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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. The Bellerophon pipeline, improving de novo transcriptomes and removing chimeras

   https://doi.org/10.1002/ece3.5571  

   Kerkvliet, Jesse ; de Fouchier, Arthur ; van Wijk, Michiel ; Groot, Astrid Tatjana ( August 2019 , Ecology and Evolution)

   Transcriptome quality control is an important step in RNA‐Seq experiments. However, the quality of de novo assembled transcriptomes is difficult to assess, due to the lack of reference genome to compare the assembly to. We developed a method to assess and improve the quality of de novo assembled transcriptomes by focusing on the removal of chimeric sequences. These chimeric sequences can be the result of faulty assembled contigs, merging two transcripts into one. The developed method is incorporated into a pipeline, which we named Bellerophon, that is broadly applicable and easy to use. Bellerophon first uses the quality assessment tool TransRate to indicate the quality, after which it uses a transcripts per million (TPM) filter to remove lowly expressed contigs and CD‐HIT‐EST to remove highly identical contigs. To validate the quality of this method, we performed three benchmark experiments: (1) a computational creation of chimeras, (2) identification of chimeric contigs in a transcriptome assembly, (3) a simulated RNA‐Seq experiment using a known reference transcriptome. Overall, the Bellerophon pipeline was able to remove between 40% and 91.9% of the chimeras in transcriptome assemblies and removed more chimeric than nonchimeric contigs. Thus, the Bellerophon sequence of filtration steps is a broadly applicable solution to improve transcriptome assemblies.

    

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3. Optimizing de novo genome assembly from PCR-amplified metagenomes

   https://doi.org/10.7717/peerj.6902  

   Roux, Simon ; Trubl, Gareth ; Goudeau, Danielle ; Nath, Nandita ; Couradeau, Estelle ; Ahlgren, Nathan A. ; Zhan, Yuanchao ; Marsan, David ; Chen, Feng ; Fuhrman, Jed A. ; et al ( May 2019 , PeerJ)

   Metagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enablingde novoassembly of genomes from metagenomes. These metagenome-assembled genomes are critical to provide ecological, evolutionary, and metabolic context for all the microbes and viruses yet to be cultivated. Metagenomes can now be generated from nanogram to subnanogram amounts of DNA. However, these libraries require several rounds of PCR amplification before sequencing, and recent data suggest these typically yield smaller and more fragmented assemblies than regular metagenomes.

   Here we evaluatede novoassembly methods of 169 PCR-amplified metagenomes, including 25 for which an unamplified counterpart is available, to optimize specific assembly approaches for PCR-amplified libraries. We first evaluated coverage bias by mapping reads from PCR-amplified metagenomes onto reference contigs obtained from unamplified metagenomes of the same samples. Then, we compared different assembly pipelines in terms of assembly size (number of bp in contigs ≥ 10 kb) and error rates to evaluate which are the best suited for PCR-amplified metagenomes.

   Read mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥10 kb by 10 to 100-fold for low input metagenomes.

   PCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improvedde novogenome assembly from PCR-amplified datasets, and enables a better genome recovery from low input metagenomes.

    

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4. Novo&Stitch: accurate reconciliation of genome assemblies via optical maps

   https://doi.org/10.1093/bioinformatics/bty255  

   Pan, Weihua ; Wanamaker, Steve I. ; Ah-Fong, Audrey M. V. ; Judelson, Howard S. ; Lonardi, Stefano ( June 2018 , Bioinformatics)

   De novo genome assembly is a challenging computational problem due to the high repetitive content of eukaryotic genomes and the imperfections of sequencing technologies (i.e. sequencing errors, uneven sequencing coverage and chimeric reads). Several assembly tools are currently available, each of which has strengths and weaknesses in dealing with the trade-off between maximizing contiguity and minimizing assembly errors (e.g. mis-joins). To obtain the best possible assembly, it is common practice to generate multiple assemblies from several assemblers and/or parameter settings and try to identify the highest quality assembly. Unfortunately, often there is no assembly that both maximizes contiguity and minimizes assembly errors, so one has to compromise one for the other.

   The concept of assembly reconciliation has been proposed as a way to obtain a higher quality assembly by merging or reconciling all the available assemblies. While several reconciliation methods have been introduced in the literature, we have shown in one of our recent papers that none of them can consistently produce assemblies that are better than the assemblies provided in input. Here we introduce Novo&Stitch, a novel method that takes advantage of optical maps to accurately carry out assembly reconciliation (assuming that the assembled contigs are sufficiently long to be reliably aligned to the optical maps, e.g. 50 Kbp or longer). Experimental results demonstrate that Novo&Stitch can double the contiguity (N50) of the input assemblies without introducing mis-joins or reducing genome completeness.

   Novo&Stitch can be obtained from https://github.com/ucrbioinfo/Novo_Stitch.

    

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5. 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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