BACKGROUND: Chromatin loops are an essential factor in the structural organization of the genome; however, their detection in Hi-C interaction matrices is a challenging and compute-intensive task. The approach presented here, integrated into the HiCExplorer software, shows a chromatin loop detection algorithm that applies a strict candidate selection based on continuous negative binomial distributions and performs a Wilcoxon rank-sum test to detect enriched Hi-C interactions. RESULTS: HiCExplorer's loop detection has a high detection rate and accuracy. It is the fastest available CPU implementation and utilizes all threads offered by modern multicore platforms. CONCLUSIONS: HiCExplorer's method to detect loops by using a continuous negative binomial function combined with the donut approach from HiCCUPS leads to reliable and fast computation of loops. All the loop-calling algorithms investigated provide differing results, which intersect by $\sim 50\%$ at most. The tested in situ Hi-C data contain a large amount of noise; achieving better agreement between loop calling algorithms will require cleaner Hi-C data and therefore future improvements to the experimental methods that generate the data.

BACKGROUND: Plazaster borealis has a unique morphology, displaying multiple arms with a clear distinction between disk and arms, rather than displaying pentaradial symmetry, a remarkable characteristic of echinoderms. Herein we report the first chromosome-level reference genome of P. borealis and an essential tool to further investigate the basis of the divergent morphology. FINDINGS: In total, 57.76 Gb of a long read and 70.83 Gb of short-read data were generated to assemble a de novo 561-Mb reference genome of P. borealis, and Hi-C sequencing data (57.47 Gb) were used for scaffolding into 22 chromosomal scaffolds comprising 92.38% of the genome. The genome completeness estimated by BUSCO was 98.0% using the metazoan set, indicating a high-quality assembly. Through the comparative genome analysis, we identified evolutionary accelerated genes known to be involved in morphogenesis and regeneration, suggesting their potential role in shaping body pattern and capacity of regeneration. CONCLUSION: This first chromosome-level genome assembly of P. borealis provides fundamental insights into echinoderm biology, as well as the genomic mechanism underlying its unique morphology and regeneration.

BACKGROUND: The blue catfish is of great value in aquaculture and recreational fisheries. The F1 hybrids of female channel catfish (Ictalurus punctatus) × male blue catfish (Ictalurusfurcatus) have been the primary driver of US catfish production in recent years because of superior growth, survival, and carcass yield. The channel-blue hybrid also provides an excellent model to investigate molecular mechanisms of environment-dependent heterosis. However, transcriptome and methylome studies suffered from low alignment rates to the channel catfish genome due to divergence, and the genome resources for blue catfish are not publicly available. RESULTS: The blue catfish genome assembly is 841.86 Mbp in length with excellent continuity (8.6 Mbp contig N50, 28.2 Mbp scaffold N50) and completeness (98.6% Eukaryota and 97.0% Actinopterygii BUSCO). A total of 30,971 protein-coding genes were predicted, of which 21,781 were supported by RNA sequencing evidence. Phylogenomic analyses revealed that it diverged from channel catfish approximately 9 million years ago with 15.7 million fixed nucleotide differences. The within-species single-nucleotide polymorphism (SNP) density is 0.32% between the most aquaculturally important blue catfish strains (D&B and Rio Grande). Gene family analysis discovered significant expansion of immune-related families in the blue catfish lineage, which may contribute to disease resistance in blue catfish. CONCLUSIONS: We reported the first high-quality, chromosome-level assembly of the blue catfish genome, which provides the necessary genomic tool kit for transcriptome and methylome analysis, SNP discovery and marker-assisted selection, gene editing and genome engineering, and reproductive enhancement of the blue catfish and hybrid catfish.

BACKGROUND: Screening for antibiotic resistance genes (ARGs) in especially environmental samples with (meta)genomic sequencing is associated with false-positive predictions of phenotypic resistance. This stems from the fact that most acquired ARGs require being overexpressed before conferring resistance, which is often caused by decontextualization of putative ARGs by mobile genetic elements (MGEs). Consequent overexpression of ARGs can be caused by strong promoters often present in insertion sequence (IS) elements and integrons and the copy number effect of plasmids, which may contribute to high expression of accessory genes. RESULTS: Here, we screen all complete bacterial RefSeq genomes for ARGs. The genetic contexts of detected ARGs are investigated for IS elements, integrons, plasmids, and phylogenetic dispersion. The ARG-MOB scale is proposed, which indicates how mobilized detected ARGs are in bacterial genomes. It is concluded that antibiotic efflux genes are rarely mobilized and even 80% of β-lactamases have never, or very rarely, been mobilized in the 15,790 studied genomes. However, some ARGs are indeed mobilized and co-occur with IS elements, plasmids, and integrons. CONCLUSIONS: In this study, ARGs in all complete bacterial genomes are classified by their association with MGEs, using the proposed ARG-MOB scale. These results have consequences for the design and interpretation of studies screening for resistance determinants, as mobilized ARGs pose a more concrete risk to human health. An interactive table of all results is provided for future studies targeting highly mobilized ARGs.

Survival analysis is a branch of statistics that deals with both the tracking of time and the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarize the survival time and perform a classification analysis. Here, we develop a benchmarking design, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics data sets. SurvBenchmark not only focuses on classical approaches such as the Cox model but also evaluates state-of-the-art machine learning survival models. All approaches were assessed using multiple performance metrics; these include model predictability, stability, flexibility, and computational issues. Our systematic comparison design with 320 comparisons (20 methods over 16 data sets) shows that the performances of survival models vary in practice over real-world data sets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies.

BACKGROUND: Amplicon sequencing (metabarcoding) is a common method to survey diversity of environmental communities whereby a single genetic locus is amplified and sequenced from the DNA of whole or partial organisms, organismal traces (e.g., skin, mucus, feces), or microbes in an environmental sample. Several software packages exist for analyzing amplicon data, among which QIIME 2 has emerged as a popular option because of its broad functionality, plugin architecture, provenance tracking, and interactive visualizations. However, each new analysis requires the user to keep track of input and output file names, parameters, and commands; this lack of automation and standardization is inefficient and creates barriers to meta-analysis and sharing of results. FINDINGS: We developed Tourmaline, a Python-based workflow that implements QIIME 2 and is built using the Snakemake workflow management system. Starting from a configuration file that defines parameters and input files-a reference database, a sample metadata file, and a manifest or archive of FASTQ sequences-it uses QIIME 2 to run either the DADA2 or Deblur denoising algorithm; assigns taxonomy to the resulting representative sequences; performs analyses of taxonomic, alpha, and beta diversity; and generates an HTML report summarizing and linking to the output files. Features include support for multiple cores, automatic determination of trimming parameters using quality scores, representative sequence filtering (taxonomy, length, abundance, prevalence, or ID), support for multiple taxonomic classification and sequence alignment methods, outlier detection, and automated initialization of a new analysis using previous settings. The workflow runs natively on Linux and macOS or via a Docker container. We ran Tourmaline on a 16S ribosomal RNA amplicon data set from Lake Erie surface water, showing its utility for parameter optimization and the ability to easily view interactive visualizations through the HTML report, QIIME 2 viewer, and R- and Python-based Jupyter notebooks. CONCLUSION: Automated workflows like Tourmaline enable rapid analysis of environmental amplicon data, decreasing the time from data generation to actionable results. Tourmaline is available for download at github.com/aomlomics/tourmaline.

BACKGROUND: Microbial community samples have been accumulating at a speed faster than ever, with hundreds of thousands of samples been sequenced each year. Mining such a huge amount of multisource heterogeneous data is becoming an increasingly difficult challenge, so efficient and accurate compare and search of samples is in urgent need: faced with millions of samples in the data repository, traditional sample comparison and search approaches fall short in speed and accuracy. FINDINGS: Here we proposed Meta-Prism 2.0, a microbial community sample analysis method that has pushed the time and memory efficiency to a new limit without compromising accuracy. Based on sparse data structure, time-saving instruction pipeline, and SIMD optimization, Meta-Prism 2.0 has enabled ultra-fast, memory-efficient, flexible, and accurate search among millions of samples. Meta-Prism 2.0 was put to test on several data sets, with the largest containing 1 million samples. Results show that Meta-Prism 2.0's 0.00001-s per sample pair compare speed and 8-GB memory needs for searching against 1 million samples have made it one of the most efficient sample analysis methods. Additionally, Meta-Prism 2.0 can achieve accuracy comparable with or better than other contemporary methods. Third, Meta-Prism 2.0 can precisely identify the original biome for samples, thus enabling sample source tracking. Finally, we have provided a web server for fast search of microbial community samples online. CONCLUSIONS: In summary, Meta-Prism 2.0 has changed the resource-intensive sample search scheme to an effective procedure, which could be conducted by researchers every day even on a laptop, for insightful sample search, similarity analysis, and knowledge discovery. Meta-Prism 2.0 can be accessed at https://github.com/HUST-NingKang-Lab/Meta-Prism-2.0, and the web server can be accessed at https://hust-ningkang-lab.github.io/Meta-Prism-2.0/.

BACKGROUND: Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques quantify messenger RNA expression levels from tissue sections and at the same time register information about the original locations of the molecules in the tissue. The resulting data sets are processed and analyzed by accompanying software that, however, is incompatible across inputs from different technologies. FINDINGS: Here, we present spacemake, a modular, robust, and scalable spatial transcriptomics pipeline built in Snakemake and Python. Spacemake is designed to handle all major spatial transcriptomics data sets and can be readily configured for other technologies. It can process and analyze several samples in parallel, even if they stem from different experimental methods. Spacemake's unified framework enables reproducible data processing from raw sequencing data to automatically generated downstream analysis reports. Spacemake is built with a modular design and offers additional functionality such as sample merging, saturation analysis, and analysis of long reads as separate modules. Moreover, spacemake employs novoSpaRc to integrate spatial and single-cell transcriptomics data, resulting in increased gene counts for the spatial data set. Spacemake is open source and extendable, and it can be seamlessly integrated with existing computational workflows.

Metazoan metabarcoding is emerging as an essential strategy for inventorying biodiversity, with diverse projects currently generating massive quantities of community-level data. The potential for integrating across such data sets offers new opportunities to better understand biodiversity and how it might respond to global change. However, large-scale syntheses may be compromised if metabarcoding workflows differ from each other. There are ongoing efforts to improve standardization for the reporting of inventory data. However, harmonization at the stage of generating metabarcode data has yet to be addressed. A modular framework for harmonized data generation offers a pathway to navigate the complex structure of terrestrial metazoan biodiversity. Here, through our collective expertise as practitioners, method developers, and researchers leading metabarcoding initiatives to inventory terrestrial biodiversity, we seek to initiate a harmonized framework for metabarcode data generation, with a terrestrial arthropod module. We develop an initial set of submodules covering the 5 main steps of metabarcode data generation: (i) sample acquisition; (ii) sample processing; (iii) DNA extraction; (iv) polymerase chain reaction amplification, library preparation, and sequencing; and (v) DNA sequence and metadata deposition, providing a backbone for a terrestrial arthropod module. To achieve this, we (i) identified key points for harmonization, (ii) reviewed the current state of the art, and (iii) distilled existing knowledge within submodules, thus promoting best practice by providing guidelines and recommendations to reduce the universe of methodological options. We advocate the adoption and further development of the terrestrial arthropod module. We further encourage the development of modules for other biodiversity fractions as an essential step toward large-scale biodiversity synthesis through harmonization.

BACKGROUND: Booklice (psocids) in the genus Liposcelis (Psocoptera: Liposcelididae) are a group of important storage pests, found in libraries, grain storages, and food-processing facilities. Booklice are able to survive under heat treatment and typically possess high resistance to common fumigant insecticides, hence posing a threat to storage security worldwide. RESULTS: We assembled the genome of the booklouse, L. brunnea, the first genome reported in Psocoptera, using PacBio long-read sequencing, Illumina sequencing, and chromatin conformation capture (Hi-C) methods. After assembly, polishing, haplotype purging, and Hi-C scaffolding, we obtained 9 linkage groups (174.1 Mb in total) ranging from 12.1 Mb to 27.6 Mb (N50: 19.7 Mb), with the BUSCO completeness at 98.9%. In total, 15,543 genes were predicted by the Maker pipeline. Gene family analyses indicated the sensing-related gene families (OBP and OR) and the resistance-related gene families (ABC, EST, GST, UGT, and P450) expanded significantly in L. brunnea compared with those of their closest relatives (2 parasitic lice). Based on transcriptomic analysis, we found that the CYP4 subfamily from the P450 gene family functioned during phosphine fumigation; HSP genes, particularly those from the HSP70 subfamily, were upregulated significantly under high temperatures. CONCLUSIONS: We present a chromosome-level genome assembly of L. brunnea, the first genome reported for the order Psocoptera. Our analyses provide new insights into the gene family evolution of the louse clade and the transcriptomic responses of booklice to environmental stresses.

Open Science has gained momentum over the past decade, and embracing that, GigaScience, from its launch a decade ago has aimed at pushing scientific publishing beyond just making articles open access toward making the entire research process open and available as an embedded part of the publishing process. Before the journal's launch in July 2012, the editors aimed to make publishing more than a narrative presentation of work already done into a fully open process. Major milestones include creating our own data repository, embracing FAIR principles, promoting and integrating preprints, and working with other platforms to contribute to a 21st century publishing infrastructure. Almost 10 years after GigaScience's launch, UNESCO published its Open Science Recommendations. With these in mind, looking back, we are happy to have contributed in various ways to UNESCO's aim to "foster a culture of Open Science and aligning incentives for Open Science" from the very beginning, and, more, to use those recommendations to guide our path into the future: to truly embrace the full spectrum of information, tools, and access to Open Science for all participants in scientific endeavours.