Dear Pantelis, at http://genomics-lab.fleming.gr/fleming/PHlab/run341/rpkm/gencode you'll find the tables RPKM_PHR20r-bcatPoly_vs_PHR24r-IgG.csv RPKM_PHR21r-bcatMono_vs_top5pct-PHR24r-IgG.csv RPKM_PHR22r-TCF4_vs_top5pct-PHR24r-IgG.csv RPKM_PHR23r-FUBP2_vs_PHR24r-IgG.csv improved as discussed: - using the lasted (lncRNA) annoation from Gencode (old ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_26/GRCh37_mapping/gencode.v26lift37.annoation.gtf.gz) ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_26/GRCh37_mapping/gencode.v26lift37.annotation.gtf.gz - elimination of duplicates by keeping only the exon with the highest RPKM foldchange (vs IgG) for each gene I also checked correalations in scatterplots using: - only RPKMs: RPKM_bcatMono_vs_bcatPoly.png Pearson's product-moment correlation: 0.9530492 RPKM_bcatMono_vs_TCF4.png Pearson's product-moment correlation: 0.9376876 RPKM_bcatPoly_vs_TCF4.png Pearson's product-moment correlation: 0.9342349 RPKM_FUBP2_vs_TCF4.png Pearson's product-moment correlation: 0.6638814 RPKM_TCF4_vs_IgG.png Pearson's product-moment correlation: 0.9251441 (!) - RPKM foldchange vs IgG or Input IgG_foldchange_bcatMono_vs_bcatPoly.png Pearson's product-moment correlation: 0.6942124 IgG_foldchange_bcatMono_vs_TCF4.png Pearson's product-moment correlation: 0.6428579 IgG_foldchange_bcatPoly_vs_TCF4.png Pearson's product-moment correlation: 0.6625278 IgG_foldchange_FUBP2_vs_TCF4.png Pearson's product-moment correlation: 0.3768401 Input_foldchange_bcatMono_vs_bcatPoly.png Pearson's product-moment correlation: 0.7967128 Input_foldchange_bcatMono_vs_TCF4.png Pearson's product-moment correlation: 0.7605851 Input_foldchange_bcatPoly_vs_TCF4.png Pearson's product-moment correlation: 0.7418563 Input_foldchange_FUBP2_vs_TCF4.png Pearson's product-moment correlation: 0.3833719 These correlations suggest: - correlation bcatMono_vs_bcatPoly is highest - correlation bcatMono_vs_TCF4 and bcatPoly_vs_TCF4 is slightly lower than bcatMono_vs_bcatPoly - using RPKM foldchange agaist IgG or Input has stronger signal than single RPKM contrasts. Please check the new tables (note that our browser needs an update for the Gencode annotation), I'll add GOterm enrichment analysis later. BW, Martin Dear Pantelis, at http://genomics-lab.fleming.gr/fleming/PHlab/run341/rpkm RPKM_PHR20r-bcatPoly.bam_vs_PHR24r-IgG.csv RPKM_PHR21r-bcatMono.bam_vs_PHR24r-IgG.csv RPKM_PHR22r-TCF4.bam_vs_PHR24r-IgG.csv RPKM_PHR23r-FUBP2.bam_vs_PHR24r-IgG.csv you'll find my custom RPKM analysis of the RIP data. Coverage on each exon is counted with featureCounts tool of the subread package. The tables contain exons that have at least 10 reads in the condition and more than 2fold higher RPKM values over IgG. The RPKM fold change vs IgG is in the colum "fc_vs_PHR24r.IgG.bam". The RPKM fold change vs Input is in the colum "fc_vs_PHR19r.Input.bam" All colum headers are: "Geneid" "Chr" "Start" "End" "Strand" "Length" "PHR23r.FUBP2.bam" "PHR22r.TCF4.bam" "PHR21r.bcatMono.bam" "PHR20r.bcatPoly.bam" "PHR24r.IgG.bam" "PHR19r.Input.bam" "con_b.bam" "con_a.bam" "RPKM_PHR22r.TCF4.bam" "RPKM_PHR24r.IgG.bam" "fc_vs_PHR24r.IgG.bam" "RPKM_PHR19r.Input.bam" "fc_vs_PHR19r.Input.bam" BW, Martin Dear Pantelis, at http://genomics-lab.fleming.gr/fleming/PHlab/run341/ripseeker/ you will find folders with RIPSeeker https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3632129/pdf/gkt142.pdf analysis for the following contrasts (1st set, 2nd set you defined): RIPSeeker_bcatMono_vs_IgG_bothStrands RIPSeeker_bcatPoly_vs_IgG_bothStrands RIPSeeker_FUBP2_vs_IgG_bothStrands RIPSeeker_TCF4_vs_IgG_bothStrands RIPSeeker_bcatMono_vs_Input_bothStrands RIPSeeker_bcatPoly_vs_Input_bothStrands RIPSeeker_FUBP2_vs_Input_bothStrands RIPSeeker_TCF4_vs_Input_bothStrands In each folder, the files are: RIPregions_annotated.txt RIPregions_enrichedGO.txt RIPregions_annotated.gff3 RIPregions.gff3 The file RIPregions_annotated.txt to be loaded with excle, has the list of RIP regions with summed read count FPK (fragment per kilobase of region length), representing a normalized read count averaged log odd scores p-value and adjuster p-value RIPregions_enrichedGO.txt has a GOterm enrichment analysis and the gff files can be added as UCSC tracks. For the 3rd set analysis, I found another tool called ASpeak https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btt428 that considers matching RNAseq data (to be defined) for normalization. BW, Martin Dear Pantelis, the tracks for run341 (sampled to PHR23r-FUBP2 with 7250244 reads) are ready at: http://genomics-lab.fleming.gr/cgi-bin/hgTracks?db=hg19&hubUrl=http://genomics-lab.fleming.gr/fleming/PHlab/run341/hub.txt Id nreads PHR19r-Input 12500491 PHR20r-bcatPoly 9498439 PHR21r-bcatMono 10141601 PHR22r-TCF4 9480555 PHR23r-FUBP2 7250244 PHR24r-IgG 9183788 BW Martin