SNP-SVant: A Computational Workflow to Predict and Annotate Genomic Variants in Organisms Lacking Benchmarked Variants.

Whole-genome sequencing is widely used to investigate population genomic variation in organisms of interest. Assorted tools have been independently developed to call variants from short-read sequencing data aligned to a reference genome, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). We developed SNP-SVant, an integrated, flexible, and computationally efficient bioinformatic workflow that predicts high-confidence SNPs and SVs in organisms without benchmarked variants, which are traditionally used for distinguishing sequencing errors from real variants. In the absence of these benchmarked datasets, we leverage multiple rounds of statistical recalibration to increase the precision of variant prediction. The SNP-SVant workflow is flexible, with user options to tradeoff accuracy for sensitivity. The workflow predicts SNPs and small insertions and deletions using the Genome Analysis ToolKit (GATK) and predicts SVs using the Genome Rearrangement IDentification Software Suite (GRIDSS), and it culminates in variant annotation using custom scripts. A key utility of SNP-SVant is its scalability. Variant calling is a computationally expensive procedure, and thus, SNP-SVant uses a workflow management system with intermediary checkpoint steps to ensure efficient use of resources by minimizing redundant computations and omitting steps where dependent files are available. SNP-SVant also provides metrics to assess the quality of called variants and converts between VCF and aligned FASTA format outputs to ensure compatibility with downstream tools to calculate selection statistics, which are commonplace in population genomics studies. By accounting for both small and large structural variants, users of this workflow can obtain a wide-ranging view of genomic alterations in an organism of interest. Overall, this workflow advances our capabilities in assessing the functional consequences of different types of genomic alterations, ultimately improving our ability to associate genotypes with phenotypes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Predicting single nucleotide polymorphisms and structural variations Support Protocol 1: Downloading publicly available sequencing data Support Protocol 2: Visualizing variant loci using Integrated Genome Viewer Support Protocol 3: Converting between VCF and aligned FASTA formats.

A Principal Components Analysis and Functional Annotation of Differentially Expressed Genes in Brain Regions of Gray Rats Selected for Tame or Aggressive Behavior.

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication.

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

Whole-genome sequencing of Ganoderma boninense, the causal agent of basal stem rot disease in oil palm, via combined short- and long-read sequencing.

The hemibiotrophic Basidiomycete pathogen Ganoderma boninense (Gb) is the dominant causal agent of oil palm basal stem rot disease. Here, we report a complete chromosomal genome map of Gb using a combination of short-read Illumina and long-read Pacific Biosciences (PacBio) sequencing platforms combined with chromatin conformation capture data from the Chicago and Hi-C platforms. The genome was 55.87 Mb in length and assembled to a high contiguity (N50: 304.34 kb) of 12 chromosomes built from 112 scaffolds, with a total of only 4.34 Mb (~ 7.77%) remaining unplaced. The final assemblies were evaluated for completeness of the genome by using Benchmarking Universal Single Copy Orthologs (BUSCO) v4.1.4, and based on 4464 total BUSCO polyporales group searches, the assemblies yielded 4264 (95.52%) of the conserved orthologs as complete and only a few fragmented BUSCO of 42 (0.94%) as well as a missing BUSCO of 158 (3.53%). Genome annotation predicted a total of 21,074 coding genes, with a GC content ratio of 59.2%. The genome features were analyzed with different databases, which revealed 2471 Gene Ontology/GO (11.72%), 5418 KEGG (Kyoto Encyclopedia of Genes and Genomes) Orthologous/KO (25.71%), 13,913 Cluster of Orthologous Groups of proteins/COG (66.02%), 60 ABC transporter (0.28%), 1049 Carbohydrate-Active Enzymes/CAZy (4.98%), 4005 pathogen-host interactions/PHI (19%), and 515 fungal transcription factor/FTFD (2.44%) genes. The results obtained in this study provide deep insight for further studies in the future.

Annotations of four high-quality indigenous chicken genomes identify more than one thousand missing genes in subtelomeric regions and micro-chromosomes with high G/C contents.

BACKGROUND: Although multiple chicken genomes have been assembled and annotated, the numbers of protein-coding genes in chicken genomes and their variation among breeds are still uncertain due to the low quality of these genome assemblies and limited resources used in their gene annotations. To fill these gaps, we recently assembled genomes of four indigenous chicken breeds with distinct traits at chromosome-level. In this study, we annotated genes in each of these assembled genomes using a combination of RNA-seq- and homology-based approaches. RESULTS: We identified varying numbers (17,497-17,718) of protein-coding genes in the four indigenous chicken genomes, while recovering 51 of the 274 "missing" genes in birds in general, and 36 of the 174 "missing" genes in chickens in particular. Intriguingly, based on deeply sequenced RNA-seq data collected in multiple tissues in the four breeds, we found 571 ~ 627 protein-coding genes in each genome, which were missing in the annotations of the reference chicken genomes (GRCg6a and GRCg7b/w). After removing redundancy, we ended up with a total of 1,420 newly annotated genes (NAGs). The NAGs tend to be found in subtelomeric regions of macro-chromosomes (chr1 to chr5, plus chrZ) and middle chromosomes (chr6 to chr13, plus chrW), as well as in micro-chromosomes (chr14 to chr39) and unplaced contigs, where G/C contents are high. Moreover, the NAGs have elevated quadruplexes G frequencies, while both G/C contents and quadruplexes G frequencies in their surrounding regions are also high. The NAGs showed tissue-specific expression, and we were able to verify 39 (92.9%) of 42 randomly selected ones in various tissues of the four chicken breeds using RT-qPCR experiments. Most of the NAGs were also encoded in the reference chicken genomes, thus, these genomes might harbor more genes than previously thought. CONCLUSION: The NAGs are widely distributed in wild, indigenous and commercial chickens, and they might play critical roles in chicken physiology. Counting these new genes, chicken genomes harbor more genes than originally thought.

Evaluating large language models for annotating proteins.

In UniProtKB, up to date, there are more than 251 million proteins deposited. However, only 0.25% have been annotated with one of the more than 15000 possible Pfam family domains. The current annotation protocol integrates knowledge from manually curated family domains, obtained using sequence alignments and hidden Markov models. This approach has been successful for automatically growing the Pfam annotations, however at a low rate in comparison to protein discovery. Just a few years ago, deep learning models were proposed for automatic Pfam annotation. However, these models demand a considerable amount of training data, which can be a challenge with poorly populated families. To address this issue, we propose and evaluate here a novel protocol based on transfer learningThis requires the use of protein large language models (LLMs), trained with self-supervision on big unnanotated datasets in order to obtain sequence embeddings. Then, the embeddings can be used with supervised learning on a small and annotated dataset for a specialized task. In this protocol we have evaluated several cutting-edge protein LLMs together with machine learning architectures to improve the actual prediction of protein domain annotations. Results are significatively better than state-of-the-art for protein families classification, reducing the prediction error by an impressive 60% compared to standard methods. We explain how LLMs embeddings can be used for protein annotation in a concrete and easy way, and provide the pipeline in a github repo. Full source code and data are available at https://github.com/sinc-lab/llm4pfam.

Annotation of nuclear lncRNAs based on chromatin interactions.

The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Some specific nuclear lncRNAs have been shown to be important regulatory components acting locally. As RNA-chromatin interaction and Hi-C chromatin conformation data showed that chromatin interactions of nuclear lncRNAs are determined by the local chromatin 3D conformation, we used Hi-C data to identify potential target genes of lncRNAs. RNA-protein interaction data suggested that nuclear lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. Nuclear lncRNAs may therefore play a role in directing regulatory factors to locations spatially close to the lncRNA gene. We provide the analysis results through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.

Chromosome-level genome assembly of the threatened resource plant Cinnamomum chago.

Cinnamomum chago is a tree species endemic to Yunnan province, China, with potential economic value, phylogenetic importance, and conservation priority. We assembled the genome of C. chago using multiple sequencing technologies, resulting in a high-quality, chromosomal-level genome with annotation information. The assembled genome size is approximately 1.06 Gb, with a contig N50 length of 92.10 Mb. About 99.92% of the assembled sequences could be anchored to 12 pseudo-chromosomes, with only one gap, and 63.73% of the assembled genome consists of repeat sequences. In total, 30,497 genes were recognized according to annotation, including 28,681 protein-coding genes. This high-quality chromosome-level assembly and annotation of C. chago will assist us in the conservation and utilization of this valuable resource, while also providing crucial data for studying the evolutionary relationships within the Cinnamomum genus, offering opportunities for further research and exploration of its diverse applications.

An improved chromosome-level genome assembly and annotation of Echeneis naucrates.

Echeneis naucrates, as known as live sharksucker, is famous for the behavior of attaching to hosts using a highly modified dorsal fin with oval-shaped sucking disc. Here, we generated an improved high-quality chromosome-level genome assembly of E. naucrates using Illumina short reads, PacBio long reads and Hi-C data. Our assembled genome spans 572.85 Mb with a contig N50 of 23.19 Mb and is positioned to 24 pseudo-chromosomes. Additionally, at least one telomere was identified for 23 out of 24 chromosomes. Furthermore, we identified a total of 22,161 protein-coding genes, of which 21,402 genes (96.9%) were annotated successfully with functions. The combination of ab initio predictions and Repbase-based searches revealed that 15.57% of the assembled E. naucrates genome was identified as repetitive sequences. The completeness of the genome assembly and the gene annotation were estimated to be 97.5% and 95.4% with BUSCO analyses. This work enhances the utility of the live sharksucker genome and provides a valuable groundwork for the future study of genomics, biology and adaptive evolution in this species.

Chromosomal-level genome assembly and annotation of the tropical sea cucumber Holothuria scabra.

Holothuria scabra, a commercially valuable yet ecologically vulnerable tropical holothuroid, has experienced a severe decline in its wild populations, especially in China. Genomic resources are crucial for the development of effective genomic breeding projects and stock conservation strategies to restore these natural populations. Until now, a high-quality, chromosome-level reference genome for H. scabra has not been available. Here, we employed Oxford Nanopore and Hi-C sequencing technologies to assemble and annotate a high-quality, chromosome-level reference genome of H. scabra. The final genome comprised 31 scaffolds with a total length of 1.19 Gb and a scaffold N50 length of 53.52 Mb. Remarkably, 1,191.67 Mb (99.95%) of the sequences were anchored to 23 pseudo-chromosomes, with the longest one spanning 79.75 Mb. A total of 34,418 protein-coding genes were annotated in the final genome, with BUSCO analysis revealing 98.01% coverage of metazoa_odb10 genes, marking a significant improvement compared to the previous report. These chromosome-level sequences and annotations will provide an essential genomic basis for further investigation into molecular breeding and conservation management of H. scabra.

Insight into the adaptive role of arachnid genome-wide duplication through chromosome-level genome assembly of the Western black widow spider.

Although spiders are one of the most diverse groups of arthropods, the genetic architecture of their evolutionary adaptations is largely unknown. Specifically, ancient genome-wide duplication occurring during arachnid evolution ~450 mya resulted in a vast assembly of gene families, yet the extent to which selection has shaped this variation is understudied. To aid in comparative genome sequence analyses, we provide a chromosome-level genome of the Western black widow spider (Latrodectus hesperus)-a focus due to its silk properties, venom applications, and as a model for urban adaptation. We used long-read and Hi-C sequencing data, combined with transcriptomes, to assemble 14 chromosomes in a 1.46 Gb genome, with 38,393 genes annotated, and a BUSCO score of 95.3%. Our analyses identified high repetitive gene content and heterozygosity, consistent with other spider genomes, which has led to challenges in genome characterization. Our comparative evolutionary analyses of eight genomes available for species within the Araneoidea group (orb weavers and their descendants) identified 1,827 single-copy orthologs. Of these, 155 exhibit significant positive selection primarily associated with developmental genes, and with traits linked to sensory perception. These results support the hypothesis that several traits unique to spiders emerged from the adaptive evolution of ohnologs-or retained ancestrally duplicated genes-from ancient genome-wide duplication. These comparative spider genome analyses can serve as a model to understand how positive selection continually shapes ancestral duplications in generating novel traits today within and between diverse taxonomic groups.

AlphaFun: Structural-Alignment-Based Proteome Annotation Reveals why the Functionally Unknown Proteins (uPE1) Are So Understudied.

With the rapid expansion of sequencing of genomes, the functional annotation of proteins becomes a bottleneck in understanding proteomes. The Chromosome-centric Human Proteome Project (C-HPP) aims to identify all proteins encoded by the human genome and find functional annotations for them. However, until now there are still 1137 identified human proteins without functional annotation, called uPE1 proteins. Sequence alignment was insufficient to predict their functions, and the crystal structures of most proteins were unavailable. In this study, we demonstrated a new functional annotation strategy, AlphaFun, based on structural alignment using deep-learning-predicted protein structures. Using this strategy, we functionally annotated 99% of the human proteome, including the uPE1 proteins and missing proteins, which have not been identified yet. The accuracy of the functional annotations was validated using the known-function proteins. The uPE1 proteins shared similar functions to the known-function PE1 proteins and tend to express only in very limited tissues. They are evolutionally young genes and thus should conduct functions only in specific tissues and conditions, limiting their occurrence in commonly studied biological models. Such functional annotations provide hints for functional investigations on the uPE1 proteins. This proteome-wide-scale functional annotation strategy is also applicable to any other species.

Genome-wide annotation of transcript boundaries using bacterial Rend-seq datasets.

Accurate annotation to single-nucleotide resolution of the transcribed regions in genomes is key to optimally analyse RNA-seq data, understand regulatory events and for the design of experiments. However, currently most genome annotations provided by GenBank generally lack information about untranslated regions. Additionally, information regarding genomic locations of non-coding RNAs, such as sRNAs, or anti-sense RNAs is frequently missing. To provide such information, diverse RNA-seq technologies, such as Rend-seq, have been developed and applied to many bacterial species. However, incorporating this vast amount of information into annotation files has been limited and is bioinformatically challenging, resulting in UTRs and other non-coding elements being overlooked or misrepresented. To overcome this problem, we present pyRAP (python Rend-seq Annotation Pipeline), a software package that analyses Rend-seq datasets to accurately resolve transcript boundaries genome-wide. We report the use of pyRAP to find novel transcripts, transcript isoforms, and RNase-dependent sRNA processing events. In Bacillus subtilis we uncovered 63 novel transcripts and provide genomic coordinates with single-nucleotide resolution for 2218 5'UTRs, 1864 3'UTRs and 161 non-coding RNAs. In Escherichia coli, we report 117 novel transcripts, 2429 5'UTRs, 1619 3'UTRs and 91 non-coding RNAs, and in Staphylococcus aureus, 16 novel transcripts, 664 5'UTRs, 696 3'UTRs, and 81 non-coding RNAs. Finally, we use pyRAP to produce updated annotation files for B. subtilis 168, E. coli K-12 MG1655, and S. aureus 8325 for use in the wider microbial genomics research community.

Integrative annotation scores of variants for impact on RNA binding protein activities.

MOTIVATION: The ENCODE project generated a large collection of eCLIP-seq RNA binding protein (RBP) profiling data with accompanying RNA-seq transcriptomes of shRNA knockdown of RBPs. These data could have utility in understanding the functional impact of genetic variants, however their potential has not been fully exploited. We implement INCA (Integrative annotation scores of variants for impact on RBP activities) as a multi-step genetic variant scoring approach that leverages the ENCODE RBP data together with ClinVar and integrates multiple computational approaches to aggregate evidence. RESULTS: INCA evaluates variant impacts on RBP activities by leveraging genotypic differences in cell lines used for eCLIP-seq. We show that INCA provides critical specificity, beyond generic scoring for RBP binding disruption, for candidate variants and their linkage-disequilibrium partners. As a result, it can, on average, augment scoring of 46.2% of the candidate variants beyond generic scoring for RBP binding disruption and aid in variant prioritization for follow-up analysis. AVAILABILITY AND IMPLEMENTATION: INCA is implemented in R and is available at https://github.com/keleslab/INCA.

Improvements in viral gene annotation using large language models and soft alignments.

BACKGROUND: The annotation of protein sequences in public databases has long posed a challenge in molecular biology. This issue is particularly acute for viral proteins, which demonstrate limited homology to known proteins when using alignment, k-mer, or profile-based homology search approaches. A novel methodology employing Large Language Models (LLMs) addresses this methodological challenge by annotating protein sequences based on embeddings. RESULTS: Central to our contribution is the soft alignment algorithm, drawing from traditional protein alignment but leveraging embedding similarity at the amino acid level to bypass the need for conventional scoring matrices. This method not only surpasses pooled embedding-based models in efficiency but also in interpretability, enabling users to easily trace homologous amino acids and delve deeper into the alignments. Far from being a black box, our approach provides transparent, BLAST-like alignment visualizations, combining traditional biological research with AI advancements to elevate protein annotation through embedding-based analysis while ensuring interpretability. Tests using the Virus Orthologous Groups and ViralZone protein databases indicated that the novel soft alignment approach recognized and annotated sequences that both blastp and pooling-based methods, which are commonly used for sequence annotation, failed to detect. CONCLUSION: The embeddings approach shows the great potential of LLMs for enhancing protein sequence annotation, especially in viral genomics. These findings present a promising avenue for more efficient and accurate protein function inference in molecular biology.

Enhanced bovine genome annotation through integration of transcriptomics and epi-transcriptomics datasets facilitates genomic biology.

BACKGROUND: The accurate identification of the functional elements in the bovine genome is a fundamental requirement for high-quality analysis of data informing both genome biology and genomic selection. Functional annotation of the bovine genome was performed to identify a more complete catalog of transcript isoforms across bovine tissues. RESULTS: A total of 160,820 unique transcripts (50% protein coding) representing 34,882 unique genes (60% protein coding) were identified across tissues. Among them, 118,563 transcripts (73% of the total) were structurally validated by independent datasets (PacBio isoform sequencing data, Oxford Nanopore Technologies sequencing data, de novo assembled transcripts from RNA sequencing data) and comparison with Ensembl and NCBI gene sets. In addition, all transcripts were supported by extensive data from different technologies such as whole transcriptome termini site sequencing, RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression, chromatin immunoprecipitation sequencing, and assay for transposase-accessible chromatin using sequencing. A large proportion of identified transcripts (69%) were unannotated, of which 86% were produced by annotated genes and 14% by unannotated genes. A median of two 5' untranslated regions were expressed per gene. Around 50% of protein-coding genes in each tissue were bifunctional and transcribed both coding and noncoding isoforms. Furthermore, we identified 3,744 genes that functioned as noncoding genes in fetal tissues but as protein-coding genes in adult tissues. Our new bovine genome annotation extended more than 11,000 annotated gene borders compared to Ensembl or NCBI annotations. The resulting bovine transcriptome was integrated with publicly available quantitative trait loci data to study tissue-tissue interconnection involved in different traits and construct the first bovine trait similarity network. CONCLUSIONS: These validated results show significant improvement over current bovine genome annotations.

KEGG orthology prediction of bacterial proteins using natural language processing.

BACKGROUND: The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, making it necessary to use auto annotation tools. These tools are now indispensable in the biological research landscape, bridging the gap between the vastness of unannotated sequences and meaningful biological insights. RESULTS: In this work, we propose a novel pipeline for KEGG orthology annotation of bacterial protein sequences that uses natural language processing and deep learning. To assess the effectiveness of our pipeline, we conducted evaluations using the genomes of two randomly selected species from the KEGG database. In our evaluation, we obtain competitive results on precision, recall, and F1 score, with values of 0.948, 0.947, and 0.947, respectively. CONCLUSIONS: Our experimental results suggest that our pipeline demonstrates performance comparable to traditional methods and excels in identifying distant relatives with low sequence identity. This demonstrates the potential of our pipeline to significantly improve the accuracy and comprehensiveness of KEGG orthology annotation, thereby advancing our understanding of functional relationships within biological systems.

The chromosome-level genome assembly of the giant dobsonfly Acanthacorydalis orientalis (McLachlan, 1899).

Acanthacorydalis orientalis (McLachlan, 1899) (Megaloptera: Corydalidae) is an important freshwater-benthic invertebrate species that serves as an indicator for water-quality biomonitoring and is valuable for conservation from East Asia. Here, a high-quality reference genome for A. orientalis was constructed using Oxford Nanopore sequencing and High throughput Chromosome Conformation Capture (Hi-C) technology. The final genome size is 547.98 Mb, with the N50 values of contig and scaffold being 7.77 Mb and 50.53 Mb, respectively. The longest contig and scaffold are 20.57 Mb and 62.26 Mb in length, respectively. There are 99.75% contigs anchored onto 13 pseudo-chromosomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the completeness of the genome assembly is 99.01%. There are 10,977 protein-coding genes identified, of which 84.00% are functionally annotated. The genome contains 44.86% repeat sequences. This high-quality genome provides substantial data for future studies on population genetics, aquatic adaptation, and evolution of Megaloptera and other related insect groups.

Chromosome-scale genome assembly and annotation of Cotoneaster glaucophyllus.

Cotoneaster glaucophyllus is a semi-evergreen plant that blossoms in late summer, producing dense, attractive, fragrant white flowers with significant ornamental and ecological value. Here, a chromosome-scale genome assembly was obtained by integrating PacBio and Illumina sequencing data with the aid of Hi-C technology. The genome assembly was 563.3 Mb in length, with contig N50 and scaffold N50 values of ~6 Mb and ~31 Mb, respectively. Most (95.59%) of the sequences were anchored onto 17 pseudochromosomes (538.4 Mb). We predicted 35,856 protein-coding genes, 1,401 miRNAs, 655 tRNAs, 425 rRNAs, and 795 snRNAs. The functions of 34,967 genes (97.52%) were predicted. The availability of this chromosome-level genome will provide valuable resources for molecular studies of this species, facilitating future research on speciation, functional genomics, and comparative genomics within the Rosaceae family.

PPanG: a precision pangenome browser enabling nucleotide-level analysis of genomic variations in individual genomes and their graph-based pangenome.

Graph-based pangenome is gaining more popularity than linear pangenome because it stores more comprehensive information of variations. However, traditional linear genome browser has its own advantages, especially the tremendous resources accumulated historically. With the fast-growing number of individual genomes and their annotations available, the demand for a genome browser to visualize genome annotation for many individuals together with a graph-based pangenome is getting higher and higher. Here we report a new pangenome browser PPanG, a precise pangenome browser enabling nucleotide-level comparison of individual genome annotations together with a graph-based pangenome. Nine rice genomes with annotations were provided by default as potential references, and any individual genome can be selected as the reference. Our pangenome browser provides unprecedented insights on genome variations at different levels from base to gene, and reveals how the structures of a gene could differ for individuals. PPanG can be applied to any species with multiple individual genomes available and it is available at https://cgm.sjtu.edu.cn/PPanG .