Single-cell sequencing technologies are revolutionizing biology, but they are limited by the need to dissociate live samples. Here, we present ACME (ACetic-MEthanol), a dissociation approach for single-cell transcriptomics that simultaneously fixes cells. ACME-dissociated cells have high RNA integrity, can be cryopreserved multiple times, and are sortable and permeable. As a proof of principle, we provide single-cell transcriptomic data of different species, using both droplet-based and combinatorial barcoding single-cell methods. ACME uses affordable reagents, can be done in most laboratories and even in the field, and thus will accelerate our knowledge of cell types across the tree of life.

Comment in Genome Biol. 2021 Apr 7;22(1):99.

The integration of a viral genome into the host genome has a major impact on the trajectory of the infected cell. Integration location and variation within the associated viral genome can influence both clonal expansion and persistence of infected cells. Methods based on short-read sequencing can identify viral insertion sites, but the sequence of the viral genomes within remains unobserved. We develop PCIP-seq, a method that leverages long reads to identify insertion sites and sequence their associated viral genome. We apply the technique to exogenous retroviruses HTLV-1, BLV, and HIV-1, endogenous retroviruses, and human papillomavirus.

de Bruijn graphs play an essential role in bioinformatics, yet they lack a universal scalable representation. Here, we introduce simplitigs as a compact, efficient, and scalable representation, and ProphAsm, a fast algorithm for their computation. For the example of assemblies of model organisms and two bacterial pan-genomes, we compare simplitigs to unitigs, the best existing representation, and demonstrate that simplitigs provide a substantial improvement in the cumulative sequence length and their number. When combined with the commonly used Burrows-Wheeler Transform index, simplitigs reduce memory, and index loading and query times, as demonstrated with large-scale examples of GenBank bacterial pan-genomes.

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

BACKGROUND: Medulloblastoma (MB) is the most common malignant pediatric brain tumor that originates in the cerebellum and brainstem. Frequent somatic mutations and deregulated expression of epigenetic regulators in MB highlight the substantial role of epigenetic alterations. 5-hydroxymethylcytosine (5hmC) is a highly abundant cytosine modification in the developing cerebellum and is regulated by ten-eleven translocation (TET) enzymes. RESULTS: We investigate the alterations of 5hmC and TET enzymes in MB and their significance to cerebellar cancer formation. We show total abundance of 5hmC is reduced in MB, but identify significant enrichment of MB-specific 5hmC marks at regulatory regions of genes implicated in stem-like properties and Nanog-binding motifs. While TET1 and TET2 levels are high in MBs, only knockout of Tet1 in the smoothened (SmoA1) mouse model attenuates uncontrolled proliferation, leading to a favorable prognosis. The pharmacological Tet1 inhibition reduces cell viability and platelet-derived growth factor signaling pathway-associated genes. CONCLUSIONS: These results together suggest a potential key role of 5hmC and indicate an oncogenic nature for TET1 in MB tumorigenesis, suggesting it as a potential therapeutic target for MBs.

While genome sequencing and assembly are now routine, we do not have a full, precise picture of polyploid genomes. No existing polyploid phasing method provides accurate and contiguous haplotype predictions. We developed nPhase, a ploidy agnostic tool that leverages long reads and accurate short reads to solve alignment-based phasing for samples of unspecified ploidy ( https://github.com/OmarOakheart/nPhase ). nPhase is validated by tests on simulated and real polyploids. nPhase obtains on average over 95% accuracy and a contiguous 1.25 haplotigs per haplotype to cover more than 90% of each chromosome (heterozygosity rate ≥ 0.5%). nPhase allows population genomics and hybrid studies of polyploids.

BACKGROUND: The vast majority of trait-associated variants identified using genome-wide association studies (GWAS) are noncoding, and therefore assumed to impact gene regulation. However, the majority of trait-associated loci are unexplained by regulatory quantitative trait loci (QTLs). RESULTS: We perform a comprehensive characterization of the putative mechanisms by which GWAS loci impact human immune traits. By harmonizing four major immune QTL studies, we identify 26,271 expression QTLs (eQTLs) and 23,121 splicing QTLs (sQTLs) spanning 18 immune cell types. Our colocalization analyses between QTLs and trait-associated loci from 72 GWAS reveals that genetic effects on RNA expression and splicing in immune cells colocalize with 40.4% of GWAS loci for immune-related traits, in many cases increasing the fraction of colocalized loci by two fold compared to previous studies. Notably, we find that the largest contributors of this increase are splicing QTLs, which colocalize on average with 14% of all GWAS loci that do not colocalize with eQTLs. By contrast, we find that cell type-specific eQTLs, and eQTLs with small effect sizes contribute very few new colocalizations. To investigate the 60% of GWAS loci that remain unexplained, we collect H3K27ac CUT&Tag data from rheumatoid arthritis and healthy controls, and find large-scale differences between immune cells from the different disease contexts, including at regions overlapping unexplained GWAS loci. CONCLUSION: Altogether, our work supports RNA splicing as an important mediator of genetic effects on immune traits, and suggests that we must expand our study of regulatory processes in disease contexts to improve functional interpretation of as yet unexplained GWAS loci.

To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

Microbiology is at a turning point in its 120-year history. Widespread next-generation sequencing has revealed genetic complexity among bacteria that could hardly have been imagined by pioneers such as Pasteur, Escherich and Koch. This data cascade brings enormous potential to improve our understanding of individual bacterial cells and the genetic basis of phenotype variation. However, this revolution in data science cannot replace established microbiology practices, presenting the challenge of how to integrate these new techniques. Contrasting comparative and functional genomic approaches, we evoke molecular microbiology theory and established practice to present a conceptual framework and practical roadmap for next-generation microbiology.

Author information: (1)The Vertebrate Genome Lab, Rockefeller University, New York, NY, USA. gformenti@rockefeller.edu. (2)Laboratory of Neurogenetics of Language, Rockefeller University, New York, NY, USA. gformenti@rockefeller.edu. (3)The Howards Hughes Medical Institute, Chevy Chase, MD, USA. gformenti@rockefeller.edu. (4)Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. (5)The Vertebrate Genome Lab, Rockefeller University, New York, NY, USA. (6)Laboratory of Neurogenetics of Language, Rockefeller University, New York, NY, USA. (7)The Howards Hughes Medical Institute, Chevy Chase, MD, USA. (8)Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia, Italy. (9)Monash University Malaysia Genomics Facility, School of Science, Bandar Sunway, Selangor Darul Ehsan, Malaysia. (10)Tropical Medicine and Biology Multidisciplinary Platform, Monash University Malaysia, Bandar Sunway, Selangor Darul Ehsan, Malaysia. (11)Qatar Falcon Genome Project, Doha, State of Qatar. (12)Department of Environmental Science and Policy, University of Milan, Milan, Italy. (13)Department of Biology, New Mexico State University, Las Cruces, NM, USA. (14)Wellcome Sanger Institute, Cambridge, UK. (15)Department of Genetics, University of Cambridge, Cambridge, UK. (16)Max Planck Institute of Molecular Cell Biology & Genetics, Dresden, Germany. (17)Hoonygen, Seoul, Korea. (18)Pacific Biosciences, Menlo Park, CA, USA. (19)DNAnexus Inc., Mountain View, CA, USA. (20)Oxford Nanopore Technologies Ltd, Oxford Science Park, Oxford, UK. (21)Department of Biosciences, University of Milan, Milan, Italy. (22)Department of Biology, University of Florida, Gainesville, FL, USA. (23)The Vertebrate Genome Lab, Rockefeller University, New York, NY, USA. ejarvis@rockefeller.edu. (24)Laboratory of Neurogenetics of Language, Rockefeller University, New York, NY, USA. ejarvis@rockefeller.edu. (25)The Howards Hughes Medical Institute, Chevy Chase, MD, USA. ejarvis@rockefeller.edu.

BACKGROUND: Millennia of directional human selection has reshaped the genomic architecture of cultivated cotton relative to wild counterparts, but we have limited understanding of the selective retention and fractionation of genomic components. RESULTS: We construct a comprehensive genomic variome based on 1961 cottons and identify 456 Mb and 357 Mb of sequence with domestication and improvement selection signals and 162 loci, 84 of which are novel, including 47 loci associated with 16 agronomic traits. Using pan-genome analyses, we identify 32,569 and 8851 non-reference genes lost from Gossypium hirsutum and Gossypium barbadense reference genomes respectively, of which 38.2% (39,278) and 14.2% (11,359) of genes exhibit presence/absence variation (PAV). We document the landscape of PAV selection accompanied by asymmetric gene gain and loss and identify 124 PAVs linked to favorable fiber quality and yield loci. CONCLUSIONS: This variation repertoire points to genomic divergence during cotton domestication and improvement, which informs the characterization of favorable gene alleles for improved breeding practice using a pan-genome-based approach.

BACKGROUND: Recent genome-wide studies of many species reveal the existence of a myriad of RNAs differing in size, coding potential and function. Among these are the long non-coding RNAs, some of them producing functional small peptides via the translation of short ORFs. It now appears that any kind of RNA presumably has a potential to encode small peptides. Accordingly, our team recently discovered that plant primary transcripts of microRNAs (pri-miRs) produce small regulatory peptides (miPEPs) involved in auto-regulatory feedback loops enhancing their cognate microRNA expression which in turn controls plant development. Here we investigate whether this regulatory feedback loop is present in Drosophila melanogaster. RESULTS: We perform a survey of ribosome profiling data and reveal that many pri-miRNAs exhibit ribosome translation marks. Focusing on miR-8, we show that pri-miR-8 can produce a miPEP-8. Functional assays performed in Drosophila reveal that miPEP-8 affects development when overexpressed or knocked down. Combining genetic and molecular approaches as well as genome-wide transcriptomic analyses, we show that miR-8 expression is independent of miPEP-8 activity and that miPEP-8 acts in parallel to miR-8 to regulate the expression of hundreds of genes. CONCLUSION: Taken together, these results reveal that several Drosophila pri-miRs exhibit translation potential. Contrasting with the mechanism described in plants, these data shed light on the function of yet undescribed primary-microRNA-encoded peptides in Drosophila and their regulatory potential on genome expression.

BACKGROUND: The binding of transcription factors (TF) to genomic targets is critical in the regulation of gene expression. Short, double-stranded DNA sequence motifs are routinely implicated in TF recruitment, but many questions remain on how binding site specificity is governed. RESULTS: Herein, we reveal a previously unappreciated role for DNA secondary structures as key features for TF recruitment. In a systematic, genome-wide study, we discover that endogenous G-quadruplex secondary structures (G4s) are prevalent TF binding sites in human chromatin. Certain TFs bind G4s with affinities comparable to double-stranded DNA targets. We demonstrate that, in a chromatin context, this binding interaction is competed out with a small molecule. Notably, endogenous G4s are prominent binding sites for a large number of TFs, particularly at promoters of highly expressed genes. CONCLUSIONS: Our results reveal a novel non-canonical mechanism for TF binding whereby G4s operate as common binding hubs for many different TFs to promote increased transcription.

BACKGROUND: DNA methylation dynamics in the brain are associated with normal development and neuropsychiatric disease and differ across functionally distinct brain regions. Previous studies of genome-wide methylation differences among human brain regions focus on limited numbers of individuals and one to two brain regions. RESULTS: Using GTEx samples, we generate a resource of DNA methylation in purified neuronal nuclei from 8 brain regions as well as lung and thyroid tissues from 12 to 23 donors. We identify differentially methylated regions between brain regions among neuronal nuclei in both CpG (181,146) and non-CpG (264,868) contexts, few of which were unique to a single pairwise comparison. This significantly expands the knowledge of differential methylation across the brain by 10-fold. In addition, we present the first differential methylation analysis among neuronal nuclei from basal ganglia tissues and identify unique CpG differentially methylated regions, many associated with ion transport. We also identify 81,130 regions of variably CpG methylated regions, i.e., variable methylation among individuals in the same brain region, which are enriched in regulatory regions and in CpG differentially methylated regions. Many variably methylated regions are unique to a specific brain region, with only 202 common across all brain regions, as well as lung and thyroid. Variably methylated regions identified in the amygdala, anterior cingulate cortex, and hippocampus are enriched for heritability of schizophrenia. CONCLUSIONS: These data suggest that epigenetic variation in these particular human brain regions could be associated with the risk for this neuropsychiatric disorder.

Our assembly-free linkage analysis pipeline (AFLAP) identifies segregating markers as k-mers in the raw reads without using a reference genome assembly for calling variants and provides genotype tables for the construction of unbiased, high-density genetic maps without a genome assembly. AFLAP is validated and contrasted to a conventional workflow using simulated data. AFLAP is applied to whole genome sequencing and genotype-by-sequencing data of F1, F2, and recombinant inbred populations of two different plant species, producing genetic maps that are concordant with genome assemblies. The AFLAP-based genetic map for Bremia lactucae enables the production of a chromosome-scale genome assembly.

BACKGROUND: Castor bean (Ricinus communis L.) is an important oil crop, which belongs to the Euphorbiaceae family. The seed oil of castor bean is currently the only commercial source of ricinoleic acid that can be used for producing about 2000 industrial products. However, it remains largely unknown regarding the origin, domestication, and the genetic basis of key traits of castor bean. RESULTS: Here we perform a de novo chromosome-level genome assembly of the wild progenitor of castor bean. By resequencing and analyzing 505 worldwide accessions, we reveal that the accessions from East Africa are the extant wild progenitors of castor bean, and the domestication occurs ~ 3200 years ago. We demonstrate that significant genetic differentiation between wild populations in Kenya and Ethiopia is associated with past climate fluctuation in the Turkana depression ~ 7000 years ago. This dramatic change in climate may have caused the genetic bottleneck in wild castor bean populations. By a genome-wide association study, combined with quantitative trait locus analysis, we identify important candidate genes associated with plant architecture and seed size. CONCLUSIONS: This study provides novel insights of domestication and genome evolution of castor bean, which facilitates genomics-based breeding of this important oilseed crop and potentially other tree-like crops in future.

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET .

Genetic maps have been fundamental to building our understanding of disease genetics and evolutionary processes. The gametes of an individual contain all of the information required to perform a de novo chromosome-scale assembly of an individual's genome, which historically has been performed with populations and pedigrees. Here, we discuss how single-cell gamete sequencing offers the potential to merge the advantages of short-read sequencing with the ability to build personalized genetic maps and open up an entirely new space in personalized genetics.

BACKGROUND: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. RESULTS: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. CONCLUSION: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

BACKGROUND: X-chromosomal genes contribute to sex differences, in particular during early development, when both X chromosomes are active in females. Double X-dosage shifts female pluripotent cells towards the naive stem cell state by increasing pluripotency factor expression, inhibiting the differentiation-promoting MAP kinase (MAPK) signaling pathway, and delaying differentiation. RESULTS: To identify the genetic basis of these sex differences, we use a two-step CRISPR screening approach to comprehensively identify X-linked genes that cause the female pluripotency phenotype in murine embryonic stem cells. A primary chromosome-wide CRISPR knockout screen and three secondary screens assaying for different aspects of the female pluripotency phenotype allow us to uncover multiple genes that act in concert and to disentangle their relative roles. Among them, we identify Dusp9 and Klhl13 as two central players. While Dusp9 mainly affects MAPK pathway intermediates, Klhl13 promotes pluripotency factor expression and delays differentiation, with both factors jointly repressing MAPK target gene expression. CONCLUSIONS: Here, we elucidate the mechanisms that drive sex-induced differences in pluripotent cells and our approach serves as a blueprint to discover the genetic basis of the phenotypic consequences of other chromosomal effects.

BACKGROUND: Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. RESULTS: All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. CONCLUSION: This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

Differential gene expression mechanisms ensure cellular differentiation and plasticity to shape ontogenetic and phylogenetic diversity of cell types. A key regulator of differential gene expression programs are the enhancers, the gene-distal cis-regulatory sequences that govern spatiotemporal and quantitative expression dynamics of target genes. Enhancers are widely believed to physically contact the target promoters to effect transcriptional activation. However, our understanding of the full complement of regulatory proteins and the definitive mechanics of enhancer action is incomplete. Here, we review recent findings to present some emerging concepts on enhancer action and also outline a set of outstanding questions.

Muscle-invasive bladder cancers are characterized by their distinct expression of luminal and basal genes, which could be used to predict key clinical features such as disease progression and overall survival. Transcriptionally, FOXA1, GATA3, and PPARG are shown to be essential for luminal subtype-specific gene regulation and subtype switching, while TP63, STAT3, and TFAP2 family members are critical for regulation of basal subtype-specific genes. Despite these advances, the underlying epigenetic mechanisms and 3D chromatin architecture responsible for subtype-specific regulation in bladder cancer remain unknown. RESULT: We determine the genome-wide transcriptome, enhancer landscape, and transcription factor binding profiles of FOXA1 and GATA3 in luminal and basal subtypes of bladder cancer. Furthermore, we report the first-ever mapping of genome-wide chromatin interactions by Hi-C in both bladder cancer cell lines and primary patient tumors. We show that subtype-specific transcription is accompanied by specific open chromatin and epigenomic marks, at least partially driven by distinct transcription factor binding at distal enhancers of luminal and basal bladder cancers. Finally, we identify a novel clinically relevant transcription factor, Neuronal PAS Domain Protein 2 (NPAS2), in luminal bladder cancers that regulates other subtype-specific genes and influences cancer cell proliferation and migration. CONCLUSION: In summary, our work identifies unique epigenomic signatures and 3D genome structures in luminal and basal urinary bladder cancers and suggests a novel link between the circadian transcription factor NPAS2 and a clinical bladder cancer subtype.

BACKGROUND: Although using a blockade of programmed death-ligand 1 (PD-L1) to enhance T cell immune responses shows great promise in tumor immunotherapy, the immune-checkpoint inhibition strategy is limited for patients with solid tumors. The mechanism and efficacy of such immune-checkpoint inhibition strategies in solid tumors remains unclear. RESULTS: Employing qRT-PCR, Sanger sequencing, and RNA BaseScope analysis, we show that human lung adenocarcinoma (LUAD) all produce a long non-coding RNA isoform of PD-L1 (PD-L1-lnc) by alternative splicing, regardless if the tumor is positive or negative for the protein PD-L1. Similar to PD-L1 mRNA, PD-L1-lnc in various lung adenocarcinoma cells is significantly upregulated by IFNγ. Both in vitro and in vivo studies demonstrate that PD-L1-lnc increases proliferation and invasion but decreases apoptosis of lung adenocarcinoma cells. Mechanistically, PD-L1-lnc promotes lung adenocarcinoma progression through directly binding to c-Myc and enhancing c-Myc transcriptional activity. CONCLUSIONS: In summary, the PD-L1 gene can generate a long non-coding RNA through alternative splicing to promote lung adenocarcinoma progression by enhancing c-Myc activity. Our results argue in favor of investigating PD-L1-lnc depletion in combination with PD-L1 blockade in lung cancer therapy.

BACKGROUND: Whole genome duplication (WGD) events have played a major role in eukaryotic genome evolution, but the consequence of these extreme events in adaptive genome evolution is still not well understood. To address this knowledge gap, we used a comparative phylogenetic model and transcriptomic data from seven species to infer selection on gene expression in duplicated genes (ohnologs) following the salmonid WGD 80-100 million years ago. RESULTS: We find rare cases of tissue-specific expression evolution but pervasive expression evolution affecting many tissues, reflecting strong selection on maintenance of genome stability following genome doubling. Ohnolog expression levels have evolved mostly asymmetrically, by diverting one ohnolog copy down a path towards lower expression and possible pseudogenization. Loss of expression in one ohnolog is significantly associated with transposable element insertions in promoters and likely driven by selection on gene dosage including selection on stoichiometric balance. We also find symmetric expression shifts, and these are associated with genes under strong evolutionary constraints such as ribosome subunit genes. This possibly reflects selection operating to achieve a gene dose reduction while avoiding accumulation of "toxic mutations". Mechanistically, ohnolog regulatory divergence is dictated by the number of bound transcription factors in promoters, with transposable elements being one likely source of novel binding sites driving tissue-specific gains in expression. CONCLUSIONS: Our results imply pervasive adaptive expression evolution following WGD to overcome the immediate challenges posed by genome doubling and to exploit the long-term genetic opportunities for novel phenotype evolution.

High-quality chromosome-scale haplotype sequences of diploid genomes, polyploid genomes, and metagenomes provide important insights into genetic variation associated with disease and biodiversity. However, whole-genome short read sequencing does not yield haplotype information spanning whole chromosomes directly. Computational assembly of shorter haplotype fragments is required for haplotype reconstruction, which can be challenging owing to limited fragment lengths and high haplotype and repeat variability across genomes. Recent advancements in long-read and chromosome-scale sequencing technologies, alongside computational innovations, are improving the reconstruction of haplotypes at the level of whole chromosomes. Here, we review recent and discuss methodological progress and perspectives in these areas.

BACKGROUND: Structured RNAs play varied bioregulatory roles within microbes. To date, hundreds of candidate structured RNAs have been predicted using informatic approaches that search for motif structures in genomic sequence data. The human microbiome contains thousands of species and strains of microbes. Yet, much of the metagenomic data from the human microbiome remains unmined for structured RNA motifs primarily due to computational limitations. RESULTS: We sought to apply a large-scale, comparative genomics approach to these organisms to identify candidate structured RNAs. With a carefully constructed, though computationally intensive automated analysis, we identify 3161 conserved candidate structured RNAs in intergenic regions, as well as 2022 additional candidate structured RNAs that may overlap coding regions. We validate the RNA expression of 177 of these candidate structures by analyzing small fragment RNA-seq data from four human fecal samples. CONCLUSIONS: This approach identifies a wide variety of candidate structured RNAs, including tmRNAs, antitoxins, and likely ribosome protein leaders, from a wide variety of taxa. Overall, our pipeline enables conservative predictions of thousands of novel candidate structured RNAs from human microbiomes.

BACKGROUND: Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions. RESULTS: To systematically reveal the pitfalls and challenges of deconvolution analyses, we investigate the impact of several technical and biological factors including simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks. These frameworks cover comparative analysis of 11 popular deconvolution methods under 1766 conditions. CONCLUSIONS: We provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.