Complementary DNA sequencing: expressed sequence tags and human genome project

The commercial roots of the genomic commons

Accounts of the origins of the genomic commons typically focus on the development of public repositories and data-sharing agreements. This article tells a different story. During the 1990s in the United States, efforts of private companies to prevent the patenting of certain kinds of DNA sequences were essentially a conservative response to shifts in the sociotechnical constitution of the pharmaceutical innovation system, and to the operation of intellectual property as one of the key knowledge control regimes that regulate that system. In this context, the idea of 'the commons' was rehabilitated from earlier tragic theorizations to argue that industry's ability to deliver new pharmaceutical products would be better served if certain kinds of intellectual property were left in the public domain. The genomic commons is not a neutral space of disinterested scientific research that naturally aligns with some abstract 'public good', but is part of an innovation system that has evolved to serve the interests of a range of stakeholders, among which the big pharmaceutical companies enjoy a dominant position.

The Evolution of Next-Generation Sequencing Technologies

The genetic information that dictates the structure and function of all life forms is encoded in the DNA. In 1953, Watson and Crick first presented the double helical structure of a DNA molecule. Their findings unearthed the desire to elucidate the exact composition and sequence of DNA molecules. Discoveries and the subsequent development and optimization of techniques that allowed for deciphering the DNA sequence has opened new doors in research, biotech, and healthcare. The application of high-throughput sequencing technologies in these industries has positively impacted and will continue to contribute to the betterment of humanity and the global economy. Improvements, such as the use of radioactive molecules for DNA sequencing to the use of florescent dyes and the implementation of polymerase chain reaction (PCR) for amplification, led to sequencing a few hundred base pairs in days, to automation, where sequencing of thousands of base pairs in hours became possible. Significant advances have been made, but there is still room for improvement. Here, we look at the history and the technology of the currently available next-generation sequencing platforms and the possible applications of such technologies to biomedical research and beyond.

Extracellular vesicles, RNA sequencing, and bioinformatic analyses: Challenges, solutions, and recommendations

Extracellular vesicles (EVs) are heterogeneous entities secreted by cells into their microenvironment and systemic circulation. Circulating EVs carry functional small RNAs and other molecular footprints from their cell of origin, and thus have evident applications in liquid biopsy, therapeutics, and intercellular communication. Yet, the complete transcriptomic landscape of EVs is poorly characterized due to critical limitations including variable protocols used for EV-RNA extraction, quality control, cDNA library preparation, sequencing technologies, and bioinformatic analyses. Consequently, there is a gap in knowledge and the need for a standardized approach in delineating EV-RNAs. Here, we address these gaps by describing the following points by (1) focusing on the large canopy of the EVs and particles (EVPs), which includes, but not limited to - exosomes and other large and small EVs, lipoproteins, exomeres/supermeres, mitochondrial-derived vesicles, RNA binding proteins, and cell-free DNA/RNA/proteins; (2) examining the potential functional roles and biogenesis of EVPs; (3) discussing various transcriptomic methods and technologies used in uncovering the cargoes of EVPs; (4) presenting a comprehensive list of RNA subtypes reported in EVPs; (5) describing different EV-RNA databases and resources specific to EV-RNA species; (6) reviewing established bioinformatics pipelines and novel strategies for reproducible EV transcriptomics analyses; (7) emphasizing the significant need for a gold standard approach in identifying EV-RNAs across studies; (8) and finally, we highlight current challenges, discuss possible solutions, and present recommendations for robust and reproducible analyses of EVP-associated small RNAs. Overall, we seek to provide clarity on the transcriptomics landscape, sequencing technologies, and bioinformatic analyses of EVP-RNAs. Detailed portrayal of the current state of EVP transcriptomics will lead to a better understanding of how the RNA cargo of EVPs can be used in modern and targeted diagnostics and therapeutics. For the inclusion of different particles discussed in this article, we use the terms large/small EVs, non-vesicular extracellular particles (NVEPs), EPs and EVPs as defined in MISEV guidelines by the International Society of Extracellular Vesicles (ISEV).

Natural antisense transcripts as versatile regulators of gene expression

Long non-coding RNAs (lncRNAs) are emerging as a major class of gene products that have central roles in cell and developmental biology. Natural antisense transcripts (NATs) are an important subset of lncRNAs that are expressed from the opposite strand of protein-coding and non-coding genes and are a genome-wide phenomenon in both eukaryotes and prokaryotes. In eukaryotes, a myriad of NATs participate in regulatory pathways that affect expression of their cognate sense genes. Recent developments in the study of NATs and lncRNAs and large-scale sequencing and bioinformatics projects suggest that whether NATs regulate expression, splicing, stability or translation of the sense transcript is influenced by the pattern and degrees of overlap between the sense-antisense pair. Moreover, epigenetic gene regulatory mechanisms prevail in somatic cells whereas mechanisms dependent on the formation of double-stranded RNA intermediates are prevalent in germ cells. The modulating effects of NATs on sense transcript expression make NATs rational targets for therapeutic interventions.

Transcriptome analysis: a powerful tool to understand individual microbial behaviors and interactions in ecosystems

Transcriptome analysis is a powerful tool for studying microbial ecology, especially individual microbial functions in an ecosystem and their interactions. With the development of high-throughput sequencing technology, great progress has been made in analytical methods for microbial communities in natural environments. 16S rRNA gene amplicon sequencing (ie microbial community structure analysis) and shotgun metagenome analysis have been widely used to determine the composition and potential metabolic capability of microorganisms in target environments without requiring culture. However, even if the types of microorganisms present and their genes are known, it is difficult to determine what they are doing in an ecosystem. Gene expression analysis (transcriptome analysis; RNA-seq) is a powerful tool to address these issues. The history and basic information of gene expression analysis, as well as examples of studies using this method to analyze microbial ecosystems, are presented.

Deep mining of the Sequence Read Archive reveals major genetic innovations in coronaviruses and other nidoviruses of aquatic vertebrates

Virus discovery by genomics and metagenomics empowered studies of viromes, facilitated characterization of pathogen epidemiology, and redefined our understanding of the natural genetic diversity of viruses with profound functional and structural implications. Here we employed a data-driven virus discovery approach that directly queries unprocessed sequencing data in a highly parallelized way and involves a targeted viral genome assembly strategy in a wide range of sequence similarity. By screening more than 269,000 datasets of numerous authors from the Sequence Read Archive and using two metrics that quantitatively assess assembly quality, we discovered 40 nidoviruses from six virus families whose members infect vertebrate hosts. They form 13 and 32 putative viral subfamilies and genera, respectively, and include 11 coronaviruses with bisegmented genomes from fishes and amphibians, a giant 36.1 kilobase coronavirus genome with a duplicated spike glycoprotein (S) gene, 11 tobaniviruses and 17 additional corona-, arteri-, cremega-, nanhypo- and nangoshaviruses. Genome segmentation emerged in a single evolutionary event in the monophyletic lineage encompassing the subfamily Pitovirinae. We recovered the bisegmented genome sequences of two coronaviruses from RNA samples of 69 infected fishes and validated the presence of poly(A) tails at both segments using 3'RACE PCR and subsequent Sanger sequencing. We report a genetic linkage between accessory and structural proteins whose phylogenetic relationships and evolutionary distances are incongruent with the phylogeny of replicase proteins. We rationalize these observations in a model of inter-family S recombination involving at least five ancestral corona- and tobaniviruses of aquatic hosts. In support of this model, we describe an individual fish co-infected with members from the families Coronaviridae and Tobaniviridae. Our results expand the scale of the known extraordinary evolutionary plasticity in nidoviral genome architecture and call for revisiting fundamentals of genome expression, virus particle biology, host range and ecology of vertebrate nidoviruses.

Full-length transcriptome analysis of Ophioglossum vulgatum: effects of experimentally identified chloroplast gene clusters on expression and evolutionary patterns

Genes with similar or related functions in chloroplasts are often arranged in close proximity, forming clusters on chromosomes. These clusters are transcribed coordinated to facilitate the expression of genes with specific function. Our previous study revealed a significant negative correlation between the chloroplast gene expression level of the rare medicinal fern Ophioglossum vulgatum and its evolutionary rates as well as selection pressure. Therefore, in this study, we employed a combination of SMRT and Illumina sequencing technology to analyze the full-length transcriptome sequencing of O. vulgatum for the first time. In particular, we experimentally identified gene clusters based on transcriptome data and investigated the effects of chloroplast gene clustering on expression and evolutionary patterns. The results revealed that the total sequenced data volume of the full-length transcriptome of O. vulgatum amounted to 71,950,652,163 bp, and 110 chloroplast genes received transcript coverage. Nine different types of gene clusters were experimentally identified in their transcripts. The chloroplast cluster genes may cause a decrease in non-synonymous substitution rate and selection pressure, as well as a reduction in transversion rate, transition rate, and their ratio. While expression levels of chloroplast cluster genes in leaf, sporangium, and stem would be relatively elevated. The Mann-Whitney U test indicated statistically significant in the selection pressure, sporangia and leaves groups (P < 0.05). We have contributed novel full-length transcriptome data resources for ferns, presenting new evidence on the effects of chloroplast gene clustering on expression land evolutionary patterns, and offering new theoretical support for transgenic research through gene clustering.

Development and validation of genome-wide SSR molecular markers of Tapes dorsatus

BACKGROUND

Tapes dorsatus is an economically important benthic animal in the Beibu Gulf of China. However, the deficiency of microsatellite markers has hindered the study of its genetics. The development of microsatellite markers will provide useful tools for genetic improvement, variety identification, phylogenetic analysis and resource conservation.

METHODS AND RESULTS

Within the genome sequence, 145,008 simple sequence repeats (SSRs) were identified, and 29,691 primer pairs were designed successfully. A total of 100 primer pairs were randomly synthesized for testing, and 93 primers yielded products. Sixty highly polymorphic primers were used to reveal the genetic diversity of 50 T. dorsatus individuals. The average number of alleles (Na) of the population was 10.40; the average number of effective alleles was 6.16, the average expected heterozygosity (He) was 0.82, and the average polymorphic information content was 0.80. The genetic structure of the population was detected, by which the population could be divided into three subpopulations.

CONCLUSION

We identified 145,008 SSRs in the genome of T. dorsatus and designed 29,691 primer pairs in this study. Of 100 synthesized primers, 60 were highly polymorphic and used to reveal the genetic diversity and structure of the population. The SSR markers identified here will provide useful tools and a foundation for genetic diversity, linkage mapping and molecular marker-aided breeding in T. dorsatus.

Transcriptomics : Approaches to Quantifying Gene Expression and Their Application to Studying the Human Brain

To date, the field of transcriptomics has been characterized by rapid methods development and technological advancement, with new technologies continuously rendering older ones obsolete.This chapter traces the evolution of approaches to quantifying gene expression and provides an overall view of the current state of the field of transcriptomics, its applications to the study of the human brain, and its place in the broader emerging multiomics landscape.

De novo transcriptome assembly of mouse male germ cells reveals novel genes, stage-specific bidirectional promoter activity, and noncoding RNA expression

In mammals, the adult testis is the tissue with the highest diversity in gene expression. Much of that diversity is attributed to germ cells, primarily meiotic spermatocytes and postmeiotic haploid spermatids. Exploiting a newly developed cell purification method, we profiled the transcriptomes of such postmitotic germ cells of mice. We used a de novo transcriptome assembly approach and identified thousands of novel expressed transcripts characterized by features distinct from those of known genes. Novel loci tend to be short in length, monoexonic, and lowly expressed. Most novel genes have arisen recently in evolutionary time and possess low coding potential. Nonetheless, we identify several novel protein-coding genes harboring open reading frames that encode proteins containing matches to conserved protein domains. Analysis of mass-spectrometry data from adult mouse testes confirms protein production from several of these novel genes. We also examine overlap between transcripts and repetitive elements. We find that although distinct families of repeats are expressed with differing temporal dynamics during spermatogenesis, we do not observe a general mode of regulation wherein repeats drive expression of nonrepetitive sequences in a cell type-specific manner. Finally, we observe many fairly long antisense transcripts originating from canonical gene promoters, pointing to pervasive bidirectional promoter activity during spermatogenesis that is distinct and more frequent compared with somatic cells.

Genetic Enhancement of Cereals Using Genomic Resources for Nutritional Food Security

Advances in genomics resources have facilitated the evolution of cereal crops with enhanced yield, improved nutritional values, and heightened resistance to various biotic and abiotic stresses. Genomic approaches present a promising avenue for the development of high-yielding varieties, thereby ensuring food and nutritional security. Significant improvements have been made within the omics domain, specifically in genomics, transcriptomics, and proteomics. The advent of Next-Generation Sequencing (NGS) techniques has yielded an immense volume of data, accompanied by substantial progress in bioinformatic tools for proficient analysis. The synergy between genomics and computational tools has been acknowledged as pivotal for unravelling the intricate mechanisms governing genome-wide gene regulation. Within this review, the essential genomic resources are delineated, and their harmonization in the enhancement of cereal crop varieties is expounded upon, with a paramount focus on fulfilling the nutritional requisites of humankind. Furthermore, an encompassing compendium of the available genomic resources for cereal crops is presented, accompanied by an elucidation of their judicious utilization in the advancement of crop attributes.

Genome annotation: From human genetics to biodiversity genomics

Within the next decade, the genomes of 1.8 million eukaryotic species will be sequenced. Identifying genes in these sequences is essential to understand the biology of the species. This is challenging due to the transcriptional complexity of eukaryotic genomes, which encode hundreds of thousands of transcripts of multiple types. Among these, a small set of protein-coding mRNAs play a disproportionately large role in defining phenotypes. Due to their sequence conservation, orthology can be established, making it possible to define the universal catalog of eukaryotic protein-coding genes. This catalog should substantially contribute to uncovering the genomic events underlying the emergence of eukaryotic phenotypes. This piece briefly reviews the basics of protein-coding gene prediction, discusses challenges in finalizing annotation of the human genome, and proposes strategies for producing annotations across the eukaryotic Tree of Life. This lays the groundwork for obtaining the catalog of all genes-the Earth's code of life.

Technical assessment of different extraction methods and transcriptome profiling of RNA isolated from small volumes of blood

Transcriptome profiling of human whole blood is used to discover biomarkers of diseases and to assess phenotypic traits. Recently, finger-stick blood collection systems have allowed a less invasive and quicker collection of peripheral blood. Such non-invasive sampling of small volumes of blood offers practical advantages. The quality of gene expression data is strictly dependent on the steps used for the sample collection, extraction, preparation and sequencing. Here we have: (i) compared the manual and automated RNA extraction of small volumes of blood using the Tempus Spin RNA isolation kit and the MagMAX for Stabilized Blood RNA Isolation kit , respectively; and (ii) assessed the effect of TURBO DNA Free treatment on the transcriptomic data of RNA isolated from small volumes of blood. We have used the QuantSeq 3' FWD mRNA-Seq Library Prep kit to prepare RNA-seq libraries, which were sequenced on the Illumina NextSeq 500 system. The samples isolated manually displayed a higher variability in the transcriptomic data as compared to the other samples. The TURBO DNA Free treatment affected the RNA samples negatively, decreasing the RNA yield and reducing the quality and reproducibility of the transcriptomic data. We conclude that automated extraction systems should be preferred over manual extraction systems for data consistency, and that the TURBO DNA Free treatment should be avoided when working on RNA samples isolated manually from small volumes of blood.

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.

Comparative Analysis of Non-Coding RNA Transcriptomics in Heart Failure

Heart failure constitutes a clinical complex syndrome with different symptomatic characteristics depending on age, sex, race and ethnicity, among others, which has become a major public health issue with an increasing prevalence. One of the most interesting tools seeking to improve prevention, diagnosis, treatment and prognosis of this pathology has focused on finding new molecular biomarkers since heart failure relies on deficient cardiac homeostasis, which is regulated by a strict gene expression. Therefore, currently, analyses of non-coding RNA transcriptomics have been oriented towards human samples. The present review develops a comparative study emphasizing the relevance of microRNAs, long non-coding RNAs and circular RNAs as potential biomarkers in heart failure. Significantly, further studies in this field of research are fundamental to supporting their widespread clinical use. In this sense, the various methodologies used by the authors should be standardized, including larger cohorts, homogeneity of the samples and uniformity of the bioinformatic pipelines used to reach stratification and statistical significance of the results. These basic adjustments could provide promising steps to designing novel strategies for clinical management of patients with heart failure.

The future of individualized cardiovascular care: how wearables could be integrated to improve outcomes

Personalized medicine is a concept all clinicians must strive to deliver. Recent advances in technology increasingly offer new opportunities to personalize care, not least in cardiovascular medicine. Health trackers and wearables are technologies in an explosive phase of development. They allow accurate and continuous measurement of bio-data, recorded and analysed using apps and mobile devices. However, although there is huge potential, most physicians and healthcare organizations are yet to realize the value of integrating wearables into routine clinical practice. We discuss how this state-of-the-art technology can support patients in making meaningful lifestyle changes and revolutionize the future of cardiovascular medicine.

Gene Expression in the Developing Seed of Wild and Domesticated Rice

The composition and nutritional properties of rice are the product of the expression of genes in the developing seed. RNA-Seq was used to investigate the level of gene expression at different stages of seed development in domesticated rice (Oryza sativa ssp. japonica var. Nipponbare) and two Australian wild taxa from the primary gene pool of rice (Oryza meridionalis and Oryza rufipogon type taxa). Transcriptome profiling of all coding sequences in the genome revealed that genes were significantly differentially expressed at different stages of seed development in both wild and domesticated rice. Differentially expressed genes were associated with metabolism, transcriptional regulation, nucleic acid processing, and signal transduction with the highest number of being linked to protein synthesis and starch/sucrose metabolism. The level of gene expression associated with domestication traits, starch and sucrose metabolism, and seed storage proteins were highest at the early stage (5 days post anthesis (DPA)) to the middle stage (15 DPA) and declined late in seed development in both wild and domesticated rice. However, in contrast, black hull colour (Bh4) gene was significantly expressed throughout seed development. A substantial number of novel transcripts (38) corresponding to domestication genes, starch and sucrose metabolism, and seed storage proteins were identified. The patterns of gene expression revealed in this study define the timing of metabolic processes associated with seed development and may be used to explain differences in rice grain quality and nutritional value.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Opportunities and Challenges of Data-Driven Virus Discovery

Virus discovery has been fueled by new technologies ever since the first viruses were discovered at the end of the 19th century. Starting with mechanical devices that provided evidence for virus presence in sick hosts, virus discovery gradually transitioned into a sequence-based scientific discipline, which, nowadays, can characterize virus identity and explore viral diversity at an unprecedented resolution and depth. Sequencing technologies are now being used routinely and at ever-increasing scales, producing an avalanche of novel viral sequences found in a multitude of organisms and environments. In this perspective article, we argue that virus discovery has started to undergo another transformation prompted by the emergence of new approaches that are sequence data-centered and primarily computational, setting them apart from previous technology-driven innovations. The data-driven virus discovery approach is largely uncoupled from the collection and processing of biological samples, and exploits the availability of massive amounts of publicly and freely accessible data from sequencing archives. We discuss open challenges to be solved in order to unlock the full potential of data-driven virus discovery, and we highlight the benefits it can bring to classical (mostly molecular) virology and molecular biology in general.

A Review of Histocytological Events and Molecular Mechanisms Involved in Intestine Regeneration in Holothurians

Most species of the class Holothuroidea are able to regenerate most of their internal organs following a typical evisceration process, which is a unique mechanism that allows sea cucumbers to survive in adverse environments. In this review, we compare autotomy among different type of sea cucumber and summarize the histocytological events that occur during the five stages of intestinal regeneration. Multiple cytological activities, such as apoptosis and dedifferentiation, take place during wound healing and anlage formation. Many studies have focused on the molecular regulation mechanisms that underlie regeneration, and herein we describe the techniques that have been used as well as the development-related signaling pathways and key genes that are significantly expressed during intestinal regeneration. Future analyses of the underlying mechanisms responsible for intestinal regeneration should include mapping at the single-cell level. Studies of visceral regeneration in echinoderms provide a unique perspective for understanding whole-body regeneration or appendage regeneration.

The Long Read Transcriptome of Rice (Oryza sativa ssp. japonica var. Nipponbare) Reveals Novel Transcripts

BACKGROUND

High-throughput next-generation sequencing technologies offer a powerful approach to characterizing the transcriptomes of plants. Long read sequencing has been shown to support the discovery of novel isoforms of transcripts. This approach enables the generation of full-length sequences revealing splice variants that may be important in regulating gene action. Investigation of the diversity of transcripts in the rice transcriptome including splice variants was conducted using PacBio long-read sequence data to improve the annotation of the rice genome.

RESULTS

A cDNA library was prepared from RNA extracted from leaves, roots, seeds, inflorescences, and panicles of O. sativa ssp. japonica var Nipponbare and sequenced on a PacBio Sequel platform. This produced 346,190 non-redundant full-length non-chimeric reads (FLNC) resulting in 33,504 high-quality transcripts. Half of the transcripts were multi-exonic and entirely matched with the reference transcripts. However, 14,874 novel isoforms were also identified resulting predominantly from intron retention and at least one novel splice site. Intron retention was the prevalent alternative splicing event and exon skipping was the least observed. Of 73,659 splice junctions, 12,755 (17%) represented novel splice junctions with canonical and non-canonical intron boundaries. The complexity of the transcriptome was examined in detail for 19 starch synthesis-related genes, defining 276 spliced isoforms of which 94 splice variants were novel.

CONCLUSION

The data reveal the great complexity of the rice transcriptome. The novel transcripts provide new insights that may be a key input in future research to improve the annotation of the rice genome.

Genetic Diversity Analysis of Sapindus in China and Extraction of a Core Germplasm Collection Using EST-SSR Markers

Sapindus is an important forest tree genus with utilization in biodiesel, biomedicine, and it harbors great potential for biochemical engineering applications. For advanced breeding of Sapindus, it is necessary to evaluate the genetic diversity and construct a rationally designed core germplasm collection. In this study, the genetic diversity and population structure of Sapindus were conducted with 18 expressed sequence tag-simple sequence repeat (EST-SSR) markers in order to establish a core germplasm collection from 161 Sapindus accessions. The population of Sapindus showed high genetic diversity and significant population structure. Interspecific genetic variation was significantly higher than intraspecific variation in the Sapindus mukorossi, Sapindus delavayi, and combined Sapindus rarak plus Sapindus rarak var. velutinus populations. S. mukorossi had abundant genetic variation and showed a specific pattern of geographical variation, whereas S. delavayi, S. rarak, and S. rarak var. velutinus showed less intraspecific variation. A core germplasm collection was created that contained 40% of genetic variation in the initial population, comprising 53 S. mukorossi and nine S. delavayi lineages, as well as single representatives of S. rarak and S. rarak var. velutinus. These results provide a germplasm basis and theoretical rationale for the efficient management, conservation, and utilization of Sapindus, as well as genetic resources for joint genomics research in the future.

Small open reading frames in plant research: from prediction to functional characterization

Gene prediction is a laborious and time-consuming task. The advancement of sequencing technologies and bioinformatics tools, coupled with accelerated rate of ribosome profiling and mass spectrometry development, have made identification of small open reading frames (sORFs) (< 100 codons) in various plant genomes possible. The past 50 years have seen sORFs being isolated from many organisms. However, to date, a comprehensive sORF annotation pipeline is as yet unavailable, hence, addressed in our review. Here, we also provide current information on classification and functions of plant sORFs and their potential applications in crop improvement programs.

Recommendations for pharmacotranscriptomic profiling of drug response in CNS disorders

Pharmacotranscriptomics is a still very new field of research that has just begun to flourish and promises to enable target discovery, inform biomarker and evaluate drug efficacy beyond pharmacogenomics. The aim of this review is to provide a critical overview of the biological foundations of transcriptomics, methodological approaches to transcriptomic studies, and their advantages and limitations. We present the different RNA species (rRNAs, tRNAs, mtRNAs, snRNAs, scRNAs, mRNAs, ncRNAs, LINE and SINE transcripts, circular RNAs, piRNAs, miRNAs, snoRNAs) and their potential for pharmacotranscriptomic studies as markers to predict treatment response in neurological and psychiatric disorders. We also review the accessible sources of RNA in patients peripheral blood cells (including platelets), plasma, microvesicles, exosomes, apoptotic bodies, and how those affect the integrity and relative abundances of RNAs and reflect the situation in the Central Nervous System (CNS). Finally, we discuss the suitability and indications of different techniques, such as microarrays and RNA-sequencing (RNA-Seq) techniques to understand gene expression differences or to reveal variation in expression levels of coding and non-coding genes. We conclude with some recommendations for future directions, e.g., gaps of knowledge and particular RNAs/tissues that have been overlooked.

Effective decoders for DNA codes

A number of applications use DNA as a storage mechanism. Because processes in these applications may cause errors in the data, the information must be encoded as one of a chosen set of words that are well separated from one another - a DNA error-correcting code. Typically, the types of errors that may occur include insertions, deletions and substitutions of symbols, making the edit metric the most suitable choice to measure the distance between strings. Decoding, the process of recovering the original word when errors occur, is complicated by biological restrictions combined with a high cost to calculate edit distance. Side effect machines (SEMs), an extension of finite state machines, can provide efficient decoding algorithms for such codes. Several codes of varying lengths are used to study the effectiveness of evolutionary programming (EP) as a general approach for finding SEMs for edit metric decoding. Two classification methods (direct and fuzzy classification) are compared, and different EP settings are examined to observe how decoding accuracy is affected. Regardless of code length, the best results are found using fuzzy classification. The best accuracy is seen for codes of length 10, for which a maximum accuracy of up to 99.4% is achieved for distance 1 and distance 2 and 3 achieve up to 97.1% and 85.9%, respectively. Additionally, the SEMs are examined for potential bloat by comparing the number of reachable states against the total number of states. Bloat is seen more in larger machines than in smaller machines. Furthermore, the results are analysed to find potential trends and relationships among the parameters, with the most consistent trend being that, when allowed, the longer codes generally show a propensity for larger machines.

Alternative Splicing in Myeloid Malignancies

Alternative RNA splicing (AS) is an essential physiologic function that diversifies the human proteome. AS also has a crucial role during cellular development. In fact, perturbations in RNA-splicing have been implicated in the development of several cancers, including myeloid malignancies. Splicing dysfunction can be independent of genetic lesions or appear as a direct consequence of mutations in components of the RNA-splicing machinery, such as in the case of mutations occurring in splicing factor genes (i.e., SF3B1, SRSF2, U2AF1) and their regulators. In addition, cancer cells exhibit marked gene expression alterations, including different usage of AS isoforms, possibly causing tissue-specific effects and perturbations of downstream pathways. This review summarizes several modalities leading to splicing diversity in myeloid malignancies.

The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications

Multiple myeloma (MM) is a genetically complex disease that results from a multistep transformation of normal to malignant plasma cells in the bone marrow. However, the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown. A fundamental step needed to understand the oncogenesis of MM and its response to therapy is the identification of driver mutations. The introduction of gene expression profiling (GEP) in MM is an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Since some mutations in myeloma occur in non-coding regions, studies based on the analysis of mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology. In this review, we discuss the role of gene expression profiling in understanding the biology of multiple myeloma together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions.

KinOrtho: a method for mapping human kinase orthologs across the tree of life and illuminating understudied kinases

BACKGROUND

Protein kinases are among the largest druggable family of signaling proteins, involved in various human diseases, including cancers and neurodegenerative disorders. Despite their clinical relevance, nearly 30% of the 545 human protein kinases remain highly understudied. Comparative genomics is a powerful approach for predicting and investigating the functions of understudied kinases. However, an incomplete knowledge of kinase orthologs across fully sequenced kinomes severely limits the application of comparative genomics approaches for illuminating understudied kinases. Here, we introduce KinOrtho, a query- and graph-based orthology inference method that combines full-length and domain-based approaches to map one-to-one kinase orthologs across 17 thousand species.

RESULTS

Using multiple metrics, we show that KinOrtho performed better than existing methods in identifying kinase orthologs across evolutionarily divergent species and eliminated potential false positives by flagging sequences without a proper kinase domain for further evaluation. We demonstrate the advantage of using domain-based approaches for identifying domain fusion events, highlighting a case between an understudied serine/threonine kinase TAOK1 and a metabolic kinase PIK3C2A with high co-expression in human cells. We also identify evolutionary fission events involving the understudied OBSCN kinase domains, further highlighting the value of domain-based orthology inference approaches. Using KinOrtho-defined orthologs, Gene Ontology annotations, and machine learning, we propose putative biological functions of several understudied kinases, including the role of TP53RK in cell cycle checkpoint(s), the involvement of TSSK3 and TSSK6 in acrosomal vesicle localization, and potential functions for the ULK4 pseudokinase in neuronal development.

CONCLUSIONS

In sum, KinOrtho presents a novel query-based tool to identify one-to-one orthologous relationships across thousands of proteomes that can be applied to any protein family of interest. We exploit KinOrtho here to identify kinase orthologs and show that its well-curated kinome ortholog set can serve as a valuable resource for illuminating understudied kinases, and the KinOrtho framework can be extended to any protein-family of interest.

The impact of gene-body H3K36me3 patterns on gene expression level changes in chronic myelogenous leukemia

Chronic myelogenous leukemia (CML) is a malignant clonal disease of hematopoietic stem cells. Researches have exhibited that the progression of CML is related to histone modifications. Here, we perform the systematic analyses of H3K36me3 patterns and gene expression level changes. We observe that the genes with higher gene-body H3K36me3 levels in normal cells show fewer expression changes during leukemogenesis, while the genes with lower gene-body H3K36me3 levels in normal cells yield obvious expression changes during leukemogenesis (ρ = -0.98, P = 9.30 × 10^-8). These findings are conserved in human lung/breast cancers and mouse CML, regardless of gene expression levels and gene lengths. Regulatory element analysis and Random Forest regression display that Hoxd13, Rara, Scl, Smad3, Smad4 and Tgif1 induce the up-regulation of genes with lower H3K36me3 levels (ρ = 0.97, P = 2.35 × 10^-56). Enrichment analysis shows that the differentially expressed genes with lower H3K36me3 levels are involved in leukemia-related pathways, such as leukocyte migration and regulation of leukocyte activation. Finally, six driver genes (Tp53, Wt1, Dnmt3a, Cacna1b, Phactr1 and Gbp4) with lower H3K36me3 levels are identified. Our analyses indicate that lower gene-body H3K36me3 levels may serve as a biomarker for the progression of CML.

Utilizing a Biology-Driven Approach to Map the Exposome in Health and Disease: An Essential Investment to Drive the Next Generation of Environmental Discovery

BACKGROUND

Recent developments in technologies have offered opportunities to measure the exposome with unprecedented accuracy and scale. However, because most investigations have targeted only a few exposures at a time, it is hypothesized that the majority of the environmental determinants of chronic diseases remain unknown.

OBJECTIVES

We describe a functional exposome concept and explain how it can leverage existing bioassays and high-resolution mass spectrometry for exploratory study. We discuss how such an approach can address well-known barriers to interpret exposures and present a vision of next-generation exposomics.

DISCUSSION

The exposome is vast. Instead of trying to capture all exposures, we can reduce the complexity by measuring the functional exposome-the totality of the biologically active exposures relevant to disease development-through coupling biochemical receptor-binding assays with affinity purification-mass spectrometry. We claim the idea of capturing exposures with functional biomolecules opens new opportunities to solve critical problems in exposomics, including low-dose detection, unknown annotations, and complex mixtures of exposures. Although novel, biology-based measurement can make use of the existing data processing and bioinformatics pipelines. The functional exposome concept also complements conventional targeted and untargeted approaches for understanding exposure-disease relationships.

CONCLUSIONS

Although measurement technology has advanced, critical technological, analytical, and inferential barriers impede the detection of many environmental exposures relevant to chronic-disease etiology. Through biology-driven exposomics, it is possible to simultaneously scale up discovery of these causal environmental factors. https://doi.org/10.1289/EHP8327.

MGN-Net: A multi-view graph normalizer for integrating heterogeneous biological network populations

With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity. Although modern network science opens new frontiers of analyzing connectivity patterns in such datasets, we still lack data-driven methods for extracting an integral connectional fingerprint of a multi-view graph population, let alone disentangling the typical from the atypical variations across the population samples. We present the multi-view graph normalizer network (MGN-Net²), a graph neural network based method to normalize and integrate a set of multi-view biological networks into a single connectional template that is centered, representative, and topologically sound. We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer's disease and Autism spectrum disorder patients. Additionally, by comparing the learned templates of healthy and disordered populations, we show that MGN-Net significantly outperforms conventional network integration methods across extensive experiments in terms of producing the most centered templates, recapitulating unique traits of populations, and preserving the complex topology of biological networks. Our evaluations showed that MGN-Net is powerfully generic and easily adaptable in design to different graph-based problems such as identification of relevant connections, normalization and integration.

Genetic Diversity of Blueberry Genotypes Estimated by Antioxidant Properties and Molecular Markers

Blueberries (Vaccinium spp.) have gained much attention worldwide because of their potential health benefits and economic importance. Genetic diversity was estimated in blueberry hybrids, wild clones and cultivars by their antioxidant efficacy, total phenolic and flavonoid contents, and express sequence tag–simple sequence repeat (SSR) (EST–SSR), genomic (G)–SSR and express sequence tag–polymerase chain reaction (EST–PCR) markers. Wide diversity existed among the genotypes for antioxidant properties, with the highest variation for DPPH radical scavenging activity (20-fold), followed by the contents of total flavonoids (16-fold) and phenolics (3.8-fold). Although a group of 11 hybrids generated the maximum diversity for antioxidant activity (15-fold), wild clones collected from Quebec, Canada, had the maximum variation for total phenolic (2.8-fold) and flavonoid contents (6.9-fold). Extensive genetic diversity was evident from Shannon’s index (0.34 for EST–SSRs, 0.29 for G–SSR, 0.26 for EST–PCR) and expected heterozygosity (0.23 for EST–SSR, 0.19 for G–SSR, 0.16 for EST–PCR). STRUCTURE analysis separated the genotypes into three groups, which were in agreement with principal coordinate and neighbour-joining analyses. Molecular variance suggested 19% variation among groups and 81% among genotypes within the groups. Clustering based on biochemical data and molecular analysis did not coincide, indicating a random distribution of loci in the blueberry genome, conferring antioxidant properties. However, the stepwise multiple regression analysis (SMRA) revealed that 17 EST–SSR, G–SSR and EST–PCR markers were associated with antioxidant properties. The study is valuable to breeding and germplasm conservation programs.

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analysis of transcriptomes. It uses high-throughput next-generation sequencing technologies and has revolutionized our understanding of the complexity and dynamics of whole transcriptomes.In this chapter, we recall the key developments in transcriptome analysis and dissect the different steps of the general workflow that can be run by users to design and perform a mRNA-seq experiment as well as to process mRNA-seq data obtained by the Illumina technology. The chapter proposes guidelines for completing a mRNA-seq study properly and makes available recommendations for best practices based on recent literature and on the latest developments in technology and algorithms. We also remark the large number of choices available (especially for bioinformatic data analysis) in front of which the scientist may be in trouble.In the last part of the chapter we discuss the new frontiers of single-cell RNA-seq and isoform sequencing by long read technology.

The Landscape of Emerging Randomized Clinical Trial Evidence for COVID-19 Disease Stages: A Systematic Review of Global Trial Registries

Purpose

A multitude of randomized controlled trials (RCTs) have emerged in response to the novel coronavirus disease (COVID-19) pandemic. Understanding the distribution of trials among various settings is important to guide future research priorities and efforts. The purpose of this review was to describe the emerging evidence base of COVID-19 RCTs by stages of disease progression, from pre-exposure to hospitalization.

Methods

We collated trial data across international registries: ClinicalTrials.gov; International Standard Randomised Controlled Trial Number Registry; Chinese Clinical Trial Registry; Clinical Research Information Service; EU Clinical Trials Register; Iranian Registry of Clinical Trials; Japan Primary Registries Network; German Clinical Trials Register (up to 7 October 2020). Active COVID-19 RCTs in international registries were eligible for inclusion. We extracted trial status, intervention(s), control, sample size, and clinical context to generate descriptive frequencies, network diagram illustrations, and statistical analyses including odds ratios and the Mann–Whitney U-test.

Results

Our search identified 11503 clinical trials registered for COVID-19 and identified 2388 RCTs. After excluding 45 suspended RCTs and 480 trials with unclear or unreported disease stages, 1863 active RCTs were included and categorized into four broad disease stages: pre-exposure (n=107); post-exposure (n=208); outpatient treatment (n=266); hospitalization, including the intensive care unit (n=1376). Across all disease stages, most trials had two arms (n=1500/1863, 80.52%), most often included (hydroxy)chloroquine (n=271/1863, 14.55%) and were US-based (n=408/1863, 21.90%). US-based trials had lower odds of including (hydroxy)chloroquine than trials in other countries (OR: 0.63, 95% CI: 0.45–0.90) and similar odds of having two arms compared to other geographic regions (OR: 1.05, 95% CI: 0.80–1.38).

Conclusion

There is a marked difference in the number of trials across settings, with limited studies on non-hospitalized persons. Focus on pre- and post-exposure, and outpatients, is worthwhile as a means of reducing infections and lessening the health, social, and economic burden of COVID-19.

Advances in the Bioinformatics Knowledge of mRNA Polyadenylation in Baculovirus Genes

Baculoviruses are a group of insect viruses with large circular dsDNA genomes exploited in numerous biotechnological applications, such as the biological control of agricultural pests, the expression of recombinant proteins or the gene delivery of therapeutic sequences in mammals, among others. Their genomes encode between 80 and 200 proteins, of which 38 are shared by all reported species. Thanks to multi-omic studies, there is remarkable information about the baculoviral proteome and the temporality in the virus gene expression. This allows some functional elements of the genome to be very well described, such as promoters and open reading frames. However, less information is available about the transcription termination signals and, consequently, there are still imprecisions about what are the limits of the transcriptional units present in the baculovirus genomes and how is the processing of the 3′ end of viral mRNA. Regarding to this, in this review we provide an update about the characteristics of DNA signals involved in this process and we contribute to their correct prediction through an exhaustive analysis that involves bibliography information, data mining, RNA structure and a comprehensive study of the core gene 3′ ends from 180 baculovirus genomes.

Transcriptome Sequencing and Development of Novel Genic SSR Markers From Pistacia vera L

In this study, we aimed to develop novel genic simple sequence repeat (eSSR) markers and to study phylogenetic relationship among Pistacia species. Transcriptome sequencing was performed in different tissues of Siirt and Atl cultivars of pistachio (Pistacia vera). A total of 37.5-Gb data were used in the assembly. The number of total contigs and unigenes was calculated as 98,831, and the length of N50 was 1,333 bp after assembly. A total of 14,308 dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexanucleotide SSR motifs (4–17) were detected, and the most abundant SSR repeat types were trinucleotide (29.54%), dinucleotide (24.06%), hexanucleotide (20.67%), pentanucleotide (18.88%), and tetranucleotide (6.85%), respectively. Overall 250 primer pairs were designed randomly and tested in eight Pistacia species for amplification. Of them, 233 were generated polymerase chain reaction products in at least one of the Pistacia species. A total of 55 primer pairs that had amplifications in all tested Pistacia species were used to characterize 11 P. vera cultivars and 78 wild Pistacia genotypes belonging to nine Pistacia species (P. khinjuk, P. eurycarpa, P. atlantica, P. mutica, P. integerrima, P. chinensis, P. terebinthus, P. palaestina, and P. lentiscus). A total of 434 alleles were generated from 55 polymorphic eSSR loci with an average of 7.89 alleles per locus. The mean number of effective allele was 3.40 per locus. Polymorphism information content was 0.61, whereas observed (Ho) and expected heterozygosity (He) values were 0.39 and 0.65, respectively. UPGMA (unweighted pair-group method with arithmetic averages) and STRUCTURE analysis divided 89 Pistacia genotypes into seven populations. The closest species to P. vera was P. khinjuk. P. eurycarpa was closer P. atlantica than P. khinjuk. P. atlantica–P. mutica and P. terebinthus–P. palaestina pairs of species were not clearly separated from each other, and they were suggested as the same species. The present study demonstrated that eSSR markers can be used in the characterization and phylogenetic analysis of Pistacia species and cultivars, as well as genetic linkage mapping and QTL (quantitative trait locus) analysis.

Evaluating the consistency of large-scale pharmacogenomic studies

Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data.

Heparanase: Historical Aspects and Future Perspectives

Heparanase is an endo-β-glucuronidase that cleaves at a limited number of internal sites the glycosaminoglycan heparan sulfate (HS). Heparanase enzymatic activity was first reported in 1975 and by 1983 evidence was beginning to emerge that the enzyme was a facilitator of tumor metastasis by cleaving HS chains present in blood vessel basement membranes and, thereby, aiding the passage of tumor cells through blood vessel walls. Due to a range of technical difficulties, it took another 16 years before heparanase was cloned and characterized in 1999 and a further 14 years before the crystal structure of the enzyme was solved. Despite these substantial deficiencies, there was steady progress in our understanding of heparanase long before the enzyme was fully characterized. For example, it was found as early as 1984 that activated T cells upregulate heparanase expression, like metastatic tumor cells, and the enzyme aids the entry of T cells and other leukocytes into inflammatory sites. Furthermore, it was discovered in 1989 that heparanase releases pre-existing growth factors and cytokines associated with HS in the extracellular matrix (ECM), the liberated growth factors/cytokines enhancing angiogenesis and wound healing. There were also the first hints that heparanase may have functions other than enzymatic activity, in 1995 it being reported that under certain conditions the enzyme could act as a cell adhesion molecule. Also, in the same year PI-88 (Muparfostat), the first heparanase inhibitor to reach and successfully complete a Phase III clinical trial was patented.Nevertheless, the cloning of heparanase (also known as heparanase-1) in 1999 gave the field an enormous boost and some surprises. The biggest surprise was that there is only one heparanase encoding gene in the mammalian genome, despite earlier research, based on substrate specificity, suggesting that there are at least three different heparanases. This surprising conclusion has remained unchanged for the last 20 years. It also became evident that heparanase is a family 79 glycoside hydrolase that is initially produced as a pro-enzyme that needs to be processed by proteases to form an enzymatically active heterodimer. A related molecule, heparanase-2, was also discovered that is enzymatically inactive but, remarkably, recently has been shown to inhibit heparanase-1 activity as well as acting as a tumor suppressor that counteracts many of the pro-tumor properties of heparanase-1.The early claim that heparanase plays a key role in tumor metastasis, angiogenesis and inflammation has been confirmed by many studies over the last 20 years. In fact, heparanase expression is enhanced in all major cancer types, namely carcinomas, sarcomas, and hematological malignancies, and correlates with increased metastasis and poor prognosis. Also, there is mounting evidence that heparanase plays a central role in the induction of inflammation-associated cancers. The enzymatic activity of heparanase has also emerged in unexpected situations, such as in the spread of HS-binding viruses and in Type-1 diabetes where the destruction of intracellular HS in pancreatic insulin-producing beta cells precipitates diabetes. But the most extraordinary recent discoveries have been with the realization that heparanase can exert a range of biological activities that are independent of its enzymatic function, most notably activation of several signaling pathways and being a transcription factor that controls methylation of histone tails. Collectively, these data indicate that heparanase is a truly multifunctional protein that has the additional property of cleaving HS chains and releasing from ECM and cell surfaces hundreds of HS-binding proteins with a plethora of functional consequences. Clearly, there are many unique features of this intriguing molecule that still remain to be explored and are highlighted in this Chapter.

Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era

Abiotic stresses are among the principal limiting factors for productivity in agriculture. In the current era of continuous climate changes, the understanding of the molecular aspects involved in abiotic stress response in plants is a priority. The rise of -omics approaches provides key strategies to promote effective research in the field, facilitating the investigations from reference models to an increasing number of species, tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular aspects involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining and integration, and necessary for extracting valuable information and for comparative efforts, paving the way to the modeling of the involved processes. We provide here an overview of bioinformatics resources for research on plant abiotic stresses, describing collections from -omics efforts in the field, ranging from raw data to complete databases or platforms, highlighting opportunities and still open challenges in abiotic stress research based on -omics technologies.

Identification of coronavirus sequences in carp cDNA from Wuhan, China

Severe acute respiratory syndrome (SARS)‐like coronavirus sequences were identified in two separate complementary DNA (cDNA) pools. The first pool was from a Carassius auratus (crusian carp) cell line and the second was from Ctenopharyngodon idella (grass carp) head kidney tissue. BLAST analysis suggests that these sequences belong to SARS‐like coronaviruses, and that they are not evolutionarily conserved in other species. Investigation of the submitting laboratories revealed that two laboratories from the Institute of Hydrobiology at the Chinese Academy of Sciences in Wuhan, China performed the research and submitted the cDNA libraries to GenBank. This institution is very close in proximity to the Wuhan South China Seafood Wholesale Market where SARS‐CoV‐2 first amplified in the human population. It is possible that these sequences are an artifact of the bioinformatics pipeline that was used. It is also possible that SARS‐like coronaviruses are a common environmental pathogen in the region that may be in aquatic habitats.

SARS‐like coronavirus sequences were found in two carp cDNA pools from separate laboratories in Wuhan, China.

Compacta: a fast contig clustering tool for de novo assembled transcriptomes

Background

RNA-Seq is the preferred method to explore transcriptomes and to estimate differential gene expression. When an organism has a well-characterized and annotated genome, reads obtained from RNA-Seq experiments can be directly mapped to that genome to estimate the number of transcripts present and relative expression levels of these transcripts. However, for unknown genomes, de novo assembly of RNA-Seq reads must be performed to generate a set of contigs that represents the transcriptome. These contig sets contain multiple transcripts, including immature mRNAs, spliced transcripts and allele variants, as well as products of close paralogs or gene families that can be difficult to distinguish. Thus, tools are needed to select a set of less redundant contigs to represent the transcriptome for downstream analyses. Here we describe the development of Compacta to produce contig sets from de novo assemblies.

Results

Compacta is a fast and flexible computational tool that allows selection of a representative set of contigs from de novo assemblies. Using a graph-based algorithm, Compacta groups contigs into clusters based on the proportion of shared reads. The user can determine the minimum coverage of the contigs to be clustered, as well as a threshold for the proportion of shared reads in the clustered contigs, thus providing a dynamic range of transcriptome compression that can be adapted according to experimental aims. We compared the performance of Compacta against state of the art clustering algorithms on assemblies from Arabidopsis, mouse and mango, and found that Compacta yielded more rapid results and had competitive precision and recall ratios. We describe and demonstrate a pipeline to tailor Compacta parameters to specific experimental aims.

Conclusions

Compacta is a fast and flexible algorithm for the determination of optimum contig sets that represent the transcriptome for downstream analyses.

Unveiling ncRNA regulatory axes in atherosclerosis progression

Completion of the human genome sequencing project highlighted the richness of the cellular RNA world, and opened the door to the discovery of a plethora of short and long non-coding RNAs (the dark transcriptome) with regulatory or structural potential, which shifted the balance of pathological gene alterations from coding to non-coding RNAs. Thus, disease risk assessment currently has to also evaluate the expression of new RNAs such as small micro RNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), competing endogenous RNAs (ceRNAs), retrogressed elements, 3'UTRs of mRNAs, etc. We are interested in the pathogenic mechanisms of atherosclerosis (ATH) progression in patients suffering Chronic Kidney Disease, and in this review, we will focus in the role of the dark transcriptome (non-coding RNAs) in ATH progression. We will focus in miRNAs and in the formation of regulatory axes or networks with their mRNA targets and with the lncRNAs that function as miRNA sponges or competitive inhibitors of miRNA activity. In this sense, we will pay special attention to retrogressed genomic elements, such as processed pseudogenes and Alu repeated elements, that have been recently seen to also function as miRNA sponges, as well as to the use or miRNA derivatives in gene silencing, anti-ATH therapies. Along the review, we will discuss technical developments associated to research in lncRNAs, from sequencing technologies to databases, repositories and algorithms to predict miRNA targets, as well as new approaches to miRNA function, such as integrative or enrichment analysis and their potential to unveil RNA regulatory networks.

Recent advances in Schistosoma mekongi ecology, transcriptomics and proteomics of relevance to snail control

Mekong schistosomiasis caused by Schistosoma mekongi is a public health problem that occurs along the border between southern Laos and northern Cambodia. Given its restricted distribution and low prevalence, eventual eradication via an effective control program can be expected to be successful. To achieve this goal detailed knowledge of its basic biology, molecular biology, biochemistry, and pathology is urgently required. In this regard, recent studies on transcriptome analysis of adult male and female S. mekongi worms, and proteome analysis of developmental stages have been reported and are discussed here. The biology, habitat, and distribution of the snail intermediate host Neotricula aperta, which are factors in disease transmission, are discussed in this review. These have initiated renewed interest in S. mekongi research and contributed promising data that will be utilized in the generation of effective control and prevention strategies.

Genetic assessment of the internal transcribed spacer region (ITS1.2) in Mangifera indica L. landraces

Mango (Mangifera indica) is one of the most important tropical fruits in the world. Twenty-two genotypes of native mangoes from different regions of southern Iran (Hormozgan and Kerman) were collected and analyzed for the ribosomal genes. GC content was found to be 55.5%. Fu and Li's D* test statistic (0.437), Fu and Li's F* test statistic (0.500) and Tajima's D (1.801) were positive and nonsignificant. A total of 769 positions were identified (319 with insertion or deletion including 250 polymorphic and 69 monomorphic loci; 450 loci without any insertion or deletion including 35 Singletons and 22 haplotypes). Nucleotide diversity of 0.309 and a high genetic differentiation including Chi square of 79.8; P value of 0.3605 and df value of 76 was observed among mango genotypes studied. The numerical value of the ratio dN/dS (0.45) indicated a pure selection in the examined gene and the absence of any key changes. Cluster analysis differentiated the mango used in this research (M. indica L.) into two genotypes but could not differentiate their geographical locations. The results of this study indicated that a high genetic distance exists between HajiGholam (Manojan) and Arbabi (Rodan) genotypes and showed higher genetic diversity in mango of Rodan region. Results of present study suggested that for successful breeding, the genotypes of Rodan region mango especially Arbabi mango can be used as a gene donor and ITS can be a suitable tool for genetic evaluations of inter and intra species.

Cap Analysis of Gene Expression (CAGE): A Quantitative and Genome-Wide Assay of Transcription Start Sites

Cap analysis of gene expression (CAGE) is an approach to identify and monitor the activity (transcription initiation frequency) of transcription start sites (TSSs) at single base-pair resolution across the genome. It has been effectively used to identify active promoter and enhancer regions in cancer cells, with potential utility to identify key factors to immunotherapy. Here, we overview a series of CAGE protocols and describe detailed experimental steps of the latest protocol based on the Illumina sequencing platform; both experimental steps (see Subheadings 3.1-3.11) and computational processing steps (see Subheadings 3.12-3.20) are described.