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1. Gd SS RS ess RR Sc S 04 eger E ih pB d L 3 8 Lu LONE Md a a Group Group 2 Ontology Report Z score Report Totals r score regulation of biological process B7 44 43 4684 5 4 1 11 64 33 31 2505 1 61 1 07 61 35 26 2790 2 03 0 68 59 30 329 2170 121 1 48 57 34 23 2720 203 1 19 1646 1 18 1 52 1083 1 55 0 21 1056 2 20 0 65 1 05 0 53 1 10 0 29 1 27 2 49 21 74 2 61 0 59 0 74 0 25 Ontology jen List Array abolic process E 222 149 73 6318 617 128 cellular process E 221 132 89 9220 4 15 1 68 biological regulation 97 51 46 4995 483 1 15 Figure 7 14 6 Gene ontology reports COMPARE GENE EXPRESSION FROM PAIRED SAMPLES OBTAINED ALTERNATE FROM TRANSCRIPTOME PROFILING ASSAYS BY NEXT GENERATION PROTOCOL 1 DNA SEQUENCING Several experiments have been published recently where NGS or Next Gen technolo gies were used for transcriptome profiling NGS experiments have three phases for data analysis First there is a data collection phase where the instrument captures information performs base calling and creates the short DNA sequences that we refer to as reads Analyzing Next there is an alignment phase where reads are aligned to a reference data set and pn atterns 7 14 13 Current Protocols in Bioinformatics Supplement 27 Analyzing Gene Expression Data Using GeneSifter 7 14 14 Supplement 27 counted Last
2. NM 009466 BE992059 BC013464 D49729 NM 008155 539 genes show a significant change Gene Name Cytochrome P450 family 2 subfamily a polypeptide 4 Glutathione 5 transferase alpha 2 Yc2 Sterol C4 methyl oxidase like C type lectin domain family 2 member h Fatty acid binding protein 5 epidermal Ectonudeoside triphosphate diphosphohydrolase 5 Alcohol dehydrogenase iron containing 1 Apolipoprotein A IV Nuclear factor interleukin 3 regulated Apolipoprotein A IV C type lectin domain family 2 member h UDP glucose dehydrogenase Glucose phosphate isomerase 1 select a name Ketohexokinase s to view the Arginine vasopressin receptor 1A gene summary Glucose phosphate isomerase 1 AE EP GE P bo Latin dee Formas T ee highly in mice fed with LRD 5001 Figure 7 14 3 Analyzing the results from a pairwise comparison for sorting and changing the views You may increase the number of genes in the list sort by the ratio p value or adjusted p value choose a p value cutoff so that genes are only shown if the p values are below a certain number and change the presentation from the raw p value to the adjusted p value After choosing selections from the menus click the Search button to show the results When this page first appears our results show a list of 764 genes that are differentially expressed Arrows on the left side of each gene ratio point up if a gene shows an increase in expression relative to the first group or d
3. Return to the results window and click the KEGG link a The KEGG report The KEGG report as shown in Figure 7 14 5 presents a list of biochemical and regulatory pathways that contain members from the list of differentially expressed genes on the results page Each row shows the name of the pathway a link to a list of gene list members that belong to that pathway with arrowheads to show if a member is up or down regulated a link to the KEGG pathway database the number of genes from the list that belong to that pathway the number of genes that are up regulated the number down regulated the total number from that pathway that were present in the array or reference data set for Next Gen data and the z scores for up and down regulated genes Z SCOICS z scores are used to evaluate whether genes from a specific pathway are enriched in your list of differentially expressed genes If genes from a specific pathway are represented in your gene list more often than they would be expected to be seen by chance the z scores reflect that occurrence A z score greater than 2 indicates that a pathway is significantly enriched in the list of differentially expressed genes while a z score below 2 indicates that a pathway is significantly under represented in the list The direction and color of the arrowheads show whether those genes are up or down regulated in the second group relative to the first group of samples Clicking the arrows above a
4. We have added fea tures for uploading large data sets aligning data to reference sequences and presenting results which make GSAE useful for NGS as well Both kinds of data analyses share several similar features Data must be entered into the system and normalized Statistical methods must be applied to identify significant differences in gene expression Once significantly different expression patterns have been identified there must be a way to uncover the biological meaning for those results GSAE provides methods for work ing with ontologies and KEGG pathways clustering options to help identify genes that share similar patterns of expression and links to access information in public databases Data management capabilities and quality control measures are also part of the GSAE system In both of the two basic protocols we will present general methods for analyzing mi croarray data follow those procedures with alternative procedures that can be used to analyze NGS data and discuss the differences between the microarray protocol and the NGS alternative Basic Protocol 1 presents a pairwise analysis of microarray data from mice that were fed different kinds of food Kozul et al 2008 The protocol uses data from the public Gene Expression Omnibus GEO database at the NCBI Barrett et al 2009 and demonstrates normalizing the data and the analyses Alternate Protocol 1 for a pairwise comparison also uses data from GEO however these
5. D mem D Li I T pos mmm a Oo mom iss mmc M 99 0 EE 0m SH a 5 Figure 7 14 15 Partitioning and silhouette data from a Next Gen experiment LITERATURE CITED Barrett T Troup D B Wilhite S E Ledoux P Rudnev D Evangelista C Kim I F Soboleva A Tomashevsky M Marshall K A Phillippy K H Sherman P M Muertter R N and Edgar R 2009 NCBI GEO Archive for high throughput functional genomic data Nucleic Acids Res 37 D885 D890 Kaufman L and Rousseeuw P 1990 Finding Groups in Data An Introduction to Cluster Analysis Wiley Series in Probability and Statistics John Wiley amp Sons Inc New York Kozul C D Nomikos A P Hampton T H Warnke L A Gosse J A Davey J C Thorpe J E Jackson B P Ihnat M A and Hamilton J W 2008 Laboratory diet profoundly alters gene expression and confounds genomic analysis in mouse liver and lung Chem Biol Interact 173 129 140 Li H and Durbin R 2009 Fast and accurate short read alignment with Burrows Wheeler transform Bioinformatics E pub May 18 Marioni J C Mason C E Mane S M Stephens M and Gilad Y 2008 RNA seq An assessment of technical reproducibility and comparison with gene expression arrays Genome Res 18 1509 1517 Millenaar F F Okyere J May S T van Zanten M Voesenek L A and Peeters A J 2006 How to decide Different methods of calculating ge
6. GS AE users can choose between the t test Welch s t test a Wilcoxon test and no statistical tests The f test is commonly used for this step when samples from a controlled experiment are being compared The f test assumes a normal distribution with equal variance Other options that may be used are the Welch s t test which does not assume equal variance and the Wilcoxon test a nonparametric rank sum test Since all of these tests look at the variation between replicates you must have at least two replicates for each group to apply these tests For the Wilcoxon test you must have at least four replicates Use the f test for this example Quality Calls The quality options in this menu are N A A absent M marginal or P present However neither RMA nor GC RMA produce quality values so N A is the appropriate choice when these normalization methods are used Exclude Control Probes Selecting this check box excludes positive and negative control probes from the analysis This step can be helpful because it cuts down on the number of tests and minimizes the penalty from the multiple testing correction For our example check this box Current Protocols in Bioinformatics e Show genes that are up regulated or down regulated Use the checkboxes to choose both sets of genes or one set Check both boxes for this example f Threshold The Lower threshold menu allows you to filter the results by the change in expression leve
7. Protocols in Bioinformatics AIN 76A 1 AIN 76A 5 AIN 76A 7 AIN 76A 8 AIN 76A 11 Figure 7 14 11 Box plots from a multiple condition experiment A Box plots from the six con ditions that were compared in Basic Protocol 2 Each plot represents the averaged data from the four to five replicates from each treatment B Box plots from biological replicates Replicates from the AIN 76 0 lead samples are shown 25 Return to the results page and choose Samples from the Cluster options These results also show us that the groups of samples are divided by the kinds of food they received The mice that ate the LRD 5001 show patterns of gene expression more similar to each other than to the patterns from the mice that ate AIN 76A We also see that the AIN 76A samples that had 100 ppb of arsenic were more different from the AIN 76A samples without arsenic than the LRD 5001 samples were from each other Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 25 Supplement 27 by sample ioe fee dd Dicas d mc Dang ees cd z A clustered ET wa by genes clustered THA i e 1 EDU aam DC pot AMG SLA aan Of A TRAD was D bd and arsenic in wate Paa ou a dua at THE mech HP nemo E A T ai rmm mem ncm rnm sBemzliB gap HalilcarEld ganpnnbippiit 4 if t Ragerta Orietogy TECG DCiuiker Saves Genes FOA iiaiai Exod Gave vere Aa borage gt mie
8. alignment algorithms The results from processing data from the AB SOLD instrument are described in Alternate Protocol 1 For Illumina data processed with the BWA we obtain the following kinds of results gene lists text and html a base composition plot a list of genes formatted for GSAE a transcript coverage plot and an analysis log Fig 7 14 14 a Gene lists text and html The gene lists show the number of reads that map to each transcript and the number mapping per transcript normalized per million The html version of the gene list includes a graph showing where the reads map which is linked to a more detailed map with each base position Links are provided to the NCBI RefSeq record b Base composition plot This graph shows the numbers of each base at each position and can be helpful for quality control If sequencing DNA we would expect the ratios to be fairly similar If sequencing single stranded RNA we would expect to see more differences c Transcript coverage plot The transcript coverage plot shows the number of reads that map to different numbers of transcripts For example in each case you can see there are a large number of transcripts that only have one mapping read Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 29 Supplement 27 Analysis Type Ireut File Tate Iritisted Compbitid Comment Mente 1D Experiment ANA Seg BWA 29M musci Olit Compite JIHNS 05
9. obtained after the multi test correction in our case Benjamini and Hochberg has been applied In this analysis choosing the adjusted p value decreases the number of differentially expressed genes from 1449 to 26 As noted earlier although the multiple testing correction provides a way to sort genes by the significance genes that truly change may be missed when these corrections are applied To view additional genes that may be candidates for study you can raise the cut off limit for the adjusted p values using the pull down menu or skip the multiple test correction altogether Current Protocols in Bioinformatics Interpreting the results After adjusting the p value only 28 genes in our set show significant changes It is helpful at this point to save our results before proceeding on to further analyses Since the reports that we would use next the scatter plot KEGG pathway information and ontology reports are the same as in Basic Protocol 1 we will leave it to the reader to refer to the earlier protocol for instruction The one point we would like to discuss here is interpreting the gene summary and the differences between the gene summaries for microarray and Next Gen data Each gene in the list is accompanied by a summary that can be accessed by clicking the gene name The summary page presents information about expression levels at the top and links to external databases in the bottom half Summaries from both microarray data an
10. settings a Normalization This step involves normalizing data for differences in signal intensity within and between arrays This type of normalization process does not apply to Next Gen data since Next Gen measurements are derived from the number of reads that map to a transcript instead of the intensity of light Next Gen sequence data are normalized by GSAE but this happens during the align ment phase During the alignment process the number of reads from each experiment is Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 17 Supplement 27 Analyzing Gene Expression Data Using GeneSifter 7 14 18 Supplement 27 20 normalized to the number of mapped reads per million reads RPM This allows data from different experiments to be compared For this example choose None from the menu Statistics The statistical tests available from this menu are used to determine if the differences between the mean numbers of read counts or intensity measurements in the case of microarrays from a set of replicate samples are significant The significance levels are reported as p values i e the probability of seeing a result by chance For this example choose t test for the statistics Quality For Next Gen data the quality values correspond to the number of reads per million transcripts and range from 0 5 to 100 For this example set the quality at 1 meaning that we w
11. there is a comparison phase where the numbers of read counts can be used to gain insights into gene expression Many of the steps in the last phase are similar to those used in the analysis of microarray data In this protocol we will describe analyzing data from two NGS data sets and their replicates These data were obtained from an experiment to assess the transcriptome from single cells mouse oocytes with different genotypes Tang et al 2009 In one case wild type mouse oocytes were used In the other case the mouse oocytes had a knock out mutation for DICER a gene required for processing microRNAs We will discuss uploading data and aligning the data view the types of information obtained from the alignment and compare the two samples to each other mentioning where the NGS data analysis process differs from a pairwise comparison of samples from microarrays Necessary Resources Software GeneSifter Analysis Edition GS ABE a trial account must be established in order to upload data files to GSAE a license for the GeneSifter Analysis Edition may be obtained from Geospiza Inc http www geospiza com GS AE is accessed over the Web therefore Internet access 1s required along with an up to date Web browser such as Mozilla Firefox MS Internet Explorer or Apple Safari Files Data files may be uploaded from a variety of sequencing instruments For the Illumina GA analyzer the data are text files containing FASTA forma
12. to sort by z scores It should also be noted that some genes may belong to multiple ontologies When we look at the ontology information for our experiment we can see that the most significant ontologies in biological processes are metabolism cellular processes and regulation for cellular components we see that cells and cell parts are significant and for the molecular function ontology catalytic activity and electron carrier activity are significant When we look at the z score report for molecular function and sort our results by up regulated genes we see that many genes show oxidoreductase and glutathione S transferase activity which is consistent with our findings from the KEGG report Selecting the Genes icon shows us that those genes are cytochrome P450s Taking all of our data together we can conclude that genes for breaking down substances like xenobiotics are expressed more highly when mice are fed LRD 5001 than when they are fed AIN 76A Current Protocols in Bioinformatics B sbogrirad Frepess Crha Cece Esse Focus Commngy Rspevt E armena Buspart fumes Reet Benen BO Lisi Arras ri amp g i E 13 WE hu Li E s E amp HM 12108 E maetabol process fu E p H im ap DB o D ww ik 1 Jgs aH TES Bran EE a d a e E N a alia a F Uu a 8 6 HM hy A D i a 9 i12 mE am 5d Terr b iB 5 5 8 B o 343 m 3 D amp tik 2 WE 41 GD D uU smi eo a 3 ti deae pera eum Be mg ER IER d
13. z score column will allow you to sort by z scores for up regulated or down regulated genes Click the arrowhead that is pointed up in the z score column to sort by up regulated genes We can see at least 20 pathways are up regulated when mice are fed LRD 5001 Genes Pick one of the top listed pathways and click the corresponding icon in the Genes column A new section will appear underneath the name of the pathway Before proceeding look at the values in the List totals and Array column We can see in our analysis that the cytochrome P450 pathway for metabolizing xenobiotics is significantly up regulated and contains 19 members from our 539 member gene list We also see that those members are all up regulated and that there are 53 genes on the array that belong to this pathway Now look at the list of genes in the newly opened section Where we had 19 genes shown as the value in the list column there are 26 genes listed below the name of the pathway Current Protocols in Bioinformatics Kec Metabgliam of aenpblo ra by ieron PATO Mus mibilius eres Father mam Patten sry hug PCL Seuls wi lm ee ee ee ti i ao click the KEGG icon to see a diagram of the pathway click the gene icon to see genes that are up regulated a list of the genes that belong to this pathway L RO CR cu 2b tad and a red border bare a bum Baird tuo it aneda TE Eriin yh Famy acid mat
14. 11 13 58 31 2009 05 11 15 26 05 Biolegscal replicates Ho 1553 muscle 01 RNA Seq BWA 2MM Info Reference Species Mus musculus Befencenoe Type a Analysis Results Gene ligt Text Base Composition Pigi gi genes Text Gene ligt HTML Transeript Coverage Piot Anahrgs Log Figure 7 14 14 Analyzing Gene Expression Data Using GeneSifter 7 14 30 Supplement 27 8 FhcBEeGRGegedn deg rir mU 1 aa Reads Transcript Illumina data Setting up a project 14 To compare multiple samples begin by setting up a project Find the Create New section in the Control Panel and click Project 15 Give the project a title and add a description Use Mouse tissues for this project 16 Use the checkboxes to select the arrays that contain your data These names cor respond to the Array Gene Set names that you assigned to the data sets during the upload process If you checked the correct box you will see the sample names ap pear in the Common Conditions box The conditions that appear should match your experimental treatments 17 Click the Continue button 18 Assign a name to this group 19 Select a normalization method Choose None for this example 20 Use the Data transformation menu to select a method for data transformation Data transformation options are no transformation log transformation or already transformed Log transformations smooth out the data and produce a more G
15. 243397 GSM243378 GSM243382 and GSM243355 for the LRD 5001 group with 10 ppb arsenic GSM243374 GSM243380 GSM243381 GSM243385 GSM243387 and for the LRD 5001 group with 100 ppb arsenic GSM243354 GSM243356 GSM243383 GSM243390 GSM243392 A demonstration site with the same files and analysis procedures can be viewed from the data center at Attp www geospiza com Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 21 Supplement 27 Uploading data 1 Create a zip archive from your microarray data files a If using a computer with a Microsoft Windows operating system a commonly used program is WinZip b If using Mac OS X select your data files click the right mouse button and choose Compress Items to create a zip archive The resulting archive file will be called Archive zip 2 Log in to GeneSifter Analysis Edition GSAE http login genesifter net 3 Locate the Import Data heading in the Control Panel on the left hand side of the screen and click Upload Tools Several types of microarray data can be uploaded and analyzed in GSAE See Basic Pro tocol 1 for detailed descriptions Our data were uploaded using the option for Advanced upload methods and normalized with GC RMA 4 On the page that appears click the Run Advanced Upload Methods button 5 Select the normalization method and the array type from pull down menus For this example use GC RMA and the 430 2 0 Mouse arra
16. 7 Current Protocols in Bioinformatics 11 12 13 14 15 16 L9 18 19 Enter the name for the control group as the group name and any descriptive informa tion in the Description field Choose a Normalization option Leave the setting at None because our data were log transformed and normalized when we used GC RMA during the upload process Choose a Data Transformation option Leave the setting at Data already log transformed because our data were log trans formed and normalized when we used GC RMA during the upload process Select a group for a control sample and use the arrow button to move that group to the box on the right side Choose AIN 76A with 0 as the control sample Select the other groups that will be part of the analysis and move them to the right side by clicking the arrow button Click the Create Group button Next select the samples for each condition Select all the experiments and click the Create Group button A new page will appear with a list of all the conditions and all the samples for each condition Choose the samples that will be used in the analysis You may choose the samples one by one or if all the samples will be used click Select All Experiments Click Select All Experiments Click the Create Group button A small window will appear while data are processing When the processing step is complete a new page will appear stating that your project has been cre
17. A does a sampling 100 and picks the best from that sample a Clusters The number chosen here determines the number of gene groups Often people try different values to see which gives the best results b Row Center The values in this set Row Mean None or Control are used to determine the centers of each row c Distance The Distance choices are Euclidean which corresponds to a straight line distance Manhattan which is a sum of linear distances and Correlation As a starting point for this example choose PAM with 4 clusters based on our Gene cluster pattern a Euclidean distance and the Row Center at the Row Mean 35 Click the Search button to begin Silhouettes When the clustering process is complete a page appears with multiple graphs one for each cluster group At the top of the page and under each graph are values called silhouettes Silhouette widths are scores that indicate how well the expression of the genes within a cluster matches that graph Values between 0 26 and 0 50 indicate a weak structure between 0 50 and 0 70 a reasonable structure and above 0 70 a strong structure The mean silhouette value for all the silhouettes appears at the top of the page with the individual values appearing below each graph along with the number of genes that show that pattern Kaufman and Rousseeuw 1990 The graphs showing the average expression pattern within each cluster and the silhouette values for our clusters are sh
18. Analyzing Gene Expression Data from UNIT 7 14 Microarray and Next Generation DNA Sequencing Transcriptome Profiling Assays Using GeneSifter Analysis Edition Sandra Porter N Eric Olson and Todd Smith Digital World Biology Seattle Washington Geospiza Inc Seattle Washington ABSTRACT Transcription profiling with microarrays has become a standard procedure for comparing the levels of gene expression between pairs of samples or multiple samples following dif ferent experimental treatments New technologies collectively known as next generation DNA sequencing methods are also starting to be used for transcriptome analysis These technologies with their low background large capacity for data collection and dynamic range provide a powerful and complementary tool to the assays that formerly relied on microarrays In this chapter we describe two protocols for working with microarray data from pairs of samples and samples treated with multiple conditions and discuss alter native protocols for carrying out similar analyses with next generation DNA sequencing data from two different instrument platforms Illumina GA and Applied Biosystems SOLID Curr Protoc Bioinform 27 7 14 1 7 14 35 2009 by John Wiley amp Sons Inc Keywords gene expression e microarray e RNA Seq e transcriptome e GeneSifter Analysis Edition e next generation DNA sequencing INTRODUCTION Transcriptome profiling is a widely used technique that allo
19. RD 5001 a standard laboratory mouse food pairwise comparison mouse 1 mouse2 mouse 3 mouse 4 mouse 5 mouse 6 Qy By Gy By By Cy no treatment N ra treatment isolate RNA microarrays upload data normalize identify differential expression fold change quality statistics e g t test others multiple testing correction Bonferroni Benjamini and Hochberg others explore biology ontology KEGG scatter plot Figure 7 14 1 Overview of the process for a pairwise comparison Current Protocols in Bioinformatics BASIC PROTOCOL 1 Analyzing Expression Patterns 7 14 3 Supplement 27 Necessary Resources Software GeneSifter Analysis Edition GS ABE a trial account must be established in order to upload data files to GSAE a trial account or license for GeneSifter Analysis Edition may be obtained from Geospiza Inc Attp www geospiza com GS AE is accessed through the Web therefore Internet access is required along with an up to date Web browser such as Mozilla Firefox MS Internet Explorer or Apple Safari Files Data files from a variety of microarray platforms may be uploaded and analyzed in GSAE including Affymetrix Illumina Codelink or Agilent arrays and custom chips The example data used in this procedure were CEL files from an Affymetrix array and were obtained from the GEO database at the NCBI Accession code GSE 9630 CEL files are the best file type for use in GSAE
20. T Click the Next button Two windows will appear for managing the upload process Use the controls in the left window to locate your data files Once you have found your data files select them with your mouse and click the blue arrowhead to move those files into the Transfer Queue Once the files you wish to transfer are in the Transfer Queue highlight those files and click the blue arrow beneath the Transfer Queue window to begin transferring data Transferring data will take a variable amount of time depending on your network the volume of network traffic and the amount of data you are transferring A 2 GB Next Gen data set will take at least 40 min to upload Aligning Next Gen data to reference data Once the data have been uploaded to GSAE the reads in each data set are aligned to a reference data source During this process the number of reads mapping to each transcript are counted and normalized to the number of reads per million reads RPM so that data may be compared between experiments 8 10 11 12 13 Access uploaded Next Gen data sets by clicking Next Gen in the Inventories section of the control panel Use the checkboxes to select data sets for analysis then click the Analyze button on the bottom right side of the table A new page will appear Choose the Analysis Type Reference species and a Reference Type from the corre sponding pull down menus a Analysis Type The Analysis Type is det
21. The analysis step may take a few hours depending on the size of your data file and the number of samples that need to be processed Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 15 Supplement 27 Read Mapping Statistics RNA Seq Analysis Gene List Total number of reads Number of unmapped reads Number of mapped reads 12537930 1993626 15 90 of all reads 10544304 84 10 of all reads Non uniquely mapped reads 1759919 14 04 of all reads Uniquely mapped reads 8784385 70 06 of all reads Uniquely mapped reads with 0 mismatches 6163136 70 16 of uniquely mapped reads Uniquely mapped reads with 1 mismatches 1746203 19 88 of uniquely mapped reads Uniquely mapped reads with 2 mismatches 875046 9 96 of uniquely mapped reads s Uniquely mapped to ribosomal 2 mismatches 0 0 00 of uniquely mapped reads gt gt Analysis Job Details Job Info Job ID 51 Uniquely Mapped Reads by Chromosome Analysis Type RNA Seq SOLID 3 passes Input File GSM365013 filtered csfasta txt State Complete Initiated 2009 05 15 13 33 10 Completed 2009 05 15 14 26 43 Comment Remote ID 2032 Experiment GSM365013 1 RNA Seq SOLID 3 passes Info Reference Species Mus musculus Reference Type mRNA Analysis Results Read alignment statistics HTML Gene list Text Gene list HTML Job log file Standard error Standard o
22. To obtain CEL files go to the GEO database at the NCBI www ncbi nih gov geo Enter the accession number in this case GSE 9630 in the section labeled Query and click the Go button In this example all the files in the data set are downloaded as a single tar file by selecting ftp from the Download column at the bottom of the page After downloading to a local computer the files are extracted unzipped then uploaded to GSAE as described in the instructions Files used for the AIN 76 group GSM243398 GSM243405 GSM243391 GSM243358 and GSM243376 Files used for the LRD 5001 group GSM243394 GSM243397 GSM243378 GSM243382 and GSM243355 A demonstration site with the steps performed below and the same data files can be accessed from the data center at http www geospiza com Uploading data 1 Create a zip archive from your microarray data files a If using a computer with a Microsoft Windows operating system a commonly used program is WinZip b If using Mac OS X select your data files click the right mouse button and choose Compress Items to create a zip archive 2 Log in to GeneSifter Analysis Edition GSAE hittp login genesifter net A user name and password are provided when a trial account is established 3 Locate the Import Data heading in the Control Panel on the left hand side of the screen and click Upload Tools Several types of microarray data can be uploaded and analyzed in GSAE Since different
23. am i Pearse Sa ie a0 RES RR RHEE RSH a Group 1 AN TGA meer 0 Group E LAD 5001 water 0 pE 5nammemenenm The number of genes from the list on the analysis page that belong to this pathway _ The number of genes in this pathway that are up regulated in group 2 relative to group 1 The number of genes in this pathway that are down regulated in group 2 relative to group 1 The number of genes on the array that belong to this pathway The z score for the number of genes that belong to this pathway and are up regulated Clicking the red arrow will sort the list by z scores 6 The z score for the number of genes that belong to this pathway and are down regulated Clicking the green arrow will sort the list by z scores Figure 7 14 5 KEGG pathway results Most of the genes have different names but some appear to be identical For example there are three listings for glutathione S transferase mu I Are they really the same gene Clicking the gene names shows us that two entries have the same accession number One possible explanation for their duplication in the list could be that they are represented multiple times on the array It could also be that the probes were originally thought to belong to different genes and now with a better map are placed in the same gene We also see that one of the three genes has a different accession number This entry might represent a different isoform t
24. are NGS data from the Applied Biosystems SOLID instrument In Alternate Protocol 1 we use a pairwise analysis to compare gene expression from single wild type mouse oocytes with gene ex pression in mouse oocytes containing a knockout mutation for DICER a gene involved in processing microRNAs Tang et al 2009 Basic Protocol 2 presents a general method for analyzing microarray data from samples that were obtained after multiple conditions were applied In this study mice were fed two kinds of food and exposed to increasing concentrations of arsenic in their water Kozul et al 2008 This protocol includes ANOVA and demonstrates options for Principal Component Analysis clustering data by samples or genes and identifying expression patterns from specific gene families Alternate Protocol 2 a variation on Basic Protocol 2 describes an analysis of NGS data from the Illumina GA analyzer comparing samples from three different tissues Mortazavi et al 2008 Cluster analysis is included in this procedure as a means of identifying genes that are expressed in a tissue specific manner As with Basic Protocol 1 these studies use data from public repositories in this case GEO and the NCBI Short Read Archive SRA Wheeler et al 2008 It should be noted for both protocols that GSAE contains alternatives to the statistical tools used in these procedures and that other tools may be more appropriate depending on the individual study Current Protoc
25. ated From this point you can continue the analysis by selecting Analyze This Project or you can analyze the project at a later time Identifying differential gene expression 20 21 Select Projects from the Analysis section of the Control Panel Choose the project name to review the box plots for the samples and replicates in the project When we analyze multiple samples GSAE creates box plots that allow us to evaluate the variation between experimental groups and the replicate samples within each group The box plot also known as a box and whiskers plot shows the averaged data either from a group of replicate samples or from the intensity values for a single sample The line within the box represents the median value for the data set The ends of the whiskers show the highest and lowest values If a box and whiskers graph is made from data with a normal distribution the graph would look like the box plot in Figure 7 14 10 Box plots are helpful for quality control If we find a box plot with a different median value from the other samples it could indicate a problem with that sample or array a Locate the Project Info section in the Project Details page and click Boxplot A box plot will appear as shown in Figure 7 14 11A with plots representing all six of the different conditions Notice that all six of the plots have similar shapes and similar values b Return to the Project Details page Locate the bottom section enti
26. aussian distribution For this example choose Log transform data from the menu Current Protocols in Bioinformatics 21 22 25 In this next step you will set the condition order The first group selected acts as a reference or control group Changes in gene expression in the other groups are all measured relative to first group that 1s chosen a Decide which group is group 1 and enter the name of that group in the Group Name box To do this select that group in the Conditions box on the left side and use the arrow key to move it to the right hand box b Select the other conditions that you wish to analyze and use the arrow key to move those to the left condition box as well c Click the Create Group button A new page will appear with a list of all the groups and samples Select the samples for each condition We will use all the samples so click Select All Experiments then click the Create Group button The processing window will appear while the data are being processed Once a project has been created you may analyze the project or create a new project or new group These steps can also be completed at a later time Comparing samples 24 Locate the Analysis section in the Control Panel select Projects and find your project in the list Once you have found your project in the list you may wish to select the project name to view some of the project details You may also wish to view the box plots fo
27. d Next Gen data Fig 7 14 8 show the number of samples N along with the values for each sample and the standard error of the mean Where the two kinds of summaries differ is in intensity and quality values For microarray data the columns labeled intensity values do show the intensity data If the data were log transformed during the upload process or the analysis then the log transformed values are reported For Next Gen data however the values in the intensity values column are not intensity values When Next Gen data are used these values refer to the normalized number of Gene Summary Cytochrome P450 family 2 subfamily a polypeptide 4 By Group sy i Quality Group Condition M Mean SEM SEM Mean Mean 1 AIN 76A water O 5 10 0036 0 1127 1 196 2 LRD 5001 water 0 5 12 7881 0 1339 195 By Target Group Sample Intensity l GSM243358 10 0985 G5M243376 10 1402 GSM243391 9 6907 GSM243398 9 7940 GSM243405 10 2946 GSM243355 12 8186 GSM243378 12 7926 LRD 5001 water 0 up regulated G5M243382 12 9859 6 89 fold 35M243394 12 2901 B compared to AIN 76A water 0 6G5M243397 13 0535 1 1 1 1 2 Z 2 2 2 Gene Summary Drebrin like By Group Group Condition N Mean SEM SEM Mean sai ii Le ri i Wild type mouse oocyte 2 3 3219 0 0000 0 0 0000 2 Dicer knock out oocyte 2 2 5029 0 0036 0 1 6 0750 Irnterisity By Target Group Sample Intensity Dicer knock 1 GSM365014 3 3219 exit oocy
28. d process Choose Data Already Log Transformed for this example i Click the Analyze button A page with results appears when the processing step is complete Investigating the biology Figure 7 14 3 shows the results from our pairwise analysis of the microarray data the differentially expressed genes Pull down menus in the middle of the page contain options Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 7 Supplement 27 view changes by ontology or the z score see the scatter plot nalysis gt Pairwise gt Resulrs 2 KEGG Scatter Plot Results Export Save Group 2 AIN 76A water 0 LRD 5001 water 0 74096 74101 74111 74115 74118 74094 74103 74106 74113 74114 1 5 t test IB save your None results None Calls Pairwise Analysis Mouse food and arsenic Reports Ontoloc Sugi Conditions Experiments Significance Normalization Quality Cutoff Data Transformation change to Log Transformed change to click search to 2 adjusted p 1 adjusted p 3 apply changes Show 20 Sort By Adj p p Cutoff 0 05 adjusted p 3 Search 539 results found 1 20 21 40 p value 0 00000 0 00000 this gene is expressed about 7 fold more Identifier NM 007812 NM 008182 AK005441 AK017207 BCO02008 NM 007647 BCO255B84 BCO10769 AY061760 AV027367 AF350410
29. e statistical methods for analyzing data have become more standardized As NGS becomes more commonplace these new methods are increasingly likely to serve as a complement Analyzing Expression Patterns Current Protocols in Bioinformatics 7 14 1 7 14 35 September 2009 7 14 1 Published online September 2009 in Wiley Interscience www interscience wiley com DOI 10 1002 0471250953 b10714s27 Supplement 27 Copyright 2009 John Wiley amp Sons Inc Analyzing Gene Expression Data Using GeneSifter 7 14 2 Supplement 27 or alternative to microarrays Since these assays are based on DNA sequencing rather than hybridization the background is low the results are digital the dynamic range 1s greater and transcripts can be detected even in the absence of a pre existing probe Marioni et al 2008 Wang et al 2009 Furthermore once the sequence data are available they can be aligned to new reference data sets making NGS data valuable for future experiments Still until NGS assays are better characterized and understood it is likely that microarrays and NGS will serve as complementary technologies for some years to come In this chapter we describe using a common platform GeneSifter Analysis Edition GSAE a registered trademark of Geospiza Inc for analyzing data from both microar ray and NGS experiments GSAE is a versatile Web based system that can already be used to analyze data from a wide variety of microarray platforms
30. e Illumina GA Analyzer and obtained from the SRA database at the NCBI Accession code SRAO001030 The data files are obtained as follows The accession number SRA001030 is entered in the data set search box at the NCBI Short Read Archive http www ncbi nih gov sra and the Go button is clicked The files are downloaded for each tissue type by clicking Download data for this experiment link After downloading the data files the text files containing the fasta sequences are uploaded to GSAE and processed as described in the instructions 1 Log in to GeneSifter Analysis Edition GSAE Aittp login genesifter net Uploading data 2 Locate the Import Data heading in the Control Panel and click Upload Tools 3 Click the Next Gen File Upload button to begin uploading Next Gen data 4 Enter a name for a folder Folders are used to organize Next Gen data sets 5 Click the Next button 6 Two windows will appear for managing the upload process Use the controls in the left window to locate your data files Once you have found your data files select them with your mouse and click the blue arrowhead to move those files into the Transfer Queue 7 Once the files you wish to transfer are in the Transfer Queue highlight those files and click the blue arrow beneath the Transfer Queue window to begin transferring data Transferring data will take a variable amount of time depending on your network the volume of network traffic and th
31. e amount of data you are transferring Illumina GA data sets are approximately 250 MB and take at least 10 min to transfer Aligning Next Gen data to reference data Once the data have been uploaded to GSAE the expression levels for each gene are measured by aligning the read sequences from the data set to a reference data source and counting the number of reads that map to each transcript 8 Access uploaded Next Gen data sets by clicking Next Gen in the Inventories section of the control panel 9 Usethe checkboxes to select the data sets then click the Analyze button on the bottom right side of the table 10 A new page will appear where you can choose analysis settings from pull down menus These settings include the File Type Analysis Type Reference Species and Reference Type Choose the appropriate Analysis Type Reference Species and Reference Type Current Protocols in Bioinformatics a File Type The file type is determined by the instrument that was used to collect the data Since our read data were generated by an Illumina Genome Analyzer choose Genome Analyzer b Analysis Type The Analysis Type is determined by the kind of data that were uploaded and the kind of experiment that was performed This setting also allows you to choose which algorithm to use for the alignment Choose RNA Seq BWA 2 MM This setting uses the Burroughs Wheeler algorithm Li and Durbin 2009 to align the reads with a tolerance setting o
32. e group 1 column to select the samples for group 1 and the checkbox in the group 2 column to select the samples for group 2 Usually the control wild type or untreated samples are assigned to group I Here assign the AIN 76A sample to group 1 and the LRD 5001 samples to group 2 14 Choose the advanced analysis settings Since the data were normalized during the uploading process by the GC RMA algorithm we can use some of the default settings for the analysis If you choose a setting that is not valid for RMA or GC RMA normalized data warnings will appear to let you know that the data are already normalized or already log transformed a Normalization Use None with RMA or GC RMA normalized data This step has already been performed since RMA and GC RMA both perform quantile normalization during the upload process b Statistics The statistical tests available from the pull down menu are used to determine the probability that the differences between the mean values for intensity measurements for each gene or probe from a set of replicate samples are Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 5 Supplement 27 Genesifter Palais bd Control Panel Analysis 1 Pairwise Projects Import Data a Upload Tools Create New Project Condition Target select Pairwise 2 select the gene set 8 assign samples to the two groups 4 choose analysis
33. elong to that ontology iii GO Clicking the GO icon opens the record for the ontology in the AmiGO database iv List The list column shows the total number of genes from the gene list both up and down regulated that have that ontology as part of their annotation v Totals up or down One column contains the values for number of up regulated genes in the list that belong to an ontology The other column shows the number of down regulated genes that belong to that ontology vi Array This value shows the number of probes on a microarray chip that could corre spond to genes in an individual ontology vii z score As with the KEGG report the z score provides a way to determine whether a specific ontology is over or under represented in the list of differentially expressed genes Significant z scores are above 2 or below negative 2 We cannot sort by z scores on the ontology report pages but we can sort by z scores from the z score report viii Pie graph The pie graph depicts the ontologies in the list and the numbers of members Z score reports Each ontology report page contains a link to a z score report Where the ontology reports show ontologies through a hierarchical organization the z score report shows all the ontologies with significant z scores without the need to drill down into the hierarchy This is helpful both because significant z scores can be hidden inside of a hierarchy and because this report allows you
34. eneSifter are PAM Partitioning Around Medoids and CLARA Clustering Around Large Applications Both of these options are variations of K means clustering K means clustering is used to break a set of objects in this case genes into set of k groups The clusters are formed by locating samples at the medoids median values to act as the seeds and clustering the other genes around the medoids In order to use the advanced clustering methods such as PAM or CLARA filters must be applied in order to limit the number of the genes to below 5000 Two ways to limit the gene number are to set a lower p value as a cutoff and to raise the threshold These filters can be used separately or in combination Current Protocols in Bioinformatics To use the advanced clustering methods 32 Choose Pattern Navigation from the analysis path 33 Choose Cluster 34 Choose a method for clustering and set the options as described below The two options for advanced cluster analysis are PAM Partitioning Around Medoids and CLARA Clustering Around Large Applications The difference between the two methods is that PAM will try to group the samples into the number of clusters that you assign while CLARA will try to find the optimum number of groups PAM is recommended for data sets smaller than 3500 genes while CLARA is more suited to larger data sets PAM is also more robust it tries all possible combinations of genes for k and picks the best clusters CLAR
35. ermined by the kind of data that were uploaded and the kind of experiment that was performed For example if you uploaded SOLiD data analysis options specific to that data type would appear as choices in the menu For SOLID data the alignment algorithm is specific for data in a csfasta format Choose RNA Seq SOLID 3 passes b Reference Species The Reference Species is determined by the source of your data If your data came from human tissues for example you would select Homo sapiens as the reference species Since our data came from mouse choose Mus musculus c Reference Type The choices for Reference Type are made available in the Refer ence Type menu after you have selected the analysis type and reference species The Reference Type refers to the kind of reference data that will be used in the alignment Since we are measuring gene expression choose mRNA as the ref erence type This reference data set contains the RNA sequences from the mouse RefSeq database at the NCBI Click the checkbox for Create Experiment s upon completion This selection organizes your data as an experiment allowing you to compare expression between samples after the analysis step is complete In order to set up experiments GeneSifter must already contain an appropriate Gene Set A Gene Set is derived from the annotations that accompany the reference data source Click the Analyze button to queue the Next Gen data set for analysis
36. es us a visual picture of gene expression in the different samples The levels of gene expression in group 1 mice fed with AIN 76A are plotted on the x axis and group 2 mice fed with LRD 5001 on the y axis Genes that are equally expressed in both samples fall on the diagonal line Genes that are expressed more in one group or in the other appear either above the line group 2 or below the line group 1 depending on the group that shows the highest level of expression If we used a method to correct for the false discovery rate then the points for genes showing nonsignificant changes would be colored gray up regulated genes showing a significant change would be colored red and down regulated genes showing a significant change would be colored blue or green The zoom window and gene summary To learn more about any gene in the graph we drag the box on top of a spot and click the zoom button After a short time up to 30 sec the highlighted spot and surrounding spots will appear in the top right window If spots overlap you may separate them by dragging them with the mouse The name of each gene will appear when the mouse is moved over a spot and clicking a spot will produce the gene summary information in the lower right corner In our experimental example clicking some of the spots will find genes that were seen ear lier in the list such as genes for members of the cytochrome p450 family and glutathione S transferase 19
37. f 2 mismatches c Reference Species The Reference Species is determined by the source of your data Since our data came from mouse choose Mus musculus d Reference Type The choices for Reference Type are made available in the Refer ence Type menu after you have selected the analysis type and reference species The Reference Type refers to the reference data that will be used in the alignment Since we are measuring gene expression pick mRNA as the reference type This reference data set corresponds to the current build for mouse RefSeq RNA 11 Click the checkbox for Create Experiment s upon completion This selection organizes your data as an experiment allowing you to compare expression between samples after the analysis step is complete 12 Click the Analyze button to queue the Next Gen data set for analysis The analysis step may take a few hours depending on the size of your data file and the number of samples waiting to be processed When the analysis has finished the information on the right side of the table in the Analysis State column will change to Complete When the analysis step is complete you will be able to view different types of information about your samples Viewing the Next Gen alignment results 13 Click the file name to get to the analysis details page for your file then click the Job ID to get the information from the analysis The kinds of analysis results obtained depend on the
38. hat is transcribed from the same gene Many arrays do not distinguish between alternative transcripts and count them all together Affymetrix arrays can also have multiple probe sets for a single gene in these cases the gene will appear multiple times since intensity measurements will be obtained from each probe It should also be noted that some genes may belong to multiple KEGG pathways see below d KEGG pathways Click the KEGG icon to access the KEGG database and view more details for a KEGG pathway Once we have identified KEGG pathways with significant changes we can investigate further by selecting the links to the individual genes in that pathway or we Current Protocols in Bioinformatics genes from the list are in green boxes are in boxes with red numbers a o 5 ee d Siete i de Praga aar 2 Haghitasi ag E RS breup i AN TE water D Lascia 8 1 1 Sid I Grneus di LED DO01 mater 0 18 zT pitaa a rate 120 TIN deat alee Pathway pe 8 UH 9 S ro CFL I Lei 12 Hagirbcqumonms Metaboimm of xancbiotics by cytochrome PASO CLE ARS Hugoni TDI etim re AsachaXitus acid rrarLRIws rari puta P IzDciuy spar 1 irl alk da GEOH ui armiran Ades magitaa j UO nie cmn B HE MLULLIT La Analyzing Expression Patterns 7 14 11 Supplement 27 Analyzing Gene Expression Data Using GeneSifter 7 14 12 Supplement 27 can select the KEGG
39. he Exclude Control Probes checkbox then click the Search button Clicking Show All Genes gives 45 101 results Returning to this page and making the choices listed here cuts the number of genes to 921 The results page appears after the threshold filtering is complete At this point you can either save these results and return or continue the analysis There are a variety of paths we can follow from this point as shown in Figure 7 14 12 We can view the ontology or KEGG reports as discussed in Basic Protocol 1 We can also use clustering to group related genes or we can change the p cutoff value to limit the number of genes even further Using clustering to identify patterns of differential gene expression 24 Choose PCA from the Cluster options The PCA option performs a type of clustering known as Principal Component Analysis PCA PCA allows you to evaluate the similarities between samples by identifying the directions where variation is maximal The idea behind PCA is that much of the variation in a data set can be explained by a small number of variables In Figure 7 14 12 we can see that principal component analysis breaks our conditions up into three groups One group contains all of the LRD 5001 samples one group contains the AIN 76A samples and another group contains the AIN 76A sample that was treated with 100 ppb arsenic These results tell us that the greatest difference between the groups resulted from the food Current
40. he NCBI GEO Gene Expression Omnibus database GEO is a convenient place to find both microarray and Next Gen transcriptome datasets http www ebi ac uk microarray The ArrayExpress database from the European Bioinformatics Institute Both microarray and Next Gen transcriptome data can be obtained here http www ncbi nlm nih gov sra The NCBI SRA Short Read Archive database Some Next Gen transcriptome data can be obtained here Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 35 Supplement 27
41. he links on this page b Gene list text A gene list can be downloaded as a text file after the alignment is complete Current Protocols in Bioinformatics c Gene list html The gene list html shows a table with information for all the transcripts identified in this experiment i Reads A read is a DNA sequence obtained together with several other reads from a single sample Typical reads from Next Gen instruments such as the ABI SOLiD and the Illumina GA are between 25 and 50 bases long The number of reads in the first column equals the number of reads from a single sample that were aligned to the reference data set in this case RefSeq RNAs ii RPKM Reads per thousand bases per million reads This column shows the number of reads for a given transcript divided by the length of the transcript and normalized to 1 million reads Dividing the number of reads by the transcript length corrects for the greater number of reads that would be expected to align to a longer molecule of RNA iii RPM Reads per million reads iv Entrez This column contains links to the corresponding entries in the Entrez Gene database v Image maps Image maps are used to show where reads align to each transcript The transcripts in these images are all different lengths vi RefSeq ID The RefSeq accession number for a given transcript vii Title The name of the gene from RefSeq viii Gene ID The symbol for that gene ix Chrom The chrom
42. icon to view the encoded enzymes in the context of a biochemical pathway Clicking the boxes in the KEGG database takes us to additional information about each enzyme In our experiment we find that 19 of the 53 genes in the array are up regulated and belong to the cytochrome P450 pathway for metabolizing xenobiotics The KEGG pathway shows some of the possible substrates for these enzymes It would be interesting to look more closely at LRD 5001 and see if it contains naphthalene or benzopyrene or one of the other compounds shown in the KEGG pathway Other pathways that are up regulated when mice are fed LRD 5001 instead of AIN 76A are pathways for biosynthesis of steroids fatty acid metabolism arachidonic acid metabolism etc Down regulated pathways include those for pyruvate metabolism and glycolysis 20 Return to the results window and click Ontology options described below a Ontology reports An overview of the ontology reports and their features is shown in Figure 7 14 6 Three kinds of ontology reports are available from Ontology a set organized by biological process another by cellular component and a third by molecular function Each report shows a list of ontologies that contain up or down regulated genes from the list of 539 genes i Ontology Selecting the name of an ontology allows you to drill down and view sub ontologies ii Genes Clicking the icon in the genes column shows the genes from the gene list that b
43. ill only look at transcripts where there the RPM value is at least 1 in one of the samples being compared Show genes that are up regulated and or down regulated Selecting the checkboxes allows you to choose whether to limit the view to up regulated or down regulated genes or to show both types For this example check both boxes Threshold Lower The threshold corresponds to the fold change For this example choose 1 5 as the lower threshold Threshold Upper This option is usually set to none however if you wish to filter out highly expressed genes you might wish to set an upper threshold For this example leave the upper threshold at none Correction For this example choose the Benjamini and Hochberg correction Data Transformation Use these buttons to choose whether the data will be log transformed or not Log transfor mations are often used with microarray data to make the data more normally distributed For this example apply a log transformation to the data Click the Analyze button When the analysis is complete the results page will appear Viewing the results The results page shows the two groups of samples that were compared and the conditions that were used for the comparison All the genes that varied in expression by at least 1 5 fold are listed in a table on this page 21 Choose adjusted p from the last menu and click the Search button Adjusted p values are the p values
44. ity value of 100 for NGS data corresponds to 100 reads per million sampled 29 Click the Search button 30 Now we have limited the number of genes to 3293 At this point it is helpful to save the results so that we can easily return to this point To do this click Save and enter a name and description for this subset of our project When saving your project it is helpful to enter information about the data transformations or statistical tests that were used during the analysis For example if your data were log transformed or statistical tests or corrections were applied it helps to enter this information in the description field 31 A page will appear asking if you wish to continue the analysis or analyze the newly created project Select Analyze newly created project and select Show All Genes from the Project Summary page Visualizing the results Now we can begin use some of the other analysis features in GSAE The ontology and KEGG reports were discussed earlier in Basic Protocol 1 and some of the clus tering options such as PCA and clustering by samples or genes were described ear lier in this protocol We will use clustering by genes here as well in order to gain insights into the possible numbers of genes with related expression patterns In this case clustering by genes suggests that there may be three to four different expression patterns Partition clustering Two of the advanced clustering methods provided in G
45. le 6 In the screen which now appears browse to locate the data file created in step 1 7 Choose an option radio button Create Groups Create New Targets or Same as File Name Since a pairwise analysis involves comparing two groups of samples choose Create Groups and set 2 as the value If the experiment were to involve comparing more than two conditions other options would be chosen These are described in Basic Protocol 2 8 Click the Next button The screen for the next step will appear after the data are uploaded 9 On the screen displayed in step 3 of 4 you will be asked to enter a title for your data set assign a condition to each group add labels to your samples if desired and identify which sample s belong to which group In this case decide that the AIN 76A mice should be condition I and the LRD 5001 mice should be condition 2 Then use the buttons to assign all the AIN 76 samples to group 1 and the LRD 5001 samples to group 2 Comparing paired groups of samples and finding differentially expressed genes 10 Begin by selecting Pairwise from the Analysis section of the control panel Fig 7 14 2 11 Find the array or gene set that corresponds to your experiment In this case our array is named Mouse food and arsenic 12 Select the spyglass to set up the analysis A new page will appear with a list of all the samples in the array as well as the analysis options 13 Use the checkboxes in th
46. ls For example picking 1 5 as the lower threshold means that genes will only appear in the list if there is at least a 1 5 fold difference in expression between the two groups of samples Use a setting of 1 5 as the Lower limit and None as the Upper limit g Correction Every time gene expression is measured in a microarray or Next Gen experiment there is a certain probability that the results will be identified as significantly different even though they are not These kinds of results can be described as false positives or as type I errors As we increase the number of the genes tested we also increase the probability of seeing false positives For example if we have a p value of 0 05 we have a 5 chance that the gene expression difference between the two groups resulted from chance When a large data set such as one generated by a microarray experiment is analyzed with a list of 10 000 genes an average sized microarray about 500 of those genes could be incorrectly identified as significant The correction methods in this menu are designed to compensate for this kind of result Four different options are available in GSAE to adjust the p values for multiple testing and minimize the false discovery rate Since these methods are used to correct the p values obtained from statistical tests these corrections are only be applied if a statistical test such as a t test has been applied GS AE offers the following correction methods Bonfe
47. microarray platforms produce data in a variety of formats each type of microarray data has its own upload wizard In this protocol we will be working with Affymetrix CEL data from the NCBI GEO database and so we choose the option for Advanced upload methods This option also allows you to normalize data during the upload process using standard techniques for Affymetrix data such as RMA GC RMA or MASS Instructions for using other GSAE upload wizards are straightforward and are available in the GSAE user manual RMA and GC RMA are commonly used normalization procedures Millenaar et al 2006 Analyzing Gene ve iu Data Both of these processes involve three distinct operations global background normaliza Using GeneSifter tion across array normalization and log2 transformations of the intensity values One 7 14 4 Supplement 27 Current Protocols in Bioinformatics point to note here is that if you plan to use RMA or GC RMA the across array normaliza tion step requires that all the data be uploaded at the same time If you wish to compare data to another experiment at a later time you will need to upload the data again together with those data from the new experiment 4 Click the Run Advanced Upload Methods button 5 Next select the normalization method and the array type from pull down menus Click the Next button at the bottom of the screen Choose GC RMA normalization and the 430 2 0 Mouse array for in this examp
48. ne expression from short oligonucleotide array data will give different results BMC Bioinformatics 7 137 Current Protocols in Bioinformatics Mortazavi A Williams B A McCue K Schaeffer L and Wold B 2008 Mapping and quantifying mammalian transcriptomes by RNA Seq Nat Methods 5 621 628 Tang F Barbacioru C Wang Y Nordman E Lee C Xu N Wang X Bodeau J Tuch B B Siddiqui A Lao K and Surani M A 2009 mRNA Seq whole transcriptome analysis of a single cell Nat Methods 5 377 382 Wang Z Gerstein M and Snyder M 2009 RNA Seq A revolutionary tool for transcriptomics Nat Rev Genet 10 57 63 Wheeler D L Barrett T Benson D A Bryant S H Canese K Chetvernin V Church D M Dicuccio M Edgar R Federhen S Feolo M Geer L Y Helmberg W Kapustin Y Khovayko O Landsman D Lipman D J Madden T L Maglott D R Miller V Ostell J Pruitt K D Schuler G D Shumway M Sequeira E Sherry S T Sirotkin K Souvorov A Starchenko G Tatusov R L Tatusova T A Wagner L and Yaschenko E 2008 Database resources of the National Center for Biotechnology Information Nucleic Acids Res 36 D13 D21 INTERNET RESOURCES http www geospiza com Support datacenter shtml The microarray data center at Geospiza Inc A diverse set of microarray data sets and tutorials on using GSAE are available from this page http www ncbi nlm nih gov geo T
49. olism and why they might be up regulated when mice are fed LRD 5001 We could also use a 2 way ANOVA COMPARE GENE EXPRESSION FROM NEXT GENERATION DNA ALTERNATE SEQUENCING DATA OBTAINED FROM MULTIPLE CONDITIONS PROTOCOL 2 This protocol discusses a general method for analyzing samples from Next Generation DNA sequencing experiments that represent different conditions In this example we will compare replicate samples n 3 from three different tissues brain liver and muscle We will also discuss using partitioning to cluster data by the pattern of gene expression Necessary Resources Software GeneSifter Analysis Edition GSAE a trial account must be established in order to upload data files to GSAE a license for the GeneSifter Analysis Edition may be obtained from Geospiza Inc http www geospiza com Analyzing Expression Patterns 7 14 27 Current Protocols in Bioinformatics Supplement 27 Analyzing Gene Expression Data Using GeneSifter 7 14 28 Supplement 27 GS AE is accessed over the Web therefore Internet access is required along with an up to date Web browser such as Mozilla Firefox MS Internet Explorer or Apple Safari Files Data files may be uploaded from a variety of sequencing instruments Illumina GA analyzer data are text files containing FASTA formatted sequences Data from the ABI SOLID instrument are uploaded as csfasta files The example data used in this procedure were generated by th
50. ols in Bioinformatics COMPARING GENE EXPRESSION FROM PAIRED SAMPLE DATA OBTAINED FROM MICROARRAY EXPERIMENTS One of the most common types of transcriptome profiling experiments involves com paring gene expression from two different kinds of samples These conditions might be an untreated and treated control or a wild type strain and a mutant Since there are two conditions we call this process a pairwise analysis Often the two conditions in volve replicates as well For example we might have four mice as untreated controls and four mice that were subjected to some kind of experimental treatment Compar ing these two sets of samples requires normalizing the data so that we can compare expression within and between arrays Next the normalized results are compared and subjected to statistical tests to determine if any differences are likely to be signifi cant Procedures can also be applied at this stage to correct for multiple testing Last we use z scores ontologies and pathway information to explore the biology and de termine if some pathways are significantly over represented and elucidate what this information is telling us about our samples Figure 7 14 1 provides an overview of this process In this analysis we compare the expression profiles from the livers of five mice that were fed for 5 weeks with a purified diet AIN 76A with the expression profiles from the livers of five mice that were fed for the same period of time with L
51. ome P450s that were induced by LRD 5001 to see if patterns of expression can be discerned 27 Click Pattern Navigation located on the right top corner of the page 28 Locate the Project Analysis section in the bottom half of the page and click Gene Analyzing Gene Navigation Expression Data Using GeneSifter 7 14 26 Supplement 27 Current Protocols in Bioinformatics food and arsenic in water Pe nigation Gene Function Pattern Navigation Cluster Export Two Way ANOV ud MEM enter part of arc ame d a gene name Name typ Option Match Amy Word Statistics ANOVA M show 50 MM search cluster by gene E Ler w Gg gc AIN TA water Los PO 5608 maier DO gd ae T Fo uter LE LES m t et a A a aa i ES 2 ss Tt eee a EE Figure 7 14 13 Gene specific navigation a Enter the gene symbol in the Name textbox as shown in Figure 7 14 13 b Choose Match Any Word from the Option pull down menu c Choose ANOVA from the Statistics menu d Click the Search button A page will appear when the filtering is complete It will indicate that 20 genes matched this query At this point clustering with the Gene option lets us see which of the cytochrome P450 genes are up regulated in the presence of LRD S001 To understand this phenomenon further we could use the ontology reports and KEGG pathways to learn about the specific roles that these cytochrome P450s play in metab
52. on 3 condition 4 isolate RNA microarrays upload data normalize P identify differential expression visualize results explore biology fold change hierarchical clustering ontology quality PCA KEGG ANOVA scatter plot multiple testing correction partitioning Bonferroni PAM Benjamini and Hochberg silhouettes others Figure 7 14 9 Overview of an experiment comparing multiple conditions The example data used in this procedure were CEL files from an Affymetrix 430 2 0 Mouse array and were obtained from the GEO database at the NCBI Accession code GSE 9630 CEL files are the best file type for use in GSAE To obtain CEL files go to the GEO database at the NCBI www ncbi nih gov geo Enter the accession number in this case GSE 9630 in the section labeled Query and click the Go button In this example all the files in the data set are downloaded as a single tar file by selecting ftp from the Download column at the bottom of the page After downloading to a local computer the files are extracted unzipped and uploaded to GSAE as described in the instructions Files used for the AIN 76 group with 0 ppb arsenic GSM243398 GSM243405 GSM243391 GSM243358 and GSM243376 for the AIN 76 group with 10 ppb arsenic GSM243359 GSM243400 GSM243403 GSM243406 GSM243410 for the AIN 76 group with 100 ppb arsenic GSM243353 GSM243365 GSM243369 GSM243377 for the LRD 5001 group with 0 ppb arsenic GSM243394 GSM
53. osomal location for a gene x Type The type of RNA molecule Comparing paired samples and finding differentially expressed genes In the next step the numbers of reads mapping to each transcript are compared in order to quantify differential gene expression between the samples This process is similar to the process that we used in Basic Protocol 1 we will set up our analysis apply statistics to correct for multiple testing then view the results from the scatter plot KEGG pathways and ontology results to explore the biology 16 Locate the Analysis section in the GSAE Control Panel and select Pairwise A list of potential array gene sets will appear The gene sets correspond to the results from analyzing Next Gen data Clicking the name of a gene set will allow you to view the samples that belong to that set 17 To set up the analysis either click the spyglass on the left of a gene set or click the name of the gene set and choose Analyze experiments from this array from the middle of the window A page will appear where you can assign samples to a group and pick the analysis settings 18 Use the checkboxes to assign one sample or set of samples to group 1 these are often the control samples and the other sample or set of samples to group 2 Assign the two sets from wild type mouse oocytes to group 1 and the two sets from the DICER knock out oocytes to group 2 19 Use the pull down menus to select the advanced analysis
54. own if a gene shows decreased expression The ratio shows the extent of up or down regulation When this page first appears the list is filtered by the raw p value 15 Filter based on the corrections for multiple testing by selecting adjusted p from the raw p value menu and clicking the Search button By choosing adjusted p from the left pull down menu to correct for the false discovery rate calculated by the Benjamini and Hochberg correction and clicking the Search button to show the p values for the differences between each gene the number of genes is changed to 539 16 Next it can be helpful to sort the data Initially the data are shown sorted by ratio so that genes with a larger fold change appear earlier in the list It can also be helpful to Analyzing Gene Expression Data sort the data by the p value or the adjusted p value to see which genes show the most Using GeneSifter 7 14 8 Supplement 27 Current Protocols in Bioinformatics significant change Choose Adj p from the Sort By menu to sort by the adjusted p value Sorting by the adjusted p value shows that the genes with the most significant changes are cytochrome p450 family 2 subfamily a polypeptide 4 and glutathione S transferase alpha 2 gene 17 We can learn more about any gene in the list by clicking its name Clicking the top gene in the list brings us to a page where we can view summarized information for this gene and obtain links
55. own in Figure 7 14 15 When a graph in GSAE is clicked the heat map containing the genes represented by the graph will appear The first graph shows a pattern that seems a bit different from the results we might expect Instead of showing the brain samples with a higher level of expression and liver and muscle lower our first 20 liver and muscle samples appear instead to up regulated This result is puzzling until we look more closely at the results and see that the first silhouette contains 1920 genes and that the variations in expression levels are small It 1s likely that looking at more genes would show us that they do follow the pattern of expression seen in the graph The other three graphs with 397 717 and 277 genes respectively match the results that we see in their respective heat maps These groups also make biological sense If we look at the genes and read about their function in the ontology and KEGG reports we can see that as expected brain genes are expressed in brain liver genes in liver muscle genes in muscle and some genes in two or more of tissues examined It should be noted that clustering is not a definitive analytical tool Clustering 1s used to try and group genes by the expression patterns that we see and we will often try multiple values for k and different ways of making the clusters Although the silhouette scores are helpful for evaluating the strength of the group ultimately we want to see if the cluster make
56. pe and mutant samples then a pairwise analysis as described in Basic Protocol 1 is used to compare the results If an experiment involves multiple conditions such as a time course different drug dosing regimes and perhaps even different genotypes then the analysis is considered a project GSAE projects have additional capabilities for analyzing these projects as well as different statistical procedures for identifying significant changes in expression Some of the tests that can be performed with GSAE are a one way ANOVA a two way balanced ANOVA and a non parametric Kruskal Wallis test Corrections for multiple testing such as those from Bonferroni Holm and Benjamini and Hochberg can also be applied Additional analyses are clustering or using the Pearson coefficient to look for patterns of expression Specific searches for genes by name characteristic or function can also be performed In Basic Protocol 2 we describe a general procedure shown in Fig 7 14 9 for ana lyzing microarray data from specimens that were obtained from different treatments An alternative procedure Alternate Protocol 2 follows in which we will demonstrate a multiple condition analysis with Next Gen data from the Illumina GA analyzer The samples used in Basic Protocol 2 were obtained from the GEO database These samples came from the same study described in Basic Protocol 1 RNA was isolated from mouse livers where two factors were examined diet and arsenic in
57. r 100 jade ior PAS Frey 4 gary as fete 15 Clutathiona S racilergpes ote 1 TE DMs S ca amp eaeegee ap I CI Cytpcheperae PSU erty DES pop cree 1 Dyce Pa S0 ry j posl s mPp ja 11 T eig qnare peoenodwe punire ODL ATP iei cruprite pot lameoy C OFTR MRPE member J C type kain rau Peevey Jona m whe oca wem unica J Dna a ces oe ey eee 1 I Fwpa Gein Auman famy Jo arse amp eee eee J reb mwaumimena l T a ee Sree A ne piter re om ae fori eee Dd Umum aca now Emha ia Say Ro Cacufecg Bjw amp 4 Lew Wein Bomae fare 2 Sarnia Lori pha4chcocy uem d piers Pato uc D aay 6 epee 533 cen purga Beet aarp ar 4 Rasse xe ete g R aac GREE raman eund fi am fi an Figure 7 14 12 Analyzing the results from comparing multiple samples 26 Return to the results page and select Genes from the Cluster options Clustering by genes produces an image consistent with our earlier results On the right half where the mice were fed LRD 5001 the three conditions show similar patterns of expression The samples in the left half are also similar to each other although it appears that some genes have changed in the sample with 100 ppb arsenic If we look more closely at the genes that appear to be up regulated in the LRD 5001 mice we can see that many of the genes belong to the cytochrome P450 family Examining differential gene expression in a specific gene family The user may decide to look further at the cytochr
58. r these data as discussed in Basic Protocol 2 Identifying differential expression 25 26 2i To begin the analysis select Projects from the Analysis section locate your project in the list and either select the spyglass or click the name or your project and then click on Analyze this Project The Project summary page appears From this page we can choose to view all the genes or apply filters to locate specific genes by name chromosome function or other distinguishing features Choose Show All Genes It will take a few moments for the results to appear especially with large data sets The Project results appear At this point we see there are 40 009 results We will need to apply a threshold and some statistics to select genes that are differentially expressed The threshold filter allows us to choose the genes that show at least a minimum change in expression Use a threshold of 1 5 for this project GSAE offers three types of statistical tests described below that can be applied at this point At least three replicates per group are recommended A balanced ANOVA can also be carried out when only one factor is varied such as time or dose and there are equal numbers of replicates for each sample a A standard 1 way ANOVA This method is used when there is a normal distribution the samples show equal variance and the samples are independent b A way ANOVA for samples with unequal variance Like the standard 1
59. rroni Holm Benjamini and Hochberg and Westfall and Young maxT corrections The Bonferroni and Westfall and Young corrections calculate a family wise error rate This is a very conser vative requirement with a 5 chance that you will have at least one error The Benjamini and Hochberg correction calculates a False Discovery Rate With this method when the error rate equals 0 05 5 of the genes considered statistically significant will be false positives Benjamin and Hochberg is the least stringent of the four choices allowing for a greater number of false positives and fewer false negatives When it comes to choosing a correction method we choose correction methods depending on our experimental goal If our goal is discovery we can tolerate a greater number of false positive results in order to minimize the number of false negatives If we choose a method to minimize false positives we have to realize that some of the real positives may be missed Genes with real differences in expression may appear to show an insignificant change after multiple testing corrections are applied One of the most important reasons for using these tests is that they allow the user to rank genes in order of the significance of change and make choices about which changes to investigate For this example choose Benjamini and Hochberg h Data Transformation Our data are already log2 transformed since RMA and GC RMA both carry out this step during the uploa
60. s biological sense with genes in a common pathway showing a pattern of coordinate control Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 33 Supplement 27 Analyzing Gene Expression Data Using GeneSifter 7 14 34 Supplement 27 res kie mm ES mo mir Funda C pukce Rud uw kinia peu eee pea L rE Ea Craps aera Lmkp md farce Lp heme bP Uem TW om Li rura gore ri Cetra ieee i S2 ma mm J mE 5 Cluster 1 1902 Genres Cluster 2 397 Genes Mean sil width 6 473 Mean sil width 0 579 Cluster 3 717 Gerts Cluster 4 277 Genes Mean gil width D S45 Megan Hi wigih Oto F Sp eui ep i Wraps mado fud J sum FA ce Reet a hh ae gS be Pepe j bert mea ig tuer s ao eee c dampna a Epimp und sra pee Cee et pie mo kg Akm wars Queer pee eee I LPS caa gU aes Peale pa ee ee ee Susi Tas APERE Lies Der Ga Pa Rug Pee Myan bged prepari ewes J eee me ee SR eee ee Els coe jip paa Chad EDWerTACItg peor Ud pee Ps IESU ae E minibar Mite ee Ta laki ua Gere fete F Po ee Bi Bead Od Ex ALBE A Par he Bum peums d eee esc cA quia b Rau curb cu Sea el ub mei Xam Pe 4 LEM Fuge ATL quib legacaoac acum dacE Epl ood deere a LP pAg Gooey ST ae d Leer sad pote l mirma j acie pn rg LI ones J Mipoass nI jo ppi MIT ag uh d bp vel keep Bap 3 aiu Ue rt va p fl pee mic LELE E Pal h EFSF
61. settings 5 click Analyze Home Support Geospiza Main iagin digzal biology gt Analysis gt su 1 Array Gene Set Description Q Mouse Tissues BWA Mouse RefSeq mafa 2 Mouse food and arsenic 430 2 0 Mouse GCRMA Mouse oocytes Mouse RefSeq mafa Tissues Mouse RefSeq ma fa Pairwise ss Analysis Mouse fooi Mouse food and arsenic OS ae pe esse U U Lip sbesm1r eemzaosoa pesmme 0 E afoso e oswzessoz ino so01 water D ee e a a Dj ERHALTEN E md M C D u zen 3 eswzessse Juven waero JO BOROTA Josraeaeos Junzen sues 3 Tet Ejum zen s osvasssze uv 76A water 3 Djuwze7 jeswens juwzew vero E amp E jure e eonzasson Juvzen waero 8 Advanced Analysis Settings 4 Normalization Threshold None B Lower LS i Upper None H8 Statistics t test eal Benjamini and Hochberg ol Show genes that are M Up regulated M Down regulated Data Transformation No Transformation D Lag Transform Data D Data Already Log Transformed 5 Analyze Reset Figure 7 14 2 Setting up a pairwise comparison Analyzing Gene Expression Data Using GeneSifter 7 14 6 Supplement 27 significant The significance level for each gene is reported as a p value When multiple replicates of a sample are used
62. te 1 G5M365013 1 3 3219 2 GSM365015 2 5993 2 G5M355016 2 6064 Dicer knock out oocyte up regulated 60 75 fold compared to Wild type mouse Figure 7 14 8 Gene summaries for microarray and NGS data A gene summary from a microar ray sample is shown in the top half of the image and a summary for a sample analyzed by NGS is shown in the bottom half Note the difference between the intensity and quality values Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 19 Supplement 27 BASIC PROTOCOL 2 Analyzing Gene Expression Data Using GeneSifter 7 14 20 Supplement 27 reads that were mapped to a gene RPM If the data were log transformed during the analysis then these values are the log transformed values The other difference between these data for the two systems is in the quality column For Next Gen data the quality column shows the RPM value for that gene In the quality column for the Next Gen data two of the samples show quality values of zero This means that zero transcripts were detected The other two samples show values around 6 indicating that approximately 6 transcripts were detected per million reads for the Drebrin like gene COMPARING GENE EXPRESSION FROM MICROARRAY EXPERIMENTS WITH MULTIPLE CONDITIONS GSAE has two modes for analyzing data depending on the number of factors that are tested If two factors are compared such as treated and untreated or wild ty
63. the drinking water Over a 5 week period the mice were fed two kinds of food AIN 76A a purified diet or LRD 5001 a standard laboratory mouse food and given arsenic in their water at three different concentrations 0 10 ppb or 100 ppb There were four to five biological repli cates mice for each treatment We will demonstrate setting up the analysis applying statistics and multiple testing corrections and using some of the clustering tools Some of the clustering methods PAM and CLARA will be discussed in Alternate Protocol 2 rather than Basic Protocol 2 Necessary Resources Software GeneSifter Analysis Edition GS ABE a trial account must be established in order to upload data files to GSAE a license for the GeneSifter Analysis Edition may be obtained from Geospiza Inc http www geospiza com GS AE is accessed over the Web therefore Internet access is required along with an up to date Web browser such as Mozilla Firefox MS Internet Explorer or Apple Safari Files Data files from a variety of microarray platforms may be uploaded and analyzed in GSAE including Affymetrix Illumina Codelink or Agilent arrays and custom chips Current Protocols in Bioinformatics multiple sample comparison mouse 1 mouse2 mouse 3 mouse 4 mouse5 mouse6 no treatment N J treatment 10 ppb As condition 1 condition 2 mouse 7 mouse 8 mouse 9 mouse 10 mouse 11 mouse 12 treatment 100 ppb AN J treatment 1000 ppb As conditi
64. tled Group Info Each of the conditions in this section has between four and five replicates and a box plot Fig 7 14 1 1B The box plot link for this section opens a window for Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 23 Supplement 27 Analyzing Gene Expression Data Using GeneSifter 7 14 24 Supplement 27 a box and whiskers plot showing a normal distribution of data highest value 3rd quartile 1st quartile lowest value Figure 7 14 10 Box plot a box for each replicate Click the box plot link for some of the replicates to see if the replicates are similar or if any of the replicates appear to be different from other members of the group 22 Click Analyze This Project to begin the analysis The Pattern Navigation page appears From the Pattern Navigation page you may view all the genes or limit the genes to those that meet certain criteria for fold change statistics or a certain pattern of expression Additional options from the Gene Navigation link allow genes to be located by name chromosome or accession number and options from the Gene Function link allow them to be located by ontology or KEGG pathway Statistics can also be applied to limit the results 23 Locate the Search by Threshold section and set the threshold choices Choose 1 5 for the Threshold ANOVA for the statistics and Benjamini and Hochberg to correct for the false discovery rate Click t
65. to more information in public databases 18 Click Scatter Plot to view the differences in gene expression another way A new window will open with the data presented as a scatter plot Fig 7 14 4 click the zoom button c umi UM eee e cytochrome P450 family 2 subfamily a LRD 5001 water 0 drag the box over the genes you wish to view in detail 10000 up regulated genes appear on this side of the diagonal line click a spot to see more details Gene Info Cytochrome P450 ami 2 subfamily a eae 4 down regulated genes appear on this side Group Mean SEM Mean Quality 1 00 1 000 1 0000 1 00000 AIN 76A water O 10 0036 0 1127 1 1396 0 0000 LRD 5001 water 0 5 12 7881 0 1339 1 0596 0 0000 AIN 76A water 0 BELGEN Open static scatter plot num summary information num in this lower corner Each spot in the graph represents the expression measurements for one gene The expression level for group 2 is plotted on the y axis and the value for group 1 is plotted on the x axis C1 Intensity o o AIN 76A LRD 5001 water O water O LRD 5001 water 0 up regulated 6 9 fold compared to AIN 76A water 0 Figure 7 14 4 Scatter plot Analyzing Expression Patterns 7 14 9 Current Protocols in Bioinformatics Supplement 27 Analyzing Gene Expression Data Using GeneSifter 7 14 10 Supplement 27 a The scatter plot The scatter plot giv
66. tted sequences Data from the ABI SOLID instrument are uploaded as csfasta files The example NGS data used in this procedure were generated by the ABI SOLID instrument and obtained as csfasta files from the GEO database at the NCBI Accession number GSE14605 The csfasta files are obtained as follows The accession number GSE14605 is entered in the data set search box at the NCBI GEO database http www ncbi nlm nih gov geo and the Go button is clicked The csfasta files are downloaded for both wild type mouse oocytes and DICER knockout mouse oocytes by clicking the links to the file names and clicking ftp for the ezipped csfasta files GSM365013 filtered csfasta txt gz GSM365014 filtered csfasta txt gz GSM365015 filtered csfasta txt gz GSM365016 filtered csfasta txt gz 1 Log in to GeneSifter Analysis Edition GSAE hittp login genesifter net Uploading data 2 Locate the Import Data heading in the Control Panel and click Upload Tools The uploading and processing steps described for Next Gen data sets require a license from Geospiza However you may access data that have already been uploaded and processed from a demonstration site The demonstration site can be accessed from the data center at http www geospiza com 3 Click the Next Gen File Upload button to begin uploading Next Gen data 4 Enter a name for a folder Folders are used to organize Next Gen data sets Current Protocols in Bioinformatics 5 6
67. utput Gene List for genes txt Summary Statistics Download Gene List SM 137275 12756 87 15627 16 12049 p s NM 013479 Bcl2 like 10 Bcl2110 9 mRNA MM H1 histone family 115536 11806 49 13152 43 171506 nul i NM_138311 member O _ oocyte specific 103361 8452 91 11766 45 21432 P NM 009337 T cell lymphoma Tcl 1 oe breakpoint NM_011462 87655 2412 60 9978 50 20729 NM 146043 Spidlin 1 Zinc finger BED 80718 4851 53 9188 80 72114 Ful t NM_028106 domain containing Zbed3 JP 3 Figure 7 14 7 Analysis results from NGS data obtained from an ABI SOLID instrument Viewing the Next Gen alignment results 14 When the alignment step is complete you will be able to view different types of 15 Analyzing Gene Expression Data Using GeneSifter 7 14 16 Supplement 27 information about your samples Click the file name to get to the analysis details page for your file then click the Job ID to get the information from the analysis The exact kinds of information will depend on the data type and the algorithms that were used to align the reads to the reference data source Fig 7 14 7 The types of information seen from Illumina data will be described in the next protocol For SOLID data you will see information that includes a Read alignment statistics These include the total number of reads and the numbers that were mapped unmapped or mapped to multiple positions Sets of reads can also be downloaded from t
68. way ANOVA this method assumes a normal distribution and independent random samples c The Kruskal Wallis test nonparametric This method assumes independent ran dom samples but does not make assumptions about the distribution or variance Choose the standard 1 way ANOVA for this analysis Current Protocols in Bioinformatics Analyzing Expression Patterns 7 14 31 Supplement 27 Analyzing Gene Expression Data Using GeneSifter 7 14 32 Supplement 27 28 After choosing a statistical method click the Search button At this point there are still over 17 000 results The advanced analysis methods in GSAE work best with gene numbers under 5000 consequently we will use some additional filters to reduce the number of genes in the list a Apply a correction to limit false discoveries The options are the Bonferroni Holm and Benjamini and Hochberg Bonferroni is the most stringent followed by Holm with Benjamini and Hochberg allowing more false positives in order to minimize false negatives Multiple testing corrections are discussed in detail in Basic Protocol 1 Used Benajmini and Hochberg in this example b Apply a p Cutoff This sets a threshold for the minimum p value Set the p value cutoff at 0 01 c Set the quality For NGS data the quality corresponds to the number of reads per million reads Set the quality level at 100 to view highly expressed genes that differ between these three tissues qual
69. ws researchers to view the response of an organism or cell to a new situation or treatment Insights into the tran scriptome have uncovered new genes helped clarify mechanisms of gene regulation and implicated new pathways in the response to different drugs or environmental condi tions Often these kinds of analyses are carried out using microarrays Microarray assays quantify gene expression indirectly by measuring the intensity of fluorescent signals from tagged RNA after it has been allowed to hybridize to thousands of probes on a single chip Recently next generation DNA sequencing technologies also known as NGS or Next Gen have emerged as an alternative method for sampling the transcriptome Un like microarrays which identify transcripts by hybridization and quantify transcripts by fluorescence intensity NGS technologies identify transcripts by sequencing DNA and quantify transcription by counting the number of sequences that align to a given tran script Although the final output from an NGS experiment is a digital measure of gene expression with the units expressed as the numbers of aligned reads instead of intensity the data and goals are similar enough that we can apply many of the statistical methods developed for working with microarrays to the analysis of NGS data There are many benefits to using microarray assays the greatest being low cost and long experience Over the years the laboratory methods for sample preparation and th
70. y Click the Next button 6 In the screen which now appears browse to locate the data file on your computer 7 Choose the option radio button for Create Groups Create New Targets or Same as File Name Our data came from mice that were given two different kinds of food and drinking water with three concentrations of arsenic so six groups were created Therefore set 6 as the value next to Create Groups 8 Click the Next button The screen for the next step will appear after the data are uploaded 9 On the screen displayed in step 3 of 4 you will be asked to enter a title for your data set assign a condition to each group add labels to your samples if desired and identify which sample s belong to which group In our case we have six conditions see Table 7 14 1 with four to five biological replicates targets for each condition We kept the original file names as the target or sample names Setting up a project for analysis 10 We begin the analysis process by creating a project Select New Project from the Create New section add a title for the project click the checkbox next to the array that contains the samples and click the Continue button Table 7 14 1 Conditions Used for the Example in Basic Protocol 2 Condition Mouse food Arsenic in water ppb 1 AIN 76A 0 2 AIN 76A 10 3 AIN 76A 100 4 LRD 5001 0 Analyzing Gene D LRD 5001 10 Expression Data Using GeneSifter 6 LRD 5001 100 7 14 22 Supplement 2

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