A reference-quality, fully annotated genome from a Puerto Rican individual

Until 2019, the human genome was available in only one fully-annotated version, GRCh38, which was the result of 18 years of continuous improvement and revision. Despite dramatic improvements in sequencing technology, no other genome was available as an annotated reference until 2019, when the genome of an Ashkenazi individual, Ash1, was released. In this study, we describe the assembly and annotation of a second individual genome, from a Puerto Rican individual whose DNA was collected as part of the Human Pangenome project. The new genome, called PR1, is the first true reference genome created from an individual of African descent. Due to recent improvements in both sequencing and assembly technology, and particularly to the use of the recently completed CHM13 human genome as a guide to assembly, PR1 is more complete and more contiguous than either GRCh38 or Ash1. Annotation revealed 37,755 genes (of which 19,999 are protein-coding), including 12 additional gene copies that are present in PR1 and missing from CHM13. 57 genes have fewer copies in PR1 than in CHM13, 9 map only partially, and 3 genes (all non-coding) from CHM13 are entirely missing from PR1.

High-efficiency CRISPR gene editing in C. elegans using Cas9 integrated into the genome

Gene editing in C. elegans using plasmid-based CRISPR reagents requires microinjection of many animals to produce a single edit. Germline silencing of plasmid-borne Cas9 is a major cause of inefficient editing. Here, we present a set of C. elegans strains that constitutively express Cas9 in the germline from an integrated transgene. These strains markedly improve the success rate for plasmid-based CRISPR edits. For simple, short homology arm GFP insertions, 50–100% of injected animals typically produce edited progeny, depending on the target locus. Template-guided editing from an extrachromosomal array is maintained over multiple generations. We have built strains with the Cas9 transgene on multiple chromosomes. Additionally, each Cas9 locus also contains a heatshock-driven Cre recombinase for selectable marker removal and a bright fluorescence marker for easy outcrossing. These integrated Cas9 strains greatly reduce the workload for producing individual genome edits.

Identification of the Genome Segments of Bluetongue Virus Type 26/Type 1 Reassortants Influencing Horizontal Transmission in a Mouse Model

Bluetongue virus serotypes 1 to 24 are transmitted primarily by infected Culicoides midges, in which they also replicate. However, “atypical” BTV serotypes (BTV-25, -26, -27 and -28) have recently been identified that do not infect and replicate in adult Culicoides, or a Culicoides derived cell line (KC cells). These atypical viruses are transmitted horizontally by direct contact between infected and susceptible hosts (primarily small ruminants) causing only mild clinical signs, although the exact transmission mechanisms involved have yet to be determined. We used reverse genetics to generate a strain of BTV-1 (BTV-1 RGC7) which is less virulent, infecting IFNAR(−/−) mice without killing them. Reassortant viruses were also engineered, using the BTV-1 RGC7 genetic backbone, containing individual genome segments derived from BTV-26. These reassortant viruses were used to explore the genetic control of horizontal transmission (HT) in the IFNAR(−/−) mouse model. Previous studies showed that genome segments 1, 2 and 3 restrict infection of Culicoides cells, along with a minor role for segment 7. The current study demonstrates that genome segments 2, 5 and 10 of BTV-26 (coding for proteins VP2, NS1 and NS3/NS3a/NS5, respectively) are individually sufficient to promote HT.

A consensus-based ensemble approach to improve transcriptome assembly

Background Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. Results In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. Conclusions Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.

Detection of F1 hybrids from single-genome data reveals frequent hybridization in Hymenoptera and particularly ants

Hybridization occupies a central role in many fundamental evolutionary processes, such as speciation or adaptation. Yet, despite its pivotal importance in evolution, little is known about the actual prevalence and distribution of hybridization across the tree of life. Here we develop and implement a new statistical method enabling the detection of F1 hybrids from single-individual genome sequencing data. Using simulations and sequencing data from known hybrid systems, we first demonstrate the specificity of the method, and identify its statistical limits. Next, we showcase the method by applying it to available sequencing data from more than 1500 species of Arthropods, including Hymenoptera, Hemiptera, Coleoptera, Diptera and Archnida. Among these taxa, we find Hymenoptera, and especially ants, to display the highest number of candidate F1 hybrids, suggesting higher rates of recent hybridization in these groups. The prevalence of F1 hybrids was heterogeneously distributed across ants, with taxa including many candidates tending to harbor specific ecological and life history traits. This work shows how large-scale genomic comparative studies of recent hybridization can be implemented, uncovering the determinants of hybridization frequency across whole taxa.

nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

High-efficiency CRISPR gene editing in C. elegans using Cas9 integrated into the genome

Gene editing in C. elegans using plasmid-based CRISPR reagents requires microinjection of many animals to produce a single edit. Germline silencing of plasmid-borne Cas9 is a major cause of inefficient editing. Here, we present a set of C. elegans strains that constitutively express Cas9 in the germline from an integrated transgene. These strains markedly improve the success rate for plasmid-based CRISPR edits. For simple GFP insertions, 60 – 100% of injected animals typically produce edited progeny, depending on the target locus. Template-guided editing from an extrachromosomal array is maintained over multiple generations. We have built strains with the Cas9 transgene on multiple chromosomes. Additionally, each Cas9 locus also contains a heatshock-driven Cre recombinase for selectable marker removal and a bright fluorescence marker for easy outcrossing. These integrated Cas9 strains greatly reduce the workload for producing individual genome edits.

Privacy Concerns regarding Open Medical Data (Preprint)

UNSTRUCTURED With next-generation sequencing (NGS), it is now feasible to sequence large amounts of DNA and publish comprehensive individual genome-phenome data sets for genome-wide association studies. The Personal Genome Project (PGP) is a collaborative research initiative that aims to sequence and publicize the complete genomes and medical records of 100,000 volunteers, in order to enable NGS into personal genomics and personalized medicine. The PGP puts genome data in an open medical database on the Internet to promote studies by many researchers and citizen scientists. However, open medical data pose significant privacy and ethical issues because it can convey identifiable and predictive information concerning particular individuals and their family members and relatives. The purpose of this paper is to identify the most important privacy and security risks of open medical data and explain how to use data protection and privacy policies to handle the risks. This paper begins by detailing the growth of genetic testing and the characteristics of genomic data. We then present the case study of the PGP’s open medical data and open consent model and highlight how open medical data could raise important questions regarding privacy and confidentiality of genomic and personal data. We further propose privacy policy considerations to resolve these challenging privacy concerns. We conclude with a discussion of the implications of the study and directions of future research.

A reference-quality, fully annotated genome from a Puerto Rican individual

Until 2019, the human genome was available in only one fully-annotated version, which was the result of 18 years of continuous improvement and revision. Despite dramatic improvements in sequencing technology, no other individual human genome was available as an annotated reference until 2019, when the genome of an Ashkenazi individual was released. In this study, we describe the assembly and annotation of a second individual genome, from a Puerto Rican individual whose DNA was collected as part of the Human Pangenome project. The new genome, called PR1, is the first true reference genome created from an individual of African descent. Due to recent improvements in both sequencing and assembly technology, PR1 is more complete and more contiguous than either the human reference genome (GRCh38) or the Ashkenazi genome. Annotation revealed 42,217 genes (of which 20,168 are protein-coding), including 107 additional gene copies that are present in PR1 and missing from GRCh38. 180 genes have fewer copies in PR1 than in GRCh38, 13 map only partially, and 3 genes (1 protein-coding) from GRCh38 are entirely missing from PR1.