Metagenome assembly is an efficient approach to reconstruct microbial genomes from metagenomic sequencing data. Although short-read sequencing has been widely used for metagenome assembly, linked- and long-read sequencing have shown their advancements in assembly by providing long-range DNA connectedness. Many metagenome assembly tools were developed to simplify the assembly graphs and resolve the repeats in microbial genomes. However, there remains no comprehensive evaluation of metagenomic sequencing technologies, and there is a lack of practical guidance on selecting the appropriate metagenome assembly tools. This paper presents a comprehensive benchmark of 19 commonly used assembly tools applied to metagenomic sequencing datasets obtained from simulation, mock communities or human gut microbiomes. These datasets were generated using mainstream sequencing platforms, such as Illumina and BGISEQ short-read sequencing, 10x Genomics linked-read sequencing, and PacBio and Oxford Nanopore long-read sequencing. The assembly tools were extensively evaluated against many criteria, which revealed that long-read assemblers generated high contig contiguity but failed to reveal some medium- and high-quality metagenome-assembled genomes (MAGs). Linked-read assemblers obtained the highest number of overall near-complete MAGs from the human gut microbiomes. Hybrid assemblers using both short- and long-read sequencing were promising methods to improve both total assembly length and the number of near-complete MAGs. This paper also discussed the running time and peak memory consumption of these assembly tools and provided practical guidance on selecting them.

Background: The complex life cycle of the coconut crab,Birgus latro, begins when an obligate terrestrial adult female visits the intertidal to hatch zoea larvae into the surf. After drifting for several weeks in the ocean, the post-larval glaucothoes settle in the shallow subtidal zone, undergo metamorphosis, and the early juveniles then subsequently make their way to land where they undergo further physiological changes that prevent them from ever entering the sea again. Here, we sequenced, assembled and analyzed the coconut crab genome to shed light on its adaptation to terrestrial life. For comparison, we also assembled the genomes of the long-tailed marine-living ornate spiny lobster,Panulirus ornatus, and the short-tailed marine-living red king crab,Paralithodes camtschaticus. Our selection of the latter two organisms furthermore allowed us to explore parallel evolution of the crab-like form in anomurans.Results: All three assembled genomes are large, repeat-rich and AT-rich. Functional analysis reveals that the coconut crab has undergone proliferation of genes involved in the visual, respiratory, olfactory and cytoskeletal systems. Given that the coconut crab has atypical mitochondrial DNA compared to other anomurans, we argue that an abundance ofkif22and other significantly proliferated genes annotated with mitochondrial and microtubule functions, point to unique mechanisms involved in providing cellular energy via nuclear protein-coding genes supplementing mitochondrial and microtubule function. We furthermore detected in the coconut crab a significantly proliferated HOX gene,caudal, that has been associated with posterior development inDrosophila, but we could not definitively associate this gene with carcinization in the Anomura since it is also significantly proliferated in the ornate spiny lobster. However, a cuticle-associated coatomer gene,gammacop, that is significantly proliferated in the coconut crab, may play a role in hardening of the adult coconut crab abdomen in order to mitigate desiccation in terrestrial environments.Conclusion: The abundance of genomic features in the three assembled genomes serve as a source of hypotheses for future studies of anomuran environmental adaptations such as shell-utilization, perception of visual and olfactory cues in terrestrial environments, and cuticle sclerotization. We hypothesize that the coconut crab exhibits gene proliferation in lieu of alternative splicing as a terrestrial adaptation mechanism and propose life-stage transcriptomic assays to test this hypothesis.

We present a full description and analysis of the complete mitochondrial genome of a Pacific Ocean specimen of the coconut crab Birgus latro (Linnaeus, 1767), the largest extant terrestrial arthropod in the world. Our de novo-assembled mitogenome has a massive 16,161 times organelle read coverage, a length of 16,411 bp, contains 22 tDNAs (20 unique), 13 protein-coding genes, two rDNAs, and a putative control region of length 1,381 bp. The control region contains three microsatellites and two pairs of inverted repeats. Contrary to the mitochondrial sentinel gene concept, two-dimensional nucleotide analysis reveals higher GC-content in cox gene families than in nadh gene families. Moreover, cox gene families are more conserved than nadh gene families among the species of Coenobitidae selected for comparison. Secondary structure prediction of the 22 tDNAs shows major deviations from the cloverleaf pattern, which points to a relatively high rate of mutation in these genes. We also present a repertoire of mitochondrial variation between our male Okinawan coconut crab and an Indian Ocean specimen that consists of one insertion, one deletion, 135 SNPs, three MNPs and nine complex polymorphisms. We provide confirmatory evidence that the superfamily Paguroidea, to which the coconut crab belongs, is polyphyletic, that all the protein-coding genes of B. latro are under purifying selection, and that a Pacific versus Indian Ocean coconut crab population divergence occurred during the Pleistocene.

Single-cell ribonucleic acid sequencing (scRNA-seq) enables the quantification of gene expression at the transcriptomic level with single-cell resolution, enhancing our understanding of cellular heterogeneity. However, the excessive missing values present in scRNA-seq data hinder downstream analysis. While numerous imputation methods have been proposed to recover scRNA-seq data, high imputation performance often comes with low or no interpretability. Here, we present IGSimpute, an accurate and interpretable imputation method for recovering missing values in scRNA-seq data with an interpretable instance-wise gene selection layer (GSL). IGSimpute outperforms 12 other state-of-the-art imputation methods on 13 out of 17 datasets from different scRNA-seq technologies with the lowest mean squared error as the chosen benchmark metric. We demonstrate that IGSimpute can give unbiased estimates of the missing values compared to other methods, regardless of whether the average gene expression values are small or large. Clustering results of imputed profiles show that IGSimpute offers statistically significant improvement over other imputation methods. By taking the heart-and-aorta and the limb muscle tissues as examples, we show that IGSimpute can also denoise gene expression profiles by removing outlier entries with unexpectedly high expression values via the instance-wise GSL. We also show that genes selected by the instance-wise GSL could indicate the age of B cells from bladder fat tissue of the Tabula Muris Senis atlas. IGSimpute can impute one million cells using 64 min, and thus applicable to large datasets.

Quantifying an individual’s risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk individuals. Although several studies have been performed to benchmark the PRS calculation tools and assess their potential to guide future clinical applications, some issues remain to be further investigated, such as lacking (i) various simulated data with different genetic effects; (ii) evaluation of machine learning models and (iii) evaluation on multiple ancestries studies. In this study, we systematically validated and compared 13 statistical methods, 5 machine learning models and 2 ensemble models using simulated data with additive and genetic interaction models, 22 common diseases with internal training sets, 4 common diseases with external summary statistics and 3 common diseases for trans-ancestry studies in UK Biobank. The statistical methods were better in simulated data from additive models and machine learning models have edges for data that include genetic interactions. Ensemble models are generally the best choice by integrating various statistical methods. LDpred2 outperformed the other standalone tools, whereas PRS-CS, lassosum and DBSLMM showed comparable performance. We also identified that disease heritability strongly affected the predictive performance of all methods. Both the number and effect sizes of risk SNPs are important; and sample size strongly influences the performance of all methods. For the trans-ancestry studies, we found that the performance of most methods became worse when training and testing sets were from different populations.