Grape RNA-Seq analysis pipeline environmentDG. Knowles 1,∗, M. Roder 1,A. Merkel1 and R. Guigo 1∗1Bioinformatics and Genomics unit, Centre for Genomic Regulation (CRG) and UPF, Dr. Aiguader88, 08003 Barcelona, Spain.

ABSTRACTMotivation: The avalanche of data arriving since the developmentof next generation sequencing (NGS) technologies has prompted theneed for developing fast, accurate, and easily automated bioinforma-tic tools capable of dealing with massive datasets. Among the mostproductive applications of NGS technologies is the sequencing of cel-lular RNA, known as RNA-Seq. While RNA-Seq provides similar orsuperior dynamic range than microarrays at similar or lower cost, thelack of standard and user-friendly pipelines is a bottleneck preventingRNA-Seq from becoming the standard for transcriptome analysis.

Results: In this work we present a pipeline for processing andanalyzing RNA-Seq data, that we have named Grape (Grape RNA-Seq Analysis Pipeline Environment). Grape supports raw sequencingreads produced by a variety of technologies, either in Fasta orFastQ format, or as prealigned reads in SAM/BAM format. A minimalGrape configuration consists of the file location of the raw sequencingreads, the genome of the species, and the corresponding gene andtranscript annotation.

Grape first runs a set of quality control (QC) steps, and then alignsthe reads to the genome, a step that is omitted for prealigned readformats. Grape next estimates gene and transcript expression levels,calculates exon inclusion levels, and identifies novel transcripts.

Grape can be run on a single computer or in parallel on a computercluster. It is distributed with specific mapping and quantification tools,but given its modular design, any tool supporting popular data inter-change formats, can be integrated.

Availability: Grape can be obtained from the Bioinformatics andGenomics website at: ’http://big.crg.cat/services/grape’.

Contact: david.gonzalez@crg.cat

1 INTRODUCTIONThe development of ultrasequencing technologies during therecent years has started a major revolution in Biology. Theability to directly survey the cell’s RNA content by applyingNGS technologies to cDNA sequencing (“RNA-Seq”), has pro-vided insights of unprecedented depth on the transcription lands-cape of many species (Wang et al. (2009)) such as H. sapiens(Wang et al. (2008); Sultan et al. (2008); Montgomery et al. (2010);Trapnell et al. (2010)), M. musculus (Mortazavi et al. (2008);

∗to whom correspondence should be addressed

Guttman et al. (2010)), A. thaliana (Lister et al. (2008)), S. cere-visiae, and (Nagalakshmi et al. (2008); Yassour et al. (2009)) S.pombe (Castle et al. (2008)). RNA-Seq has proven particularlypowerful on tasks such as identifying novel genes and novel spliceforms, detecting low abundance transcripts, and finding sequencevariations, such as SNPs (Marguerat et al. (2008)). It is graduallysubstituting microarrays as the technology of choice for transcrip-tome analyses, providing access to a greater dynamic range of RNAexpression levels (Marioni et al. (2008)).

The throughput of NGS technologies is continuously accelera-ting, and the cost per sequenced nucleotide is rapidly falling. As aconsequence, an unprecedented amount of data is being produced,pressing for the development of fast and efficient methods of ana-lysis, as the bottleneck for scientific discovery is gradually shiftingfrom data production to data analysis.

In the analysis of NGS data, processing efficiency is governedpredominantly by two factors: First, the sheer size of the individualdata sets and second, the vast number of data sets to be analyzed.The current generation of sequencing machines, for example Illu-mina HighSeq, can produce the equivalent of 20x the coverage ofthe human genome in a single run, delivering ∼ 600 million readswith a length of > 100nt. This has spawned a new generationof aligners optimized for aligning short sequences to the genome,for example Bowtie (Langmead et al. (2009)), BWA (Li and Durbin(2009)), BFAst (Homer et al. (2009)), and GEM (Marco-Sola et al.(2012)), which are much faster than previous tools such as BLAST,FASTA, or SSEARCH (see (Trapnell and Salzberg (2009)) for ashort review). Additionally, an increasing number of projectsinvolve large sample sizes to be analyzed often under complexexperimental designs.

In the specific case of RNA-Seq, mapping of reads is only thefirst step of a quite complex data processing schema, the final goalof which is to produce accurate gene and transcript quantifications,and to delineate novel transcript structures. The lack of easy to usepipelines to perform such a processing out of the box in a transpa-rent and streamlined fashion is actually a bottleneck that preventsthe expansion of RNA-Seq, and prompts users with little accessto sophisticated bioinformatic resources to prefer microarrays, forwhich standard ready to use processing pipelines, and user-friendlybioinformatic analysis tools exist.

To address this need, specific pipelines have recently been deve-loped for analyzing RNA-Seq data, such as the pipeline developedby Goncalves et al. (Goncalves et al. (2011)), an analysis pipelinedeveloped in R within the context of the ArrayExpress Database.These and other tools facilitate the analysis of RNA-Seq data, but

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still require the user to have a significant bioinformatics background,specifically when complex experiments with a large number of datasets need to be analyzed.

Here we describe Grape, a workflow for the analysis of RNA-Seqdata that automates all the steps from RNA-Seq reads to transcriptquantification and discovery. Its user friendly interface provides thenecessary overview of all the data, and is therefore particularlysuited for processing numerous files, produced in complex experi-mental set-ups. The results, both intermediate and final, are stored ina MySQL database allowing for access through the database inter-face, but can also be visualized through Raisin, a user friendly webapplication. The same html interface that is used for standalone localanalysis, is easily deployed on a web server for remote access incollaborative projects, or data dissemination.

Grape takes three types of input files. First, the read files, whichmay be aligned or not, the reference genome sequence file, and thecorresponding gene annotation file. Alternatively, Grape can alsotake read alignment files as input, rather than raw read files. Grapeproduces a number of output files in tabular format, so they canbe easily loaded into most statistical packages. While this is thecase for quantifications, which are made available individually atthe gene, transcript and exon level, the BAM format is used as anoutput format for alignments.

More specifically, processing of RNA-Seq reads by Grape invol-ves the following steps: (a) Sequence read evaluation, and trimmingif required. (b) Mapping to the genome, and the transcriptome.(c) Assembly of reads in absence of a reference genome, or priorto genome mapping. (d) Identification of novel exons, and splicejunctions and modeling of transcript structures. (e) Gene and trans-cript quantification. Grape pays special attention to Quality Controls(QC), that trace quality scores and uncalled bases along the reads,looks for biased nucleotide composition, and inspects the distri-bution of reads along transcripts. These can be used for qualitycontrol, although Grape leaves any decision to modify the data suchas trimming and filtering to the user.

Grape is based on the PIP pipeline management system(Conery et al. (2005)). The current Grape distribution uses SAM-tools (Li et al. (2009)), the GEM mapper (Marco-Sola et al.(2012)), the Flux Capacitor (Montgomery et al. (2010)), and Cuff-links (Trapnell et al. (2010)), but any tool compliant with populardata interchange formats like GFF, BAM/SAM, and BED can beemployed. Grape can be run locally or on a computer cluster.Speed-up of the analyses is achieved by parallelizing certain stepsand taking advantage of multithreading where possible. The Grapeimplementation conserves a copy of the exact software and con-figuration used for a given set of analyses, guaranteeing forwardreproducibility.

2 APPROACH2.1 Grape descriptionGrape is an automated workflow integrating the management, analy-sis, and visualization of RNA-Seq data. GRAPE can map the readsto the genome and/or transcriptome, and it can also work with singleor paired end reads, both stranded or not.

It organizes the RNA-Seq datasets in projects, that is, sets of data-sets, that are generated within the same study and are all analyzed

using the same annotation files and reference genome sequence.

Two MySQL databases store the information generated by theindividual steps in the pipeline allowing for efficient storage andretrieval of data. One of these is used to store the metadata (celltype, RNAextract, etc...) as well as information that is independentof the reads (junction libraries, indices location, etc...) and the otherfor the results priduced for each of the datasets

Grape uses the Buildout package (’http://www.buildout.org’)to set up all necessary components for running the analysis, andRaisin to visualize the data through a web browser.

Buildout is a widely used Python package for assembling anddeploying applications from multiple parts. In our case, Buildoutinstalls all the programs used by Grape, like GEM and the FluxCapacitor, and makes the Grape pipeline scripts available to the indi-vidually configured RNA-Seq analysis pipelines. Each pipeline islinked to one of the datasets in the project, and has its own indepen-dent directory structure. The pipelines are run in parallel, and canbe inspected through log files tracing all execution steps.

The only requirement for the Buildout is a set of configurationfiles containing information on the read files and parameters to beused during the analysis, such as annotation, number of mismatchesallowed during mapping, location of data files, etc. These confi-guration files also serve as a way to track all analysis performedfor a project and the metadata associated to a project. Most import-antly, they allow future reproducibility of the results. Internally, wemanage all project configurations within the version control systemSubversion (SVN), so they can be easily updated and traced.

Buildout installs two central scripts that are needed for runningthe individual pipelines. The first is the start.sh, that insertsthe pipeline information into the metadata MySQL database. Thesecond is the execute.sh script that runs the actual pipeline andstores all its results in the results MySQL database. The analysesare executed as so called ’rules’, through a template file (similarto a Makefile). Grape can be run on a single data set or simulta-neously on several data sets. The first common database holds allmeta data concerning the pipeline runs, such as information regar-ding the input data. It is also used as a persistent cache for someshared data, like the library of splice junction coordinates retrievedfrom the annotation. The second database contains all informationfrom the individual runs that are specific to each dataset, and inclu-des the statistics needed for subsequent analysis. Finally, it stores thetime stamp of each executed rule of a dataset, which makes Grapecapable of resuming work seamlessly after interruption.

Using a rule based approach offers the flexibility of being able toselect only a subset of steps to execute, while automatically devo-ting resources only to the inferred analysis steps. Each step containsa set of prerequisite steps that need to be completed before the stepitself can be exectued, and a set of instructions that will be perfor-med by the step. This information, encoded in a template file, is readby the execution script. This script builds a graph of dependenciesbetween each of the different steps and executes those necessaryto reach the required step. This allows Grape to perform all ana-lyis steps in an ordered and easily reproducible manner, and at thesame time, to store results and keep track of successfully comple-ted steps This is particularly important when the aim is to analyzemany large datasets in a consistent manner. An additional advantage

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is that the template file contains all the steps executed by the pipe-line in a human readable way. By editing the template file, steps canbe removed, added or modified.

Grape results include expression levels provided as RPKMsand/or unique read counts for different features, such as genes,transcripts, exons and junctions. They can be accessed through thecommand line by directly querying the Grape databases or throughthe Raisin web-application. Raisin is also based on Buildout, anduses the same configuration files used for producing the pipelines.Raisin accesses the results MySQL database created by Grape, anddisplays and summarizes information produced at the major steps inthe analysis pipeline (see below).

The Raisin web server comes with two configurations, one foraccessing Grape locally on a workstation, and another, for dis-semination of the results on a local Intranet or the Internet forcollaborative projects.

Currently, RNA-Seq data processed by Grape from severalpublic projects, such as the Illumina Body Map Project (HBM) orENCODE (ENCODE Consortium (2004, 2011)) can be browsed at’http://rnaseq.crg.es’ 1.

2.2 Analysis implemented in GrapeHere we describe the analysis steps implemented in Grape and thetools used to perform them. These tools are included in the stan-dard distribution of Grape. However, alternative tools can be usedfor the analysis, provided they comply with popular input/outputformats. To illustrate the Grape analyses, we use an RNA-Seqdata set from the Illumina Human Body Map (HBM) project2

’http://rnaseq.crg.es/project/ENCODE/tab/experiments/’as an example. Here, PolyA+/random primed RNA from a mix-ture of tissues was interrogated. Three different isolation methodswere employed: PolyA+, PolyA+ normalizing the RNA in orderto more efficiently sample lowly expressed transcripts, and Ribominus instead of PolyA+ to remove the ribosomal RNA. The threesamples were sequenced using the Illumina HiSeq 2000 at a readlength of 100nt. Each of these sequencing experiments producedon average 400 million reads. The Raisin interface to the out-put produced by the Grape analyis of this data is available at’http://rnaseq.crg.es/project/HBM/tab/experiments/’.

The main steps in the Grape analysis (illustrated in fig 1) are

• Preprocessing and Quality checks.

• Mapping

• Post-Mapping

• Transcript Quantification

• Discovery and delineation of novel transcribed elements

• Summary statistics

Preprocessing and Quality Checks: Before processing thereads, Grape creates a number of files and database tables required

1 ’http://rnaseq.crg.es/project/HBM/tab/experiments’and ’http://rnaseq.crg.es/project/ENCODE/tab/experiments/’2 Additional information on the sample and the raw data can be obtainedfrom ’http://www.ncbi.nlm.nih.gov/’

Fig. 1: Overview of the three main components of the Grape RNA-Seq analysis pipeline environment. Buildout: Performs the initialconfiguration. MySQL: Two databases are used, one containinginformation specific to the datasets analyzed such as quantificationsdetected elementes, etc... Another contains information dependenton genome and annotation, as well as metainformation that allowsfor the linking of the different datasets. Raisin web application:Allows the visualization of the analysis summaries using a webbrowser

for performing the analyses, and storing the results. Experimentsare organized according to the metadata given in the Buildoutconfiguration files.

Next, Grape produces some basic statistics, and checks the qualityof the RNA-Seq data by verifying the data format, calculating thedistribution of ambiguous bases 3 and tracing quality scores alongthe reads. Figures 2a, 2b and 2d illustrate the Raisin interface tosome of these QC steps. These initial QC steps contribute to assesswhether additional preprocessing, such as trimming and/or filteringof the reads is necessary. However, Grape only provides the QCinformation, and it is up to the user to decide whether additionalpre-processing is required. Grape’s architecture allows for conveni-ently running it in stages. For example, Grape may be run first upto the QC step, and re-started from there after completion of the QCanalysis.

Mapping: Grape’s next step is the alignment of the shortsequence reads to the reference genome. This step is crucial forthe RNA-Seq analysis, and particular care has to be taken, since itwill condition any downstream analyses. Grape alignment moduleis quite complex, performing several alignments, and then combi-nes them into a final mapping, from which a BAM file is produced.By default, Grape uses GEM (Marco-Sola et al. (2012)), an exhau-stive short read mapper, allowing mismatches and indels (but otheraligners could be used as long as the output format is or can beconverted to standard BAM/SAM).

3 Bases where the sequencer was unable to call the base correctly andassigned it an N

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](a) Quality scores (b) Ambiguous bases

(c) Summary of the reads in the dataset

(d) Example of a summary shown for the reads in the dataset beforemapping

Fig. 2: Raisin visualization of Grape’s QC step. Panels a and b showthe distribution of quality scores and ambiguous nucleotides alongthe length of the reads. Panel c and d show summaries of the numberof reads in the dataset as well as the fraction of reads with no ambi-guous bases and the number of unique sequences as a percentage ofthe total.

As most NGS aligners, GEM requires an “index creation” stepprior to the actual read mapping. This step preprocesses the refe-rence sequence, creating a data structure that can be searched fastand efficiently. The choice of indices determines which referencesequences the raw reads will be searched against. The most obviousindex to be used is the one corresponding to the genome sequenceof the species investigated. However, when examining transcrip-tome data, reads mapping across splice junctions will not match thegenome sequence, and it is convenient to create a specific index cor-responding to the splice junctions. Thus, in addition to the genomeindex, Grape generates a junctions index that contains annotatedsplice junctions, plus all possible junctions that can be obtained bybiologically legal combinations of exons within each locus. Thistype of approach has already been show to distinguish differentalternative splice forms when used to design splicing microarrays(Johnson et al. (2003)).

Grape aligns reads against both the genome and the junction refe-rences. Junction mappings are filtered to remove those reads thatdo not span the splice site. Next, the remaining unmapped reads

Fig. 3: Overview of Grape’s mapping strategy. The initial genomeand junctions mapping is followed by a round of split mapping.Remaining unmaped reads are aligned with additional mismatches,and the still remaing ones are iteratively trimmed. The mappingsresulting from the different steps are combined into a final mergedmapping.

are mapped using the GEM split-mapper (Marco-Sola et al. (2012)).This tool will attempt to divide the read into two parts and align eachfragment independently to the genome. Only alignments matchingconsensus splice sites are further considered. This allows Grape torecover the reads mapping to unannotated “bona fide” splice sites,

After this step there may still be some unmapped reads–corresponding to unsequenced regions of the genome, genomecontaminants, large number of mismatches with the references, etc.Grape follows a quite agressive strategy in an attempt to reliablymap as many reads as possible, and towards that end it performsadditional rounds of mapping. First, it successively increases byone (by default) the number of allowed mismatches up to a numberwhich is proportional to the length of the read (by default, up to 1mismatch per 25 nt of read length). For each number of mismatches,a new genome mapping (including split-mapping) is performed.Second, remaining unmapped reads are successively trimmed by aset number of nucleotides (10 by default) and a new genome (andsplit) mapping performed after each trimming. The agressiveness ofthe trimming can be controlled by the user. A parameter sets theshortest length to which the read can be trimmed. If this length isequal to the read length, then no trimming is performed.

This iterative mapping ends when all reads are mapped, or thelength of the reads falls below a certain threshold (25 nt, by default).A diagram describing it can be seen in figure 3. This approachis similar to the one described in (Cloonan et al. (2009)).The Rai-sin ouptut, providing summary statistics of the Grape mappings, isshown in Figure 4.

Post-mapping: The reads that align in the initial round of map-ping (genome, junction and split-mapping) are examined and divi-ded into those reads that map to one location better than to any other

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(a) Mapping Summary

(b) Mapping Merged (c) Mapping Genome

(d) Mapping Junctions (e) Mapping Split

Fig. 4: Raisin visualization of Grape’s mapping step. Panel a showsthe overall mapping results as well as the information on the genomeannotation and number of mismatches used for the alignments.Panel b shows the fraction of reads aligned in the final merged map-ping. Panels c, d and e show the same type of information for thedifferent components of the mapping process.

(unique maps) and those that align to at least two positions equallywell (multi-maps).

This is an important filtering step since a unique map implieshigher reliabilty in the alignment than a multi map. However, exclu-ding all multimaps from an analysis results in the potential loss ofinformation. The eventual use of one read alignment type versus theother will depend on the type of the analysis. For example, for taskssuch as the identification of novel genes, or the detection of lowabundance transcripts, the confidence of the results increases if onlyuniquely mapped reads are considered. Other tasks, like the calcu-lation of genome/transcriptome coverage may use all the mappedreads.

Those reads that can be aligned to a unique position in the genomeand also to a unique position in the junctions are also consideredmulti-maps, and are in many cases indicators of the presence ofa pseudogene or processed copy of a gene in the genome. In thecase of paired end reads, if one of the members of the pair can bealigned uniquely to a certain position, the corresponding mate cansometimes be rescued if it is a multi-map. However, in the case ofRNA-Seq reads, in contrast genome DNA reads, the insert size can-not always be used to identify the correct mate, as the presence ofintrons can alter the distance between mates. Grape chooses the clo-sest mapping position to the uniquely mapping mate in these cases.In the case of the example analyzed here, Grape assigns on average52% of the reads to a unique position while 25% of the reads aremultimapping. (Figure 4a)

Fig. 5: Distribution of uniquely mapping reads along the annotatedtranscripts. This allows us to identify biases that may be caused byissues such as RNA degradation.

The results of the different mapping steps are combined into afinal mapping results file in GFF, Bed or SAM/BAM format. Thisfile can be uploaded to genome browsers for visualization and com-parison or be analysed by other programs. Grape uses this filespecifically for the quantification of genes and transcripts, and forthe delineation of transcript structures (see next).

Transcript Quantification: Grape uses the mapping results toproduce quantifications of the abundance of a number of transcribedelements: exons, splice junctions, genes and transcripts, as well asinclusion indices for exons.

Prior to the quantification, Grape investigates the distribution ofmapped reads along transcripts to identify potential 3’ to 5’ biases.Towards that end, the annotated transcripts are binned according totheir length (See figure 5.)

We employ two strategies for quantifying exons and genes: 1)simple overlap, and 2) read deconvolution. Under the first scenario,exons and genes are quantified by simply summing all the uniquelymapping reads that are fully included within the boundaries of theexon or gene. Results are given in Reads Per Kilobase per Millionmapped reads (RPKM (Mortazavi et al. (2008))). Note that readscompletely included in two or more overlapping exons will be coun-ted separately for each exon. Exon quantifications in these cases willbe over-estimated.

To produce quantifications of individual transcripts we use theFluxCapacitor (Montgomery et al. (2010)) The FluxCapacitor con-verts the transcript structure of each annotated locus into a splicinggraph, where junctions are represented as nodes and exons as edges.The mapping of the reads into the graph imposes a number ofconstraints that the FluxCapacitor represents as a system of linearequations, which can be solved using linear programming.

Raisin plots the distribution of expression of all genes (Fig. 6a),and lists the top 20 highly expressed transcripts (Fig. 6c), and genes(Fig. 6b), and from the Raisin interface it is possible to navigate tothe expression values of all transcripts and genes (in html, csv andexcel formats).

Splice junctions are quantified using the number of reads span-ning the junction. In this case no need for feature length normaliza-tion is required, and the choice of normalization based on numberof reads in different samples is left to the criteria of the user (Fig.7a). The detected splice sites are further classified: “Known” are

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(a) Expression profile of the detected genes

(b) Top genes (c) Top transcripts

Fig. 6: Raisin visualization of the transcript quantification step.Panel a shows the distribution of gene expression. Panels b andc show respectively the top genes and transcripts detected in thedifferent lanes of the analyzed samples.

junctions which appear in the annotation. “Novel” are junctions for-med between annotated exons from the same gene, but not presentin the annotation. “Novel from unannotated exons” are split-mapjunctions, in which at least one of the two connected “exons” is notannotated. In this last group we also include any junction detectedbetween exons from different genes.

Grape also computes an exon inclusion ratio for each annotatedinternal exon. Exon inclusion is computed as the ratio of all readssupporting the inclusion of the exon (reads mapping to exon junc-tions that include it and the exon itself) to all reads supporting itsexclusion. These are reads mapping to junctions from that gene thatskip the exon. See figure 7c

Discovery of novel transcribed elements: Grape runs a numberof analysis to identify novel transcribed elements. First, RNA-Seqclusters are built from uniquely mapping reads, using either geno-mic mappings, junctions maps, or split reads. There is no thresholdfor the number of reads that make a cluster, but Grape providesthe number of reads, and of staggered reads making each cluster.Grape also detects novel splice junctions through the split mappingof reads. These are the junctions classified as novel from unanno-tated exons (see above). Finally Cufflinks (Trapnell et al. (2010)) isemployed to infer transcript structures.

Grape also implements a simple procedure to identify chimericRNAs independently of those cases found by Cufflinks, which canbe used if the input is paired end reads. Here, both mates of all readpairs mapping uniquely (with up to the specified number of mis-matches) to the transcriptome are evaluated. If they map to different

(a) Detected splice sites (b) Reads Supporting Exon inclu-sion

(c) Distribution of inclusion values

Fig. 7: Raisin visualization of Grape’s splicing analysis. Panel ashows the summary table for the detected splice sites. Panel b con-tains a table with the top included exons in the samples examinedand panel c shows the distribution of the inclusion values over allinternal exons.

Fig. 8: Raisin overview of Grape’s analysis results.

transcripts, they are classified as unannotated splice variants if bothof these transcripts belong to the same gene. Otherwise, they mapto transcripts from different annotated genes, and are classified asputative chimeric RNAs or fusions.

Summary statistics: A page including summary statistics fromall the different analysis steps in Grape is produced and it can beaccessed through Raisin (Figure 8). From this page it is possible tonavigate to each specific analysis.

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Additional analysis: Additional analyses can be implementedwithin Grape by simply adding an entry to the workflow templatefile specifying the computations to be performed in the additionalstep. The new entry is a sequence of shell commands that specifythe computations that need to be performed. The sequence of com-mands is preceded by a line which contains the name of the newstep and its dependencies. These dependencies are just the names ofthose other steps that need to be executed before in order to generatethe input required by the new step. For instance, in the entry

preprocess: start$BIN/preprocess.RNAseq.plmv preprocess.RNAseq.log $LOG

The first line contains the name of the step, (’preprocess’) itsdependencies, in this case a step named start and the followinglines the commands to be run. The variables set by Grape, suchas BINandLOG, are listed at the start of this file and can be usedby any of the commands in the template file

3 DISCUSSIONHere we presented and discussed Grape, an architecture for a com-putational pipeline for the analysis of millions or billions of shortreads obtained from (potentially many) high throughput RNA-Seqexperiments. This is a general and flexible pipeline that combinescontributions of previous studies with our own experience dealingwith RNA-Seq data. Grape attempts to address the challenges bothfrom the processing and management stanpoints associated to theanalysis ot sheer amounts of data. It automates the processing andanalysis steps, while at the same time providing an organizationalframework that simplifies the management and summarizing of theanalysis.

The full workflow of the pipeline fits in a single text file spe-cifying the dependency graph of the pipeline’s rules. Adding a newanalysis step in Grape is as simple as defining the parent steps thathave to be executed beforehand, and the programs and scripts thatneed to be executed by the new step.

Grape’s objective is to produce quantifications of transcript abun-dances (and of the abundances of other transcriptional elements:genes, exons, splice junctions, etc). Grape does not perform stati-stical analysis and comparisons of expression or splicing usage bet-ween and/or accross samples. From Grape’s output quantifications,however, other methods and tools can be employed to perform suchanalysis (for instance, R statistical pachages within Bioconductor(’http://pypi-ranking.info/module/zc.buildou’) forexpression analyses, etc...). Integrating Grape’s quantification withanalyis of gene expression and alternative spilcing is among the fur-ther developments planned wihin Grape’s roadmap. In the very shortterm, we plan to incorporate a method to assess data reproducibility.The method, called Irreproducible Discovery Rate (Li et al. (2011)),has been pioneered in the framework of the ENCODE project, andit can be applied to experiments with two replicates. Other deve-lopments include transcript assembly and quantifications in absenceof a genome of reference. This could be particularly useful for spe-cies with transcriptome data, but not sequenced genome. Note thatGrape can already be directly used to produce quantifications if anindex from a reference transcriptome–independently assembled–is

generated. Current efforts are, however, mostly focused to stream-line the pipeline in order to maximize speed and minimize memoryusage, to simplify installation of the package, to provide graphicalsupport for interactive usage, and to improve graphical reporting ofthe results. Currently, as of July 2012, Grape is on version 1.6, andwe provide regular upgrades.

We have so far used succesfully Grape for the in house analy-sis of over a thousand large RNA-Seq data sets (from projects suchas ENCODE (ENCODE Consortium (2004, 2011)), Illumina BodyMap (’rnaseq.crg.es/project/ENCODE/tab/experiments/’),GTEx (’www.genome.gov/gtex’), ICGC (Puente et al. (2011)),Geuvadis (’www.geuvadis.org’), Quantomics (’www.quantomics.eu’)and others.

Given the unique role of RNA as both a proxy and a determi-nant of the cellular and organism phenotype, profiling of RNA byRNA-Seq will spread beyond basic research, in Medicine, Agricul-ture, Biotechnology, and other technical applications of Biology.RNA-Seq could be used, for instance, for continuous ambulatorymonitoring of tumor response to treatment. It could become a stan-dard component of blood tests, a single assay monitoring many morevariables that current biomarker assay. Such applications of RNA-Seq require, however, analysis tools (mapping, quantification, etc.)that are order of magnitude more efficient than currently existingones. They require, in addition, robust, efficient and scalable soft-ware systems for RNA-Seq data storage, organization and analysis.The lack of such systems is often seriously limiting the utility ofRNA-Seq data, and may prevent researchers to embark in a medium-to large-scale RNA-Seq projects–which are otherwise within tech-nological reach. We believe that Grape may contribute to fill such avoid.

ACKNOWLEDGEMENTWe would like to thank Giovanni Bussotti, Jia-Ming Chang, AndreaTanzer and Darek Dedra for their patience testing the first imple-mentations as well as their helpful comments and suggestions. JensSteuck (Bioinformatik, University of Leipzig) for testing it.

Funding: The research leading to these results has received fun-ding from the European Union Seventh Framework Programme(FP7/2007-2013) under grant agreement 282510. This workhas been carried under grants BIO2011-26205 from Ministeriode Educacin y Ciencia (Spain) and INB GNV-1 and RETICSRD07/0067/0012 from ISCIII (Spain).

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