APPLICATION INFORMATION

Transcription

1 GeXP-A-0002 APPLICATION INFORMATION G e n e t i c A n a l y s i s : G e X p Multiple Gene Normalisation and Alternative Export of Gexp Data Expression analysis on the GeXP platform. James Studd MSc Manfred Souquet Ph.D. European GA Applications Support Team Beckman Coulter Europe Notes When analysing expression data it may be advisable to normalise m-rna levels against multiple housekeeping genes. This may help offset expression instability between samples of individual normalisation genes. The number of genes chosen for normalisation is a compromise between accuracy and practicality. Ideally normalisation should be carried out against the highest number of genes possible. Practically, it may be inconvenient to quantify large numbers of house keepers, especially if the number of genes of interest in a multiplex is low. Potential house keeper stability can be assessed using a separate multiplex of candidate genes such as Beckman Coulter s Human Reference plex and genorm a software package for statistical evaluation of expression stability (see applications note GeXP-A-0001). If house keeper stability has not been validated prior to multiplex design it may be desirable to include a larger number of candidate house keepers to allow a degree of flexibility. Those found to exhibit instability considered too great can be removed from subsequent normalisation. Generally we recommend the use of at least three house keepers although user demands may vary. In order to normalise GeXP data against multiple housekeepers it is necessary to use an alterative method of data export, this is described below. Items required 1) CSV transformer.exe document * 2) GeXP analysis template * * Items available on request from GenomeLab Application Support Team The work described in this Note is for guidance only and has not been fully validated. Applications Team Europe Page 1 of 9 Revision 1.0

2 Protocol - Export of GeXP Data 1) Open the fragment analysis module of the Genome Lab software. 2) Select samples intended for analysis 3) Analyse using default GeXP analysis parameters 4) In the study explorer right click on Binnings and select New Binning. you must have at least 10 samples in your study in order to conduct a binning analysis. Additional samples not intended for analysis can easily be removed at a later stage. Overall every GeXP product should be at a minimum of two times detected in the number of samples. 5) - Set Dye to D4, default representation blue, dye 4 is used to label all fragments in the GeXP PCR reaction. - Set the Fragment Range to exclude any peaks outside the range of expected GeXP products - Leave the minimum Number of data points per bin on two - Set the Bin width, for example a bin at 200bp with a bin width of 0.80nt will include fragments between and 200.4nt - Set repeat unit length to 1, this will instruct the software to try to place a bin every base pair. This feature was designed for microsatellite binning which may have repeat units of 2/3/4 base pairs for di, tri and tetra nucleotide repeats respectively - then click next Applications Team Europe Page 2 of 9 Revision 1.0

3 6) Sort data by clicking on column Num. Points - highlight unwanted bins either with no peaks or no designed peaks and right click and select Remove Alleles. In the Bin View horizontal grey bars represent bins, blue squares represent peaks in bins Applications Team Europe Page 3 of 9 Revision 1.0

4 and purple squares represent peaks outside of bins. 7) Adjust the Minimum Relative Peak Height. Default 0.01, any peak with a height in RFU below 1% of the largest peak in all samples is excluded from binning. Consider lowering to 0 to capture low expressing genes. 8) Evaluate individual peaks by double clicking peak squares to display electropherogram of relevant sample. 9) Add locus ID int his case the gene name. Add Prefix HK and a space for housekeeping genes (e.g. HK GAPDH). 10) Adjust the placement of individual bins to include designed peaks or exclude shoulder peaks (for this edit Apparent Size). 11) When all gene specific peaks are binned complete binning by clicking next. 12) Enter user chosen binning name under Locus tag and Locus Name then click finish. Applications Team Europe Page 4 of 9 Revision 1.0

5 13) Finish binning by clicking next or finish. 14) Save your study under an appropriate name 15) Highlight results and right click. Select Reanalyse Results Using Additional/Edited Locus Tag, 15) Highlight binning name from list. b) move wanted STR Locus Tag from Available to Selected c) click next Applications Team Europe Page 5 of 9 Revision 1.0

6 16) Analyse data then click Finish 17) View electropherogram with fragment/allele IDs. Therefore generate the Fragment list (click on gold nugget) and create exclusion filter (alleleid = blank) 18) A single result now should look like this 19) Export fragments/genotypes, to create a.csv document containing a list of fragments with ID tags Applications Team Europe Page 6 of 9 Revision 1.0

7 20) Open the CSV transformer document. Applications Team Europe Page 7 of 9 Revision 1.0

8 21) Ensure default settings are Transfer Format GeXP normalisation format and Output By Peak Area 22) Set the Size difference range to below the size difference of the most closely spaced fragments in your multiplex. 23) Select Input File (means generated csv-fragment list) 24) Click transform to create transformed excel document 25) View transformed format 0 means: no peak in this result detected The format of the csv transformer is amenable to further analysis by 3 rd party software packages such as GeNorm. Multiple Gene Normalisation 1) Denote housekeeper genes to be used for normalisation with the prefix HK. This number may range from 1 to every gene in the multiplex. 2) Open the GeXP analysis template. 3) Copy data (including sample names) from CSV transfer document and paste into the GeXP analysis template at position B1 4) Click Generate Normalisation Factor Applications Team Europe Page 8 of 9 Revision 1.0

9 5) Normalisation factors are displayed for each sample. Click Normalised Data tab to view normalised data 6) Normalisation factors are displayed for each sample. Click Normalised Data tab to view normalised data. 7) Click Insert Names and Normalise Data to display normalised data. Applications Team Europe Page 9 of 9 Revision 1.0