BACKGROUND: Oocyte maturation arrest and early embryonic arrest are important reproductive phenotypes resulting in female infertility and cause the recurrent failure of assisted reproductive technology (ART). However, the genetic etiologies of these female infertility-related phenotypes are poorly understood. Previous studies have mainly focused on inherited mutations based on large pedigrees or consanguineous patients. However, the role of de novo mutations (DNMs) in these phenotypes remains to be elucidated. RESULTS: To decipher the role of DNMs in ART failure and female infertility with oocyte and embryo defects, we explore the landscape of DNMs in 473 infertile parent-child trios and identify a set of 481 confident DNMs distributed in 474 genes. Gene ontology analysis reveals that the identified genes with DNMs are enriched in signaling pathways associated with female reproductive processes such as meiosis, embryonic development, and reproductive structure development. We perform functional assays on the effects of DNMs in a representative gene Tubulin Alpha 4a (TUBA4A), which shows the most significant enrichment of DNMs in the infertile parent-child trios. DNMs in TUBA4A disrupt the normal assembly of the microtubule network in HeLa cells, and microinjection of DNM TUBA4A cRNAs causes abnormalities in mouse oocyte maturation or embryo development, suggesting the pathogenic role of these DNMs in TUBA4A. CONCLUSIONS: Our findings suggest novel genetic insights that DNMs contribute to female infertility with oocyte and embryo defects. This study also provides potential genetic markers and facilitates the genetic diagnosis of recurrent ART failure and female infertility.

Long-read single-cell RNA sequencing (scRNA-seq) enables the quantification of RNA isoforms in individual cells. However, long-read scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes. We introduce BLAZE, which accurately and efficiently identifies 10x cell barcodes using only nanopore long-read scRNA-seq data. BLAZE outperforms the existing tools and provides an accurate representation of the cells present in long-read scRNA-seq when compared to matched short reads. BLAZE simplifies long-read scRNA-seq while improving the results, is compatible with downstream tools accepting a cell barcode file, and is available at https://github.com/shimlab/BLAZE .

Nanopore sequencing is being rapidly adopted in genomics. We recently developed SLOW5, a new file format with advantages for storage and analysis of raw signal data from nanopore experiments. Here we introduce slow5tools, an intuitive toolkit for handling nanopore data in SLOW5 format. Slow5tools enables lossless data conversion and a range of tools for interacting with SLOW5 files. Slow5tools uses multi-threading, multi-processing, and other engineering strategies to achieve fast data conversion and manipulation, including live FAST5-to-SLOW5 conversion during sequencing. We provide examples and benchmarking experiments to illustrate slow5tools usage, and describe the engineering principles underpinning its performance.

N6-methyladenosine (m6A) modification has been shown to regulate RNA metabolism. Here, we investigate m6A dynamics during maternal-to-zygotic transition (MZT) in mice through multi-omic analysis. Our results show that m6A can be maternally inherited or de novo gained after fertilization. Interestingly, m6A modification on maternal mRNAs not only correlates with mRNA degradation, but also maintains the stability of a small group of mRNAs thereby promoting their translation after fertilization. We identify Ythdc1 and Ythdf2 as key m6A readers for mouse preimplantation development. Our study reveals a key role of m6A mediated RNA metabolism during MZT in mammals.

BACKGROUND: The NSD2 p.E1099K (EK) mutation is shown to be enriched in patients with relapsed acute lymphoblastic leukemia (ALL), indicating a role in clonal evolution and drug resistance. RESULTS: To uncover 3D chromatin architecture-related mechanisms underlying drug resistance, we perform Hi-C on three B-ALL cell lines heterozygous for NSD2 EK. The NSD2 mutation leads to widespread remodeling of the 3D genome, most dramatically in terms of compartment changes with a strong bias towards A compartment shifts. Systematic integration of the Hi-C data with previously published ATAC-seq, RNA-seq, and ChIP-seq data show an expansion in H3K36me2 and a shrinkage in H3K27me3 within A compartments as well as increased gene expression and chromatin accessibility. These results suggest that NSD2 EK plays a prominent role in chromatin decompaction through enrichment of H3K36me2. In contrast, we identify few changes in intra-topologically associating domain activity. While compartment changes vary across cell lines, a common core of decompacting loci are shared, driving the expression of genes/pathways previously implicated in drug resistance. We further perform RNA sequencing on a cohort of matched diagnosis/relapse ALL patients harboring the relapse-specific NSD2 EK mutation. Changes in patient gene expression upon relapse significantly correlate with core compartment changes, further implicating the role of NSD2 EK in genome decompaction. CONCLUSIONS: In spite of cell-context-dependent changes mediated by EK, there appears to be a shared transcriptional program dependent on compartment shifts which could explain phenotypic differences across EK cell lines. This core program is an attractive target for therapeutic intervention.

BACKGROUND: Homoeologs are defined as homologous genes resulting from allopolyploidy. Bread wheat, Triticum aestivum, is an allohexaploid species with many homoeologs. Homoeolog expression bias, referring to the relative contribution of homoeologs to the transcriptome, is critical for determining the traits that influence wheat growth and development. Asymmetric transcription of homoeologs has been so far investigated in a tissue or organ-specific manner, which could be misleading due to a mixture of cell types. RESULTS: Here, we perform single nuclei RNA sequencing and ATAC sequencing of wheat root to study the asymmetric gene transcription, reconstruct cell differentiation trajectories and cell-type-specific gene regulatory networks. We identify 22 cell types. We then reconstruct cell differentiation trajectories that suggest different origins between epidermis/cortex and endodermis, distinguishing bread wheat from Arabidopsis. We show that the ratio of asymmetrically transcribed triads varies greatly when analyzing at the single-cell level. Hub transcription factors determining cell type identity are also identified. In particular, we demonstrate that TaSPL14 participates in vasculature development by regulating the expression of BAM1. Combining single-cell transcription and chromatin accessibility data, we construct the pseudo-time regulatory network driving root hair differentiation. We find MYB3R4, REF6, HDG1, and GATAs as key regulators in this process. CONCLUSIONS: Our findings reveal the transcriptional landscape of root organization and asymmetric gene transcription at single-cell resolution in polyploid wheat.

The telomere-to-telomere (T2T) complete human reference has significantly improved our ability to characterize genome structural variation. To understand its impact on inversion polymorphisms, we remapped data from 41 genomes against the T2T reference genome and compared it to the GRCh38 reference. We find a ~ 21% increase in sensitivity improving mapping of 63 inversions on the T2T reference. We identify 26 misorientations within GRCh38 and show that the T2T reference is three times more likely to represent the correct orientation of the major human allele. Analysis of 10 additional samples reveals novel rare inversions at chromosomes 15q25.2, 16p11.2, 16q22.1-23.1, and 22q11.21.

Elucidating the sources of a microbiome can provide insight into the ecological dynamics responsible for the formation of these communities. Source tracking approaches to date leverage species abundance information; however, single nucleotide variants (SNVs) may be more informative because of their high specificity to certain sources. To overcome the computational burden of utilizing all SNVs for a given sample, we introduce a novel method to identify signature SNVs for source tracking. Signature SNVs used as input into a previously designed source tracking algorithm, FEAST, can more accurately estimate contributions than species and provide novel insights, demonstrated in three case studies.

BACKGROUND: Cas12a (formerly known as Cpf1), the class II type V CRISPR nuclease, has been widely used for genome editing in mammalian cells and plants due to its distinct characteristics from Cas9. Despite being one of the most robust Cas12a nucleases, LbCas12a in general is less efficient than SpCas9 for genome editing in human cells, animals, and plants. RESULTS: To improve the editing efficiency of LbCas12a, we conduct saturation mutagenesis in E. coli and identify 1977 positive point mutations of LbCas12a. We selectively assess the editing efficiency of 56 LbCas12a variants in human cells, identifying an optimal LbCas12a variant (RVQ: G146R/R182V/E795Q) with the most robust editing activity. We further test LbCas12a-RV, LbCas12a-RRV, and LbCas12a-RVQ in plants and find LbCas12a-RV has robust editing activity in rice and tomato protoplasts. Interestingly, LbCas12a-RRV, resulting from the stacking of RV and D156R, displays improved editing efficiency in stably transformed rice and poplar plants, leading to up to 100% editing efficiency in T0 plants of both plant species. Moreover, this high-efficiency editing occurs even at the non-canonical TTV PAM sites. CONCLUSIONS: Our results demonstrate that LbCas12a-RVQ is a powerful tool for genome editing in human cells while LbCas12a-RRV confers robust genome editing in plants. Our study reveals the tremendous potential of these LbCas12a variants for advancing precision genome editing applications across a wide range of organisms.

BACKGROUND: Caloric restriction (CR) has been known to promote health by reprogramming metabolism, yet little is known about how the epigenome and microbiome respond during metabolic adaptation to CR. RESULTS: We investigate chromatin modifications, gene expression, as well as alterations in microbiota in a CR mouse model. Collectively, short-term CR leads to altered gut microbial diversity and bile acid metabolism, improving energy expenditure. CR remodels the hepatic enhancer landscape at genomic loci that are enriched for binding sites for signal-responsive transcription factors, including HNF4α. These alterations reflect a dramatic reprogramming of the liver transcriptional network, including genes involved in bile acid metabolism. Transferring CR gut microbiota into mice fed with an obesogenic diet recapitulates the features of CR-related bile acid metabolism along with attenuated fatty liver. CONCLUSIONS: These findings suggest that CR-induced microbiota shapes the hepatic epigenome followed by altered expression of genes responsible for bile acid metabolism.

BACKGROUND: RNA N6-methyladenosine (m6A) modification is critical for plant growth and crop yield. m6A reader proteins can recognize m6A modifications to facilitate the functions of m6A in gene regulation. ECT2, ECT3, and ECT4 are m6A readers that are known to redundantly regulate trichome branching and leaf growth, but their molecular functions remain unclear. RESULTS: Here, we show that ECT2, ECT3, and ECT4 directly interact with each other in the cytoplasm and perform genetically redundant functions in abscisic acid (ABA) response regulation during seed germination and post-germination growth. We reveal that ECT2/ECT3/ECT4 promote the stabilization of their targeted m6A-modified mRNAs, but have no function in alternative polyadenylation and translation. We find that ECT2 directly interacts with the poly(A) binding proteins, PAB2 and PAB4, and maintains the stabilization of m6A-modified mRNAs. Disruption of ECT2/ECT3/ECT4 destabilizes mRNAs of ABA signaling-related genes, thereby promoting the accumulation of ABI5 and leading to ABA hypersensitivity. CONCLUSION: Our study reveals a unified functional model of m6A mediated by m6A readers in plants. In this model, ECT2/ECT3/ECT4 promote stabilization of their target mRNAs in the cytoplasm.

Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.

Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions and estimate their allele frequency in populations. Benchmarking with simulated data revealed that TrEMOLO outperforms other state-of-the-art computational tools. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics. TrEMOLO is available under GNU GPL3.0 at https://github.com/DrosophilaGenomeEvolution/TrEMOLO .

BACKGROUND: Glucokinase (GCK) regulates insulin secretion to maintain appropriate blood glucose levels. Sequence variants can alter GCK activity to cause hyperinsulinemic hypoglycemia or hyperglycemia associated with GCK-maturity-onset diabetes of the young (GCK-MODY), collectively affecting up to 10 million people worldwide. Patients with GCK-MODY are frequently misdiagnosed and treated unnecessarily. Genetic testing can prevent this but is hampered by the challenge of interpreting novel missense variants. RESULT: Here, we exploit a multiplexed yeast complementation assay to measure both hyper- and hypoactive GCK variation, capturing 97% of all possible missense and nonsense variants. Activity scores correlate with in vitro catalytic efficiency, fasting glucose levels in carriers of GCK variants and with evolutionary conservation. Hypoactive variants are concentrated at buried positions, near the active site, and at a region of known importance for GCK conformational dynamics. Some hyperactive variants shift the conformational equilibrium towards the active state through a relative destabilization of the inactive conformation. CONCLUSION: Our comprehensive assessment of GCK variant activity promises to facilitate variant interpretation and diagnosis, expand our mechanistic understanding of hyperactive variants, and inform development of therapeutics targeting GCK.

BACKGROUND: The microrchidia (MORC) proteins are a family of evolutionarily conserved GHKL-type ATPases involved in chromatin compaction and gene silencing. Arabidopsis MORC proteins act in the RNA-directed DNA methylation (RdDM) pathway, where they act as molecular tethers to ensure the efficient establishment of RdDM and de novo gene silencing. However, MORC proteins also have RdDM-independent functions although their underlying mechanisms are unknown. RESULTS: In this study, we examine MORC binding regions where RdDM does not occur in order to shed light on the RdDM-independent functions of MORC proteins. We find that MORC proteins compact chromatin and reduce DNA accessibility to transcription factors, thereby repressing gene expression. We also find that MORC-mediated repression of gene expression is particularly important under conditions of stress. MORC-regulated transcription factors can in some cases regulate their own transcription, resulting in feedback loops. CONCLUSIONS: Our findings provide insights into the molecular mechanisms of MORC-mediated chromatin compaction and transcription regulation.

BACKGROUND: Apple is an economically important fruit crop. Changes in metabolism accompanying human-guided evolution can be revealed using a multiomics approach. We perform genome-wide metabolic analysis of apple fruits collected from 292 wild and cultivated accessions representing various consumption types. RESULTS: We find decreased amounts of certain metabolites, including tannins, organic acids, phenolic acids, and flavonoids as the wild accessions transition to cultivated apples, while lysolipids increase in the "Golden Delicious" to "Ralls Janet" pedigree, suggesting better storage. We identify a total of 222,877 significant single-nucleotide polymorphisms that are associated with 2205 apple metabolites. Investigation of a region from 2.84 to 5.01 Mb on chromosome 16 containing co-mapping regions for tannins, organic acids, phenolic acids, and flavonoids indicates the importance of these metabolites for fruit quality and nutrition during breeding. The tannin and acidity-related genes Myb9-like and PH4 are mapped closely to fruit weight locus fw1 from 3.41 to 3.76 Mb on chromosome 15, a region under selection during domestication. Lysophosphatidylethanolamine (LPE) 18:1, which is suppressed by fatty acid desaturase-2 (FAD2), is positively correlated to fruit firmness. We find the fruit weight is negatively correlated with salicylic acid and abscisic acid levels. Further functional assays demonstrate regulation of these hormone levels by NAC-like activated by Apetala3/Pistillata (NAP) and ATP binding cassette G25 (ABCG25), respectively. CONCLUSIONS: This study provides a metabolic perspective for selection on fruit quality during domestication and improvement, which is a valuable resource for investigating mechanisms controlling apple metabolite content and quality.

BACKGROUND: Phenotypic plasticity is defined as the phenotypic variation of a trait when an organism is exposed to different environments, and it is closely related to genotype. Exploring the genetic basis behind the phenotypic plasticity of ear traits in maize is critical to achieve climate-stable yields, particularly given the unpredictable effects of climate change. Performing genetic field studies in maize requires development of a fast, reliable, and automated system for phenotyping large numbers of samples. RESULTS: Here, we develop MAIZTRO as an automated maize ear phenotyping platform for high-throughput measurements in the field. Using this platform, we analyze 15 common ear phenotypes and their phenotypic plasticity variation in 3819 transgenic maize inbred lines targeting 717 genes, along with the wild type lines of the same genetic background, in multiple field environments in two consecutive years. Kernel number is chosen as the primary target phenotype because it is a key trait for improving the grain yield and ensuring yield stability. We analyze the phenotypic plasticity of the transgenic lines in different environments and identify 34 candidate genes that may regulate the phenotypic plasticity of kernel number. CONCLUSIONS: Our results suggest that as an integrated and efficient phenotyping platform for measuring maize ear traits, MAIZTRO can help to explore new traits that are important for improving and stabilizing the yield. This study indicates that genes and alleles related with ear trait plasticity can be identified using transgenic maize inbred populations.

Long-read RNA sequencing (lrRNA-seq) produces detailed information about full-length transcripts, including novel and sample-specific isoforms. Furthermore, there is an opportunity to call variants directly from lrRNA-seq data. However, most state-of-the-art variant callers have been developed for genomic DNA. Here, there are two objectives: first, we perform a mini-benchmark on GATK, DeepVariant, Clair3, and NanoCaller primarily on PacBio Iso-Seq, data, but also on Nanopore and Illumina RNA-seq data; second, we propose a pipeline to process spliced-alignment files, making them suitable for variant calling with DNA-based callers. With such manipulations, high calling performance can be achieved using DeepVariant on Iso-seq data.

BACKGROUND: DNA double-strand breaks (DSBs) are among the most deleterious DNA lesions, and they can cause cancer if improperly repaired. Recent chromosome conformation capture techniques, such as Hi-C, have enabled the identification of relationships between the 3D chromatin structure and DSBs, but little is known about how to explain these relationships, especially from global contact maps, or their contributions to DSB formation. RESULTS: Here, we propose a framework that integrates graph neural network (GNN) to unravel the relationship between 3D chromatin structure and DSBs using an advanced interpretable technique GNNExplainer. We identify a new chromatin structural unit named the DNA fragility-associated chromatin interaction network (FaCIN). FaCIN is a bottleneck-like structure, and it helps to reveal a universal form of how the fragility of a piece of DNA might be affected by the whole genome through chromatin interactions. Moreover, we demonstrate that neck interactions in FaCIN can serve as chromatin structural determinants of DSB formation. CONCLUSIONS: Our study provides a more systematic and refined view enabling a better understanding of the mechanisms of DSB formation under the context of the 3D genome.

BACKGROUND: Extensive studies have revealed the function and mechanism of lncRNAs in development and differentiation, but the majority have focused on those lncRNAs adjacent to protein-coding genes. In contrast, lncRNAs located in gene deserts are rarely explored. Here, we utilize multiple differentiation systems to dissect the role of a desert lncRNA, HIDEN (human IMP1-associated "desert" definitive endoderm lncRNA), in definitive endoderm differentiation from human pluripotent stem cells. RESULTS: We show that desert lncRNAs are highly expressed with cell-stage-specific patterns and conserved subcellular localization during stem cell differentiation. We then focus on the desert lncRNA HIDEN which is upregulated and plays a vital role during human endoderm differentiation. We find depletion of HIDEN by either shRNA or promoter deletion significantly impairs human endoderm differentiation. HIDEN functionally interacts with RNA-binding protein IMP1 (IGF2BP1), which is also required for endoderm differentiation. Loss of HIDEN or IMP1 results in reduced WNT activity, and WNT agonist rescues endoderm differentiation deficiency caused by the depletion of HIDEN or IMP1. Moreover, HIDEN depletion reduces the interaction between IMP1 protein and FZD5 mRNA and causes the destabilization of FZD5 mRNA, which is a WNT receptor and necessary for definitive endoderm differentiation. CONCLUSIONS: These data suggest that desert lncRNA HIDEN facilitates the interaction between IMP1 and FZD5 mRNA, stabilizing FZD5 mRNA which activates WNT signaling and promotes human definitive endoderm differentiation.

BACKGROUND: Multiple genetic modifications may be required to develop potent off-the-shelf chimeric antigen receptor (CAR) T cell therapies. Conventional CRISPR-Cas nucleases install sequence-specific DNA double-strand breaks (DSBs), enabling gene knock-out or targeted transgene knock-in. However, simultaneous DSBs provoke a high rate of genomic rearrangements which may impede the safety of the edited cells. RESULTS: Here, we combine a non-viral CRISPR-Cas9 nuclease-assisted knock-in and Cas9-derived base editing technology for DSB free knock-outs within a single intervention. We demonstrate efficient insertion of a CAR into the T cell receptor alpha constant (TRAC) gene, along with two knock-outs that silence major histocompatibility complexes (MHC) class I and II expression. This approach reduces translocations to 1.4% of edited cells. Small insertions and deletions at the base editing target sites indicate guide RNA exchange between the editors. This is overcome by using CRISPR enzymes of distinct evolutionary origins. Combining Cas12a Ultra for CAR knock-in and a Cas9-derived base editor enables the efficient generation of triple-edited CAR T cells with a translocation frequency comparable to unedited T cells. Resulting TCR- and MHC-negative CAR T cells resist allogeneic T cell targeting in vitro. CONCLUSIONS: We outline a solution for non-viral CAR gene transfer and efficient gene silencing using different CRISPR enzymes for knock-in and base editing to prevent translocations. This single-step procedure may enable safer multiplex-edited cell products and demonstrates a path towards off-the-shelf CAR therapeutics.

BACKGROUND: Ocular neovascularization is a leading cause of blindness. Retinal microglia have been implicated in hypoxia-induced angiogenesis and vasculopathy, but the underlying mechanisms are not entirely clear. Lactylation is a novel lactate-derived posttranslational modification that plays key roles in multiple cellular processes. Since hypoxia in ischemic retinopathy is a precipitating factor for retinal neovascularization, lactylation is very likely to be involved in this process. The present study aimed to explore the role of lactylation in retinal neovascularization and identify new therapeutic targets for retinal neovascular diseases. RESULTS: Microglial depletion by the colony-stimulating factor 1 receptor (CSF1R) inhibitor PLX3397 suppresses retinal neovascularization in oxygen-induced retinopathy. Hypoxia increased lactylation in microglia and accelerates FGF2 expression, promoting retinal neovascularization. We identify 77 sites of 67 proteins with increased lactylation in the context of increased lactate under hypoxia. Our results show that the nonhistone protein Yin Yang-1 (YY1), a transcription factor, is lactylated at lysine 183 (K183), which is regulated by p300. Hyperlactylated YY1 directly enhances FGF2 transcription and promotes angiogenesis. YY1 mutation at K183 eliminates these effects. Overexpression of p300 increases YY1 lactylation and enhances angiogenesis in vitro and administration of the p300 inhibitor A485 greatly suppresses vascularization in vivo and in vitro. CONCLUSIONS: Our results suggest that YY1 lactylation in microglia plays an important role in retinal neovascularization by upregulating FGF2 expression. Targeting the lactate/p300/YY1 lactylation/FGF2 axis may provide new therapeutic targets for proliferative retinopathies.

Detecting and mitigating off-target activity is critical to the practical application of CRISPR-mediated genome and epigenome editing. While numerous methods have been developed to map Cas9 binding specificity genome-wide, they are generally time-consuming and/or expensive, and not applicable to catalytically dead CRISPR enzymes. We have developed CasKAS, a rapid, inexpensive, and facile assay for identifying off-target CRISPR enzyme binding and cleavage by chemically mapping the unwound single-stranded DNA structures formed upon binding of a sgRNA-loaded Cas9 protein. We demonstrate this method in both in vitro and in vivo contexts.

With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent increased sparsity due to more zero counts being measured for many genes, we demonstrate here that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~ 50-fold more cells that can be analyzed using the same computational resources. We also highlight the possibilities provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.

We present pycoMeth, a toolbox to store, manage and analyze DNA methylation calls from long-read sequencing data obtained using the Oxford Nanopore Technologies sequencing platform. Building on a novel, rapid-access, read-level and reference-anchored methylation storage format MetH5, we propose efficient algorithms for haplotype aware, multi-sample consensus segmentation and differential methylation testing. We show that MetH5 is more efficient than existing solutions for storing Oxford Nanopore Technologies methylation calls, and carry out benchmarking for pycoMeth segmentation and differential methylation testing, demonstrating increased performance and sensitivity compared to existing solutions designed for short-read methylation data.

The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.

As interest in using unsupervised deep learning models to analyze gene expression data has grown, an increasing number of methods have been developed to make these models more interpretable. These methods can be separated into two groups: post hoc analyses of black box models through feature attribution methods and approaches to build inherently interpretable models through biologically-constrained architectures. We argue that these approaches are not mutually exclusive, but can in fact be usefully combined. We propose PAUSE ( https://github.com/suinleelab/PAUSE ), an unsupervised pathway attribution method that identifies major sources of transcriptomic variation when combined with biologically-constrained neural network models.

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.

BACKGROUND: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. RESULTS: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. CONCLUSION: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.

We present JBrowse 2, a general-purpose genome annotation browser offering enhanced visualization of complex structural variation and evolutionary relationships. It retains core features of JBrowse while adding new views for synteny, dotplots, breakpoints, gene fusions, and whole-genome overviews. It allows users to share sessions, open multiple genomes, and navigate between views. It can be embedded in a web page, used as a standalone application, or run from Jupyter notebooks or R sessions. These improvements are enabled by a ground-up redesign using modern web technology. We describe application functionality, use cases, performance benchmarks, and implementation notes for web administrators and developers.

BACKGROUND: Changes in microbial community composition as a function of human health and disease states have sparked remarkable interest in the human gut microbiome. However, establishing reproducible insights into the determinants of microbial succession in disease has been a formidable challenge. RESULTS: Here we use fecal microbiota transplantation (FMT) as an in natura experimental model to investigate the association between metabolic independence and resilience in stressed gut environments. Our genome-resolved metagenomics survey suggests that FMT serves as an environmental filter that favors populations with higher metabolic independence, the genomes of which encode complete metabolic modules to synthesize critical metabolites, including amino acids, nucleotides, and vitamins. Interestingly, we observe higher completion of the same biosynthetic pathways in microbes enriched in IBD patients. CONCLUSIONS: These observations suggest a general mechanism that underlies changes in diversity in perturbed gut environments and reveal taxon-independent markers of "dysbiosis" that may explain why widespread yet typically low-abundance members of healthy gut microbiomes can dominate under inflammatory conditions without any causal association with disease.

Single-molecule detection and phasing of A-to-I RNA editing events remain an unresolved problem. Long-read and PCR-free nanopore native RNA sequencing offers a great opportunity for direct RNA editing detection. Here, we develop a neural network model, DeepEdit, that not only recognizes A-to-I editing events in single reads of Oxford Nanopore direct RNA sequencing, but also resolves the phasing of RNA editing events on transcripts. We illustrate the robustness of DeepEdit by applying it to Schizosaccharomyces pombe and Homo sapiens transcriptome data. We anticipate DeepEdit to be a powerful tool for the study of RNA editing from a new perspective.

Whole -genome sequencing projects of millions of subjects contain enormous genotypes, entailing a huge memory burden and time for computation. Here, we present GBC, a toolkit for rapidly compressing large-scale genotypes into highly addressable byte-encoding blocks under an optimized parallel framework. We demonstrate that GBC is up to 1000 times faster than state-of-the-art methods to access and manage compressed large-scale genotypes while maintaining a competitive compression ratio. We also showed that conventional analysis would be substantially sped up if built on GBC to access genotypes of a large population. GBC's data structure and algorithms are valuable for accelerating large-scale genomic research.

We present RCRUNCH, an end-to-end solution to CLIP data analysis for identification of binding sites and sequence specificity of RNA-binding proteins. RCRUNCH can analyze not only reads that map uniquely to the genome but also those that map to multiple genome locations or across splice boundaries and can consider various types of background in the estimation of read enrichment. By applying RCRUNCH to the eCLIP data from the ENCODE project, we have constructed a comprehensive and homogeneous resource of in-vivo-bound RBP sequence motifs. RCRUNCH automates the reproducible analysis of CLIP data, enabling studies of post-transcriptional control of gene expression.

BACKGROUND: Tibetans are genetically adapted to high-altitude environments. Though many studies have been conducted, the genetic basis of the adaptation remains elusive due to the poor reproducibility for detecting selective signatures in the Tibetan genomes. RESULTS: Here, we present whole-genome sequencing (WGS) data of 1001 indigenous Tibetans, covering the major populated areas of the Qinghai-Tibetan Plateau in China. We identify 35 million variants, and more than one-third of them are novel variants. Utilizing the large-scale WGS data, we construct a comprehensive map of allele frequency and linkage disequilibrium and provide a population-specific genome reference panel, referred to as 1KTGP. Moreover, with the use of a combined approach, we redefine the signatures of Darwinian-positive selection in the Tibetan genomes, and we characterize a high-confidence list of 4320 variants and 192 genes that have undergone selection in Tibetans. In particular, we discover four new genes, TMEM132C, ATP13A3, SANBR, and KHDRBS2, with strong signals of selection, and they may account for the adaptation of cardio-pulmonary functions in Tibetans. Functional annotation and enrichment analysis indicate that the 192 genes with selective signatures are likely involved in multiple organs and physiological systems, suggesting polygenic and pleiotropic effects. CONCLUSIONS: Overall, the large-scale Tibetan WGS data and the identified adaptive variants/genes can serve as a valuable resource for future genetic and medical studies of high-altitude populations.

Microbiome data from sequencing experiments contain the relative abundance of a large number of microbial taxa with their evolutionary relationships represented by a phylogenetic tree. The compositional and high-dimensional nature of the microbiome mediator challenges the validity of standard mediation analyses. We propose a phylogeny-based mediation analysis method called PhyloMed to address this challenge. Unlike existing methods that directly identify individual mediating taxa, PhyloMed discovers mediation signals by analyzing subcompositions defined on the phylogenic tree. PhyloMed produces well-calibrated mediation test p-values and yields substantially higher discovery power than existing methods.

BACKGROUND: Nanopore-based DNA sequencing relies on basecalling the electric current signal. Basecalling requires neural networks to achieve competitive accuracies. To improve sequencing accuracy further, new models are continuously proposed with new architectures. However, benchmarking is currently not standardized, and evaluation metrics and datasets used are defined on a per publication basis, impeding progress in the field. This makes it impossible to distinguish data from model driven improvements. RESULTS: To standardize the process of benchmarking, we unified existing benchmarking datasets and defined a rigorous set of evaluation metrics. We benchmarked the latest seven basecaller models by recreating and analyzing their neural network architectures. Our results show that overall Bonito's architecture is the best for basecalling. We find, however, that species bias in training can have a large impact on performance. Our comprehensive evaluation of 90 novel architectures demonstrates that different models excel at reducing different types of errors and using recurrent neural networks (long short-term memory) and a conditional random field decoder are the main drivers of high performing models. CONCLUSIONS: We believe that our work can facilitate the benchmarking of new basecaller tools and that the community can further expand on this work.