Optimizing Transcriptome Assemblies for Eleusine indica Leaf and Seedling by Combining Multiple Assemblies from Three De Novo Assemblers.

Due to rapid advances in sequencing technology, increasing amounts of genomic and transcriptomic data are available for plant species, presenting enormous challenges for biocomputing analysis. A crucial first step for a successful transcriptomics-based study is the building of a high-quality assembly. Here, we utilized three different de novo assemblers (Trinity, Velvet, and CLC) and the EvidentialGene pipeline tr2aacds to assemble two optimized transcript sets for the notorious weed species, Eleusine indica. Two RNA sequencing (RNA-seq) datasets from leaf and aboveground seedlings were processed using three assemblers, which resulted in 20 assemblies for each dataset. The contig numbers and N50 values of each assembly were compared to study the effect of read number, k-mer size, and in silico normalization on assembly output. The 20 assemblies were then processed through the tr2aacds pipeline to remove redundant transcripts and to select the transcript set with the best coding potential. Each assembly contributed a considerable proportion to the final transcript combination with the exception of the CLC-k14. Thus each assembler and parameter set did assemble better contigs for certain transcripts. The redundancy, total contig number, N50, fully assembled contig number, and transcripts related to target-site herbicide resistance were evaluated for the EvidentialGene and Trinity assemblies. Comparing the EvidentialGene set with the Trinity assembly revealed improved quality and reduced redundancy in both leaf and seedling EvidentialGene sets. The optimized transcriptome references will be useful for studying herbicide resistance in E. indica and the evolutionary process in the three allotetraploid E. indica offspring.

Chen S ，McElroy JS ，Dane F ，Peatman E ... -  《Plant Genome》

Combining transcriptome assemblies from multiple de novo assemblers in the allo-tetraploid plant Nicotiana benthamiana.

Nakasugi K ，Crowhurst R ，Bally J ，Waterhouse P ... -  《PLoS One》

Optimizing an efficient ensemble approach for high-quality de novo transcriptome assembly of Thymus daenensis.

Non-erroneous and well-optimized transcriptome assembly is a crucial prerequisite for authentic downstream analyses. Each de novo assembler has its own algorithm-dependent pros and cons to handle the assembly issues and should be specifically tested for each dataset. Here, we examined efficiency of seven state-of-art assemblers on ~ 30 Gb data obtained from mRNA-sequencing of Thymus daenensis. In an ensemble workflow, combining the outputs of different assemblers associated with an additional redundancy-reducing step could generate an optimized outcome in terms of completeness, annotatability, and ORF richness. Based on the normalized scores of 16 benchmarking metrics, EvidentialGene, BinPacker, Trinity, rnaSPAdes, CAP3, IDBA-trans, and Velvet-Oases performed better, respectively. EvidentialGene, as the best assembler, totally produced 316,786 transcripts, of which 235,730 (74%) were predicted to have a unique protein hit (on uniref100), and also half of its transcripts contained an ORF. The total number of unique BLAST hits for EvidentialGene was approximately three times greater than that of the worst assembler (Velvet-Oases). EvidentialGene could even capture 17% and 7% more average BLAST hits than BinPacker and Trinity. Although BinPacker and CAP3 produced longer transcripts, the EvidentialGene showed a higher collinearity between transcript size and ORF length. Compared with the other programs, EvidentialGene yielded a higher number of optimal transcript sets, further full-length transcripts, and lower possible misassemblies. Our finding corroborates that in non-model species, relying on a single assembler may not give an entirely satisfactory result. Therefore, this study proposes an ensemble approach of accompanying EvidentialGene pipelines to acquire a superior assembly for T. daenensis.

Ahmadi H ，Sheikh-Assadi M ，Fatahi R ，Zamani Z ，Shokrpour M ... -  《Scientific Reports》

Fragmentation and Coverage Variation in Viral Metagenome Assemblies, and Their Effect in Diversity Calculations.

Metagenomic libraries consist of DNA fragments from diverse species, with varying genome size and abundance. High-throughput sequencing platforms produce large volumes of reads from these libraries, which may be assembled into contigs, ideally resembling the original larger genomic sequences. The uneven species distribution, along with the stochasticity in sample processing and sequencing bias, impacts the success of accurate sequence assembly. Several assemblers enable the processing of viral metagenomic data de novo, generally using overlap layout consensus or de Bruijn graph approaches for contig assembly. The success of viral genomic reconstruction in these datasets is limited by the degree of fragmentation of each genome in the sample, which is dependent on the sequencing effort and the genome length. Depending on ecological, biological, or procedural biases, some fragments have a higher prevalence, or coverage, in the assembly. However, assemblers must face challenges, such as the formation of chimerical structures and intra-species variability. Diversity calculation relies on the classification of the sequences that comprise a metagenomic dataset. Whenever the corresponding genomic and taxonomic information is available, contigs matching the same species can be classified accordingly and the coverage of its genome can be calculated for that species. This may be used to compare populations by estimating abundance and assessing species distribution from this data. Nevertheless, the coverage does not take into account the degree of fragmentation, or else genome completeness, and is not necessarily representative of actual species distribution in the samples. Furthermore, undetermined sequences are abundant in viral metagenomic datasets, resulting in several independent contigs that cannot be assigned by homology or genomic information. These may only be classified as different operational taxonomic units (OTUs), sometimes remaining inadvisably unrelated. Thus, calculations using contigs as different OTUs ultimately overestimate diversity when compared to diversity calculated from species coverage. In order to compare the effect of coverage and fragmentation, we generated three sets of simulated Illumina paired-end reads with different sequencing depths. We compared different assemblies performed with RayMeta, CLC Assembly Cell, MEGAHIT, SPAdes, Meta-IDBA, SOAPdenovo, Velvet, Metavelvet, and MIRA with the best attainable assemblies for each dataset (formed by arranging data using known genome coordinates) by calculating different assembly statistics. A new fragmentation score was included to estimate the degree of genome fragmentation of each taxon and adjust the coverage accordingly. The abundance in the metagenome was compared by bootstrapping the assembly data and hierarchically clustering them with the best possible assembly. Additionally, richness and diversity indexes were calculated for all the resulting assemblies and were assessed under two distributions: contigs as independent OTUs and sequences classified by species. Finally, we search for the strongest correlations between the diversity indexes and the different assembly statistics. Although fragmentation was dependent of genome coverage, it was not as heavily influenced by the assembler. The sequencing depth was the predominant attractor that influenced the success of the assemblies. The coverage increased notoriously in larger datasets, whereas fragmentation values remained lower and unsaturated. While still far from obtaining the ideal assemblies, the RayMeta, SPAdes, and the CLC assemblers managed to build the most accurate contigs with larger datasets while Meta-IDBA showed a good performance with the medium-sized dataset, even after the adjusted coverage was calculated. Their resulting assemblies showed the highest coverage scores and the lowest fragmentation values. Alpha diversity calculated from contigs as OTUs resulted in significantly higher values for all assemblies when compared with actual species distribution, showing an overestimation due to the increased predicted abundance. Conversely, using PHACCS resulted in lower values for all assemblers. Different association methods (random-forest, generalized linear models, and the Spearman correlation index) support the number of contigs, the coverage, and fragmentation as the assembly parameters that most affect the estimation of the alpha diversity. Coverage calculations may provide an insight into relative completeness of a genome but they overlook missing fragments or overly separated sequences in a genome. The assembly of a highly fragmented genomes with high coverage may still lead to the clustering of different OTUs that are actually different fragments of a genome. Thus, it proves useful to penalize coverage with a fragmentation score. Using contigs for calculating alpha diversity result in overestimation but it is usually the only approach available. Still, it is enough for sample comparison. The best approach may be determined by choosing the assembler that better fits the sequencing depth and adjusting the parameters for longer accurate contigs whenever possible whereas diversity may be calculated considering taxonomical and genomic information if available.

García-López R ，Vázquez-Castellanos JF ，Moya A 《Frontiers in Bioengineering and Biotechnology》

Combining independent de novo assemblies to optimize leaf transcriptome of Persian walnut.

Sadat-Hosseini M ，Bakhtiarizadeh MR ，Boroomand N ，Tohidfar M ，Vahdati K ... -  《PLoS One》