At 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 



Dear Dimitris,

attached are those genes that have more than 2 "ATTTA" motifs and the location of those motifs.
Using these genes, the results are at
http://genomics-lab.fleming.gr/fleming/DKlab/run343/metaseqr_quantseq_run343c/index.html 

BW,
Martin




#@
28042017
Yes, please use the latest edition and use the "ATTTA" motif at first-we seek those that have more than 2 of those.

#@
Dear Dimitris,

at
http://genomics-lab.fleming.gr/fleming/DKlab/run343/metaseqr_quantseq_run343b/index.html 
are the comparisons as you defined them
(note again the used naming convention: in A_vs_B, A is always the reference).

Concerning the AU-rich elements, should we use the latest edition at
http://rna.tbi.univie.ac.at/AREsite2/welcome
and shall we include all motifs?

BW,
Martin


#@

Dimitris Kontoyiannis
11:31 AM (1 hour ago)

to me, Dimitris, Pantelis, Harokopos 
Hi Martin,

Thanks for the update.The comparisons that i am interested in are:

C vs K which should be derived from C=C1,C2  and K=K23, K2,K3,
CL4 vs KL4 which should be derived from CL4=C1L4, C2L4    and KL4=K23L4,K2L4,K3L4
C vs CL4 & 
K vs KL4

Now given the low count of reads and since the samples are to be run again, i am excluding C3 and C3L4 since they appear as outliers in the MDS plots. 

After the final analysis, i would require an extra filtering if possible-to remove genes with less than 2 AU-rich elements. People are doing this by taking datasets from the ARESite database.

Best,

Dimitris
#@

Dear Dimitris,

the processing of the 3UTRseq samples in run343 has finished.
At
http://genomics-lab.fleming.gr/cgi-bin/hgTracks?db=mm10&hubUrl=http://genomics-lab.fleming.gr/fleming/DKlab/run343/hub.txt
you will find tracks, normalized to the smallest library, DK3R6-K3 with 1586921 reads.

The library sizes are as follows:
sample        n_reads
DK3R1r-C1     2031406
DK3R2r-C2     2073352
DK3R3r-C3     2474771
DK3R4b-K23    2049128
DK3R5-K2      2181338
DK3R6-K3      1586921
DK3R7-C1L4    2564939
DK3R8-C2L4    2316573
DK3R9r-C3L4   1994621
DK3R10b-K23L4 2251092
DK3R11-K2L4   2209027
DK3R12-K3L4   2980532

At http://genomics-lab.fleming.gr/fleming/DKlab/run343/metaseqr_quantseq_run343/index.html 
you will find the metaseqR [1] differential gene expression analysis using
the contrasts
"C_vs_K","C_vs_C_L4","C_vs_K_L4","K_vs_C_L4","K_vs_K_L4","C_L4_vs_K_L4"
(note the used naming convention: in A_vs_B, A is always the reference).
Let me know if you need other contrasts.

Best wishes,
Martin

[1] Moulos P and Hatzis P “Systematic integration of RNA-Seq statistical
algorithms for accurate detection of differential gene expression
patterns.” Nucl. Acids Res. (2014)
doi: 10.1093/nar/gku1273
http://nar.oxfordjournals.org/content/early/2014/12/01/nar.gku1273.abstract.