia to control CaPV throughout the country. This vaccine strain is clinically safe to control CaPV in small ruminants but not in cattle which may be associated with insufficient vaccination coverage and the production of low-quality vaccines.

CAMPAREE: a robust and configurable RNA expression simulator

Background

The accurate interpretation of RNA-Seq data presents a moving target as scientists continue to introduce new experimental techniques and analysis algorithms. Simulated datasets are an invaluable tool to accurately assess the performance of RNA-Seq analysis methods. However, existing RNA-Seq simulators focus on modeling the technical biases and artifacts of sequencing, rather than on simulating the original RNA samples. A first step in simulating RNA-Seq is to simulate RNA.

Results

To fill this need, we developed the Configurable And Modular Program Allowing RNA Expression Emulation (CAMPAREE), a simulator using empirical data to simulate diploid RNA samples at the level of individual molecules. We demonstrated CAMPAREE’s use for generating idealized coverage plots from real data, and for adding the ability to generate allele-specific data to existing RNA-Seq simulators that do not natively support this feature.

Conclusions

Separating input sample modeling from library preparation/sequencing offers added flexibility for both users and developers to mix-and-match different sample and sequencing simulators to suit their specific needs. Furthermore, the ability to maintain sample and sequencing simulators independently provides greater agility to incorporate new biological findings about transcriptomics and new developments in sequencing technologies. Additionally, by simulating at the level of individual molecules, CAMPAREE has the potential to model molecules transcribed from the same genes as a heterogeneous population of transcripts with different states of degradation and processing (splicing, editing, etc.). CAMPAREE was developed in Python, is open source, and freely available at https://github.com/itmat/CAMPAREE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07934-2.

Method for quick DNA barcode reference library construction

DNA barcoding has become one of the most important techniques in plant species identification. Successful application of this technology is dependent on the availability of reference database of high species coverage. Unfortunately, there are experimental and data processing challenges to construct such a library within a short time. Here, we present our solutions to these challenges. We sequenced six conventional DNA barcode fragments (ITS1, ITS2, matK1, matK2, rbcL1, and rbcL2) of 380 flowering plants on next-generation sequencing (NGS) platforms (Illumina Hiseq 2500 and Ion Torrent S5) and the Sanger sequencing platform. After comparing the sequencing depths, read lengths, base qualities, and base accuracies, we conclude that Illumina Hiseq2500 PE250 run is suitable for conventional DNA barcoding. We developed a new "Cotu" method to create consensus sequences from NGS reads for longer output sequences and more reliable bases than the other three methods. Step-by-step instructions to our method are provided. By using high-throughput machines (PCR and NGS), labeling PCR, and the Cotu method, it is possible to significantly reduce the cost and labor investments for DNA barcoding. A regional or even global DNA barcoding reference library with high species coverage is likely to be constructed in a few years.

RNA Sequencing Data from Human Intracranial Aneurysm Tissue Reveals a Complex Inflammatory Environment Associated with Rupture

BACKGROUND

Intracranial aneurysm (IA) rupture leads to deadly subarachnoid hemorrhages. However, the mechanisms leading to rupture remain poorly understood. Altered gene expression within IA tissue is linked to the pathobiology of aneurysm development and progression. Here, we analyzed expression patterns of control tissue samples and compared them to those of unruptured and ruptured IA tissue samples using data from the Gene Expression Omnibus (GEO).

METHODS

FASTQ files for 21 ruptured IAs, 21 unruptured IAs, and 16 control tissue samples were accessed from the GEO database. DESeq2 was used for differential expression analysis in three comparisons: unruptured IA versus control, ruptured IA versus control, and ruptured versus unruptured IA. Genes that were differentially expressed in multiple comparisons were evaluated to find those progressively increasing/decreasing from control to unruptured to ruptured. Significance was tested by either analysis of variance/Gabriel or Brown-Forsythe/Games Howell (p < 0.05 was considered significant). We used additional RNA sequencing and proteomics datasets to evaluate if our differentially expressed genes (DEGs) were present in other studies. Bioinformatics analyses were performed with g:Profiler and Ingenuity Pathway Analysis.

RESULTS

In total, we identified 1768 DEGs, of which 318 were found in multiple comparisons. Unruptured versus control reflected vascular remodeling processes, while ruptured versus control reflected inflammatory responses and cell activation/signaling. When comparing ruptured to unruptured IAs, we found massive activation of inflammation, inflammatory responses, and leukocyte responses. Of the 318 genes in multiple comparisons, 127 were found to be significant in the multi-cohort correlation analysis. Those that progressively increased (70 genes) were associated with immune system processes, while those that progressively decreased (38 genes) did not return any gene ontology terms. Many of our DEGs were also found in the other IA tissue sequencing studies.

CONCLUSIONS

We found unruptured IAs relate more to remodeling processes, while ruptured IAs reflect more inflammatory and immune responses.

Deploying new generation sequencing for the study of flesh color depletion in Atlantic Salmon (Salmo salar)

BACKGROUND

The flesh pigmentation of farmed Atlantic salmon is formed by accumulation of carotenoids derived from commercial diets. In the salmon gastrointestinal system, the hindgut is considered critical in the processes of carotenoids uptake and metabolism. In Tasmania, flesh color depletion can noticeably affect farmed Atlantic salmon at different levels of severity following extremely hot summers. In this study, RNA sequencing (RNA-Seq) was performed to investigate the reduction in flesh pigmentation. Library preparation is a key step that significantly impacts the effectiveness of RNA sequencing (RNA-Seq) experiments. Besides the commonly used whole transcript RNA-Seq method, the 3' mRNA-Seq method is being applied widely, owing to its reduced cost, enabling more repeats to be sequenced at the expense of lower resolution. Therefore, the output of the Illumina TruSeq kit (whole transcript RNA-Seq) and the Lexogen QuantSeq kit (3' mRNA-Seq) was analyzed to identify genes in the Atlantic salmon hindgut that are differentially expressed (DEGs) between two flesh color phenotypes.

RESULTS

In both methods, DEGs between the two color phenotypes were associated with metal ion transport, oxidation-reduction processes, and immune responses. We also found DEGs related to lipid metabolism in the QuantSeq method. In the TruSeq method, a missense mutation was detected in DEGs in different flesh color traits. The number of DEGs found in the TruSeq libraries was much higher than the QuantSeq; however, the trend of DEGs in both library methods was similar and validated by qPCR.

CONCLUSIONS

Flesh coloration in Atlantic salmon is related to lipid metabolism in which apolipoproteins, serum albumin and fatty acid-binding protein genes are hypothesized to be linked to the absorption, transport and deposition of carotenoids. Our findings suggest that Grp could inhibit the feeding behavior of low color-banded fish, resulting in the dietary carotenoid shortage. Several SNPs in genes involving in carotenoid-binding cholesterol and oxidative stress were detected in both flesh color phenotypes. Regarding the choice of the library preparation method, the selection criteria depend on the research design and purpose. The 3' mRNA-Seq method is ideal for targeted identification of highly expressed genes, while the whole RNA-Seq method is recommended for identification of unknown genes, enabling the identification of splice variants and trait-associated SNPs, as we have found for duox2 and duoxa1.

Human Papillomavirus Detection by Whole-Genome Next-Generation Sequencing: Importance of Validation and Quality Assurance Procedures

Next-generation sequencing (NGS) yields powerful opportunities for studying human papillomavirus (HPV) genomics for applications in epidemiology, public health, and clinical diagnostics. HPV genotypes, variants, and point mutations can be investigated in clinical materials and described in previously unprecedented detail. However, both the NGS laboratory analysis and bioinformatical approach require numerous steps and checks to ensure robust interpretation of results. Here, we provide a step-by-step review of recommendations for validation and quality assurance procedures of each step in the typical NGS workflow, with a focus on whole-genome sequencing approaches. The use of directed pilots and protocols to ensure optimization of sequencing data yield, followed by curated bioinformatical procedures, is particularly emphasized. Finally, the storage and sharing of data sets are discussed. The development of international standards for quality assurance should be a goal for the HPV NGS community, similar to what has been developed for other areas of sequencing efforts including microbiology and molecular pathology. We thus propose that it is time for NGS to be included in the global efforts on quality assurance and improvement of HPV-based testing and diagnostics.

Pervasive transcription of the mitochondrial genome in Candida albicans is revealed in mutants lacking the mtEXO RNase complex

The mitochondrial genome of the pathogenic yeast Candida albicans displays a typical organization of several (eight) primary transcription units separated by noncoding regions. Presence of genes encoding Complex I subunits and the stability of its mtDNA sequence make it an attractive model to study organellar genome expression using transcriptomic approaches. The main activity responsible for RNA degradation in mitochondria is a two-component complex (mtEXO) consisting of a 3ʹ-5ʹ exoribonuclease, in yeasts encoded by the DSS1 gene, and a conserved Suv3p helicase. In C. albicans, deletion of either DSS1 or SUV3 gene results in multiple defects in mitochondrial genome expression leading to the loss of respiratory competence. Transcriptomic analysis reveals pervasive transcription in mutants lacking the mtEXO activity, with evidence of the entire genome being transcribed, whereas in wild-type strains no RNAs corresponding to a significant fraction of the noncoding genome can be detected. Antisense (‘mirror’) transcripts, absent from normal mitochondria are also prominent in the mutants. The expression of multiple mature transcripts, particularly those translated from bicistronic mRNAs, as well as those that contain introns is affected in the mutants, resulting in a decreased level of proteins and reduced respiratory complex activity. The phenotype is most severe in the case of Complex IV, where a decrease of mature COX1 mRNA level to ~5% results in a complete loss of activity. These results show that RNA degradation by mtEXO is essential for shaping the mitochondrial transcriptome and is required to maintain the functional demarcation between transcription units and non-coding genome segments.

Constrained Standardization of Count Data from Massive Parallel Sequencing

In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets at well over 1 million count records per second whilst mitigating unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd's simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.

Differentiation of Capripox Viruses by Nanopore Sequencing

The genus capripoxvirus (CaPV), family Poxviridae, includes three virus species: goatpox virus (GPV), sheeppox virus (SPV) and lumpy skin disease virus (LSDV). CaPV causes disease outbreaks with consequent economic losses in Africa and the Middle East. LSDV has recently spread to Southeast Europe. As CaPVs share 96–97% genetic similarity along the length of the entire genome and are difficult to distinguish using serological assays, simple, reliable and fast methods for diagnosis and species differentiation are crucial in cases of disease outbreak. The present study aimed to develop a field-applicable CaPV differentiation method. Nanopore technology was used for whole genome sequencing. A local database of complete CaPV genomes and partial sequences of three genes (RPO30, P32 and GPCR) was established for offline Basic Local Alignment Search Tool (BLAST). Specificities of 98.04% in whole genome and 97.86% in RPO30 gene runs were obtained among the three virus species, while other databases were less specific. The total run time was shortened to approximately 2 h. Functionality of the developed procedure was proved by samples with high host background sequences. Reliable differentiation options for the quality and capacity of hardware, and sample quality of suspected cases, were derived from these findings. The whole workflow can be performed rapidly with a mobile suitcase laboratory and mini-computer, allowing application at the point-of-need with limited resource settings.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, MS is difficult to diagnose and responses to specific treatments may vary between individuals. Therefore, there is an indisputable need for biomarkers for early diagnosis, prediction of disease exacerbations, monitoring the progression of disease, and for measuring responses to therapy. Genomic and proteomic studies have sought to understand the molecular basis of MS and find biomarker candidates. Advances in next-generation sequencing and mass-spectrometry techniques have yielded an unprecedented amount of genomic and proteomic data; yet, translation of the results into the clinic has been underwhelming. This has prompted the development of novel data science techniques for exploring these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we discuss optimization of omics study designs, advances in the generation of omics data, and systems biology approaches aimed at improving biomarker discovery and translation to the clinic for MS.

Advancing brain barriers RNA sequencing: guidelines from experimental design to publication

BACKGROUND

RNA sequencing (RNA-Seq) in its varied forms has become an indispensable tool for analyzing differential gene expression and thus characterization of specific tissues. Aiming to understand the brain barriers genetic signature, RNA seq has also been introduced in brain barriers research. This has led to availability of both, bulk and single-cell RNA-Seq datasets over the last few years. If appropriately performed, the RNA-Seq studies provide powerful datasets that allow for significant deepening of knowledge on the molecular mechanisms that establish the brain barriers. However, RNA-Seq studies comprise complex workflows that require to consider many options and variables before, during and after the proper sequencing process.

MAIN BODY

In the current manuscript, we build on the interdisciplinary experience of the European PhD Training Network BtRAIN ( https://www.btrain-2020.eu/ ) where bioinformaticians and brain barriers researchers collaborated to analyze and establish RNA-Seq datasets on vertebrate brain barriers. The obstacles BtRAIN has identified in this process have been integrated into the present manuscript. It provides guidelines along the entire workflow of brain barriers RNA-Seq studies starting from the overall experimental design to interpretation of results. Focusing on the vertebrate endothelial blood-brain barrier (BBB) and epithelial blood-cerebrospinal-fluid barrier (BCSFB) of the choroid plexus, we provide a step-by-step description of the workflow, highlighting the decisions to be made at each step of the workflow and explaining the strengths and weaknesses of individual choices made. Finally, we propose recommendations for accurate data interpretation and on the information to be included into a publication to ensure appropriate accessibility of the data and reproducibility of the observations by the scientific community.

CONCLUSION

Next generation transcriptomic profiling of the brain barriers provides a novel resource for understanding the development, function and pathology of these barrier cells, which is essential for understanding CNS homeostasis and disease. Continuous advancement and sophistication of RNA-Seq will require interdisciplinary approaches between brain barrier researchers and bioinformaticians as successfully performed in BtRAIN. The present guidelines are built on the BtRAIN interdisciplinary experience and aim to facilitate collaboration of brain barriers researchers with bioinformaticians to advance RNA-Seq study design in the brain barriers community.

Prognostic and Predictive Value of Cadherin 11 for Patients with Gastric Cancer and Its Correlation with Tumor Microenvironment: Results from Microarray Analysis

Gastric cancer is a disease characterized by inflammation, and epithelial-to-mesenchymal transition (EMT) and tumor-associated macrophages (TAMs) both play a vital role in epithelial-driven malignancy. In the present study, we performed an integrated bioinformatics analysis of transcriptome data from multiple databases of gastric cancer patients and worked on a biomarker for evaluating tumor prognosis. We found that cadherin 11 (CDH11) is highly expressed not only in gastric cancer tissues but also in EMT molecular subtypes and metastatic patients. Also, we obtained evidence that CDH11 has a significant correlation with infiltrating immune cells in the tumor microenvironment (TME). Our findings reflected that CDH11 likely plays an important role in tumor immune escape and could provide a prognostic biomarker and potential therapeutic target for patients with gastric cancer.

RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.

Dlx1/2 mice have abnormal enteric nervous system function

Decades ago, investigators reported that mice lacking DLX1 and DLX2, transcription factors expressed in the enteric nervous system (ENS), die with possible bowel motility problems. These problems were never fully elucidated. We found that mice lacking DLX1 and DLX2 (Dlx1/2-/- mice) had slower small bowel transit and reduced or absent neurally mediated contraction complexes. In contrast, small bowel motility seemed normal in adult mice lacking DLX1 (Dlx1-/-). Even with detailed anatomic studies, we found no defects in ENS precursor migration, or neuronal and glial density in Dlx1/2-/- or Dlx1-/- mice. However, RNA sequencing of Dlx1/2-/- ENS revealed dysregulation of many genes, including vasoactive intestinal peptide (Vip). Using immunohistochemistry and reporter mice, we then found that Dlx1/2-/- mice have reduced VIP expression and fewer VIP-lineage neurons in their ENS. Our study reveals what we believe is a novel connection between Dlx genes and Vip and highlights the observation that dangerous bowel motility problems can occur in the absence of easily identifiable ENS structural defects. These findings may be relevant for disorders like chronic intestinal pseudo-obstruction (CIPO) syndrome.

RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism

The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.

Comparative evaluation of RNA-Seq library preparation methods for strand-specificity and low input

Library preparation is a key step in sequencing. For RNA sequencing there are advantages to both strand specificity and working with minute starting material, yet until recently there was no kit available enabling both. The Illumina TruSeq stranded mRNA Sample Preparation kit (TruSeq) requires abundant starting material while the Takara Bio SMART-Seq v4 Ultra Low Input RNA kit (V4) sacrifices strand specificity. The SMARTer Stranded Total RNA-Seq Kit v2 - Pico Input Mammalian (Pico) by Takara Bio claims to overcome these limitations. Comparative evaluation of these kits is important for selecting the appropriate protocol. We compared the three kits in a realistic differential expression analysis. We prepared and sequenced samples from two experimental conditions of biological interest with each of the three kits. We report differences between the kits at the level of differential gene expression; for example, the Pico kit results in 55% fewer differentially expressed genes than TruSeq. Nevertheless, the agreement of the observed enriched pathways suggests that comparable functional results can be obtained. In summary we conclude that the Pico kit sufficiently reproduces the results of the other kits at the level of pathway analysis while providing a combination of options that is not available in the other kits.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Preanalytic Variables and Tissue Stewardship for Reliable Next-Generation Sequencing (NGS) Clinical Analysis

An enduring goal of personalized medicine in cancer is the ability to identify patients who are likely to respond to specific therapies. Our growing understanding of the biology and molecular signatures of individual tumor types has facilitated the identification of predictive biomarkers and has led to an increasing number of diagnostic tests to be performed, often as serial and distinct assays on limited tumor specimens. The biomarker diagnostics field has been revolutionized by next-generation sequencing (NGS), which provides a comprehensive overview of the genomic profile of a tumor. Many preanalytic variables can influence the accuracy and reliability of NGS results. Standardization of preanalytic variables is, however, complicated by the plethora of specimen acquisition and processing methods. Variables across the tissue journey, including specimen acquisition, specimen fixation, and sectioning, as well as postfixation processing, such as nucleic acid extraction, library preparation, and choice of sequencing methods, are critical for the reliability of NGS analysis; thus, standardization would be beneficial. In this article, each step in the tissue journey is outlined, with specific focus on preanalytic variables that can influence NGS results. Practical considerations for standardization of these variables are provided to facilitate accurate, reliable, and reproducible NGS-based molecular characterization of tumors, ultimately informing diagnosis and guiding treatment.

Current Coverage of the mTOR Pathway by Next-Generation Sequencing Oncology Panels

The mTOR pathway is in the process of establishing itself as a key access-point of novel oncological drugs and targeted therapies. This is also reflected by the growing number of mTOR pathway genes included in commercially available next-generation sequencing (NGS) oncology panels. This review summarizes the portfolio of medium sized diagnostic, as well as research destined NGS panels and their coverage of the mTOR pathway, including 16 DNA-based panels and the current gene list of Foundation One as a major reference entity. In addition, we give an overview of interesting, mTOR-associated somatic mutations that are not yet incorporated. Especially eukaryotic translation initiation factors (eIFs), a group of mTOR downstream proteins, are on the rise as far as diagnostics and drug targeting in precision medicine are concerned. This review aims to raise awareness for the true coverage of NGS panels, which should be valuable in selecting the ideal platform for diagnostics and research.

Environmental DNA: A New Low-Cost Monitoring Tool for Pathogens in Salmonid Aquaculture

Environmental DNA (eDNA) metabarcoding is a relatively new monitoring tool featuring in an increasing number of applications such as the facilitation of the accurate and cost effective detection of species in environmental samples. eDNA monitoring is likely to have a major impact on the ability of salmonid aquaculture industry producers and their regulators to detect the presence and abundance of pathogens and other biological threats in the surrounding environment. However, for eDNA metabarcoding to develop into a useful bio-monitoring tool it is necessary to (a) validate that sequence datasets derived from amplification of metabarcoding markers reflect the true species' identity, (b) test the sensitivity under different abundance levels and environmental noise and (c) establish a low-cost sequencing method to enable the bulk processing of field samples. In this study, we employed an elaborate experimental design whereby different combinations of five biological agents were crossed at three abundance levels and exposed to sterile pre-filtered and unfiltered seawater, prior to coarse filtering and then eDNA ultrafiltration of the resultant material. We then benchmarked the low-cost, scalable, Ion Torrent sequencing method against the current gold-standard Illumina platform for eDNA surveys in aquaculture. Based on amplicon-seq of the 18S SSU rDNA v9 region, we were able to identify two parasites (Lepeophtheirus salmonis and Paramoeba perurans) to species level, whereas the microalgae species Prymnesium parvum, Pseudo-nitzschia seriata, and P. delicatissima could be assigned correctly only to the genus level. Illumina and Ion Torrent provided near identical results in terms of community composition in our samples, whereas Ion Torrent was more sensitive in detecting species richness when the medium was unfiltered seawater. Both methods were able to reflect the difference in relative abundance between treatments in 4 out of 5 species when samples were exposed to the unfiltered seawater, despite the significant amount of background noise from both bacteria and eukaryotes. Our findings indicate that eDNA metabarcoding offers significant potential in the monitoring of species harmful to aquaculture and for this purpose, the low-cost Ion Torrent sequencing is as accurate as Illumina in determining differences in their relative abundance between samples.

Methodological Approaches to Study Extracellular Vesicle miRNAs in Epstein⁻Barr Virus-Associated Cancers

Epstein Barr-virus (EBV) was the first virus identified to be associated with human cancer in 1964 and is found ubiquitously throughout the world's population. It is now established that EBV contributes to the development and progression of multiple human cancers of both lymphoid and epithelial cell origins. EBV encoded miRNAs play an important role in tumor proliferation, angiogenesis, immune escape, tissue invasion, and metastasis. Recently, EBV miRNAs have been found to be released from infected cancer cells in extracellular vesicles (EVs) and regulate gene expression in neighboring uninfected cells present in the tumor microenvironment and possibly at distal sites. As EVs are abundant in many biological fluids, the viral and cellular miRNAs present within EBV-modified EVs may serve as noninvasion markers for cancer diagnosis and prognosis. In this review, we discuss recent advances in EV isolation and miRNA detection, and provide a complete workflow for EV purification from plasma and deep-sequencing for biomarker discovery.

Transcriptional alterations during proliferation and lignification in Phyllostachys nigra cells

Highly-lignified culms of bamboo show distinctive anatomical and mechanical properties compared with the culms of other grass species. A cell culture system for Phyllostachys nigra has enabled investigating the alterations in cellular states associated with secondary cell wall formation during its proliferation and lignification in woody bamboos. To reveal transcriptional changes related to lignification in bamboo, we analyzed transcriptome in P. nigra cells treated with the synthetic auxin 2,4-dichlorophenoxyacetic acid (2,4-D) and the synthetic cytokinin benzylaminopurine (BA) by RNA-seq analysis. We found that some genes putatively involved in cell wall biogenesis and cell division were up-regulated in response to the 2,4-D treatment, and the induction of lignification by the BA treatment was correlated with up-regulation of genes involved in the shikimate pathway. We also found that genes encoding MYB transcription factors (TFs) show correlated expression patterns with those encoding cinnamyl alcohol dehydrogenase (CAD), suggesting that MYB TFs presumably regulate secondary cell wall formation in the bamboo cells. These findings suggest that cytokinin signaling may regulate lignification in P. nigra cells through coordinated transcriptional regulation and metabolic alterations. Our results have also produced a useful resource for better understanding of secondary cell wall formation in bamboo plants.

Identification of novel ΔNp63α-regulated miRNAs using an optimized small RNA-Seq analysis pipeline

Advances in high-throughput sequencing have enabled profiling of microRNAs (miRNAs), however, a consensus pipeline for sequencing of small RNAs has not been established. We built and optimized an analysis pipeline using Partek Flow, circumventing the need for analyzing data via scripting languages. Our analysis assessed the effect of alignment reference, normalization method, and statistical model choice on biological data. The pipeline was evaluated using sequencing data from HaCaT cells transfected with either a non-silencing control or siRNA against ΔNp63α, a p53 family member protein which is highly expressed in non-melanoma skin cancer and shown to regulate a number of miRNAs. We posit that 1) alignment and quantification to the miRBase reference provides the most robust quantitation of miRNAs, 2) normalizing sample reads via Trimmed Mean of M-values is the most robust method for accurate downstream analyses, and 3) use of the lognormal with shrinkage statistical model effectively identifies differentially expressed miRNAs. Using our pipeline, we identified previously unrecognized regulation of miRs-149-5p, 18a-5p, 19b-1-5p, 20a-5p, 590-5p, 744-5p and 93-5p by ΔNp63α. Regulation of these miRNAs was validated by RT-qPCR, substantiating our small RNA-Seq pipeline. Further analysis of these miRNAs may provide insight into ΔNp63α's role in cancer progression. By defining the optimal alignment reference, normalization method, and statistical model for analysis of miRNA sequencing data, we have established an analysis pipeline that may be carried out in Partek Flow or at the command line. In this manner, our pipeline circumvents some of the major hurdles encountered during small RNA-Seq analysis.

Dietary challenges differentially affect activity and sleep/wake behavior in mus musculus: Isolating independent associations with diet/energy balance and body weight

BACKGROUND AND AIMS

Associated with numerous metabolic and behavioral abnormalities, obesity is classified by metrics reliant on body weight (such as body mass index). However, overnutrition is the common cause of obesity, and may independently contribute to these obesity-related abnormalities. Here, we use dietary challenges to parse apart the relative influence of diet and/or energy balance from body weight on various metabolic and behavioral outcomes.

MATERIALS AND METHODS

Seventy male mice (mus musculus) were subjected to the diet switch feeding paradigm, generating groups with various body weights and energetic imbalances. Spontaneous activity patterns, blood metabolite levels, and unbiased gene expression of the nutrient-sensing ventral hypothalamus (using RNA-sequencing) were measured, and these metrics were compared using standardized multivariate linear regression models.

RESULTS

Spontaneous activity patterns were negatively related to body weight (p<0.0001) but not diet/energy balance (p = 0.63). Both body weight and diet/energy balance predicted circulating glucose and insulin levels, while body weight alone predicted plasma leptin levels. Regarding gene expression within the ventral hypothalamus, only two genes responded to diet/energy balance (neuropeptide y [npy] and agouti-related peptide [agrp]), while others were related only to body weight.

CONCLUSIONS

Collectively, these results demonstrate that individual components of obesity-specifically obesogenic diets/energy imbalance and elevated body mass-can have independent effects on metabolic and behavioral outcomes. This work highlights the shortcomings of using body mass-based indices to assess metabolic health, and identifies novel associations between blood biomarkers, neural gene expression, and animal behavior following dietary challenges.