Optical map guided genome assembly

De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

Annotated genome and transcriptome of the endangered Caribbean mountainous star coral (Orbicella faveolata) using PacBio long-read sequencing

Long-read sequencing is revolutionizing de-novo genome assemblies, with continued advancements making it more readily available for previously understudied, non-model organisms. Stony corals are one such example, with long-read de-novo genome assemblies now starting to be publicly available, opening the door for a wide array of 'omics-based research. Here we present a new de-novo genome assembly for the endangered Caribbean star coral, Orbicella faveolata, using PacBio circular consensus reads. Our genome assembly improved the contiguity (51 versus 1,933 contigs) and complete and single copy BUSCO orthologs (93.6% versus 85.3%, database metazoa_odb10), compared to the currently available reference genome generated using short-read methodologies. Our new de-novo assembled genome also showed comparable quality metrics to other coral long-read genomes. Telomeric repeat analysis identified putative chromosomes in our scaffolded assembly, with these repeats at either one, or both ends, of scaffolded contigs. We identified 32,172 protein coding genes in our assembly through use of long-read RNA sequencing (ISO-seq) of additional O. faveolata fragments exposed to a range of abiotic and biotic treatments, and publicly available short-read RNA-seq data. With anthropogenic influences heavily affecting O. faveolata, as well as its increasing incorporation into reef restoration activities, this updated genome resource can be used for population genomics and other 'omics analyses to aid in the conservation of this species.

Contrasting new and available reference genomes to highlight uncertainties in assemblies and areas for future improvement: an example with monodontid species

BACKGROUND

Reference genomes provide a foundational framework for evolutionary investigations, ecological analysis, and conservation science, yet uncertainties in the assembly of reference genomes are difficult to assess, and by extension rarely quantified. Reference genomes for monodontid cetaceans span a wide spectrum of data types and analytical approaches, providing the context to derive broader insights related to discrepancies and regions of uncertainty in reference genome assembly. We generated three beluga (Delphinapterus leucas) and one narwhal (Monodon monoceros) reference genomes and contrasted these with published chromosomal scale assemblies for each species to quantify discrepancies associated with genome assemblies.

RESULTS

The new reference genomes achieved chromosomal scale assembly using a combination of PacBio long reads, Illumina short reads, and Hi-C scaffolding data. For beluga, we identified discrepancies in the order and orientation of contigs in 2.2-3.7% of the total genome depending on the pairwise comparison of references. In addition, unsupported higher order scaffolding was identified in published reference genomes. In contrast, we estimated 8.2% of the compared narwhal genomes featured discrepancies, with inversions being notably abundant (5.3%). Discrepancies were linked to repetitive elements in both species.

CONCLUSIONS

We provide several new reference genomes for beluga (Delphinapterus leucas), while highlighting potential avenues for improvements. In particular, additional layers of data providing information on ultra-long genomic distances are needed to resolve persistent errors in reference genome construction. The comparative analyses of monodontid reference genomes suggested that the three new reference genomes for beluga are more accurate compared to the currently published reference genome, but that the new narwhal genome is less accurate than one published. We also present a conceptual summary for improving the accuracy of reference genomes with relevance to end-user needs and how they relate to levels of assembly quality and uncertainty.

The first complete genome of the simian malaria parasite Plasmodium brasilianum

Naturally occurring human infections by zoonotic Plasmodium species have been documented for P. knowlesi, P. cynomolgi, P. simium, P. simiovale, P. inui, P. inui-like, P. coatneyi, and P. brasilianum. Accurate detection of each species is complicated by their morphological similarities with other Plasmodium species. PCR-based assays offer a solution but require prior knowledge of adequate genomic targets that can distinguish the species. While whole genomes have been published for P. knowlesi, P. cynomolgi, P. simium, and P. inui, no complete genome for P. brasilianum has been available. Previously, we reported a draft genome for P. brasilianum, and here we report the completed genome for P. brasilianum. The genome is 31.4 Mb in size and comprises 14 chromosomes, the mitochondrial genome, the apicoplast genome, and 29 unplaced contigs. The chromosomes consist of 98.4% nucleotide sites that are identical to the P. malariae genome, the closest evolutionarily related species hypothesized to be the same species as P. brasilianum, with 41,125 non-synonymous SNPs (0.0722% of genome) identified between the two genomes. Furthermore, P. brasilianum had 4864 (82.1%) genes that share 80% or higher sequence similarity with 4970 (75.5%) P. malariae genes. This was demonstrated by the nearly identical genomic organization and multiple sequence alignments for the merozoite surface proteins msp3 and msp7. We observed a distinction in the repeat lengths of the circumsporozoite protein (CSP) gene sequences between P. brasilianum and P. malariae. Our results demonstrate a 97.3% pairwise identity between the P. brasilianum and the P. malariae genomes. These findings highlight the phylogenetic proximity of these two species, suggesting that P. malariae and P. brasilianum are strains of the same species, but this could not be fully evaluated with only a single genomic sequence for each species.

Evaluating Illumina-, Nanopore-, and PacBio-based genome assembly strategies with the bald notothen, Trematomus borchgrevinki

For any genome-based research, a robust genome assembly is required. De novo assembly strategies have evolved with changes in DNA sequencing technologies and have been through at least 3 phases: (1) short-read only, (2) short- and long-read hybrid, and (3) long-read only assemblies. Each of the phases has its own error model. We hypothesized that hidden short-read scaffolding errors and erroneous long-read contigs degrade the quality of short- and long-read hybrid assemblies. We assembled the genome of Trematomus borchgrevinki from data generated during each of the 3 phases and assessed the quality problems we encountered. We developed strategies such as k-mer-assembled region replacement, parameter optimization, and long-read sampling to address the error models. We demonstrated that a k-mer-based strategy improved short-read assemblies as measured by Benchmarking Universal Single-Copy Ortholog while mate-pair libraries introduced hidden scaffolding errors and perturbed Benchmarking Universal Single-Copy Ortholog scores. Furthermore, we found that although hybrid assemblies can generate higher contiguity they tend to suffer from lower quality. In addition, we found long-read-only assemblies can be optimized for contiguity by subsampling length-restricted raw reads. Our results indicate that long-read contig assembly is the current best choice and that assemblies from phase I and phase II were of lower quality.

HGGA: hierarchical guided genome assembler

BACKGROUND

De novo genome assembly typically produces a set of contigs instead of the complete genome. Thus additional data such as genetic linkage maps, optical maps, or Hi-C data is needed to resolve the complete structure of the genome. Most of the previous work uses the additional data to order and orient contigs.

RESULTS

Here we introduce a framework to guide genome assembly with additional data. Our approach is based on clustering the reads, such that each read in each cluster originates from nearby positions in the genome according to the additional data. These sets are then assembled independently and the resulting contigs are further assembled in a hierarchical manner. We implemented our approach for genetic linkage maps in a tool called HGGA.

CONCLUSIONS

Our experiments on simulated and real Pacific Biosciences long reads and genetic linkage maps show that HGGA produces a more contiguous assembly with less contigs and from 1.2 to 9.8 times higher NGA50 or N50 than a plain assembly of the reads and 1.03 to 6.5 times higher NGA50 or N50 than a previous approach integrating genetic linkage maps with contig assembly. Furthermore, also the correctness of the assembly remains similar or improves as compared to an assembly using only the read data.

An Overview of the Genetics and Genomics of the Urochloa Species Most Commonly Used in Pastures

Pastures based on perennial monocotyledonous plants are the principal source of nutrition for ruminant livestock in tropical and subtropical areas across the globe. The Urochloa genus comprises important species used in pastures, and these mainly include Urochloa brizantha, Urochloa decumbens, Urochloa humidicola, and Urochloa ruziziensis. Despite their economic relevance, there is an absence of genomic-level information for these species, and this lack is mainly due to genomic complexity, including polyploidy, high heterozygosity, and genomes with a high repeat content, which hinders advances in molecular approaches to genetic improvement. Next-generation sequencing techniques have enabled the recent release of reference genomes, genetic linkage maps, and transcriptome sequences, and this information helps improve our understanding of the genetic architecture and molecular mechanisms involved in relevant traits, such as the apomictic reproductive mode. However, more concerted research efforts are still needed to characterize germplasm resources and identify molecular markers and genes associated with target traits. In addition, the implementation of genomic selection and gene editing is needed to reduce the breeding time and expenditure. In this review, we highlight the importance and characteristics of the four main species of Urochloa used in pastures and discuss the current findings from genetic and genomic studies and research gaps that should be addressed in future research.

Finding Overlapping Rmaps via Clustering

Optical mapping has been largely automated, and first produces single molecule restriction maps, called Rmaps, which are assembled to generate genome wide optical maps. Since the location and orientation of each Rmap is unknown, the first problem in the analysis of this data is finding related Rmaps, i.e., pairs of Rmaps that share the same orientation and have significant overlap in their genomic location. Although heuristics for identifying related Rmaps exist, they all require quantization of the data which leads to a loss in the precision. In this paper, we propose a Gaussian mixture modelling clustering based method, which we refer to as O, that finds overlapping Rmaps without quantization. Using both simulated and real datasets, we show that OMclust substantially improves the precision (from 48.3% to 73.3%) over the state-of-the art methods while also reducing CPU time and memory consumption. Further, we integrated OMclust into the error correction methods (Elmeri and Comet) to demonstrate the increase in the performance of these methods. When OMclust was combined with Comet to error correct Rmap data generated from human DNA, it was able to error correct close to 3x more Ramps, and reduced the CPU time by more than 35x.

FaNDOM: Fast nested distance-based seeding of optical maps

Optical mapping (OM) provides single-molecule readouts of fluorescently labeled sequence motifs on long fragments of DNA, resolved to nucleotide-level coordinates. With the advent of microfluidic technologies for analysis of DNA molecules, it is possible to inexpensively generate long OM data ( > 150 kbp) at high coverage. In addition to scaffolding for de novo assembly, OM data can be aligned to a reference genome for identification of genomic structural variants. We introduce FaNDOM (Fast Nested Distance Seeding of Optical Maps)-an optical map alignment tool that greatly reduces the search space of the alignment process. On four benchmark human datasets, FaNDOM was significantly (4-14×) faster than competing tools while maintaining comparable sensitivity and specificity. We used FaNDOM to map variants in three cancer cell lines and identified many biologically interesting structural variants, including deletions, duplications, gene fusions and gene-disrupting rearrangements. FaNDOM is publicly available at https://github.com/jluebeck/FaNDOM.