AutoFlow, a Versatile Workflow Engine Illustrated by Assembling an Optimised de novo Transcriptome for a Non-Model Species, such as Faba Bean (Vicia faba)

Seoane, Pedro; Ocaña, Sara; Carmona, Rosario; Bautista, Rocí­o; Madrid, Eva; Torres, A. M.; Claros, M. Gonzalo

doi:10.2174/1574893611666160212235117

Cited by 20 publications

(21 citation statements)

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“…1 a for the Arabidopsis datasets. It has been constructed using AutoFlow [ 29 ], a workflow manager developed in Ruby in our laboratory. The workflow receives as input files the raw reads and the transcriptome, both in Fasta format.…”

Section: Methodsmentioning

confidence: 99%

Automated identification of reference genes based on RNA-seq data

et al. 2017

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BackgroundGene expression analyses demand appropriate reference genes (RGs) for normalization, in order to obtain reliable assessments. Ideally, RG expression levels should remain constant in all cells, tissues or experimental conditions under study. Housekeeping genes traditionally fulfilled this requirement, but they have been reported to be less invariant than expected; therefore, RGs should be tested and validated for every particular situation. Microarray data have been used to propose new RGs, but only a limited set of model species and conditions are available; on the contrary, RNA-seq experiments are more and more frequent and constitute a new source of candidate RGs.ResultsAn automated workflow based on mapped NGS reads has been constructed to obtain highly and invariantly expressed RGs based on a normalized expression in reads per mapped million and the coefficient of variation. This workflow has been tested with Roche/454 reads from reproductive tissues of olive tree (Olea europaea L.), as well as with Illumina paired-end reads from two different accessions of Arabidopsis thaliana and three different human cancers (prostate, small-cell cancer lung and lung adenocarcinoma). Candidate RGs have been proposed for each species and many of them have been previously reported as RGs in literature. Experimental validation of significant RGs in olive tree is provided to support the algorithm.ConclusionRegardless sequencing technology, number of replicates, and library sizes, when RNA-seq experiments are designed and performed, the same datasets can be analyzed with our workflow to extract suitable RGs for subsequent PCR validation. Moreover, different subset of experimental conditions can provide different suitable RGs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-017-0356-5) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Automated identification of reference genes based on RNA-seq data

et al. 2017

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“…The proportion of unknown transcripts was 60% in v 1.1, compared to 65% in v 1.0 and 52% in Kaur et al [ 23 ]. This indicates that the update of sequences and software in the workflow described in Seoane et al [ 36 ] clearly improved the faba bean transcriptome. These transcripts can be faba/legume-specific transcripts (without a clear orthologue in the databases) or assembling artefacts.…”

Section: Resultsmentioning

confidence: 88%

“…Approximately half of the sequences (16,567,244) belong to the 29H parental line and the other half (16,455,916) to line Vf136. A total of 30,907,202 (93.59%) useful reads were submitted to different assembly procedures as described in Seoane et al [ 36 ]. Additionally, 105,094 sequences from ENA and Kaur et al [ 23 ] were pre-processed to provide 87,269 useful reads to be assembled and merged with the previous assemblies.…”

Section: Resultsmentioning

confidence: 99%

“…For this purpose, sequences from three different data sets were combined: newly obtained transcripts from Illumina sequencing, those obtained from FLX/454 reads [ 23 ] and the ESTs sequences available at public databases. The completeness and quality of the new assembly was superior to earlier faba transcriptome assemblies [ 23 , 36 ]. For instance, when the datasets were analyzed individually, a wide range of counts were reported: 85,844 transcripts from 304,680 FLX/454 reads [ 23 ] and 98,195 transcripts from 30,907,202 Illumina reads.…”

Section: Discussionmentioning

confidence: 98%

“…With the aim of constructing a faba bean transcriptome that integrates all known sequences in this species, 19,067 entries from European Nucleotide Archive (ENA), and 79,657 singletons and 6,370 contigs from Kaur et al [ 23 ] were incorporated. Illumina reads and these additional faba bean sequences were assembled following an updated version of the workflow described in Seoane et al [ 36 ]. Differences with respect to the published pipeline are that (i) more ENA entries were incorporated, (ii) the pre-processing included more putative contaminant sources of microorganisms, (iii) Full-LengtherNext analyses can now detect and split chimeric transcripts, (iv) UniProtKB database for Full-LengtherNext analyses was updated on September 4 th , 2014, and (v) the prediction of species-specific coding transcripts was carried out with TransDecoder ( http://transdecoder.github.io ).…”

Section: Methodsmentioning

confidence: 99%

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Large-Scale Transcriptome Analysis in Faba Bean (Vicia faba L.) under Ascochyta fabae Infection

et al. 2015

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Faba bean is an important food crop worldwide. However, progress in faba bean genomics lags far behind that of model systems due to limited availability of genetic and genomic information. Using the Illumina platform the faba bean transcriptome from leaves of two lines (29H and Vf136) subjected to Ascochyta fabae infection have been characterized. De novo transcriptome assembly provided a total of 39,185 different transcripts that were functionally annotated, and among these, 13,266 were assigned to gene ontology against Arabidopsis. Quality of the assembly was validated by RT-qPCR amplification of selected transcripts differentially expressed. Comparison of faba bean transcripts with those of better-characterized plant genomes such as Arabidopsis thaliana, Medicago truncatula and Cicer arietinum revealed a sequence similarity of 68.3%, 72.8% and 81.27%, respectively. Moreover, 39,060 single nucleotide polymorphism (SNP) and 3,669 InDels were identified for genotyping applications. Mapping of the sequence reads generated onto the assembled transcripts showed that 393 and 457 transcripts were overexpressed in the resistant (29H) and susceptible genotype (Vf136), respectively. Transcripts involved in plant-pathogen interactions such as leucine rich proteins (LRR) or plant growth regulators involved in plant adaptation to abiotic and biotic stresses were found to be differently expressed in the resistant line. The results reported here represent the most comprehensive transcript database developed so far in faba bean, providing valuable information that could be used to gain insight into the pathways involved in the resistance mechanism against A. fabae and to identify potential resistance genes to be further used in marker assisted selection.

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