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1. ExpressionPlot 2way 4way account correlation ecdf event heatmap genelev heatmap manual pairdist pairplot read types seqview 2way Plotter Away Plotter User Configuration Area Pairwise correlations of transcriptional profiles Empirical Cumulative Distribution Function Plotter event heatmap Heatmap levels or changes across multiple events and projects Gene Level Barplotter Compare change profiles from different sets User s Guide Paired end summaries Paired end Plotter Read types quantify aligning exonic intron etc Readmap Browser Figure 2 Wesst ntron o m m exon V 29IosnwIexs H wean V gon H eumg v eumg H Lotsnwieg V Lotsnwiesg Oo wean H Len V Haan H tug v burg Matching read types ANM oo O Ce ao tN 0 100 speal Buiyoyew jo abeyusoe4 KI SE BE 36 0a SE g t 3560 z BN V Zeisnwiexs H zie v wean H zung v zug H Letrsrwieug V L trsmwieusg 9 uen g uev V uev g jumig V umg T 0 0 10 0 A 3 20 Spee jo suoi 0t 8 0 90 v0 zo speey pu3 paireg jeoiuoue jo uonoe14 2 v v v oo van uan uaan van uan zepenpims 1 epeniexG 1 epeniexs Zeg pug Paired End Distance Figure 3 A gene level changes Upin brain 370 1e 04 1e 02 1e 00 1e 02 1e 04 1B435 T47D MAQC_human MAQC_UHR liver heart skel_muscle colon adipo RPKM Human Tissues brain Up skipped exons Platform2. g and g of which align to a particular gene of interest we model g2 as a binomial distribution with parameters q and g where q2 n ni n2 and g g g is the total number of reads aligning to the gene in either sample The two tailed P value is then calculated using R s binom test function Minimize Significant Changes MSC method to estimate effective total read numbers To estimate the effective total number of reads n and n in a pair of samples or pair of groups of samples we estimate q2 which is the fraction of reads in the second sample and then set n ON and n N nz where N is the total number of uniquely aligning reads from either sample The theory of our calculation of q2 is that once a P value cutoffs is set any potential choice of q will lead to a certain number of significantly changed genes say C q which could be calculated by applying the procedure described above to every gene for example 27 389 genes in mouse Thus we have the optimization problem minC qg 0x qs 1 q2 Solving the problem by convex optimization methods would be feasible but slow due to the cost of re calculating C qz Instead we use the binconf function from R s Hmisc library 22 to calculate a 95 confidence interval for q for every gene based on the observed number of reads This interval corresponds to the range of q for which that gene is not significantly changed Then the range O to 1 is split into
3. RNASeg P 1e 4 FC 1 5 Showing 144 144 Up skipped _ exons cmo 1 36199064 36190317 ce 1 33518208 35518249 chrii 1 118031780 118031812 70282417 70282583 1 1 B cassette exon splicing changes Upin brain 144 brain Incl Skip Ratio muscle colon adipose testes lymph node BT474 152 1e 04 1e 01 1e 02 1e 05 1B435 T47D MAQC_human MAQC_UHR liver heart skel_muscle colon adipo Incl Skip Ratio lt lt pst EE Convert nes ove esto GR GD Make Background controlling for L peese geg g s iz OVIN s u f g 6y jeueg enssiy uewny xujewAYY IAY JOAII Q seerued g seejued v seemued eupni g auppi y Aap XO PO LOED xot ON LO X0 91 T seueB g 6y jeueg enssiy uewny xujeui gy d AIHW eut seemued g seemued y seemued euppi g euppiev Aen XO v9 xX0 9l LO ON XOT c e osnu g s 2s OOV ueuin snui ui 2 Jexs t1en Hn snui ur g Autzg 2 F i gr 3 3 4 a D 5 e 3 3 Q a 3 S dipe uojoo ajosnw LEE D zzl x 07102 91 172CXZ HOT 0 9Sp S CXZ d c H0o1n2753 LOOF HON d old uosueduio em p m c a a 9 m c T E 3 D 5 u KA d y D 3 OC 3 vm ZS T eo a a 2 a 02 6 8 2X2 HOl Z2 H 96 2 2XS d CH 1000 0 LO UO old uosueduio Aem p y eiser snw Sejsepsso V y ainbi4 Figure 5 2 o a a e Ww 4 36 200 000 e amp 4845 poat n Tissue Panel Log2 Fold Change 3
4. from five human tissues is shown on the top The heights of black bars indicate numbers of reads overlapping each genomic position whereas the heights of blue brackets indicate numbers of reads overlapping splice junctions Data from RNA Seq human tissue panel 1 Figure 6 ExpressionPlot screen shots examining spleen enriched genes in human exon array tissue panel data 24 A Levels of Myd88 a key signaling protein in the innate immune system 25 in human tissues using the genelev tool B ecdf showing tissue enrichment fold change relative to all other tissues of the 316 genes least 5 fold enriched in the spleen at a P value cutoff of 10 The sharp angle at 2 3 in the spleen curve indicates the 5 fold cutoff The position of the cerebellum curve to the left of all the others may reflect the general depletion of immune cells which are characteristic of the spleen within the nervous system C event heatmap showing the fold enrichments of the 316 spleen enriched genes in all 11 tissues in the panel Screen shot was edited by removing many of the genes from the middle for formatting purposes and adding an arrow to indicate Myd88 which is part of a cluster of spleen enriched genes also enriched in the liver The depletion of the spleen enriched genes in the cerebellum is evident by the excess blue color in the cerebellum row Figure 1 ExpressionPlot Web Server account correlation ecdf genelev heatmap Manual
5. top right are present on every page during the website experience for easy navigation The manual link opens the page of the User s Guide relevant to the currently selected tool Quality Control The ExpressionPlot front end provides several quality control tools for RNA Seq data The read types tool graphs the number of reads in each sample of each type non aligning multiply aligning paired end uniquely aligning or single end uniquely aligning Figure 2A The user can also run this tool looking at only the uniquely aligning reads to see if they align to exons introns intergenic regions or junctions Figure 2B The correlation tool generates either a heatmap or a hierarchical clustering dendrogram showing the pairwise correlations of gene expression profiles in the RNA Seq or microarray samples of your project Figure 2C Supplementary Methods For paired end data sets the pairdist tool shows the fraction of paired end reads for which 1 the two ends align to different chromosomes 2 the two ends align to the same chromosome but on the same strand 3 the two ends align to the same chromosome and different strands but the minus end strand is upstream of the plus end strand and 4 the two ends align to the same chromosome different strands minus end downstream of the plus end but there is at least one intron between the two ends The fifth category of reads where the two ends don t flank any known intron can be used to estima
6. windows of width 0 0001 and the number of genes whose confidence interval overlaps each of these windows is counted The uncertainty introduced by using windows as point estimates is mitigated by their small radius a difference of 0 0001 0 01 in the sample size estimate will have a minute effect on resultant gene levels The value of q2 for the window overlapped by the confidence intervals of the most genes or the mean of the qz for the several windows if there is a tie for the most intervals is then taken as the optimum Empirical tests show that this method is extremely robust to the choice of P value cutoff data not shown This is implemented in a very short R function called minimize significant changes in BradStats R 23 ENA Accession Numbers The previously unpublished and de identified data sets used to create figures 2D S7 and S9 are available from the European Nucleotide Archive under accession number ERPOO00619 available at http www ebi ac uk ena data view ERPO000619 References 1 Wang ET Sandberg R Luo S Khrebtukova I Zhang L Mayr C Kingsmore SF Schroth GP Burge CB Alternative isoform regulation in human tissue transcriptomes Nature 2008 456 470 47610 1038 nature07509 Available Accessed 1 November 2010 2 Nagalakshmi U Waern K Snyder M RNA Seq a method for comprehensive transcriptome analysis Curr Protoc Mol Biol 2010 Chapter 4 Unit 4 11 1 1310 1002 0471142727 mb0411s89Available Accessed 1 Nov
7. 009 10 3 r25Available Accessed 14 September 2010 10 Wu Z Jenkins B Rynearson T Dyhrman S Saito M Mercier M Whitney L Empirical bayes analysis of sequencing based transcriptional profiling without replicates BMC Bioinformatics 2010 11 56410 1186 1471 2105 11 564Available Accessed 24 May 2011 11 Goecks J Nekrutenko A Taylor J Galaxy a comprehensive approach for supporting accessible reproducible and transparent computational research in the life sciences Genome Biol 2010 11 R8610 1186 gb 2010 11 8 r86Available Accessed 14 September 2010 12 Reich M Liefeld T Gould J Lerner J Tamayo P Mesirov JP GenePattern 2 0 Nat Genet 2006 38 500 50110 1038 ng0506 500Available Accessed 16 March 2011 13 Fujita PA Rhead B Zweig AS Hinrichs AS Karolchik D Cline MS Goldman M Barber GP Clawson H Coelho A Diekhans M Dreszer TR Giardine BM Harte RA Hillman Jackson J Hsu F Kirkup V Kuhn RM Learned K Li CH Meyer LR Pohl A Raney BJ Rosenbloom KR Smith KE Haussler D Kent WJ The UCSC Genome Browser database update 2011 Nucleic Acids Res 2010 10 1093 nar gkq963 Available http www ncbi nlm nih gov ezp prod1 hul harvard edu pubmed 20959295 A ccessed 1 November 2010 14 Hubbard TJP Aken BL Ayling S Ballester B Beal K Bragin E Brent S Chen Y Clapham P Clarke L Coates G Fairley S Fitzgerald S Fernandez Banet J Gordon L Graf S Haider S Hammond M Holland R Howe K Jenkinson A Johnson N Kahar
8. 6 195 000 ECDFs of Gene Set in Affy_HuExo go 9o vo zo B spleen enriched genes seus eo 9 LE seuec 91 jo uogos o e Q ei o v o e i H3 i 3 E D a o JO mo mo mo E 999 36 185 000 n Tissu TEE n gmn Di 8 ge 22 SS Affy HuExo GEERT 323223853 Hy iH D coding CLTA protein coding CLTA protein coding CLTA E 2 8 EEEEE EES Fl 2 8 Gene level in set H mo mo mo mo m H breast No Reads No Reads No Reads Skel muscle No Reads protein coding CLTA Figure 6 A MyD88 gene levels protein
9. ExpressionPlot A web based framework for analysis of RNA Seq and microarray gene expression data Brad A Friedman corresponding Harvard University Department of Molecular and Cell Biology and Koch Institute at MIT Cambridge MA USA brad aaron friedman 2 gmail com Tom Maniatis Columbia University College of Physicians and Surgeons Department of Biochemistry and Molecular Biophysics New York NY USA tm2472 columbia edu Abstract RNA Seq and microarray platforms have emerged as important tools for detecting changes in gene expression and RNA processing in biological samples We present ExpressionPlot a software package consisting of a default back end which prepares raw sequencing or Affymetrix microarray data and a web based front end which offers a biologically centered interface to browse visualize and compare different data sets Download and Installation instructions user s manual discussion group and a prototype are available at http expressionplot com Main Text RNA Seq has emerged in recent years as the eminent platform for analysis of gene expression and RNA processing 1 3 However processing the raw sequence data to get useful and accurate information about gene expression and RNA processing is still a daunting task even for computationally inclined researchers High quality software packages now exist to perform specific steps in the analysis pipeline 4 10 as well as web based systems such as Galax
10. ed statistics such as read numbers RPKM values and P values The table can be sorted by clicking on the header of the desired field or filtered using a text string or a numeric filter Action buttons allow for the export of the table into other software such as R or OpenOffice or Excel for automatic conversion of the genes into other IDs such as Ensembl or Entrez and for the automatic generation of expression controlled background sets of similarly expressed but unchanged genes in terms of either RPKM or raw read numbers the user chooses although we recommend raw read numbers to avoid transcript length biases 21 These background sets are appropriate for downstream gene ontology or motif analysis A convenient feature of the table browser is the ability to click on any row to be presented with a link to the ExpressionPlot genome browser seqview This browser displays both RNA Seq reads including those spanning junctions as well as array probe intensities along with gene annotations described below Comparison of changes from different experiments data sets Having examined changes in two different conditions of a single experiment it is natural to ask how these changes compare to another experiment Sometimes this second experiment may be part of the same project but in other cases it could be part of another project and maybe even have been performed on another platform e g RNA Seq versus microarray or in another organ
11. ember 2010 3 Mortazavi A Williams BA McCue K Schaeffer L Wold B Mapping and quantifying mammalian transcriptomes by RNA Seq Nat Methods 2008 5 621 62810 1038 nmeth 1226Available Accessed 14 September 2010 4 Trapnell C Pachter L Salzberg SL TopHat discovering splice junctions with RNA Seq Bioinformatics 2009 25 1105 111110 1093 bioinformatics btp120Available Accessed 14 December 2010 5 Trapnell C Williams BA Pertea G Mortazavi A Kwan G van Baren MJ Salzberg SL Wold BJ Pachter L Transcript assembly and quantification by RNA Seq reveals unannotated transcripts and isoform switching during cell differentiation Nat Biotechnol 2010 28 511 51510 1038 nbt 1621Available Accessed 14 December 2010 6 Li H Ruan J Durbin R Mapping short DNA sequencing reads and calling variants using mapping quality scores Genome Research 2008 18 1851 185810 1101 gr 078212 108Available Accessed 14 December 2010 7 Katz Y Wang ET Airoldi EM Burge CB Analysis and design of RNA sequencing experiments for identifying isoform regulation Nat Methods 2010 7 1009 101510 1038 nmeth 1528 Available Accessed 14 December 2010 8 Lander E Getz G Mesirov J with Robinson J Thrvaldsdottir H Winckler W M Integrative Genomics Viewer Nature Biotechnology In Press 9 Langmead B Trapnell C Pop M Salzberg SL Ultrafast and memory efficient alignment of short DNA sequences to the human genome Genome Biol 2009 10 R2510 1186 gb 2
12. i A Keefe D Keenan S Kinsella R Kokocinski F Kulesha E Lawson D Longden I et al Ensembl 2009 Nucleic Acids Res 2009 37 D690 69710 1093 nar gkn828Available Accessed 1 November 2010 15 Anders S Huber W Differential expression analysis for sequence count data Genome Biol 2010 11 R10610 1186 gb 2010 11 10 r106Available Accessed 21 November 2010 16 Marioni JC Mason CE Mane SM Stephens M Gilad Y RNA seq an assessment of technical reproducibility and comparison with gene expression arrays Genome Res 2008 18 1509 151710 1101 gr 079558 108Available Accessed 1 November 2010 17 Robinson MD Oshlack A A scaling normalization method for differential expression analysis of RNA seq data Genome Biol 2010 11 R2510 1186 gb 2010 11 3 r25Available Accessed 21 November 2010 18 Bullard J Purdom E Hansen K Dudoit S Evaluation of statistical methods for normalization and differential expression in mRNA Seq experiments BMC Bioinformatics 2010 11 9410 1186 1471 2105 11 94Available Accessed 23 May 2011 19 Affymetrix Affymetrix Power Tools http www affymetrix com partners_programs programs developer tools powertools affx 20 Smyth GK Linear models and empirical bayes methods for assessing differential expression in microarray experiments Stat Appl Genet Mol Biol 2004 3 Article310 2202 1544 6115 1027Available Accessed 24 May 2011 21 Oshlack A Wakefield M Transcript length bias in RNA seq data confounds system
13. in its design and coordination and in drafting the manuscript Additional Data Files Additional File 1 pdf Supplementary Figures Methods References and description of other additional files Additional File 2 zip Data for Figure S7 Additional File 3 zip Data for Figure 2D Additional File 4 zip Archival copy of software Figure Legends Figure 1 The ExpressionPlot home page The website opens with this screen giving a list of tools available in ExpressionPlot and a login box in the top right The navigation bar on top appears on all pages giving links to the other tools The manual link is context aware it automatically opens the User s Guide in another tab to the page explaining the current tool Figure 2 Screen shots of ExpressionPlot quality control tools A read types tool showing all read types Numbers of non aligning Nonmatch mulitply aligning Mult unique genome aligning Genomic and unique junction aligning Junction reads are shown for each lane from a mouse tissue transcriptome dataset 3 Numbers 1 2 indicate different libraries letters A B C indicate different lanes of the same library B read types tool showing matching read types normalized to 10096 C Pairwise correlation heatmap of gene expression profiles generated from each lane D pairdist tool shows ECDF of paired end distances of canonical reads same chromosome different strand minus strand read downstream of plu
14. ism e g human versus mouse The 4way tool and its associated table browser automatically match up changed genes or RNA processing events from different experiments and presents them in a similar manner to its 2way cousin After selecting two projects and a pairwise comparison P value and fold change cutoff for each ExpressionPlot generates a scattergram where each point corresponds to a gene or event Here the x axis shows the change in that gene event in the first comparison and the y axis shows the change in the second comparison Figure 4 For example points in the upper right quadrant would correspond to genes events increased in both experiments whereas those in the upper left quadrant would be decreased in the x axis experiment but increased in the y axis experiment Points are colored according to whether the gene event is significantly changed in one or both experiments with blue representing those changed in both experiments As with the 2way tool after the plot is generated ExpressionPlot offers the user action buttons to select a group of genes events to further examine in the 4way table browser For example clicking Up Up would show a table of genes events increased in both experiments This table shows the annotation of the gene event identifier chromosome position strand etc as well as all the associated statistics It has the same fields that would be shown in the 2way browser but they are then repeated for both e
15. ltant number of significantly changed genes a procedure we call Minimize Significant Changes MSC see Methods Finally a binomial test is performed on the number of reads aligning to a particular gene from the two samples to determine if the ratio is significantly different from the ratio of total numbers of reads in the two samples See Supplemental Methods For the RNA processing events we form two by two contingency tables looking at the numbers of reads supporting the two isoforms in the different samples e g see Figures S3 S4 and S6 and Supplementary Methods The P values are then derived from either Fisher s Exact Test which is known to be conservative in this regime see Supplementary or if all the expected values are greater than 5 the Chi Squared Test By default the ExpressionPlot back end generates P values that are not adjusted for multiple testing This should be kept in mind when setting cutoffs on the website We usually use a P value cutoff of 10 For example using the UCSC genes cluster for mouse mm9 there are 27389 genes so on average this cutoff would yield no more than 3 false positives Actually in most RNA Seq data sets many of the genes are not expressed or at extremely low levels and so the expected false positives is even lower since the small P values are not achievable for these genes Users who prefer to work with Benjamini Hochberg corrected P values can choose to do so by providing the correct s
16. nnotation all transcripts flanking that intron contain those exons Figure S5 As with skipped exons the pre computed sets contain candidate events for all known introns Finally alternative terminal exon events are created for genes with multiple transcript start sites TSS or multiple poly adenylation cleavage sites PACS These events compare reads supporting a candidate terminal exon with more distal 5 of TSS or 3 of PACS exons Such events are created for all but the 5 most TSS and 3 most PACS Figure S6 Support for other types of events include alternative splice sites and sequence variants due to SNPs or RNA editing is planned for a future release Statistical Calculations For changes in gene expression ExpressionPlot uses the DESeq package 15 to model biological variation in the calculation of P values This package normalizes samples using median fold change and models the read counts using the negative binomial distribution including a term for both sampling and biological noise Alternatively users can choose a modification of a previously described procedure 16 to detect technical differences between two lanes or groups of lanes In a similar spirit to DESeq and other existing packages 17 18 total read counts are normalized using a robust procedure that is not dominated by the mostly highly expressed genes In this step the effective total number of reads in each sample is optimized to minimize the resu
17. pare different datasets including from different organisms or between microarray and RNA Seq generate empirical cumulative distribution functions ECDFs to look at levels or changes in a cohort of genes and look up levels of specific genes The ExpressionPlot back end can also generate BAM and BigWig files upon request and for downstream analysis the web based front end can output spreadsheets with gene and exon statistics ExpressionPlot includes a web controllable user account and access control system by which pre published data can be shared with other users or when appropriate made public Finally ExpressionPlot does not require a cluster it can run on any machine with sufficient memory to hold the bowtie indexes usually at least 3 or 4 GB and hard drive space to hold the sequencing data and processed files roughly 1 2 GB per lane In short ExpressionPlot is a unified solution for gene expression analysis of RNA Seq and microarray data Tasks of Gene Expression Analysis RNA Seq and microarray analyses begin with these pre processing tasks Back End Pre processing Tasks RNA Seq 1 Alignment 2 Read accumulation 3 Statistical calculations Back End Pre processing tasks microarrays 1 Background subtraction 2 Probe normalization 3 Probe accumulation 4 Statistical Calculations The pre processing tasks are sequential and usually performed for all analysis projects In ExpressionPlot they are performed b
18. ript which comes with ExpressionPlot ExpressionPlot s alignment strategy is to find and use only unique best alignments either to the genome or to the splice junction database Figure S1 For paired end data an additional step is taken to try to align the single ends individually Figure S2 Counting Reads for Genes and RNA Processing Events Aligned reads are then mapped to gene models and alternative splicing events Users can supply their own models and events or download and install pre computed annotations using EP manage pl currently available for human mouse and rat The pre computed gene models are built from all exons of any transcript based on UCSC known genes 13 or Ensembl 14 A read is counted towards any gene that contains the aligned positions possibly split by a junction on either strand within its exons Scripts and detailed instructions to generate annotations for other genomes are included Pre computed candidate skipped exon events are created from all known exons regardless of whether or not they are known to be skipped For skipped exons skipping reads are considered as splice junction spanning reads that both skip the exon and are additionally anchored in known splice sites of the host genes Figure S3 For intron retention the number of reads aligning to the intron is compared to the number aligning to locally constitutive flanking exons Figure S4 Locally constitutive means that based on the underlying a
19. s biology Biology Direct 2009 4 1410 1186 1745 6150 4 14Available Accessed 16 March 2011 22 CRAN Package Hmisc http cran r project org web packages Hmisc index html 23 BradStats R expressionplot Project Hosting on Google Code http code google com p expressionplot source browse trunk lib R BradStats R 24 Affymetrix Sample Data Exon 1 0 ST Array Dataset http www affymetrix com support technical sample_data exon_array_data affx 25 Akira S Takeda K Toll like receptor signalling Nat Rev Immunol 2004 4 499 51110 1038 nri1391Available Accessed 4 November 2010 Acknowledgments We would like to thank Y Katz SL Ng J Gertz and M Muratet for critical reading of the manuscript S O Keeffe and M Muratet for extensive software testing and technical suggestions CB Burge for hosting our prototype server D Housman for scientific advice and laboratory space during the development of this software IK Friedman and B Lewis for administrative support HP Phatnani C Lobsiger J Cahoy J Zamanian and other members of the Barres Lab Stanford Myers Lab HudsonAlpha Institute Ravits Lab Benaroya Institute and Maniatis Lab Harvard Columbia for providing data and or user feedback This work was supported by a grant from the ALS Therapy Alliance Competing Interests The authors declare that they have no competing interests Authors Contributions BF conceived of and wrote the software and the manuscript TM helped
20. s strand read Distance is defined as the genomic distance in nucleotides between the aligned positions of the last sequenced bases of the two reads can be negative if the alignments overlap The samples have been de identified data in Additional File 3 Numbers in parentheses indicate median paired end distance for each sample add 36 for both sequences and 50 for both Illumina adaptors 172 to get complete library size Figure 3 Screen shots of ExpressionPlot 2way plot and table browser 2way plot of human tissue panel RNA Seq data 1 showing brain gene expression on y axis and average expression in all other tissues pooled on x axis Blue points correspond to genes significantly higher P lt 10 fold change 2 20 370 points in brain relative to the other tissues green correspond to significantly lower B 2way plot showing cassette exon usage inclusion skip read ratios instead of gene levels in the same data set The heavy lobe above the diagonal corresponds to exons with zero skipping reads in the brain and the lighter lobe below the diagonal corresponds to exons with zero skipping reads in all other tissues Although the P values are still valid in these regimes the inclusion skip ratio statistic is less precise C Partial screen shot of table browser showing brain enriched cassette exons in the same data set The context menu was triggered by the mouse clicking on the row for CLTA clathrin light chain A and offer
21. s the user links to open the seqview genome browser tool in a window covering either the entire gene or just the alternative exon In either case the exon will be automatically highlighted See Figure 5 Figure 4 Screen shots of ExpressionPlot 4way plots showing cross platform and cross species comparisons A Heart enriched gene expression in human tissue panel exon array 24 x axis and RNA Seq 1 y axis data sets Points correspond to genes Fold change of expression in heart is plotted versus all other samples in corresponding data set Genes enriched in heart are plotted further to the right exon array and or up RNA Seq and those higher in other samples are further to the left and or down Genes significantly different only on one platform are colored red exon array or green RNA Seq and those different on both platforms are colored blue P value cutoffs are 0 01 for exon array and 10 for RNA Seq and fold change cutoffs are 2 for both platforms Colored numbers show number of genes in each category B Similar plot comparing the same x axis human heart enriched gene expression by exon array to mouse heart enriched gene expression also by exon array y axis Figure 5 Screen shots of ExpressionPlot s genome browser seqview The region of the CLTA gene which contains a brain enriched exon pink is shown Known transcripts of CLTA are seen along the bottom arrowheads indicate plus strand The accumulation of RNA Seq reads
22. te the insert size and empirical cumultative distribution functions ECDFs of the insert sizes defined as the length of the un sequenced part of the library between the paired ends for the different lanes are also plotted by this tool Figure 2D Generation of plots and tables of changed genes events The 2way tool and its associated table browser are the basic tools to examine the relationships between gene levels or RNA processing events in two different samples The x axis will correspond to one sample such as wildtype and the y axis to another such as mutant The project and pair of samples are chosen by the user from drop down menus and the plots like all the other plots in ExpressionPlot are generated on demand by the web server The 2way plot is a scattergram where points correspond to genes or RNA processing events e g cassette exons and are colored according to whether they are significantly different in the two samples Figure 3A B P value and fold change cutoffs for significance can be controlled by the user After the plot is generated action buttons are presented to the user to access the significantly changed genes or RNA processing events in the table browser This screen presents the user with a dynamic table whose rows correspond to changed genes events Figure 3C The columns of the table contain identifiers for the gene or event like gene name chromsome strand and position as well as all the associat
23. that region with strands separated or merged as requested by the user or of the hybridization intensities of microarray probes in that region Zooming and scrolling is implemented and users can also highlight specific genomic coordinates Barplots are automatically generated showing levels of genes within the requested regions The pairplot tool is a genome browser specifically designed to visualize the relationship between the aligned positions of paired ends Only one sample can be visualized at a time The gene annotation of the requested region is shown as well as the pileup track from the seqview tool showing total numbers of reads Above this a scattergram shows a point for each paired end read aligning to the genomic region The x axis gives the position of the plus strand end and the y axis gives the position of the minus strand end The colors and sizes of the points indicate the number of reads aligning to each pair of coordinates Under conditions of constitutive splicing the scattergram should form a series of segments above each exon and parallel to the diagonal with the distance to the diagonal dictated by the paired end insert and intron size Alternatively spliced regions however will show multiple parallel segments corresponding to the different isoforms The relative strength of the segments corresponds to the abundances of the two isoforms Figure S9 Examining levels or changes of particular genes or events The genelev
24. to satisfy any that are missing It then downloads and installs the latest version of ExpressionPlot Alternatively a VirtualBox hard drive is available running Ubuntu linux with ExpressionPlot already installed In either case after installation is complete the EP manage p1 script can be used to download and add on bowtie indexes annotations and microarray library files as required Example data sets both unprocessed and processed can also be installed using the same script The User s Guide can be found at http expressionplot com wiki and contains detailed instructions on setting up and running ExpressionPlot Please use the ExpressionPlot discussion group to post technical questions or hints This can be accessed by visiting http groups google com group expressionplot or by sending e mail to expressionplot googlegroups com Extracting biological meaning from high throughput data ExpressionPlot offers the gene expression community an easy to use tool for automated analysis of gene expression and RNA processing data The back end offers a solution to the problem of detecting significant changes in gene expression and RNA processing while the web based interface offers data analysis visualization and browsing tools that realize the biological potential of this new technology Methods Calculating P values for significance of changes in gene expression Given total numbers of reads in two samples or two groups of samples n and n2
25. tool generates barplots of gene levels RPKM with error bars Figure 6A The ecdf tool allows the user to visualize the levels or fold changes of a set of genes by plotting the cumulative distribution of those genes levels in the samples of a project or fold changes in the pairwise comparisons of a project Figure 6B Instead of looking at the distribution of the whole set the event heatmap tool visualizes the individual levels or fold change of all the genes the set as a heatmap Figure 6C Administrative tasks ExpressionPlot has an access management system that makes it easy for end users to share their data or release it publicly New user accounts can be made automatically through the website including an e mail based password recovery feature When invoking the back end for a given project one user is assigned admin privileges Users can then assign either view or admin privileges to other users on projects for which they are admin or can add a public flag to the project to make it visible without login These permissions are all controlled via a simple web interface Download installation help Visit the ExpressionPlot website at http expressionplot com for instructions on how to download and install the latest version ExpressionPlot requires an existing MySQL and Apache web server as well as the RApache module The install p1 script checks all the dependencies and tries to satisfy or make suggestions on how
26. witches as described in the User s Guide Pre processing Tasks Microarrays Background subtraction and probe normalization ExpressionPlot uses Affymetrix Power Tools 19 to perform the background subtraction using either mismatch probes 3 UTR arrays or GC control probes exon arrays and follows this with quantile normalization of background subtracted probe intensities Users can use any affymetrix array for which they have the appropriate library files but for the following arrays those files can be automatically downloaded and installed by EP manage p1 HG U133 A B HG U133 Plus 2 HuExon MOE430 A B MoExon and Rat230 2 Statistical Calculations For microarray data gene levels are estimated first by finding all detected probes which are defined as probes with positive background subtracted intensities across all arrays in the project Once these probes are defined the gene level in each array is summarized as the median probe intensity P values for gene level changes are calculated by default using the Limma package 20 or optionally the t test As with the RNA Seq pipeline the P values are not by corrected for multiple testing unless specifically requested Web based Front End Global Tasks Website users are initially presented with a landing page with links and short descriptions of all the different tools available in ExpressionPlot Figure 1 The navigation bar at the top as well as the login box on the
27. xperiments This includes the annotation fields since sometimes they are from different organisms As with the 2way browser there are action buttons to download convert IDs and generate background sets Finally clicking on a row of the table opens a context menu with links that will automatically open the genome browser to the right part of the genome for the two experiments In the case of RNA processing events the correct genomic region will be automatically highlighted within the browser so the user can quickly find for example a differentially spliced cassette exon The heatmap tool Figure S8 allows the user to compare larger numbers of change profiles Here all the different comparisons from one project are laid out along the x axis and all the comparisons from a second possibly different project are laid out along the y axis The color of each square of the heatmap indicates the similarity of the two comparisons The user can choose from a variety of statistics to quantify similarity This tool is a useful way to look for relationships within larger numbers of experiments Web based Front End Specific tasks Examining reads from a particular genomic region The seqview tool is ExpressionPlot s genome browser Figure 5 With it the user can select the project of interest then query either by a gene name or genomic region One of several annotations can be chosen and then a plot is generated showing either the pileup of reads in
28. y 11 and GenePattern 12 that enable the management of data flow through these tools We present ExpressionPlot an open source solution consisting of a back end pipeline which performs alignment and statistical analyses and a web based front end which allows users to explore and further compare the completed analyses Compared to Galaxy and GenePattern ExpressionPlot s web based front end is novel in the ease with which one can browse and manipulate gene expression results gene isoform lists are one click filterable sortable and hyperlinked to the underlying genomic regions in the table browser tool Furthermore even with differing platforms such as microarray versus RNA Seq or organisms such as mouse versus human the front end can automatically compare changes in gene expression across different experiments using the 4way and heatmap tools ExpressionPlot can be tested as a virtual machine running under VirtualBox or installed directly into an existing web server Input to ExpressionPlot can be raw sequence data FASTQ files or Affymetrix array data CEL files completed alignments BAM files or tables of gene expression values and changes generated by other back ends Once data is pre processed the web based front end allows users to easily browse measures of quality control plot changes in gene expression and RNA processing browse hyperlinked tables of changed genes and splicing events generate read plots from a genomic view com
29. y the back end which is started from the command line on the server A typical RNA Seq data set might take a few days to to run most of which is spent on alignments Using pre aligned data sets is possible by importing from BAM files Once completed the subsequent tasks can be considered a mixture of global discovery based and specific hypothesis based tasks In ExpressionPlot these tasks are the domain of the web based front end and all run on demand within seconds Global tasks e Quality Control e Generation of plots and tables of changed genes events e Genome wide comparison of changes from different experiments data sets Specific tasks e Examining reads probe intensities from a particular genomic region e Examining levels changes of a particular gene splicing event or set of genes splicing events ExpressionPlot provides simple mechanisms to perform all of these steps Back End Pre processing Tasks RNA Seq Alignment ExpressionPlot uses bowtie 9 to align reads to the genome and then a database of splice junctions The splice junction databases that come with ExpressionPlot were generated by combining the known half junctions from each gene in every possible forward splicing combination exon n splices to exon m where m gt n Precomputed junction databases can be downloaded and installed with the EP manage p1 script human mouse and rat as of press time or can easily be generated using the make junctions database pl sc
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