Next generation sequencing in functional genomics

Recent Advances in Genome Editing and Bioinformatics: Addressing Challenges in Genome Editing Implementation and Genome Sequencing

Genome-editing technology has advanced significantly since the 2020 Nobel Prize in Chemistry was awarded for the development of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9). While CRISPR-Cas9 has become widely used in academic research, its social implementation has lagged due to unresolved patent disputes and slower progress in gene function analysis. To address this, new approaches bypassing direct gene function analysis are needed, with bioinformatics and next-generation sequencing (NGS) playing crucial roles. NGS is essential for sequencing the genome of target species, but challenges such as data quality, genome heterogeneity, ploidy, and small individual sizes persist. Despite these issues, advancements in sequencing technologies, like PacBio high-fidelity (HiFi) long reads and high-throughput chromosome conformation capture (Hi-C), have improved genome sequencing. Bioinformatics contributes to genome editing through off-target prediction and target gene selection, both of which require accurate genome sequence information. In this review, I will give updates on the development of genome editing and bioinformatics technologies with a focus on the rapid progress in genome sequencing.

Proteogenomic Approaches for Diseasome Studies

During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches.

Transcriptomic analysis of potato (Solanum tuberosum L.) tuber development reveals new insights into starch biosynthesis

Potato tubers are rich sources of various nutrients and unique sources of starch. Many genes play major roles in different pathways, including carbohydrate metabolism during the potato tuber's life cycle. Despite substantial scientific evidence about the physiological and morphological development of potato tubers, the molecular genetic aspects of mechanisms underlying tuber formation have not yet been fully understood. In this study, for the first time, RNA-seq analysis was performed to shed light on the expression of genes involved in starch biosynthesis during potato tuber development. To this end, samples were collected at the hook-like stolon (Stage I), swollen tips stolon (Stage II), and tuber initiation (Stage III) stages of tuber formation. Overall, 23 GB of raw data were generated and assembled. There were more than 20000 differentially expressed genes (DEGs); the expression of 73 genes involved in starch metabolism was further studied. Moreover, qRT-PCR analysis revealed that the expression profile of the starch biosynthesis DEGs was consistent with that of the RNA-seq data, which further supported the role of the DEGs in starch biosynthesis. This study provides substantial resources on potato tuber development and several starch synthesis isoforms associated with starch biosynthesis.

The role of zygotic genome activation in genetic-related reproductive medicine: Technological perspective, religious and bioethical concerns, challenges and benefits

Zygotic Genome Activation (ZGA) is a crucial developmental milestone in early embryogenesis, marking the transition from maternal to embryonic control of development. This process, which varies in timing across species, involves the activation of the embryonic genome, paving the way for subsequent cell differentiation and organismal development. Recent advances in genomics and reproductive medicine have highlighted the potential of ZGA in the realm of genetic screening, providing a window into the genetic integrity of the developing embryo at its earliest stages. The intersection of ZGA and genetic screening primarily emerges in the context of preimplantation genetic diagnosis (PGD) and preimplantation genetic screening (PGS). These techniques, often employed during assisted reproductive technologies, aim to detect potential genetic abnormalities or chromosomal imbalances before embryo implantation. Given that ZGA represents the onset of embryonic gene expression, understanding its intricacies can significantly enhance the accuracy and predictive power of these screening processes. With the advent of next-generation sequencing and other high-throughput genomic techniques, detailed mapping of the transcriptomic changes during ZGA has become feasible. Such advancements have deepened our insights into the dynamics of early embryonic development and the onset of genetic disorders. As our knowledge in this realm expands, it promises to revolutionize our capabilities in detecting, understanding, and potentially rectifying genetic anomalies at the earliest stages of human life, thereby optimizing reproductive outcomes.

De Novo Assembly of the Genome of the Sea Urchin Paracentrotus lividus (Lamarck 1816)

The Mediterranean purple sea urchin Paracentrotus lividus (Lamarck 1816) is a remarkable model system for molecular, evolutionary and cell biology studies, particularly in the field of developmental biology. We sequenced the genome, performed a de novo assembly, and analysed the assembly content. The genome of P. lividus was sequenced using Illumina NextSeq 500 System (Illumina) in a 2 × 150 paired-end format. More than 30,000 open reading frames (ORFs), (more than 8000 are unique), were identified and analysed to provide molecular tools accessible for the scientific community. In particular, several genes involved in complex innate immune responses, oxidative metabolism, signal transduction, and kinome, as well as genes regulating the membrane receptors, were identified in the P. lividus genome. In this way, the employment of the Mediterranean sea urchin for investigations and comparative analyses was empowered, leading to the explanation of cis-regulatory networks and their evolution in a key developmental model occupying an important evolutionary position with respect to vertebrates and humans.

Characterizing the bacterial communities associated with Mediterranean sponges: a metataxonomic analysis

The oceans cover over 70% of our planet, hosting a biodiversity of tremendous wealth. Sponges are one of the major ecosystem engineers on the seafloor, providing a habitat for a wide variety of species to be considered a good source of bioactive compounds. In this study, a metataxonomic approach was employed to describe the bacterial communities of the sponges collected from Faro Lake (Sicily) and Porto Paone (Gulf of Naples). Morphological analysis and amplification of the conserved molecular markers, including 18S and 28S (RNA ribosomal genes), CO1 (mitochondrial cytochrome oxidase subunit 1), and ITS (internal transcribed spacer), allowed the identification of four sponges. Metataxonomic analysis of sponges revealed a large number of amplicon sequence variants (ASVs) belonging to the phyla Proteobacteria, Cloroflexi, Dadabacteria, and Poribacteria. In particular, Myxilla (Myxilla) rosacea and Clathria (Clathria) toxivaria displayed several classes such as Alphaproteobacteria, Dehalococcoidia, Gammaproteobacteria, Cyanobacteria, and Bacteroidia. On the other hand, the sponges Ircinia oros and Cacospongia mollior hosted bacteria belonging to the classes Dadabacteriia, Anaerolineae, Acidimicrobiia, Nitrospiria, and Poribacteria. Moreover, for the first time, the presence of Rhizobiaceae bacteria was revealed in the sponge M. (Myxilla) rosacea, which was mainly associated with soil and plants and involved in biological nitrogen fixation.

Image-Based High-Throughput Phenotyping in Horticultural Crops

Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.

Genome Privacy and Trust

Genomics data are important for advancing biomedical research, improving clinical care, and informing other disciplines such as forensics and genealogy. However, privacy concerns arise when genomic data are shared. In particular, the identifying nature of genetic information, its direct relationship to health status, and the potential financial harm and stigmatization posed to individuals and their blood relatives call for a survey of the privacy issues related to sharing genetic and related data and potential solutions to overcome these issues. In this work, we provide an overview of the importance of genomic privacy, the information gleaned from genomics data, the sources of potential private information leakages in genomics, and ways to preserve privacy while utilizing the genetic information in research. We discuss the relationship between trust in the scientific community and protecting privacy, illuminating a future roadmap for data sharing and study participation.

Increase Crop Resilience to Heat Stress Using Omic Strategies

Varieties of various crops with high resilience are urgently needed to feed the increased population in climate change conditions. Human activities and climate change have led to frequent and strong weather fluctuation, which cause various abiotic stresses to crops. The understanding of crops' responses to abiotic stresses in different aspects including genes, RNAs, proteins, metabolites, and phenotypes can facilitate crop breeding. Using multi-omics methods, mainly genomics, transcriptomics, proteomics, metabolomics, and phenomics, to study crops' responses to abiotic stresses will generate a better, deeper, and more comprehensive understanding. More importantly, multi-omics can provide multiple layers of information on biological data to understand plant biology, which will open windows for new opportunities to improve crop resilience and tolerance. However, the opportunities and challenges coexist. Interpretation of the multidimensional data from multi-omics and translation of the data into biological meaningful context remained a challenge. More reasonable experimental designs starting from sowing seed, cultivating the plant, and collecting and extracting samples were necessary for a multi-omics study as the first step. The normalization, transformation, and scaling of single-omics data should consider the integration of multi-omics. This review reports the current study of crops at abiotic stresses in particular heat stress using omics, which will help to accelerate crop improvement to better tolerate and adapt to climate change.

Differentially expressed genes prediction by multiple self-attention on epigenetics data

Predicting differentially expressed genes (DEGs) from epigenetics signal data is the key to understand how epigenetics controls cell functional heterogeneity by gene regulation. This knowledge can help developing 'epigenetics drugs' for complex diseases like cancers. Most of existing machine learning-based methods suffer defects in prediction accuracy, interpretability or training speed. To address these problems, in this paper, we propose a Multiple Self-Attention model for predicting DEGs on Epigenetic data (Epi-MSA). Epi-MSA first uses convolutional neural networks for neighborhood bins information embedding, and then employs multiple self-attention encoders on different input epigenetics factors data to learn which locations of genes are important for predicting DEGs. Next it trains a soft attention module to pick out which epigenetics factors are significant. The attention mechanism makes the model interpretable, and the pure matrix operation of self-attention enables the model to be parallel calculated and speeds up the training. Experiments on datasets from the Roadmap Epigenome Project and BluePrint Data Analysis Portal (BDAP) show that the performance of Epi-MSA is better than existing competitive methods, and Epi-MSA also has a smaller standard deviation, which shows that Epi-MSA is effective and stable. In addition, Epi-MSA has a good interpretability, this is confirmed by referring its attention weight matrix with existing biological knowledge.

Functional genomics data: privacy risk assessment and technological mitigation

The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.

Model-based plant phenomics on morphological traits using morphometric descriptors

The morphological traits of plants contribute to many important functional features such as radiation interception, lodging tolerance, gas exchange efficiency, spatial competition between individuals and/or species, and disease resistance. Although the importance of plant phenotyping techniques is increasing with advances in molecular breeding strategies, there are barriers to its advancement, including the gap between measured data and phenotypic values, low quantitativity, and low throughput caused by the lack of models for representing morphological traits. In this review, we introduce morphological descriptors that can be used for phenotyping plant morphological traits. Geometric morphometric approaches pave the way to a general-purpose method applicable to single units. Hierarchical structures composed of an indefinite number of multiple elements, which is often observed in plants, can be quantified in terms of their multi-scale topological characteristics using topological data analysis. Theoretical morphological models capture specific anatomical structures, if recognized. These morphological descriptors provide us with the advantages of model-based plant phenotyping, including robust quantification of limited datasets. Moreover, we discuss the future possibilities that a system of model-based measurement and model refinement would solve the lack of morphological models and the difficulties in scaling out the phenotyping processes.

Finding Gene Associations by Text Mining and Annotating it with Gene Ontology

Digitalization of the research articles and their maintenance in a database was the first stage toward the development of biomedical research. With the large amounts of research being published daily, it has created a large gap in accessing all the articles for review to a given problem. To understand any biological process, an insight into the role of each element in the genome is essential. But with this gap in manual curation of literature, there are chances that important biological information may be lost. Hence, text mining plays an important role in bridging this gap and extracting important biological information from the text, finding associations among them and predicting annotations. An annotation may be gene, gene products, gene names, their physical and functional characteristics, and so on. The process of annotations may be classified as structural annotation, functional annotation, and relational annotation. In this chapter, a basic protocol utilizing text mining to extract biological information and predict their functional role based on Gene Ontology is provided.

Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review

Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.

Assessment of Multiple Anaerobic Co-Digestions and Related Microbial Community of Molasses with Rice-Alcohol Wastewater

Molasses is a highly dense and refined byproduct produced in the sugarcane industry, and it contains high amounts of degradable compounds. Through bioconversion, these compounds can be transformed into renewable products. However, the involved biological process is negatively influenced by the high chemical oxygen demand (COD) of molasses and ion concentration. The co-digestion of molasses with rice-alcohol wastewater (RAW) was compared with its mono-digestion at an increasing organic loading rate (OLR). Both processes were assessed by detecting the COD removal rate, the methane contents of biogas, and the structure and composition of microbial communities at different stages. Results showed that the co-digestion is stable up to a maximum OLR of 16 g COD L−1 d−1, whereas after the acclimatization phase, the mono-digestion process was disturbed two times, which occurred at a maximum OLR of 9 and 10 g COD L−1 d−1. The volatile fatty acids (VFAs) observed were 2059.66 mg/L and 1896.9 mg/L, which in mono-digestion causes the inhibition at maximum OLRs. In the co-digestion process, the concomitant COD removal rates and methane content recorded was 90.72 ± 0.63% 64.47% ± 0.59% correspondingly. While in the mono-digestion process, high COD removal rate and methane contents observed were 89.29 ± 0.094% and 61.37 ± 1.06% respectively. From the analysis of microbial communities, it has been observed that both the bacterial and archaeal communities respond differently at unlike stages. However, in both processes, Propionibacteriaceae was the most abundant family in the bacterial communities, whereas Methanosaetaceae was abundant in the archaeal communities. From the current study, it has been concluded that that rice-alcohol wastewater could be a good co-substrate for the anaerobic digestion of molasses in terms of COD removal rate and methane contents production, that could integrate molasses into progressive biogas production with high OLR.

Development of fly tolerance to consuming a high-protein diet requires physiological, metabolic and transcriptional changes

Mortality in insects consuming high-protein-and-low-carbohydrate diets resembles a type III lifespan curve with increased mortality at an early age and few survivors that live a relatively long lifespan. We selected for a Drosophila line able to live for a long time on an imbalanced high-protein-low-carbohydrate diet by carrying out five rounds of breeding to select for the most long-lived survivors. Adaptation to this diet in the selected line was studied at the biochemical, physiological and transcriptomic levels. The selected line of flies consumed less of the imbalanced food but also accumulated more storage metabolites: glycogen, triacylglycerides, and trehalose. Selected flies also had a higher activity of alanine transaminase and a higher urea content. Adaptation of the selected line on the transcriptomic level was characterized by down-regulation of genes encoding serine endopeptidases (Jon25i, Jon25ii, betaTry, and others) but up-regulation of genes encoding proteins related to the immune system, such as antimicrobial peptides, Turandot-family humoral factors, hexamerin isoforms, and vitellogenin. These sets of down- and up-regulated genes were similar to those observed in fruit flies with suppressed juvenile hormone signaling. Our data show that the physiological adaptation of fruit flies to a high-protein-low-carbohydrate diet occurs via intuitive pathways, namely a decrease in food consumption, conversion of amino acids into ketoacids to compensate for the lack of carbohydrate, and accumulation of storage metabolites to eliminate the negative effects of excess amino acids. Nevertheless, transcriptomic adaptation occurs in a counter-intuitive way likely via an influence of gut microbiota on food digestion.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients

BACKGROUND

Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part. Yet the importance of these RNAs especially for higher biological functions remains under investigation.

METHODS

In this paper, we analyze RNA-seq data, including non-coding and protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype of non-small-cell lung cancer, with deep learning neural networks and other state-of-the-art classification methods. The purpose of our paper is three-fold. First, we compare the classification performance of different versions of deep belief networks with SVMs, decision trees and random forests. Second, we compare the classification capabilities of protein coding and non-coding RNAs. Third, we study the influence of feature selection on the classification performance.

RESULTS

As a result, we find that deep belief networks perform at least competitively to other state-of-the-art classifiers. Second, data from non-coding RNAs perform better than coding RNAs across a number of different classification methods. This demonstrates the equivalence of predictive information as captured by non-coding RNAs compared to protein coding RNAs, conventionally used in computational diagnostics tasks. Third, we find that feature selection has in general a negative effect on the classification performance which means that unfiltered data with all features give the best classification results.

CONCLUSIONS

Our study is the first to use ncRNAs beyond miRNAs for the computational classification of cancer and for performing a direct comparison of the classification capabilities of protein coding RNAs and non-coding RNAs.

Differential binding cell-SELEX method to identify cell-specific aptamers using high-throughput sequencing

Aptamers have in recent years emerged as a viable alternative to antibodies. High-throughput sequencing (HTS) has revolutionized aptamer research by increasing the number of reads from a few (using Sanger sequencing) to millions (using an HTS approach). Despite the availability and advantages of HTS compared to Sanger sequencing, there are only 50 aptamer HTS sequencing samples available on public databases. HTS data in aptamer research are primarily used to compare sequence enrichment between subsequent selection cycles. This approach does not take full advantage of HTS because the enrichment of sequences during selection can be due to inefficient negative selection when using live cells. Here, we present a differential binding cell-SELEX (systematic evolution of ligands by exponential enrichment) workflow that adapts the FASTAptamer toolbox and bioinformatics tool edgeR, which are primarily used for functional genomics, to achieve more informative metrics about the selection process. We propose a fast and practical high-throughput aptamer identification method to be used with the cell-SELEX technique to increase the aptamer selection rate against live cells. The feasibility of our approach is demonstrated by performing aptamer selection against a clear cell renal cell carcinoma (ccRCC) RCC-MF cell line using the RC-124 cell line from healthy kidney tissue for negative selection.

Omics approaches to study gene regulatory networks for development in echinoderms

Gene regulatory networks (GRNs) describe the interactions for a developmental process at a given time and space. Historically, perturbation experiments represent one of the key methods for analyzing and reconstructing a GRN, and the GRN governing early development in the sea urchin embryo stands as one of the more deeply dissected so far. As technology progresses, so do the methods used to address different biological questions. Next-generation sequencing (NGS) has become a standard experimental technique for genome and transcriptome sequencing and studies of protein-DNA interactions and DNA accessibility. While several efforts have been made toward the integration of different omics approaches for the study of the regulatory genome in many animals, in a few cases, these are applied with the purpose of reconstructing and experimentally testing developmental GRNs. Here, we review emerging approaches integrating multiple NGS technologies for the prediction and validation of gene interactions within echinoderm GRNs. These approaches can be applied to both 'model' and 'non-model' organisms. Although a number of issues still need to be addressed, advances in NGS applications, such as assay for transposase-accessible chromatin sequencing, combined with the availability of embryos belonging to different species, all separated by various evolutionary distances and accessible to experimental regulatory biology, place echinoderms in an unprecedented position for the reconstruction and evolutionary comparison of developmental GRNs. We conclude that sequencing technologies and integrated omics approaches allow the examination of GRNs on a genome-wide scale only if biological perturbation and cis-regulatory analyses are experimentally accessible, as in the case of echinoderm embryos.

The aquatic animals' transcriptome resource for comparative functional analysis

Background

Aquatic animals have great economic and ecological importance. Among them, non-model organisms have been studied regarding eco-toxicity, stress biology, and environmental adaptation. Due to recent advances in next-generation sequencing techniques, large amounts of RNA-seq data for aquatic animals are publicly available. However, currently there is no comprehensive resource exist for the analysis, unification, and integration of these datasets. This study utilizes computational approaches to build a new resource of transcriptomic maps for aquatic animals. This aquatic animal transcriptome map database dbATM provides de novo assembly of transcriptome, gene annotation and comparative analysis of more than twenty aquatic organisms without draft genome.

Results

To improve the assembly quality, three computational tools (Trinity, Oases and SOAPdenovo-Trans) were employed to enhance individual transcriptome assembly, and CAP3 and CD-HIT-EST software were then used to merge these three assembled transcriptomes. In addition, functional annotation analysis provides valuable clues to gene characteristics, including full-length transcript coding regions, conserved domains, gene ontology and KEGG pathways. Furthermore, all aquatic animal genes are essential for comparative genomics tasks such as constructing homologous gene groups and blast databases and phylogenetic analysis.

Conclusion

In conclusion, we establish a resource for non model organism aquatic animals, which is great economic and ecological importance and provide transcriptomic information including functional annotation and comparative transcriptome analysis. The database is now publically accessible through the URL http://dbATM.mbc.nctu.edu.tw/.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4463-x) contains supplementary material, which is available to authorized users.

A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer

Clear cell renal cell carcinoma (ccRCC) is the most common and most aggressive form of renal cell cancer (RCC). The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways.

Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

Characterization of sequence-specific errors in various next-generation sequencing systems

Next-generation sequencing (NGS) is a powerful method for functional microbial ecology in a variety of environments including human's body. In this work, novel sequence-specific errors (SSEs) from the currently popular NGS systems and their hotspots were discovered, providing a scientific basis for filtering poor-quality sequence reads from the different NGS systems.

Deep sequencing-generated modules demonstrate coherent expression patterns for various cardiac diseases

As sequencing technology rapidly develops, gene annotations have also become increasingly sophisticated with incorporation of information regarding the temporal-spatial context of alternative splicing patterns, developmental stages, and tissue specificity. The present study aimed to identify the heart-enriched genes based on next-generation sequencing data and to investigate the gene modules demonstrating coherent expression patterns for various cardiac disease-related perturbations. Seven gene modules, including 382 heart-enriched genes, were identified. At least two modules containing differentially expressed genes were experimentally confirmed to be highly sensitive to various cardiac diseases. Transcription factors regulating the gene modules were then analyzed based on knowledgebase information; the expression of eight transcription factors changed significantly during pressure-overload cardiac hypertrophy, suggesting possible regulation of the modules by the identified transcription factors. Collectively, our results contribute to the classification of heart-enriched genes and their modules and would aid in identification of the transcription factors involved in cardiac pathogenesis in the future.

Characterization of the Transcriptional Complexity of the Receptive and Pre-receptive Endometria of Dairy Goats

Endometrium receptivity is essential for successful embryo implantation in mammals. However, the lack of genetic information remains an obstacle to understanding the mechanisms underlying the development of a receptive endometrium from the pre-receptive phase in dairy goats. In this study, more than 4 billion high-quality reads were generated and de novo assembled into 102,441 unigenes; these unigenes were annotated using published databases. A total of 3,255 unigenes that were differentially expressed (DEGs) between the PE and RE were discovered in this study (P-values < 0.05). In addition, 76,729–77,102 putative SNPs and 12,837 SSRs were discovered in this study. Bioinformatics analysis of the DEGs revealed a number of biological processes and pathways that are potentially involved in the establishment of the RE, notably including the GO terms proteolysis, apoptosis, and cell adhesion and the KEGG pathways Cell cycle and extracellular matrix (ECM)-receptor interaction. We speculated that ADCY8, VCAN, SPOCK1, THBS1, and THBS2 may play important roles in the development of endometrial receptivity. The de novo assembly provided a good starting point and will serve as a valuable resource for further investigations into endometrium receptivity in dairy goats and future studies on the genomes of goats and other related mammals.

Sequencing and Characterization of Divergent Marbling Levels in the Beef Cattle (Longissimus dorsi Muscle) Transcriptome

Marbling is an important trait regarding the quality of beef. Analysis of beef cattle transcriptome and its expression profile data are essential to extend the genetic information resources and would support further studies on beef cattle. RNA sequencing was performed in beef cattle using the Illumina High-Seq2000 platform. Approximately 251.58 million clean reads were generated from a high marbling (H) group and low marbling (L) group. Approximately 80.12% of the 19,994 bovine genes (protein coding) were detected in all samples, and 749 genes exhibited differential expression between the H and L groups based on fold change (>1.5-fold, p<0.05). Multiple gene ontology terms and biological pathways were found significantly enriched among the differentially expressed genes. The transcriptome data will facilitate future functional studies on marbling formation in beef cattle and may be applied to improve breeding programs for cattle and closely related mammals.

Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment-based mathematical model

Changes in transcription factor levels, epigenetic status, splicing kinetics and mRNA degradation can each contribute to changes in the mRNA dynamics of a gene. We present a novel method to identify which of these processes is changed in cells in response to external signals or as a result of a diseased state. The method employs a mathematical model, for which the kinetics of gene regulation, splicing, elongation and mRNA degradation were estimated from experimental data of transcriptional dynamics. The time-dependent dynamics of several species of adipose differentiation-related protein (ADRP) mRNA were measured in response to ligand activation of the transcription factor peroxisome proliferator-activated receptor δ (PPARδ). We validated the method by monitoring the mRNA dynamics upon gene activation in the presence of a splicing inhibitor. Our mathematical model correctly identifies splicing as the inhibitor target, despite the noise in the data.

De novo transcriptome assembly for a non-model species, the blood-sucking bug Triatoma brasiliensis, a vector of Chagas disease

High throughput sequencing (HTS) provides new research opportunities for work on non-model organisms, such as differential expression studies between populations exposed to different environmental conditions. However, such transcriptomic studies first require the production of a reference assembly. The choice of sampling procedure, sequencing strategy and assembly workflow is crucial. To develop a reliable reference transcriptome for Triatoma brasiliensis, the major Chagas disease vector in Northeastern Brazil, different de novo assembly protocols were generated using various datasets and software. Both 454 and Illumina sequencing technologies were applied on RNA extracted from antennae and mouthparts from single or pooled individuals. The 454 library yielded 278 Mb. Fifteen Illumina libraries were constructed and yielded nearly 360 million RNA-seq single reads and 46 million RNA-seq paired-end reads for nearly 45 Gb. For the 454 reads, we used three assemblers, Newbler, CAP3 and/or MIRA and for the Illumina reads, the Trinity assembler. Ten assembly workflows were compared using these programs separately or in combination. To compare the assemblies obtained, quantitative and qualitative criteria were used, including contig length, N50, contig number and the percentage of chimeric contigs. Completeness of the assemblies was estimated using the CEGMA pipeline. The best assembly (57,657 contigs, completeness of 80 %, <1 % chimeric contigs) was a hybrid assembly leading to recommend the use of (1) a single individual with large representation of biological tissues, (2) merging both long reads and short paired-end Illumina reads, (3) several assemblers in order to combine the specific advantages of each.

An extended gene protein/products Boolean network model including post-transcriptional regulation

Background

Networks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs.

Results

In this paper we show how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN. The enhanced BN model is implemented in a software toolkit (EBNT) that allows to analyze boolean GRNs from both a structural and a dynamic point of view. The open-source toolkit is compatible with available visualization tools like Cytoscape and allows to run detailed analysis of the network topology as well as of its attractors, trajectories, and state-space. In the paper, a small GRN built around the mTOR gene is used to demonstrate the main capabilities of the toolkit.

Conclusions

The extended model proposed in this paper opens new opportunities in the study of gene regulation. Several of the successful researches done with the support of BN to understand high-level characteristics of regulatory networks, can now be improved to better understand the role of post-transcriptional regulation for example as a network-wide noise-reduction or stabilization mechanisms.

Whole genome sequencing of the black grouse (Tetrao tetrix): reference guided assembly suggests faster-Z and MHC evolution

BACKGROUND

The different regions of a genome do not evolve at the same rate. For example, comparative genomic studies have suggested that the sex chromosomes and the regions harbouring the immune defence genes in the Major Histocompatability Complex (MHC) may evolve faster than other genomic regions. The advent of the next generation sequencing technologies has made it possible to study which genomic regions are evolutionary liable to change and which are static, as well as enabling an increasing number of genome studies of non-model species. However, de novo sequencing of the whole genome of an organism remains non-trivial. In this study, we present the draft genome of the black grouse, which was developed using a reference-guided assembly strategy.

RESULTS

We generated 133 Gbp of sequence data from one black grouse individual by the SOLiD platform and used a combination of de novo assembly and chicken reference genome mapping to assemble the reads into 4572 scaffolds with a total length of 1022 Mb. The draft genome well covers the main chicken chromosomes 1 ~ 28 and Z which have a total length of 1001 Mb. The draft genome is fragmented, but has a good coverage of the homologous chicken genes. Especially, 33.0% of the coding regions of the homologous genes have more than 90% proportion of their sequences covered. In addition, we identified ~1 M SNPs from the genome and identified 106 genomic regions which had a high nucleotide divergence between black grouse and chicken or between black grouse and turkey.

CONCLUSIONS

Our results support the hypothesis that the chromosome X (Z) evolves faster than the autosomes and our data are consistent with the MHC regions being more liable to change than the genome average. Our study demonstrates how a moderate sequencing effort can be combined with existing genome references to generate a draft genome for a non-model species.

Uncovering the genetic basis for early isogamete differentiation: a case study of Ectocarpus siliculosus

BACKGROUND

The phenomenon of sexual reproduction characterizes nearly all eukaryotes, with anisogamy being the most prevalent form of gamete discrimination. Since dimorphic gametes most likely descend from equal-sized specialized germ cells, identifying the genetic bases of the early functional diversification in isogametes can provide better understanding of the evolution of sexual dimorphism. However, despite the potential importance to the evolutionary biology field, no comprehensive survey of the transcriptome profiling in isomorphic gametes has been reported hitherto.

RESULTS

Gamete differentiation on the genomic level was investigated using Ectocarpus siliculosus, a model organism for brown algal lineage which displays an isogamous sexual reproduction cycle. Transcriptome libraries of male and female gametes were generated using Next Generation Sequencing technology (SOLiD) and analyzed to identify differentially regulated genes and pathways with potential roles in fertilization and gamete specialization. Gamete transcriptomes showed a high level of complexity with a large portion of gender specific gene expression. Our results indicate that over 4,000 of expressed genes are differentially regulated between male and female, including sequences related to cell movement, carbohydrate and lipid metabolism, signaling, transport and RNA processing.

CONCLUSIONS

This first comprehensive transcriptomic study of protist isogametes describes considerable adaptation to distinct sexual roles, suggesting that functional anisogamy precedes morphological differentiation. Several sex-biased genes and pathways with a putative role in reproduction were identified, providing the basis for more detailed investigations of the mechanisms underlying evolution of mating types and sexual dimorphism.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

Omic-profiling in breast cancer metastasis to bone: implications for mechanisms, biomarkers and treatment

Despite well-recognised advances in breast cancer treatment, there remain substantial numbers of patients who develop metastatic disease, of which up to 70% involves spread to bone, resulting in skeletal complications which have a major negative impact on mortality and quality of life. Bisphosphonates and newer bone-targeted agents have reduced the prevalence of skeletal complications, yet there remains significant unmet clinical need, particularly for the development of more specific therapies for the prevention and treatment of metastatic bone disease, for the prediction of risk of its development in individual patients and for the prediction of response to treatments. Modern 'omic' strategies can potentially make a major contribution to meeting this need. Technological advances in the field of nucleic acid sequencing, mass spectrometry and metabolic profiling have driven progress in genomics, transcriptomics (functional genomics), proteomics and metabolomics. This review appraises the recent application of these approaches to studies of breast cancer metastasis (particularly to bone), with a focus on understanding how omic approaches may lead to new therapeutic options and to novel biomarker molecules or molecular signatures with potential value in clinical practise. The increasingly recognised need for rigorous sample quality control and both pre-clinical and clinical validation to meet the ultimate goals of clinical utility and patient benefit is discussed. Future directions of omic driven research in breast cancer metastasis are considered, in particular micro-RNAs and their role in the post-transcriptional regulation of gene function and the possible role of cancer-stem cells and epigenetic modifications in the development of distant metastases.

Characterization of transcriptional complexity during longissimus muscle development in bovines using high-throughput sequencing

BACKGROUND

Beef cattle are among the most economically important animals in the world because they are farmed for their meat and leather. However, a lack of genetic information remains an obstacle to understanding the mechanisms behind the development of this animal. Analysis of the beef cattle transcriptome and its expression profile data are essential to extending the genetic information resources for this species and would support studies on this animal.

RESULTS

RNA sequencing of beef cattle was performed using the Illumina High-Seq2000 platform. A total of 25,605,140 and 26,214,800 reads were sequenced for embryonic and adult pooled samples, respectively. We identified 24,464-29,994 novel transcript units in two pooled samples. In addition, 8,533-10,144 genes showed evidence of alternative splicing, in agreement with the finding that alternative 3' splicing is the most common type of alternative splicing event in cattle. We detected the expression levels of 16,174 genes, and 6,800 genes exhibited differential expression between the two pooled samples with a false discovery rate ≤0.001. Using GO enrichment and KEGG pathway analysis, multiple GO term and biological pathways were found to be significantly enriched for differentially expressed genes. In addition, we discovered that 30,618-31,334 putative single nucleotide polymorphisms were located in coding regions.

CONCLUSIONS

We obtained a high-quality beef cattle reference transcriptome using a high throughput sequencing approach, thereby providing a valuable resource for better understanding the beef cattle genome. The transcriptome data will facilitate future functional studies on the beef cattle genome and can be applied to breeding programs for cattle and closely related mammals.

High-throughput RNA sequencing of a formalin-fixed, paraffin-embedded autopsy lung tissue sample from the 1918 influenza pandemic

Most biopsy and autopsy tissues are formalin-fixed and paraffin-embedded (FFPE), but this process leads to RNA degradation that limits gene expression analysis. The RNA genome of the 1918 pandemic influenza virus was previously determined in a 9-year effort by overlapping RT-PCR from post-mortem samples. Here, the full genome of the 1918 virus at 3000× coverage was determined in one high-throughput sequencing run of a library derived from total RNA of a 1918 FFPE sample after duplex-specific nuclease treatments. Bacterial sequences associated with secondary bacterial pneumonias were also detected. Host transcripts were well represented in the library. Compared to a 2009 pandemic influenza virus FFPE post-mortem library, the 1918 sample showed significant enrichment for host defence and cell death response genes, concordant with prior animal studies. This methodological approach should assist in the analysis of FFPE tissue samples isolated over the past century from a variety of diseases.

Multiple interferon regulatory factor and NF-κB sites cooperate in mediating cell-type- and maturation-specific activation of the human CD83 promoter in dendritic cells

CD83 is one of the best-known surface markers for fully mature dendritic cells (mature DCs), and its cell-type- and maturation-specific regulation makes the CD83 promoter an interesting tool for the genetic modulation of DCs. To determine the mechanisms regulating this DC- and maturation-specific CD83 expression, chromatin immunoprecipitation (ChIP)-on-chip microarray, biocomputational, reporter, electrophoretic mobility shift assay (EMSA), and ChIP analyses were performed. These studies led to the identification of a ternary transcriptional activation complex composed of an upstream regulatory element, a minimal promoter, and an enhancer, which have not been reported in this arrangement for any other gene so far. Notably, these DNA regions contain a complex framework of interferon regulatory factor (IRF)- and NF-κB transcription factor-binding sites mediating their arrangement. Mutation of any of the IRF-binding sites resulted in a significant loss of promoter activity, whereas overexpression of NF-κB transcription factors clearly enhanced transcription. We identified IRF-1, IRF-2, IRF-5, p50, p65, and cRel to be involved in regulating maturation-specific CD83 expression in DCs. Therefore, the characterization of this promoter complex not only contributes to the knowledge of DC-specific gene regulation but also suggests the involvement of a transcriptional module with binding sites separated into distinct regions in transcriptional activation as well as cell-type- and maturation-specific transcriptional targeting of DCs.

The emerging spectrum of allelic variation in schizophrenia: current evidence and strategies for the identification and functional characterization of common and rare variants

After decades of halting progress, recent large genome-wide association studies (GWAS) are finally shining light on the genetic architecture of schizophrenia. The picture emerging is one of sobering complexity, involving large numbers of risk alleles across the entire allelic spectrum. The aims of this article are to summarize the key genetic findings to date and to compare and contrast methods for identifying additional risk alleles, including GWAS, targeted genotyping and sequencing. A further aim is to consider the challenges and opportunities involved in determining the functional basis of genetic associations, for instance using functional genomics, cellular models, animal models and imaging genetics. We conclude that diverse approaches will be required to identify and functionally characterize the full spectrum of risk variants for schizophrenia. These efforts should adhere to the stringent standards of statistical association developed for GWAS and are likely to entail very large sample sizes. Nonetheless, now more than any previous time, there are reasons for optimism and the ultimate goal of personalized interventions and therapeutics, although still distant, no longer seems unattainable.

Merging multiple omics datasets in silico: statistical analyses and data interpretation

By the combinations of high-throughput analytical technologies in the fields of transcriptomics, proteomics, and metabolomics, we are now able to gain comprehensive and quantitative snapshots of the intracellular processes. Dynamic intracellular activities and their regulations can be elucidated by systematic observation of these multi-omics data. On the other hand, careful statistical analysis is necessary for such integration, since each of the omics layers as well as the specific analytical methodologies harbor different levels of noise and variations. Moreover, interpretation of such multitude of data requires an intuitive pathway context. Here we describe such statistical methods for the integration and comparison of multi-omics data, as well as the computational methods for pathway reconstruction, ID conversion, mapping, and visualization that play key roles for the efficient study of multi-omics information.

Genome-wide nucleosome positioning during embryonic stem cell development

We determined genome-wide nucleosome occupancies in mouse embryonic stem cells and their neural progenitor and embryonic fibroblast counterparts to assess features associated with nucleosome positioning during lineage commitment. Cell-type- and protein-specific binding preferences of transcription factors to sites with either low (Myc, Klf4 and Zfx) or high (Nanog, Oct4 and Sox2) nucleosome occupancy as well as complex patterns for CTCF were identified. Nucleosome-depleted regions around transcription start and transcription termination sites were broad and more pronounced for active genes, with distinct patterns for promoters classified according to CpG content or histone methylation marks. Throughout the genome, nucleosome occupancy was correlated with certain histone methylation or acetylation modifications. In addition, the average nucleosome repeat length increased during differentiation by 5-7 base pairs, with local variations for specific regions. Our results reveal regulatory mechanisms of cell differentiation that involve nucleosome repositioning.

Technical considerations for reduced representation bisulfite sequencing with multiplexed libraries

Reduced representation bisulfite sequencing (RRBS), which couples bisulfite conversion and next generation sequencing, is an innovative method that specifically enriches genomic regions with a high density of potential methylation sites and enables investigation of DNA methylation at single-nucleotide resolution. Recent advances in the Illumina DNA sample preparation protocol and sequencing technology have vastly improved sequencing throughput capacity. Although the new Illumina technology is now widely used, the unique challenges associated with multiplexed RRBS libraries on this platform have not been previously described. We have made modifications to the RRBS library preparation protocol to sequence multiplexed libraries on a single flow cell lane of the Illumina HiSeq 2000. Furthermore, our analysis incorporates a bioinformatics pipeline specifically designed to process bisulfite-converted sequencing reads and evaluate the output and quality of the sequencing data generated from the multiplexed libraries. We obtained an average of 42 million paired-end reads per sample for each flow-cell lane, with a high unique mapping efficiency to the reference human genome. Here we provide a roadmap of modifications, strategies, and trouble shooting approaches we implemented to optimize sequencing of multiplexed libraries on an a RRBS background.

Molecular genomic research designs

Genetic and genomic research approaches have the capability to expand our understanding of the complex pathophysiology of disease susceptibility, susceptibility to complications related to disease, trajectory of recovery from acquired injuries and infections, patient response to interventions and therapeutics, as well as informing diagnoses and prognoses. Nurse scientists are actively involved in all of these fields of inquiry, and the goal of this chapter is to assist with incorporation of genetic and genomic trajectories into their research and facilitate the design and execution of these studies. New studies that are going to embark on recruitment, phenotyping, and sample collection will benefit from forethought about research design to ensure that it addresses the research questions or hypotheses being tested. Studies that will use existing data or samples will also benefit from forethought about research design for the same reason but will also address the fact that some designs may not be feasible with the available data or samples. This chapter discusses candidate gene association, genome-wide association, candidate gene expression, global gene expression, and epigenetic/epigenomic study designs. Information provided includes rationale for selecting an appropriate study design, important methodology considerations for each design, key technologies available to accomplish each type of study, and online resources available to assist in executing each type of study design.

Transcriptomic changes in the bovine conceptus between the blastocyst stage and initiation of implantation

Conceptus-maternal communication is vital for the successful establishment and maintenance of pregnancy, yet relatively little information exists for many of the mechanisms and the nature of the conceptus signals responsible for this cross-talk. Sub-optimal communication, resulting from impairment of conceptus development and/or from abnormal uterine receptivity, contributes to a high incidence of embryonic mortality. Therefore, detailed examination of the mechanisms regulating both pre- and peri-implantation conceptus development are necessary to fully understand the factors regulating successful post-hatching development, pregnancy recognition and implantation signaling. Despite significant progress in understanding of the temporal changes in the transcriptome of the uterine endometrium, there is only a rudimentary knowledge of the genes and pathways governing growth and development of the cattle conceptus. Furthermore, although there are a large number of studies describing gene expression profiles in bovine embryos focused mainly during the earlier preimplantation stages (up to and including Day 7), very little information exists for the post-hatching embryo and elongating conceptus. This period of development is arguably more important as a significant proportion of all embryonic loss occurs between Days 8 and 16 of pregnancy in cattle, corresponding to the time of hatching of the blastocyst from the zona pellucida and its subsequent elongation coincident with the time of maternal recognition of pregnancy. Given that this is a critical period in development leading up to maternal recognition and establishment of pregnancy, the identification of key genes and pathways regulating these crucial developmental events is essential.