Background With more than 30,000 species, fish—including bony, jawless, and cartilaginous fish—are the largest vertebrate group, and include some of the earliest vertebrates. Despite their critical roles in many ecosystems and human society, fish genomics lags behind work on birds and mammals. This severely limits our understanding of evolution and hinders progress on the conservation and sustainable utilization of fish. Results Here, we announce the Fish10K project, a portion of the Earth BioGenome Project aiming to sequence 10,000 representative fish genomes in a systematic fashion within 10 years, and we officially welcome collaborators to join this effort. As a step towards this goal, we herein describe a feasible workflow for the procurement and storage of biospecimens, as well as sequencing and assembly strategies. Conclusions To illustrate, we present the genomes of 10 fish species from a cohort of 93 species chosen for technology development.
With more than 30,000 species, fish are the largest and most ancient vertebrate group.Despite their critical roles in many ecosystems and human society, fish genomics lags behind work on birds and mammals. This severely limits our understanding of evolution and hinders progress on the conservation and sustainable utilization of fish.Here, we announce the Fish10K project, an international collaborative project or initiative? aiming to sequence 10,000 representative fish genomes under a systematic context within ten years, and officially welcome collaborators to join this effort. As a step towards this goal, we herein describe a feasible workflow for the procurement and storage of biospecimens, and sequencing and assembly strategies. To illustrate, we present the genomes of ten fish species from a cohort of 93 species chosen for technology development.
With the continuing development of sequencing technology, genomics has been applied in a variety of biological research areas. In particular, the application of genomics to marine species, which boast a high diversity, promises great scientific and industrial potential. Significant progress has been made in marine genomics especially over the past few years. Consequently, BGI, leveraging its prominent contributions in genomics research, established BGI-Qingdao, an institute specifically aimed at exploring marine genomics. In order to accelerate marine genomics research and related applications, BGI-Qingdao initiated the International Conference on Genomics of the Ocean (ICG-Ocean) to develop international collaborations and establish a focused and coherent global research plan. Last year, the first ICG-Ocean conference was held in Qingdao, China, during which 47 scientists in marine genomics from all over the world reported on their research progress to an audience of about 300 attendees. This year, we would like to build on that success, drafting a report on marine genomics to draw global attention to marine genomics. We summarized the recent progress, proposed future directions, and we would like to enable additional profound insights on marine genomics. Similar to the annual report on plant and fungal research by Kew Gardens, and the White Paper of ethical issues on experimental animals, we hope our first report on marine genomics can provide some useful insights for researchers, funding agencies as well as industry, and that future versions will expand upon the foundation established here in both breadth and depth of knowledge.This report summarizes the recent progress in marine genomics in six parts including: marine microorganisms, marine fungi, marine algae and plants, marine invertebrates, marine vertebrates and genomics-based applications.
Motivation Single-cell sequencing brings about a revolutionarily high resolution for finding differentially expressed genes by disentangling highly heterogeneous cell tissues. Yet, such analysis is so far mostly focused on comparing between different cell types from the same individual. As single-cell sequencing becomes cheaper and easier to use, an increasing number of datasets from case-control studies are becoming available, which call for new methods for identifying differential expressions between case and control individuals. Results To bridge this gap, we propose Barycenter Single-cell Differential Expression (BSDE), a nonparametric method for finding differentially expressed genes for case-control studies. Through the use of optimal transportation for aggregating distributions and computing their distances, our method overcomes the restrictive parametric assumptions imposed by standard mixed-effect-modeling approaches. Through simulations, we show that BSDE can accurately detect a variety of differential expressions while maintaining the type-I error at a prescribed level. Further, 1345 and 1568 cell type specific differentially expressed genes are identified by BSDE from datasets on pulmonary fibrosis and multiple sclerosis, among which the top findings are supported by previous results from the literature. Availability R package BSDE is freely available from doi.org/10.5281/zenodo.6332254. For real data analysis with the R package, see doi.org/10.5281/zenodo.6332566. These can also be accessed thorough GitHub at github.com/mqzhanglab/BSDE and github.com/mqzhanglab/BSDE_pipeline. The two single cell sequencing datasets can be download with UCSC cell browser from cells.ucsc.edu/?ds=ms and cells.ucsc.edu/?ds=lung-pf-control. Supplementary information Supplementary data are available at Bioinformatics online.
Gene-set analyses are used to assess whether there is any evidence of association with disease among a set of biologically related genes. Such an analysis typically treats all genes within the sets similarly, even though there is substantial, external, information concerning the likely importance of each gene within each set. For example, for traits that are under purifying selection, we would expect genes showing extensive genic constraint to be more likely to be trait associated than unconstrained genes. Here we improve gene-set analyses by incorporating such external information into a higher-criticism-based signal detection analysis. We show that when this external information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. Further, our approach is particularly powerful when the signal is sparse, that is when only a small number of genes within the set are associated with the trait. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS) and implicate a number of genesets containing SOD1 and NEK1 as well as showing enrichment of small p values for gene-sets containing known ALS genes. We implement our approach in the R package wHC. K E Y W O R D Samyotrophic lateral sclerosis, gene-set-based analysis, higher criticism, prior information, weighted p values
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