Six million Jews were killed by Nazi Germany and its collaborators during World War II. Archaeological excavations in the area of the death camp in Sobibór, Poland, revealed ten sets of human skeletal remains presumptively assigned to Polish victims of the totalitarian regimes. However, their genetic analyses indicate that the remains are of Ashkenazi Jews murdered as part of the mass extermination of European Jews by the Nazi regime and not of otherwise hypothesised non-Jewish partisan combatants. In accordance with traditional Jewish rite, the remains were reburied in the presence of a Rabbi at the place of their discovery.

Erratum in Genome Biol. 2021 Aug 25;22(1):246.

Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIAN is a general method that can be applied to bulk or single-cell data, but has particular utility for single-cell analysis due to that data's unique challenges and opportunities for discovery. SICILIAN's precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, and increases agreement between biological replicates. SICILIAN detects unannotated splicing in single cells, enabling the discovery of novel splicing regulation through single-cell analysis workflows.

BACKGROUND: Human-to-human transmission of symbiotic, anaerobic bacteria is a fundamental evolutionary adaptation essential for membership of the human gut microbiota. However, despite its importance, the genomic and biological adaptations underpinning symbiont transmission remain poorly understood. The Firmicutes are a dominant phylum within the intestinal microbiota that are capable of producing resistant endospores that maintain viability within the environment and germinate within the intestine to facilitate transmission. However, the impact of host transmission on the evolutionary and adaptive processes within the intestinal microbiota remains unknown. RESULTS: We analyze 1358 genomes of Firmicutes bacteria derived from host and environment-associated habitats. Characterization of genomes as spore-forming based on the presence of sporulation-predictive genes reveals multiple losses of sporulation in many distinct lineages. Loss of sporulation in gut Firmicutes is associated with features of host-adaptation such as genome reduction and specialized metabolic capabilities. Consistent with these data, analysis of 9966 gut metagenomes from adults around the world demonstrates that bacteria now incapable of sporulation are more abundant within individuals but less prevalent in the human population compared to spore-forming bacteria. CONCLUSIONS: Our results suggest host adaptation in gut Firmicutes is an evolutionary trade-off between transmission range and colonization abundance. We reveal host transmission as an underappreciated process that shapes the evolution, assembly, and functions of gut Firmicutes.

BACKGROUND: Spatial interactions and insulation of chromatin regions are associated with transcriptional regulation. Domains of frequent chromatin contacts are proposed as functional units, favoring and delimiting gene regulatory interactions. However, contrasting evidence supports the association between chromatin domains and transcription. RESULT: Here, we assess gene co-regulation in chromatin domains across multiple human cancers, which exhibit great transcriptional heterogeneity. Across all datasets, gene co-regulation is observed only within a small yet significant number of chromatin domains. We design an algorithmic approach to identify differentially active domains (DADo) between two conditions and show that these provide complementary information to differentially expressed genes. Domains comprising co-regulated genes are enriched in the less active B sub-compartments and for genes with similar function. Notably, differential activation of chromatin domains is not associated with major changes of domain boundaries, but rather with changes of sub-compartments and intra-domain contacts. CONCLUSION: Overall, gene co-regulation is observed only in a minority of chromatin domains, whose systematic identification will help unravel the relationship between chromatin structure and transcription.

RNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

We develop a novel computational method, NucHMM, to identify functional nucleosome states associated with cell type-specific combinatorial histone marks and nucleosome organization features such as phasing, spacing and positioning. We test it on publicly available MNase-seq and ChIP-seq data in MCF7, H1, and IMR90 cells and identify 11 distinct functional nucleosome states. We demonstrate these nucleosome states are distinctly associated with the splicing potentiality of skipping exons. This advances our understanding of the chromatin function at the nucleosome level and offers insights into the interplay between nucleosome organization and splicing processes.

Single-cell RNA-seq datasets are often first analyzed independently without harnessing model fits from previous studies, and are then contextualized with public data sets, requiring time-consuming data wrangling. We address these issues with sfaira, a single-cell data zoo for public data sets paired with a model zoo for executable pre-trained models. The data zoo is designed to facilitate contribution of data sets using ontologies for metadata. We propose an adaption of cross-entropy loss for cell type classification tailored to datasets annotated at different levels of coarseness. We demonstrate the utility of sfaira by training models across anatomic data partitions on 8 million cells.

Comment in Nat Rev Rheumatol. 2021 Nov;17(11):646.

Nullomers and nullpeptides are short DNA or amino acid sequences that are absent from a genome or proteome, respectively. One potential cause for their absence could be their having a detrimental impact on an organism. RESULTS: Here, we identify all possible nullomers and nullpeptides in the genomes and proteomes of thirty eukaryotes and demonstrate that a significant proportion of these sequences are under negative selection. We also identify nullomers that are unique to specific functional categories: coding sequences, exons, introns, 5'UTR, 3'UTR, promoters, and show that coding sequence and promoter nullomers are most likely to be selected against. By analyzing all protein sequences across the tree of life, we further identify 36,081 peptides up to six amino acids in length that do not exist in any known organism, termed primes. We next characterize all possible single base pair mutations that can lead to the appearance of a nullomer in the human genome, observing a significantly higher number of mutations than expected by chance for specific nullomer sequences in transposable elements, likely due to their suppression. We also annotate nullomers that appear due to naturally occurring variants and show that a subset of them can be used to distinguish between different human populations. Analysis of nullomers and nullpeptides across vertebrate evolution shows they can also be used as phylogenetic classifiers. CONCLUSIONS: We provide a catalog of nullomers and nullpeptides in distinct functional categories, develop methods to systematically study them, and highlight the use of variability in these sequences in other analyses.

BACKGROUND: The transcription factor CTCF appears indispensable in defining topologically associated domain boundaries and maintaining chromatin loop structures within these domains, supported by numerous functional studies. However, acute depletion of CTCF globally reduces chromatin interactions but does not significantly alter transcription. RESULTS: Here, we systematically integrate multi-omics data including ATAC-seq, RNA-seq, WGBS, Hi-C, Cut&Run, and CRISPR-Cas9 survival dropout screens, and time-solved deep proteomic and phosphoproteomic analyses in cells carrying auxin-induced degron at endogenous CTCF locus. Acute CTCF protein degradation markedly rewires genome-wide chromatin accessibility. Increased accessible chromatin regions are frequently located adjacent to CTCF-binding sites at promoter regions and insulator sites associated with enhanced transcription of nearby genes. In addition, we use CTCF-associated multi-omics data to establish a combinatorial data analysis pipeline to discover CTCF co-regulatory partners. We successfully identify 40 candidates, including multiple established partners. Interestingly, many CTCF co-regulators that have alterations of their respective downstream gene expression do not show changes of their own expression levels across the multi-omics measurements upon acute CTCF loss, highlighting the strength of our system to discover hidden co-regulatory partners associated with CTCF-mediated transcription. CONCLUSIONS: This study highlights that CTCF loss rewires genome-wide chromatin accessibility, which plays a critical role in transcriptional regulation.

BACKGROUND: The succession of the gut microbiota during the first few years plays a vital role in human development. We elucidate the characteristics and alternations of the infant gut microbiota to better understand the correlation between infant health and microbiota maturation. RESULTS: We collect 13,776 fecal samples or datasets from 1956 infants between 1 and 3 years of age, based on multi-population cohorts covering 17 countries. The characteristics of the gut microbiota are analyzed based on enterotype and an ecological model. Clinical information (n = 2287) is integrated to understand outcomes of different developmental patterns. Infants whose gut microbiota are dominated by Firmicutes and Bifidobacterium exhibit typical characteristics of early developmental stages, such as unstable community structure and low microbiome maturation, while those driven by Bacteroides and Prevotella are characterized by higher diversity and stronger connections in the gut microbial community. We further reveal a geography-related pattern in global populations. Through ecological modeling and functional analysis, we demonstrate that the transition of the gut microbiota from infants towards adults follows a deterministic pattern; as infants grow up, the dominance of Firmicutes and Bifidobacterium is replaced by that of Bacteroides and Prevotella, along with shifts in specific metabolic pathways. CONCLUSIONS: By leveraging the extremely large datasets and enterotype-based microbiome analysis, we decipher the colonization and transition of the gut microbiota in infants from a new perspective. We further introduce an ecological model to estimate the tendency of enterotype transitions, and demonstrated that the transition of infant gut microbiota was deterministic and predictable.

BACKGROUND: The human genome encodes over 14,000 pseudogenes that are evolutionary relics of protein-coding genes and commonly considered as nonfunctional. Emerging evidence suggests that some pseudogenes may exert important functions. However, to what extent human pseudogenes are functionally relevant remains unclear. There has been no large-scale characterization of pseudogene function because of technical challenges, including high sequence similarity between pseudogene and parent genes, and poor annotation of transcription start sites. RESULTS: To overcome these technical obstacles, we develop an integrated computational pipeline to design the first genome-wide library of CRISPR interference (CRISPRi) single-guide RNAs (sgRNAs) that target human pseudogene promoter-proximal regions. We perform the first pseudogene-focused CRISPRi screen in luminal A breast cancer cells and reveal approximately 70 pseudogenes that affect breast cancer cell fitness. Among the top hits, we identify a cancer-testis unitary pseudogene, MGAT4EP, that is predominantly localized in the nucleus and interacts with FOXA1, a key regulator in luminal A breast cancer. By enhancing the promoter binding of FOXA1, MGAT4EP upregulates the expression of oncogenic transcription factor FOXM1. Integrative analyses of multi-omic data from the Cancer Genome Atlas (TCGA) reveal many unitary pseudogenes whose expressions are significantly dysregulated and/or associated with overall/relapse-free survival of patients in diverse cancer types. CONCLUSIONS: Our study represents the first large-scale study characterizing pseudogene function. Our findings suggest the importance of nuclear function of unitary pseudogenes and underscore their underappreciated roles in human diseases. The functional genomic resources developed here will greatly facilitate the study of human pseudogene function.

Genome-wide association studies reveal many non-coding variants associated with complex traits. However, model organism studies largely remain as an untapped resource for unveiling the effector genes of non-coding variants. We develop INFIMA, Integrative Fine-Mapping, to pinpoint causal SNPs for diversity outbred (DO) mice eQTL by integrating founder mice multi-omics data including ATAC-seq, RNA-seq, footprinting, and in silico mutation analysis. We demonstrate INFIMA's superior performance compared to alternatives with human and mouse chromatin conformation capture datasets. We apply INFIMA to identify novel effector genes for GWAS variants associated with diabetes. The results of the application are available at http://www.statlab.wisc.edu/shiny/INFIMA/ .

Update of medRxiv. 2021 Apr 13;:

Functional genomics experiments, like ChIP-Seq or ATAC-Seq, produce results that are summarized as a region set. There is no way to objectively evaluate the effectiveness of region set similarity metrics. We present Bedshift, a tool for perturbing BED files by randomly shifting, adding, and dropping regions from a reference file. The perturbed files can be used to benchmark similarity metrics, as well as for other applications. We highlight differences in behavior between metrics, such as that the Jaccard score is most sensitive to added or dropped regions, while coverage score is most sensitive to shifted regions.

Several bioinformatic tools have been developed for genome-wide identification of orthologous and paralogous genes. However, no corresponding tool allows the detection of exon homology relationships. Here, we present ExOrthist, a fully reproducible Nextflow-based software enabling inference of exon homologs and orthogroups, visualization of evolution of exon-intron structures, and assessment of conservation of alternative splicing patterns. ExOrthist evaluates exon sequence conservation and considers the surrounding exon-intron context to derive genome-wide multi-species exon homologies at any evolutionary distance. We demonstrate its use in different evolutionary scenarios: whole genome duplication in frogs and convergence of Nova-regulated splicing networks ( https://github.com/biocorecrg/ExOrthist ).

BACKGROUND: After repairing double-strand breaks (DSBs) caused by CRISPR-Cas9 cleavage, genomic damage, such as large deletions, may have pathogenic consequences. RESULTS: We show that large deletions are ubiquitous but are dependent on editing sites and cell types. Human primary T cells display more significant deletions than hematopoietic stem and progenitor cells (HSPCs), whereas we observe low levels in induced pluripotent stem cells (iPSCs). We find that the homology-directed repair (HDR) with single-stranded oligodeoxynucleotides (ssODNs) carrying short homology reduces the deletion damage by almost half, while adeno-associated virus (AAV) donors with long homology reduce large deletions by approximately 80%. In the absence of HDR, the insertion of a short double-stranded ODN by NHEJ reduces deletion indexes by about 60%. CONCLUSIONS: Timely bridging of broken ends by HDR and NHEJ vastly decreases the unintended consequences of dsDNA cleavage. These strategies can be harnessed in gene editing applications to attenuate unintended outcomes.

BACKGROUND: Structural variants (SVs) significantly drive genome diversity and environmental adaptation for diverse species. Unlike the prevalent small SVs (< kilobase-scale) in higher eukaryotes, large-size SVs rarely exist in the genome, but they function as one of the key evolutionary forces for speciation and adaptation. RESULTS: In this study, we discover and characterize several megabase-scale presence-absence variations (PAVs) in the maize genome. Surprisingly, we identify a 3.2 Mb PAV fragment that shows high integrity and is present as complete presence or absence in the natural diversity panel. This PAV is embedded within the nucleolus organizer region (NOR), where the suppressed recombination is found to maintain the PAV against the evolutionary variation. Interestingly, by analyzing the sequence of this PAV, we not only reveal the domestication trace from teosinte to modern maize, but also the footprints of its origin from Tripsacum, shedding light on a previously unknown contribution from Tripsacum to the speciation of Zea species. The functional consequence of the Tripsacum segment migration is also investigated, and environmental fitness conferred by the PAV may explain the whole segment as a selection target during maize domestication and improvement. CONCLUSIONS: These findings provide a novel perspective that Tripsacum contributes to Zea speciation, and also instantiate a strategy for evolutionary and functional analysis of the "fossil" structure variations during genome evolution and speciation.

Prime editing is a revolutionary genome-editing technology that can make a wide range of precise edits in DNA. However, designing highly efficient prime editors (PEs) remains challenging. We develop Easy-Prime, a machine learning-based program trained with multiple published data sources. Easy-Prime captures both known and novel features, such as RNA folding structure, and optimizes feature combinations to improve editing efficiency. We provide optimized PE design for installation of 89.5% of 152,351 GWAS variants. Easy-Prime is available both as a command line tool and an interactive PE design server at: http://easy-prime.cc/ .

Efficiently scaling genomic variant search indexes to thousands of samples is computationally challenging due to the presence of multiple coordinate systems to avoid reference biases. We present VariantStore, a system that indexes genomic variants from multiple samples using a variation graph and enables variant queries across any sample-specific coordinate system. We show the scalability of VariantStore by indexing genomic variants from the TCGA project in 4 h and the 1000 Genomes project in 3 h. Querying for variants in a gene takes between 0.002 and 3 seconds using memory only 10% of the size of the full representation.

Single-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data, thus providing an objective means to estimating the number of possible groups or cell-type populations present.

Erratum in Genome Biol. 2021 Sep 20;22(1):272.

Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease genes by adjusting confounders without prior knowledge of control variables in single-cell RNA-seq data. We demonstrate that our method substantially improves statistical power in simulations and real-world data analysis of 70k brain cells collected for dissecting Alzheimer's disease. We identify 215 differentially regulated causal genes in various cell types, including highly relevant genes with a proper cell type context. Genes found in different types enrich distinctive pathways, implicating the importance of cell types in understanding multifaceted disease mechanisms.

BACKGROUND: Biomolecular condensates have been implicated in multiple cellular processes. However, the global role played by condensates in 3D chromatin organization remains unclear. At present, 1,6-hexanediol (1,6-HD) is the only available tool to globally disrupt condensates, yet the conditions of 1,6-HD vary considerably between studies and may even trigger apoptosis. RESULTS: In this study, we first analyzed the effects of different concentrations and treatment durations of 1,6-HD and found that short-term exposure to 1.5% 1,6-HD dissolved biomolecular condensates whereas long-term exposure caused aberrant aggregation without affecting cell viability. Based on this condition, we drew a time-resolved map of 3D chromatin organization and found that short-term treatment with 1.5% 1,6-HD resulted in reduced long-range interactions, strengthened compartmentalization, homogenized A-A interactions, B-to-A compartment switch and TAD reorganization, whereas longer exposure had the opposite effects. Furthermore, the long-range interactions between condensate-component-enriched regions were markedly weakened following 1,6-HD treatment. CONCLUSIONS: In conclusion, our study finds a proper 1,6-HD condition and provides a resource for exploring the role of biomolecular condensates in 3D chromatin organization.

BACKGROUND: Liquid-liquid phase separation (LLPS) is an important organizing principle for biomolecular condensation and chromosome compartmentalization. However, while many proteins have been reported to undergo LLPS, quantitative and global analysis of chromatin LLPS property remains absent. RESULTS: Here, by combining chromatin-associated protein pull-down, quantitative proteomics and 1,6-hexanediol (1,6-HD) treatment, we develop Hi-MS and define an anti-1,6-HD index of chromatin-associated proteins (AICAP) to quantify 1,6-HD sensitivity of chromatin-associated proteins under physiological conditions. Compared with known physicochemical properties involved in phase separation, we find that proteins with lower AICAP are associated with higher content of disordered regions, higher hydrophobic residue preference, higher mobility and higher predicted LLPS potential. We also construct BL-Hi-C libraries following 1,6-HD treatment to study the sensitivity of chromatin conformation to 1,6-HD treatment. We find that the active chromatin and high-order structures, as well as the proteins enriched in corresponding regions, are more sensitive to 1,6-HD treatment. CONCLUSIONS: Our work provides a global quantitative measurement of LLPS properties of chromatin-associated proteins and higher-order chromatin structure. Hi-MS and AICAP data provide an experimental tool and quantitative resources valuable for future studies of biomolecular condensates.

BACKGROUND: Retrotransposons are genetic elements inducing mutations in all domains of life. Despite their detrimental effect, retrotransposons can become temporarily active during epigenetic reprogramming and cellular stress response, which may accelerate host genome evolution. In fungal pathogens, a positive role has been attributed to retrotransposons when shaping genome architecture and expression of genes encoding pathogenicity factors; thus, retrotransposons are known to influence pathogenicity. RESULTS: We uncover a hitherto unknown role of fungal retrotransposons as being pathogenicity factors, themselves. The aggressive fungal plant pathogen, Botrytis cinerea, is known to deliver some long-terminal repeat (LTR) deriving regulatory trans-species small RNAs (BcsRNAs) into plant cells to suppress host gene expression for infection. We find that naturally occurring, less aggressive B. cinerea strains possess considerably lower copy numbers of LTR retrotransposons and had lost retrotransposon BcsRNA production. Using a transgenic proof-of-concept approach, we reconstitute retrotransposon expression in a BcsRNA-lacking B. cinerea strain, which results in enhanced aggressiveness in a retrotransposon and BcsRNA expression-dependent manner. Moreover, retrotransposon expression in B. cinerea leads to suppression of plant defence-related genes during infection. CONCLUSIONS: We propose that retrotransposons are pathogenicity factors that manipulate host plant gene expression by encoding trans-species BcsRNAs. Taken together, the novelty that retrotransposons are pathogenicity factors will have a broad impact on studies of host-microbe interactions and pathology.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

Tandem repeat (TR) expansion is the underlying cause of over 40 neurological disorders. Long-read sequencing offers an exciting avenue over conventional technologies for detecting TR expansions. Here, we present Straglr, a robust software tool for both targeted genotyping and novel expansion detection from long-read alignments. We benchmark Straglr using various simulations, targeted genotyping data of cell lines carrying expansions of known diseases, and whole genome sequencing data with chromosome-scale assembly. Our results suggest that Straglr may be useful for investigating disease-associated TR expansions using long-read sequencing.

BACKGROUND: Nonsense-mediated mRNA decay (NMD) is a eukaryotic, translation-dependent degradation pathway that targets mRNAs with premature termination codons and also regulates the expression of some mRNAs that encode full-length proteins. Although many genes express NMD-sensitive transcripts, identifying them based on short-read sequencing data remains a challenge. RESULTS: To identify and analyze endogenous targets of NMD, we apply cDNA Nanopore sequencing and short-read sequencing to human cells with varying expression levels of NMD factors. Our approach detects full-length NMD substrates that are highly unstable and increase in levels or even only appear when NMD is inhibited. Among the many new NMD-targeted isoforms that our analysis identifies, most derive from alternative exon usage. The isoform-aware analysis reveals many genes with significant changes in splicing but no significant changes in overall expression levels upon NMD knockdown. NMD-sensitive mRNAs have more exons in the 3΄UTR and, for those mRNAs with a termination codon in the last exon, the length of the 3΄UTR per se does not correlate with NMD sensitivity. Analysis of splicing signals reveals isoforms where NMD has been co-opted in the regulation of gene expression, though the main function of NMD seems to be ridding the transcriptome of isoforms resulting from spurious splicing events. CONCLUSIONS: Long-read sequencing enables the identification of many novel NMD-sensitive mRNAs and reveals both known and unexpected features concerning their biogenesis and their biological role. Our data provide a highly valuable resource of human NMD transcript targets for future genomic and transcriptomic applications.

Most eukaryotic genes express alternative polyadenylation (APA) isoforms. A growing number of RNA sequencing methods, especially those used for single-cell transcriptome analysis, generate reads close to the polyadenylation site (PAS), termed nearSite reads, hence inherently containing information about APA isoform abundance. Here, we present a probabilistic model-based method named MAAPER to utilize nearSite reads for APA analysis. MAAPER predicts PASs with high accuracy and sensitivity and examines different types of APA events with robust statistics. We show MAAPER's performance with both bulk and single-cell data and its applicability in unpaired or paired experimental designs.

Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3' tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identity analysis beyond gene expression, enriching information extracted from scRNA-seq data. Using SCAPTURE, we show changes of PAS usage in PBMCs from infected versus healthy individuals at single-cell resolution.