Recent proteogenomic studies revealed extensive translation outside of annotated protein coding regions, such as non-coding RNAs and untranslated regions of mRNAs. This non-canonical translation is largely due to start codon plurality within the same RNA. This plurality is often due to the failure of some scanning ribosomes to recognize potential start codons leading to initiation downstream-a process termed leaky scanning. Codons other than AUG (non-AUG) are particularly leaky due to their inefficiency. Here we discuss our current understanding of non-AUG initiation. We argue for a near-ubiquitous role of non-AUG initiation in shaping the dynamic composition of mammalian proteomes.

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

Variant benchmarking is often performed by comparing a test callset to a gold standard set of variants. In repetitive regions of the genome, it may be difficult to establish what is the truth for a call, for example, when different alignment scoring metrics provide equally supported but different variant calls on the same data. Here, we provide an alternative approach, TT-Mars, that takes advantage of the recent production of high-quality haplotype-resolved genome assemblies by providing false discovery rates for variant calls based on how well their call reflects the content of the assembly, rather than comparing calls themselves.

BACKGROUND: The plasticity along the epithelial-mesenchymal transition (EMT) spectrum has been shown to be regulated by various epigenetic repertoires. Emerging evidence of local chromatin conformation changes suggests that regulation of EMT may occur at a higher order of three-dimensional genome level. RESULTS: We perform Hi-C analysis and combine ChIP-seq data across cancer cell lines representing different EMT states. We demonstrate that the epithelial and mesenchymal genes are regulated distinctively. We find that EMT genes are regulated within their topologically associated domains (TADs), with only a subset of mesenchymal genes being influenced by A/B compartment switches, indicating topological remodeling is required in the transcriptional regulation of these genes. At the TAD level, epithelial and mesenchymal genes are associated with different regulatory trajectories. The epithelial gene-residing TADs are enriched with H3K27me3 marks in the mesenchymal-like states. The mesenchymal gene-residing TADs, which do not show enrichment of H3K27me3 in epithelial-like states, exhibit increased interaction frequencies with regulatory elements in the mesenchymal-like states. CONCLUSIONS: We propose a novel workflow coupling immunofluorescence and dielectrophoresis to unravel EMT heterogeneity at single-cell resolution. The predicted three-dimensional structures of chromosome 10, harboring Vimentin, identify cell clusters of different states. Our results pioneer a novel avenue to decipher the complexities underlying the regulation of EMT and may infer the barriers of plasticity in the 3D genome context.

DNA methylation plays vital roles in both prokaryotes and eukaryotes. There are three forms of DNA methylation in prokaryotes: N6-methyladenine (6mA), N4-methylcytosine (4mC), and 5-methylcytosine (5mC). Although many sequencing methods have been developed to sequence specific types of methylation, few technologies can be used for efficiently mapping multiple types of methylation. Here, we present NT-seq for mapping all three types of methylation simultaneously. NT-seq reliably detects all known methylation motifs in two bacterial genomes and can be used for identifying de novo methylation motifs. NT-seq provides a simple and efficient solution for detecting multiple types of DNA methylation.

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each sequenced cell-is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.

BACKGROUND: The analysis of chromatin binding patterns of proteins in different biological states is a main application of chromatin immunoprecipitation followed by sequencing (ChIP-seq). A large number of algorithms and computational tools for quantitative comparison of ChIP-seq datasets exist, but their performance is strongly dependent on the parameters of the biological system under investigation. Thus, a systematic assessment of available computational tools for differential ChIP-seq analysis is required to guide the optimal selection of analysis tools based on the present biological scenario. RESULTS: We created standardized reference datasets by in silico simulation and sub-sampling of genuine ChIP-seq data to represent different biological scenarios and binding profiles. Using these data, we evaluated the performance of 33 computational tools and approaches for differential ChIP-seq analysis. Tool performance was strongly dependent on peak size and shape as well as on the scenario of biological regulation. CONCLUSIONS: Our analysis provides unbiased guidelines for the optimized choice of software tools in differential ChIP-seq analysis.

BACKGROUND: Hybridization is associated with the activation of transposable elements and changes in the patterns of gene expression leading to phenotypic changes. However, the underlying mechanisms are not well understood. RESULTS: Here, we describe the changes to the gene expression in interspecific Solanum hybrids that are associated with small RNAs derived from endogenous (para)retroviruses (EPRV). There were prominent changes to sRNA profiles in these hybrids involving 22-nt species produced in the DCL2 biogenesis pathway, and the hybridization-induced changes to the gene expression were similar to those in a dcl2 mutant. CONCLUSIONS: These findings indicate that hybridization leads to activation of EPRV, perturbation of small RNA profiles, and, consequently, changes in the gene expression. Such hybridization-induced variation in the gene expression could increase the natural phenotypic variation in natural evolution or in breeding for agriculture.

BACKGROUND: Spatial transcriptomics are a set of new technologies that profile gene expression on tissues with spatial localization information. With technological advances, recent spatial transcriptomics data are often in the form of sparse counts with an excessive amount of zero values. RESULTS: We perform a comprehensive analysis on 20 spatial transcriptomics datasets collected from 11 distinct technologies to characterize the distributional properties of the expression count data and understand the statistical nature of the zero values. Across datasets, we show that a substantial fraction of genes displays overdispersion and/or zero inflation that cannot be accounted for by a Poisson model, with genes displaying overdispersion substantially overlapped with genes displaying zero inflation. In addition, we find that either the Poisson or the negative binomial model is sufficient for modeling the majority of genes across most spatial transcriptomics technologies. We further show major sources of overdispersion and zero inflation in spatial transcriptomics including gene expression heterogeneity across tissue locations and spatial distribution of cell types. In particular, when we focus on a relatively homogeneous set of tissue locations or control for cell type compositions, the number of detected overdispersed and/or zero-inflated genes is substantially reduced, and a simple Poisson model is often sufficient to fit the gene expression data there. CONCLUSIONS: Our study provides the first comprehensive evidence that excessive zeros in spatial transcriptomics are not due to zero inflation, supporting the use of count models without a zero inflation component for modeling spatial transcriptomics.

BACKGROUND: Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely between tumor types, but common gene signatures that relate to core cancer pathways have also been identified. It is not clear, however, whether there exist additional sets of genes or transcriptomic features that are less well known in cancer biology but that are also commonly deregulated across several cancer types. RESULTS: Here, we agnostically identify transcriptomic features that are commonly shared between cancer types using 13,461 RNA-seq samples from 19 normal tissue types and 18 solid tumor types to train three feed-forward neural networks, based either on protein-coding gene expression, lncRNA expression, or splice junction use, to distinguish between normal and tumor samples. All three models recognize transcriptome signatures that are consistent across tumors. Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints. Importantly, we find that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer. CONCLUSIONS: Our results highlighted that deregulation of RNA-processing genes and aberrant splicing are pervasive features on which core cancer pathways might converge across a large array of solid tumor types.

Several high-throughput antibody-free methods for RNA modification detection from sequencing data have been developed. We present JACUSA2 as a versatile software solution and comprehensive analysis framework for RNA modification detection assays that are based on either the Illumina or Nanopore platform. Importantly, JACUSA2 can integrate information from multiple experiments, such as replicates and different conditions, and different library types, such as first- or second-strand cDNA libraries. We demonstrate its utility, showing analysis workflows for N6-methyladenosine (m6A) and pseudouridine (Ψ) detection on Illumina and Nanopore sequencing data sets. Our software and its R helper package are available as open source solutions.

BACKGROUND: Pleiotropy describes the phenomenon in which a gene affects multiple phenotypes. The extent of pleiotropy is still disputed, mainly because of issues of inadequate power of analyses. A further challenge is that empirical tests of pleiotropy are restricted to a small subset of all possible phenotypes. To overcome these limitations, we propose a new measurement of pleiotropy that integrates across many phenotypes and multiple generations to improve power. RESULTS: We infer pleiotropy from the fitness cost imposed by frequency changes of pleiotropic loci. Mixing Drosophila simulans populations, which adapted independently to the same new environment using different sets of genes, we show that the adaptive frequency changes have been accompanied by measurable fitness costs. CONCLUSIONS: Unlike previous studies characterizing the molecular basis of pleiotropy, we show that many loci, each of weak effect, contribute to genome-wide pleiotropy. We propose that the costs of pleiotropy are reduced by the modular architecture of gene expression, which facilitates adaptive gene expression changes with low impact on other functions.

Erratum in Genome Biol. 2022 Oct 13;23(1):213.

Erratum in Genome Biol. 2022 Jul 13;23(1):156.