BACKGROUND: Hybridization and backcrossing are commonly used in animal and plant breeding to induce heritable variation including epigenetic changes such as paramutation. However, the molecular basis for hybrid-induced epigenetic memory remains elusive. RESULTS: Here, we report that hybridization between the inbred parents B73 and Mo17 induces trans-acting hypermethylation and hypomethylation at thousands of loci; several hundreds (~ 3%) are transmitted through six backcrossing and three selfing generations. Notably, many transgenerational methylation patterns resemble epialleles of the nonrecurrent parent, despite > 99% of overall genomic loci are converted to the recurrent parent. These epialleles depend on 24-nt siRNAs, which are eliminated in the isogenic hybrid Mo17xB73:mop1-1 that is defective in siRNA biogenesis. This phenomenon resembles paramutation-like events and occurs in both intraspecific (Mo17xB73) and interspecific (W22xTeosinte) hybrid maize populations. Moreover, siRNA abundance and methylation levels of these epialleles can affect expression of their associated epigenes, many of which are related to stress responses. CONCLUSION: Divergent siRNAs between the hybridizing parents can induce trans-acting epialleles in the hybrids, while the induced epigenetic status is maintained for transgenerational inheritance during backcross and hybrid breeding, which alters epigene expression to enhance growth and adaptation. These genetic and epigenetic principles may apply broadly from plants to animals.

BACKGROUND: Light switches on the photomorphogenic development of young plant seedlings, allowing young seedlings to acquire photosynthetic capacities and gain survival fitness. Light regulates gene expression at all levels of the central dogma, including alternative splicing (AS) during the photomorphogenic development. However, accurate determination of full-length (FL) splicing variants has been greatly hampered by short-read RNA sequencing technologies. RESULT: In this study, we adopt PacBio isoform sequencing (Iso-seq) to overcome the limitation of the short-read RNA-seq technologies. Normalized cDNA libraries used for Iso-seq allows for comprehensive and effective identification of FL AS variants. Our analyses reveal more than 30,000 splicing variant models from approximately 16,500 gene loci and additionally identify approximately 700 previously unannotated genes. Among the variants, approximately 12,000 represent new gene models. Intron retention (IR) is the most frequently observed form of variants, and many IR-containing AS variants show evidence of engagement in translation. Our study reveals the formation of heterodimers of transcription factors composed of annotated and IR-containing AS variants. Moreover, transgenic plants overexpressing the IR forms of two B-BOX DOMAIN PROTEINs exhibits light-hypersensitive phenotypes, suggesting their regulatory roles in modulating optimal light responses. CONCLUSIONS: This study provides an accurate and comprehensive portrait of full-length transcript isoforms and experimentally confirms the presence of de novo synthesized AS variants that impose regulatory functions in photomorphogenic development in Arabidopsis.

BACKGROUND: The high-activity adenine base editors (ABEs), engineered with the recently-developed tRNA adenosine deaminases (TadA8e and TadA9), show robust base editing activity but raise concerns about off-target effects. RESULTS: In this study, we perform a comprehensive evaluation of ABE8e- and ABE9-induced DNA and RNA mutations in Oryza sativa. Whole-genome sequencing analysis of plants transformed with four ABEs, including SpCas9n-TadA8e, SpCas9n-TadA9, SpCas9n-NG-TadA8e, and SpCas9n-NG-TadA9, reveal that ABEs harboring TadA9 lead to a higher number of off-target A-to-G (A>G) single-nucleotide variants (SNVs), and that those harboring CRISPR/SpCas9n-NG lead to a higher total number of off-target SNVs in the rice genome. An analysis of the T-DNAs carrying the ABEs indicates that the on-target mutations could be introduced before and/or after T-DNA integration into plant genomes, with more off-target A>G SNVs forming after the ABEs had integrated into the genome. Furthermore, we detect off-target A>G RNA mutations in plants with high expression of ABEs but not in plants with low expression of ABEs. The off-target A>G RNA mutations tend to cluster, while off-target A>G DNA mutations rarely clustered. CONCLUSION: Our findings that Cas proteins, TadA variants, temporal expression of ABEs, and expression levels of ABEs contribute to ABE specificity in rice provide insight into the specificity of ABEs and suggest alternative ways to increase ABE specificity besides engineering TadA variants.

BACKGROUND: A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the sample, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. RESULTS: We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by sampling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. CONCLUSIONS: We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( https://github.com/PYangLab/scCCESS ).

High-throughput single-cell analysis today is facilitated by protocols like the 10X Genomics platform or Drop-Seq which generate cDNA pools in which the origin of a transcript is encoded at its 5' or 3' end. Here, we used R2C2 to sequence and demultiplex 12 million full-length cDNA molecules generated by the 10X Genomics platform from ~3000 peripheral blood mononuclear cells. We use these reads, independent from Illumina data, to identify B cell, T cell, and monocyte clusters and generate isoform-level transcriptomes for cells and cell types. Finally, we extract paired adaptive immune receptor sequences unique to each T and B cell.

BACKGROUND: Circular RNAs (circRNAs), a novel class of poorly conserved non-coding RNAs that regulate gene expression, are highly enriched in the human brain. Despite increasing discoveries of circRNA function in human neurons, the circRNA landscape and function in developing human oligodendroglia, the myelinating cells that govern neuronal conductance, remains unexplored. Meanwhile, improved experimental and computational tools for the accurate identification of circRNAs are needed. RESULTS: We adopt a published experimental approach for circRNA enrichment and develop CARP (CircRNA identification using A-tailing RNase R approach and Pseudo-reference alignment), a comprehensive 21-module computational framework for accurate circRNA identification and quantification. Using CARP, we identify developmentally programmed human oligodendroglia circRNA landscapes in the HOG oligodendroglioma cell line, distinct from neuronal circRNA landscapes. Numerous circRNAs display oligodendroglia-specific regulation upon differentiation, among which a subclass is regulated independently from their parental mRNAs. We find that circRNA flanking introns often contain cis-regulatory elements for RNA editing and are predicted to bind differentiation-regulated splicing factors. In addition, we discover novel oligodendroglia-specific circRNAs that are predicted to sponge microRNAs, which co-operatively promote oligodendroglia development. Furthermore, we identify circRNA clusters derived from differentiation-regulated alternative circularization events within the same gene, each containing a common circular exon, achieving additive sponging effects that promote human oligodendroglia differentiation. CONCLUSIONS: Our results reveal dynamic regulation of human oligodendroglia circRNA landscapes during early differentiation and suggest critical roles of the circRNA-miRNA-mRNA axis in advancing human oligodendroglia development.

Adjustment for confounding sources of expression variation is an important preprocessing step in large gene expression studies, but the effect of confound adjustment on co-expression network analysis has not been well-characterized. Here, we demonstrate that the choice of confound adjustment method can have a considerable effect on the architecture of the resulting co-expression network. We compare standard and alternative confound adjustment methods and provide recommendations for their use in the construction of gene co-expression networks from bulk tissue RNA-seq datasets.

BACKGROUND: The human pathogen Haemophilus influenzae was the main cause of bacterial meningitis in children and a major cause of worldwide infant mortality before the introduction of a vaccine in the 1980s. Although the occurrence of serotype b (Hib), the most virulent type of H. influenzae, has since decreased, reports of infections with other serotypes and non-typeable strains are on the rise. While non-typeable strains have been studied in-depth, very little is known of the pathogen's evolutionary history, and no genomes dating prior to 1940 were available. RESULTS: We describe a Hib genome isolated from a 6-year-old Anglo-Saxon plague victim, from approximately 540 to 550 CE, Edix Hill, England, showing signs of invasive infection on its skeleton. We find that the genome clusters in phylogenetic division II with Hib strain NCTC8468, which also caused invasive disease. While the virulence profile of our genome was distinct, its genomic similarity to NCTC8468 points to mostly clonal evolution of the clade since the 6th century. We also reconstruct a partial Yersinia pestis genome, which is likely identical to a published first plague pandemic genome of Edix Hill. CONCLUSIONS: Our study presents the earliest genomic evidence for H. influenzae, points to the potential presence of larger genomic diversity in the phylogenetic division II serotype b clade in the past, and allows the first insights into the evolutionary history of this major human pathogen. The identification of both plague and Hib opens questions on the effect of plague in immunocompromised individuals already affected by infectious diseases.

BACKGROUND: Despite remarkable advances in our knowledge of epigenetically mediated transcriptional programming of cell differentiation in plants, little is known about chromatin topology and its functional implications in this process. RESULTS: To interrogate its significance, we establish the dynamic three-dimensional (3D) genome architecture of the allotetraploid cotton fiber, representing a typical single cell undergoing staged development in plants. We show that the subgenome-relayed switching of the chromatin compartment from active to inactive is coupled with the silencing of developmentally repressed genes, pinpointing subgenome-coordinated contribution to fiber development. We identify 10,571 topologically associating domain-like (TAD-like) structures, of which 25.6% are specifically organized in different stages and 75.23% are subject to partition or fusion between two subgenomes. Notably, dissolution of intricate TAD-like structure cliques showing long-range interactions represents a prominent characteristic at the later developmental stage. Dynamic chromatin loops are found to mediate the rewiring of gene regulatory networks that exhibit a significant difference between the two subgenomes, implicating expression bias of homologous genes. CONCLUSIONS: This study sheds light on the spatial-temporal asymmetric chromatin structures of two subgenomes in the cotton fiber and offers a new insight into the regulatory orchestration of cell differentiation in plants.

BACKGROUND: Single-stranded DNA (ssDNA) coated with replication protein A (RPA) acts as a key platform for the recruitment and exchange of genome maintenance factors in DNA damage response. Yet, how the formation of the ssDNA-RPA intermediate is regulated remains elusive. RESULTS: Here, we report that the lamin-associated protein LAP2α is physically associated with RPA, and LAP2α preferentially facilitates RPA deposition on damaged chromatin via physical contacts between LAP2α and RPA1. Importantly, LAP2α-promoted RPA binding to ssDNA plays a critical role in protection of replication forks, activation of ATR, and repair of damaged DNA. We further demonstrate that the preference of LAP2α-promoted RPA loading on damaged chromatin depends on poly ADP-ribose polymerase PARP1, but not poly(ADP-ribosyl)ation. CONCLUSIONS: Our study provides mechanistic insight into RPA deposition in response to DNA damage and reveals a genome protection role of LAP2α.

Identifying genomic regions with hypervariable ChIP-seq or ATAC-seq signals across given samples is essential for large-scale epigenetic studies. In particular, the hypervariable regions across tumors from different patients indicate their heterogeneity and can contribute to revealing potential cancer subtypes and the associated epigenetic markers. We present HyperChIP as the first complete statistical tool for the task. HyperChIP uses scaled variances that account for the mean-variance dependence to rank genomic regions, and it increases the statistical power by diminishing the influence of true hypervariable regions on model fitting. A pan-cancer case study illustrates the practical utility of HyperChIP.

Recovering high-quality metagenome-assembled genomes (MAGs) from complex microbial ecosystems remains challenging. Recently, high-throughput chromosome conformation capture (Hi-C) has been applied to simultaneously study multiple genomes in natural microbial communities. We develop HiCBin, a novel open-source pipeline, to resolve high-quality MAGs utilizing Hi-C contact maps. HiCBin employs the HiCzin normalization method and the Leiden clustering algorithm and includes the spurious contact detection into binning pipelines for the first time. HiCBin is validated on one synthetic and two real metagenomic samples and is shown to outperform the existing Hi-C-based binning methods. HiCBin is available at https://github.com/dyxstat/HiCBin .

BACKGROUND: Gut microbial alteration is implicated in inflammatory bowel disease but is noted in other diseases. Systematic comparison to define similarities and specificities is hampered since most studies focus on a single disease. RESULTS: We develop a pipeline to compare between disease cohorts starting from the raw V4 16S amplicon sequence variants. Including 12,838 subjects, from 59 disease cohorts, we demonstrate a predominant shared signature across diseases, indicating a common bacterial response to different diseases. We show that classifiers trained on one disease cohort predict relatively well other diseases due to this shared signal, and hence, caution should be taken when using such classifiers in real-world scenarios, where diseases are intermixed. Based on this common signature across a large array of diseases, we develop a universal dysbiosis index that successfully differentiates between cases and controls across various diseases and can be used for prioritizing fecal donors and samples with lower disease probability. Finally, we identify a set of IBD-specific bacteria, which can direct mechanistic studies and design of IBD-specific microbial interventions. CONCLUSIONS: A robust non-specific general response of the gut microbiome is detected in a large array of diseases. Disease classifiers may confuse between different diseases due to this shared microbial response. Our universal dysbiosis index can be used as a tool to prioritize fecal samples and donors. Finally, the IBD-specific taxa may indicate a more direct association to gut inflammation and disease pathogenesis, and those can be further used as biomarkers and as future targets for interventions.

Erratum in Genome Biol. 2022 Jun 29;23(1):142.

The decreasing cost of sequencing and concomitant augmentation of publicly available genomes have created an acute need for automated software to assess genomic contamination. During the last 6 years, 18 programs have been published, each with its own strengths and weaknesses. Deciding which tools to use becomes more and more difficult without an understanding of the underlying algorithms. We review these programs, benchmarking six of them, and present their main operating principles. This article is intended to guide researchers in the selection of appropriate tools for specific applications. Finally, we present future challenges in the developing field of contamination detection.

BACKGROUND: Unraveling the relationship between genetic variation and phenotypic traits remains a fundamental challenge in biology. Mapping variants underlying complex traits while controlling for confounding environmental factors is often problematic. To address this, we establish a vertebrate genetic resource specifically to allow for robust genotype-to-phenotype investigations. The teleost medaka (Oryzias latipes) is an established genetic model system with a long history of genetic research and a high tolerance to inbreeding from the wild. RESULTS: Here we present the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel: the first near-isogenic panel of 80 inbred lines in a vertebrate model derived from a wild founder population. Inbred lines provide fixed genomes that are a prerequisite for the replication of studies, studies which vary both the genetics and environment in a controlled manner, and functional testing. The MIKK panel will therefore enable phenotype-to-genotype association studies of complex genetic traits while allowing for careful control of interacting factors, with numerous applications in genetic research, human health, drug development, and fundamental biology. CONCLUSIONS: Here we present a detailed characterization of the genetic variation across the MIKK panel, which provides a rich and unique genetic resource to the community by enabling large-scale experiments for mapping complex traits.

Although the use of long-read sequencing improves the contiguity of assembled viral genomes compared to short-read methods, assembling complex viral communities remains an open problem. We describe the viralFlye tool for identification and analysis of metagenome-assembled viruses in long-read assemblies. We show it significantly improves viral assemblies and demonstrate that long-reads result in a much larger array of predicted virus-host associations as compared to short-read assemblies. We demonstrate that the identification of novel CRISPR arrays in bacterial genomes from a newly assembled metagenomic sample provides information for predicting novel hosts for novel viruses.

BACKGROUND: The teleost medaka (Oryzias latipes) is a well-established vertebrate model system, with a long history of genetic research, and multiple high-quality reference genomes available for several inbred strains. Medaka has a high tolerance to inbreeding from the wild, thus allowing one to establish inbred lines from wild founder individuals. RESULTS: We exploit this feature to create an inbred panel resource: the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel. This panel of 80 near-isogenic inbred lines contains a large amount of genetic variation inherited from the original wild population. We use Oxford Nanopore Technologies (ONT) long read data to further investigate the genomic and epigenomic landscapes of a subset of the MIKK panel. Nanopore sequencing allows us to identify a large variety of high-quality structural variants, and we present results and methods using a pan-genome graph representation of 12 individual medaka lines. This graph-based reference MIKK panel genome reveals novel differences between the MIKK panel lines and standard linear reference genomes. We find additional MIKK panel-specific genomic content that would be missing from linear reference alignment approaches. We are also able to identify and quantify the presence of repeat elements in each of the lines. Finally, we investigate line-specific CpG methylation and performed differential DNA methylation analysis across these 12 lines. CONCLUSIONS: We present a detailed analysis of the MIKK panel genomes using long and short read sequence technologies, creating a MIKK panel-specific pan genome reference dataset allowing for investigation of novel variation types that would be elusive using standard approaches.

Clonal analysis of tumour sequencing data enables the evaluation of the relationship of histologically distinct synchronous lesions, such as co-existing benign areas, and temporally distinct tumours, such as primary-recurrence comparisons. In this review, we summarise statistical approaches that are commonly employed to define tumour clonal relatedness using data from bulk DNA technologies. We discuss approaches using total copy number, allele-specific copy number and mutation data, and the relative genomic resolution required for analysis and summarise some of the current tools for inferring clonal relationships. We argue that the impact of the biological context is critical in selecting any particular approach, such as the relative genomic complexity of the lesions being compared, and we recommend considering this context before employing any method to a new dataset.

BACKGROUND: Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called "hashing." RESULTS: Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects. CONCLUSIONS: Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.

BACKGROUND: Computational biology provides software tools for testing and making inferences about biological data. In the face of increasing volumes of data, heuristic methods that trade software speed for accuracy may be employed. We have studied these trade-offs using the results of a large number of independent software benchmarks, and evaluated whether external factors, including speed, author reputation, journal impact, recency and developer efforts, are indicative of accurate software. RESULTS: We find that software speed, author reputation, journal impact, number of citations and age are unreliable predictors of software accuracy. This is unfortunate because these are frequently cited reasons for selecting software tools. However, GitHub-derived statistics and high version numbers show that accurate bioinformatic software tools are generally the product of many improvements over time. We also find an excess of slow and inaccurate bioinformatic software tools, and this is consistent across many sub-disciplines. There are few tools that are middle-of-road in terms of accuracy and speed trade-offs. CONCLUSIONS: Our findings indicate that accurate bioinformatic software is primarily the product of long-term commitments to software development. In addition, we hypothesise that bioinformatics software suffers from publication bias. Software that is intermediate in terms of both speed and accuracy may be difficult to publish-possibly due to author, editor and reviewer practises. This leaves an unfortunate hole in the literature, as ideal tools may fall into this gap. High accuracy tools are not always useful if they are slow, while high speed is not useful if the results are also inaccurate.

BACKGROUND: Autism spectrum disorder (ASD) involves complex genetics interacting with the perinatal environment, complicating the discovery of common genetic risk. The epigenetic layer of DNA methylation shows dynamic developmental changes and molecular memory of in utero experiences, particularly in placenta, a fetal tissue discarded at birth. However, current array-based methods to identify novel ASD risk genes lack coverage of the most structurally and epigenetically variable regions of the human genome. RESULTS: We use whole genome bisulfite sequencing in placenta samples from prospective ASD studies to discover a previously uncharacterized ASD risk gene, LOC105373085, renamed NHIP. Out of 134 differentially methylated regions associated with ASD in placental samples, a cluster at 22q13.33 corresponds to a 118-kb hypomethylated block that replicates in two additional cohorts. Within this locus, NHIP is functionally characterized as a nuclear peptide-encoding transcript with high expression in brain, and increased expression following neuronal differentiation or hypoxia, but decreased expression in ASD placenta and brain. NHIP overexpression increases cellular proliferation and alters expression of genes regulating synapses and neurogenesis, overlapping significantly with known ASD risk genes and NHIP-associated genes in ASD brain. A common structural variant disrupting the proximity of NHIP to a fetal brain enhancer is associated with NHIP expression and methylation levels and ASD risk, demonstrating a common genetic influence. CONCLUSIONS: Together, these results identify and initially characterize a novel environmentally responsive ASD risk gene relevant to brain development in a hitherto under-characterized region of the human genome.

BACKGROUND: Ribosomal DNA (rDNA) displays substantial inter-individual genetic variation in human and mouse. A systematic analysis of how this variation impacts epigenetic states and expression of the rDNA has thus far not been performed. RESULTS: Using a combination of long- and short-read sequencing, we establish that 45S rDNA units in the C57BL/6J mouse strain exist as distinct genetic haplotypes that influence the epigenetic state and transcriptional output of any given unit. DNA methylation dynamics at these haplotypes are dichotomous and life-stage specific: at one haplotype, the DNA methylation state is sensitive to the in utero environment, but refractory to post-weaning influences, whereas other haplotypes entropically gain DNA methylation during aging only. On the other hand, individual rDNA units in human show limited evidence of genetic haplotypes, and hence little discernible correlation between genetic and epigenetic states. However, in both species, adjacent units show similar epigenetic profiles, and the overall epigenetic state at rDNA is strongly positively correlated with the total rDNA copy number. Analysis of different mouse inbred strains reveals that in some strains, such as 129S1/SvImJ, the rDNA copy number is only approximately 150 copies per diploid genome and DNA methylation levels are < 5%. CONCLUSIONS: Our work demonstrates that rDNA-associated genetic variation has a considerable influence on rDNA epigenetic state and consequently rRNA expression outcomes. In the future, it will be important to consider the impact of inter-individual rDNA (epi)genetic variation on mammalian phenotypes and diseases.

Advances in multi-omics have led to an explosion of multimodal datasets to address questions from basic biology to translation. While these data provide novel opportunities for discovery, they also pose management and analysis challenges, thus motivating the development of tailored computational solutions. Here, we present a data standard and an analysis framework for multi-omics, MUON, designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible and interoperable data structure. MUON enables a versatile range of analyses, from data preprocessing to flexible multi-omics alignment.