LASER: Large genome ASsembly EvaluatoR

But where did the centromeres go in the chicken genome models?

The chicken genome was the third vertebrate to be sequenced. To date, its sequence and feature annotations are used as the reference for avian models in genome sequencing projects developed on birds and other Sauropsida species, and in genetic studies of domesticated birds of economic and evolutionary biology interest. Therefore, an accurate description of this genome model is important to a wide number of scientists. Here, we review the location and features of a very basic element, the centromeres of chromosomes in the galGal5 genome model. Centromeres are elements that are not determined by their DNA sequence but by their epigenetic status, in particular by the accumulation of the histone-like protein CENP-A. Comparison of data from several public sources (primarily marker probes flanking centromeres using fluorescent in situ hybridization done on giant lampbrush chromosomes and CENP-A ChIP-seq datasets) with galGal5 annotations revealed that centromeres are likely inappropriately mapped in 9 of the 16 galGal5 chromosome models in which they are described. Analysis of karyology data confirmed that the location of the main CENP-A peaks in chromosomes is the best means of locating the centromeres in 25 galGal5 chromosome models, the majority of which (16) are fully sequenced and assembled. This data re-analysis reaffirms that several sources of information should be examined to produce accurate genome annotations, particularly for basic structures such as centromeres that are epigenetically determined.

Genomic repeats, misassembly and reannotation: a case study with long-read resequencing of Porphyromonas gingivalis reference strains

BACKGROUND

Without knowledge of their genomic sequences, it is impossible to make functional models of the bacteria that make up human and animal microbiota. Unfortunately, the vast majority of publicly available genomes are only working drafts, an incompleteness that causes numerous problems and constitutes a major obstacle to genotypic and phenotypic interpretation. In this work, we began with an example from the class Bacteroidia in the phylum Bacteroidetes, which is preponderant among human orodigestive microbiota. We successfully identify the genetic loci responsible for assembly breaks and misassemblies and demonstrate the importance and usefulness of long-read sequencing and curated reannotation.

RESULTS

We showed that the fragmentation in Bacteroidia draft genomes assembled from massively parallel sequencing linearly correlates with genomic repeats of the same or greater size than the reads. We also demonstrated that some of these repeats, especially the long ones, correspond to misassembled loci in three reference Porphyromonas gingivalis genomes marked as circularized (thus complete or finished). We prove that even at modest coverage (30X), long-read resequencing together with PCR contiguity verification (rrn operons and an integrative and conjugative element or ICE) can be used to identify and correct the wrongly combined or assembled regions. Finally, although time-consuming and labor-intensive, consistent manual biocuration of three P. gingivalis strains allowed us to compare and correct the existing genomic annotations, resulting in a more accurate interpretation of the genomic differences among these strains.

CONCLUSIONS

In this study, we demonstrate the usefulness and importance of long-read sequencing in verifying published genomes (even when complete) and generating assemblies for new bacterial strains/species with high genomic plasticity. We also show that when combined with biological validation processes and diligent biocurated annotation, this strategy helps reduce the propagation of errors in shared databases, thus limiting false conclusions based on incomplete or misleading information.

HISEA: HIerarchical SEed Aligner for PacBio data

Background

The next generation sequencing (NGS) techniques have been around for over a decade. Many of their fundamental applications rely on the ability to compute good genome assemblies. As the technology evolves, the assembly algorithms and tools have to continuously adjust and improve. The currently dominant technology of Illumina produces reads that are too short to bridge many repeats, setting limits on what can be successfully assembled. The emerging SMRT (Single Molecule, Real-Time) sequencing technique from Pacific Biosciences produces uniform coverage and long reads of length up to sixty thousand base pairs, enabling significantly better genome assemblies. However, SMRT reads are much more expensive and have a much higher error rate than Illumina’s – around 10-15% – mostly due to indels. New algorithms are very much needed to take advantage of the long reads while mitigating the effect of high error rate and lowering the required coverage.

Methods

An essential step in assembling SMRT data is the detection of alignments, or overlaps, between reads. High error rate and very long reads make this a much more challenging problem than for Illumina data. We present a new pairwise read aligner, or overlapper, HISEA (Hierarchical SEed Aligner) for SMRT sequencing data. HISEA uses a novel two-step k-mer search, employing consistent clustering, k-mer filtering, and read alignment extension.

Results

We compare HISEA against several state-of-the-art programs – BLASR, DALIGNER, GraphMap, MHAP, and Minimap – on real datasets from five organisms. We compare their sensitivity, precision, specificity, F1-score, as well as time and memory usage. We also introduce a new, more precise, evaluation method. Finally, we compare the two leading programs, MHAP and HISEA, for their genome assembly performance in the Canu pipeline.

Discussion

Our algorithm has the best alignment detection sensitivity among all programs for SMRT data, significantly higher than the current best. The currently best assembler for SMRT data is the Canu program which uses the MHAP aligner in its pipeline. We have incorporated our new HISEA aligner in the Canu pipeline and benchmarked it against the best pipeline for multiple datasets at two relevant coverage levels: 30x and 50x. Our assemblies are better than those using MHAP for both coverage levels. Moreover, Canu+HISEA assemblies for 30x coverage are comparable with Canu+MHAP assemblies for 50x coverage, while being faster and cheaper.

Conclusions

The HISEA algorithm produces alignments with highest sensitivity compared with the current state-of-the-art algorithms. Integrated in the Canu pipeline, currently the best for assembling PacBio data, it produces better assemblies than Canu+MHAP.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1953-9) contains supplementary material, which is available to authorized users.