Showing papers in "Briefings in Functional Genomics in 2019"

Computational models for lncRNA function prediction and functional similarity calculation.

Abstract: From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.

Deep learning in omics: a survey and guideline.

Abstract: Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fortunately, deep learning technology can contribute toward resolving these challenges. There is evidence that deep learning can handle omics data well and resolve omics problems. This survey aims to provide an entry-level guideline for researchers, to understand and use deep learning in order to solve omics problems. We first introduce several deep learning models and then discuss several research areas which have combined omics and deep learning in recent years. In addition, we summarize the general steps involved in using deep learning which have not yet been systematically discussed in the existent literature on this topic. Finally, we compare the features and performance of current mainstream open source deep learning frameworks and present the opportunities and challenges involved in deep learning. This survey will be a good starting point and guideline for omics researchers to understand deep learning.

Interplay between miRNAs and host genes and their role in cancer.

Abstract: MicroRNAs (miRNAs) are small endogenous non-coding functional RNAs that post-transcriptionally regulate gene expression. They play essential roles in nearly all biological processes including cell development and differentiation, DNA damage repair, cell death as well as intercellular communication. They are highly involved in cancer, acting as tumor suppressors and/or promoters to modulate cell proliferation, epithelial-mesenchymal transition and tumor invasion and metastasis. Recent studies have shown that more than half of miRNAs are located within protein-coding or non-coding genes. Intragenic miRNAs and their host genes either share the promoter or have independent transcription. Meanwhile, miRNAs work as partners or antagonists of their host genes by fine-tuning their target genes functionally associated with host genes. This review outlined the complicated relationship between intragenic miRNAs and host genes. Focusing on miRNAs known as oncogenes or tumor suppressors in specific cancer types, it studied co-expression relationships between these miRNAs and host genes in the cancer types using TCGA data sets, which validated previous findings and revealed common, tumor-specific and even subtype-specific patterns. These observations will help understand the function of intragenic miRNAs and further develop miRNA therapeutics in cancer.

CD8+ T-cell exhaustion in cancer: mechanisms and new area for cancer immunotherapy.

Abstract: Immunotherapies have emerged as the most promising area in cancer treatments in recent years. CD8+ T cells, as one of the primary effector cells of anticancer immunity, however, when infiltrating in cancer tissues, are generally in dysfunctional states termed T-cell exhaustion. Exhausted CD8+ T cells are characterized by impaired activity and proliferative ability, increased apoptotic rate and reduced production of effector cytokines. Such dysfunctional CD8+ T cells serve as a barrier in successful cancer elimination. Investigation on the mechanism of T-cell exhaustion was aiming to sustain or restore the efficiency of CD8+ T cells infiltrating in cancer, which may help to develop novel strategies to overcome cancer. Recent studies have found several vital mechanisms of CD8+ T-cell exhaustion and provided novel avenues through targeting CD8+ T-cell exhaustion to enhance anticancer immunity. Here, we review the recent progress in the study of CD8+ T-cell exhaustion to make a summary and to provide a framework for further researches.

A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae.

Abstract: N6-methyladenosine (m6A) modification, as one of the commonest post-transcription modifications in RNAs, has been reported to be highly related to many biological processes. Over the past decade, several tools for m6A sites prediction of Saccharomyces cerevisiae have been developed and are freely available online. However, the quality of predictions by these tools is difficult to quantify and compare. In this study, an independent dataset M6Atest6540 was compiled to systematically evaluate nine publicly available m6A prediction tools for S. cerevisiae. The experimental results indicate that RAM-ESVM achieved the best performance on M6Atest6540; however, most models performed substantially worse than their performances reported in the original papers. The benchmark dataset Met2614, which was used as the training dataset for the nine methods, were further analyzed by using a position bias index. The results demonstrated the significantly different bias of dataset Met2614 compared with the RNA segments around m6A sites recorded in RMBase. Moreover, newMet2614 was collected by randomly selecting RNA segments from non-redundant data recorded in RMBase, and three different kinds of features were extracted. The performances of the models built on Met2614 and newMet2614 with the features were compared, which shows the better generalization of models built on newMet2614. Our results also indicate the position-specific propensity-based features outperform other features, although they are also easily over-fitted on a biased dataset.

The genomic architecture of antimalarial drug resistance

Abstract: Plasmodium falciparum and Plasmodium vivax, the two protozoan parasite species that cause the majority of cases of human malaria, have developed resistance to nearly all known antimalarials. The ability of malaria parasites to develop resistance is primarily due to the high numbers of parasites in the infected person's bloodstream during the asexual blood stage of infection in conjunction with the mutability of their genomes. Identifying the genetic mutations that mediate antimalarial resistance has deepened our understanding of how the parasites evade our treatments and reveals molecular markers that can be used to track the emergence of resistance in clinical samples. In this review, we examine known genetic mutations that lead to resistance to the major classes of antimalarial medications: the 4-aminoquinolines (chloroquine, amodiaquine and piperaquine), antifolate drugs, aryl amino-alcohols (quinine, lumefantrine and mefloquine), artemisinin compounds, antibiotics (clindamycin and doxycycline) and a napthoquinone (atovaquone). We discuss how the evolution of antimalarial resistance informs strategies to design the next generation of antimalarial therapies.

Cutting back malaria: CRISPR/Cas9 genome editing of Plasmodium.

Abstract: CRISPR/Cas9 approaches are revolutionizing our ability to perform functional genomics across a wide range of organisms, including the Plasmodium parasites that cause malaria. The ability to deliver single point mutations, epitope tags and gene deletions at increased speed and scale is enabling our understanding of the biology of these complex parasites, and pointing to potential new therapeutic targets. In this review, we describe some of the biological and technical considerations for designing CRISPR-based experiments, and discuss potential future developments that broaden the applications for CRISPR/Cas9 interrogation of the malaria parasite genome.

Research progress in protein posttranslational modification site prediction.

Abstract: Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.

The expanding roles of APETALA2/Ethylene Responsive Factors and their potential applications in crop improvement.

Abstract: Understanding the molecular basis of the gene-regulatory networks underlying agronomic traits or plant responses to abiotic/biotic stresses is very important for crop improvement. In this context, transcription factors, which either singularly or in conjugation directly control the expression of many target genes, are suitable candidates for improving agronomic traits via genetic engineering. In this regard, members of one of the largest class of plant-specific APETALA2/Ethylene Response Factor (AP2/ERF) superfamily, which is implicated in various aspects of development and plant stress adaptation responses, are considered high-value targets for crop improvement. Besides their long-known regulatory roles in mediating plant responses to abiotic stresses such as drought and submergence, the novel roles of AP2/ERFs during fruit ripening or secondary metabolites production have also recently emerged. The astounding functional plasticity of AP2/ERF members is considered to be achieved by their interplay with other regulatory networks and signalling pathways. In this review, we have integrated the recently accumulated evidence from functional genomics studies and described their newly emerged functions in plants. The key structural features of AP2/ERF proteins and the modes of their action are briefly summarized. The importance of AP2/ERFs in plant development and stress responses and a summary of the event of their successful applications in crop improvement programs are also provided. Altogether, we envisage that the synthesized information presented in this review will be useful to design effective strategies for improving agronomic traits in crop plants.

Microfluidic devices for embryonic and larval zebrafish studies.

Abstract: Zebrafish or Danio rerio is an established model organism for studying the genetic, neuronal and behavioral bases of diseases and for toxicology and drug screening. The embryonic and larval stages of zebrafish have been used extensively in fundamental and applied research due to advantages offered such as body transparency, small size, low cost of cultivation and high genetic homology with humans. However, the manual experimental methods used for handling and investigating this organism are limited due to their low throughput, labor intensiveness and inaccuracy in delivering external stimuli to the zebrafish while quantifying various neuronal and behavioral responses. Microfluidic and lab-on-a-chip devices have emerged as ideal technologies to overcome these challenges. In this review paper, the current microfluidic approaches for investigation of behavior and neurobiology of zebrafish at embryonic and larval stages will be reviewed. Our focus will be to provide an overview of the microfluidic methods used to manipulate (deliver and orient), immobilize and expose or inject zebrafish embryos or larvae, followed by quantification of their responses in terms of neuron activities and movement. We will also provide our opinion in terms of the direction that the field of zebrafish microfluidics is heading toward in the area of biomedical engineering.

Can luteolin be a therapeutic molecule for both colon cancer and diabetes

Abstract: Diabetes and colon cancer are the leading cause of mortality worldwide. According to World Health Organization, the number of patients with diabetes and cancer is going to be elevated by 50% in 2020. However, several flavonoids have been known to be useful in reducing the chance of cancer/diabetes but the hunt of a single biomolecule that can act as therapeutic and preventive molecules for future epidemic continues. In this review, we aim to perform an illustration of all researches done that target molecular signaling using luteolin in cancer/diabetes and predicted target protein using PharmMapper. The search confirms that luteolin can be a remedial molecule for both cancer and diabetes via acting on variety of signaling pathway. Furthermore, we also intend to illustrate/compare the predicted and verified molecular modes of action of luteolin. Fluorescence in situ hybridization analysis confirms the expression of CCND1 in colon cancer while immunofluorescence analysis confirms the CDK4 in diabetes. Finally, an effort has been made to map docking of marker protein-luteolin at a particular site using docking software. This review gives a holistic overview about luteolin as a therapeutic molecule for cancer/diabetes via acting on multiple signaling cascade such as p53, Wnt, eNOS, iNOS, SOD and MMP9, with especial emphasis on the cyclin-CDK pathway. Altogether, the review concludes that luteolin can be a molecule for the therapy of both cancer and diabetes by acting on broad signaling pathway.

The bioinformatics tools for the genome assembly and analysis based on third-generation sequencing

Abstract: The application of third-generation sequencing (TGS) technology in genetics and genomics have provided opportunities to categorize and explore the individual genomic landscapes and mutations relevant for diagnosis and therapy using whole genome sequencing and de novo genome assembly. In general, the emerging TGS technology can produce high quality long reads for the determination of overlapping reads and transcript isoforms. However, this technology still faces challenges such as the accuracy for the identification of nucleotide bases and high error rates. Here, we surveyed 39 TGS-related tools for de novo assembly and genome analysis to identify the differences among their characteristics, such as the required input, the interaction with the user, sequencing platforms, type of reads, error models, the possibility of introducing coverage bias, the simulation of genomic variants and outputs provided. The decision trees are summarized to help researchers to find out the most suitable tools to analyze the TGS data. Our comprehensive survey and evaluation of computational features of existing methods for TGS may provide a valuable guideline for researchers.

Transcriptional variation in malaria parasites: why and how

Abstract: Transcriptional differences enable the generation of alternative phenotypes from the same genome. In malaria parasites, transcriptional plasticity plays a major role in the process of adaptation to fluctuations in the environment. Multiple studies with culture-adapted parasites and field isolates are starting to unravel the different transcriptional alternatives available to Plasmodium falciparum and the underlying molecular mechanisms. Here we discuss how epigenetic variation, directed transcriptional responses and also genetic changes that affect transcript levels can all contribute to transcriptional variation and, ultimately, parasite survival. Some transcriptional changes are driven by stochastic events. These changes can occur spontaneously, resulting in heterogeneity within parasite populations that provides the grounds for adaptation by dynamic natural selection. However, transcriptional changes can also occur in response to external cues. A better understanding of the mechanisms that the parasite has evolved to alter its transcriptome may ultimately contribute to the design of strategies to combat malaria to which the parasite cannot adapt.

What functional genomics has taught us about transcriptional regulation in malaria parasites.

Abstract: Malaria parasites are characterized by a complex life cycle that is accompanied by dynamic gene expression patterns. The factors and mechanisms that regulate gene expression in these parasites have been searched for even before the advent of next generation sequencing technologies. Functional genomics approaches have substantially boosted this area of research and have yielded significant insights into the interplay between epigenetic, transcriptional and post-transcriptional mechanisms. Recently, considerable progress has been made in identifying sequence-specific transcription factors and DNA-encoded regulatory elements. Here, we review the insights obtained from these efforts including the characterization of core promoters, the involvement of sequence-specific transcription factors in life cycle progression and the mapping of gene regulatory elements. Furthermore, we discuss recent developments in the field of functional genomics and how they might contribute to further characterization of this complex gene regulatory network.

The role of epigenetics and chromatin structure in transcriptional regulation in malaria parasites.

Abstract: Due to the unique selective pressures and extreme changes faced by the human malaria parasite Plasmodium falciparum throughout its life cycle, the parasite has evolved distinct features to alter its gene expression patterns. Along with classical gene regulation by transcription factors (TFs), of which only one family, the AP2 TFs, has been described in the parasite genome, a large body of evidence points toward chromatin structure and epigenetic factors mediating the changes in gene expression associated with parasite life cycle stages. These attributes may be critically important for immune evasion, host cell invasion and development of the parasite in its two hosts, the human and the Anopheles vector. Thus, the factors involved in the maintenance and regulation of chromatin and epigenetic features represent potential targets for antimalarial drugs. In this review, we discuss the mechanisms in P. falciparum that regulate chromatin structure, nucleosome landscape, the 3-dimensional structure of the genome and additional distinctive features created by parasite-specific genes and gene families. We review conserved traits of chromatin in eukaryotes in order to highlight what is unique in the parasite.

Polycomb proteins as organizers of 3D genome architecture in embryonic stem cells.

Abstract: Polycomb group proteins (PcGs) control the epigenetic and transcriptional state of developmental genes and regulatory elements during mammalian embryogenesis. Moreover, PcGs can also contribute to 3D genome organization, adding an additional layer of complexity to their regulatory functions. Understanding the mechanistic basis and the dynamics of PcG-dependent chromatin structures will help us untangle the full complexity of PcG function during development. Since most studies concerning the 3D organization of PcG-bound chromatin in mammals have been performed in embryonic stem cells (ESCs), here we will focus on this cell type characterized by its unique self-renewal and pluripotency properties. More specifically, we will highlight recent findings and discuss open questions regarding how PcG-dependent changes in 3D chromatin architecture control gene expression, cellular identity and differentiation potential in ESCs. We believe that this can serve to illustrate the diverse regulatory mechanisms by which PcG proteins control the proper execution of gene expression programs during mammalian embryogenesis.

Immune checkpoints and cancer in the immunogenomics era

Abstract: Immune checkpoints have been the subject of a wave of new studies. Among these checkpoints are tytotoxic T-lymphocyte-associated antigen 4, checkpoints programmed death-1 and programmed death-ligand 1; their blockades have been approved by the Food and Drug Administration for therapy of melanoma and other types of cancers. Immunogenomics, which combines the latest nucleic acid sequencing strategy with immunotherapy, provides precise information about genomic alterations (e.g. mutations) and enables a paradigm shift of immune checkpoint therapy from tumor types to molecular signatures. Studying these critical checkpoints in relation to genomic mutations and neoantigens has produced groundbreaking results. This article examines these studies and delves into the relationships between immune checkpoint blockade and tumors harboring certain genomic mutations. Moreover, this article reviews recent studies on resistance to immune checkpoint therapy.

Prediction of tumor metastasis from sequencing data in the era of genome sequencing

Abstract: Tumor metastasis is the key reason for the high mortality rate of tumor. Growing number of scholars have begun to pay attention to the research on tumor metastasis and have achieved satisfactory results in this field. The advent of the era of sequencing has enabled us to study cancer metastasis at the molecular level, which is essential for understanding the molecular mechanism of metastasis, identifying diagnostic markers and therapeutic targets and guiding clinical decision-making. We reviewed the metastasis-related studies using sequencing data, covering detection of metastasis origin sites, determination of metastasis potential and identification of distal metastasis sites. These findings include the discovery of relevant markers and the presentation of prediction tools. Finally, we discussed the challenge of studying metastasis considering the difficulty of obtaining metastatic cancer data, the complexity of tumor heterogeneity and the uncertainty of sample labels.

The application of CRISPR-Cas9 genome editing tool in cancer immunotherapy.

Abstract: Clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (CRISPR-Cas9) system was originally discovered in prokaryotes functioned as a part of the adaptive immune system. Because of its high efficiency and easy operability, CRISPR-Cas9 system has been developed to be a powerful and versatile gene editing tool shortly after its discovery. Given that multiple genetic alterations are the main factors that drive genesis and development of tumor, CRISPR-Cas9 system has been applied to correct cancer-causing gene mutations and deletions and to engineer immune cells, such as chimeric antigen receptor T (CAR T) cells, for cancer immunotherapeutic applications. Recently, CRISPR-Cas9-based CAR T-cell preparation has been an important breakthrough in antitumor therapy. Here, we summarize the mechanism, delivery and the application of CRISPR-Cas9 in gene editing, and discuss the challenges and future directions of CRISPR-Cas9 in cancer immunotherapy.

Single-nucleotide variants in human RNA: RNA editing and beyond.

Abstract: Through analysis of paired high-throughput DNA-Seq and RNA-Seq data, researchers quickly recognized that RNA-Seq can be used for more than just gene expression quantification. The alternative applications of RNA-Seq data are abundant, and we are particularly interested in its usefulness for detecting single-nucleotide variants, which arise from RNA editing, genomic variants and other RNA modifications. A stunning discovery made from RNA-Seq analyses is the unexpectedly high prevalence of RNA-editing events, many of which cannot be explained by known RNA-editing mechanisms. Over the past 6-7 years, substantial efforts have been made to maximize the potential of RNA-Seq data. In this review we describe the controversial history of mining RNA-editing events from RNA-Seq data and the corresponding development of methodologies to identify, predict, assess the quality of and catalog RNA-editing events as well as genomic variants.

Technologies to study spatial genome organization: beyond 3C.

Abstract: The way that chromatin is organized in three-dimensional nuclear space is now acknowledged as a factor critical for the major cell processes, like transcription, replication and cell division. Researchers have been armed with new molecular and imaging technologies to study this structure-to-function link of genomes, spearheaded by the introduction of the 'chromosome conformation capture' technology more than a decade ago. However, this technology is not without shortcomings, and novel variants and orthogonal approaches are being developed to overcome these. As a result, the field of nuclear organization is constantly fueled by methods of increasing resolution and/or throughput that strive to eliminate systematic biases and increase precision. In this review, we attempt to highlight the most recent advances in technology that promise to provide novel insights on how chromosomes fold and function.

The second life of Plasmodium in the mosquito host: gene regulation on the move.

Abstract: Malaria parasites face dynamically changing environments and strong selective constraints within human and mosquito hosts. To survive such hostile and shifting conditions, Plasmodium switches transcriptional programs during development and has evolved mechanisms to adjust its phenotype through heterogeneous patterns of gene expression. In vitro studies on culture-adapted isolates have served to set the link between chromatin structure and functional gene expression. Yet, experimental evidence is limited to certain stages of the parasite in the vertebrate, i.e. blood, while the precise mechanisms underlying the dynamic regulatory landscapes during development and in the adaptation to within-host conditions remain poorly understood. In this review, we discuss available data on transcriptional and epigenetic regulation in Plasmodium mosquito stages in the context of sporogonic development and phenotypic variation, including both bet-hedging and environmentally triggered direct transcriptional responses. With this, we advocate the mosquito offers an in vivo biological model to investigate the regulatory networks, transcription factors and chromatin-modifying enzymes and their modes of interaction with regulatory sequences, which might be responsible for the plasticity of the Plasmodium genome that dictates stage- and cell type-specific blueprints of gene expression.

Functional genomics of simian malaria parasites and host-parasite interactions

Abstract: Two simian malaria parasite species, Plasmodium knowlesi and Plasmodium cynomolgi, cause zoonotic infections in Southeast Asia, and they have therefore gained recognition among scientists and public health officials. Notwithstanding, these species and others including Plasmodium coatneyi have served for decades as sources of knowledge on the biology, genetics and evolution of Plasmodium, and the diverse ramifications and outcomes of malaria in their monkey hosts. Experimental analysis of these species can help to fill gaps in knowledge beyond what may be possible studying the human malaria parasites or rodent parasite species. The genome sequences for these simian malaria parasite species were reported during the last decade, and functional genomics research has since been pursued. Here research on the functional genomics analysis involving these species is summarized and their importance is stressed, particularly for understanding host-parasite interactions, and potentially testing novel interventions. Importantly, while Plasmodium falciparum and Plasmodium vivax can be studied in small New World monkeys, the simian malaria parasites can be studied more effectively in the larger Old World monkey macaque hosts, which are more closely related to humans. In addition to ex vivo analyses, experimental scenarios can include passage through Anopheline mosquito hosts and longitudinal infections in monkeys to study acute and chronic infections, as well as relapses, all in the context of the in vivo host environment. Such experiments provide opportunities for understanding functional genomic elements that govern host-parasite interactions, immunity and pathogenesis in-depth, addressing hypotheses not possible from in vitro cultures or cross-sectional clinical studies with humans.

Gene-specific metrics to facilitate identification of disease genes for molecular diagnosis in patient genomes: a systematic review

Abstract: The evolution of next-generation sequencing technologies has facilitated the detection of causal genetic variants in diseases previously undiagnosed at a molecular level. However, in genome sequencing studies, the identification of disease genes among a candidate gene list is often difficult because of the large number of apparently damaging (but usually neutral) variants. A number of variant prioritization tools have been developed to help detect disease-causal sites. However, the results may be misleading as many variants scored as damaging by these tools are often tolerated, and there are inconsistencies in prediction results among the different variant-level prediction tools. Recently, studies have indicated that understanding gene properties might improve detection of genes liable to have associated disease variation and that this information improves molecular diagnostics. The purpose of this systematic review is to evaluate how understanding gene-specific properties might improve filtering strategies in clinical sequence data to prioritize potential disease variants. Improved understanding of the 'disease genome', which includes coding, noncoding and regulatory variation, might help resolve difficult cases. This review provides a comprehensive assessment of existing gene-level approaches, the relationships between measures of gene-pathogenicity and how use of these prediction tools can be developed for molecular diagnostics.

Human core duplicon gene families: game changers or game players?

Abstract: Illuminating the role of specific gene duplications within the human lineage can provide insights into human-specific adaptations. The so-called human core duplicon gene families have received particular attention in this respect, due to special features, such as expansion along single chromosomes, newly acquired protein domains and signatures of positive selection. Here, we summarize the data available for 10 such families and include some new analyses. A picture emerges that suggests broad functions for these protein families, possibly through modification of core cellular pathways. Still, more dedicated studies are required to elucidate the function of core-duplicons gene families and how they have shaped adaptations and evolution of humans.

Functional genomics: paving the way for more successful cancer immunotherapy.

Abstract: Immunotherapies have revolutionized cancer treatment. Immunotherapy is effective for the treatment of a wide range of cancer types and can mediate complete and durable tumor regression. Nonetheless, the field still faces many significant challenges, such as the need for personalized therapeutic strategies and better biomarkers, the difficulty of selecting the right combination therapy, and resistance to currently available immunotherapies. Both cancer and host immunity comprise significantly diverse and complex ecosystems, making immunogenomics an ideal field for functional genomics analysis. In this review, we describe the cancer-immunity cycle, how cancer cells manage to evade immune attack and the current hurdles in the path of cancer immunotherapy. Then, we discuss how functional genomics approaches can pave the way for more successful cancer immunotherapies.

Identification and validation of plant miRNA from NGS data-an experimental approach.

Abstract: miRNAs are class of endogenously initiated noncoding RNAs, which are most critical in gene expression and regulation at posttranscriptional level. They do so either by cleavage of the target mRNA or by translational repression. miRNAs are being given enough attention in recent years because of its role in myriad developmental processes including tumorogenesis and host-pathogen interaction. Advent of Next Generation Sequencing (NGS) technology and computational approach made it possible to pinpoint the precise role of miRNA and their target. Identification of miRNAs and their target has several approaches depending on efficiency, cost and time. The present review summarizes the developments in the field of plant miRNA w.r.t. to experimental approaches that are being followed to identify and validate the miRNAs and their targets.

Multi-omics investigations within the Phylum Mollusca, Class Gastropoda: from ecological application to breakthrough phylogenomic studies

Abstract: Gastropods are the largest and most diverse class of mollusc and include species that are well studied within the areas of taxonomy, aquaculture, biomineralization, ecology, microbiome and health. Gastropod research has been expanding since the mid-2000s, largely due to large-scale data integration from next-generation sequencing and mass spectrometry in which transcripts, proteins and metabolites can be readily explored systematically. Correspondingly, the huge data added a great deal of complexity for data organization, visualization and interpretation. Here, we reviewed the recent advances involving gastropod omics ('gastropodomics') research from hundreds of publications and online genomics databases. By summarizing the current publicly available data, we present an insight for the design of useful data integrating tools and strategies for comparative omics studies in the future. Additionally, we discuss the future of omics applications in aquaculture, natural pharmaceutical biodiscovery and pest management, as well as to monitor the impact of environmental stressors.

Next-generation sequencing technologies accelerate advances in T-cell therapy for cancer

Abstract: Next-generation sequencing has produced a large quantity of DNA or RNA sequences related to the processes occurring within tumors and their microenvironment in a reasonable time and cost. These data have been used to guide the identification of neoantigens and to determine their specific T-cell receptors. Furthermore, adoptive T-cell therapy targeting neoantigens is under development for cancer treatment. In this review, we first provide an overview of sequencing technologies and the updated findings concerning neoantigens related to adoptive T-cell therapy and then summarize the methods and principles underlying the development of next-generation sequencing-based neoantigen-reactive T-cell therapy for cancer.

Tantalizing dilemma in risk prediction from disease scoring statistics

Abstract: Over the past decade, human host genome-wide association studies (GWASs) have contributed greatly to our understanding of the impact of host genetics on phenotypes. Recently, the microbiome has been recognized as a complex trait in host genetic variation, leading to microbiome GWAS (mGWASs). For these, many different statistical methods and software tools have been developed for association mapping. Applications of these methods and tools have revealed several important findings; however, the establishment of causal factors and the direction of causality in the interactive role between human genetic polymorphisms, the microbiome and the host phenotypes are still a huge challenge. Here, we review disease scoring approaches in host and mGWAS and their underlying statistical methods and tools. We highlight the challenges in pinpointing the genetic-associated causal factors in host and mGWAS and discuss the role of multi-omic approach in disease scoring statistics that may provide a better understanding of human phenotypic variation by enabling further system biological experiment to establish causality.