Third-generation sequencing techniques and applications to drug discovery

Bioherbicidal potential of Bacillus altitudinis D30202 on Avena fatua L.: a whole-genome sequencing analysis

Avena fatua L. (wild oat) is one of the most harmful gramineous weeds that can affect the yield and quality of infiltrating crops. Bacillus altitudinis D30202 exhibits an excellent biocontrol activity against wild oat. To elucidate the biocontrol mechanisms of B. altitudinis D30202, the genome structure of this strain was assessed via whole-genome sequencing analysis. We predicted and analyzed secondary metabolite synthesis gene clusters to elucidate the mechanisms underlying the biocontrol of weeds. The whole-genome sequencing data indicated that B. altitudinis D30202 had the genome size and GC content of 3,777,154 bp and 41.32%, respectively, and 3809 coding genes were identified. Moreover, this strain could generate several compounds with bioherbicidal activity, including 4-hydroxy-3-methoxycinnamic acid and two indole derivatives. Bioinformatics prediction and comparative genomic analysis revealed that the strain had 6 secondary metabolite gene clusters. Furthermore, the taxonomic position of B. altitudinis D30202 was assessed, confirming its uniqueness and novelty within the Bacillus genus. Comparative genomic analysis showed differences in gene distribution, suggesting potential adaptations to different environments. In conclusion, B. altitudinis D30202 possesses a genome with unique characteristics, encoding enzymes and pathways related to herbicidal potential and biocontrol. This study provides a reference basis for understanding the molecular mechanisms of weed inhibition.

Retinitis Pigmentosa: Current Clinical Management and Emerging Therapies

Retinitis pigmentosa (RP) comprises a group of inherited retinal dystrophies characterized by the degeneration of rod photoreceptors, followed by the degeneration of cone photoreceptors. As a result of photoreceptor degeneration, affected individuals experience gradual loss of visual function, with primary symptoms of progressive nyctalopia, constricted visual fields and, ultimately, central vision loss. The onset, severity and clinical course of RP shows great variability and unpredictability, with most patients already experiencing some degree of visual disability in childhood. While RP is currently untreatable for the majority of patients, significant efforts have been made in the development of genetic therapies, which offer new hope for treatment for patients affected by inherited retinal dystrophies. In this exciting era of emerging gene therapies, it remains imperative to continue supporting patients with RP using all available options to manage their condition. Patients with RP experience a wide variety of physical, mental and social-emotional difficulties during their lifetime, of which some require timely intervention. This review aims to familiarize readers with clinical management options that are currently available for patients with RP.

Single-Molecule Real-Time Sequencing of Full-Length Transcriptome and Identification of Genes Related to Male Development in Cannabis sativa

Female Cannabis sativa plants have important therapeutic properties. The sex ratio of the dioecious cannabis is approximately 1:1. Cultivating homozygous female plants by inducing female plants to produce male flowers is of great practical significance. However, the mechanism underlying cannabis male development remains unclear. In this study, single-molecule real-time (SMRT) sequencing was performed using a mixed sample of female and induced male flowers from the ZYZM1 cannabis variety. A total of 15,241 consensus reads were identified, and 13,657 transcripts were annotated across seven public databases. A total of 48 lncRNAs with an average length of 986.54 bp were identified. In total, 8202 transcripts were annotated as transcription factors, the most common of which were bHLH transcription factors. Moreover, tissue-specific expression pattern analysis showed that 13 MADS transcription factors were highly expressed in male flowers. Furthermore, 232 reads of novel genes were predicted and enriched in lipid metabolism, and qRT-PCR results showed that CER1 may be involved in the development of cannabis male flowers. In addition, 1170 AS events were detected, and two AS events were further validated. Taken together, these results may improve our understanding of the complexity of full-length cannabis transcripts and provide a basis for understanding the molecular mechanism of cannabis male development.

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

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

Innovative in Silico Approaches for Characterization of Genes and Proteins

Bioinformatics is an amalgamation of biology, mathematics and computer science. It is a science which gathers the information from biology in terms of molecules and applies the informatic techniques to the gathered information for understanding and organizing the data in a useful manner. With the help of bioinformatics, the experimental data generated is stored in several databases available online like nucleotide database, protein databases, GENBANK and others. The data stored in these databases is used as reference for experimental evaluation and validation. Till now several online tools have been developed to analyze the genomic, transcriptomic, proteomics, epigenomics and metabolomics data. Some of them include Human Splicing Finder (HSF), Exonic Splicing Enhancer Mutation taster, and others. A number of SNPs are observed in the non-coding, intronic regions and play a role in the regulation of genes, which may or may not directly impose an effect on the protein expression. Many mutations are thought to influence the splicing mechanism by affecting the existing splice sites or creating a new sites. To predict the effect of mutation (SNP) on splicing mechanism/signal, HSF was developed. Thus, the tool is helpful in predicting the effect of mutations on splicing signals and can provide data even for better understanding of the intronic mutations that can be further validated experimentally. Additionally, rapid advancement in proteomics have steered researchers to organize the study of protein structure, function, relationships, and dynamics in space and time. Thus the effective integration of all of these technological interventions will eventually lead to steering up of next-generation systems biology, which will provide valuable biological insights in the field of research, diagnostic, therapeutic and development of personalized medicine.

Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics

Although next-generation sequencing (NGS) technology revolutionized sequencing, offering a tremendous sequencing capacity with groundbreaking depth and accuracy, it continues to demonstrate serious limitations. In the early 2010s, the introduction of a novel set of sequencing methodologies, presented by two platforms, Pacific Biosciences (PacBio) and Oxford Nanopore Sequencing (ONT), gave birth to third-generation sequencing (TGS). The innovative long-read technologies turn genome sequencing into an ease-of-handle procedure by greatly reducing the average time of library construction workflows and simplifying the process of de novo genome assembly due to the generation of long reads. Long sequencing reads produced by both TGS methodologies have already facilitated the decipherment of transcriptional profiling since they enable the identification of full-length transcripts without the need for assembly or the use of sophisticated bioinformatics tools. Long-read technologies have also provided new insights into the field of epitranscriptomics, by allowing the direct detection of RNA modifications on native RNA molecules. This review highlights the advantageous features of the newly introduced TGS technologies, discusses their limitations and provides an in-depth comparison regarding their scientific background and available protocols as well as their potential utility in research and clinical applications.

Functional Annotation of a Full-Length Transcriptome and Identification of Genes Associated with Flower Development in Rhododendronsimsii (Ericaceae)

Rhododendronsimsii is one of the top ten famous flowers in China. Due to its historical value and high aesthetic, it is widely popular among Chinese people. Various colors are important breeding objectives in Rhododendron L. The understanding of the molecular mechanism of flower color formation can provide a theoretical basis for the improvement of flower color in Rhododendron L. To generate the R.simsii transcriptome, PacBio sequencing technology has been used. A total of 833,137 full-length non-chimeric reads were obtained and 726,846 high-quality full-length transcripts were found. Moreover, 40,556 total open reading frames were obtained; of which 36,018 were complete. In gene annotation analyses, 39,411, 18,565, 16,102 and 17,450 transcriptions were allocated to GO, Nr, KEGG and COG databases, correspondingly. To identify long non-coding RNAs (lncRNAs), we utilized four computational methods associated with Protein families (Pfam), Cooperative Data Classification (CPC), Coding Assessing Potential Tool (CPAT) and Coding Non Coding Index (CNCI) databases and observed 6170, 2265, 4084 and 1240 lncRNAs, respectively. Based on the results, most genes were enriched in the flavonoid biosynthetic pathway. The eight key genes on the anthocyanin biosynthetic pathway were further selected and analyzed by qRT-PCR. The F3'H and ANS showed an upward trend in the developmental stages of R. simsii. The highest expression of F3'5'H and FLS in the petal color formation of R. simsii was observed. This research provided a huge number of full-length transcripts, which will help to proceed genetic analyses of R.simsii. native, which is a semi-deciduous shrub.

Development and validation of 9-long Non-coding RNA signature to predicting survival in hepatocellular carcinoma

Primary hepatic carcinoma is 1 of the most common malignant tumors globally, of which hepatocellular carcinoma (HCC) accounts for 85% to 90%. Due to the high degree of deterioration and low early detection rate of HCC, most patients are diagnosed when they are already in the middle and advanced stages, and the prognosis are always poor.RNA sequencing data from the cancer genome atlas was used to explore differences in lncRNA expression profiles. LncRNA was extracted by gdcRNAtools in R package. Multivariate cox analysis was performed on the screened lncRNAs. The relationship between the lncRNA model and prognosis as well as clinical characteristics of patients with HCC was analyzed. Finally, a predictive nomogram in the the cancer genome atlas cohort was established and verified internallyBased on the RNA sequencing survival analysis, a 9- lncRNAs prognosis model, including TMCC1-AS1, AC008892.1, AL031985.3, L34079.2, U95743.1, KDM4A-AS1, SACS-AS1, AC005534.1, LINC01116 was established. The 9-lncRNA prognosis model was a reliable tool for predicting prognosis of HCC, and the nomogram of this prognosis model could help clinicians to choose personalized treatment for HCC patientsThis model was significant to complement clinic characteristics of HCC and to promote personalized management of patients, it also provided a new idea for researches on the prognosis of HCC.

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.

Novel Ternary Vectors for Efficient Sorghum Transformation

Sorghum has been considered a recalcitrant crop for tissue culture and genetic transformation. A breakthrough in Agrobacterium-mediated sorghum transformation was achieved with the use of super-binary cointegrate vectors based on plasmid pSB1. However, even with pSB1, transformation capability was restricted to certain sorghum genotypes, excluding most of the important African sorghum varieties. We recently developed a ternary vector system incorporating the pVIR accessory plasmid. The ternary vector system not only doubled the transformation frequency (TF) in Tx430, but also extended the transformation capability into an important African sorghum elite variety.

LONG-NONCODING RNAs in gastroesophageal cancers

Despite continuing improvements in multimodal therapies, gastro-esophageal malignances remain widely prevalent in the population and is characterized by poor overall and disease-free survival rates. Due to the lack of understanding about the pathogenesis and absence of reliable markers, gastro-esophageal cancers are associated with delayed diagnosis. The increasing understanding about cancer's molecular landscape in the recent years, offers the possibility of identifying 'targetable' molecular events and in particular facilitates novel treatment strategies and development of biomarkers for early stage diagnosis. At least 98% of our genome is actively transcribed into non-coding RNAs encompassing long non-coding RNAs (lncRNAs) constituted of transcripts longer than 200 nucleotides with no protein-coding capacity. Many studies have demonstrated that lncRNAs are functional genomic elements playing pivotal roles in main oncogenic processes. LncRNA can act at multiple levels developing a complex molecular network that can modulate directly or indirectly the expression of genes involved in tumorigenesis. In this review, we focus on lncRNAs as emerging players in gastro-esophageal carcinogenesis and critically assess their potential as reliable noninvasive biomarkers and in next generation targeted therapies.

Genome Sequencing

Genome sequencing is an important step toward correlating genotypes with phenotypic characters. Sequencing technologies are important in many fields in the life sciences, including functional genomics, transcriptomics, oncology, evolutionary biology, forensic sciences, and many more. The era of sequencing has been divided into three generations. First generation sequencing involved sequencing by synthesis (Sanger sequencing) and sequencing by cleavage (Maxam-Gilbert sequencing). Sanger sequencing led to the completion of various genome sequences (including human) and provided the foundation for development of other sequencing technologies. Since then, various techniques have been developed which can overcome some of the limitations of Sanger sequencing. These techniques are collectively known as "Next-generation sequencing" (NGS), and are further classified into second and third generation technologies. Although NGS methods have many advantages in terms of speed, cost, and parallelism, the accuracy and read length of Sanger sequencing is still superior and has confined the use of NGS mainly to resequencing genomes. Consequently, there is a continuing need to develop improved real time sequencing techniques. This chapter reviews some of the options currently available and provides a generic workflow for sequencing a genome.

Managing risks in drug discovery: reproducibility of published findings

In spite of tremendous advances in biopharmaceutical science and technology, the productivity of pharmaceutical research and development has been steadily declining over the last decades. The reasons for this decline are manifold and range from improved standard of care that is more and more difficult to top to inappropriate management of technical and translational risks along the R&D value chain. In this short review, major types of risks in biopharmaceutical R&D and means to address them will be described. A special focus will be on a risk, i.e., the lack of reproducibility of published information, that has so far not been fully appreciated and systematically analyzed. Measures to improve reproducibility and trust in published information will be discussed.

Cancer modelling in the NGS era - Part I: Emerging technology and initial modelling

It is today indisputable that great progresses have been made in our molecular understanding of cancer cells, but an effective implementation of such knowledge into dramatic cancer-cures is still belated and yet desperately needed. This review gives a snapshot at where we stand today in this search for cancer understanding and definitive treatments, how far we have progressed and what are the major obstacles we will have to overcome both technologically and for disease modelling. In the first part, promising 3rd/4th Generation Sequencing Technologies will be summarized (particularly IonTorrent and OxfordNanopore technologies). Cancer modelling will be then reviewed from its origin in XIX Century Germany to today's NGS applications for cancer understanding and therapeutic interventions. Developments after Molecular Biology revolution (1953) are discussed as successions of three phases. The first, PH1, labelled "Clonal Outgrowth" (from 1960s to mid 1980s) was characterized by discoveries in cytogenetics (Nowell, Rowley) and viral oncology (Dulbecco, Bishop, Varmus), which demonstrated clonality. Treatments were consequently dominated by a "cytotoxic eradication" strategy with chemotherapeutic agents. In PH2, (from the mid 1980s to our days) the description of cancer as "Gene Networks" led to targeted-gene-therapies (TGTs). TGTs are the focus of Section 3: in view of their apparent failing (Ephemeral Therapies), alternative strategies will be discussed in review part II (particularly cancer immunotherapy, CIT). Additional Pitfalls impinge on the concepts of tumour heterogeneity (inter/intra; ITH). The described pitfalls set the basis for a new phase, PH3, which is called "NGS Era" and will be also discussed with ten emerging cancer models in the Review 2nd part.

Plant exomics: concepts, applications and methodologies in crop improvement

Molecular breeding has a crucial role in improvement of crops. Conventional breeding techniques have failed to ameliorate food production. Next generation sequencing has established new concepts of molecular breeding. Exome sequencing has proven to be a significant tool for assessing natural evolution in plants, studying host pathogen interactions and betterment of crop production as exons assist in interpretation of allelic variation with respect to their phenotype. This review covers the platforms for exome sequencing, next generation sequencing technologies that have revolutionized exome sequencing and led toward development of third generation sequencing. Also discussed in this review are the uses of these sequencing technologies to improve wheat, rice and cotton yield and how these technologies are used in exploring the biodiversity of crops, providing better understanding of plant-host pathogen interaction and assessing the process of natural evolution in crops and it also covers how exome sequencing identifies the gene pool involved in symbiotic and other co-existential systems. Furthermore, we conclude how integration of other methodologies including whole genome sequencing, proteomics, transcriptomics and metabolomics with plant exomics covers the areas which are left untouched with exomics alone and in the end how these integration will transform the future of crops.

Plant pathogen nanodiagnostic techniques: forthcoming changes?

Plant diseases are among the major factors limiting crop productivity. A first step towards managing a plant disease under greenhouse and field conditions is to correctly identify the pathogen. Current technologies, such as quantitative polymerase chain reaction (Q-PCR), require a relatively large amount of target tissue and rely on multiple assays to accurately identify distinct plant pathogens. The common disadvantage of the traditional diagnostic methods is that they are time consuming and lack high sensitivity. Consequently, developing low-cost methods to improve the accuracy and rapidity of plant pathogens diagnosis is needed. Nanotechnology, nano particles and quantum dots (QDs) have emerged as essential tools for fast detection of a particular biological marker with extreme accuracy. Biosensor, QDs, nanostructured platforms, nanoimaging and nanopore DNA sequencing tools have the potential to raise sensitivity, specificity and speed of the pathogen detection, facilitate high-throughput analysis, and to be used for high-quality monitoring and crop protection. Furthermore, nanodiagnostic kit equipment can easily and quickly detect potential serious plant pathogens, allowing experts to help farmers in the prevention of epidemic diseases. The current review deals with the application of nanotechnology for quicker, more cost-effective and precise diagnostic procedures of plant diseases. Such an accurate technology may help to design a proper integrated disease management system which may modify crop environments to adversely affect crop pathogens.

DNA fingerprinting in zoology: past, present, future

In 1962, Thomas Kuhn famously argued that the progress of scientific knowledge results from periodic 'paradigm shifts' during a period of crisis in which new ideas dramatically change the status quo. Although this is generally true, Alec Jeffreys' identification of hypervariable repeat motifs in the human beta-globin gene, and the subsequent development of a technology known now as 'DNA fingerprinting', also resulted in a dramatic shift in the life sciences, particularly in ecology, evolutionary biology, and forensics. The variation Jeffreys recognized has been used to identify individuals from tissue samples of not just humans, but also of many animal species. In addition, the technology has been used to determine the sex of individuals, as well as paternity/maternity and close kinship. We review a broad range of such studies involving a wide diversity of animal species. For individual researchers, Jeffreys' invention resulted in many ecologists and evolutionary biologists being given the opportunity to develop skills in molecular biology to augment their whole organism focus. Few developments in science, even among the subsequent genome discoveries of the 21st century, have the same wide-reaching significance. Even the later development of PCR-based genotyping of individuals using microsatellite repeats sequences, and their use in determining multiple paternity, is conceptually rooted in Alec Jeffreys' pioneering work.

Quantitative polyadenylation site mapping with single-molecule direct RNA sequencing

The known regulatory role of 3' untranslated regions (3'UTRs) and poly(A) tails in RNA localization, stability, and translation, and polyadenylation regulation defects leading to human diseases such as oculopharyngeal muscular dystrophy, thalassemias, thrombophilia, and IPEX syndrome underline the need to fully characterize genome-wide polyadenylation states and mechanisms across normal physiological and disease states. This chapter outlines the quantitative polyadenylation site mapping and analysis strategies developed with the single-molecule direct RNA sequencing technology.

High-throughput, multiparameter analysis of single cells

Heterogeneity of cell populations in various biological systems has been widely recognized, and the highly heterogeneous nature of cancer cells has been emerging with clinical relevance. Single-cell analysis using a combination of high-throughput and multiparameter approaches is capable of reflecting cell-to-cell variability, and at the same time of unraveling the complexity and interdependence of cellular processes in the individual cells of a heterogeneous population. In this review, analytical methods and microfluidic tools commonly used for high-throughput, multiparameter single-cell analysis of DNA, RNA, and proteins are discussed. Applications and limitations of currently available technologies for cancer research and diagnostics are reviewed in the light of the ultimate goal to establish clinically applicable assays.

Biases in small RNA deep sequencing data

High-throughput RNA sequencing (RNA-seq) is considered a powerful tool for novel gene discovery and fine-tuned transcriptional profiling. The digital nature of RNA-seq is also believed to simplify meta-analysis and to reduce background noise associated with hybridization-based approaches. The development of multiplex sequencing enables efficient and economic parallel analysis of gene expression. In addition, RNA-seq is of particular value when low RNA expression or modest changes between samples are monitored. However, recent data uncovered severe bias in the sequencing of small non-protein coding RNA (small RNA-seq or sRNA-seq), such that the expression levels of some RNAs appeared to be artificially enhanced and others diminished or even undetectable. The use of different adapters and barcodes during ligation as well as complex RNA structures and modifications drastically influence cDNA synthesis efficacies and exemplify sources of bias in deep sequencing. In addition, variable specific RNA G/C-content is associated with unequal polymerase chain reaction amplification efficiencies. Given the central importance of RNA-seq to molecular biology and personalized medicine, we review recent findings that challenge small non-protein coding RNA-seq data and suggest approaches and precautions to overcome or minimize bias.

Analysis of epigenetic modifications of DNA in human cells

Epigenetics, the study of somatically heritable changes in gene expression not related to changes in the DNA sequence, is a rapidly expanding research field that plays important roles in healthy as well as in diseased cells. DNA methylation and hydroxymethylation are epigenetic modifications found in human cells, which are deeply implicated in normal cellular processes as well as in several major human diseases. Here, a range of different methods for the analyses of DNA methylation and hydroxymethylation at locus-specific and genome-wide scales is described.

Genes, behavior and next-generation RNA sequencing

Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g. nonsynonymous coding single nucleotide polymorphisms. Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.

Targeted transcript profiling by sequencing

In this work we present a targeted gene expression strategy employing trinucleotide threading (TnT) amplification and massive parallel sequencing. We have previously shown that TnT combined with array readout accurately monitors expression levels. However, with this detection strategy spurious products go undetected. Accordingly, we adapted the TnT protocol to massive parallel sequencing to acquire an unbiased view of the entire TnT-generated product population. In this manner we investigated the identity of undesired products, their extent at different oligonucleotide:RNA ratios and their effect on the expression levels. We demonstrate that TnT gene expression profiling with massive sequencing readout renders reliable expression data from as low as 3.5 ng of total RNA. Moreover, using 350 ng of total RNA results in only 0.7% to 1.1% undesired products. When lowering the amount of input material, the undesired product fraction increases but this does not influence the expression profiles.