MOTIVATION: Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual's genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. RESULTS: Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual's genetic similarity to a continental group. AVAILABILITY AND IMPLEMENTATION: AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

MOTIVATION: In clinical trials, individuals are matched using demographic criteria, paired and then randomly assigned to treatment and control groups to determine a drug's efficacy. A chief cause for the irreproducibility of results across pilot to Phase-III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups. RESULTS: Pair Matcher (PaM) addresses stratification bias by optimizing pairing assignments a priori and/or a posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on competing methods, including the commonly used principal component analysis (PCA). Homogenizing the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the trial or genetic study. PaM's ancestral inferences also allow characterizing responders and developing a precision medicine approach to treatment. AVAILABILITY AND IMPLEMENTATION: PaM is freely available via Rhttps://github.com/eelhaik/PAM and a web-interface at http://elhaik-matcher.sheffield.ac.uk/ElhaikLab/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The methylation of cytosines at CpG dinucleotides, which plays an important role in gene expression regulation, is one of the most studied epigenetic modifications. Thus far, the detection of DNA methylation has been determined mostly by experimental methods, which are not only prone to bench effects and artifacts but are also time-consuming, expensive, and cannot be easily scaled up to many samples. It is therefore useful to develop computational prediction methods for DNA methylation. Our previous studies highlighted the existence of correlations between the GC content of the third codon position (GC₃), methylation, and gene expression. We thus designed a model to predict methylation in Oryza sativa based on genomic sequence features and gene expression data. RESULTS: We first derive equations to describe the relationship between gene methylation levels, GC₃, expression, length, and other gene compositional features. We next assess gene compositional features involving sixmers and their association with methylation levels and other gene level properties. By applying our sixmer-based approach on rice gene expression data we show that it can accurately predict methylation (Pearson's correlation coefficient r = 0.79) for the majority (79%) of the genes. Matlab code with our model is included. CONCLUSIONS: Gene expression variation can be used as predictors of gene methylation levels.

For the past four decades the compositional organization of the mammalian genome posed a formidable challenge to molecular evolutionists attempting to explain it from an evolutionary perspective. Unfortunately, most of the explanations adhered to the "isochore theory," which has long been rebutted. Recently, an alternative compositional domain model was proposed depicting the human and cow genomes as composed mostly of short compositionally homogeneous and nonhomogeneous domains and a few long ones. We test the validity of this model through a rigorous sequence-based analysis of eleven completely sequenced mammalian and avian genomes. Seven attributes of compositional domains are used in the analyses: (1) the number of compositional domains, (2) compositional domain-length distribution, (3) density of compositional domains, (4) genome coverage by the different domain types, (5) degree of fit to a power-law distribution, (6) compositional domain GC content, and (7) the joint distribution of GC content and length of the different domain types. We discuss the evolution of these attributes in light of two competing phylogenetic hypotheses that differ from each other in the validity of clade Euarchontoglires. If valid, the murid genome compositional organization would be a derived state and exhibit a high similarity to that of other mammals. If invalid, the murid genome compositional organization would be closer to an ancestral state. We demonstrate that the compositional organization of the murid genome differs from those of primates and laurasiatherians, a phenomenon previously termed the "murid shift," and in many ways resembles the genome of opossum. We find no support to the "isochore theory." Instead, our findings depict the mammalian genome as a tapestry of mostly short homogeneous and nonhomogeneous domains and few long ones thus providing strong evidence in favor of the compositional domain model and seem to invalidate clade Euarchontoglires.