research article workflow for genome-wide...

Research ArticleWorkflow for Genome-Wide Determination of Pre-mRNASplicing Efficiency from Yeast RNA-seq Data

Martin Plevorovskyacute Martina Haacutelovaacute Katelina Abrhaacutemovaacute Jiliacute Libus and Petr Folk

Department of Cell Biology Faculty of Science Charles University Vinicna 7 128 43 Prague 2 Czech Republic

Correspondence should be addressed to Martin Prevorovsky prevorovnaturcunicz

Received 8 September 2016 Accepted 2 November 2016

Academic Editor Guohua Xiao

Copyright copy 2016 Martin Prevorovsky et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Pre-mRNA splicing represents an important regulatory layer of eukaryotic gene expression In the simple budding yeastSaccharomyces cerevisiae about one-third of all mRNA molecules undergo splicing and splicing efficiency is tightly regulatedfor example during meiotic differentiation S cerevisiae features a streamlined evolutionarily highly conserved splicing machineryand serves as a favourite model for studies of various aspects of splicing RNA-seq represents a robust versatile and affordabletechnique for transcriptome interrogation which can also be used to study splicing efficiency However convenient bioinformaticstools for the analysis of splicing efficiency from yeast RNA-seq data are lackingWe present a complete workflow for the calculationof genome-wide splicing efficiency in S cerevisiae using strand-specific RNA-seq data Our pipeline takes sequencing reads in theFASTQ format and provides splicing efficiency values for the 51015840 and 31015840 splice junctions of each intron The pipeline is based on up-to-date open-source software tools and requires very limited input from the user We provide all relevant scripts in a ready-to-useform We demonstrate the functionality of the workflow using RNA-seq datasets from three spliceosome mutants The workflowshould prove useful for studies of yeast splicing mutants or of regulated splicing for example under specific growth conditions

1 Introduction

In eukaryotes coding parts of genes the exons are inter-rupted by noncoding parts the introns The process throughwhich introns are removed and exons are joined togetheris called splicing It occurs via two consecutive transester-ification reactions which are catalysed by the spliceosomea large dynamic ribonucleoprotein complex composed offive snRNP particles (U1 U2 U4U6 and U5) and otherassociated protein complexes like the Nineteen Complex(NTC in yeast CDC5L in mammals) (reviewed in [1])Splicing must occur very precisely as even a single nucleotideshift may lead to a frameshift which could cause manydisorders including cancer [2 3] Therefore regulation ofsplicing has an important role in gene expression

Introns are defined by core sequences comprising the 51015840splice site branch site and the 31015840 splice site In metazoansadditional sequences are needed for recruiting various trans-acting regulatory factors which modulate the binding ofspliceosome subunits and splice site choice and efficiency

deciding on the splicing outcomeThis is important especiallyfor alternative splicing (reviewed in [4])

In contrast to higher eukaryotes whose genes typicallycontain multiple short exons alternating with introns up toseveral kilobases long [5] gene structure in the buddingyeast Saccharomyces cerevisiae is much simpler Only fivepercent of the almost 6000 yeast genes contain intronsusually just one [6] However the intron containing genesare very highly expressed As a result about one-third of alltranscripts are spliced [7] The consensus sequences of theyeast core spliceosome signals are well defined (GUAUGUfor the 51015840 splice site UACUAAC for the branch site with thebranching A in bold and AG for the 31015840 splice site) [6] Alsothere are only few cases of alternative splicing in buddingyeast (reviewed in [8]) and regulation of splicing efficiencyplays a more prominent role for example during meiosis[9] or under environmental stress [10 11] For example theconstitutively transcribed intron containing genes REC107AMA1 SPO22 and MER3 are efficiently spliced and pro-cessed to form functional mRNAs only during meiosis when

Hindawi Publishing CorporationBioMed Research InternationalVolume 2016 Article ID 4783841 9 pageshttpdxdoiorg10115520164783841

2 BioMed Research International

Table 1 RNA-seq datasets used in this study

Genotype ArrayExpress acc numbera ENA acc numberb Read length (nt) Total reads Reads with MAPQge 10

reads with MAPQge 10

WT E-MTAB-5149 ERR1709739 100 27 789 829 25 329 092 912ERR1709740 100 22 000 062 20 402 556 927

prp45(1-169) E-MTAB-5149 ERR1709737 100 27 842 215 25 491 566 916ERR1709738 100 25 156 639 23 359 541 929

WT E-GEOD-44219 SRX233529 100 21 012 048 17 127 536 815prp4-1 E-GEOD-44219 SRX233535 100 17 142 559 14 457 015 843WT E-GEOD-49966 SRR953535 101 35 203 753 7 655 225 218prp40-1 E-GEOD-49966 SRR953537 101 17 326 529 3 596 304 208aAccession number for the ArrayExpress database (httpswwwebiacukarrayexpress)bAccession number for the European Nucleotide Archive (httpwwwebiacukena)

the Mer1 splicing factor is expressed [9 12ndash14] Also variousenvironmental stresses can lead to differential changes insplicing efficiency in specific groups of genes amino acidstarvation inhibits splicing of the ribosomal protein geneswhile ethanol stress has no effect on this group of genes butalters splicing in another group [10 11]

The yeast spliceosome consists of sim90 proteins thatis around half of the proteins identified in the humanspliceosome However nearly all of the yeast spliceosomecomponents have counterparts in human Therefore it wassuggested that the S cerevisiae spliceosome represents an evo-lutionarily conserved core of the splicingmachinery Accord-ingly many of the human-specific spliceosomal proteins areneeded for the regulation of alternative splicing a featurealmost missing in the budding yeast [15] This together withthe ease of cultivation and genetic manipulation led to theestablishment of the budding yeast as a favourite model forstudying the basic mechanisms of pre-mRNA splicing

To study the influence of genetic perturbations or envi-ronmental conditions on splicing it is important to quantifythe splicing efficiency Splicing efficiency is traditionallycalculated as the amount of mRNA divided by the amountof pre-mRNA The gold standard for mRNA and pre-mRNAquantification is the use of quantitative PCR (RT-qPCR) withprimers spanning exon-intron and exon-exon junctions (eg[16])However this approach is feasible formeasuringmRNAand pre-mRNA levels for only a limited number of genesBy contrast ultrahigh-throughput sequencing of RNA (RNA-seq) allows comprehensive splicing analysis at the genome-wide scale [17ndash19]There are multiple paradigms for calculat-ing splicing efficiency from RNA-seq data which are basedon comparing sequencing read counts from intronic andexonic regions or also take into account exon-exon junctionreads (transreads)Themethods also vary in the length of thewindow considered (eg 25 bp around a splice site versus awhole exon) [17 18 20ndash24] RNA-seq is a simple robust andaffordable technique and there is now a wealth of publiclyavailable RNA-seq datasets [25 26] Together this makesRNA-seq a convenient method for genome-wide determi-nation of splicing efficiency although the bioinformaticsanalyses involved might be challenging for nonspecialistsHere we present a complete and documented up-to-date

workflow for semiautomatic calculation of genome-widesplicing efficiencies from strand-specific RNA-seq data in Scerevisiae

2 Materials and Methods

21 RNA-seq Datasets Sequencing reads from strand-specific transcriptome profiling of splicing mutants (prp45(1-169) prp4-1 and prp40-1) and their corresponding wild-typeS cerevisiae strains [17 24] were downloaded from theEuropean Nucleotide Archive (httpwwwebiacukena)in FASTQ format (more information about the various fileformats used in this study can be found at httpsgenomeucsceduFAQFAQformathtml) and experiment metadatawere obtained from ArrayExpress (httpswwweabiacukarrayexpress) The relevant database accession numbersare given in Table 1 Importantly to simplify downstreamanalyses only ldquoread 1rdquo FASTQ files were used from paired-end sequencing datasets

Thepipeline requires that strand-specific sequencing readdata are used (true for most currently generated RNA-seq datasets) As pervasive antisense transcription has beenreported in many eukaryotic organisms including yeasts[27] strand specificity of sequencing reads helps to separatethe contributions of potential overlapping senseantisensetranscripts

Sequencing read quality and potential contamination byadapters andor PCR primers was checked with FastQC0115 (httpwwwbioinformaticsbabrahamacukprojectsfastqc) All datasets were found suitable for further anal-yses However if needed contaminating and low-qualitysequences can be filtered andor trimmed using tools such asTrimmomatic [28]

22 Read Mapping Reads were aligned to S cerevisiaegenome (Ensembl R64-1-1) with the fast splice-awareHISAT2 aligner (version 204) using S cerevisiae genomeindex containing transcript structures [29] Minimum andmaximum intron length parameters were set to 20 and10000 nt respectively More details on HISAT2 parametersettings can be found in the ldquoworkflowshrdquo shell script in theSupplementary Material available online at httpdxdoiorg10115520164783841

BioMed Research International 3

Subsequently samtools 131 were used to filter reads bytheir mapping quality score (MAPQ ge 10) to keep only readsthat aligned unambiguously to a single locus and to sort andindex the resulting BAM files [30] Read mapping was alsoassessed visually in the IGV browser 2369 [31]

We also tried aligning reads from the PRP45-relateddatasets with TopHat2 [32] the widely used predecessor ofHISAT2 Compared to TopHat2 the HISAT2 alignment stepwas sim2-fold faster and the mapping supported identificationof sim11 more transreads and calculation of splicing efficien-cies for sim4 more splice sites

23 Splice Site Identification and Counting of Transreads Pu-tative splicing events were detected by regtools 020 (httpsregtoolsreadthedocsioenlatest) These tools look for pre-sumed transreads (reads spanning exon-exon junction) inBAMfiles compile a table of all identified putative splice sitesand their characteristics and provide the counts of transreadsspanning these splice sites Minimum and maximum intronlength parameters were set to 20 and 10000 nt respectivelyDetected splice sites were also annotated and classified asknown or novel using regtools and S cerevisiae genomeannotation (Ensembl R64-1-1) in GTF format The outputfrom regtools was further processed and coordinates of basesat the very 51015840 and 31015840 ends of each known intron wereextracted (into BED format) by a custom R script (R version331 httpswwwr-projectorg) See the ldquoworkflowshrdquo andldquojunctionsRrdquo scripts in the SupplementaryMaterial for moredetails

24 Determination of 51015840 and 31015840 Intron End Coverage Readcoverage of the very first (51015840 end) and the very last (31015840 end)base of each known intron was determined from all BAMfiles in parallel using bedtools 2250 (bedtoolsmulticov) [33]It is critical to set the -split parameter to avoid includingtransreads in the intronic read counts It is also important tocorrectly set the -s-S parameters according to the sequencinglibrary preparation protocol employed to ensure that onlyreads mapped to the ldquosenserdquo strand will be counted (see theldquoworkflowshrdquo script in the Supplementary Material)

25 Splicing Efficiency Calculation The method for splicingefficiency calculation was derived from the ldquo31015840 splice siteratiordquo method described in [20] For each intron splicingefficiency was determined separately for the 51015840 splice site and31015840 splice site using the following formulas

Efficiency 51015840 = transread count51015840 intron end first base coverage

Efficiency 31015840 = transread count31015840 intron end last base coverage

(1)

Transreads only arise from spliced transcripts and thusreflect directly the abundance of mRNA molecules in whichthe particular intron has been spliced out By definitiontransreads must cover at least the very last base of exon X andthe very first base of exon X + 1 To match this single-baseresolution while counting intronic reads only those reads

covering the very first (for 51015840 splice site) and the very last (for31015840 splice site) base of the corresponding intron are countedThis allows direct comparison of pre-mRNA and splicedmRNA molecule levels (an approach most similar to the RT-qPCR gold standard see below) and separate calculation ofsplicing efficiencies at the 51015840 splice site and 31015840 splice site ofeach intron reflecting the efficiencies of the two splicingsteps

We note that for some genes our method may use only afraction of all available intronic reads (eg for mitochondrialgenes with long introns) Importantly the introns of somegenes harbour nested snoRNAs or are predicted to containpotential stable structures suggesting the presence of so faruncharacterised nested noncoding RNAs It was shown thatsuch introns can be maintained in the cell after splicing [34]potentially affecting the determination of splicing efficiencywhen sequencing reads along the whole intron are consid-ered

Depending on the sequencing library size strain geno-type or cultivation conditions many introns typically havevery low read coverage which might produce unreliablesplicing efficiency data Therefore in the final spreadsheetwe do not report splicing efficiency values for junctions withread counts below an arbitrary threshold of 5 reads for bothtransread count and intron end read count Usersmay changethis threshold if required see the ldquoefficiencyRrdquo script in theSupplementary Material for more details

26 Yeast Strains and Cell Cultivation Wild-type (BY4741-MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) and the prp45(1-169) mutant (AVY17-MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0prp45(1-169)-3-HANatMX6) strains were grown in theYPADmedium (2 peptone 1 yeast extract 001 adenineand 2 glucose) at 30∘C to 15 times 107 cellsmL Two millilitresof each culture was harvested by centrifugation and cellpellets were stored at minus80∘C

27 RNA Isolation Reverse Transcription and RT-qPCRAnalysis Total RNA was isolated with the MasterPure YeastRNA Isolation Kit (Epicentre) according to the manufac-turerrsquos protocol cDNA was prepared from 2 120583g of the totalRNA using the RevertAid First Strand cDNA Synthesis Kit(Thermo Fisher Scientific) with random hexamer primersRT-qPCR was performed using LightCycler 480 II (Roche)Each reaction (total volume of 10 120583L) was performed intriplicate and consisted of 5 120583L of the MESA GREEN qPCRMasterMix Plus for SYBR Assay No-ROX (Eurogentec) 4 120583Lof 100-fold diluted cDNA and a pair of primers 03mM each(for sequences see the SupplementaryMaterial) Primer pairswere designed to specifically amplify either the spliced or theunspliced transcripts of the ECM33 ACT1 COF1 RPL22Aand RPL22B genes Amplicons from unspliced transcriptscovered the 31015840 splice junction in ECM33 while the 51015840 splicejunction was covered in the other four genes Four to sixbiological replicates were analysed for each gene and theTOM22 and SPT15 genes were used as reference controlsRelative pre-mRNA andmRNA quantities were calculated bythe ΔΔCt method [35]


3 Results and Discussion

31 Pipeline for Determination of Splicing Efficiency We haveput together a workflow for calculating splicing efficiencyof each intron from standard RNA-seq data in S cerevisiae(Figure 1) The pipeline consists mostly of established open-source tools formanipulation and analysis of next-generationsequencing data (FastQC HISAT2 samtools regtools andbedtools) and simple custom scripts (Linux shell and R) Allscripts including parameter settings for all tools are availablein the Supplementary Material

Briefly after input quality control (FastQC) reads aremapped into S cerevisiae reference genome (HISAT2 [29])and filtered keeping only uniquely mapped reads (samtools[30] MAPQ ge 10) Positions of splice junctions (both knownand novel) and transread counts for these junctions areextracted (regtools) Read coverage of the 51015840 and 31015840 intronend base is determined (bedtools [33]) and splicing efficiencycalculated for each intron at both 51015840 and 31015840 splice junctioncorresponding to the efficiency of the first and the second stepof splicing respectively

The description of folder structure and content bothrequired before starting the analysis and produced dur-ing the analysis is given in the file ldquofolders readmerdquo inthe Supplementary Material Users need to supply genomesequence (in FASTA format) annotation (in GTF format)and HISAT2 genome index containing transcript struc-tures (in the ldquogenomerdquo folder) A ready-made S cerevisiaegenome index can be downloaded from the HISAT2 website(ldquogenome tranrdquo httpsccbjhuedusoftwarehisat2) Usersfurther need to supply RNA-seq reads in FASTQ format (inthe ldquoFASTQrdquo folder) the pipeline is designed for single-end strand-specific sequencing data Depending on theprotocol for sequencing library preparation that was usedusers might need to adjust strandness-related parameters forHISAT2 and bedtools (see theMaterials andMethods and theldquoworkflowshrdquo file in the Supplementary Material) to obtaincorrect read counts and splicing efficiency values

The main outputs of the pipeline are

(1) a table (CSV format) of transread counts from allsamples for known splice junctions (folder ldquotran-sreadsrdquo file ldquosplice junctions coverageknowncsvrdquonote that only junctions of expressed genes for whichtransreads were detected are reported in all analyses)

(2) tables of read coverage from all samples for 51015840 and31015840 terminal bases of known introns (folder ldquoin-tronsrdquo files ldquointrons known 5ssbedcountscsvrdquo andldquointrons known 3ssbedcountscsvrdquo)

(3) tables of read threshold-filtered splicing efficienciesin all samples calculated separately for 51015840 and 31015840splice sites of known introns (folder ldquoefficiencyrdquofiles ldquosplicing efficiency 5ss confcsvrdquo and ldquosplic-ing efficiency 3ss confcsvrdquo)

Users can also define a set of sample (FASTQ filename) pairsto be compared (eg mutant versus wild type see the ldquoeffi-ciencyRrdquo script in the SupplementaryMaterial)The pipelinethen produces a table of ldquorelativerdquo splicing efficiencies (folder

ldquoefficiencyrdquo files ldquorelative splicing efficiency 5ss confcsvrdquoand ldquorelative splicing efficiency 3ss confcsvrdquo) and scatter-plot images (folder ldquoimagesrdquo PDF format see Figure 2 for anexample)

Multiple approaches have been used in the past todetermine splicing efficiency from yeast RNA-seq data forexample [17 18 21 24] However the bioinformatics toolsused are often outdated now in terms of speed of processingand their abilities to work with split reads (transreads)Also the complete workflow including all relevant scriptsis usually not provided By contrast the pipeline presentedin this study is based upon the latest tools with advancedcapabilities for fast and accurate split read processing suitablefor studies of splicing efficiency [29 30 33] The pipelinealso allows convenient processing of multiple FASTQ files(samples) with very limited input from the user requiredIn case of the PRP45-related datasets mentioned below sim55million 100 nt reads were processed by the pipeline in 38minutes on a standard desktop PC (quad-core AMDA8-3870APU CPU with 8GB RAM) running 64-bit Ubuntu 1604

While the scripts provided in the SupplementaryMaterialare customized for analysis of S cerevisiae RNA-seq datathe pipeline can be easily adapted for other yeast speciesby providing the corresponding genome sequence genomeannotation and genome index (built using hisat2-build[29]) files and altering the relevant filename variables inthe ldquoworkflowshrdquo script accordingly The SupplementaryMaterial contains one such example adaptation of the pipelinefor the fission yeast Schizosaccharomyces pombe

32 Analysis of Splicing Efficiency in Spliceosome MutantsAs we are primarily interested in studying altered splicingpatterns in various spliceosome mutants we selected threepublicly available RNA-seq datasets for S cerevisiae spliceo-some mutants which show global reductions in splicingefficiency to demonstrate the function of our workflow Alldatasets contain samples with roughly similar amounts ofsequencing reads (sim17ndash35 million) of very similar length(sim100 nt) but differ markedly in data quality in terms of thepercentage of uniquely mappable reads (Table 1)

The first example dataset is focused on Prp45 an essentialsplicing factor and a component of the so-called NTC-relatedcomplex [36] Based on cryo-EM structural informationPrp45 stabilizes the catalytic centre of the spliceosomethrough interactions with many proteins and with all threespliceosomal snRNAs [37] Cells bearing the C-terminallytruncated prp45(1-169) allele are temperature-sensitive andhave deformed shapes [38] We analysed two biologicalreplicates of RNA-seq data for wild-type and prp45(1-169)cells grown at permissive temperature (30∘C) First weused the data pooled by genotype and found a globaldecrease of splicing efficiency in the mutant (Figures 2(a)and 2(b)) Next we calculated relative splicing efficiencies(ie mutant normalized to wild type) in each replicateseparately to assess reproducibility Results for introns withsufficient splice junctions coverage (ge5 transreads and ge5reads covering intron end base) showed good agreementbetween the two independent replicates (Figures 2(c) and2(d)) Finally to validate the results of our pipeline we used


RNA-seq reads(FASTQ)

Sequencing QC(FastQC)(i) Base calling quality

(ii) Adapter content

Yes

No

Clean reads(FASTQ)

Genome

Uniquely

Splice sites

Extract junction

intron ends(BED)

5 and 3

intron endcoverage

Count 5 and 3

intron end reads(bedtools)

Calculate splicingefficiency

(R)

Splicing efficiency

Genome annotation

ReadFilter amp trim reads(eg Trimmomatic) quality

ok

(GTF)(i) Exonintron

sequence(FASTA)

Map reads(HISAT2)

(i) Splice-aware

mapped reads(BAM)

Find splicing events(regtools)

(ii) Count transreads(i) Find junctions

(ii) Transread counts(i) Knownnovel

coordinates(R)

5 and 3

(ii) Plots(i) Per intron

Filter reads

(i) MAPQ ge 10(samtools)

Figure 1 Workflow for calculating splicing efficiency from RNA-seq data Files and datasets are represented by blue parallelograms (fileformats given in parentheses) and processing steps are represented by orange rectangles (tool names given in parentheses) Some filesdatasetsare used repeatedly in several steps of the workflow as signified bymultiple flow lines going from these filesdatasetsThe diagramwas createdusing drawio (httpswwwdrawio)


WT

50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)

log2(splicing efficiency)

5 splice site

Coveragege

lt5 readsge5 reads

(a)W

T

Coveragege5 readslt5 reads

50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


3 splice site

(b)

minus6 minus4 minus2 0

Pearsonrsquos r = 0925

5 splice site

log2(relative splicing efficiency)

log 2

(rela

tive

splic

ing

effici

ency

)

Replicate 1

Repl

icat

e2

minus6

minus4

minus2

0

(c)

Pearsonrsquos r = 0758minus6

minus4

minus2

0

minus4 minus2 0minus6

3 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Repl

icat

e2

Replicate 1

(d)

minus5

minus4

minus3

minus2

minus1

0

1

2

log 2

(rela

tive

splic

ing

effici

ency

)

RT-qPCRRNA-seq 5

ssRNA-seq 3

ss

ECM33 ACT1 COF1 RPL22A RPL22B

(e)

Figure 2 Splicing efficiency in the prp45(1-169) mutant Splicing efficiencies for all known introns (for 51015840 and 31015840 splice sites separately) werecalculated using the pipeline described in Figure 1 (a b) Results for two pooled biological replicates of the prp45(1-169) mutant and itscorresponding wild-type strain Higher values correspond to more efficient splicing Full circles represent values for introns with sufficientcoverage (ge5 transreads and ge5 reads covering intron end base) open circles represent low-confidence values for introns with low sequencingread coverage (c d) Relative splicing efficiencies (prp45(1-169) normalized to wild type) at the 51015840 and 31015840 splice sites were calculated foreach biological replicate separately Only introns with sufficient read coverage were considered Pearsonrsquos 119903 values for the two replicates areindicated (e) Comparison of relative splicing efficiencies at the 51015840 and 31015840 splice sites of selected genes calculated from the pooled RNA-seqdata with relative splicing efficiencies determined by RT-qPCR (means of 4ndash6 independent RT-qPCR experiments plusmn SD)


5 splice site

WT


minus5

0

5

10

0 5 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(a)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

50 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(b)

5 splice site

WT

0

5

minus5

10

50 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)



(c)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

0 5 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)


(d)

Figure 3 Splicing efficiency in the prp4-1 and prp40-1mutants Splicing efficiencies for all known introns (for 51015840 and 31015840 splice sites separately)were calculated using the pipeline described in Figure 1 (a b) Results for the prp4-1 mutant and its corresponding wild-type strain [17](c d) Results for the prp40-1 mutant and its corresponding wild-type strain [24] Higher values correspond to more efficient splicing Fullcircles represent values for introns with sufficient coverage (ge5 transreads and ge5 reads covering intron end base) open circles representlow-confidence values for introns with low sequencing read coverage

RT-qPCR to measure the levels of spliced and unsplicedtranscripts for five selected genes showing various degreesof splicing impairment in the prp45(1-169) mutant RNA-seq dataset Reassuringly the splicing efficiencies calculatedby our pipeline were concordant with those determined byRT-qPCR pointing to possible differential requirements forPrp45 in pre-mRNA splicing of specific genes (Figure 2(e))

Next we analysed data for Prp4 a structural componentof the U4U6 di- andU4U6-U5 tri-snRNP particles [39 40]

The prp4-1 temperature-sensitive allele blocks U4 snRNPdissociation during the catalytic activation of the spliceosome[41 42] Using splicing-sensitive microarrays it was shownthat this mutation causes a genome-wide splicing defect evenat the permissive temperature of 26∘C [43] We used theprp4-1 RNA-seq dataset from [17] and we confirmed theglobal impairment of splicing efficiency in this mutant atpermissive temperature (Figures 3(a) and 3(b)) Howeverfor a number of introns splicing efficiency could not be


convincingly determined (especially at the 51015840 splice site)due to low read coverage at the splice junctions This wasunexpected since the prp4-1 sequencing library sizes andmappability were comparable to the prp45(1-169) dataset(Table 1) Furthermore the whole distribution of splicingefficiencies at the 31015840 splice site (for both prp4-1 and itswild-type control) was shifted compared to the other twosplicing mutants we analysed Visual inspection of mappedprp4-1 reads revealed decreasing coverage towards 51015840 endsof genes where introns are typically located in S cerevisiaesuggesting possible problems with RNA sample preparationandor processing

The third example relates to Prp40 a component ofthe U1 snRNP particle [44] Through interactions withmany spliceosome subunits and with the phosphorylated C-terminal domain of the RNA polymerase II the Prp40 pro-tein is important for cotranscriptional spliceosome assem-bly (reviewed in [45]) The prp40-1 temperature-sensitivemutant [44] exerts a global splicing defect when shiftedto nonpermissive temperature [24] which we confirmedby running the published RNA-seq dataset through ourpipeline (Figures 3(c) and 3(d)) It should be noted thatthis dataset had poor mappability (Table 1) and yieldedrelatively low read coverage decreasing the number ofjunctions for which splicing efficiency could be calculatedconvincingly

Thus these three proof-of-principle scenarios demon-strated that our workflow is able to recapitulate previouslyidentified genome-wide decreases in splicing efficiency usingRNA-seq data The results also highlight a critical require-ment for sufficient sequencing library quality and size forsuccessful analysis

4 Conclusions

We present a complete bioinformatics workflow for deter-mining splicing efficiency in the budding yeast S cerevisiaeusing data produced by the simple and affordable RNA-seqtechnique Starting with strand-specific sequencing reads inthe FASTQ format our pipeline is able to calculate splicingefficiency at the 51015840 and 31015840 splice junctions of each intronwith very limited input required from the user All relevantscripts are provided in a documented and ready-to-use formTheworkflow should prove useful for studies of yeast splicingmutants or of regulated splicing for example under variousgrowth conditions

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

This work was supported by the Czech Science FoundationGrant 14-19002S and Charles University Grants SVV 260310UNCE 204013 and GAUK 8214

References

[1] C L Will and R Luhrmann ldquoSpliceosome structure andfunctionrdquo Cold Spring Harbor Perspectives in Biology vol 3 no7 pp 1ndash23 2011

[2] A J Ward and T A Cooper ldquoThe pathobiology of splicingrdquoJournal of Pathology vol 220 no 2 pp 152ndash163 2010

[3] R K Singh and T A Cooper ldquoPre-mRNA splicing in diseaseand therapeuticsrdquo Trends in Molecular Medicine vol 18 no 8pp 472ndash482 2012

[4] Z Wang and C B Burge ldquoSplicing regulation from a parts listof regulatory elements to an integrated splicing coderdquoRNA vol14 no 5 pp 802ndash813 2008

[5] MDeutsch andM Long ldquoIntron-exon structures of eukaryoticmodel organismsrdquo Nucleic Acids Research vol 27 no 15 pp3219ndash3228 1999

[6] M Spingola L Grate D Haussler and AManuel Jr ldquoGenome-wide bioinformatic andmolecular analysis of introns in Saccha-romyces cerevisiaerdquo RNA vol 5 no 2 pp 221ndash234 1999

[7] M Ares L Grate and M H Pauling ldquoA handful of intron-containing genes produces the lionrsquos share of yeast mRNArdquoRNA vol 5 no 9 pp 1138ndash1139 1999

[8] F Kempken ldquoAlternative splicing in ascomycetesrdquo AppliedMicrobiology and Biotechnology vol 97 no 10 pp 4235ndash42412013

[9] J Engebrecht K Voelkel-Meiman and G S Roeder ldquoMeiosis-specific RNA splicing in yeastrdquoCell vol 66 no 6 pp 1257ndash12681991

[10] M Bergkessel G B Whitworth and C Guthrie ldquoDiverseenvironmental stresses elicit distinct responses at the level ofpre-mRNA processing in yeastrdquo RNA vol 17 no 8 pp 1461ndash1478 2011

[11] J A Pleiss G B Whitworth M Bergkessel and C GuthrieldquoRapid transcript-specific changes in splicing in response toenvironmental stressrdquoMolecular Cell vol 27 no 6 pp 928ndash9372007

[12] T Nakagawa and H Ogawa ldquoThe Saccharomyces cerevisiaeMER3 gene encoding a novel helicase-like protein is requiredfor crossover control in meiosisrdquo EMBO Journal vol 18 no 20pp 5714ndash5723 1999

[13] E M Munding A H Igel L Shiue K M Dorighi L RTrevino and M Ares Jr ldquoIntegration of a splicing regulatorynetwork within themeiotic gene expression program of Saccha-romyces cerevisiaerdquo Genes amp Development vol 24 no 23 pp2693ndash2704 2010

[14] C A Davis L Grate M Spingola and M Ares Jr ldquoTest ofintron predictions reveals novel splice sites alternatively splicedmRNAs and new introns in meiotically regulated genes ofyeastrdquoNucleic Acids Research vol 28 no 8 pp 1700ndash1706 2000

[15] P Fabrizio J Dannenberg P Dube et al ldquoThe evolutionarilyconserved core design of the catalytic activation step of the yeastspliceosomerdquoMolecular Cell vol 36 no 4 pp 593ndash608 2009

[16] S Hao and D Baltimore ldquoRNA splicing regulates the temporalorder of TNF-induced gene expressionrdquo Proceedings of theNational Academy of Sciences of the United States of Americavol 110 no 29 pp 11934ndash11939 2013

[17] E M Munding L Shiue S Katzman J P Donohue andM Ares ldquoCompetition between pre-mRNAs for the splicingmachinery drives global regulation of splicingrdquoMolecular Cellvol 51 no 3 pp 338ndash348 2013


[18] T Kawashima S Douglass J Gabunilas M Pellegrini and GF Chanfreau ldquoWidespread use of non-productive alternativesplice sites in saccharomyces cerevisiaerdquo PLoS Genetics vol 10no 4 Article ID e1004249 2014

[19] D A Bitton C Rallis D C Jeffares et al ldquoLaSSO a strategyfor genome-wide mapping of intronic lariats and branch pointsusing RNA-seqrdquo Genome Research vol 24 no 7 pp 1169ndash11792014

[20] L Herzel and K M Neugebauer ldquoQuantification of co-tran-scriptional splicing from RNA-Seq datardquo Methods vol 85 pp36ndash43 2015

[21] S B Livesay S E Collier D A Bitton J Bahler andM D OhildquoStructural and functional characterization of the N terminusof Schizosaccharomyces pombe Cwf10rdquo Eukaryotic Cell vol 12no 11 pp 1472ndash1489 2013

[22] C J Grisdale L C Bowers E S Didier and N M Fast ldquoTran-scriptome analysis of the parasite Encephalitozoon cuniculian in-depth examination of pre-mRNA splicing in a reducedeukaryoterdquo BMC Genomics vol 14 no 1 article 207 2013

[23] G M Gould J M Paggi Y Guo et al ldquoIdentification of newbranch points and unconventional introns in Saccharomycescerevisiaerdquo RNA vol 22 no 10 pp 1522ndash1534 2016

[24] A Volanakis M Passoni R D Hector et al ldquoSpliceosome-mediated decay (SMD) regulates expression of nonintronicgenes in budding yeastrdquo Genes and Development vol 27 no 18pp 2025ndash2038 2013

[25] F Ozsolak and P M Milos ldquoRNA sequencing advanceschallenges and opportunitiesrdquo Nature Reviews Genetics vol 12no 2 pp 87ndash98 2011

[26] J A Reuter D V Spacek and M P Snyder ldquoHigh-throughputsequencing technologiesrdquoMolecular Cell vol 58 no 4 pp 586ndash597 2015

[27] T H Jensen A Jacquier and D Libri ldquoDealing with pervasivetranscriptionrdquoMolecular Cell vol 52 no 4 pp 473ndash484 2013

[28] AM BolgerM Lohse and B Usadel ldquoTrimmomatic a flexibletrimmer for Illumina sequence datardquoBioinformatics vol 30 no15 pp 2114ndash2120 2014

[29] D Kim B Langmead and S L Salzberg ldquoHISAT a fast splicedaligner with low memory requirementsrdquo Nature Methods vol12 no 4 pp 357ndash360 2015

[30] H Li B Handsaker A Wysoker et al ldquoThe sequence align-mentmap format and SAMtoolsrdquoBioinformatics vol 25 no 16pp 2078ndash2079 2009

[31] HThorvaldsdottir J T Robinson and J PMesirov ldquoIntegrativeGenomics Viewer (IGV) high-performance genomics datavisualization and explorationrdquo Briefings in Bioinformatics vol14 no 2 pp 178ndash192 2013

[32] D Kim G Pertea C Trapnell H Pimentel R Kelley and SL Salzberg ldquoTopHat2 accurate alignment of transcriptomes inthe presence of insertions deletions and gene fusionsrdquo GenomeBiology vol 14 no 4 article R36 2013

[33] A R Quinlan and I M Hall ldquoBEDTools a flexible suite ofutilities for comparing genomic featuresrdquo Bioinformatics vol26 no 6 Article ID btq033 pp 841ndash842 2010

[34] K B Hooks S Naseeb S Parker S Griffiths-Jones and D Del-neri ldquoNovel intronic RNA structures contribute tomaintenanceof phenotype in Saccharomyces cerevisiaerdquo Genetics vol 203no 3 pp 1469ndash1481 2016

[35] T D Schmittgen and K J Livak ldquoAnalyzing real-time PCR databy the comparative 119862T methodrdquo Nature Protocols vol 3 no 6pp 1101ndash1108 2008

[36] M D Ohi A J Link L Ren J L Jennings W H McDonaldand K L Gould ldquoProteomics analysis reveals stable multipro-tein complexes in both fission and budding yeasts containingMyb-related Cdc5pCef1p novel pre-mRNA splicing factorsand snRNAsrdquoMolecular and Cellular Biology vol 22 no 7 pp2011ndash2024 2002

[37] R Wan C Yan R Bai G Huang and Y Shi ldquoStructure of ayeast catalytic step I spliceosome at 34 A resolutionrdquo Sciencevol 353 no 6302 pp 895ndash904 2016

[38] O Gahura K Abrhamova M Skruzny et al ldquoPrp45 affectsPrp22 partition in spliceosomal complexes and splicing effi-ciency of non-consensus substratesrdquo Journal of Cellular Bio-chemistry vol 106 no 1 pp 139ndash151 2009

[39] J Banroques and J N Abelson ldquoPRP4 a protein of the yeastU4U6 small nuclear ribonucleoprotein particlerdquoMolecular andCellular Biology vol 9 no 9 pp 3710ndash3719 1989

[40] S P Bjoslashrn A Soltyk J D Beggs and J D Friesen ldquoPRP4(RNA4) from Saccharomyces cerevisiae its gene product isassociated with the U4U6 small nuclear ribonucleoproteinparticlerdquoMolecular and Cellular Biology vol 9 no 9 pp 3698ndash3709 1989

[41] L H Hartwell C S McLaughlin and J R Warner ldquoIdentifi-cation of ten genes that control ribosome formation in yeastrdquoMGG Molecular amp General Genetics vol 109 no 1 pp 42ndash561970

[42] L Ayadi M Miller and J Banroques ldquoMutations withinthe yeast U4U6 snRNP protein Prp4 affect a late stage ofspliceosome assemblyrdquo RNA vol 3 no 2 pp 197ndash209 1997

[43] T A Clark C W Sugnet and M Ares Jr ldquoGenomewideanalysis of mRNA processing in yeast using splicing-specificmicroarraysrdquo Science vol 296 no 5569 pp 907ndash910 2002

[44] H-Y Kao and P G Siliciano ldquoIdentification of Prp40 a novelessential yeast splicing factor associated with the U1 smallnuclear ribonucleoprotein particlerdquo Molecular and CellularBiology vol 16 no 3 pp 960ndash967 1996

[45] S Becerra E Andres-Leon S Prieto-Sanchez C Hernandez-Munain and C Sune ldquoPrp40 and early events in splice sitedefinitionrdquo Wiley Interdisciplinary Reviews RNA vol 7 no 1pp 17ndash32 2016

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


Table 1 RNA-seq datasets used in this study

Genotype ArrayExpress acc numbera ENA acc numberb Read length (nt) Total reads Reads with MAPQge 10

reads with MAPQge 10

WT E-MTAB-5149 ERR1709739 100 27 789 829 25 329 092 912ERR1709740 100 22 000 062 20 402 556 927

prp45(1-169) E-MTAB-5149 ERR1709737 100 27 842 215 25 491 566 916ERR1709738 100 25 156 639 23 359 541 929

WT E-GEOD-44219 SRX233529 100 21 012 048 17 127 536 815prp4-1 E-GEOD-44219 SRX233535 100 17 142 559 14 457 015 843WT E-GEOD-49966 SRR953535 101 35 203 753 7 655 225 218prp40-1 E-GEOD-49966 SRR953537 101 17 326 529 3 596 304 208aAccession number for the ArrayExpress database (httpswwwebiacukarrayexpress)bAccession number for the European Nucleotide Archive (httpwwwebiacukena)

the Mer1 splicing factor is expressed [9 12ndash14] Also variousenvironmental stresses can lead to differential changes insplicing efficiency in specific groups of genes amino acidstarvation inhibits splicing of the ribosomal protein geneswhile ethanol stress has no effect on this group of genes butalters splicing in another group [10 11]

The yeast spliceosome consists of sim90 proteins thatis around half of the proteins identified in the humanspliceosome However nearly all of the yeast spliceosomecomponents have counterparts in human Therefore it wassuggested that the S cerevisiae spliceosome represents an evo-lutionarily conserved core of the splicingmachinery Accord-ingly many of the human-specific spliceosomal proteins areneeded for the regulation of alternative splicing a featurealmost missing in the budding yeast [15] This together withthe ease of cultivation and genetic manipulation led to theestablishment of the budding yeast as a favourite model forstudying the basic mechanisms of pre-mRNA splicing

To study the influence of genetic perturbations or envi-ronmental conditions on splicing it is important to quantifythe splicing efficiency Splicing efficiency is traditionallycalculated as the amount of mRNA divided by the amountof pre-mRNA The gold standard for mRNA and pre-mRNAquantification is the use of quantitative PCR (RT-qPCR) withprimers spanning exon-intron and exon-exon junctions (eg[16])However this approach is feasible formeasuringmRNAand pre-mRNA levels for only a limited number of genesBy contrast ultrahigh-throughput sequencing of RNA (RNA-seq) allows comprehensive splicing analysis at the genome-wide scale [17ndash19]There are multiple paradigms for calculat-ing splicing efficiency from RNA-seq data which are basedon comparing sequencing read counts from intronic andexonic regions or also take into account exon-exon junctionreads (transreads)Themethods also vary in the length of thewindow considered (eg 25 bp around a splice site versus awhole exon) [17 18 20ndash24] RNA-seq is a simple robust andaffordable technique and there is now a wealth of publiclyavailable RNA-seq datasets [25 26] Together this makesRNA-seq a convenient method for genome-wide determi-nation of splicing efficiency although the bioinformaticsanalyses involved might be challenging for nonspecialistsHere we present a complete and documented up-to-date

workflow for semiautomatic calculation of genome-widesplicing efficiencies from strand-specific RNA-seq data in Scerevisiae

2 Materials and Methods

21 RNA-seq Datasets Sequencing reads from strand-specific transcriptome profiling of splicing mutants (prp45(1-169) prp4-1 and prp40-1) and their corresponding wild-typeS cerevisiae strains [17 24] were downloaded from theEuropean Nucleotide Archive (httpwwwebiacukena)in FASTQ format (more information about the various fileformats used in this study can be found at httpsgenomeucsceduFAQFAQformathtml) and experiment metadatawere obtained from ArrayExpress (httpswwweabiacukarrayexpress) The relevant database accession numbersare given in Table 1 Importantly to simplify downstreamanalyses only ldquoread 1rdquo FASTQ files were used from paired-end sequencing datasets

Thepipeline requires that strand-specific sequencing readdata are used (true for most currently generated RNA-seq datasets) As pervasive antisense transcription has beenreported in many eukaryotic organisms including yeasts[27] strand specificity of sequencing reads helps to separatethe contributions of potential overlapping senseantisensetranscripts

Sequencing read quality and potential contamination byadapters andor PCR primers was checked with FastQC0115 (httpwwwbioinformaticsbabrahamacukprojectsfastqc) All datasets were found suitable for further anal-yses However if needed contaminating and low-qualitysequences can be filtered andor trimmed using tools such asTrimmomatic [28]

22 Read Mapping Reads were aligned to S cerevisiaegenome (Ensembl R64-1-1) with the fast splice-awareHISAT2 aligner (version 204) using S cerevisiae genomeindex containing transcript structures [29] Minimum andmaximum intron length parameters were set to 20 and10000 nt respectively More details on HISAT2 parametersettings can be found in the ldquoworkflowshrdquo shell script in theSupplementary Material available online at httpdxdoiorg10115520164783841









(1)


























Yes

No

Clean reads(FASTQ)

Genome

Uniquely

Splice sites

Extract junction

intron ends(BED)

5 and 3

intron endcoverage

Count 5 and 3



(R)

Splicing efficiency

Genome annotation


ok

(GTF)(i) Exonintron

sequence(FASTA)

Map reads(HISAT2)

(i) Splice-aware

mapped reads(BAM)




coordinates(R)

5 and 3


Filter reads




WT

50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


5 splice site

Coveragege

lt5 readsge5 reads

(a)W

T


50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


3 splice site

(b)



5 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Replicate 1

Repl

icat

e2

minus6

minus4

minus2

0

(c)


minus4

minus2

0


3 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Repl

icat

e2

Replicate 1

(d)

minus5

minus4

minus3

minus2

minus1

0

1

2

log 2

(rela

tive

splic

ing

effici

ency

)

RT-qPCRRNA-seq 5

ssRNA-seq 3

ss


(e)



5 splice site

WT


minus5

0

5

10

0 5 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(a)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

50 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(b)

5 splice site

WT

0

5

minus5

10

50 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)



(c)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

0 5 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)


(d)









4 Conclusions


Competing Interests


Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology









(1)


























Yes

No

Clean reads(FASTQ)

Genome

Uniquely

Splice sites

Extract junction

intron ends(BED)

5 and 3

intron endcoverage

Count 5 and 3



(R)

Splicing efficiency

Genome annotation


ok

(GTF)(i) Exonintron

sequence(FASTA)

Map reads(HISAT2)

(i) Splice-aware

mapped reads(BAM)




coordinates(R)

5 and 3


Filter reads




WT

50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


5 splice site

Coveragege

lt5 readsge5 reads

(a)W

T


50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


3 splice site

(b)



5 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Replicate 1

Repl

icat

e2

minus6

minus4

minus2

0

(c)


minus4

minus2

0


3 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Repl

icat

e2

Replicate 1

(d)

minus5

minus4

minus3

minus2

minus1

0

1

2

log 2

(rela

tive

splic

ing

effici

ency

)

RT-qPCRRNA-seq 5

ssRNA-seq 3

ss


(e)



5 splice site

WT


minus5

0

5

10

0 5 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(a)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

50 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(b)

5 splice site

WT

0

5

minus5

10

50 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)



(c)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

0 5 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)


(d)









4 Conclusions


Competing Interests


Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




















Yes

No

Clean reads(FASTQ)

Genome

Uniquely

Splice sites

Extract junction

intron ends(BED)

5 and 3

intron endcoverage

Count 5 and 3



(R)

Splicing efficiency

Genome annotation


ok

(GTF)(i) Exonintron

sequence(FASTA)

Map reads(HISAT2)

(i) Splice-aware

mapped reads(BAM)




coordinates(R)

5 and 3


Filter reads




WT

50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


5 splice site

Coveragege

lt5 readsge5 reads

(a)W

T


50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


3 splice site

(b)



5 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Replicate 1

Repl

icat

e2

minus6

minus4

minus2

0

(c)


minus4

minus2

0


3 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Repl

icat

e2

Replicate 1

(d)

minus5

minus4

minus3

minus2

minus1

0

1

2

log 2

(rela

tive

splic

ing

effici

ency

)

RT-qPCRRNA-seq 5

ssRNA-seq 3

ss


(e)



5 splice site

WT


minus5

0

5

10

0 5 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(a)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

50 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(b)

5 splice site

WT

0

5

minus5

10

50 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)



(c)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

0 5 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)


(d)









4 Conclusions


Competing Interests


Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


WT

50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


5 splice site

Coveragege

lt5 readsge5 reads

(a)W

T


50 10minus5

prp45(1-169)

minus5

0

5

10

log 2

(spl

icin

geffi

cien

cy)


3 splice site

(b)



5 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Replicate 1

Repl

icat

e2

minus6

minus4

minus2

0

(c)


minus4

minus2

0


3 splice site


log 2

(rela

tive

splic

ing

effici

ency

)

Repl

icat

e2

Replicate 1

(d)

minus5

minus4

minus3

minus2

minus1

0

1

2

log 2

(rela

tive

splic

ing

effici

ency

)

RT-qPCRRNA-seq 5

ssRNA-seq 3

ss


(e)



5 splice site

WT


minus5

0

5

10

0 5 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(a)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

50 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(b)

5 splice site

WT

0

5

minus5

10

50 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)



(c)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

0 5 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)


(d)









4 Conclusions


Competing Interests


Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


5 splice site

WT


minus5

0

5

10

0 5 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(a)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

50 10minus5

prp4-1

log 2

(spl

icin

geffi

cien

cy)


(b)

5 splice site

WT

0

5

minus5

10

50 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)



(c)

Coverage

3 splice site

WT

ge5 readslt5 reads

minus5

0

5

10

0 5 10minus5

prp40-1

log 2

(spl

icin

geffi

cien

cy)


(d)









4 Conclusions


Competing Interests


Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





4 Conclusions


Competing Interests


Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

research article workflow for genome-wide...

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