Chromosome-scale Elaeis guineensis and E. oleifera assemblies: Comparative genomics of oil palm and other Arecaceae.

Elaeis guineensis and E. oleifera are the two species of oil palm. E. guineensis is the most widely cultivated commercial species, and introgression of desirable traits from E. oleifera is ongoing. We report an improved E. guineensis genome assembly with substantially increased continuity and completeness, as well as the first chromosome-scale E. oleifera genome assembly. Each assembly was obtained by integration of long-read sequencing, proximity ligation sequencing, optical mapping and genetic mapping. High interspecific genome conservation is observed between the two species. The study provides the most extensive gene annotation to date, including 46,697 E. guineensis and 38,658 E. oleifera gene predictions. Analyses of repetitive element families further resolve the DNA repeat architecture of both genomes. Comparative genomic analyses identified experimentally validated small structural variants between the oil palm species and resolved the mechanism of chromosomal fusions responsible for the evolutionary descending dysploidy from 18 to 16 chromosomes.

Small RNAs and Karma methylation in Elaeis guineensis mother palms are linked to high clonal mantling.

The mantled phenotype is an abnormal somaclonal variant arising from the oil palm cloning process and severe phenotypes lead to oil yield losses. Hypomethylation of the Karma retrotransposon within the B-type MADS-box EgDEF1 gene has been associated with this phenotype. While abnormal Karma-EgDEF1 hypomethylation was detected in mantled clones, we examined the methylation state of Karma in ortets that gave rise to high mantling rates in their clones. Small RNAs (sRNAs) were proposed to play a role in Karma hypomethylation as part of the RNA-directed DNA methylation process, hence differential expression analysis of sRNAs between the ortet groups was conducted. While no sRNA was differentially expressed at the Karma-EgDEF1 region, three sRNA clusters were differentially regulated in high-mantling ortets. The first two down-regulated clusters were possibly derived from long non-coding RNAs while the third up-regulated cluster was derived from the intron of a DnaJ chaperone gene. Several predicted mRNA targets for the first two sRNA clusters conversely displayed increased expression in high-mantling relative to low-mantling ortets. These predicted mRNA targets may be associated with defense or pathogenesis response. In addition, several differentially methylated regions (DMRs) were identified in Karma and its surrounding regions, mainly comprising subtle CHH hypomethylation in high-mantling ortets. Four of the 12 DMRs were located in a region corresponding to hypomethylated areas at the 3'end of Karma previously reported in mantled clones. Further investigations on these sRNAs and DMRs may indicate the predisposition of certain ortets towards mantled somaclonal variation.

Mitochondrial DNA haplogroup, genetic ancestry, and susceptibility to Ewing sarcoma.

Based on current studies, the incidence of Ewing sarcoma (ES) varies significantly by race and ethnicity, with the disease being most common in patients of European ancestry. However, race/ethnicity has generally been self-reported rather than formally evaluated at a population level using DNA evidence. Additionally, mitochondrial dysfunction is a hallmark of ES, yet there have been no reported studies of mitochondrial genetics in ES. Thus, we evaluated both the mitochondrial and nuclear ancestries of 420 pediatric ES patients in the United States using whole-genome sequencing. We found that the mitochondrial DNA (mtDNA) genomes of only six (1.4 %) patients belonged to African L haplogroups, while those of 90 % of the patients belonged to macrohaplogroup R, which includes haplogroup H, the most common maternal lineage in Europe. Compared to the general US population, European haplogroups were significantly enriched in ES patients (p < 2.2e-16) and the African haplogroups are significantly impoverished (p < 4.6e-16). Using the ancestry informative markers defined in a National Genographic study, the vast majority of patients exhibited significant nuclear ancestry originating from the Mediterranean, Northern Europe, and Southwest Asia, including all six patients with African L mtDNAs. Very few had primarily African nuclear ancestry. This is the first genomic epidemiology study to simultaneously interrogate the mitochondrial and nuclear ancestries of ES patients. While supporting previous findings of enriched European ancestry in ES patients, these results also suggest alternative hypotheses for the significant contribution of mitochondrial ancestry in ES patients, as well as the protective role of African ancestry.

Genome-Wide Prediction of Transcription Start Sites in Conifers.

The identification of promoters is an essential step in the genome annotation process, providing a framework for gene regulatory networks and their role in transcription regulation. Despite considerable advances in the high-throughput determination of transcription start sites (TSSs) and transcription factor binding sites (TFBSs), experimental methods are still time-consuming and expensive. Instead, several computational approaches have been developed to provide fast and reliable means for predicting the location of TSSs and regulatory motifs on a genome-wide scale. Numerous studies have been carried out on the regulatory elements of mammalian genomes, but plant promoters, especially in gymnosperms, have been left out of the limelight and, therefore, have been poorly investigated. The aim of this study was to enhance and expand the existing genome annotations using computational approaches for genome-wide prediction of TSSs in the four conifer species: loblolly pine, white spruce, Norway spruce, and Siberian larch. Our pipeline will be useful for TSS predictions in other genomes, especially for draft assemblies, where reliable TSS predictions are not usually available. We also explored some of the features of the nucleotide composition of the predicted promoters and compared the GC properties of conifer genes with model monocot and dicot plants. Here, we demonstrate that even incomplete genome assemblies and partial annotations can be a reliable starting point for TSS annotation. The results of the TSS prediction in four conifer species have been deposited in the Persephone genome browser, which allows smooth visualization and is optimized for large data sets. This work provides the initial basis for future experimental validation and the study of the regulatory regions to understand gene regulation in gymnosperms.

Bioinformatics Applications to Reveal Molecular Mechanisms of Gene Expression Regulation in Model Organisms.

Gene expression regulation at the transcriptome, genome, cell, and tissue levels is a complex phenomenon demanding the development of bioinformatics tools [...].

Genome-wide analysis of genetic diversity and artificial selection in Large White pigs in Russia.

Breeding practices adopted at different farms are aimed at maximizing the profitability of pig farming. In this work, we have analyzed the genetic diversity of Large White pigs in Russia. We compared genomes of historic and modern Large White Russian breeds using 271 pig samples. We have identified 120 candidate regions associated with the differentiation of modern and historic pigs and analyzed genomic differences between the modern farms. The identified genes were associated with height, fitness, conformation, reproductive performance, and meat quality.

Detection of genomic regions associated malformations in newborn piglets: a machine-learning approach.

BACKGROUND: A significant proportion of perinatal losses in pigs occurs due to congenital malformations. The purpose of this study is the identification of genomic loci associated with fetal malformations in piglets. METHODS: The malformations were divided into two groups: associated with limb defects (piglet splay leg) and associated with other congenital anomalies found in newborn piglets. 148 Landrace and 170 Large White piglets were selected for the study. A genome-wide association study based on the gradient boosting machine algorithm was performed to identify markers associated with congenital anomalies and piglet splay leg. RESULTS: Forty-nine SNPs (23 SNPs in Landrace pigs and 26 SNPs in Large White) were associated with congenital anomalies, 22 of which were localized in genes. A total of 156 SNPs (28 SNPs in Landrace; 128 in Large White) were identified for piglet splay leg, of which 79 SNPs were localized in genes. We have demonstrated that the gradient boosting machine algorithm can identify SNPs and their combinations associated with significant selection indicators of studied malformations and productive characteristics. DATA AVAILABILITY: Genotyping and phenotyping data are available at http://www.compubioverne.group/data-and-software/.

Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.

As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper, we present TransPrise-an efficient deep learning tool for predicting positions of eukaryotic transcription start sites. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise with the TSSPlant approach for well-annotated genome of Oryza sativa. Using a computer with a graphics processing unit, the run time of TransPrise is 250 min on a genome of 374 Mb long.We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all the necessary packages, models, and code as well as the source code of the TransPrise algorithm are available at http://compubioverne.group/ . The source code is ready to use and to be customized to predict TSS in any eukaryotic organism.

Local ancestry prediction with PyLAE.

SUMMARY: We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations. AVAILABILITY AND IMPLEMENTATION: The source code and installation manual are available at https://github.com/smetam/pylae.

Bioinformatics Methods in Medical Genetics and Genomics.

Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the "Centenary of Human Population Genetics" Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein-protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at "Systems Biology and Bioinformatics"-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools.

DNA methylation changes in clonally propagated oil palm.

KEY MESSAGE: Several hypomethylated sites within the Karma region of EgDEF1 and hotspot regions in chromosomes 1, 2, 3, and 5 may be associated with mantling. One of the main challenges faced by the oil palm industry is fruit abnormalities, such as the "mantled" phenotype that can lead to reduced yields. This clonal abnormality is an epigenetic phenomenon and has been linked to the hypomethylation of a transposable element within the EgDEF1 gene. To understand the epigenome changes in clones, methylomes of clonal oil palms were compared to methylomes of seedling-derived oil palms. Whole-genome bisulfite sequencing data from seedlings, normal, and mantled clones were analyzed to determine and compare the context-specific DNA methylomes. In seedlings, coding and regulatory regions are generally hypomethylated while introns and repeats are extensively methylated. Genes with a low number of guanines and cytosines in the third position of codons (GC3-poor genes) were increasingly methylated towards their 3' region, while GC3-rich genes remain demethylated, similar to patterns in other eukaryotic species. Predicted promoter regions were generally hypomethylated in seedlings. In clones, CG, CHG, and CHH methylation levels generally decreased in functionally important regions, such as promoters, 5' UTRs, and coding regions. Although random regions were found to be hypomethylated in clonal genomes, hypomethylation of certain hotspot regions may be associated with the clonal mantling phenotype. Our findings, therefore, suggest other hypomethylated CHG sites within the Karma of EgDEF1 and hypomethylated hotspot regions in chromosomes 1, 2, 3 and 5, are associated with mantling.

PigLeg: prediction of swine phenotype using machine learning.

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

The Impact of MNRI Therapy on the Levels of Neurotransmitters Associated with Inflammatory Processes.

The neurotransmitter levels of representatives from five different diagnosis groups were tested before and after participation in the MNRI®-Masgutova Neurosensorimotor Reflex Intervention. The purpose of this study was to ascertain neurological impact on (1) Developmental disorders, (2) Anxiety disorders/OCD (Obsessive Compulsive Disorder), PTSD (Post-Traumatic Stress disorder), (3) Palsy/Seizure disorders, (4) ADD/ADHD (Attention Deficit Disorder/Attention Deficit Disorder Hyperactive Disorder), and (5) ASD (Autism Spectrum Disorder) disorders. Each participant had a form of neurological dysregulation and typical symptoms respective to their diagnosis. These diagnoses have a severe negative impact on the quality of life, immunity, stress coping, cognitive skills, and social assimilation. This study showed a trend towards optimization and normalization of neurological and immunological functioning, thus supporting the claim that the MNRI method is an effective non-pharmacological neuromodulation treatment of neurological disorders. The effects of MNRI on inflammation have not yet been assessed. The resulting post-MNRI changes in participants' neurotransmitters show significant adjustments in the regulation of the neurotransmitter resulting in being calmer, a decrease of hypervigilance, an increase in stress resilience, behavioral and emotional regulation improvements, a more positive emotional state, and greater control of cognitive processes. In this paper, we demonstrate that the MNRI approach is an intervention that reduces inflammation. It is also likely to reduce oxidative stress and encourage homeostasis of excitatory neurotransmitters. MNRI may facilitate neurodevelopment, build stress resiliency, neuroplasticity, and optimal learning opportunity. There have been no reported side effects of MNRI treatments.