Dear Yiota, attached are my findings concerning alternative splicing in the RNAseq data. Comparing all wt vs all ko samples, I get 23 differentially expressed transcripts (q-value <=0.05). Using the criterium for alternative splicing, that a different transcript of the same gene should also be differentially expressed (p-value < 0.05), 14 genes are alternatively spliced (see attached xls). I've generated transcript visualizations for those 14 (attached). The polysome seq tracks are at: http://genomics-lab.fleming.gr/cgi-bin/hgTracks?db=mm9&hubUrl=http://genomics-lab.fleming.gr/fleming/DKlab/mr/Polysomes/www/Phub.txt These are generated by merging replicates and sampling to the smallest library pair (WT_IL4 containing 9826554 reads). The libraries without replicates are not sampled. BW, Martin #@ 14062017 Dear Martin, I think I understand the visualizations you sent me, and they are fine, according to what we expect. I just manage to figure out what is what and I have attached Orsalia's word file, where highlighted in yellow you will see the conditions that correspond to the polysomal-RNA seq files. Please, do keep me informed and let me know if I need to send you something more for now. Cheers, Yiota #@ 13062017 Dear Yiota, at http://genomics-lab.fleming.gr/cgi-bin/hgTracks?db=mm9&hubUrl=http://genomics-lab.fleming.gr/fleming/DKlab/orsalia/Phub.txt you will find the merged (for the same condition) and normalized tracks for the RNAseq samples. Sampling is to the smallest library pair (wt0h) containing 159034728 reads. I performed expression modelling for isoforms using Stringtie. To assess these results, can you please specify the genomic location of the exon known to be alternatively spliced? Bw, Martin ok. First of all, the second exon of HuR should be very low in KO samples. The coordinates for this exon are: chr8:4,311,599-4,311,788 Another example is the gene Dlst, for which an alternative exon 10b has been reported to be included upon HuR knock-out. Its coordinates are: chr8:4,311,599-4,311,788 I checked them both at your tracks and they looked fine. I can not think of any other controls, but I will see what I could find. Are you going to run cuffdif or something else for the alternative splicing events? I am not sure what the final decision was in that meeting. #@ 12062017 Dear Martin, I am still waiting to hear from ORsalia about the barcodes etc. However, so that you can at least proceed with the rest of the experiments, I am sending you the list of experiments that have been done and analyzed by Orsalia: RNAseq and polysome seq: wild type macrophages untreated: wt0h wild type macrophages treated with LPS for 2h: wt2h wild type macrophages treated with LPS for 6h: wt6h wild type macrophages treated with IFN:wtIFN wild type macrophages treated with IL4: wtIL4. Similarly, for the knock-out (ko) macrophages, you have: ko0h, ko2h, ko6h, koIFN and koIL4. For the PAR-CLIPS: The HuR samples are as I described in the previous e-mail that I sent you. The TTP samples are: wild type macrophages treated with LPS for 6h: BMX_6h wild type macrophages treated with IFN: BMX_IFN wild type macrophages treated with IL4: BMX_IL4 knock-out macrophages treated with LPS for 6h: Cl6_6h knock-out macrophages treated with IFN: Cl6_IFN knock-out macrophages treated with IL4: Cl6_IL4 and one sample IgG control: rabbit IgG The TIA samples are: wild type macrophages untreated: BMX_0h wild type macrophages treated with LPS for 2h: BMX_2h wild type macrophages treated with LPS for 6h: BMX_6h wild type macrophages treated with IFN: BMX_IFN wild type macrophages treated with IL4: BMX_IL4 knock-out macrophages untreated: Cl6_0h knock-out macrophages treated with LPS for 2h: Cl6_2h knock-out macrophages treated with LPS for 6h: Cl6_6h knock-out macrophages treated with IFN: Cl6_IFN knock-out macrophages treated with IL4: Cl6_IL4 and one sample IgG control: goat IgG As soon as I have a reply from Orsalia, I will specify which file corresponds to which sample and send you again her document. Best, Yiota #@ 16052017,15:00 Dear Dimitris, inspecting the recent results I found a bug in Panagiotis metaSeqR package that he introduced when he prepared the QuanSeq aware version of his program a while ago. This bug treats stranded RNAseq data as unstranded. The number if significantly detected DE genes changes by <~3%, in most cases fewer genes are detected. The corrected analysis are here: RNAseq: http://genomics-lab.fleming.gr/fleming/DKlab/orsalia/metaseqr_WT_KO_IFN_IL4_V2/index.html [ 2017-05-16 13:39:59 ] INFO Counting reads overlapping with given annotation... [ 2017-05-16 13:39:59 ] INFO ...for paired-end reads... [ 2017-05-16 13:39:59 ] INFO ...assuming reverse sequenced reads... QuantSeq: http://genomics-lab.fleming.gr/fleming/DKlab/run343/metaseqr_quantseq_run343V2/index.html More than 2 "ATTTA" motifs gene set: http://genomics-lab.fleming.gr/fleming/DKlab/run343/metaseqr_quantseq_run343cV2/index.html BW, Martin 16052017,12:00 Dear Dimitris, the DE analysis of the data specified by Orsalia is ready at: http://genomics-lab.fleming.gr/fleming/DKlab/orsalia/metaseqr_WT_KO_IFN_IL4/index.html The summary is: Number of differentially expressed genes per contrast: WT0_vs_KO0: 1671 (502) statistically significant genes of which 120 (83) up regulated, 227 (136) down regulated and 1324 (283) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log2 scale. WT2_vs_KO2: 1665 (486) statistically significant genes of which 158 (83) up regulated, 149 (99) down regulated and 1358 (304) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log2 scale. WT6_vs_KO6: 569 (25) statistically significant genes of which 21 (6) up regulated, 88 (12) down regulated and 460 (7) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log2 scale. WTIFN_vs_KOIFN: 395 (25) statistically significant genes of which 22 (6) up regulated, 40 (12) down regulated and 333 (7) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log2 scale. WTIL4_vs_KOIL4: 273 (22) statistically significant genes of which 26 (9) up regulated, 30 (6) down regulated and 217 (7) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log2 scale. BW, Martin #@ 15052017 Dimitris Kontoyiannis to me Hi Martin: For the metaseeker analysis we will do the simple comparison between control and knock-outs Data are in duplicates: wt01, wt02 compared to Ko01, Ko02 wt21, wt22 compared to Ko21, Ko22 wt61, wt62 compared to Ko61, Ko62 wtIFN1, wtIFN2 compared to KoIFN1, KoIFN2 wt01IL4, wt02IL4 compared to Ko01IL4, Ko02IL4 Let me know if there is anything we can do from our side. Best, Dimitris