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



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.


De novo transcriptome assembly of short-read RNA-seq data followed by prediction of open reading frames (ORFs) and automated annotation of predicted proteins is widely used for studying non-model eukaryotic organisms without a reference genome [1, 2]. The NCBI Sequence Read Archive (SRA) database currently contains over 3 million RNA-seq datasets, including hundreds of thousands from non-model eukaryotes [3]. These datasets represent a rich and continuously growing resource for diverse biological research across the tree of life. In contrast, only ~ 6900 eukaryotic transcriptome assemblies have been uploaded to the NCBI Transcriptome Shotgun Assembly (TSA) database to date, reflecting the difficulties in producing and uploading high-quality assemblies [4]. Generating and annotating a de novo transcriptome assembly requires numerous bioinformatic tools that can be difficult to install, and best practices are not always followed [5].

We surveyed existing pipelines for RNA-seq data analysis, including de novo transcriptome assembly and gene annotation tasks (Table 1). To date, four pipelines have been published for de novo transcriptome assembly, two of which (Rnnotator [6] and themira [7]) have been discontinued since their publication. Several other pipelines are available for aligning RNA-seq reads to a reference genome. Only a few of them support alignment of raw reads to a de novo assembled or reference transcriptome, depending mostly on the read aligner used. However, such pipelines generally were not designed to assist with gene discovery in non-model organisms. Presently, Pincho [8] is the only maintained pipeline that supports both de novo transcriptome assembly and transcript annotation using a variety of tools. However, Pincho does not support distributed computing on high-performance computational clusters (HPCs), and therefore has limited utility for processing large sequencing datasets.

Table 1 Overview of existing pipelines for RNA-seq data analysis

Here, we present a new de novo transcriptome assembly pipeline, transXpress, which streamlines reproducible assembly of transcripts, quantification of transcript expression levels, and gene and protein prediction and annotation. transXpress also supports parallel execution on heterogeneous cluster computing hardware.


Workflow engine

Older RNA-seq pipelines were typically implemented as shell scripts with the use of Perl, Python or R to execute the relevant downstream analyses. Recently, there is a strong tendency to employ bioinformatic workflow engines such as Snakemake, Nextflow or Galaxy [20,21,22]. Owing to its general simplicity and ease of use, we selected Snakemake to handle the dependencies between the executed tasks, to avoid repeated computations upon pipeline re-execution, and to support cluster computing [20]. The users of transXpress are advised to install required dependencies using Conda [23] and Python’s PIP package management systems, as described on the transXpress GitHub page [24].

The transXpress pipeline (Fig. 1) performs parallel execution of the underlying tools whenever possible. Furthermore, it splits the input datafiles (e.g., for the Trimmomatic and the FASTA annotation steps) into multiple partitions (batches) to speed up even single-threaded tasks by parallelization. The partial results files from such split tasks are then merged automatically back into a single output file. In the case of the Trinity assembler, the individual jobs generated within Trinity by the ‘Chrysalis’ phase as input for the ‘Butterfly’ phase, are automatically parallelized by transXpress [25, 26]. The output files from all the underlying tools, including their graphical results, are retained in the project folder.

Fig. 1
figure 1

A schema of the data processing steps performed by the transXpress pipeline. The input data are on the very left in a yellow-colored frame. Initial data pre-treatment tasks are on the left, followed by assembly and tasks executed largely in parallel (annotation and expression analysis). Output data types are in a purple background on the very right. This is a manually simplified version of the directed acyclic graph (DAG) of Snakemake tasks. The DAG can be automatically generated by Snakemake for each transXpress run

Data pre-treatment

The quality of the input sequencing reads has a major impact on the quality of the final transcriptome assembly [27]. To assess the quality of the provided reads, transXpress uses the FastQC tool [28]. Its wrapper add-on MultiQC [29] further aggregates and summarizes FastQC reports of all samples into a single report, providing an easy overview of the quality of sample preparation, library construction, and sequencing across all samples. Such a report is fundamental for the subsequent interpretation of the data.

Sequencing adapters and poor quality reads are removed using Trimmomatic [30]. Trimming the reads is very important for de novo assembly, since artificially introduced sequences (various types of adapters and their dimers, multimers, partial copies, or PCR-based artifacts) may interfere with the extension of contigs. After read trimming, transXpress performs another round of FastQC/MultiQC quality assessment and checks the generated report for potential warnings.

de novo transcriptome assembly

Roughly ten de novo transcriptome assemblers for short RNA-seq reads have been developed and are in common use [31]. Among them, Trinity [25], rnaSPAdes [32] and TransAbyss [26], are the most widely used tools, and a recent evaluation indicated these three assemblers generally outperformed other tools [33]. All three utilize kmer-based De Bruijn graph assembly, which often requires a large amount of memory for the kmer frequency counting step. transXpress pools the sequencing reads for all provided samples and performs de novo assembly either using Trinity or rnaSPAdes, depending on the configuration settings provided by the user. Since these assemblers were primarily developed for high-quality short-read sequences, the range of supported sequencers includes Illumina, DNBSEQ, MGISEQ, or BGISEQ platforms, as well as older Roche/454 instruments [34]. transXpress does not support assembly from long-read sequencers such as PacBio or Nanopore. The assembled transcripts are further processed with TransDecoder [26] to identify likely protein-coding regions (ORFs). In case multiple potential ORFs are identified within a single transcript, TransDecoder reports all of them, leading to multiple protein sequences being subject to downstream annotation tasks in transXpress.

For each assembled transcriptome, transXpress reports simple statistics using scripts provided by the Trinity assembler (e.g., the number of assembled isoforms and genes, median contig length, contig Nx and ExN50 values) [35]. Further, transXpress runs the Benchmarking Universal Single-Copy Orthologs (BUSCO) tool to assess the completeness of the transcriptome by estimating completeness and redundancy in terms of expected gene content [36].

Expression analysis and transcriptome annotation

The underlying RNA-seq reads used for the transcriptome assembly are also used to estimate transcript expression levels (transcript-per-million or TPM values) using kallisto, a fast alignment-free method for near-optimal expression quantification at the transcript isoform level [37]. As an optional step, full read-to-transcript local alignments can also be performed using Bowtie2 [38], to allow for troubleshooting and manual inspection of read coverage, for example in Integrated Genomics Viewer [39]. If multiple samples are included, transXpress performs differential expression analysis using edgeR [40]. This step also generates graphical output in the form of heat maps with hierarchical clustering analysis, using Perl and R scripts provided by the Trinity assembler [26]. The information about sample groups for differential expression analyses is obtained automatically from the transXpress main input file samples.txt, which defines the sample groups, replicates, and paths to raw sequencing reads (FASTQ files) for each sample.

The assembled transcriptome is further decorated with automated annotations. NCBI BLAST + [41] searches (blastx and blastp) are performed against the curated UniProtKB/Swiss-Prot database [42]; hmmer3 [43] is used to search through the Pfam-A database of protein domains [44]; and cmscan from the Infernal package [45] is used to search the Rfam database of non-coding RNA sequences [46]. Moreover, transXpress uses SignalP 6.0 and TargetP 2.0 to predict N-terminal signaling and targeting peptides [47, 48]. A Python re-implementation of the widely used TMHMM algorithm is employed for prediction of transmembrane helices [49].

The resulting flat files are parsed via custom Python scripts and the collected annotations are used to decorate the output FASTA files with transcripts and predicted protein coding sequences.

Transcriptome mining

The most user-friendly way to mine the annotated FASTA files generated by transXpress is to use SequenceServer [50], which enables performing BLAST + [51] searches against custom FASTA sequence databases. For every hit, SequenceServer displays its alignment to the query and also the FASTA headers of each sequence, which include functional annotations created with transXpress—expression levels in different samples, the best BLAST hit in SwissProt, identified Pfam domains, topology prediction for transmembrane proteins, subcellular localization and prediction of targeting peptides, and auto-generated external hyperlinks to relevant Pfam and UniProt entries (Fig. 2).

Fig. 2
figure 2

An example entry of a transcript annotated by the transXpress pipeline and rendered through SequenceServer 2.0.0 [50]. A number of annotations including TPM (expression quantification) values, protein domain and transmembrane domain predictions, subcellular localization, and signaling peptide predictions are annotated (underlined in purple). Auto-generated external hyperlinks are added as well (underlined in green). The example protein sequence was shortened for clarity

Results and discussion

To demonstrate the utility of the transXpress pipeline, we processed RNA-seq reads from long pepper (Piper longum), also known as pippali, a non-model plant used in Indian Ayurvedic medicine [52]. P. longum plants have been used in traditional medicine from ancient times and are known to produce biochemically interesting alkaloids with anticancer and nootropic effects in humans [53, 54]. The RNA-seq data were downloaded from NCBI Sequence Read Archive (SRA) and contained Illumina stranded, paired-end 2 × 150 bp reads from Piper longum leaf, spike and root samples. The transXpress pipeline was run on a computational cluster with either Trinity or rnaSPADES as the assembler of choice. Notably, both de novo assemblers generated over 200 thousand unique transcripts with an average predicted ORF length of 282 and 255 amino acids, respectively (Table 2). In comparison, a recent genome assembly of the closely related black pepper (Piper nigrum) [55] contains 63,466 genes with the average protein coding sequence length 1347 nt (449 amino acids). This difference is likely related to the large proportion (22%) of 5′-partial transcripts, possibly caused by incomplete PCR amplification using oligo (dT) primers, as commonly performed in RNA-seq protocols. It is worth noting that for such 5′-partial protein sequences, targeting peptide prediction is not possible.

Table 2 Descriptive statistics of the P. longum transcriptomes assembled with transXpress using the Trinity and rnaSPADES assemblers

Targeting peptides were found in 11.8% of the protein sequences using TargetP. The most common targeting sequence was a signal peptide for endoplasmic reticulum, followed by a chloroplast transit peptide (Fig. 3A, B). About 19% of all protein sequences were predicted to contain transmembrane domains (Fig. 3C). Differential expression analysis of the three tissue samples was performed using edgeR [40] (Fig. 4).

Fig. 3
figure 3

Statistics of the predicted P. longum protein sequences (n = 131,098) generated automatically using the tools included in transXpress. Data from the Trinity assembly is shown, as the results for the rnaSPADES assembly were very similar. A N-terminal targeting peptides predicted by TargetP. B N-terminal signaling peptides predicted by SignalP. C Transmembrane proteins predicted by TMHMM

Fig. 4
figure 4

A hierarchically-clustered heatmap showing differential expression between root, leaf and tissue RNA-Seq samples from Piper longum [52]. This figure was automatically generated by the differential expression analysis step of transXpress from the transcriptome assembled with Trinity


The transXpress pipeline is an easy-to-install, integrated tool that generates reproducible, annotated FASTA files ready for downstream mining. With this, transXpress facilitates rapid discovery of new genes and proteins in non-model organisms. The pipeline is actively maintained and is already used by many labs. For experienced users, transXpress can provide a good starting point to develop customized workflows.

Availability and requirements

Project name: transXpress.

Project home page:

Operating system(s): Linux.

Programming language: Snakemake (Python), bash.

Other requirements: Dependencies installed via Conda or pip.

License: GNU GPLv3.

Any restrictions to use by non-academics: none.

Availability of data and materials

The datasets analyzed during the current study are available in the NCBI SRA repository, containing Piper longum leaf (SRR10362954), spike (SRR10362953) and root (SRR10583928) RNA-seq datasets [52]. Two archives with the output files produced by the transXpress runs using Trinity and rnaSPADES on the Piper longum sequencing datasets were deposited into Zenodo under [56].


  1. Torrens-Spence MP, Fallon TR, Weng JK. A workflow for studying specialized metabolism in nonmodel eukaryotic organisms. In: O’Connor SE, editor. Methods in enzymology. Academic Press; 2016. p. 69–97.

    Google Scholar 

  2. Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years. Nat Rev Genet. 2019;20:631–56.

    Article  CAS  PubMed  Google Scholar 

  3. RNA-Seq datasets in NCBI SRA. Accessed 24 Oct 2022.

  4. NCBI TSA. Accessed 24 Oct 2022.

  5. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Martin J, Bruno VM, Fang Z, Meng X, Blow M, Zhang T, et al. Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads. BMC Genom. 2010;11:663.

    Article  CAS  Google Scholar 

  7. Melicher D, Torson AS, Dworkin I, Bowsher JH. A pipeline for the de novo assembly of the Themira biloba (Sepsidae: Diptera) transcriptome using a multiple k-mer length approach. BMC Genom. 2014;15:188.

    Article  Google Scholar 

  8. Ortiz R, Gera P, Rivera C, Santos JC. Pincho: a modular approach to high quality de novo transcriptomics. Genes. 2021;12:953.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lataretu M, Hölzer M. RNAflow: an effective and simple RNA-Seq differential gene expression pipeline using nextflow. Genes. 2020;11:1487.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020;38:276–8.

    Article  CAS  PubMed  Google Scholar 

  11. Federico A, Karagiannis T, Karri K, Kishore D, Koga Y, Campbell JD, et al. Pipeliner: a nextflow-based framework for the definition of sequencing data processing pipelines. Front Genet. 2019;10:614.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Cornwell M, Vangala M, Taing L, Herbert Z, Köster J, Li B, et al. VIPER: visualization pipeline for RNA-seq, a snakemake workflow for efficient and complete RNA-seq analysis. BMC Bioinform. 2018;19:135.

    Article  Google Scholar 

  13. Zhang X, Jonassen I. RASflow: an RNA-Seq analysis workflow with snakemake. BMC Bioinform. 2020;21:110.

    Article  Google Scholar 

  14. Wang D. hppRNA—a snakemake-based handy parameter-free pipeline for RNA-Seq analysis of numerous samples. Brief Bioinform. 2018;19:622–6.

    CAS  PubMed  Google Scholar 

  15. Wolfien M, Rimmbach C, Schmitz U, Jung JJ, Krebs S, Steinhoff G, et al. TRAPLINE: a standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation. BMC Bioinform. 2016;17:21.

    Article  Google Scholar 

  16. Zhao S, Xi L, Quan J, Xi H, Zhang Y, von Schack D, et al. QuickRNASeq lifts large-scale RNA-seq data analyses to the next level of automation and interactive visualization. BMC Genom. 2016;17:39.

    Article  CAS  Google Scholar 

  17. Orjuela S, Huang R, Hembach KM, Robinson MD, Soneson C. ARMOR: an automated reproducible modular workflow for preprocessing and differential analysis of RNA-seq data. G3. 2019;9:2089–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gadepalli VS, Ozer HG, Yilmaz AS, Pietrzak M, Webb A. BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation. BMC Bioinform. 2019;20(Suppl 24):670.

    Article  CAS  Google Scholar 

  19. Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, et al. RNA-seq analysis is easy as 1–2–3 with limma, Glimma and edgeR. F1000Res. 2016;5.

  20. Köster J, Rahmann S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics. 2012;28:2520–2.

    Article  PubMed  Google Scholar 

  21. Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017;35:316–9.

    Article  PubMed  Google Scholar 

  22. Goecks J, Nekrutenko A, Taylor J, Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010;11:R86.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018;15:475–6.

    Article  PubMed  Google Scholar 

  24. transXpress GitHub page. Accessed 30 Nov 2022.

  25. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.

    Article  CAS  PubMed  Google Scholar 

  27. Smith-Unna R, Boursnell C, Patro R, Hibberd JM, Kelly S. TransRate: reference-free quality assessment of de novo transcriptome assemblies. Genome Res. 2016;26:1134–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Babraham bioinformatics—FastQC A quality control tool for high throughput sequence data. Accessed 11 Oct 2021.

  29. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Geniza M, Jaiswal P. Tools for building de novo transcriptome assembly. Curr Plant Biol. 2017;11–12:41–5.

    Article  Google Scholar 

  32. Bushmanova E, Antipov D, Lapidus A, Prjibelski AD. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. Gigascience. 2019;8:100.

    Article  Google Scholar 

  33. Hölzer M, Marz M. De novo transcriptome assembly: a comprehensive cross-species comparison of short-read RNA-Seq assemblers. Gigascience. 2019;8:039.

    Article  Google Scholar 

  34. Ren X, Liu T, Dong J, Sun L, Yang J, Zhu Y, et al. Evaluating de Bruijn graph assemblers on 454 transcriptomic data. PLoS ONE. 2012;7: e51188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Trinity Wiki—assembly statistics. Accessed 24 Oct 2022.

  36. Manni M, Berkeley MR, Seppey M, Simão FA, Zdobnov EM. BUSCO update: novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol Biol Evol. 2021;38:4647–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–7.

    Article  CAS  PubMed  Google Scholar 

  38. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29:24–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    Article  CAS  PubMed  Google Scholar 

  41. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421.

    Article  Google Scholar 

  42. UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49:D480–9.

    Article  Google Scholar 

  43. Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14:755–63.

    Article  CAS  PubMed  Google Scholar 

  44. Sonnhammer EL, Eddy SR, Durbin R. Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins. 1997;28:405–20.

    Article  CAS  PubMed  Google Scholar 

  45. Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29:2933–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kalvari I, Nawrocki EP, Ontiveros-Palacios N, Argasinska J, Lamkiewicz K, Marz M, et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res. 2021;49:D192-200.

    Article  CAS  PubMed  Google Scholar 

  47. Teufel F, Almagro Armenteros JJ, Johansen AR, Gíslason MH, Pihl SI, Tsirigos KD, et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol. 2022;40:1023–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Almagro Armenteros JJ, Salvatore M, Emanuelsson O, Winther O, von Heijne G, Elofsson A, et al. Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance. 2019;2:5.

    Article  Google Scholar 

  49. Sonnhammer EL, von Heijne G, Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol. 1998;6:175–82.

    CAS  PubMed  Google Scholar 

  50. Priyam A, Woodcroft BJ, Rai V, Moghul I, Mungala A, Ter F, et al. Sequenceserver: a modern graphical user interface for custom BLAST databases. Mol Biol Evol. 2019.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.

    Article  CAS  PubMed  Google Scholar 

  52. Dantu PK, Prasad M, Ranjan R. Elucidating biosynthetic pathway of piperine using comparative transcriptome analysis of leaves, root and spike in Piper longum L. bioRxiv. 2021; 2021.01.03.425108.

  53. Salehi B, Zakaria ZA, Gyawali R, Ibrahim SA, Rajkovic J, Shinwari ZK, et al. Piper species: a comprehensive review on their phytochemistry. Biol Act Appl Mol. 2019;24:1364.

    Google Scholar 

  54. Choudhary N, Singh V. A census of P. longum’s phytochemicals and their network pharmacological evaluation for identifying novel drug-like molecules against various diseases, with a special focus on neurological disorders. PLoS ONE. 2018;13:e0191006.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Hu L, Xu Z, Wang M, Fan R, Yuan D, Wu B, et al. The chromosome-scale reference genome of black pepper provides insight into piperine biosynthesis. Nat Commun. 2019;10:1–11.

    Article  Google Scholar 

  56. Čalounová T. Piper longum transcriptomes generated using transXpress. 2022.

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We thank Brian Hass for his support with numerous issues and questions related to the Trinity assembler. The transXpress logo was designed by the Whitehead Institute Bioinformatics & Research Computing group.


T.R.F. is supported by the National Institute of Environmental Health Sciences, Kirschstein-NRSA postdoctoral fellowship (grant number F32-ES032276). This work is supported by the Family Larsson-Rosenquist Foundation (J.K.W.), the National Science Foundation (MCB-1818132, J.K.W.), Chan Zuckerberg Foundation (2020-221485, J.K.W.), Gordon and Betty Moore Foundation (9331, J.K.W.), the Czech Science Foundation—GA CR (21-11563M, T.P.), and the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant 891397, T.P.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding providers.

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TRF and TP developed the initial version of the pipeline. TČ added edgeR and documentation. TP wrote the draft of the manuscript. JKW supervised the initial development of the pipeline and edited the manuscript. MM provided testing and functionality improvements of the pipeline and contributed to the manuscript.

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Correspondence to Jing-Ke Weng or Tomáš Pluskal.

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Competing interests

J.K.W. is a member of the Scientific Advisory Board and a shareholder of DoubleRainbow Biosciences, Galixir, and Inari Agriculture, which develop biotechnologies related to natural products, drug discovery and agriculture. All other authors have no competing interests.

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Fallon, T.R., Čalounová, T., Mokrejš, M. et al. transXpress: a Snakemake pipeline for streamlined de novo transcriptome assembly and annotation. BMC Bioinformatics 24, 133 (2023).

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  • De novo transcriptome assembly
  • RNA-seq
  • Non-model organisms
  • Transcriptome annotation
  • Differential expression analysis
  • Reproducible software
  • High-performance computing