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1. A consensus-based ensemble approach to improve transcriptome assembly

   https://doi.org/10.1186/s12859-021-04434-8  

   Voshall, Adam ; Behera, Sairam ; Li, Xiangjun ; Yu, Xiao-Hong ; Kapil, Kushagra ; Deogun, Jitender S. ; Shanklin, John ; Cahoon, Edgar B. ; Moriyama, Etsuko N. ( October 2021 , BMC Bioinformatics)

   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.

   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.

   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 genome-guided 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://bioinfolab.unl.edu/emlab/consemble/.

    

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2. transXpress: a Snakemake pipeline for streamlined de novo transcriptome assembly and annotation

   https://doi.org/10.1186/s12859-023-05254-8  

   Fallon, Timothy R. ; Čalounová, Tereza ; Mokrejš, Martin ; Weng, Jing-Ke ; Pluskal, Tomáš ( April 2023 , BMC Bioinformatics)

   RNA-seq followed by de novo transcriptome assembly has been a transformative technique in biological research of non-model organisms, but the computational processing of RNA-seq data entails many different software tools. The complexity of these de novo transcriptomics workflows therefore presents a major barrier for researchers to adopt best-practice methods and up-to-date versions of software.

   Here we present a streamlined and universal de novo transcriptome assembly and annotation pipeline, transXpress, implemented in Snakemake. transXpress supports two popular assembly programs, Trinity and rnaSPAdes, and allows parallel execution on heterogeneous cluster computing hardware.

   transXpress simplifies the use of best-practice methods and up-to-date software for de novo transcriptome assembly, and produces standardized output files that can be mined using SequenceServer to facilitate rapid discovery of new genes and proteins in non-model organisms.

    

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3. Grouper: graph-based clustering and annotation for improved de novo transcriptome analysis

   https://doi.org/10.1093/bioinformatics/bty378  

   Malik, Laraib ; Almodaresi, Fatemeh ; Patro, Rob ; Birol, ed., Inanc ( May 2018 , Bioinformatics)

   De novo transcriptome analysis using RNA-seq offers a promising means to study gene expression in non-model organisms. Yet, the difficulty of transcriptome assembly means that the contigs provided by the assembler often represent a fractured and incomplete view of the transcriptome, complicating downstream analysis. We introduce Grouper, a new method for clustering contigs from de novo assemblies that are likely to belong to the same transcripts and genes; these groups can subsequently be analyzed more robustly. When provided with access to the genome of a related organism, Grouper can transfer annotations to the de novo assembly, further improving the clustering.

   On de novo assemblies from four different species, we show that Grouper is able to accurately cluster a larger number of contigs than the existing state-of-the-art method. The Grouper pipeline is able to map greater than 10% more reads against the contigs, leading to accurate downstream differential expression analyses. The labeling module, in the presence of a closely related annotated genome, can efficiently transfer annotations to the contigs and use this information to further improve clustering. Overall, Grouper provides a complete and efficient pipeline for processing de novo transcriptomic assemblies.

   The Grouper software is freely available at https://github.com/COMBINE-lab/grouper under the 2-clause BSD license.

   Supplementary data are available at Bioinformatics online.

    

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4. Pincho: A Modular Approach to High Quality De Novo Transcriptomics

   https://doi.org/10.3390/genes12070953  

   Ortiz, Randy ; Gera, Priyanka ; Rivera, Christopher ; Santos, Juan C. ( July 2021 , Genes)

   Transcriptomic reconstructions without reference (i.e., de novo) are common for data samples derived from non-model biological systems. These assemblies involve massive parallel short read sequence reconstructions from experiments, but they usually employ ad-hoc bioinformatic workflows that exhibit limited standardization and customization. The increasing number of transcriptome assembly software continues to provide little room for standardization which is exacerbated by the lack of studies on modularity that compare the effects of assembler synergy. We developed a customizable management workflow for de novo transcriptomics that includes modular units for short read cleaning, assembly, validation, annotation, and expression analysis by connecting twenty-five individual bioinformatic tools. With our software tool, we were able to compare the assessment scores based on 129 distinct single-, bi- and tri-assembler combinations with diverse k-mer size selections. Our results demonstrate a drastic increase in the quality of transcriptome assemblies with bi- and tri- assembler combinations. We aim for our software to improve de novo transcriptome reconstructions for the ever-growing landscape of RNA-seq data derived from non-model systems. We offer guidance to ensure the most complete transcriptomic reconstructions via the inclusion of modular multi-assembly software controlled from a single master console. 

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5. The draft genome sequence of the Japanese rhinoceros beetle Trypoxylus dichotomus septentrionalis towards an understanding of horn formation

   https://doi.org/10.1038/s41598-023-35246-w  

   Morita, Shinichi ; Shibata, Tomoko F ; Nishiyama, Tomoaki ; Kobayashi, Yuuki ; Yamaguchi, Katsushi ; Toga, Kouhei ; Ohde, Takahiro ; Gotoh, Hiroki ; Kojima, Takaaki ; Weber, Jesse N ; et al ( December 2023 , Scientific Reports)

   The Japanese rhinoceros beetleTrypoxylus dichotomusis a giant beetle with distinctive exaggerated horns present on the head and prothoracic regions of the male.T. dichotomushas been used as a research model in various fields such as evolutionary developmental biology, ecology, ethology, biomimetics, and drug discovery. In this study, de novo assembly of 615 Mb, representing 80% of the genome estimated by flow cytometry, was obtained using the 10 × Chromium platform. The scaffold N50 length of the genome assembly was 8.02 Mb, with repetitive elements predicted to comprise 49.5% of the assembly. In total, 23,987 protein-coding genes were predicted in the genome. In addition, de novo assembly of the mitochondrial genome yielded a contig of 20,217 bp. We also analyzed the transcriptome by generating 16 RNA-seq libraries from a variety of tissues of both sexes and developmental stages, which allowed us to identify 13 co-expressed gene modules. We focused on the genes related to horn formation and obtained new insights into the evolution of the gene repertoire and sexual dimorphism as exemplified by the sex-specific splicing pattern of thedoublesexgene. This genomic information will be an excellent resource for further functional and evolutionary analyses, including the evolutionary origin and genetic regulation of beetle horns and the molecular mechanisms underlying sexual dimorphism.

    

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