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. 2015 May 24;11:97–104. doi: 10.4137/EBO.S23873

TRUFA: A User-Friendly Web Server for de novo RNA-seq Analysis Using Cluster Computing

Etienne Kornobis 1,✉, Luis Cabellos 2, Fernando Aguilar 2, Cristina Frías-López 3, Julio Rozas 3, Jesús Marco 2, Rafael Zardoya 1
PMCID: PMC4444131  PMID: 26056424

Abstract

Application of next-generation sequencing (NGS) methods for transcriptome analysis (RNA-seq) has become increasingly accessible in recent years and are of great interest to many biological disciplines including, eg, evolutionary biology, ecology, biomedicine, and computational biology. Although virtually any research group can now obtain RNA-seq data, only a few have the bioinformatics knowledge and computation facilities required for transcriptome analysis. Here, we present TRUFA (TRanscriptome User-Friendly Analysis), an open informatics platform offering a web-based interface that generates the outputs commonly used in de novo RNA-seq analysis and comparative transcriptomics. TRUFA provides a comprehensive service that allows performing dynamically raw read cleaning, transcript assembly, annotation, and expression quantification. Due to the computationally intensive nature of such analyses, TRUFA is highly parallelized and benefits from accessing high-performance computing resources. The complete TRUFA pipeline was validated using four previously published transcriptomic data sets. TRUFA’s results for the example datasets showed globally similar results when comparing with the original studies, and performed particularly better when analyzing the green tea dataset. The platform permits analyzing RNA-seq data in a fast, robust, and user-friendly manner. Accounts on TRUFA are provided freely upon request at https://trufa.ifca.es.

Keywords: transcriptomics, RNA-seq, de novo assembly, read cleaning, annotation, expression quantification

Introduction

Since the introduction of the RNA-seq methodology around 2006,1–6 studies based on whole transcriptomes of both model and non-model species have been flourishing. RNA-seq data are widely used for discovering novel transcripts and splice variants, finding candidate genes, or comparing differential gene expression patterns. The applications of this technology in many fields are vast,1,7 including researches on, eg, splicing signatures of breast cancer,8 host–pathogen interactions,9 the evolution of the frog immunome,10 the plasticity of butterfly wing patterns,11 the study of conotoxin diversity in Conus tribblei,12 and the optimization of trimming parameters for de novo assemblies.13

Despite the tremendous decrease in sequencing costs, which allows virtually any laboratory to obtain RNA-seq data, transcriptome analyses are still challenging and remain the main bottleneck for the widespread use of this technology. User-friendly applications are scarce,14 and the post-analysis of generated sequence data demands appropriate bioinformatics know-how and suitable computing infrastructures.

When a reference genome is available, which is normally the case for model system species, a reference-guided assembly is preferable to a de novo assembly. However, an increasing number of RNA-seq studies are performed on non-model organisms with no available reference genome for read mapping (particularly those studies focused on comparative transcriptomics above the species level), and thus require a de novo assembly approach. Moreover, when a reference genome is available, combining both de novo and reference-based approaches can lead to better assemblies.15,16 Analysis pipelines encompassing de novo assemblies are varied, and generally include steps such as cleaning and assembly of the reads, annotation of transcripts, and gene expression quantification.16 A variety of software programs have been developed to perform different steps of the RNA-seq analysis,17–19 but most of them are computationally intensive. The vast majority of these programs run solely with command lines. Processing the data to connect one step to the next in RNA-seq pipelines can be cumbersome in many instances, mainly due to the variety of output formats produced and the postprocessing needed to accept them further as input. Moreover, as soon as a large computing effort is required, interactive execution is usually not feasible and an interface with the underlying batch systems used in clusters or supercomputers is needed. In order to provide users with such a bioinformatics tool that solve the above-mentioned problems, we have developed TRUFA (TRanscriptomes User-Friendly Analysis), an informatics platform for RNA-seq data analysis, which runs on the ALTAMIRA supercomputer at the Instituto de Fisica de Cantabria (IFCA), Spain.20 The platform is highly parallelized both at the pipeline and program level. It can access up to 256 cores per execution instance for certain components of the pipeline. On top of allowing the user to obtain results in a relatively short time thanks to HPC (high-performance computing) resources, TRUFA is an integrative and graphical web tool for performing the main and most computationally demanding steps of a de novo RNA-seq analysis.

The first step of a de novo RNA-seq analysis consists in assessing data quality and cleaning raw reads. The output of a next-generation sequencing (NGS) reaction contains traces of polymerase chain reaction (PCR) primers and sequencing adapters as well as poor-quality bases/reads. Hence, it is advised to perform read trimming, which has been shown to have a positive effect on the rest of the RNA-seq analysis,21 although parameter values for such trimming have to be optimized.13

Once reads have been cleaned, they are assembled into transcripts, which are subsequently categorized into functional classes in order to understand their biological meaning. Finally, it is possible to perform expression quantification analyses by estimating the amount of reads sequenced per assembled transcript and taking into account that the number of reads sequenced theoretically correlates with the number of copies of the corresponding mRNA in vivo.6 All the above-mentioned steps in the RNA-seq analysis pipeline are included in TRUFA and correspond to distinct sections in the web-based user interface (see Figs. 1 and 2). For each step, the options available are those that are either critical to the analysis or, to our knowledge, the most widely used in the literature.

Figure 1.

Figure 1

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Overview of the TRUFA pipeline.

Figure 2.

Figure 2

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Snapshot of the TRUFA web page for running RNA-seq analysis.

There are several online platforms already available to perform different parts of a RNA-seq analysis. For example, Galaxy (https://usegalaxy.org/)22 allows analyzing RNA-seq data with a reference genome (using Tophat23 and Cufflinks24), whereas GigaGalaxy (http://galaxy.cbiit.cuhk.edu.hk/) can produce de novo assemblies using SOAPdenovo.25 Another transcriptome analysis package integrated in Galaxy, Oqtans,26 provides numerous features including de novo assembly with Trinity, read mapping, and differential expression. Nonetheless, to our knowledge, GigaGalaxy or Oqtans do not perform de novo annotations. Conversely, Fastannotator27 is a platform specialized in transcript annotations using Blast2GO,28 PRIAM,29 and domain identification pipelines, but does not perform other steps of the RNA-seq analysis.

The TRUFA platform has been designed to be interactive, user-friendly, and to cover a large part of a RNA-seq analysis pipeline. Users can launch the pipeline from raw or cleaned Illumina reads as well as from already assembled transcripts. Each of the implemented programs (Table 1) can be easily integrated into the analysis and tuned depending on the needs of the user. TRUFA provides a comprehensive output, including read quality reports, cleaned read files, assembled transcript files, assembly quality statistics, Blast, Blat, and HMMER search results, read alignment files (BAM files), and expression quantification files (including values of read counts, expected counts, and TPM, ie, transcripts per million30). Some outputs can be directly visualized from the web server, and all outputs can be downloaded in order to locally perform further analyses such as single nucleotide polymorphisms (SNPs) calling and differential expression quantification. The platform is mainly written in Javascript, Python, and Bash. The source code is available at Github (https://github.com/TRUFA-rnaseq). The long-term availability of the TRUFA web server (and further developed versions) is ensured given that it is currently installed in the ALTAMIRA supercomputer, a facility integrated in the Spanish Supercomputing Network (RES). The number of users is currently not limited and accounts are freely provided upon request.

Table 1.

List of available software on TRUFA.

RNA-SEQ STEPS AVAILABLE PROGRAMS VERSIONS
Read cleaning PRINSEQ 0.20.3
CUTADAPT 1.3
BLAT v.35
Assembly and mapping Trinity r2012–06–08
CD-HIT 4.5.4
CEGMA 2.4
Bowtie 0.12.8
Bowtie2 2.0.2
Annotation BLAT v.35
HMMER 3.0
Blast+ 2.2.28
Blast2GO 2.5.0
Expression quantification RSEM 1.2.8
eXpress 1.5.1
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Implementation

The overall workflow of TRUFA is shown in Figure 1. The input, output, and different components of the pipeline are the following:

Input

Currently, the input data accepted by TRUFA includes Illumina read files and/or reads already assembled into contigs. Read files should be in FASTQ format and can be uploaded as gzip compressed files (reducing uploading times). Reads from the NCBI SRA databases can be used but should be first formatted into FASTQ format using, eg, the SRA toolkit.31 Already assembled contigs should be uploaded as FASTA files. Other FASTA files and HMM profiles can be uploaded as well for custom blast-like and protein profile-based transcript annotation steps, respectively. Thus far, no data size limitation is set.

Pipeline

Several programs can be called during the cleaning step (Table 1). The program FASTQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc) has been implemented to assess the quality of raw reads and give the statistics necessary to tune cleaning parameters (Fig. 1). After the quality of the data is determined, CUTADAPT32 and PRINSEQ33 allow, among other functionalities, the removal of adapters as well as low quality bases/reads. In particular, PRINSEQ has been chosen for its ability to treat both single and paired-end reads and to perform read quality trimming as well as duplicate removal. Using the BLAT fast similarity search tool, reads can be compared against databases of potential contaminants such as, eg, UniVec (which allows identifying sequences of vector origin; http://www.ncbi.nlm. nih.gov/VecScreen/UniVec.html) or user-specified databases. TRUFA’s scripts will automatically remove those reads, giving hits with such queried databases.

Cleaned reads, after passing an optional second quality control with FASTQC to verify the overall efficiency of the first cleaning step, are ready for assembly. TRUFA implements the software Trinity,34 which is an extensively used de novo assembler and has been shown to perform better than other single k-mer assemblers.35 After the assembly, an in-house script provides basic statistics describing transcripts lengths distribution, total bases incorporated in the assembly, N50, and GC content. In addition, to evaluate the completeness of the assembly, a Blast+36 similarity search is performed against the UniProtKB/Swiss-Prot database, and a Trinity script evaluates whether those assembled transcripts with hits are full-length or nearly full-length. The CEGMA software can also provide a measure of the completeness of the assembly by comparing the transcripts to a set of 248 core eukaryotic genes, which are conserved in highly divergent eukaryotic taxa.37 Both the number of recovered genes from the total of 248 and their completeness have been used for de novo assembly quality assessments.38,39

The newly assembled transcripts can be used as query for similarity searches with BLAT40 or Blast+ against the NCBI nr and UniRef90 databases. In parallel, HMMER41 searches can be performed applying hidden markov models (HMM) against the PFAM-A database. Both analysis can be run as well with user-specified databases or models respectively. Further annotation and assignation of gene ontology (GO) terms can be obtained with Blast2GO28 for the transcripts with blast hits against the nr database.

For expression quantification, Bowtie242 is used to produce alignments of the reads against the assembled transcripts. Alignments are then properly formatted using SAMtools43 and Picard (http://broadinstitute.github.io/picard/).43 Using these alignments, eXpress44 can be used to quantify the expression of all isoforms. Additionally, the script “run_RSEM_align_n_estimate” of the Trinity package implemented in TRUFA uses Bowtie45 and RSEM46 to provide an alternative procedure for expression quantification of both genes and isoforms. Moreover, the percentage of reads mapping back to the assembled transcripts (obtained with Bowtie and Bowtie2) can be used as another indication of the assembly quality.35,38

Output

TRUFA generates a large amount of output information from the different programs used in the customized pipeline. Briefly, a user should be able to download FastQC html reports, FASTQ files with cleaned reads (without duplicated reads and/or trimmed), Trinity-assembled transcripts (FASTA), read alignments against the transcripts (BAM files), GO annotations (.txt and.dat files which can be imported into the Blast2GO java application), and read counts (text files providing read counts and TPM). Various statistics are computed at each step and are reported in text files, such as the percentage of duplicated/trimmed reads, CEGMA completeness report, assembly sequence composition, percentage of mapped reads, and read count distributions.

Results and Discussion

We have built an informatics platform that performs a nearly complete de novo RNA-seq analysis in a user-friendly manner (amenable to the nonexpert user, avoiding command lines, and providing a lightweight visual interface), and tested its performance using four publicly available transcriptome datasets. A small dataset of the fission yeast, Schizosaccharomyces pombe, which is provided in a published Trinity tutorial,47 was used to test the correct functioning of the assembly process on TRUFA. Two previously well-characterized datasets from the green tea, Camelia sinensis (SRX020193), and the fruit fly, Drosophila melanogaster (SRR023199, SRR023502, SRR023504, SRR023538, SRR023539, SRR023540, SRR023600, SRR023602, SRR023604, SRR027109, SRR027110, SRR027114 and SRR035403), were used to compare assembly and read mapping statistics with the results from Zhao et al.35 Finally, TRUFA was tested using a rice (Oryza sativa) dataset48,49 (SRX017630, SRX017631, SRX017632, SRX017633). When applicable, reads corresponding to each end of a pair-ended reaction were concatenated separately into two files, and all files were compressed with gzip before uploading to the platform. Each of the compressed read files was uploaded to TRUFA in less than a day (typical uploading times from a personal computer anywhere ranging from 30 seconds to 12 hours for files ranging from 200 MB to 12 GB, ie, between 0.25 and 25 Gbp).

The results of a first run performing only a FASTQC analysis were used to set the parameters (see Supplementary Table 1) for the cleaning process, except for the yeast dataset, which was assembled without preprocessing. Read cleaning, assembly, mapping, and annotation statistics are shown in Tables 2 and 3. The yeast dataset showed highly similar results to the original analysis, validating the TRUFA assembly. The difference observed in the number of transcripts is most likely due to the not fully deterministic nature of the Trinity algorithm.47 However, the percentage of reads mapped back to the transcripts was slightly higher in the original study.47 For the other three datasets, TRUFA showed globally comparable results. Except for the mean transcript length for the C. sinensis assembly, all other statistics for both C. sinensis and D. melanogaster assemblies were higher in the present analyses with respect to the original ones (Table 2). Remarkably, the percentage of reads mapping back to the transcripts was significantly higher for the green tea dataset using TRUFA. This could be due to a more efficient read-cleaning step or to differences between Bowtie2 (used in TRUFA) and Bowtie used by Zhao et al (2011) mappings. CEGMA analysis showed that more than 80% (range 85.5%–98.39%) of the core eukaryotic genes are fully recovered and more than 98% (range 98.8%–100%) are partially recovered in all dataset assemblies (Fig. 3). This indicates an overall high completeness of the assemblies performed herein with TRUFA. In addition to the assembly and the mapping of the reads, TRUFA was able to annotate de novo 25%–42% of the transcripts using the Blat, Blast+, and Blast2GO pipeline with an e-value of <10−6 (Table 3). HMMER searches identified 17%–60% of the transcripts with at least one hit with an e-value <10−6. The expression of each transcript was quantified using RSEM and eXpress, although no data were available for comparison with the original studies.

Table 2.

Comparison of outputs between original and TRUFA analyses.

NO. OF RAW BASES S. pombe C. sinensis O. sativa D. melanogaster
PESS PE PE PE
544M 2320M 5983M 24740M
Pipeline Trufa Haas et al (2013) Trufa Zhao et al (2011) Trufa Xie et al (2014) Trufa Zhao et al (2011)
No. of bases after cleaning No cleaning No cleaning 2,017M NA 5,342M NA 5,028M NA
No. of transcripts 9,370 9,299 201,892 188,950 166,512 170,880 80,999 70,906
Mean transcript length 1,014 NA 319 332 480 552 847 751
No. of bases in the assembly 9M NA 64M 63M 80M 94M 69M 53M
N50 1,585 1,585 542 525 1,205 1,392 2,960 2,499
No. of transcripts >1000 nt 3,680 NA 13,276 12,495 22,317 28,578 17,251 12,511
Total alignment rate 94.98% 99.93% 88.84% 61.04% 94.76% NA 92.39% 89.9
Concordant pairs 92.21% 93.12% 74.45% NA 87.51% NA 84.73% NA
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Note: Concordant pairs are considered when they report at least one alignment.

Abbreviations: PE, Paired-end; SS, strand-specific; M, million; NA, data not available.

Table 3.

Summary of the de novo annotation step for the four assembled transcriptomes.

S. pombe C. sinensis O. sativa D. melanogaster
# transcripts 9,370 201,892 166,512 80,999
# Blast Hits 8,257 72,559 66,129 29,924
# Annotations 3,922 51,272 50,721 22,534
% of annotated transcripts 42% 25% 30% 28%
# HMMER hits 5,588 34,689 28,736 16,552
User time 11 h 3 d 19 h 6 d 8 h 4 d 15 h
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Notes: # Transcripts, number of transcripts assembled by Trinity; # Blast hits, number of transcripts with at least one hit against the NCBI nr database (e-value <10−6); # Annotation, number of transcripts with at least one annotation after Blast2GO analysis; # HMMER hits, number of transcripts with at least one hit against the Pfam A database (e-value <10−6); User time, time needed to perform the complete pipeline (cleaning, assembly, annotation, and expression quantification).

Figure 3.

Figure 3

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Measures of completeness and read usage for the assemblies produced with TRUFA. CEGMA results represent the percentage of completely and partially recovered genes in the assemblies for a subset of 248 highly conserved core eukaryotic genes. Overall alignment rate and concordant pairs (providing at least one alignment) were computed with Bowtie2.

Considering the entire pipeline, each testing dataset was analyzed by TRUFA in less than a week (Table 3), confirming a good time efficiency of the platform. According to Macmanes13 on the effect of read trimming for RNA-seq analysis, optimizing trimming parameters leads to better assembly results. This optimization should take no longer than 3 days of computation for datasets such as the ones used here and can be easily done with TRUFA by producing in parallel various assemblies and their quality statistics with different sets of trimming parameters and parameter values.

In Prospect

To complete the RNA-seq analysis pipeline available in TRUFA, we plan to expand the platform by incorporating programs for differential expression analysis and SNP calling. Other programs, especially for assembly (eg, SOAPdenovo-Trans, Velvet-Oases) and visualization (eg, GBrowse) of the data, are planned to be also included in the future. In addition, integrating GO terms for each annotated transcripts would permit the user to browse sequences of interest directly from the web server without the need to download large quantities of output. We also plan to complete the platform by providing features for read mapping against a reference genome (such as, eg, STAR,50 Tophat, and Cufflinks). A cloud version of TRUFA, which would increase considerably its global capabilities, is also envisioned to be run in the EGI.eu Federated Cloud (see https://www.egi.eu/infrastructure/cloud/) in the near future.

Conclusion

We presented TRUFA, a bioinformatics platform offering a web interface for de novo RNA-seq analysis. It is intended for scientists analyzing transcriptome data who do not have either bioinformatics skills or access to fast computing services (or both). TRUFA is essentially a wrapper of various widely used RNA-seq analysis tools, allowing the generation of RNA-seq outputs in an efficient, consistent, and user-friendly manner, based on a pipeline approach.

The trimming and assembly steps are guided by the integration of widely used quality control programs toward the optimization of the assembly process. Moreover, the implementation of HMMER, BLAST+, and Blast2GO to the platform for de novo annotation is, to our knowledge, a feature not available in other RNA-seq analysis web servers such as GigaGalaxy or Oqtans. This step is the most computationally demanding among all RNA-seq analysis steps (including SNPs calling and differential expression), and TRUFA uses highly parallelized steps to obtain annotations in a relatively short time frame. Although annotations can be performed in other platforms such as FastAnnotator, having all these steps from cleaning to annotations and expression quantification in the same pipeline reduces unnecessary transfer of large outputs and provides an advantage to the nonexpert user.

Data Accessibility

TRUFA platform, user manual, example data sets and tutorial videos are accessible at the web page https://trufa.ifca.es/web. Accession numbers to the read files used in this study are provided in the Results and Discussion section and can be obtained from http://www.ncbi.nlm.nih.gov/sra/.

Supplementary Material

Supplementary Table 1. List of the main command lines used for the analysis of each data sets. Datasets: 1, S. pombe; 2, C. sinensis; 3, O. sativa; 4, D. melanogaster.

Acknowledgments

We would like to thank Beatríz Ranz for her help during the validation process and Iván Cabrillo for his help managing the accounts on the Altamira supercomputer. We are grateful to Federico Abascal for his input in the review process, his suggestions, and his help during the beta testing. Thanks to all other beta testers: Anna María Addamo, Carlos Canchaya, Michel Domínguez, Iván Gómez-Mestre, Iker Irisarri, Nathan Kenny, Diushi Keri, David Osca, Snæbjörn Pálsson, Sara Rocha, Diego San Mauro, Maria Torres, Juan E. Uribe, Ignacio Varela, and Joel Vizueta. We thank five anonymous reviewers for their valuable feedback on a previous version of the manuscript. The platform was developed thanks to the use of bootstrap (http://getbootstrap.com/), jquery (http://jquery.com/), sqlite (https://sqlite.org/), webpy (http://webpy.org/), and filemanager (https://github.com/simogeo/Filemanager). The Altamira supercomputer is member of the Spanish Supercomputing Network.

Glossary

Abbreviations

TRUFA

transcriptome user-friendly analysis

TPM

transcripts per million

SNP

single nucleotide polymorphism

HPC

high performance computing

Footnotes

ACADEMIC EDITOR: Jike Cui, Associate Editor

FUNDING: This work was partially funded with Spanish Ministry of Science and Innovation grants CGL2010–18216 and CGL2013–45211-C2–2-P to RZ and CGL2013–45211-C2–1-P to JR. JR was partially supported by ICREA Academia (Generalitat de Catalunya; Spain). The authors confirm that the funder had no influence over the study design, content of the article, or selection of this journal.

COMPETING INTERESTS: Authors disclose no potential conflicts of interest.

Paper subject to independent expert blind peer review by minimum of two reviewers. All editorial decisions made by independent academic editor. Upon submission manuscript was subject to anti-plagiarism scanning. Prior to publication all authors have given signed confirmation of agreement to article publication and compliance with all applicable ethical and legal requirements, including the accuracy of author and contributor information, disclosure of competing interests and funding sources, compliance with ethical requirements relating to human and animal study participants, and compliance with any copyright requirements of third parties. This journal is a member of the Committee on Publication Ethics (COPE).

Author Contributions

Conceived the study: RZ, EK. Constructed the pipeline: EK. Performed testing runs: EK, CFL. Implemented the web version: LC, FA, EK, JM. Tuned parts of the pipeline: CFL, JR. All authors contributed to the writing and improving of the manuscript, and read and approved the final version.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1. List of the main command lines used for the analysis of each data sets. Datasets: 1, S. pombe; 2, C. sinensis; 3, O. sativa; 4, D. melanogaster.

EBO-11-2015-097-s001.csv (1.7KB, csv)

Data Availability Statement

TRUFA platform, user manual, example data sets and tutorial videos are accessible at the web page https://trufa.ifca.es/web. Accession numbers to the read files used in this study are provided in the Results and Discussion section and can be obtained from http://www.ncbi.nlm.nih.gov/sra/.

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