TransPI: A balancing act between transcriptome assemblers
TransPi - a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assembly
Recommendation: posted 27 June 2021, validated 19 July 2021
Ever since the introduction of the first widely usable assemblers for transcriptomic reads (Huang and Madan 1999; Schulz et al. 2012; Simpson et al. 2009; Trapnell et al. 2010, and many more), it has been a technical challenge to compare different methods and to choose the “right” or “best” assembly. It took years until the first widely accepted set of benchmarks beyond raw statistical evaluation became available (e.g., Parra, Bradnam, and Korf 2007; Simão et al. 2015). However, an approach to find the right balance between the number of transcripts or isoforms vs. evolutionary completeness measures has been lacking. This has been particularly pronounced in the field of non-model organisms (i.e., wild species that lack a genomic reference). Often, studies in this area employed only one set of assembly tools (the most often used to this day being Trinity, Haas et al. 2013; Grabherr et al. 2011). While it was relatively straightforward to obtain an initial assembly, its validation, annotation, as well its application to the particular purpose that the study was designed for (phylogenetics, differential gene expression, etc) lacked a clear workflow. This led to many studies using a custom set of tools with ensuing various degrees of reproducibility.
TransPi (Rivera-Vicéns et al. 2021) fills this gap by first employing a meta approach using several available transcriptome assemblers and algorithms to produce a combined and reduced transcriptome assembly, then validating and annotating the resulting transcriptome. Notably, TransPI performs an extensive analysis/detection of chimeric transcripts, the results of which show that this new tool often produces fewer misassemblies compared to Trinity. TransPI not only generates a final report that includes the most important plots (in clickable/zoomable format) but also stores all relevant intermediate files, allowing advanced users to take a deeper look and/or experiment with different settings. As running TransPi is largely automated (including its installation via several popular package managers), it is very user-friendly and is likely to become the new "gold standard" for transcriptome analyses, especially of non-model organisms.
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Rivera-Vicéns RE, Garcia-Escudero CA, Conci N, Eitel M, Wörheide G (2021) TransPi – a comprehensive TRanscriptome ANalysiS PIpeline for de novo transcriptome assembly. bioRxiv, 2021.02.18.431773, ver. 3 peer-reviewed and recommended by Peer Community in Genomics. https://doi.org/10.1101/2021.02.18.431773
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Oleg Simakov (2021) TransPI: A balancing act between transcriptome assemblers. Peer Community in Genomics, 100009. 10.24072/pci.genomics.100009
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article. The authors declared that they comply with the PCI rule of having no financial conflicts of interest in relation to the content of the article.
Evaluation round #1
DOI or URL of the preprint: https://doi.org/10.1101/2021.02.18.431773
Version of the preprint: 1
Author's Reply, 16 Jun 2021
Decision by Oleg Simakov, posted 23 Mar 2021
The article presents a meta approach to transcriptome assembly, validation, and annotation. Based on multiple available tools, the TransPI aims to find the best suitable assembler/algorithm combination for a given set of data, followed by automated annotation. One of the main advantages of this approach is its flexibility in working on data from both "model" and "non-model" organisms and various levels of user expertise. TransPI also provides a clear and reproducible workflow. The manuscript it clearly written, and I will be happy to recommend it once the authors address the few points raised by the reviewers (in particular reviewer 2's concerns). These are very detailed and mostly can help phrase the manuscript better. They also include some important additional validation ideas, including an assessment of chimeric transcripts and merged/unmerged isoforms against ‘golden-standard’ data available for some of the species (i.e., not be limited to mainly BUSCO scores).