Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays

Recent progress in DNA data storage based on high-throughput DNA synthesis

DNA data storage has emerged as a solution for storing massive volumes of data by utilizing nucleic acids as a digital information medium. DNA offers exceptionally high storage density, long durability, and low maintenance costs compared to conventional storage media such as flash memory and hard disk drives. DNA data storage consists of the following steps: encoding, DNA synthesis (i.e., writing), preservation, retrieval, DNA sequencing (i.e., reading), and decoding. Out of these steps, DNA synthesis presents a bottleneck due to imperfect coupling efficiency, low throughput, and excessive use of organic solvents. Overcoming these challenges is essential to establish DNA as a viable data storage medium. In this review, we provide the overall process of DNA data storage, presenting the recent progress of each step. Next, we examine a detailed overview of DNA synthesis methods with an emphasis on their limitations. Lastly, we discuss the efforts to overcome the constraints of each method and their prospects.

Next generation plant biostimulants & genome sequencing strategies for sustainable agriculture development

The best environment for plant growth and development contains certain essential metabolites. A broad category of metabolites known as "plant biostimulants" (PBs) includes biomolecules such as proteins, carbohydrates, lipids, and other secondary metabolites related to groups of terpenes, specific nitrogen-containing compounds, and benzene ring-conjugated compounds. The formation of biomolecules depends on both biotic and abiotic factors, such as the release of PB by plants, animals, and microorganisms, or it can result from the control of temperature, humidity, and pressure in the atmosphere, in the case of humic substances (HSs). Understanding the genomic outputs of the concerned organism (may be plants or others than them) becomes crucial for identifying the underlying behaviors that lead to the synthesis of these complex compounds. For the purposes of achieving the objectives of sustainable agriculture, detailed research on PBs is essential because they aid in increasing yield and other growth patterns of agro-economic crops. The regulation of homeostasis in the plant-soil-microbe system for the survival of humans and other animals is mediated by the action of plant biostimulants, as considered essential for the growth of plants. The genomic size and gene operons for functional and regulation control have so far been revealed through technological implementations, but important gene annotations are still lacking, causing a delay in revealing the information. Next-generation sequencing techniques, such as nanopore, nanoball, and Illumina, are essential in troubleshooting the information gaps. These technical advancements have greatly expanded the candidate gene openings. The secondary metabolites being important precursors need to be studied in a much wider scale for accurate calculations of biochemical reactions, taking place inside and outside the synthesized living cell. The present review highlights the sequencing techniques to provide a foundation of opportunity generation for agricultural sustainability.

A highly sensitive biotin-based probe for small RNA northern blot and its application in dissecting miRNA function in pepper

Small RNAs play important roles in regulation of plant development and response to various stresses. Northern blot is an important technique in small RNA research. Isotope- and biotin- (or digoxigenin) labeled probes are frequently used in small RNA northern blot. However, isotope-based probe is limited by strict environmental regulation and availability in many places in the world while biotin-based probe is usually suffered from low sensitivity. In this study, we developed a T4 DNA polymerase-based method for incorporation of a cluster of 33 biotin-labeled C in small RNA probe (T4BC33 probe). T4BC33 probe reaches similar sensitivity as 32P-labeled probe in dot blot and small RNA northern blot experiments. Addition of locked nucleic acids in T4BC33 probe further enhanced its sensitivity in detecting low-abundance miRNAs. With newly developed northern blot method, expression of miR6027 and miR6149 family members was validated. Northern blot analysis also confirmed the successful application of virus-based miRNA silencing in pepper, knocking down accumulation of Can-miR6027a and Can-miR6149L. Importantly, further analysis showed that knocking-down Can-miR6027a led to upregulation of a nucleotide binding-leucine rich repeat domain protein coding gene (CaRLb1) and increased immunity against Phytophthora capsici in pepper leaves. Our study provided a highly sensitive and convenient method for sRNA research and identified new targets for genetic improvement of pepper immunity against P. capsici.

Quantitative transcriptomic and epigenomic data analysis: a primer

The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.

Wastewater microbial diversity versus molecular analysis at a glance: a mini-review

Microorganisms play a vital role in biological wastewater treatment by converting organic and toxic materials into harmless substances. Understanding microbial communities' structure, taxonomy, phylogeny, and metabolic activities is essential to improve these processes. Molecular microbial ecology employs molecular techniques to study community profiles and phylogenetic information since culture-dependent approaches have limitations in providing a comprehensive understanding of microbial diversity in a system. Genomic advancements such as DNA hybridization, microarray analysis, sequencing, and reverse sample genome probing have enabled the detailed characterization of microbial communities in wastewater treatment facilities. This mini-review summarizes the current state of knowledge on the diversity of microorganisms in wastewater treatment plants, emphasizing critical microbial processes such as nitrogen and phosphorus removal.

Genetic Databases and Gene Editing Tools for Enhancing Crop Resistance against Abiotic Stress

Abiotic stresses extensively reduce agricultural crop production globally. Traditional breeding technology has been the fundamental approach used to cope with abiotic stresses. The development of gene editing technology for modifying genes responsible for the stresses and the related genetic networks has established the foundation for sustainable agriculture against environmental stress. Integrated approaches based on functional genomics and transcriptomics are now expanding the opportunities to elucidate the molecular mechanisms underlying abiotic stress responses. This review summarizes some of the features and weblinks of plant genome databases related to abiotic stress genes utilized for improving crops. The gene-editing tool based on clustered, regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) has revolutionized stress tolerance research due to its simplicity, versatility, adaptability, flexibility, and broader applications. However, off-target and low cleavage efficiency hinder the successful application of CRISPR/Cas systems. Computational tools have been developed for designing highly competent gRNA with better cleavage efficiency. This powerful genome editing tool offers tremendous crop improvement opportunities, overcoming conventional breeding techniques' shortcomings. Furthermore, we also discuss the mechanistic insights of the CRISPR/Cas9-based genome editing technology. This review focused on the current advances in understanding plant species' abiotic stress response mechanism and applying the CRISPR/Cas system genome editing technology to develop crop resilience against drought, salinity, temperature, heavy metals, and herbicides.

More than Three Decades of Bm86: What We Know and Where to Go

Tick and tick-borne disease control have been a serious research focus for many decades. In a global climate of increasing acaricide resistance, host immunity against tick infestation has become a much-needed complementary strategy to common chemical control. From the earliest acquired resistance studies in small animal models to proof of concept in large production animals, it was the isolation, characterization, and final recombinant protein production of the midgut antigen Bm86 from the Australian cattle tick strain of Rhipicephalus (Boophilus) microplus (later reinstated as R. (B.) australis) that established tick subunit vaccines as a viable alternative in tick and tick-borne disease control. In the past 37 years, this antigen has spawned numerous tick subunit vaccines (either Bm86-based or novel), and though we are still describing its molecular structure and function, this antigen remains the gold standard for all tick vaccines. In this paper, advances in tick vaccine development over the past three decades are discussed alongside the development of biotechnology, where existing gaps and future directives in the field are highlighted.

Multi-Omics Profiling for Health

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment

Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.

From Genomics to Metagenomics in the Era of Recent Sequencing Technologies

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms obtained from a common habitat. Metagenomics and other "omics" disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. Through sampling the sequences of microbial genomes within a certain environment, metagenomics allows study of the functional metabolic capacity of a community as well as its structure based upon distribution and richness of species. Exponentially increasing number of microbiome literatures illustrate the importance of sequencing techniques which have allowed the expansion of microbial research into areas, including the human gut, antibiotics, enzymes, and more. This chapter illustrates how metagenomics field has evolved with the progress of sequencing technologies.Further, from this chapter, researchers will be able to learn about all current options for sequencing techniques and comparison of their cost and read statistics, which will be helpful for planning their own studies.

Establishing Linkages Among DNA Damage, Mutagenesis, and Genetic Diseases

DNA damage by chemicals, radiation, or oxidative stress leads to a mutational spectrum, which is complex because it is determined in part by lesion structure, the DNA sequence context of the lesion, lesion repair kinetics, and the type of cells in which the lesion is replicated. Accumulation of mutations may give rise to genetic diseases such as cancer and therefore understanding the process underlying mutagenesis is of immense importance to preserve human health. Chemical or physical agents that cause cancer often leave their mutational fingerprints, which can be used to back-calculate the molecular events that led to disease. To make a clear link between DNA lesion structure and the mutations a given lesion induces, the field of single-lesion mutagenesis was developed. In the last three decades this area of research has seen much growth in several directions, which we attempt to describe in this Perspective.

Contribution and Future of High-Throughput Transcriptomics in Battling Tuberculosis

While Tuberculosis (TB) infection remains a serious challenge worldwide, big data and “omic” approaches have greatly contributed to the understanding of the disease. Transcriptomics have been used to tackle a wide variety of queries including diagnosis, treatment evolution, latency and reactivation, novel target discovery, vaccine response or biomarkers of protection. Although a powerful tool, the elevated cost and difficulties in data interpretation may hinder transcriptomics complete potential. Technology evolution and collaborative efforts among multidisciplinary groups might be key in its exploitation. Here, we discuss the main fields explored in TB using transcriptomics, and identify the challenges that need to be addressed for a real implementation in TB diagnosis, prevention and therapy.

Global Analysis of Transcriptome and Translatome Revealed That Coordinated WNT and FGF Regulate the Carapacial Ridge Development of Chinese Soft-Shell Turtle

The turtle carapace is composed of severely deformed fused dorsal vertebrae, ribs, and bone plates. In particular, the lateral growth in the superficial layer of turtle ribs in the dorsal trunk causes an encapsulation of the scapula and pelvis. The recent study suggested that the carapacial ridge (CR) is a new model of epithelial–mesenchymal transition which is essential for the arrangement of the ribs. Therefore, it is necessary to explore the regulatory mechanism of carapacial ridge development to analyze the formation of the turtle shell. However, the current understanding of the regulatory network underlying turtle carapacial ridge development is poor due to the lack of both systematic gene screening at different carapacial ridge development stages and gene function verification studies. In this study, we obtained genome-wide gene transcription and gene translation profiles using RNA sequencing and ribosome nascent-chain complex mRNA sequencing from carapacial ridge tissues of Chinese soft-shell turtle at different development stages. A correlation analysis of the transcriptome and translatome revealed that there were 129, 670, and 135 codifferentially expressed genes, including homodirection and opposite-direction differentially expressed genes, among three comparison groups, respectively. The pathway enrichment analysis of codifferentially expressed genes from the Kyoto Encyclopedia of Genes and Genomes showed dynamic changes in signaling pathways involved in carapacial ridge development. Especially, the results revealed that the Wnt signaling pathway and MAPK signaling pathway may play important roles in turtle carapacial ridge development. In addition, Wnt and Fgf were expressed during the carapacial ridge development. Furthermore, we discovered that Wnt5a regulated carapacial ridge development through the Wnt5a/JNK pathway. Therefore, our studies uncover that the morphogenesis of the turtle carapace might function through the co-operation between conserved WNT and FGF signaling pathways. Consequently, our findings revealed the dynamic signaling pathways acting on the carapacial ridge development of Chinese soft-shell turtle and provided new insights into uncover the molecular mechanism underlying turtle shell morphogenesis.

MP3RNA-seq: Massively parallel 3' end RNA sequencing for high-throughput gene expression profiling and genotyping

Transcriptome deep sequencing (RNA-seq) has become a routine method for global gene expression profiling. However, its application to large-scale experiments remains limited by cost and labor constraints. Here we describe a massively parallel 3' end RNA-seq (MP3RNA-seq) method that introduces unique sample barcodes during reverse transcription to permit sample pooling immediately following this initial step. MP3RNA-seq allows for handling of hundreds of samples in a single experiment, at a cost of about $6 per sample for library construction and sequencing. MP3RNA-seq is effective for not only high-throughput gene expression profiling, but also genotyping. To demonstrate its utility, we applied MP3RNA-seq to 477 double haploid lines of maize. We identified 19,429 genes expressed in at least 50% of the lines and 35,836 high-quality single nucleotide polymorphisms for genotyping analysis. Armed with these data, we performed expression and agronomic trait quantitative trait locus (QTL) mapping and identified 25,797 expression QTLs for 15,335 genes and 21 QTLs for plant height, ear height, and relative ear height. We conclude that MP3RNA-seq is highly reproducible, accurate, and sensitive for high-throughput gene expression profiling and genotyping, and should be generally applicable to most eukaryotic species.

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.

DNA-Barcoded Fluorescence Microscopy for Spatial Omics

Innovation in genomics, transcriptomics, and proteomics research has created a plethora of state-of-the-art techniques such as nucleic acid sequencing and mass-spectrometry-based proteomics with paramount impact in the life sciences. While current approaches yield quantitative abundance analysis of biomolecules on an almost routine basis, coupling this high content to spatial information in a single cell and tissue context is challenging. Here, current implementations of spatial omics are discussed and recent developments in the field of DNA-barcoded fluorescence microscopy are reviewed. Light is shed on the potential of DNA-based imaging techniques to provide a comprehensive toolbox for spatial genomics and transcriptomics and discuss current challenges, which need to be overcome on the way to spatial proteomics using high-resolution fluorescence microscopy.

Differential expression of microRNA between normally developed and underdeveloped female worms of Schistosoma japonicum

Eggs produced by bisexual infected mature female worms (MF) of Schistosoma japonicum are important in the transmission of the parasite and responsible for the pathogenesis of schistosomiasis. The single-sex infected female worms (SF) cannot mature and do not produce normal eggs; also they do not induce severe damage to the host. In this study, the microRNA (miRNA) expression profiles of 25d MF and 25d SF were investigated through Solexa deep-sequencing technology to explore the developmental mechanisms of schistosome female worms. There were 36 differentially expressed miRNA, 20 up-regulated and 16 down-regulated found in MF/SF worms, including some development related miRNA such as bantam (ban), let-7, miR-124, miR-8, miR-1, miR-7. There were 166 target genes of up-regulated miRNA and 201 target genes of down-regulated miRNA after comparing the target gene prediction software results with RNA-Seq transcriptome results. Analysis of the target genes shows that different ones are involved in MF and SF worms in Gene Ontology terms, with a similar situation in KEGG. This observation indicates that different genes regulated by differentially expressed miRNA take part in MF and SF and lead to differential sexual status. This means that the sexual status of female worms is regulated by miRNA.

Insights Into the Albinism Mechanism for Two Distinct Color Morphs of Northern Snakehead, Channa argus Through Histological and Transcriptome Analyses

The great northern snakehead (Channa argus) is one of the most important economic and conservational fish in China. In this study, the melanocytes in the skin of two distinct color morphs C. argus were investigated and compared through employment of the microscopic analysis, hematoxylin and eosin (H&E) and Masson Fontana staining. Our results demonstrated the uneven distribution of melanocytes with extremely low density and most of them were in the state of aging or death. Meanwhile, there was no obvious pigment layer and melanocytes distribution pattern found in the albino-type (AT), while the melanocytes were evenly distributed with abundance in the bicolor-type (BT). The transcriptome analysis through Illumina HiSeq sequencing showed that a total of 34.93 Gb Clean Data was obtained, and Q30 base percentage reached 92.66%. The BT and AT northern snakeheads transcriptome data included a total of 56,039,701 and 60,410,063 clean reads (n = 3), respectively. In gene expression analyses, the sample correlation coefficients (r) were ranged between 0.92 and 1.00; the contribution of PC1 and PC2 were 50.25 and 13.73% by using PCA cluster analysis, the total number of DEGs were 1024 (559 up-regulated and 465 down-regulated), and the number of annotated DEGs was 767 (COG 172, KEGG 262, GO 288, SwissProt 548, Pfam 579 and NR 765). Additionally, 46,363 ± 873 and 44,947 ± 392 single nucleotide polymorphisms (SNPs) were compiled via genetic structure analysis, respectively. Ten key pigment-related genes were screened using qRT-PCR. And all of them revealed extremely higher expression levels in the skin of BT than those of AT. This is the first study to analyze the mechanism of albino characteristics of Channa via histology and transcriptomics, and also provide the oretical and practical support for the protection and development of germplasm resources for C. argus.

GSVA score reveals molecular signatures from transcriptomes for biomaterials comparison

Two in silico methodologies were implemented to reveal the molecular signatures of inorganic hydroxyapatite and β-TCP materials from a transcriptome database to compare biomaterials. To test this new methodology, we choose the array E-MTAB-7219, which contains the transcription profile of osteoblastic cell line seeded onto 15 different biomaterials up to 48 hr. The expansive potential of the methodology was tested from the construction of customized signatures. We present, for the first time, a methodology to compare the performance of different biomaterials using the transcriptome profile of the cell through the Gene set variation analysis (GSVA) score. To test this methodology, we implemented two methods based on MSigDB collections, using all the collections and sub-collections except the Hallmark collection, which was used in the second method. The result of this analysis provided an initial understanding of biomaterial grouping based on the cell transcriptional landscape. The comparison using GSVA score combined efforts and expand the potential to compare biomaterials using transcriptome profile. Altogether, our results provide a better understanding of the comparison of different biomaterials and suggest a possibility of the new methodology be applied to the prospection of new biomaterials.

The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.

Synonymous genome recoding: a tool to explore microbial biology and new therapeutic strategies

Synthetic genome recoding is a new means of generating designed organisms with altered phenotypes. Synonymous mutations introduced into the protein coding region tolerate modifications in DNA or mRNA without modifying the encoded proteins. Synonymous genome-wide recoding has allowed the synthetic generation of different small-genome viruses with modified phenotypes and biological properties. Recently, a decreased cost of chemically synthesizing DNA and improved methods for assembling DNA fragments (e.g. lambda red recombination and CRISPR-based editing) have enabled the construction of an Escherichia coli variant with a 4-Mb synthetic synonymously recoded genome with a reduced number of sense codons (n = 59) encoding the 20 canonical amino acids. Synonymous genome recoding is increasing our knowledge of microbial interactions with innate immune responses, identifying functional genome structures, and strategically ameliorating cis-inhibitory signaling sequences related to splicing, replication (in eukaryotes), and complex microbe functions, unraveling the relevance of codon usage for the temporal regulation of gene expression and the microbe mutant spectrum and adaptability. New biotechnological and therapeutic applications of this methodology can easily be envisaged. In this review, we discuss how synonymous genome recoding may impact our knowledge of microbial biology and the development of new and better therapeutic methodologies.

Holographic molecular binding assays

We demonstrate that holographic particle characterization can directly detect binding of proteins to functionalized colloidal probe particles by monitoring the associated change in the particles' size. This label-free molecular binding assay uses in-line holographic video microscopy to measure the diameter and refractive index of individual probe spheres as they flow down a microfluidic channel. Pooling measurements on 104 particles yields the population-average diameter with an uncertainty smaller than 0.5 nm, which is sufficient to detect sub-monolayer coverage by bound proteins. We demonstrate this method by monitoring binding of NeutrAvidin to biotinylated spheres and binding of immunoglobulin G to spheres functionalized with protein A.

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery

The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on “-omics” have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

Long walk to genomics: History and current approaches to genome sequencing and assembly

Genomes represent the starting point of genetic studies. Since the discovery of DNA structure, scientists have devoted great efforts to determine their sequence in an exact way. In this review we provide a comprehensive historical background of the improvements in DNA sequencing technologies that have accompanied the major milestones in genome sequencing and assembly, ranging from early sequencing methods to Next-Generation Sequencing platforms. We then focus on the advantages and challenges of the current technologies and approaches, collectively known as Third Generation Sequencing. As these technical advancements have been accompanied by progress in analytical methods, we also review the bioinformatic tools currently employed in de novo genome assembly, as well as some applications of Third Generation Sequencing technologies and high-quality reference genomes.

Genetics and Extracellular Vesicles of Pediatrics Sleep Disordered Breathing and Epilepsy

Sleep remains one of the least understood phenomena in biology, and sleep disturbances are one of the most common behavioral problems in childhood. The etiology of sleep disorders is complex and involves both genetic and environmental factors. Epilepsy is the most popular childhood neurological condition and is characterized by an enduring predisposition to generate epileptic seizures, and the neurobiological, cognitive, psychological, and social consequences of this condition. Sleep and epilepsy are interrelated, and the importance of sleep in epilepsy is less known. The state of sleep also influences whether a seizure will occur at a given time, and this differs considerably for various epilepsy syndromes. The development of epilepsy has been associated with single or multiple gene variants. The genetics of epilepsy is complex and disorders exhibit significant genetic heterogeneity and variability in the expressivity of seizures. Phenobarbital (PhB) is the most widely used antiepileptic drug. With its principal mechanism of action to prolong the opening time of the γ-aminobutyric acid (GABA)-A receptor-associated chloride channel, it enhances chloride anion influx into neurons, with subsequent hyperpolarization, thereby reducing excitability. Enzymes that metabolize pharmaceuticals including PhB are well known for having genetic polymorphisms that contribute to adverse drug–drug interactions. PhB metabolism is highly dependent upon the cytochrome P450 (CYP450) and genetic polymorphisms can lead to variability in active drug levels. The highly polymorphic CYP2C19 isozymes are responsible for metabolizing a large portion of routinely prescribed drugs and variants contribute significantly to adverse drug reactions and therapeutic failures. A limited number of CYP2C19 single nucleotide polymorphisms (SNPs) are involved in drug metabolism. Extracellular vesicles (EVs) are circular membrane fragments released from the endosomal compartment as exosomes are shed from the surfaces of the membranes of most cell types. Increasing evidence indicated that EVs play a pivotal role in cell-to-cell communication. Theses EVs may play an important role between sleep, epilepsy, and treatments. The discovery of exosomes provides potential strategies for the diagnosis and treatment of many diseases including neurocognitive deficit. The aim of this study is to better understand and provide further knowledge about the metabolism and interactions between phenobarbital and CYP2C19 polymorphisms in children with epilepsy, interplay between sleep, and EVs. Understanding this interplay between epilepsy and sleep is helpful in the optimal treatment of all patients with epileptic seizures. The use of genetics and extracellular vesicles as precision medicine for the diagnosis and treatment of children with sleep disorder will improve the prognosis and the quality of life in patients with epilepsy.

Omics Analysis for Dinoflagellates Biology Research

Dinoflagellates are important primary producers for marine ecosystems and are also responsible for certain essential components in human foods. However, they are also notorious for their ability to form harmful algal blooms, and cause shellfish poisoning. Although much work has been devoted to dinoflagellates in recent decades, our understanding of them at a molecular level is still limited owing to some of their challenging biological properties, such as large genome size, permanently condensed liquid-crystalline chromosomes, and the 10-fold lower ratio of protein to DNA than other eukaryotic species. In recent years, omics technologies, such as genomics, transcriptomics, proteomics, and metabolomics, have been applied to the study of marine dinoflagellates and have uncovered many new physiological and metabolic characteristics of dinoflagellates. In this article, we review recent application of omics technologies in revealing some of the unusual features of dinoflagellate genomes and molecular mechanisms relevant to their biology, including the mechanism of harmful algal bloom formations, toxin biosynthesis, symbiosis, lipid biosynthesis, as well as species identification and evolution. We also discuss the challenges and provide prospective further study directions and applications of dinoflagellates.

Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions

A number of specialized clustering methods have been developed so far for the accurate analysis of single-cell RNA-sequencing (scRNA-seq) expression data, and several reports have been published documenting the performance measures of these clustering methods under different conditions. However, to date, there are no available studies regarding the systematic evaluation of the performance measures of the clustering methods taking into consideration the sample size and cell composition of a given scRNA-seq dataset. Herein, a comprehensive performance evaluation study of 11 selected scRNA-seq clustering methods was performed using synthetic datasets with known sample sizes and number of subpopulations, as well as varying levels of transcriptome complexity. The results indicate that the overall performance of the clustering methods under study are highly dependent on the sample size and complexity of the scRNA-seq dataset. In most of the cases, better clustering performances were obtained as the number of cells in a given expression dataset was increased. The findings of this study also highlight the importance of sample size for the successful detection of rare cell subpopulations with an appropriate clustering tool.

Evolving Clinical Applications of Tissue Transcriptomics in Kidney Disease

Nephrotic syndrome is classically categorized by the histopathology with examples including focal segmental glomerulosclerosis (FSGS) and minimal change disease. Pediatric patients are also classified by whether their nephrotic syndrome is sensitive to, dependent on, or resistant to steroids. However, this traditional classification system overlooks the frequent clinical conundrum when, for example, one patient with FSGS responds briskly to steroids, and another quickly progresses to end stage kidney disease despite therapy. Two patients may have similar histopathologic appearances on kidney biopsy but entirely different clinical characteristics, rates of progression, and treatment responses. Transcriptional regulation of gene activation and posttranscriptional processing of mRNA may drive the unique and heterogeneous phenotypes which are incompletely understood in kidney disease and are a recent focus of research. Gene expression profiles provide insight on active transcriptional programs in tissues, are being used to understand biologic mechanisms of progressive chronic kidney disease, and may help to identify patients with shared mechanisms of kidney damage. This mini-review discusses clinically relevant techniques of bulk tissue and single cell transcriptomics, as well as strengths and limitations of each methodology. Further, we summarize recent examples in kidney research achieved through transcriptomics. This review offers an outlook on the role of transcriptomics in an integrative systems biology model with the goal of defining unique disease subgroups, finding targets for drug development, and aligning the right drug with the right patient.

Applications of the Soil, Plant and Rumen Microbiomes in Pastoral Agriculture

The production of dairy, meat, and fiber by ruminant animals relies on the biological processes occurring in soils, forage plants, and the animals' rumens. Each of these components has an associated microbiome, and these have traditionally been viewed as distinct ecosystems. However, these microbiomes operate under similar ecological principles and are connected via water, energy flows, and the carbon and nitrogen nutrient cycles. Here, we summarize the microbiome research that has been done in each of these three environments (soils, forage plants, animals' rumen) and investigate what additional benefits may be possible through understanding the interactions between the various microbiomes. The challenge for future research is to enhance microbiome function by appropriate matching of plant and animal genotypes with the environment to improve the output and environmental sustainability of pastoral agriculture.

Sequencing in High Definition Drives a Changing Worldview of the Epigenome

Single-molecule sequencing approaches have transformed the study of the human epigenome, accelerating efforts to describe genome function beyond the sequences that encode proteins. The post-genome era has ignited strong interest in the noncoding genome and profiling epigenetic signatures genome-wide have been critical for the identification and characterization of noncoding gene-regulatory sequences in various cellular and developmental contexts. These technologies enable quantification of epigenetic marks through digital assessment of DNA fragments. With the capacity to probe both the DNA sequence and count DNA molecules at once with unparalleled throughput and sensitivity, deep sequencing has been especially transformative to the study of DNA methylation. This review will discuss advances in epigenome profiling with a particular focus on DNA methylation, highlighting how deep sequencing has generated new insights into the role of DNA methylation in gene regulation. Technical aspects of profiling DNA methylation, remaining challenges, and the future of DNA methylation sequencing are also described.

Carbon-based archiving: current progress and future prospects of DNA-based data storage

The information explosion has led to a rapid increase in the amount of data requiring physical storage. However, in the near future, existing storage methods (i.e., magnetic and optical media) will be insufficient to store these exponentially growing data. Therefore, data scientists are continually looking for better, more stable, and space-efficient alternatives to store these huge datasets. Because of its unique biological properties, highly condensed DNA has great potential to become a storage material for the future. Indeed, DNA-based data storage has recently emerged as a promising approach for long-term digital information storage. This review summarizes state-of-the-art methods, including digital-to-DNA coding schemes and the media types used in DNA-based data storage, and provides an overview of recent progress achieved in this field and its exciting future.

Molecular Characterization of Primary Human Astrocytes Using Digital Gene Expression Analysis

Objective

Astrocyte dysfunctions are related to several central nervous system (CNS) pathologies. Transcriptomic profiling of human mRNAs to investigate astrocyte functions may provide the basic molecular-biological data pertaining to the cellular activities of astrocytes.

Methods

Human Primary astrocytes (HPAs) and human neural stem cell line (HB1.F3) were used for differential digital gene analysis. In this study, a massively parallel sequencing platform, next-generation sequencing (NGS), was used to obtain the digital gene expression (DGE) data from HPAs. A comparative analysis of the DGE from HPA and HB1.F3 cells was performed. Sequencing was performed using NGS platform, and subsequently, bioinformatic analyses were implemented to reveal the identity of the pathways, relatively up- or down-regulated in HPA cells.

Results

The top, novel canonical pathways up-regulated in HPA cells than in the HB1.F3 cells were "Cyclins and cell cycle regulation," "Integrin signaling," "Regulation of eIF4 and p70S6K signaling," "Wnt/β-catenin signaling," "mTOR signaling," "Aryl hydrocarbon receptor signaling," "Hippo signaling," "RhoA signaling," "Signaling by Rho family GTPases," and "Glioma signaling" pathways. The down-regulated pathways were "Cell cycle: G1/S checkpoint regulation," "eIF2 signaling," "Cell cycle: G2/M DNA damage checkpoint regulation," "Telomerase signaling," "RhoGDI signaling," "NRF2-mediated oxidative stress response," "ERK/MAPK signaling," "ATM signaling," "Pancreatic adenocarcinoma signaling," "VEGF signaling," and "Role of CHK proteins in cell cycle checkpoint control" pathways.

Conclusion

This study would be a good reference to understand astrocyte functions at the molecular level, and to develop a diagnostic test, based on the DGE pattern of astrocytes, as a powerful, new clinical tool in many CNS diseases.

Reviving the Transcriptome Studies: An Insight Into the Emergence of Single-Molecule Transcriptome Sequencing

Advances in transcriptomics have provided an exceptional opportunity to study functional implications of the genetic variability. Technologies such as RNA-Seq have emerged as state-of-the-art techniques for transcriptome analysis that take advantage of high-throughput next-generation sequencing. However, similar to their predecessors, these approaches continue to impose major challenges on full-length transcript structure identification, primarily due to inherent limitations of read length. With the development of single-molecule sequencing (SMS) from PacBio, a growing number of studies on the transcriptome of different organisms have been reported. SMS has emerged as advantageous for comprehensive genome annotation including identification of novel genes/isoforms, long non-coding RNAs and fusion transcripts. This approach can be used across a broad spectrum of species to better interpret the coding information of the genome, and facilitate the biological function study. We provide an overview of SMS platform and its diverse applications in various biological studies, and our perspective on the challenges associated with the transcriptome studies.

A theoretical analysis of single molecule protein sequencing via weak binding spectra

We propose and theoretically study an approach to massively parallel single molecule peptide sequencing, based on single molecule measurement of the kinetics of probe binding (Havranek, et al., 2013) to the N-termini of immobilized peptides. Unlike previous proposals, this method is robust to both weak and non-specific probe-target affinities, which we demonstrate by applying the method to a range of randomized affinity matrices consisting of relatively low-quality binders. This suggests a novel principle for proteomic measurement whereby highly non-optimized sets of low-affinity binders could be applicable for protein sequencing, thus shifting the burden of amino acid identification from biomolecular design to readout. Measurement of probe occupancy times, or of time-averaged fluorescence, should allow high-accuracy determination of N-terminal amino acid identity for realistic probe sets. The time-averaged fluorescence method scales well to weakly-binding probes with dissociation constants of tens or hundreds of micromolar, and bypasses photobleaching limitations associated with other fluorescence-based approaches to protein sequencing. We argue that this method could lead to an approach with single amino acid resolution and the ability to distinguish many canonical and modified amino acids, even using highly non-optimized probe sets. This readout method should expand the design space for single molecule peptide sequencing by removing constraints on the properties of the fluorescent binding probes.

A new reporter design based on DNA origami nanostructures for quantification of short oligonucleotides using microbeads

The DNA origami technique has great potential for the development of brighter and more sensitive reporters for fluorescence based detection schemes such as a microbead-based assay in diagnostic applications. The nanostructures can be programmed to include multiple dye molecules to enhance the measured signal as well as multiple probe strands to increase the binding strength of the target oligonucleotide to these nanostructures. Here we present a proof-of-concept study to quantify short oligonucleotides by developing a novel DNA origami based reporter system, combined with planar microbead assays. Analysis of the assays using the VideoScan digital imaging platform showed DNA origami to be a more suitable reporter candidate for quantification of the target oligonucleotides at lower concentrations than a conventional reporter that consists of one dye molecule attached to a single stranded DNA. Efforts have been made to conduct multiplexed analysis of different targets as well as to enhance fluorescence signals obtained from the reporters. We therefore believe that the quantification of short oligonucleotides that exist in low copy numbers is achieved in a better way with the DNA origami nanostructures as reporters.

Representation and relative abundance of cell-type selective markers in whole-kidney RNA-Seq data

Bulk-tissue RNA-Seq is increasingly being used in the study of physiological and pathophysiological processes in the kidney; however, the presence of multiple cell types in kidney tissue complicates data interpretation. We addressed the question of which cell types are represented in whole-kidney RNA-Seq data in order to identify circumstances in which bulk-kidney RNA-Seq can be successfully interpreted. We carried out RNA-Seq in mouse whole kidneys and in microdissected renal tubule segments. To aid in the interpretation of the data, we compiled a database of cell-type selective protein markers for 43 cell types believed to be present in kidney tissue. The whole-kidney RNA-Seq analysis identified transcripts corresponding to 17,742 genes, distributed over 5 orders of magnitude of expression level. Markers for all 43 curated cell types were detectable. Analysis of the cellular makeup of mouse and rat kidney, calculated from published literature, suggests that proximal tubule cells account for more than half of the mRNA in a kidney. Comparison of RNA-Seq data from microdissected proximal tubules with data from whole kidney supports this view. RNA-Seq data for cell-type selective markers in bulk-kidney samples provide a valid means to identify changes in minority-cell abundances in kidney tissue. Because proximal tubules make up a substantial fraction of whole-kidney samples, changes in proximal tubule gene expression can be assessed presumptively by bulk-kidney RNA-Seq, although results could potentially be complicated by the presence of mRNA from other cell types.

Genome-Wide Identification and Characterization of the ALOG Domain Genes in Rice

The ALOG domain genes, named after the Arabidopsis LSH1 and Oryza G1 (ALOG) proteins, have frequently been reported as key developmental regulators in rice and Arabidopsis. However, the investigation of the ALOG gene family is limited. Here, we conducted a genome-wide investigation of the ALOG gene family in rice and six other species. In total, eighty-four ALOG domain genes were identified from the seven species, of which fourteen ALOG domain genes (OsG1/G1Ls) were identified in the rice genome. The fourteen OsG1/G1Ls were unevenly distributed on eight chromosomes, and we found that eight segmental duplications contributed to the expansion of OsG1/G1Ls in the rice genome. The eighty-four ALOG family genes from seven species were classified into six clusters, and the ALOG domain-defined motifs 1, 2, and 3 were highly conserved across species according to the phylogenetic and structural analysis. However, the newly identified motifs 4 and 5 were only present in monocots, indicating a specified function in monocots. Moreover, OsG1/G1Ls exhibited tissue-specific expression patterns. Coexpression analysis suggested that OsG1 integrates OsMADS50 and the downstream MADS-box genes, such as OsMADS1, to regulate the development of rice inflorescence; OsG1L7 potentially associates with OsMADS22 and OsMADS55 to regulate stem elongation. In addition, comparative expression analysis revealed the conserved biological functions of ALOG family genes among rice, maize, and Arabidopsis. These results have shed light on the functional study of ALOG family genes in rice and other plants.

Now Is the Time to Use Molecular Gene Testing for the Diagnosis of Primary Immune Deficiencies

The discovery of chromosomes, genes, and DNA in the early 20th century paved the way for the development of techniques to examine the role of these elements in disease pathogenesis. Since the start of the 21st century, genetic testing and particularly next-generation sequencing has allowed for a rapid rate of gene disease associations for a broad range of primary immunodeficiency patients. At the same time, biologic and small molecule-based therapies targeting specific molecular pathways have been developed and are being applied clinically and in research settings to treat genetically defined immunodeficiencies. In recent years, both the American Academy of Allergy Asthma and Immunology and the Clinical Immunology Society have recommended the use of genetic testing for diagnosis, therapy guidance, and genetic counseling in patients with clinical symptoms of primary immunodeficiency.

Spectral-optical-tweezer-assisted fluorescence multiplexing system for QDs-encoded bead-array bioassay

As an efficient tool in the multiplexed detection of biomolecules, bead-array could achieve separation-free detection to multiple targets, making it suitable to analyze valuable and scarce samples like antigen and antibody from living organism. Herein, we propose a spectral-optical-tweezer-assisted fluorescence multiplexing system to analyze biomolecule-conjugated bead-array. Using optical tweezer, we trapped and locked beads at the focus to accept stimulation, offering a stable and optimized analysis condition. Moving the system focus and scanning the sample slide, we achieved emissions collection to QDs-encoded bead-array after the multiplexed detection. The emission spectra of fluorescence were collected and recorded by the spectrometer. By recognizing locations of decoding peaks and counting the intensities of label signals of emission spectra, we achieved qualitative and quantitative detection to targets. As proof-of-concept studies, we use this system to carry out multiplexed detection to various types of anti-IgG in the single sample and the detection limit reaches 1.52 pM with a linear range from 0.31 to 10 nM. Through further optimization of experimental conditions, we achieved specific detection to target IgG with sandwich method in human serum and the detection limit reaches as low as 0.23 pM with a linear range from 0.88 to 28 pM, validating the practical application of this method in real samples.

An Overview of Methodologies in Studying lncRNAs in the High-Throughput Era: When Acronyms ATTACK!

The discovery of pervasive transcription in eukaryotic genomes provided one of many surprising (and perhaps most surprising) findings of the genomic era and led to the uncovering of a large number of previously unstudied transcriptional events. This pervasive transcription leads to the production of large numbers of noncoding RNAs (ncRNAs) and thus opened the window to study these diverse, abundant transcripts of unclear relevance and unknown function. Since that discovery, recent advances in high-throughput sequencing technologies have identified a large collection of ncRNAs, from microRNAs to long noncoding RNAs (lncRNAs). Subsequent discoveries have shown that many lncRNAs play important roles in various eukaryotic processes; these discoveries have profoundly altered our understanding of the regulation of eukaryotic gene expression. Although the identification of ncRNAs has become a standard experimental approach, the functional characterization of these diverse ncRNAs remains a major challenge. In this chapter, we highlight recent progress in the methods to identify lncRNAs and the techniques to study the molecular function of these lncRNAs and the application of these techniques to the study of plant lncRNAs.

Feature selection of gene expression data for Cancer classification using double RBF-kernels

BACKGROUND

Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem.

RESULTS

The modified method was tested on four benchmark datasets with either two-class phenotypes or multiclass phenotypes, outperforming previous methods, with relatively higher accuracy, true positive rate, false positive rate and reduced runtime.

CONCLUSIONS

This paper proposes an effective feature selection method, combining double RBF-kernels with weighted analysis, to extract feature genes from gene expression data, by exploring its nonlinear mapping ability.

Gene editing in the context of an increasingly complex genome

The reporting of the first draft of the human genome in 2000 brought with it much hope for the future in what was felt as a paradigm shift toward improved health outcomes. Indeed, we have now mapped the majority of variation across human populations with landmark projects such as 1000 Genomes; in cancer, we have catalogued mutations across the primary carcinomas; whilst, for other diseases, we have identified the genetic variants with strongest association. Despite this, we are still awaiting the genetic revolution in healthcare to materialise and translate itself into the health benefits for which we had hoped. A major problem we face relates to our underestimation of the complexity of the genome, and that of biological mechanisms, generally. Fixation on DNA sequence alone and a 'rigid' mode of thinking about the genome has meant that the folding and structure of the DNA molecule -and how these relate to regulation- have been underappreciated. Projects like ENCODE have additionally taught us that regulation at the level of RNA is just as important as that at the spatiotemporal level of chromatin.In this review, we chart the course of the major advances in the biomedical sciences in the era pre- and post the release of the first draft sequence of the human genome, taking a focus on technology and how its development has influenced these. We additionally focus on gene editing via CRISPR/Cas9 as a key technique, in particular its use in the context of complex biological mechanisms. Our aim is to shift the mode of thinking about the genome to that which encompasses a greater appreciation of the folding of the DNA molecule, DNA- RNA/protein interactions, and how these regulate expression and elaborate disease mechanisms.Through the composition of our work, we recognise that technological improvement is conducive to a greater understanding of biological processes and life within the cell. We believe we now have the technology at our disposal that permits a better understanding of disease mechanisms, achievable through integrative data analyses. Finally, only with greater understanding of disease mechanisms can techniques such as gene editing be faithfully conducted.

Loss and gain of N-linked glycosylation sequons due to single-nucleotide variation in cancer

Despite availability of sequence site-specific information resulting from years of sequencing and sequence feature curation, there have been few efforts to integrate and annotate this information. In this study, we update the number of human N-linked glycosylation sequons (NLGs), and we investigate cancer-relatedness of glycosylation-impacting somatic nonsynonymous single-nucleotide variation (nsSNV) by mapping human NLGs to cancer variation data and reporting the expected loss or gain of glycosylation sequon. We find 75.8% of all human proteins have at least one NLG for a total of 59,341 unique NLGs (includes predicted and experimentally validated). Only 27.4% of all NLGs are experimentally validated sites on 4,412 glycoproteins. With respect to cancer, 8,895 somatic-only nsSNVs abolish NLGs in 5,204 proteins and 12,939 somatic-only nsSNVs create NLGs in 7,356 proteins in cancer samples. nsSNVs causing loss of 24 NLGs on 23 glycoproteins and nsSNVs creating 41 NLGs on 40 glycoproteins are identified in three or more cancers. Of all identified cancer somatic variants causing potential loss or gain of glycosylation, only 36 have previously known disease associations. Although this work is computational, it builds on existing genomics and glycobiology research to promote identification and rank potential cancer nsSNV biomarkers for experimental validation.

Differential gene expression analysis in ageing muscle and drug discovery perspectives

Identifying therapeutic target genes represents the key step in functional genomics-based therapies. Within this context, the disease heterogeneity, the exogenous factors and the complexity of genomic structure and function represent important challenges. The functional genomics aims to overcome such obstacles via identifying the gene functions and therefore highlight disease-causing genes as therapeutic targets. Genomic technologies promise to reshape the research on ageing muscle, exercise response and drug discovery. Herein, we describe the functional genomics strategies, mainly differential gene expression methods microarray, serial analysis of gene expression (SAGE), massively parallel signature sequence (MPSS), RNA sequencing (RNA seq), representational difference analysis (RDA), and suppression subtractive hybridization (SSH). Furthermore, we review these illustrative approaches that have been used to discover new therapeutic targets for some complex diseases along with the application of these tools to study the modulation of the skeletal muscle transcriptome.

Molecular characterization of the 14-3-3 gene family in rice and its expression studies under abiotic stress

14-3-3 isoforms were relatively less conserved at the C-terminal region across plant groups. Both Os 14-3-3f and Os 14-3-3g were inducible with differential gene expression levels under different abiotic stress and developmental stages in sensitive and tolerant indica rice cultivars as confirmed both at transcript and protein level. Plant 14-3-3s has been well characterized to function in several signaling pathways, biotic as well as abiotic stress and nutrient metabolism. We attempted comprehensive analysis of 14-3-3 genes in different plant lineages such as green algae (Chlamydomonas reinhardtii), moss (Physcomitrella patens) and lycophyte (Selaginella moellendorffii), dicot Arabidopsis thaliana and monocot Oryza sativa sub sp. japonica at the gene and protein level. Sequence alignment results revealed that 14-3-3 isoforms were evolutionarily conserved across all taxa with variable C-terminal end. Phylogenetic analysis indicated that the majority of 14-3-3 isoforms in rice belong to the non-epsilon group that clustered separately from the dicot group. Segmental duplication event played a significant role in the expansion of both, Arabidopsis and rice, 14-3-3 isoforms as revealed by synteny studies. In silico gene expression using Massive Parallel Signature Sequencing and microarray analysis revealed that 14-3-3 isoforms have variable expression in different tissue types and under different abiotic stress regime in Arabidopsis and japonica rice. Both, semi-quantitative and qPCR results, confirmed that Os14-3-3f and Os14-3-3g were inducible under abiotic stress in lamina and roots of indica rice and relatively higher under salinity and cold stress in Nonabokra, under dehydration stress in N-22 and under exogenous ABA in IR-29 usually after 3-6 h of treatment. Both, 14-3-3f and 14-3-3g, were highly expressed in flag leaves, stems and panicles and mature roots. These results were further confirmed by immunoblot analysis of rice cultivars using Os14-3-3f antibody generated from recombinant Os14-3-3f protein. The results provide the first comprehensive report of Os14-3-3 gene expression in indica rice cultivars which differ in tolerance to abiotic stress that might be useful for further research.