Using Growing Self-Organising Maps to Improve the Binning Process in Environmental Whole-Genome Shotgun Sequencing

Metagenomic projects using whole-genome shotgun (WGS) sequencing produces many unassembled DNA sequences and small contigs. The step of clustering these sequences, based on biological and molecular features, is called binning. A reported strategy for binning that combines oligonucleotide frequency and self-organising maps (SOM) shows high potential. We improve this strategy by identifying suitable training features, implementing a better clustering algorithm, and defining quantitative measures for assessing results. We investigated the suitability of each of di-, tri-, tetra-, and pentanucleotide frequencies. The results show that dinucleotide frequency is not a sufficiently strong signature for binning 10 kb long DNA sequences, compared to the other three. Furthermore, we observed that increased order of oligonucleotide frequency may deteriorate the assignment result in some cases, which indicates the possible existence of optimal species-specific oligonucleotide frequency. We replaced SOM with growing self-organising map (GSOM) where comparable results are obtained while gaining 7%–15% speed improvement

Binning sequences using very sparse labels within a metagenome

<p>Abstract</p> <p>Background</p> <p>In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, <it>k</it>-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels) to assign other reads based on their compositional similarity.</p> <p>Results</p> <p>The proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM), and called Seeded GSOM (S-GSOM). We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments sampled from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds.</p> <p>The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, <it>k</it>-mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all <it>k</it>-mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the ≥ 10 reads datasets and comparable in the ≥ 8 kb benchmark tests.</p> <p>Conclusion</p> <p>In the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of the methods tested. Most importantly, the proposed method does not require knowledge from known genomes and uses only very few labels (one per species is sufficient in most cases), which are extracted from the metagenome itself. These advantages make it a very attractive binning method. S-GSOM outperformed the binning methods that depend on already-sequenced genomes, and compares well to the current most advanced binning method, PhyloPythia.</p

The pangenome of hexaploid bread wheat

There is an increasing understanding that variation in gene presence–absence plays an important role in the heritability of agronomic traits; however, there have been relatively few studies on variation in gene pres- ence–absence in crop species. Hexaploid wheat is one of the most important food crops in the world and intensive breeding has reduced the genetic diversity of elite cultivars. Major efforts have produced draft genome assemblies for the cultivar Chinese Spring, but it is unknown how well this represents the genome diversity found in current modern elite cultivars. In this study we build an improved reference for Chinese Spring and explore gene diversity across 18 wheat cultivars. We predict a pangenome size of 140 500 102 genes, a core genome of 81 070 1631 genes and an average of 128 656 genes in each cultivar. Functional annotation of the variable gene set suggests that it is enriched for genes that may be associated with important agronomic traits. In addition to variation in gene presence, more than 36 million intervarietal sin- gle nucleotide polymorphisms were identified across the pangenome. This study of the wheat pangenome provides insight into genome diversity in elite wheat as a basis for genomics-based improvement of this important crop. A wheat pangenome, GBrowse, is available at http://appliedbioinformatics.com.au/cgi-bin/ gb2/gbrowse/WheatPan/, and data are available to download from http://wheatgenome.info/wheat_ge nome_databases.php

Assembly and comparison of two closely related Brassica napus genomes

As an increasing number of plant genome sequences become available, it is clear that gene content varies between individuals, and the challenge arises to predict the gene content of a species. However, genome comparison is often confounded by variation in assembly and annotation. Differentiating between true gene absence and variation in assembly or annotation is essential for the accurate identification of conserved and variable genes in a species. Here, we present the de novo assembly of the B. napus cultivar Tapidor and comparison with an improved assembly of the Brassica napus cultivar Darmor-bzh. Both cultivars were annotated using the same method to allow comparison of gene content. We identified genes unique to each cultivar and differentiate these from artefacts due to variation in the assembly and annotation. We demonstrate that using a common annotation pipeline can result in different gene predictions, even for closely related cultivars, and repeat regions which collapse during assembly impact whole genome comparison. After accounting for differences in assembly and annotation, we demonstrate that the genome of Darmor-bzh contains a greater number of genes than the genome of Tapidor. Our results are the first step towards comparison of the true differences between B. napus genomes and highlight the potential sources of error in future production of a B. napus pangenome

An efficient approach to BAC based assembly of complex genomes

Background: There has been an exponential growth in the number of genome sequencing projects since the introduction of next generation DNA sequencing technologies. Genome projects have increasingly involved assembly of whole genome data which produces inferior assemblies compared to traditional Sanger sequencing of genomic fragments cloned into bacterial artificial chromosomes (BACs). While whole genome shotgun sequencing using next generation sequencing (NGS) is relatively fast and inexpensive, this method is extremely challenging for highly complex genomes, where polyploidy or high repeat content confounds accurate assembly, or where a highly accurate ‘gold’ reference is required. Several attempts have been made to improve genome sequencing approaches by incorporating NGS methods, to variable success. Results: We present the application of a novel BAC sequencing approach which combines indexed pools of BACs, Illumina paired read sequencing, a sequence assembler specifically designed for complex BAC assembly, and a custom bioinformatics pipeline. We demonstrate this method by sequencing and assembling BAC cloned fragments from bread wheat and sugarcane genomes. Conclusions: We demonstrate that our assembly approach is accurate, robust, cost effective and scalable, with applications for complete genome sequencing in large and complex genomes

Investigation of average mutual information for species separation using GSOM

The average mutual information (AMI) has been claimed to be a strong genome signature in some literatures. The range of k values is an important parameter in AMI but no standard range of k value is yet proposed. We introduce a new growth threshold (GT) equation in Growing Self-Organising Maps (GSOM) to identify the best k range for clustering prokaryotic sequence fragments of 10 kb. However, the results using the best k range of AMI were still worse than our previously published results using oligonucleotide frequencies. These experiments showed that the newly proposed GT equation makes GSOM able to efficiently and effectively analyse different data features for the same data

A new generalized growth threshold for dynamic SOM for comparing average mutual information and oligonucleotide frequency as a species signature

The average mutual information (AMI) known from information theory has been reported as a strong genome signature in some literature and we have reported the use of oligonucleotide frequencies as a genome signature. In this work we improve the use of AMI as a training feature for Growing Self Organising Maps (GSOM). Although the range of k is considered as an important parameter in AMI, no standard range for k is proposed. Our first contribution is to introduce a new growth threshold (GT) for GSOM and use it to identify the best range of k for clustering prokaryotic sequence fragments of 10 kb. We then, compare the results using the best k range of AMI against our previously published results using oligonucleotide frequencies. These experiments showed that the newly proposed GT equation makes GSOM able to efficiently and effectively analyse different data features for the same data. The results also emphasize our use of oligonucleotide frequencies as opposed to AMI