k mer

Motivation: De novo transcriptome assembly is an integral part for many RNA-seq workflows. Common applications include sequencing of non-model organisms, cancer or meta transcriptomes. Most de novo transcriptome assemblers use the de Bruijn graph (DBG) as the underlying data structure. The quality of the assemblies produced by such assemblers is highly influenced by the exact word length k. As such no single kmer value leads to optimal results. Instead, DBGs over different kmer values are built and the assemblies are merged to improve sensitivity. However, no studies have investigated thoroughly the problem of automatically learning at which kmer value to stop the assembly. Instead a suboptimal selection of kmer values is often used in practice. Results: Here we investigate the contribution of a single kmer value in a multi-kmer based assembly approach. We find that a comparative clustering of related assemblies can be used to estimate the importance of an additional kmer assembly. Using a model fit based algorithm we predict the kmer value at which no further assemblies are necessary. Our approach is tested with different de novo assemblers for datasets with different coverage values and read lengths. Further, we suggest a simple post processing step that significantly improves the quality of multi-kmer assemblies. Conclusion: We provide an automatic method for limiting the number of kmer values without a significant loss in assembly quality but with savings in assembly time. This is a step forward to making multi-kmer methods more reliable and easier to use. Availability and Implementation:A general implementation of our approach can be found under: https://github.com/SchulzLab/KREATION. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected]

Transcriptome Analysis for Non-Model Organism: Current Status and Best-Practices

Since transcriptome analysis provides genome-wide sequence and gene expression information, transcript reconstruction using RNA-Seq sequence reads has become popular during recent years. For non-model organism, as distinct from the reference genome-based mapping, sequence reads are processed via de novo transcriptome assembly approaches to produce large numbers of contigs corresponding to coding or non-coding, but expressed, part of genome. In spite of immense potential of RNA-Seq–based methods, particularly in recovering full-length transcripts and spliced isoforms from short-reads, the accurate results can be only obtained by the procedures to be taken in a step-by-step manner. In this chapter, we aim to provide an overview of the state-of-the-art methods including (i) quality check and pre-processing of raw reads, (ii) the pros and cons of de novo transcriptome assemblers, (iii) generating non-redundant transcript data, (iv) current quality assessment tools for de novo transcriptome assemblies, (v) approaches for transcript abundance and differential expression estimations and finally (vi) further mining of transcriptomic data for particular biological questions. Our intention is to provide an overview and practical guidance for choosing the appropriate approaches to best meet the needs of researchers in this area and also outline the strategies to improve on-going projects

A consensus‑based ensemble approach to improve transcriptome assembly

Background: Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. Results: In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. Conclusions: Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genomeguided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http:// bioin folab. unl. edu/ emlab/ conse mble/

Methods for Transcriptome Assembly in the Allopolyploid Brassica napus

Canada is the world’s largest producer of canola and the trend of production is ever increasing with an annual growth rate of 9.38% according to FAOSTAT. In 2017, canola acreage surpassed wheat in Saskatchewan, the highest producer of both crops in Canada. Country-wide, the total farming area of canola increased by 9.9% to 22.4 million acres while wheat area saw a slight decline to 23.3 million acres. While Canada is the highest producer of the crop, yields are lower than other countries. To maximize the benefit of this market, canola cultivation could be made more efficient with further characterization of the organism’s genes and their involvement in plant robustness. Such studies using transcriptome analysis have been successful in organisms with relatively small and simple genomes. However, such analyses in B. napus are complicated by the allopolyploid genome structure resulting from ancestral whole genome duplications in the species’ evolutionary history. Homeologous gene pairs originating from the orthology between the two B. napus progenitor species complicate the process of transcriptome assembly. Modern assemblers: Trinity, Oases and SOAPdenovo-Trans were used to generate several de novo transcriptome assemblies for B. napus. A variety of metrics were used to determine the impact that the complex genome structure has on transcriptome studies. In particular, the most important questions for transcriptome assembly in B. napus were how does varying the k-mer parameter effect assembly quality, and to what extent do similar genes resulting from homeology within B. napus complicate the process of assembly. These metrics used for evaluating the assemblies include basic assembly statistics such as the number of contigs and contig lengths (via N25, N50 and N75 statistics); as well as more involved investigation via comparison to annotated coding DNA sequences; evaluation softwares scores for de novo transcriptome assemblies and finally; quantification of homeolog differentiation by alignment to previously identified pairs of homeologous genes. These metrics provided a picture of the trade-offs between assembly softwares and the k-parameter determining the length of subsequences used to build de Bruijn graphs for de novo transcriptome assembly. It was shown that shorter k-mer lengths produce fewer, and more complete contigs due to the shorter required overlap between read sequences; while longer k-mer lengths increase the sensitivity of an assembler to sequence variation between similar gene sequences. The Trinity assembler outperformed Oases and SOAPdenovo-Trans when considering the total breadth of evaluation metrics, generating longer transcripts with fewer chimers between homeologous gene pairs

Novel graph based algorithms for transcriptome sequence analysis

RNA-sequencing (RNA-seq) is one of the most-widely used techniques in molecular biology. A key bioinformatics task in any RNA-seq workflow is the assembling the reads. As the size of transcriptomics data sets is constantly increasing, scalable and accurate assembly approaches have to be developed.Here, we propose several approaches to improve assembling of RNA-seq data generated by second-generation sequencing technologies. We demonstrated that the systematic removal of irrelevant reads from a high coverage dataset prior to assembly, reduces runtime and improves the quality of the assembly. Further, we propose a novel RNA-seq assembly work- flow comprised of read error correction, normalization, assembly with informed parameter selection and transcript-level expression computation. In recent years, the popularity of third-generation sequencing technologies in- creased as long reads allow for accurate isoform quantification and gene-fusion detection, which is essential for biomedical research. We present a sequence-to-graph alignment method to detect and to quantify transcripts for third-generation sequencing data. Also, we propose the first gene-fusion prediction tool which is specifically tailored towards long-read data and hence achieves accurate expression estimation even on complex data sets. Moreover, our method predicted experimentally verified fusion events along with some novel events, which can be validated in the future