Alternative polyadenylation (APA) is an important post-transcriptional mechanism that has major implications in biological processes and diseases. Although specialized sequencing methods for polyadenylation exist, availability of these data are limited compared to RNA-sequencing data. We developed REPAC, a framework for the analysis of APA from RNA-sequencing data. Using REPAC, we investigate the landscape of APA caused by activation of B cells. We also show that REPAC is faster than alternative methods by at least 7-fold and that it scales well to hundreds of samples. Overall, the REPAC method offers an accurate, easy, and convenient solution for the exploration of APA.

Studies have shown a link between colorectal cancer (CRC) and gut microbiome compositions. In these studies, machine learning is used to infer CRC biomarkers using global explanation methods. While these methods allow the identification of bacteria generally correlated with CRC, they fail to recognize species that are only influential for some individuals. In this study, we investigate the potential of Shapley Additive Explanations (SHAP) for a more personalized CRC biomarker identification. Analyses of five independent datasets show that this method can even separate CRC subjects into subgroups with distinct CRC probabilities and bacterial biomarkers.

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

Kinship analysis from very low-coverage ancient sequences has been possible up to the second degree with large uncertainties. We propose a new, accurate, and fast method, correctKin, to estimate the kinship coefficient and the confidence interval using low-coverage ancient data. We perform simulations and also validate correctKin on experimental modern and ancient data with widely different genome coverages (0.12×-11.9×) using samples with known family relations and known/unknown population structure. Based on our results, correctKin allows for the reliable identification of relatedness up to the 4th degree from variable/low-coverage ancient or badly degraded forensic whole genome sequencing data.

BACKGROUND: Codon usage and nucleotide composition of coding sequences have profound effects on protein expression. However, while it is recognized that different tissues have distinct tRNA profiles and codon usages in their transcriptomes, the effect of tissue-specific codon optimality on protein synthesis remains elusive. RESULTS: We leverage existing state-of-the-art transcriptomics and proteomics datasets from the GTEx project and the Human Protein Atlas to compute the protein-to-mRNA ratios of 36 human tissues. Using this as a proxy of translational efficiency, we build a machine learning model that identifies codons enriched or depleted in specific tissues. We detect two clusters of tissues with an opposite pattern of codon preferences. We then use these identified patterns for the development of CUSTOM, a codon optimizer algorithm which suggests a synonymous codon design in order to optimize protein production in a tissue-specific manner. In human cell-line models, we provide evidence that codon optimization should take into account particularities of the translational machinery of the tissues in which the target proteins are expressed and that our approach can design genes with tissue-optimized expression profiles. CONCLUSIONS: We provide proof-of-concept evidence that codon preferences exist in tissue-specific protein synthesis and demonstrate its application to synthetic gene design. We show that CUSTOM can be of benefit in biological and biotechnological applications, such as in the design of tissue-targeted therapies and vaccines.

BACKGROUND: Mapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases, though most studies have focused on individuals of European descent. While important progress has been made to study a greater diversity of human populations, many groups remain unstudied, particularly among indigenous populations within Africa. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations. RESULTS: We find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine-mapping of causal variation. Integrating our QTL scans with signatures of natural selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation. CONCLUSION: Extending QTL mapping studies beyond European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.

Insertions and deletions (indels) enable evolution and cause disease. Due to technical challenges, indels are left out of most mutational scans, limiting our understanding of them in disease, biology, and evolution. We develop a low cost and bias method, DIMPLE, for systematically generating deletions, insertions, and missense mutations in genes, which we test on a range of targets, including Kir2.1. We use DIMPLE to study how indels impact potassium channel structure, disease, and evolution. We find deletions are most disruptive overall, beta sheets are most sensitive to indels, and flexible loops are sensitive to deletions yet tolerate insertions.

Using latent variables in gene expression data can help correct unobserved confounders and increase statistical power for expression quantitative trait Loci (eQTL) detection. The probabilistic estimation of expression residuals (PEER) and principal component analysis (PCA) are widely used methods that can remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, their performance has not been evaluated extensively in single-cell eQTL analysis, especially for different cell types. Potential challenges arise due to the structure of single-cell RNA-seq data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that PEER and PCA require additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid latent variables; otherwise, it can result in highly correlated factors (Pearson's correlation r = 0.63 ~ 0.99). Incorporating valid PFs/PCs in the eQTL association model would identify 1.7 ~ 13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and fitted PFs/PCs varied significantly in different cell types. In addition, using highly variable genes to generate latent variables could achieve similar eGenes discovery power as using all genes but save considerable computational resources (~ 6.2-fold faster).

The current version of the human reference genome, GRCh38, contains a number of errors including 1.2 Mbp of falsely duplicated and 8.04 Mbp of collapsed regions. These errors impact the variant calling of 33 protein-coding genes, including 12 with medical relevance. Here, we present FixItFelix, an efficient remapping approach, together with a modified version of the GRCh38 reference genome that improves the subsequent analysis across these genes within minutes for an existing alignment file while maintaining the same coordinates. We showcase these improvements over multi-ethnic control samples, demonstrating improvements for population variant calling as well as eQTL studies.

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

BACKGROUND: The Ccr4-Not complex is mostly known as the major eukaryotic deadenylase. However, several studies have uncovered roles of the complex, in particular of the Not subunits, unrelated to deadenylation and relevant for translation. In particular, the existence of Not condensates that regulate translation elongation dynamics has been reported. Typical studies that evaluate translation efficiency rely on soluble extracts obtained after the disruption of cells and ribosome profiling. Yet cellular mRNAs in condensates can be actively translated and may not be present in such extracts. RESULTS: In this work, by analyzing soluble and insoluble mRNA decay intermediates in yeast, we determine that insoluble mRNAs are enriched for ribosomes dwelling at non-optimal codons compared to soluble mRNAs. mRNA decay is higher for soluble RNAs, but the proportion of co-translational degradation relative to the overall mRNA decay is higher for insoluble mRNAs. We show that depletion of Not1 and Not4 inversely impacts mRNA solubilities and, for soluble mRNAs, ribosome dwelling according to codon optimality. Depletion of Not4 solubilizes mRNAs with lower non-optimal codon content and higher expression that are rendered insoluble by Not1 depletion. By contrast, depletion of Not1 solubilizes mitochondrial mRNAs, which are rendered insoluble upon Not4 depletion. CONCLUSIONS: Our results reveal that mRNA solubility defines the dynamics of co-translation events and is oppositely regulated by Not1 and Not4, a mechanism that we additionally determine may already be set by Not1 promoter association in the nucleus.

Microarray technology has been used to measure genome-wide DNA methylation in thousands of individuals. These studies typically test the associations between individual DNA methylation sites ("probes") and complex traits or diseases. The results can be used to generate methylation profile scores (MPS) to predict outcomes in independent data sets. Although there are many parallels between MPS and polygenic (risk) scores (PGS), there are key differences. Here, we review motivations, methods, and applications of DNA methylation-based trait prediction, with a focus on common diseases. We contrast MPS with PGS, highlighting where assumptions made in genetic modeling may not hold in epigenetic data.

A long-standing goal of evolutionary biology is to decode how gene regulation contributes to organismal diversity. Doing so is challenging because it is hard to predict function from non-coding sequence and to perform molecular research with non-model taxa. Massively parallel reporter assays (MPRAs) enable the testing of thousands to millions of sequences for regulatory activity simultaneously. Here, we discuss the execution, advantages, and limitations of MPRAs, with a focus on evolutionary questions. We propose solutions for extending MPRAs to rare taxa and those with limited genomic resources, and we underscore MPRA's broad potential for driving genome-scale, functional studies across organisms.

BACKGROUND: The histone variant H3.3 is enriched at active regulatory elements such as promoters and enhancers in mammalian genomes. These regions are highly accessible, creating an environment that is permissive to transcription factor binding and the recruitment of transcriptional coactivators that establish a unique chromatin post-translational landscape. How H3.3 contributes to the establishment and function of chromatin states at these regions is poorly understood. RESULTS: We perform genomic analyses of features associated with active promoter chromatin in mouse embryonic stem cells (ESCs) and find evidence of subtle yet widespread promoter dysregulation in the absence of H3.3. Loss of H3.3 results in reduced chromatin accessibility and transcription factor (TF) binding at promoters of expressed genes in ESCs. Likewise, enrichment of the transcriptional coactivator p300 and downstream histone H3 acetylation at lysine 27 (H3K27ac) is reduced at promoters in the absence of H3.3, along with reduced enrichment of the acetyl lysine reader BRD4. Despite the observed chromatin dysregulation, H3.3 KO ESCs maintain transcription from ESC-specific genes. However, upon undirected differentiation, H3.3 KO cells retain footprinting of ESC-specific TF motifs and fail to generate footprints of lineage-specific TF motifs, in line with their diminished capacity to differentiate. CONCLUSIONS: H3.3 facilitates DNA accessibility, transcription factor binding, and histone post-translational modification at active promoters. While H3.3 is not required for maintaining transcription in ESCs, it does promote de novo transcription factor binding which may contribute to the dysregulation of cellular differentiation in the absence of H3.3.

We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at [Formula: see text] of the associated loci.

Quality control (QC) is a critical component of single-cell RNA-seq (scRNA-seq) processing pipelines. Current approaches to QC implicitly assume that datasets are comprised of one cell type, potentially resulting in biased exclusion of rare cell types. We introduce SampleQC, which robustly fits a Gaussian mixture model across multiple samples, improves sensitivity, and reduces bias compared to current approaches. We show via simulations that SampleQC is less susceptible to exclusion of rarer cell types. We also demonstrate SampleQC on a complex real dataset (867k cells over 172 samples). SampleQC is general, is implemented in R, and could be applied to other data types.

BACKGROUND: The CRISPR-Cas13 system is an RNA-guided RNA-targeting system and has been widely used in transcriptome engineering with potentially important clinical applications. However, it is still controversial whether Cas13 exhibits collateral activity in mammalian cells. RESULTS: Here, we find that knocking down gene expression using RfxCas13d in the adult brain neurons caused death of mice, which may result from the collateral activity of RfxCas13d rather than the loss of target gene function or off-target effects. Mechanistically, we show that RfxCas13d exhibits collateral activity in mammalian cells, which is positively correlated with the abundance of target RNA. The collateral activity of RfxCas13d could cleave 28s rRNA into two fragments, leading to translation attenuation and activation of the ZAKα-JNK/p38-immediate early gene pathway. CONCLUSIONS: These findings provide new mechanistic insights into the collateral activity of RfxCas13d in mammalian cells and warn that the biosafety of the CRISPR-Cas13 system needs further evaluation before application to clinical treatments.