Synthetic ShK-like Peptide from the Jellyfish Nemopilema nomurai Has Human Voltage-Gated Potassium-Channel-Blocking Activity.

We identified a new human voltage-gated potassium channel blocker, NnK-1, in the jellyfish Nemopilema nomurai based on its genomic information. The gene sequence encoding NnK-1 contains 5408 base pairs, with five introns and six exons. The coding sequence of the NnK-1 precursor is 894 nucleotides long and encodes 297 amino acids containing five presumptive ShK-like peptides. An electrophysiological assay demonstrated that the fifth peptide, NnK-1, which was chemically synthesized, is an effective blocker of hKv1.3, hKv1.4, and hKv1.5. Multiple-sequence alignment with cnidarian Shk-like peptides, which have Kv1.3-blocking activity, revealed that three residues (3Asp, 25Lys, and 34Thr) of NnK-1, together with six cysteine residues, were conserved. Therefore, we hypothesized that these three residues are crucial for the binding of the toxin to voltage-gated potassium channels. This notion was confirmed by an electrophysiological assay with a synthetic peptide (NnK-1 mu) where these three peptides were substituted with 3Glu, 25Arg, and 34Met. In conclusion, we successfully identified and characterized a new voltage-gated potassium channel blocker in jellyfish that interacts with three different voltage-gated potassium channels. A peptide that interacts with multiple voltage-gated potassium channels has many therapeutic applications in various physiological and pathophysiological contexts.

Young adults' reasoning for involving a parent in a genomic decision-making research study.

Young adults have increasing genomic testing opportunities; however, little is known about how equipped they feel about making decisions to learn personal genomic information. We conducted qualitative interviews with 19 young adults, ages 18-21 years old, enrolled in a research study where they made decisions about learning personal genomic risk for developing preventable, treatable, and adult-onset conditions and carrier status for autosomal recessive conditions. Participants had the option to include a parent in their study visit and the decision-making process. The goal of this project was to explore young adults' reasons for involving or not involving a parent in the study and to assess young adults' perspectives about parental roles in their healthcare. Nine participants included a parent in the study and ten did not include a parent. Eleven participants received genomic test results before the interview, while eight participants had not yet received their results at the time of the interview. The study team developed a coding guide and coded interview transcripts inductively and deductively using an interpretive descriptive-analytic approach. Logistical issues dominated solo participants' reasons for not involving a parent in the study, whereas those who involved a parent often cited a close relationship with the parent and the parent's previous involvement in the participant's healthcare as reasons for involving them. Both groups of participants described gradually transitioning to independent healthcare decision-making with age and felt their comfort in medical decision-making depends on the severity of and their familiarity with the situation. Participants recommended that future genomic researchers or clinicians give young adults the option to involve a parent or friend as a support person in research or clinical visits. Although young adults may have different journeys toward independent healthcare decision-making, some may benefit from continued parental or peer involvement after reaching the age of legal adulthood.

Changes in genetic trends in US dairy cattle since the implementation of genomic selection.

Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values (PBV¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability PBV¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era.

Relationship between inbreeding and milk production traits in two Italian dairy sheep breeds.

The effects of inbreeding in livestock species breeds have been well documented and they have a negative impact on profitability. The objective of this study was to evaluate the levels of inbreeding in Sarda (SAR, n = 785) and Valle del Belice (VdB, n = 473) dairy sheep breeds and their impact on milk production traits. Two inbreeding coefficients (F) were estimated: using pedigree (FPED ), or runs of homozygosity (ROH; FROH ) at different minimum ROH lengths and different ROH classes. After the quality control, 38,779 single nucleotide polymorphisms remained for further analyses. A mixed-linear model was used to evaluate the impact of inbreeding coefficients on production traits within each breed. VdB showed higher inbreeding coefficients compared to SAR, with both breeds showing lower estimates as the minimum ROH length increased. Significant inbreeding depression was found only for milk yield, with a loss of around 7 g/day (for SAR) and 9 g/day (VdB) for a 1% increase of FROH . The present study confirms how the use of genomic information can be used to manage intra-breed diversity and to calculate the effects of inbreeding on phenotypic traits.

A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research.

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine.

The relative effect of genomic information on efficiency of Bayesian analysis of the mixed linear model with unknown variance.

This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single-step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single-step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance-covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.

Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle.

The use of multi-trait across-country evaluation (MACE) and the exchange of genomic information among countries allows national breeding programs to combine foreign and national data to increase the size of the training populations and potentially increase accuracy of genomic prediction of breeding values. By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (GBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. A single-step genomic BLUP approach, which enables integration of data from MACE evaluations, can be used to obtain genomic predictions while avoiding double-counting of information. The objectives of this study were to apply a single-step approach that simultaneously includes domestic and MACE information for genomic evaluation of workability traits in Canadian Holstein cattle, and compare the results obtained with this methodology with those obtained using a multi-step approach (msGBLUP). By including MACE bulls in the training population, msGBLUP led to an increase in reliability of genomic predictions of 4.8 and 15.4% for milking temperament and milking speed, respectively, compared with a traditional evaluation using only pedigree and phenotypic information. Integration of MACE data through a single-step approach (ssGBLUPIM) yielded the highest reliabilities compared with other considered methods. Integration of MACE data also helped reduce bias of genomic predictions. When using ssGBLUPIM, the bias of genomic predictions decreased by half compared with msGBLUP using domestic and MACE information. Therefore, the reliability and bias of genomic predictions for both traits improved substantially when a single-step approach was used for evaluation compared with a multi-step approach. The use of a single-step approach with integration of MACE information provides an alternative to the current method used in Canadian genomic evaluations.

Developing a framework to assess the costeffectiveness of COMPARE - a global platform for the exchange of sequence-based pathogen data.

Analysing the genomic data of pathogens with the help of next-generation sequencing (NGS) is an increasingly important part of disease outbreak investigations and helps guide responses. While this technology has already been successfully employed to elucidate and control disease outbreaks, wider implementation of NGS also depends on its cost-effectiveness. COMPARE - short for 'Collaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks' - is a major project, funded by the European Union, to develop a global platform for sharing and analysing NGS data and thereby improve the rapid identification, containment and mitigation of emerging infectious diseases and foodborne outbreaks. This article introduces the project and presents the results of a review of the literature, composed of previous relevant cost-benefit and cost-effectiveness analyses. The authors also outline the implications for a methodological framework to assess the costeffectiveness of COMPARE and similar systems.

L'analyse des données sur le génome des agents pathogènes grâce au séquençage de nouvelle génération (SNG) joue un rôle de plus en plus important dans les enquêtes sur les foyers de maladies et contribue à l'élaboration de stratégies de réponse. Si cette technologie a été utilisée avec succès pour élucider la cause des certains foyers et pour les contrôler, une application plus large du SNG dépend également de sa rentabilité. La plate-forme COMPARE (plate-forme de gestion collaborative pour la détection et l'analyse des foyers émergents et ré-émergents et des toxi-infections alimentaires) est un projet de grande envergure financé par l'Union européenne, visant à mettre en place une plate-forme mondiale d'échanges et d'analyse des données de séquençage de nouvelle génération et à améliorer ainsi l'identification précoce, le confinement et l'atténuation des maladies infectieuses émergentes et des foyers de toxiinfections alimentaires. Les auteurs présentent le projet ainsi que les résultats d'une étude bibliographique intégrant des analyses pertinentes coûts­avantages et coûts­efficacité réalisées dans le passé. Ils soulignent également les enseignements de ces analyses pour l'élaboration d'un cadre méthodologique d'évaluation de la relation coûts­efficacité applicable au système COMPARE et à d'autres systèmes similaires.

El análisis de datos genómicos de los patógenos con ayuda de técnicas de secuenciación de próxima generación es un componente cada vez más importante de la investigación de brotes infecciosos, que resulta de utilidad para guiar las medidas de respuesta. Aunque estas técnicas ya se han utilizado con éxito para elucidar y combatir brotes de enfermedad, su aplicación generalizada también dependerá de la relación costo-eficacia que ofrezcan. COMPARE (acrónimo inglés de «plataforma de gestión colectiva para la detección y análisis de brotes (re)emergentes y de transmisión alimentaria¼) es un vasto proyecto financiado por la Unión Europea que apunta a instituir un dispositivo mundial de intercambio y análisis de datos de secuenciación de próxima generación y lograr así más eficacia en la rápida identificación, contención y mitigación de brotes de transmisión alimentaria y de enfermedades infecciosas emergentes. Los autores exponen el proyecto y presentan los resultados de un repaso bibliográfico de anteriores análisis de las relaciones costo-beneficio y costo-eficacia de estas técnicas. Además, explican brevemente lo que puede aportar un marco metodológico para evaluar la relación costo-eficacia del sistema COMPARE y de otros sistemas similares.

Identification and characterization of a thermostable bifunctional enzyme with phosphomannose isomerase and sugar-1-phosphate nucleotidylyltransferase activities from a hyperthermophilic archaeon, Pyrococcus horikoshii OT3.

Mannosylglycerate is known as a compatible solute, and plays important roles for salinity adaptation and high temperature stability of microorganisms. In the gene cluster for the mannosylglycerate biosynthetic pathway predicted from the genomic data of Pyrococcus horikoshii OT3, the PH0925 protein was found as a putative bifunctional enzyme with phosphomannose isomerase (PMI) and mannose-1-phosphate guanylyltransferase (Man-1-P GTase) activities, which can synthesize GDP-mannose when accompanied by a phosphomannomutase/phosphoglucomutase (PMM/PGM) enzyme (PH0923). The recombinant PH0925 protein, expressed in E. coli, exhibited both expected PMI and Man-1-P GTase activities, as well as absolute thermostability; 95 °C was the optimum reaction temperature. According to the guanylyltransferase activity (GTase) of the PH0925 protein, it was found that the protein can catalyze glucose-1-phosphate (Glc-1-P) and glucosamine-1-phosphate (GlcN-1-P) in addition to Man-1-P. The analyses of C-terminus-truncated forms of the PH0925 protein indicated that sugar-1-phosphate nucleotidylyltransferase (Sugar-1-P NTase) activity was located in the region from the N-terminus to the 345th residue, and that the C-terminal 114 residue region of the PH0925 protein inhibited the Man-1-P GTase activity. Conversely, the PMI activity was abolished by deletion of the C-terminal 14 residues. This is the first report of a thermostable enzyme with both PMI and multiple Sugar-1-P NTase activities.

Temporal dynamics of genetic parameters and SNP effects for performance and disorder traits in poultry undergoing genomic selection.

Accurate genetic parameters are crucial for predicting breeding values and selection responses in breeding programs. Genetic parameters change with selection, reducing additive genetic variance and changing genetic correlations. This study investigates the dynamic changes in genetic parameters for residual feed intake (RFI), gain (GAIN), breast percentage (BP), and femoral head necrosis (FHN) in a broiler population that undergoes selection, both with and without the use of genomic information. Changes in single nucleotide polymorphism (SNP) effects were also investigated when including genomic information. The dataset containing 200,093 phenotypes for RFI, 42,895 for BP, 203,060 for GAIN, and 63,349 for FHN was obtained from 55 mating groups. The pedigree included 1,252,619 purebred broilers, of which 154,318 were genotyped with a 60K Illumina Chicken SNP BeadChip. A Bayesian approach within the GIBBSF90 + software was applied to estimate the genetic parameters for single-, two-, and four-trait models with sliding time intervals. For all models, we used genomic-based (GEN) and pedigree-based approaches (PED), meaning with or without genotypes. For GEN (PED), heritability varied from 0.19 to 0.2 (0.31 to 0.21) for RFI, 0.18 to 0.11 (0.25 to 0.14) for GAIN, 0.45 to 0.38 (0.61 to 0.47) for BP, and 0.35 to 0.24 (0.53 to 0.28) for FHN, across the intervals. Changes in genetic correlations estimated by GEN (PED) were 0.32 to 0.33 (0.12 to 0.25) for RFI-GAIN, -0.04 to -0.27 (-0.18 to -0.27) for RFI-BP, -0.04 to -0.07 (-0.02 to -0.08) for RFI-FHN, -0.04 to 0.04 (0.06 to 0.2) for GAIN-BP, -0.17 to -0.06 (-0.02 to -0.01) for GAIN-FHN, and 0.02 to 0.07 (0.06 to 0.07) for BP-FHN. Heritabilities tended to decrease over time while genetic correlations showed both increases and decreases depending on the traits. Similar to heritabilities, correlations between SNP effects declined from 0.78 to 0.2 for RFI, 0.8 to 0.2 for GAIN, 0.73 to 0.16 for BP, and 0.71 to 0.14 for FHN over the eight intervals with genomic information, suggesting potential epistatic interactions affecting genetic trait architecture. Given rapid genetic architecture changes and differing estimates between genomic and pedigree-based approaches, using more recent data and genomic information to estimate variance components is recommended for populations undergoing genomic selection to avoid potential biases in genetic parameters.

Genetic parameters are used to predict breeding values for individuals in breeding programs undergoing selection. However, inaccurate genetic parameters can cause breeding values to be biased, and genetic parameters can change over time due to multiple factors. This study aimed to investigate how genetic parameters changed over time in a broiler population using time intervals and observing the behavior of single nucleotide polymorphism (SNP) effects. We studied four traits related to production and disorders while also studying the impact of using genomic information on the estimates. Genetic variances showed an overall decreasing trend, whereas residual variances increased during each interval, resulting in decreasing heritability estimates. Genetic correlations between traits varied but with no major changes over time. Estimates tended to be lower when genomic information was included in the analysis. SNP effects showed changes over time, indicating changes to the genetic background of this population. Using outdated variance components in a population under selection may not represent the current population. Furthermore, when genomic selection is practiced, accounting for this information while estimating variance components is important to avoid biases.

Integrating Multi-Organ Imaging-Derived Phenotypes and Genomic Information for Predicting the Occurrence of Common Diseases.

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06.

Leveraging primate-specific genomic information for genetic studies of complex diseases.

Genomic changes specific to higher primates are regarded as primate-specific genomic information (PSI). Using PSI to inform genetic studies is highly desirable but hampered by three factors: heterogeneity among PSI studies, lack of integrated profiles of the identified PSI elements and dearth of relevant functional information. We report a database of 19,767 PSI elements collated from nine types of brain-related studies, which form 19,473 non-overlapping PSI regions that distribute unevenly but jointly cover only 0.81% of the genome. About 2.5% of the PSI regions colocalized with variants identified in genome-wide association studies, with disease loci more likely colocalized than quantitative trait loci (p = 1.6 × 10-5), particularly in regions without obvious regulatory roles. We further showed an LRP4 exemplar region with PSI elements orchestrated with common and rare disease variants and other functional elements. Our results render PSI elements as a valuable source to inform genetic studies of complex diseases.

Structural, genomic information and computational analysis of emerging coronavirus (SARS-CoV-2).

Background: The emerging viral pandemic worldwide is associated with a novel coronavirus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). This virus is said to emerge from its epidemic center in Wuhan, China, in 2019. Coronaviruses (CoVs) are single-stranded, giant, enveloped RNA viruses that come under the family of coronaviridae and order Nidovirales which are the crucial pathogens for humans and other vertebrates. Main body: Coronaviruses are divided into several subfamilies and genera based on the genomic structure and phylogenetic relationship. The name corona is raised due to the presence of spike protein on the envelope of the virus. The structural and genomic study revealed that the total genome size of SARS-CoV-2 is from 29.8 kb to 29.9 kb. The spike protein (S) is a glycoprotein that attaches to the receptor of host cells for entry into the host cell, followed by the attachment of virus RNA to the host ribosome for translation. The phylogenetic analysis of SARS-CoV-2 revealed the similarity (75-88%) with bat SARS-like coronavirus. Conclusion: The sign and symptoms of novel severe acute respiratory syndrome coronavirus 2 are also discussed in this paper. The worldwide outbreak and prevention from severe acute respiratory syndrome coronavirus 2 are overviewed in the present article. The latest variant of coronavirus and the status of vaccines are also overviewed in the present article.

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (GAPY~-1) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H-1) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h2 = 0.3) were available for all animals in generations 1-9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G-1 and GAPY~-1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.

The estimation of variance components is computationally expensive under large-scale genetic evaluations due to several inversions of the coefficient matrix. Variance components are used as parameters for estimating breeding values in mixed model equations (MME). However, resulting breeding values are not Best Linear Unbiased Predictions (BLUP) unless the variance components approach the true parameters. The increasing availability of genomic data requires the development of new methods for improving the efficiency of variance component estimations. Therefore, this study aimed to reduce the costs of single-step genomic REML (ssGREML) with the Algorithm for Proven and Young (APY) for estimating variance components with truncated pedigree and phenotypes using simulated data. In addition, we investigated the influence of truncation on variance components and genetic parameter estimates. Under APY, the size of the core group influences the similarity of breeding values and their reliability compared to the full genomic matrix. In this study, we found that to ensure reliable variance component estimation, it is required to consider a core size that corresponds to the number of largest eigenvalues explaining around 98% of the total variation in G to avoid biased parameters. In terms of costs, the use of APY slightly decreased the time for ordering and symbolic factorization with no impact on estimations.

New Drug Development and Clinical Trial Design by Applying Genomic Information Management.

Depending on the patients' genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

FAIR Aspects of a Genomic Information Protection and Management System.

To handle genomic information while supporting FAIR principles, we present GIPAMS, a modular architecture. GIPAMS provides security and privacy to manage genomic information by means of several independent services and modules that interact among them in an orchestrated way. The paper analyzes how some security and privacy aspects of the FAIRification process are covered by the GIPAMS platform.

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses.

The COVID-19 disease for Novel coronavirus (SARS-CoV-2) has turned out to be a global pandemic. The high transmission rate of this pathogenic virus demands an early prediction and proper identification for the subsequent treatment. However, polymorphic nature of this virus allows it to adapt and sustain in different kinds of environment which makes it difficult to predict. On the other hand, there are other pathogens like SARS-CoV-1, MERS-CoV, Ebola, Dengue, and Influenza as well, so that a predictor is highly required to distinguish them with the use of their genomic information. To mitigate this problem, in this work COVID-DeepPredictor is proposed on the framework of deep learning to identify an unknown sequence of these pathogens. COVID-DeepPredictor uses Long Short Term Memory as Recurrent Neural Network for the underlying prediction with an alignment-free technique. In this regard, k-mer technique is applied to create Bag-of-Descriptors (BoDs) in order to generate Bag-of-Unique-Descriptors (BoUDs) as vocabulary and subsequently embedded representation is prepared for the given virus sequences. This predictor is not only validated for the dataset using K -fold cross-validation but also for unseen test datasets of SARS-CoV-2 sequences and sequences from other viruses as well. To verify the efficacy of COVID-DeepPredictor, it has been compared with other state-of-the-art prediction techniques based on Linear Discriminant Analysis, Random Forests, and Gradient Boosting Method. COVID-DeepPredictor achieves 100% prediction accuracy on validation dataset while on test datasets, the accuracy ranges from 99.51 to 99.94%. It shows superior results over other prediction techniques as well. In addition to this, accuracy and runtime of COVID-DeepPredictor are considered simultaneously to determine the value of k in k-mer, a comparative study among k values in k-mer, Bag-of-Descriptors (BoDs), and Bag-of-Unique-Descriptors (BoUDs) and a comparison between COVID-DeepPredictor and Nucleotide BLAST have also been performed. The code, training, and test datasets used for COVID-DeepPredictor are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-DeepPredictor/.

The Importance of Henry H. Heng's Genome Architecture Theory.

A recent symposium on cancer and evolution has bought many innovative thinkers together to challenge the status quo of current cancer research. Professor Henry Heng's presentation considers cancer as a new system emerging via macro-evolution, where genome chaos-mediated information creation and maintenance plays an important role. This concept departs from the neo-Darwinian influenced somatic mutation theory of cancer. To appreciate his theory, it is helpful to briefly review several of his heterodox findings in the fields of oncology and evolutionary biology. This letter summarizes and highlights these findings and calls for a medical and scientific reckoning as well as integration within and between these fields.

Two-phased evolution: Genome chaos-mediated information creation and maintenance.

Cancer is traditionally labeled a "cellular growth problem." However, it is fundamentally an issue of macroevolution where new systems emerge from tissue by breaking various constraints. To study this process, we used experimental platforms to "watch evolution in action" by comparing the profiles of karyotypes, transcriptomes, and cellular phenotypes longitudinally before, during, and after key phase transitions. This effort, alongside critical rethinking of current gene-based genomic and evolutionary theory, led to the development of the Genome Architecture Theory. Following a brief historical review, we present four case studies and their takeaways to describe the pattern of genome-based cancer evolution. Our discoveries include 1. The importance of non-clonal chromosome aberrations or NCCAs; 2. Two-phased cancer evolution, comprising a punctuated phase and a gradual phase, dominated by karyotype changes and gene mutation/epigenetic alterations, respectively; 3. How the karyotype codes system inheritance, which organizes gene interactions and provides the genomic basis for physiological regulatory networks; and 4. Stress-induced genome chaos, which creates genomic information by reorganizing chromosomes for macroevolution. Together, these case studies redefine the relationship between cellular macro- and microevolution: macroevolution does not equal microevolution + time. Furthermore, we incorporate genome chaos and gene mutation in a general model: genome reorganization creates new karyotype coding, then diverse cancer gene mutations can promote the dominance of tumor cell populations. Finally, we call for validation of the Genome Architecture Theory of cancer and organismal evolution, as well as the systematic study of genomic information flow in evolutionary processes.