What is a gene, post-ENCODE? History and updated definition

Evaluation of efficacy of non-coding RNA in abiotic stress management of field crops: Current status and future prospective

Abiotic stresses are responsible for the major losses in crop yield all over the world. Stresses generate harmful ROS which can impair cellular processes in plants. Therefore, plants have evolved antioxidant systems in defence against the stress-induced damages. The frequency of occurrence of abiotic stressors has increased several-fold due to the climate change experienced in recent times and projected for the future. This had particularly aggravated the risk of yield losses and threatened global food security. Non-coding RNAs are the part of eukaryotic genome that does not code for any proteins. However, they have been recently found to have a crucial role in the responses of plants to both abiotic and biotic stresses. There are different types of ncRNAs, for example, miRNAs and lncRNAs, which have the potential to regulate the expression of stress-related genes at the levels of transcription, post-transcription, and translation of proteins. The lncRNAs are also able to impart their epigenetic effects on the target genes through the alteration of the status of histone modification and organization of the chromatins. The current review attempts to deliver a comprehensive account of the role of ncRNAs in the regulation of plants' abiotic stress responses through ROS homeostasis. The potential applications ncRNAs in amelioration of abiotic stresses in field crops also have been evaluated.

The conservation of gene models can support genome annotation

Many genome annotations include false-positive gene models, leading to errors in phylogenetic and comparative studies. Here, we propose a method to support gene model prediction based on evolutionary conservation and use it to identify potentially erroneous annotations. Using this method, we developed a set of 15,345 representative gene models from 12 legume assemblies that can be used to support genome annotations for other legumes.

Genome annotation: From human genetics to biodiversity genomics

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

Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection

Despite extensive research efforts, reconstruction of gene regulatory networks (GRNs) from transcriptomics data remains a pressing challenge in systems biology. While non-linear approaches for reconstruction of GRNs show improved performance over simpler alternatives, we do not yet have understanding if joint modelling of multiple target genes may improve performance, even under linearity assumptions. To address this problem, we propose two novel approaches that cast the GRN reconstruction problem as a blend between regularized multivariate regression and graphical models that combine the L2,1-norm with classical regularization techniques. We used data and networks from the DREAM5 challenge to show that the proposed models provide consistently good performance in comparison to contenders whose performance varies with data sets from simulation and experiments from model unicellular organisms Escherichia coli and Saccharomyces cerevisiae. Since the models' formulation facilitates the prediction of master regulators, we also used the resulting findings to identify master regulators over all data sets as well as their plasticity across different environments. Our results demonstrate that the identified master regulators are in line with experimental evidence from the model bacterium E. coli. Together, our study demonstrates that simultaneous modelling of several target genes results in improved inference of GRNs and can be used as an alternative in different applications.

Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Development as explanation: Understanding phenotypic stability and variability after the failure of genetic determinism

In the predominately gene-centered view of 20th century biology, the relationship between genotype and phenotype was essentially a relationship between cause and effect, between a plan and a product. Abandoning the idea of genes as inherited instructions or blueprints for phenotypes raises the question of how to best account for observed phenotypic stability and variability within and across generations of a population. We argue that the processes responsible for phenotypic stability and the processes responsible for phenotypic variability are one and the same, namely, the dynamics of development. This argument proposes that stability of phenotypic form is found not because of the transmission of genotypes, genetic programs, or the transfer of internal blueprints, but because similar internal and external conditions-collectively conceptualized as resources of development-can be reliably reconstituted in each generation. Variability of phenotypic form, which is an indispensable feature of any evolving system, relies on these same resources, but because the internal and external conditions of development are not reconstituted identically in succeeding generations, these conditions-and the phenotypes to which they give rise-will always be characterized by at least some variability.

Is RNA the working genome in eukaryotes ? The 60 year evolution of a conceptual challenge

About 80 years ago, in 1943, after a century of biochemical and genetic research, DNA was established as the carrier of genetic information. At the onset of Molecular Biology around 1960, the genome of living organisms embodied 3 basic, still unknown paradigms: its composition, organisation and expression. Between 1980 and 1990, its replication was understood, and ideas about its 3D-organisation were suggested and finally confirmed by 2010. The basic mechanisms of gene expression in higher organisms, the synthesis of precursor RNAs and their processing into functional RNAs, were also discovered about 60 years ago in 1961/62. However, some aspects were then, and are still now debated, although the latest results in post-genomic research have confirmed the basic principles. When my history-essay was published in 2003, describing the discovery of RNA processing 40 years earlier, the main facts were not yet generally confirmed or acknowledged. The processing of pre-rRNA to 28 S and 18 S rRNA was clearly demonstrated, confirmed by others and generally accepted as a fact. However, the "giant" size of pre-mRNA 10-100 kb-long and pervasive DNA transcription were still to be confirmed by post-genomic methods. It was found, surprisingly, that up to 90% of DNA is transcribed in the life cycle of eukaryotic organisms thus showing that pervasive transcription was the general rule. In this essay, we shall take a journey through the 60-year history of evolving paradigms of gene expression which followed the emergence of Molecular Biology, and we will also evoke some of the "folklore" in research throughout this period. Most important was the growing recognition that although the genome is encoded in DNA, the Working Genome in eukaryotic organisms is RNA.

De novo emergence, existence, and demise of a protein-coding gene in murids

BACKGROUND

Genes, principal units of genetic information, vary in complexity and evolutionary history. Less-complex genes (e.g., long non-coding RNA (lncRNA) expressing genes) readily emerge de novo from non-genic sequences and have high evolutionary turnover. Genesis of a gene may be facilitated by adoption of functional genic sequences from retrotransposon insertions. However, protein-coding sequences in extant genomes rarely lack any connection to an ancestral protein-coding sequence.

RESULTS

We describe remarkable evolution of the murine gene D6Ertd527e and its orthologs in the rodent Muroidea superfamily. The D6Ertd527e emerged in a common ancestor of mice and hamsters most likely as a lncRNA-expressing gene. A major contributing factor was a long terminal repeat (LTR) retrotransposon insertion carrying an oocyte-specific promoter and a 5' terminal exon of the gene. The gene survived as an oocyte-specific lncRNA in several extant rodents while in some others the gene or its expression were lost. In the ancestral lineage of Mus musculus, the gene acquired protein-coding capacity where the bulk of the coding sequence formed through CAG (AGC) trinucleotide repeat expansion and duplications. These events generated a cytoplasmic serine-rich maternal protein. Knock-out of D6Ertd527e in mice has a small but detectable effect on fertility and the maternal transcriptome.

CONCLUSIONS

While this evolving gene is not showing a clear function in laboratory mice, its documented evolutionary history in Muroidea during the last ~ 40 million years provides a textbook example of how a several common mutation events can support de novo gene formation, evolution of protein-coding capacity, as well as gene's demise.

Learning in Individual Organisms, Genes, Machines, and Groups: A New Way of Defining and Relating Learning in Different Systems

Learning is a central concept in many scientific disciplines. Communication about research on learning is, however, hampered by the fact that different researchers define learning in different ways. In this article, we introduce the extended functional definition of learning that can be used across scientific disciplines. We provide examples of how the definition can be applied to individual organisms, genes, machines, and groups. Using the extended functional definition (a) reveals a heuristic framework for research that can be applied across scientific disciplines, (b) allows researchers to engage in intersystem analyses that relate the behavior and learning of different systems, and (c) clarifies how learning differs from other phenomena such as (changes in) behavior, damaging systems, and programming systems.

Individual Genetic Heterogeneity

Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.

The Role of Pharmacogenomics in Opioid Prescribing

Pharmacogenomics is increasingly important to guide objective, safe, and effective individualised prescribing. Personalised prescribing has revolutionised treatments in the past decade, allowing clinicians to maximise drug efficacy and minimise adverse effects based on a person’s genetic profile. Opioids, the gold standard for cancer pain relief, are among the commonest medications prescribed in palliative care practice. This narrative review examines the literature surrounding opioid pharmacogenomics and its applicability to the palliative care cancer population. There is currently limited intersection between the fields of palliative care and pharmacogenomics, but growing evidence presents a need to build linkages between the two disciplines. Pharmacogenomic evidence guiding opioid prescribing is currently available for codeine and tramadol, which relates to CYP2D6 gene variants. However, these medications are prescribed less commonly for pain in palliative care. Research is accelerating with other opioids, where oxycodone (CYP2D6) and methadone (CYP2B6, ABCB1) already have moderate evidence of an association in terms of drug metabolism and downstream analgesic response and side effects. OPRM1 and COMT are receiving increasing attention and have implications for all opioids, with changes in opioid dosage requirements observed but they have not yet been studied widely enough to be considered clinically actionable. Current evidence indicates that incorporation of pharmacogenomic testing into opioid prescribing practice should focus on the CYP2D6 gene and its actionable variants. Although opioid pharmacogenomic tests are not widely used in clinical practice, the progressively reducing costs and rapid turnover means greater accessibility and affordability to patients, and thus, clinicians will be increasingly asked to provide guidance in this area. The upsurge in pharmacogenomic research will likely discover more actionable gene variants to expand international guidelines to impact opioid prescribing. This rapidly expanding area requires consideration and monitoring by clinicians in order for key findings with clinical implications to be accessible, meaningfully interpretable and communicated.

Analysis of repeat elements in the Pristionchus pacificus genome reveals an ancient invasion by horizontally transferred transposons

BACKGROUND

Repetitive sequences and mobile elements make up considerable fractions of individual genomes. While transposition events can be detrimental for organismal fitness, repetitive sequences form an enormous reservoir for molecular innovation. In this study, we aim to add repetitive elements to the annotation of the Pristionchus pacificus genome and assess their impact on novel gene formation.

RESULTS

Different computational approaches define up to 24% of the P. pacificus genome as repetitive sequences. While retroelements are more frequently found at the chromosome arms, DNA transposons are distributed more evenly. We found multiple DNA transposons, as well as LTR and LINE elements with abundant evidence of expression as single-exon transcripts. When testing whether transposons disproportionately contribute towards new gene formation, we found that roughly 10-20% of genes across all age classes overlap transposable elements with the strongest trend being an enrichment of low complexity regions among the oldest genes. Finally, we characterized a horizontal gene transfer of Zisupton elements into diplogastrid nematodes. These DNA transposons invaded nematodes from eukaryotic donor species and experienced a recent burst of activity in the P. pacificus lineage.

CONCLUSIONS

The comprehensive annotation of repetitive elements in the P. pacificus genome builds a resource for future functional genomic analyses as well as for more detailed investigations of molecular innovations.

Chimeric RNA TNNI2-ACTA1-V1 Regulates Cell Proliferation by Regulating the Expression of NCOA3

Chimeric RNA is a crucial target for tumor diagnosis and drug therapy, also having its unique biological role in normal tissues. TNNI2-ACTA1-V1 (TA-V1), a chimeric RNA discovered by our laboratory in porcine muscle tissue, can inhibit the proliferation of Porcine Skeletal Muscle Satellite Cells (PSCs). The regulatory mechanism of TA-V1 in PSCs remains unclear, but we speculate that NCOA3, DDR2 and RDX may be the target genes of TA-V1. In this study, we explored the effects of NCOA3, DDR2 and RDX on cell viability and cell proliferation by CCK-8 assay, EdU staining and flow cytometry. Furthermore, the regulatory pathway of proliferation in PSCs mediated by TA-V1 through NCOA3 or CyclinD1 was elucidated by co-transfection and co-immunoprecipitation (Co-IP). The results revealed that overexpression of NCOA3 significantly increased cell viability and the expression level of CyclinD1, and also promotes cell proliferation by changing cells from the G1 phase to the S phase. In addition, inhibiting the expression of NCOA3 substantially reduced cell viability and inhibited cell proliferation. Overexpression of DDR2 and RDX had no significant effect on cell viability and proliferation. Co-transfection experiments showed that NCOA3 could rescue the proliferation inhibition of PSCs caused by TA-V1. Co-IP assay indicated that TA-V1 directly interacts with NCOA3. Our study explores the hypothesis that TA-V1 directly regulates NCOA3, indirectly regulating CyclinD1, thereby regulating PSCs proliferation. We provide new putative mechanisms of porcine skeletal muscle growth and lay the foundation for the study of chimeric RNA in normal tissues.

Evaluating Plant Gene Models Using Machine Learning

Gene models are regions of the genome that can be transcribed into RNA and translated to proteins, or belong to a class of non-coding RNA genes. The prediction of gene models is a complex process that can be unreliable, leading to false positive annotations. To help support the calling of confident conserved gene models and minimize false positives arising during gene model prediction we have developed Truegene, a machine learning approach to classify potential low confidence gene models using 14 gene and 41 protein-based characteristics. Amino acid and nucleotide sequence-based features were calculated for conserved (high confidence) and non-conserved (low confidence) annotated genes from the published Pisum sativum Cameor genome. These features were used to train eXtreme Gradient Boost (XGBoost) classifier models to predict whether a gene model is likely to be real. The optimized models demonstrated a prediction accuracy ranging from 87% to 90% and an F-1 score of 0.91–0.94. We used SHapley Additive exPlanations (SHAP) and feature importance plots to identify the features that contribute to the model predictions, and we show that protein and gene-based features can be used to build accurate models for gene prediction that have applications in supporting future gene annotation processes.

Controlling the Effects of External Perturbations on a Gene Regulatory Network Using Proportional-Integral-Derivative Controller

Gene regulatory networks are biologically robust, which imparts resilience to living systems against most external perturbations affecting them. However, there is a limit to this and disturbances beyond this limit can impart unwanted signalling on one or more master regulators in a network. Certain disturbances may affect the functioning of other constituent genes of the same network. In most cases, this phenomenon can have some effect on the functioning of the living organism. In this investigation, we have proposed a methodology to mitigate the effects of external perturbations on a genetic network using a proportional-integral-derivative controller. The proposed controller has been used to perturb one or more of the other unaffected master regulators such that the most affected gene/s of the network revert to their normal state. The only required condition of such type of manoeuvring is that there should be multiple master regulators in a network. The proposed technique has been experimented on a 10-gene DREAM4 benchmark network and also on a larger 20-gene network, where only downregulation has been considered due to data constraints. Simulation results indicate that the most vulnerable genes can be reverted to their normal expression levels in 10 out of the 16 simulations performed.

Prediction and comparative analysis of CTCF binding sites based on a first principle approach

We calculated the patterns for the CCCTC transcription factor (CTCF) binding sites across many genomes on a first principle approach. The validation of the first principle method was done on the human as well as on the mouse genome. The predicted human CTCF binding sites are consistent with the consensus sequence, ChIP-seq data for the K562 cell, nucleosome positions for IMR90 cell as well as the CTCF binding sites in the mouse HOXA gene. The analysis of Homo sapiens, Mus musculus, Sus scrofa, Capra hircus and Drosophila melanogaster whole genomes shows: binding sites are organized in cluster-like groups, where two consecutive sites obey a power-law with coefficient ranging from to 0.3292 0.0068 to 0.5409 0.0064; the distance between these groups varies from 18.08 0.52kbp to 42.1 2.0kbp. The genome of Aedes aegypti does not show a power law, but 19.9% of binding sites are 144 4 and 287 5bp distant of each other. We run negative tests, confirming the under-representation of CTCF binding sites in Caenorhabditis elegans, Plasmodium falciparum and Arabidopsis thaliana complete genomes.

The emergence of the postgenomic gene

The identity and the existence of genes has been challenged by postgenomic discoveries. Specifically, the consideration of molecular and cellular phenomena in which genes are embedded has proved relevant for their understanding. In response to these challenges, I will argue that the complexity of genetic phenomena supports the weak emergence of genes from the DNA. In Section 2, I will expose what genes are taken to be in the postgenomic world. In Section 3, I will present the relevant account of emergence. I consider weak emergence as in Franklin and Knox (Studies for the History and Philosophy of Modern Physics, 64, 68-78, 2018), for which a phenomenon is emergent when it displays novelty and robustness. In Section 4, I will argue that genes are weakly emergent since they are novel, improving explanations, and robust in respect to some perturbations. Then, I will conclude in Section 5 that genes' emergence is a way to allow genes' flexibility and context dependency, without compromising their existence.

Foster thy young: enhanced prediction of orphan genes in assembled genomes

Proteins encoded by newly-emerged genes ('orphan genes') share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene prediction pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes, 99% of ancient genes, and give the highest sensitivity score regardless dataset in Arabidopsis. We provide a light weight, flexible, reproducible, and well-documented solution to improve gene prediction.

MicroRNA-Related Strategies to Improve Cardiac Function in Heart Failure

Heart failure (HF) describes a group of manifestations caused by the failure of heart function as a pump that supports blood flow through the body. MicroRNAs (miRNAs), as one type of non-coding RNA molecule, have crucial roles in the etiology of HF. Accordingly, miRNAs related to HF may represent potential novel therapeutic targets. In this review, we first discuss the different roles of miRNAs in the development and diseases of the heart. We then outline commonly used miRNA chemical modifications and delivery systems. Further, we summarize the opportunities and challenges for HF-related miRNA therapeutics targets, and discuss the first clinical trial of an antisense drug (CDR132L) in patients with HF. Finally, we outline current and future challenges and potential new directions for miRNA-based therapeutics for HF.

Transcriptome-Wide Analysis of Human Liver Reveals Age-Related Differences in the Expression of Select Functional Gene Clusters and Evidence for a PPP1R10-Governed 'Aging Cascade'

A transcriptome-wide analysis of human liver for demonstrating differences between young and old humans has not yet been performed. However, identifying major age-related alterations in hepatic gene expression may pinpoint ontogenetic shifts with important hepatic and systemic consequences, provide novel pharmacogenetic information, offer clues to efficiently counteract symptoms of old age, and improve the overarching understanding of individual decline. Next-generation sequencing (NGS) data analyzed by the Mann-Whitney nonparametric test and Ensemble Feature Selection (EFS) bioinformatics identified 44 transcripts among 60,617 total and 19,986 protein-encoding transcripts that significantly (p = 0.0003 to 0.0464) and strikingly (EFS score > 0.3:16 transcripts; EFS score > 0.2:28 transcripts) differ between young and old livers. Most of these age-related transcripts were assigned to the categories 'regulome', 'inflammaging', 'regeneration', and 'pharmacogenes'. NGS results were confirmed by quantitative real-time polymerase chain reaction. Our results have important implications for the areas of ontogeny/aging and the age-dependent increase in major liver diseases. Finally, we present a broadly substantiated and testable hypothesis on a genetically governed 'aging cascade', wherein PPP1R10 acts as a putative ontogenetic master regulator, prominently flanked by IGFALS and DUSP1. This transcriptome-wide analysis of human liver offers potential clues towards developing safer and improved therapeutic interventions against major liver diseases and increased insights into key mechanisms underlying aging.

Using Cartesian Doubt To Build a Sequencing-Based View of Microbiology

The technological leap of DNA sequencing generated a tension between modern metagenomics and historical microbiology. We are forcibly harmonizing the output of a modern tool with centuries of experimental knowledge derived from culture-based microbiology. As a thought experiment, we borrow the notion of Cartesian doubt from philosopher Rene Descartes, who used doubt to build a philosophical framework from his incorrigible statement that "I think therefore I am." We aim to cast away preconceived notions and conceptualize microorganisms through the lens of metagenomic sequencing alone. Specifically, we propose funding and building analysis and engineering methods that neither search for nor rely on the assumption of independent genomes bound by lipid barriers containing discrete functional roles and taxonomies. We propose that a view of microbial communities based in sequencing will engender novel insights into metagenomic structure and may capture functional biology not reflected within the current paradigm.

On how to generalize species-specific conceptual schemes to generate a species-independent Conceptual Schema of the Genome

BACKGROUND

Understanding the genome, with all of its components and intrinsic relationships, is a great challenge. Conceptual modeling techniques have been used as a means to face this challenge. The heterogeneity and idiosyncrasy of genomic use cases mean that conceptual modeling techniques are used to generate conceptual schemes that focus on too specific scenarios (i.e., they are species-specific conceptual schemes). Our research group developed two different conceptual schemes. The first one is the Conceptual Schema of the Human Genome, which is intended to improve Precision Medicine and genetic diagnosis. The second one is the Conceptual Schema of the Citrus Genome, which is intended to identify the genetic cause of relevant phenotypes in the agri-food field.

METHODS

Our two conceptual schemes have been ontologically compared to identify their similarities and differences. Based on this comparison, several changes have been performed in the Conceptual Schema of the Human Genome in order to obtain the first version of a species-independent Conceptual Schema of the Genome. Identifying the different genome information items used in each genomic case study has been essential in achieving our goal. The changes needed to provide an expanded, more generic version of the Conceptual Schema of the Human Genome are analyzed and discussed.

RESULTS

This work presents a new CS called the Conceptual Schema of the Genome that is ready to be adapted to any specific working genome-based context (i.e., species-independent).

CONCLUSION

The generated Conceptual Schema of the Genome works as a global, generic element from which conceptual views can be created in order to work with any specific species. This first working version can be used in the human use case, in the citrus use case, and, potentially, in more use cases of other species.

Design in biology and rational design in vaccinology: A conceptual analysis

This review discusses the philosophical foundations of what used to be called "the scientific method" and is nowadays often known as the scientific attitude. It used to be believed that scientific theories and methods aimed at the truth especially in the case of physics, chemistry and astronomy because these sciences were able to develop numerous scientific laws that made it possible to understand and predict many physical phenomena. The situation is different in the case of the biological sciences which deal with highly complex living organisms made up of huge numbers of constituents that undergo continuous dynamic processes; this leads to novel emergent properties in organisms that cannot be predicted because they are not present in the constituents before they have interacted with each other. This is one of the reasons why there are no universal scientific laws in biology. Furthermore, all scientific theories can only achieve a restricted level of predictive success because they remain valid only under the limited range of conditions that were used for establishing the theory' in the first place. Many theories that used to be accepted were subsequently shown to be false, demonstrating that scientific theories always remain tentative and can never be proven beyond and doubt. It is ironical that as scientists have finally accepted that approximate truths are perfectly adequate and that absolute truth is an illusion, a new irrational sociological phenomenon called Post-Truth conveyed by social media, the Internet and fake news has developed in the Western world that is convincing millions of people that truth simply does not exist. Misleading information is circulated with the intention to deceive and science denialism is promoted by denying the remarkable achievements of science and technology during the last centuries. Although the concept of intentional design is widely used to describe the methods that biologists use to make discoveries and inventions, it will be argued that the term is not appropriate for explaining the appearance of life on our planet nor for describing the scientific creativity of scientific investigators. The term rational for describing the development of new vaccines is also unjustified. Because the analysis of the COVID-19 pandemic requires contributions from biomedical and psycho-socioeconomic sciences, one scientific method alone would be insufficient for combatting the pandemic.

Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping

Over the last few decades, genomics is leading toward audacious future, and has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. The whole genome and exome sequencing (WGS/WES) are two of the most popular next-generation sequencing (NGS) methodologies that are currently being used to detect genetic variations of clinical significance. Investigating WGS/WES data for the variant discovery and genotyping is based on the nexus of different data analytic applications. Although several bioinformatics applications have been developed, and many of those are freely available and published. Timely finding and interpreting genetic variants are still challenging tasks among diagnostic laboratories and clinicians. In this study, we are interested in understanding, evaluating, and reporting the current state of solutions available to process the NGS data of variable lengths and types for the identification of variants, alleles, and haplotypes. Residing within the scope, we consulted high quality peer reviewed literature published in last 10 years. We were focused on the standalone and networked bioinformatics applications proposed to efficiently process WGS and WES data, and support downstream analysis for gene-variant discovery, annotation, prediction, and interpretation. We have discussed our findings in this manuscript, which include but not are limited to the set of operations, workflow, data handling, involved tools, technologies and algorithms and limitations of the assessed applications.

Gene expression regulates metabolite homeostasis during the Crabtree effect: Implications for the adaptation and evolution of Metabolism

A key issue in both molecular and evolutionary biology has been to define the roles of genes and phenotypes in the adaptation of organisms to environmental changes. The dominant view has been that an organism's metabolic adaptations are driven by gene expression and that gene mutations, independent of the starting phenotype, are responsible for the evolution of new metabolic phenotypes. We propose an alternate hypothesis, in which the phenotype and genotype together determine metabolic adaptation both in the lifetime of the organism and in the evolutionary selection of adaptive metabolic traits. We tested this hypothesis by flux-balance and metabolic-control analysis of the relative roles of the starting phenotype and gene expression in regulating the metabolic adaptations during the Crabtree effect in yeast, when they are switched from a low- to high-glucose environment. Critical for successful short-term adaptation was the ability of the glycogen/trehalose shunt to balance the glycolytic pathway. The role of later gene expression of new isoforms of glycolytic enzymes, rather than flux control, was to provide additional homeostatic mechanisms allowing an increase in the amount and efficiency of adenosine triphosphate and product formation while maintaining glycolytic balance. We further showed that homeostatic mechanisms, by allowing increased phenotypic plasticity, could have played an important role in guiding the evolution of the Crabtree effect. Although our findings are specific to Crabtree yeast, they are likely to be broadly found because of the well-recognized similarities in glucose metabolism across kingdoms and phyla from yeast to humans.

Role of Pharmacogenetics in Adverse Drug Reactions: An Update towards Personalized Medicine

Adverse drug reactions (ADRs) are an important and frequent cause of morbidity and mortality. ADR can be related to a variety of drugs, including anticonvulsants, anaesthetics, antibiotics, antiretroviral, anticancer, and antiarrhythmics, and can involve every organ or apparatus. The causes of ADRs are still poorly understood due to their clinical heterogeneity and complexity. In this scenario, genetic predisposition toward ADRs is an emerging issue, not only in anticancer chemotherapy, but also in many other fields of medicine, including hemolytic anemia due to glucose-6-phosphate dehydrogenase (G6PD) deficiency, aplastic anemia, porphyria, malignant hyperthermia, epidermal tissue necrosis (Lyell's Syndrome and Stevens-Johnson Syndrome), epilepsy, thyroid diseases, diabetes, Long QT and Brugada Syndromes. The role of genetic mutations in the ADRs pathogenesis has been shown either for dose-dependent or for dose-independent reactions. In this review, we present an update of the genetic background of ADRs, with phenotypic manifestations involving blood, muscles, heart, thyroid, liver, and skin disorders. This review aims to illustrate the growing usefulness of genetics both to prevent ADRs and to optimize the safe therapeutic use of many common drugs. In this prospective, ADRs could become an untoward "stress test," leading to new diagnosis of genetic-determined diseases. Thus, the wider use of pharmacogenetic testing in the work-up of ADRs will lead to new clinical diagnosis of previously unsuspected diseases and to improved safety and efficacy of therapies. Improving the genotype-phenotype correlation through new lab techniques and implementation of artificial intelligence in the future may lead to personalized medicine, able to predict ADR and consequently to choose the appropriate compound and dosage for each patient.

HRT Atlas v1.0 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets

Housekeeping (HK) genes are constitutively expressed genes that are required for the maintenance of basic cellular functions. Despite their importance in the calibration of gene expression, as well as the understanding of many genomic and evolutionary features, important discrepancies have been observed in studies that previously identified these genes. Here, we present Housekeeping and Reference Transcript Atlas (HRT Atlas v1.0, www.housekeeping.unicamp.br) a web-based database which addresses some of the previously observed limitations in the identification of these genes, and offers a more accurate database of human and mouse HK genes and transcripts. The database was generated by mining massive human and mouse RNA-seq data sets, including 11 281 and 507 high-quality RNA-seq samples from 52 human non-disease tissues/cells and 14 healthy tissues/cells of C57BL/6 wild type mouse, respectively. User can visualize the expression and download lists of 2158 human HK transcripts from 2176 HK genes and 3024 mouse HK transcripts from 3277 mouse HK genes. HRT Atlas also offers the most stable and suitable tissue selective candidate reference transcripts for normalization of qPCR experiments. Specific primers and predicted modifiers of gene expression for some of these HK transcripts are also proposed. HRT Atlas has also been integrated with a regulatory elements resource from Epiregio server.

Multiple information carried by RNAs: total eclipse or a light at the end of the tunnel?

The findings that an RNA is not necessarily either coding or non-coding, or that a precursor RNA can produce different types of mature RNAs, whether coding or non-coding, long or short, have challenged the dichotomous view of the RNA world almost 15 years ago. Since then, and despite an increasing number of studies, the diversity of information that can be conveyed by RNAs is rarely searched for, and when it is known, it remains largely overlooked in further functional studies. Here, we provide an update with prominent examples of multiple functions that are carried by the same RNA or are produced by the same precursor RNA, to emphasize their biological relevance in most living organisms. An important consequence is that the overall function of their locus of origin results from the balance between various RNA species with distinct functions and fates. The consideration of the molecular basis of this multiplicity of information is obviously crucial for downstream functional studies when the targeted functional molecule is often not the one that is believed.

EvoDevo: An Ongoing Revolution?

Since its appearance, Evolutionary Developmental Biology (EvoDevo) has been called an emerging research program, a new paradigm, a new interdisciplinary field, or even a revolution. Behind these formulas, there is the awareness that something is changing in biology. EvoDevo is characterized by a variety of accounts and by an expanding theoretical framework. From an epistemological point of view, what is the relationship between EvoDevo and previous biological tradition? Is EvoDevo the carrier of a new message about how to conceive evolution and development? Furthermore, is it necessary to rethink the way we look at both of these processes? EvoDevo represents the attempt to synthesize two logics, that of evolution and that of development, and the way we conceive one affects the other. This synthesis is far from being fulfilled, but an adequate theory of development may represent a further step towards this achievement. In this article, an epistemological analysis of EvoDevo is presented, with particular attention paid to the relations to the Extended Evolutionary Synthesis (EES) and the Standard Evolutionary Synthesis (SET).

Genes and gene models, an important distinction

Genome sequencing has fundamentally changed how plant biologists think about genes. All or nearly all genes can ultimately be associated with a gene model. However, many gene models appear to play little or no role in the traits of an organism. A range of structural, molecular, population and evolutionary features all show a separation between genes with known phenotypes and the overall set of annotated gene models. These different features could be combined to develop models to distinguish the genes that determine the traits of plants from the subset gene other annotated gene models which are unlikely to play a role in doing so. Efforts to identify the subset of annotated gene models likely involved in specifying the characteristics of plants would help aid a wide range of researchers.

Terminus-Associated Non-coding RNAs: Trash or Treasure?

3′ untranslated regions (3′ UTRs) of protein-coding genes are well known for their important roles in determining the fate of mRNAs in diverse processes, including trafficking, stabilization, translation, and RNA–protein interactions. However, non-coding RNAs (ncRNAs) scattered around 3′ termini of the protein-coding genes, here referred to as terminus-associated non-coding RNAs (TANRs), have not attracted wide attention in RNA research. Indeed, whether TANRs are transcriptional noise, degraded mRNA products, alternative 3′ UTRs, or functional molecules has remained unclear for a long time. As a new category of ncRNAs, TANRs are widespread, abundant, and conserved in diverse eukaryotes. The biogenesis of TANRs mainly follows the same promoter model, the RNA-dependent RNA polymerase activity-dependent model, or the independent promoter model. Functional studies of TANRs suggested that they are significantly involved in the versatile regulation of gene expression. For instance, at the transcriptional level, they can lead to transcriptional interference, induce the formation of gene loops, and participate in transcriptional termination. Furthermore, at the posttranscriptional level, they can act as microRNA sponges, and guide cleavage or modification of target RNAs. Here, we review current knowledge of the potential role of TANRs in the modulation of gene expression. In this review, we comprehensively summarize the current state of knowledge about TANRs, and discuss TANR nomenclature, relation to ncRNAs, cross-talk biogenesis pathways and potential functions. We further outline directions of future studies of TANRs, to promote investigations of this emerging and enigmatic category of RNA.

Do weak interactions affect the biological behavior of DNA? A DFT study of CpG island-like chains

The origin, stability, and contribution to the formation of noncovalent interactions, such as hydrogen bonds and π - π stacking, have been already widely discussed. However, there are few discussions about the relevance of these weak interactions in DNA performance. In this work, we seek to shed light on the effect of hydrogen bonds and π - π stacking interactions on the biological behavior of DNA through the description of these intermolecular forces in CpG island-like (GC-rich) chains. Furthermore, we made some comparisons with TATA box-like (TA-rich) chains in order to describe hydrogen bond and π - π stacking interactions as a function of the DNA sequence. For hydrogen bonds, we found that there is not a significant effect related to the number of base pairs. Whereas for π - π stacking interactions, the energy tended to decrease as the number of base pairs increased. We observed anticooperative effects for both hydrogen bonds and π - π stacking interactions. These results are in contrast with those of TATA box-like chains since cooperative and additive effects were found for both hydrogen bonds and π - π stacking, respectively. Based on the chemical hardness and density of states, we can conclude that proteins may interact easier with GC-rich chains. We conclude that regardless of the chain length, a protein could interact more easily with these genomics regions because the π - π stacking energies did not increase as a function of the number of base pairs, making, for the first time, a first approximation of the influence of noncovalent interaction on DNA behavior. We did all this work by means of DFT framework included in the DMol³ code (M06-L/DNP). Graphical Abstract Cartoon representation of how nocovalent interactions affect the interaction of DNA with a protein, i.e., how hydrogen bond and π - π stacking interactions influence the biological behavior of DNA.

[Non-coding Natural Antisense RNA: Mechanisms of Action in the Regulation of Target Gene Expression and Its Clinical Implications]

Recent advances in high-throughput technologies have revealed that 75% of the human genome is transcribed to RNA, whereas only 3% of transcripts are translated into proteins. Consequently, many long non-coding RNAs (lncRNAs) have been identified, which has improved our understanding of the complexity of biological processes. LncRNAs comprise multiple classes of RNA transcripts that regulate the transcription, stability and translation of protein-coding genes in a genome. Natural antisense transcripts (NATs) form one such class, and the GENCODE v30 catalog contains 16193 lncRNA loci, of which 5611 are antisense loci. This review outlines our emerging understanding of lncRNAs, with a particular focus on how lncRNAs regulate gene expression using interferon-α1 (IFN-α1) mRNA and its antisense partner IFN-α1 antisense (as)RNA as an example. We have identified and characterized the asRNA that determines post-transcriptional IFN-α1 mRNA levels. IFN-α1 asRNA stabilizes IFN-α1 mRNA by cytoplasmic sense-antisense duplex formation, which may enhance the accessibility of an RNA stabilizer protein or decrease the affinity of an RNA decay factor for the RNA. IFN-α1 asRNA can also act as competing molecules in the competing endogenous (ce)RNA network with other members of the IFNA multigene family mRNAs/asRNAs, and other cellular mRNA transcripts. Furthermore, antisense oligoribonucleotides representing functional domains of IFN-α1 asRNA inhibit influenza virus proliferation in the respiratory tract of virus-infected animals. Thus, these findings support, at least in part, the rationale that dissecting the activity of NAT on gene expression regulation promises to reveal previously unanticipated biology, with potential to provide new therapeutic approaches to diseases.

Psychology Today: Still in Denial, Still Outdated

The questionable state of psychology as a science has been pointed out repeatedly over last hundred years. Sometimes programs to overcome the obvious limitations of psychology have been also proposed. So far, in vain. Zagaria with coauthors (this issue) bring the subject up again. They demonstrate that psychology today is characterized by the incoherence of definitions of core constructs and lack of consensus in the scientific community. The authors also suggest that psychology would do better by adopting a research program of a specific form of evolutionary psychology. In this paper I show, mostly on the basis of my earlier works on the same subject, that shortcomings of psychology today go much deeper than the authors of the target article have discussed. Psychology today is characterized by fundamental epistemological and methodological problems. As the same shortcomings characterize the version of evolutionary psychology advocated by Zagaria and coauthors, it is not the best candidate to ground the future of psychology. I suggest the psychology misses unifying psychology of a specific kind, which basic principles were outlined by Vygotsky almost a century ago.

Progress, Challenges, and Surprises in Annotating the Human Genome

Our understanding of the human genome has continuously expanded since its draft publication in 2001. Over the years, novel assays have allowed us to progressively overlay layers of knowledge above the raw sequence of A's, T's, G's, and C's. The reference human genome sequence is now a complex knowledge base maintained under the shared stewardship of multiple specialist communities. Its complexity stems from the fact that it is simultaneously a template for transcription, a record of evolution, a vehicle for genetics, and a functional molecule. In short, the human genome serves as a frame of reference at the intersection of a diversity of scientific fields. In recent years, the progressive fall in sequencing costs has given increasing importance to the quality of the human reference genome, as hundreds of thousands of individuals are being sequenced yearly, often for clinical applications. Also, novel sequencing-based assays shed light on novel functions of the genome, especially with respect to gene expression regulation. Keeping the human genome annotation up to date and accurate is therefore an ongoing partnership between reference annotation projects and the greater community worldwide.

Genetic architecture of complex traits and disease risk predictors

Genomic prediction of complex human traits (e.g., height, cognitive ability, bone density) and disease risks (e.g., breast cancer, diabetes, heart disease, atrial fibrillation) has advanced considerably in recent years. Using data from the UK Biobank, predictors have been constructed using penalized algorithms that favor sparsity: i.e., which use as few genetic variants as possible. We analyze the specific genetic variants (SNPs) utilized in these predictors, which can vary from dozens to as many as thirty thousand. We find that the fraction of SNPs in or near genic regions varies widely by phenotype. For the majority of disease conditions studied, a large amount of the variance is accounted for by SNPs outside of coding regions. The state of these SNPs cannot be determined from exome-sequencing data. This suggests that exome data alone will miss much of the heritability for these traits-i.e., existing PRS cannot be computed from exome data alone. We also study the fraction of SNPs and of variance that is in common between pairs of predictors. The DNA regions used in disease risk predictors so far constructed seem to be largely disjoint (with a few interesting exceptions), suggesting that individual genetic disease risks are largely uncorrelated. It seems possible in theory for an individual to be a low-risk outlier in all conditions simultaneously.

The power and limitations of gene expression pathway analyses toward predicting population response to environmental stressors

Rapid environmental changes impact the global distribution and abundance of species, highlighting the urgency to understand and predict how populations will respond. The analysis of differentially expressed genes has elucidated areas of the genome involved in adaptive divergence to past and present environmental change. Such studies however have been hampered by large numbers of differentially expressed genes and limited knowledge of how these genes work in conjunction with each other. Recent methods (broadly termed "pathway analyses") have emerged that aim to group genes that behave in a coordinated fashion to a factor of interest. These methods aid in functional annotation and uncovering biological pathways, thereby collapsing complex datasets into more manageable units, providing more nuanced understandings of both the organism-level effects of modified gene expression, and the targets of adaptive divergence. Here, we reanalyze a dataset that investigated temperature-induced changes in gene expression in marine-adapted and freshwater-adapted threespine stickleback (Gasterosteus aculeatus), using Weighted Gene Co-expression Network Analysis (WGCNA) with PANTHER Gene Ontology (GO)-Slim overrepresentation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Six modules exhibited a conserved response and six a divergent response between marine and freshwater stickleback when acclimated to 7°C or 22°C. One divergent module showed freshwater-specific response to temperature, and the remaining divergent modules showed differences in height of reaction norms. PPARAa, a transcription factor that regulates fatty acid metabolism and has been implicated in adaptive divergence, was located in a module that had higher expression at 7°C and in freshwater stickleback. This updated methodology revealed patterns that were not found in the original publication. Although such methods hold promise toward predicting population response to environmental stressors, many limitations remain, particularly with regard to module expression representation, database resources, and cross-database integration.

Machine learning: A powerful tool for gene function prediction in plants

Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule-based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning-based approaches in plants.

Genome-wide analysis of the U-box E3 ubiquitin ligase enzyme gene family in tomato

E3 ubiquitin ligases are a central modifier of plant signaling pathways that act through targeting proteins to the degradation pathway. U-box E3 ubiquitin ligases are a distinct class of E3 ligases that utilize intramolecular interactions for its scaffold stabilization. U-box E3 ubiquitin ligases are prevalent in plants in comparison to animals. However, the evolutionary aspects, genetic organizations, and functional fate of the U-box E3 gene family in plant development, especially in tomato is not well understood. In the present study, we have performed in-silico genome-wide analysis of the U-box E3 ubiquitin ligase gene family in Solanum lycopersicum. We have identified 62 U-box genes with U-box/Ub Fusion Degradation 2 (UFD2) domain. The chromosomal localization, phylogenetic analysis, gene structure, motifs, gene duplication, syntenic regions, promoter, physicochemical properties, and ontology were investigated. The U-box gene family showed significant conservation of the U-box domain throughout the gene family. Duplicated genes discerned noticeable functional transitions among duplicated genes. The gene expression profiles of U-box E3 family members show involvement in abiotic and biotic stress signaling as well as hormonal pathways. We found remarkable participation of the U-box gene family in the vegetative and reproductive tissue development. It is predicted to be actively regulating flowering time and endosperm formation. Our study provides a comprehensive picture of distribution, structural features, promoter elements, evolutionary relationship, and gene expression of the U-box gene family in the tomato. We predict the crucial participation of the U-box gene family in tomato plant development and stress responses.

Evolution of novel genes in three-spined stickleback populations

Eukaryotic genomes frequently acquire new protein-coding genes which may significantly impact an organism’s fitness. Novel genes can be created, for example, by duplication of large genomic regions or de novo, from previously non-coding DNA. Either way, creation of a novel transcript is an essential early step during novel gene emergence. Most studies on the gain-and-loss dynamics of novel genes so far have compared genomes between species, constraining analyses to genes that have remained fixed over long time scales. However, the importance of novel genes for rapid adaptation among populations has recently been shown. Therefore, since little is known about the evolutionary dynamics of transcripts across natural populations, we here study transcriptomes from several tissues and nine geographically distinct populations of an ecological model species, the three-spined stickleback. Our findings suggest that novel genes typically start out as transcripts with low expression and high tissue specificity. Early expression regulation appears to be mediated by gene-body methylation. Although most new and narrowly expressed genes are rapidly lost, those that survive and subsequently spread through populations tend to gain broader and higher expression levels. The properties of the encoded proteins, such as disorder and aggregation propensity, hardly change. Correspondingly, young novel genes are not preferentially under positive selection but older novel genes more often overlap with F_ST outlier regions. Taken together, expression of the surviving novel genes is rapidly regulated, probably via epigenetic mechanisms, while structural properties of encoded proteins are non-debilitating and might only change much later.

Understanding biochemistry: structure and function of nucleic acids

Nucleic acids, deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), carry genetic information which is read in cells to make the RNA and proteins by which living things function. The well-known structure of the DNA double helix allows this information to be copied and passed on to the next generation. In this article we summarise the structure and function of nucleic acids. The article includes a historical perspective and summarises some of the early work which led to our understanding of this important molecule and how it functions; many of these pioneering scientists were awarded Nobel Prizes for their work. We explain the structure of the DNA molecule, how it is packaged into chromosomes and how it is replicated prior to cell division. We look at how the concept of the gene has developed since the term was first coined and how DNA is copied into RNA (transcription) and translated into protein (translation).

About three-fourths of mouse proteins unexpectedly appear at a low position of SDS-PAGE, often as additional isoforms, questioning whether all protein isoforms have been eliminated in gene-knockout cells or organisms

Most genes in evolutionarily complex genomes are expressed to multiple protein isoforms, but there is not yet any simple high-throughput approach to identify these isoforms. Using an oversimplified top-down LC-MS/MS strategy, we detected, around the 26-kD position of SDS-PAGE, proteins produced from 782 genes in a Cdk4-/- mouse embryonic fibroblast cell line. Interestingly, only 213 (27.24%, about one-fourth) of these 782 genes have their proteins with a theoretical molecular mass (TMM) 10% smaller or larger than 26 kD, that is, between 23 and 29 kD, the range set as allowed variation in SDS-PAGE. These 213 proteins are considered as the wild type (WT). The remaining three-fourths includes proteins from 66 (9.44%) genes with a TMM smaller than 23 kD and proteins from 503 (64.32%, nearly two-thirds) genes with a TMM larger than 29 kD; these proteins are categorized into a larger-group or a smaller-group, respectively, for their appearance at a higher or lower position of SDS-PAGE. For instance, at this 26-kD position we detected proteins from the Rps27a, Snrpf, Hist1h4a, and Rps25 genes whose proteins' TMM is 8.6, 9.7, 11.4, and 13.7 kD, respectively, and detected proteins from the Plelc1 and Prkdc genes, whose largest isoform is 533.9 and 471.1 kD, respectively. We extrapolate that many of those proteins migrating unexpectedly in SDS-PAGE may be isoforms besides the WT protein. Moreover, we also detected a Cdk4 protein in this Cdk4-/- cell line, thus wondering whether some of other gene-knockout cells or organisms show similar incompleteness of the knockout.

Hadoop and PySpark for reproducibility and scalability of genomic sequencing studies

Modern genomic studies are rapidly growing in scale, and the analytical approaches used to analyze genomic data are increasing in complexity. Genomic data management poses logistic and computational challenges, and analyses are increasingly reliant on genomic annotation resources that create their own data management and versioning issues. As a result, genomic datasets are increasingly handled in ways that limit the rigor and reproducibility of many analyses. In this work, we examine the use of the Spark infrastructure for the management, access, and analysis of genomic data in comparison to traditional genomic workflows on typical cluster environments. We validate the framework by reproducing previously published results from the Alzheimer's Disease Sequencing Project. Using the framework and analyses designed using Jupyter notebooks, Spark provides improved workflows, reduces user-driven data partitioning, and enhances the portability and reproducibility of distributed analyses required for large-scale genomic studies.

DNA finger printing of S. Mutans present in the saliva of caries active children and those associated with intellectual disability - An RAPD analysis

Aim

The aim of this study is, to evaluate and compare the diversity of S. Mutans genotypes with respect to caries activity among normal children and intellectually disabled children, which would enable the clinician to plan better strategies for early caries detection, management and prevention.

Materials and methods

Genotyping of S. Mutans was done by collecting the saliva samples from 40 caries active children (20 normal and 20 children associated with intellectual disability by Rapid amplified polymorphic DNA analysis using three arbitrarily primers (P1, P2, P3). Rapid amplified polymorphic DNA (RAPD) is preferred because of its reliability, reproducibility in generating genetic fingerprints of Streptococcus isolates.

Results

Number of bacterial counts in Group I showed a mean of 111.6500 followed by the Group II with a mean of 102.6500. Therefore, the difference in the number of bacterial counts was not significant between the two groups (p < 0.001). Genotype encoding Primer 1 was present in almost 82.5% of the total population of both groups. Genotype encoding Primer 2 was present in 95% of the total population. Whereas, Genotype encoding Primer 3 was present in 20% of children associated with intellectual disability and 95% of normal children.

Interpretation and conclusion

There was no significant difference in S. Mutans count of normal caries active children to that of caries active children with intellectual disability, but, there was a significance difference in the distribution of S. Mutans genotypes in both the groups.

Long Noncoding RNAs and Repetitive Elements: Junk or Intimate Evolutionary Partners?

Our recent ability to sequence entire genomes, along with all of their transcribed RNAs, has led to the surprising finding that only ∼1% of the human genome is used to encode proteins. This finding has led to vigorous debate over the functional importance of the transcribed but untranslated portions of the genome. Currently, scientists tend to assume coding genes are functional until proven not to be, while the opposite is true for noncoding genes. This review takes a new look at the evidence for and against widespread noncoding gene functionality. We focus in particular on long noncoding RNA (noncoding RNAs longer than 200 nucleotides) genes and their 'junk' associates, transposable elements, and satellite repeats. Taken together, the suggestion put forward is that more of this junk DNA may be functional than nonfunctional and that noncoding RNAs and transposable elements act symbiotically to drive evolution.