High-quality reference genome and annotation aids understanding of berry development for evergreen blueberry (Vaccinium darrowii)

Vaccinium darrowii Camp (2n = 2x = 24) is a native North American blueberry species and an important source of traits such as low chill requirement in commercial southern highbush blueberry breeding (Vaccinium corymbosum, 2n = 4x = 48). We present a chromosomal-scale genome of V. darrowii generated by the combination of PacBio sequencing and high throughput chromatin conformation capture (Hi–C) scaffolding technologies, yielding a total length of 1.06 Gigabases (Gb). Over 97.8% of the genome sequences are scaffolded into 24 chromosomes representing the two haplotypes. The primary haplotype assembly of V. darrowii contains 34,809 protein-coding genes. Comparison to a V. corymbosum haplotype assembly reveals high collinearity between the two genomes with small intrachromosomal rearrangements in eight chromosome pairs. With small RNA sequencing, the annotation was further expanded to include more than 200,000 small RNA loci and 638 microRNAs expressed in berry tissues. Transcriptome analysis across fruit development stages indicates that genes involved in photosynthesis are downregulated, while genes involved in flavonoid and anthocyanin biosynthesis are significantly increased at the late stage of berry ripening. A high-quality reference genome and accompanying annotation of V. darrowii is a significant new resource for assessing the evergreen blueberry contribution to the breeding of southern highbush blueberries.

A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model

Decreasing the cost of high-throughput DNA sequencing technologies, provides a huge amount of data that enables researchers to determine haplotypes for diploid and polyploid organisms. Although various methods have been developed to reconstruct haplotypes in diploid form, their accuracy is still a challenging task. Also, most of the current methods cannot be applied to polyploid form. In this paper, an iterative method is proposed, which employs hypergraph to reconstruct haplotype. The proposed method by utilizing chaotic viewpoint can enhance the obtained haplotypes. For this purpose, a haplotype set was randomly generated as an initial estimate, and its consistency with the input fragments was described by constructing a weighted hypergraph. Partitioning the hypergraph specifies those positions in the haplotype set that need to be corrected. This procedure is repeated until no further improvement could be achieved. Each element of the finalized haplotype set is mapped to a line by chaos game representation, and a coordinate series is defined based on the position of mapped points. Then, some positions with low qualities can be assessed by applying a local projection. Experimental results on both simulated and real datasets demonstrate that this method outperforms most other approaches, and is promising to perform the haplotype assembly.

A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data. High-throughput sequencing technologies generate enormous amounts of short fragments (reads) which essentially oversample components of a mixture; the representation redundancy enables reconstruction of the components (haplotypes, viral strains). The reconstruction problem, known to be NP-hard, boils down to grouping together reads originating from the same component in a mixture. Existing methods struggle to solve this problem with required level of accuracy and low runtimes; the problem is becoming increasingly more challenging as the number and length of the components increase. This paper proposes a read clustering method based on a convolutional auto-encoder designed to first project sequenced fragments to a low-dimensional space and then estimate the probability of the read origin using learned embedded features. The components are reconstructed by finding consensus sequences that agglomerate reads from the same origin. Mini-batch stochastic gradient descent and dimension reduction of reads allow the proposed method to efficiently deal with massive numbers of long reads. Experiments on simulated, semi-experimental and experimental data demonstrate the ability of the proposed method to accurately reconstruct haplotypes and viral quasispecies, often demonstrating superior performance compared to state-of-the-art methods.

ComHapDet: a spatial community detection algorithm for haplotype assembly

Background Haplotypes, the ordered lists of single nucleotide variations that distinguish chromosomal sequences from their homologous pairs, may reveal an individual’s susceptibility to hereditary and complex diseases and affect how our bodies respond to therapeutic drugs. Reconstructing haplotypes of an individual from short sequencing reads is an NP-hard problem that becomes even more challenging in the case of polyploids. While increasing lengths of sequencing reads and insert sizes helps improve accuracy of reconstruction, it also exacerbates computational complexity of the haplotype assembly task. This has motivated the pursuit of algorithmic frameworks capable of accurate yet efficient assembly of haplotypes from high-throughput sequencing data. Results We propose a novel graphical representation of sequencing reads and pose the haplotype assembly problem as an instance of community detection on a spatial random graph. To this end, we construct a graph where each read is a node with an unknown community label associating the read with the haplotype it samples. Haplotype reconstruction can then be thought of as a two-step procedure: first, one recovers the community labels on the nodes (i.e., the reads), and then uses the estimated labels to assemble the haplotypes. Based on this observation, we propose – a novel assembly algorithm for diploid and ployploid haplotypes which allows both bialleleic and multi-allelic variants. Conclusions Performance of the proposed algorithm is benchmarked on simulated as well as experimental data obtained by sequencing Chromosome 5 of tetraploid biallelic Solanum-Tuberosum (Potato). The results demonstrate the efficacy of the proposed method and that it compares favorably with the existing techniques.

Hap10: reconstructing accurate and long polyploid haplotypes using linked reads

BackgroundHaplotype information is essential for many genetic and genomic analyses, including genotype-phenotype associations in human, animals and plants. Haplotype assembly is a method for reconstructing haplotypes from DNA sequencing reads. By the advent of new sequencing technologies, new algorithms are needed to ensure long and accurate haplotypes. While a few linked-read haplotype assembly algorithms are available for diploid genomes, there are no algorithms yet for polyploids.ResultsThe first haplotyping algorithm designed for 10X linked reads generated from a polyploid genome is presented, built on a typical short-read haplotyping method, SDhaP. Using the input aligned reads and called variants, the haplotype-relevant information is extracted. Next, reads with the same barcodes are combined to produce molecule-specific fragments. Then, these fragments are clustered into strongly connected components which are then used as input of a haplotype assembly core in order to estimate accurate and long haplotypes.ConclusionsHap10 is a novel algorithm for haplotype assembly of polyploid genomes using linked reads. The performance of the algorithms is evaluated in a number of simulation scenarios and its applicability is demonstrated on a real dataset of sweet potato.

MAtCHap: an ultra fast algorithm for solving the single individual haplotype assembly problem

BackgroundHuman genomes are diploid, which means they have two homologous copies of each chromosome and the assignment of heterozygous variants to each chromosome copy, the haplotype assembly problem, is of fundamental importance for medical and population genetics.While short reads from second generation sequencing platforms drastically limit haplotype reconstruction as the great majority of reads do not allow to link many variants together, novel long reads from third generation sequencing can span several variants along the genome allowing to infer much longer haplotype blocks.However, the great majority of haplotype assembly algorithms, originally devised for short sequences, fail when they are applied to noisy long reads data, and although novel algorithm have been properly developed to deal with the properties of this new generation of sequences, these methods are capable to manage only datasets with limited coverages.ResultsTo overcome the limits of currently available algorithms, I propose a novel formulation of the single individual haplotype assembly problem, based on maximum allele co-occurrence (MAC) and I develop an ultra-fast algorithm that is capable to reconstruct the haplotype structure of a diploid genome from low- and high-coverage long read datasets with high accuracy. I test my algorithm (MAtCHap) on synthetic and real PacBio and Nanopore human dataset and I compare its result with other eight state-of-the-art algorithms. All the results obtained by these analyses show that MAtCHap outperforms other methods in terms of accuracy, contiguity, completeness and computational speed.AvailabilityMAtCHap is publicly available at https://sourceforge.net/projects/matchap/.