Differential isoform usage

uniqueg commented 7 years ago

Dear SUPPA developers,

first of all: thanks a lot for providing your nice tool set!

I have one feature suggestion though: Since you have now included a script for the differential analysis of splicing events, and since SUPPA also reports PSIs per isoform, it would be nice to provide an analogous tool for differential transcript/isoform usage analysis, i.e. a tool that identifies robust isoform switches by making use of replicates and expression levels/abundances similar to diffSplice.

Any chance of getting that anytime soon? :-)

Thanks and best, Alex

JLTrincado commented 7 years ago

Dear Alex,

thanks for your interest in our work. We have developed this tool for testing differential isoform usage between conditions and we're right now testing it. We expect to make it public soon.

Thanks for your suggestion and best, Juanlu.

uniqueg commented 7 years ago

Thanks Juanlu, sounds good!

JLTrincado commented 7 years ago

Dear Alex,

we have uploaded a pre-release of SUPPA with the differential isoform usage. This functionality is working, but we still need to update the README. If you want to test it, it will be really helpful if you give us some feedback about it.

Thanks again and best, Juanlu

JLTrincado commented 7 years ago

It's in another branch, so if you want to download please go to the realases section

uniqueg commented 7 years ago

Awesome, thanks a lot! I'm going to be away for a while from the end of next week, I hope I will have a chance to run it until then. Can I find the usage in the script itself?

JLTrincado commented 7 years ago

Yes, you have in the Release notes what have changed and in the script itself how to use it. But we will update the README for explaining well how to proceed with the differential isoforms analysis

uniqueg commented 7 years ago

Great, thanks.

EduEyras commented 7 years ago

Hi, we have updated the README in the testing branch. There it is explained how to run SUPPA for differential transcript usage. Some of the options have changed to accommodate that and make it similar to the analysis with events. Let us know if you encounter any problem best E.

evaruud commented 7 years ago

Dear SUPPA developers,

Thank you for a great tool!

I am following the tutorial on the wiki (https://github.com/comprna/SUPPA/wiki), and encountered a problem. When I attempt to generate the .ioi file, it returns and error message "suppa.py: error: unrecognized arguments: -f ioi".

This is the code I run in the console: suppa generateEvents -f ioi -i path/to/Homo_sapiens.GRCh38.89.gtf -o path/to/suppa_events'/pdx' -e SE SS MX RI FL

Am I doing anything wrong? Why is the -f ioi argument not working?

Best, Eva

JLTrincado commented 7 years ago

Dear Eva,

thank for your interest in our tool. We are still testing differential transcript usage. Because of this, we have the version with this new functionality in the "testing" branch. Did you download this version? Perhaps you're running the version in the master branch. In the main code page, you can switch to "testing" branch and download the corresponding version.

Thanks again and regards, Juanlu

evaruud commented 7 years ago

Dear Juanlu,

Thank you for your quick reply, that makes sense! I haven’t downloaded the testing branch, but I will try that.

Is it safe to use, now that you call it “testing branch”? And if I use this branch, will the differential splicing for local events, and the rest of the analysis, work the same way?

Best, Eva

JLTrincado commented 7 years ago

Hi again,

yes. Since some users show interest on this option, we decided to upload it. We called it "testing" because we wanted to do some benchmark before put it in the main branch. But the rest of the functionalities of SUPPA are the same as in the master branch, they're stable.

So any feedback on your experience on using differential transcript usage will be greatly appreciated. Thanks again and best, Juanlu

evaruud commented 7 years ago

Thanks a lot for your help!

cym0304 commented 7 years ago

Dear SUPPA developers, I use the diffSplice to calculate the p-value of isoforms, but the SUPPA always note me the message: "ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f7a50fa2d08>)". This is my command: python3 /software/SUPPA-2.1/suppa.py diffSplice -m empirical -i ./transcript_out/LR.isoforms.ioi -p ./split/L2_1_SE_isoform.psi ./split/L2_2_SE_isoform.psi -e ./split/L2_1_simulate.tpm ./split/L2_2_simulate.tpm -gc -o ./transcript_out/isoform_dpsi_1_2 Could you give me some adavises please?

EduEyras commented 7 years ago

Dear Yunmei,

thanks for your email. I am not sure what the problem could be, since the command line looks like the one we have in the tutorial: https://github.com/comprna/SUPPA/wiki

A simple guess is whether you're using the right version of Python.

On the other hand, the error looks like some of the files might leave some variables empty. Perhaps you can check that all fields are defined in those input files?

Let us know if this helps best

E.

On Sat, Sep 16, 2017 at 4:26 PM, YunmeiChen notifications@github.com wrote:

Dear SUPPA developers, I use the diffSplice to calculate the p-value of isoforms, but the SUPPA always note me the message: "ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f7a50fa2d08>)". This is my command: python3 /software/SUPPA-2.1/suppa.py diffSplice -m empirical -i ./transcript_out/LR.isoforms.ioi -p ./split/L2_1_SE_isoform.psi ./split/L2_2_SE_isoform.psi -e ./split/L2_1_simulate.tpm ./split/L2_2_simulate.tpm -gc -o ./transcript_out/isoform_dpsi_1_2 Could you give me some adavises please?

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cym0304 commented 7 years ago

Dear Eduardo, Thank you for your reply. The python I used is "Python 3.6.2 :: Anaconda custom (64-bit)". And I checked all fields of input files. Except the head line (only has the sample ID), every line has the same fields. I used the diffSplice module to calculate the p-value of event, and I got the results. So I don't know what are the reasons. Please give me some help. I must get the result these tow days, it's very important for me. Thank you.

EduEyras commented 7 years ago

Did you check the header lines in all files?

If you send me a sample of your files I could test it

E.

On Sat, Sep 16, 2017 at 5:38 PM, YunmeiChen notifications@github.com wrote:

Dear Eduardo, Thank you for your reply. The python I used is "Python 3.6.2 :: Anaconda custom (64-bit)". And I checked all fields of input files. Except the head line (only has the sample ID), every line has the same fields. I used the diffSplice module to calculate the p-value of event, and I got the results. So I don't know what are the reasons. Please give me some help. I must get the result these tow days, it's very important for me. Thank you.

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/comprna/SUPPA/issues/2#issuecomment-329975920, or mute the thread https://github.com/notifications/unsubscribe-auth/AMWVB97VgJxehdW05RYFDUVzysvSQ6E7ks5si-t3gaJpZM4L5vqJ .

-- Dr E Eyras

ICREA Research Professor Universitat Pompeu Fabra PRBB, Dr Aiguader 88 Tel: +34 93 316 0502 (ext 1502) E08003 Barcelona, Spain Fax: +34 93 316 0550

http://scholar.google.com/citations?user=LiojlGoAAAAJ http://www.researcherid.com/rid/L-1053-2014 http://regulatorygenomics.upf.edu/

cym0304 commented 7 years ago

Dear Eduardo, Thank you. I checked the header lines of my input files, but I can't notice the error. I calculate these samples together, and then split them based on the samples. I will send you my data files and my command lines. Could you help me to find my error about this step? Thank you.

HaoZhang17 commented 7 years ago

@YunmeiChen Hi Yunmei, I think the problem could be the new blank line that generated unintentionally in the final part of your input file(s), you can check it out.

cym0304 commented 7 years ago

@HaoZhang17 Thank you. This problem has been solved. But I have a mass of data that need to do the AS analysis, I will pay attention to this situation.

ali0010 commented 7 years ago

Hello. I have been trying to run the diffSplice for some time now but I didn't manage to make it work. Keep getting the following error when running in the 'empirical' mode:

python3.6 suppa.py diffSplice --method empirical --ioe refGene_SE_strict.ioe --psi ctrl.psi zrsr2KD.psi -e ctrl.tpm zrsr2KD.tpm --area 1000 --lower-bound 0.05 --gene-correction --output ./diffSpliceRes/ Calculating differential analysis between conditions: ctrl and zrsr2KD ERROR:main:Unknown error: (<class 'UnboundLocalError'>, UnboundLocalError("local variable 'i' referenced before assignment",), <traceback object at 0x7f48e6f2dbc8>)

and the following error when running in 'classical' mode:

python3.6 suppa.py diffSplice --method classical --ioe refGene_SE_strict.ioe --psi ctrl.psi zrsr2KD.psi -e ctrl.tpm zrsr2KD.tpm --area 1000 --lower-bound 0.05 --gene-correction --output ./diffSpliceRes/ Calculating differential analysis between conditions: ctrl and zrsr2KD /usr/local/lib/python3.6/site-packages/scipy/stats/stats.py:4897: RuntimeWarning: divide by zero encountered in double_scalars z = (bigu - meanrank) / sd ERROR:main:Unknown error: (<class 'TypeError'>, TypeError("calculate_delta_psi() missing 1 required positional argument: 'nan_th'",), <traceback object at 0x7f4e7500be88>)

Do you know what might be the problem and how I can make it work?

Cheers!

EduEyras commented 7 years ago

Hi,

in principle everything looks ok in the command line, so maybe there is something in the input files? Have you checked that there is nothing unusual in the input?

E.

On Wed, Nov 1, 2017 at 10:44 AM, ali0010 notifications@github.com wrote:

Hello. I have been trying to run the diffSplice for some time now but I didn't manage to make it work. Keep getting the following error when running in the 'empirical' mode:

python3.6 suppa.py diffSplice --method empirical --ioe refGene_SE_strict.ioe --psi ctrl.psi zrsr2KD.psi -e ctrl.tpm zrsr2KD.tpm --area 1000 --lower-bound 0.05 --gene-correction --output ./diffSpliceRes/ Calculating differential analysis between conditions: ctrl and zrsr2KD ERROR:main:Unknown error: (<class 'UnboundLocalError'>, UnboundLocalError("local variable 'i' referenced before assignment",), <traceback object at 0x7f48e6f2dbc8>)

and the following error when running in 'classical' mode:

python3.6 suppa.py diffSplice --method classical --ioe refGene_SE_strict.ioe --psi ctrl.psi zrsr2KD.psi -e ctrl.tpm zrsr2KD.tpm --area 1000 --lower-bound 0.05 --gene-correction --output ./diffSpliceRes/ Calculating differential analysis between conditions: ctrl and zrsr2KD /usr/local/lib/python3.6/site-packages/scipy/stats/stats.py:4897: RuntimeWarning: divide by zero encountered in double_scalars z = (bigu - meanrank) / sd ERROR:main:Unknown error: (<class 'TypeError'>, TypeError("calculate_delta_psi() missing 1 required positional argument: 'nan_th'",), <traceback object at 0x7f4e7500be88>)

Do you know what might be the problem and how I can make it work?

Cheers!

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ali0010 commented 7 years ago

Hi Eduardo! Thank you for your response. Actually I just rechecked them and they seem OK! Here are their contents:

head zrsr2KD.tpm
A005    A006    A007    A008
NM_001040071    0.0396222       0.0467011       0.024225        0.0577172
NM_001077637    0       0       0       0.00355455
NM_001242750    0.0443395       0.0781386       0.0271606       0.032285
NM_001195272    0       0       0.00371751      0.00441067
NR_002826       0       0       0.0163621       0
NR_002144       0       0       0       0
NR_003665       0       0       0       0
NR_027761       0.00476498      0.00560017      0       0.00694743
NR_033267       0.0982477       0.185681        0.103826        0.228722

head zrsr2KD.psi
A005    A006    A007    A008
ZFYVE28;NM_001172659    0.164831064811509       0.0516279525110283      1.63581063029275e-07    5.79243936283768e-07
ZFYVE28;NM_001172656    0.0699402337386467      0       0.0576878187006207      0
ZFYVE28;NM_020972       0.378860765098825       0.555111453633919       0.506845536084027       0.557769217334216
ZFYVE28;NM_001172660    0.310698116289648       0.345495960021994       0.337293341403167       0.325476259419332
ZFYVE28;NM_001172658    0.0756698200613701      0       0.04683402799969        0.0957049821768377
ZFYVE28;NM_001172657    0       0.0477646338330584      0.0513391122314321      0.021048961825678
PAQR3;NM_001040202      1       1       1       1
NELFA;NM_005663 1       1       1       1
CASP6;NM_001226 1       1       1       0.925227306726949

head ctrl.tpm
A001    A002    A003    A004
NM_001040071    0.0474572       0.107048        0.0377436       0
NM_001077637    0       0       0.00283034      0
NM_001242750    0.0264464       0.0357259       0.0518425       0.0465723
NM_001195272    0       0       0       0.00316265
NR_002826       0       0.00712944      0.00387732      0
NR_002144       0       0       0       0.00587538
NR_003665       0       0       0.00910444      0.00825432
NR_027761       0       0       0       0
NR_033267       0.156549        0.13652 0.197628        0.176897

head ctrl.psi
A001    A002    A003    A004
ZFYVE28;NM_001172659    0       0.379733980683996       0       0
ZFYVE28;NM_001172656    0.0527966941612684      0       0.209271105102184       0
ZFYVE28;NM_020972       0.576730352266039       0.282879201974376       0.417800975234592       0.663815782136577
ZFYVE28;NM_001172660    0.285903158079492       0.215129882269024       0.296090877264047       0.229055891297946
ZFYVE28;NM_001172658    0.0845697954932001      0.0606514127663197      0.0768370423991778      0.0579840256290471
ZFYVE28;NM_001172657    0       0.0616055223062837      0       0.0491443009364301
PAQR3;NM_001040202      1       1       1       1
NELFA;NM_005663 1       1       1       1
CASP6;NM_001226 1       0.963539048926798       0.974659104153931       1

head refGene_SE_strict.ioe
seqname gene_id event_id        alternative_transcripts total_transcripts
chr4    ZFYVE28 ZFYVE28;SE:chr4:2321998-2337432:2337521-2339134:-       NM_001172659,NM_020972  NM_001172659,NM_020972,NM_001172656
chr4    ZFYVE28 ZFYVE28;SE:chr4:2343342-2355660:2355800-2420012:-       NM_001172656,NM_020972,NM_001172657     NM_001172656,NM_020972,NM_001172657,NM_001172658
chr4    ZFYVE28 ZFYVE28;SE:chr4:2330869-2337432:2337521-2339134:-       NM_001172660    NM_001172660,NM_001172658
chr4    CAMK2D  CAMK2D;SE:chr4:114375671-114376882:114376977-114378491:-        NM_001221       NM_001221,NM_172128
chr4    CAMK2D  CAMK2D;SE:chr4:114421667-114424092:114424133-114430794:-        NM_172114       NM_172114,NM_001221,NM_172128,NM_172127,NM_172115
chr4    CAMK2D  CAMK2D;SE:chr4:114421667-114429392:114429424-114430794:-        NM_172129       NM_172129,NM_001221,NM_172128,NM_172127,NM_172115
chr4    HPSE    HPSE;SE:chr4:84232043-84234267:84234440-84240497:-      NM_001098540,NM_001166498,NM_006665     NM_001098540,NM_001166498,NM_006665,NM_001199830
chr4    FBXL5   FBXL5;SE:chr4:15642506-15643147:15643250-15646116:-     NR_036464       NR_036464,NM_001193535,NM_001193534,NM_012161
chr4    ABLIM2  ABLIM2;SE:chr4:8010829-8021345:8021398-8031383:-        NM_001286688    NM_001286688,NM_001130087,NM_032432,NM_001130086,NM_001130088

tail ctrl.psi
LOC100288966;NM_001257362       NA      NA      NA      NA
MIR3118-1;NR_036061     NA      NA      NA      NA
FRG2;NM_001286820       1       1       1       1
DUX4;NM_001306068       0       0       0       0
DUX4;NM_001293798       1       1       1       1
FAM230C;NR_027278       1       1       1       1
MGC70870;NR_003682      1       1       1       1
FLJ43315;NR_033856      1       1       1       1
MGC39584;NR_038377      1       1       1       1
LOC283788;NR_027436     1       1       1       1

tail refGene_SE_strict.ioe
chr19_gl000209_random   KIR3DS1 KIR3DS1;SE:chr19_gl000209_random:70141-71141:71176-73694:+      NM_001282170    NM_001282170,NM_001282171
chr19_gl000209_random   KIR3DS1 KIR3DS1;SE:chr19_gl000209_random:71176-71921:72205-73694:+      NM_001083539    NM_001083539,NM_001282170
chr19_gl000209_random   KIR2DS2 KIR2DS2;SE:chr19_gl000209_random:132329-134766:135065-136584:+  NM_012312,NM_001291695,NM_001291701     NM_012312,NM_001291695,NM_001291701,NM_001291696
chr19_gl000209_random   KIR2DS2 KIR2DS2;SE:chr19_gl000209_random:136877-140046:140096-144361:+  NM_012312,NM_001291695,NM_001291696     NM_012312,NM_001291695,NM_001291696,NM_001291701
chr19_gl000209_random   KIR2DS2 KIR2DS2;SE:chr19_gl000209_random:132329-136584:136877-140046:+  NM_001291696    NM_001291696,NM_001291700
chr19_gl000209_random   KIR2DL4 KIR2DL4;SE:chr19_gl000209_random:62486-66730:66834-67296:+      NM_002255_2     NM_002255_2,NM_001080770_2
chr19_gl000209_random   KIR2DL4 KIR2DL4;SE:chr19_gl000209_random:62486-66730:66833-67296:+      NM_001080772_2  NM_001080772_2,NM_001080770_2
chrUn_gl000223  ZNF26   ZNF26;SE:chrUn_gl000223:95942-98546:98641-98910:-       NM_019591_2,NM_001256279_2      NM_019591_2,NM_001256279_2,NM_001256280_2
chrUn_gl000223  ZNF26   ZNF26;SE:chrUn_gl000223:99036-104347:104445-119205:-    NM_001256279_2  NM_001256279_2,NM_019591_2,NM_001256280_2
chrUn_gl000222  LINC00960       LINC00960;SE:chrUn_gl000222:74835-77483:77643-77731:-   NR_040004_2     NR_040004_2,NR_040005_2

Cheers,

Ali

EduEyras commented 7 years ago

Hi Ali,

It seems you have a psi file for isoforms and a ioe, which is for events. You need the ioi, which is the equivalent for isoforms.

To generate an ioi file, e.g:

python3.6 ~/SUPPA-testing/suppa.py generateEvents -f ioi -i annotation.gtf -o isoforms

This is in the testing branch. I hope this helps.

Best

Eduardo

On Wed, Nov 1, 2017 at 2:07 PM, ali0010 notifications@github.com wrote:

Hi Eduardo! Thank you for your response. Actually I just rechecked them and they seem OK! Here are their contents:

head zrsr2KD.tpm A005 A006 A007 A008 NM_001040071 0.0396222 0.0467011 0.024225 0.0577172 NM_001077637 0 0 0 0.00355455 NM_001242750 0.0443395 0.0781386 0.0271606 0.032285 NM_001195272 0 0 0.00371751 0.00441067 NR_002826 0 0 0.0163621 0 NR_002144 0 0 0 0 NR_003665 0 0 0 0 NR_027761 0.00476498 0.00560017 0 0.00694743 NR_033267 0.0982477 0.185681 0.103826 0.228722

head zrsr2KD.psi A005 A006 A007 A008 ZFYVE28;NM_001172659 0.164831064811509 0.0516279525110283 1.63581063029275e-07 5.79243936283768e-07 ZFYVE28;NM_001172656 0.0699402337386467 0 0.0576878187006207 0 ZFYVE28;NM_020972 0.378860765098825 0.555111453633919 0.506845536084027 0.557769217334216 ZFYVE28;NM_001172660 0.310698116289648 0.345495960021994 0.337293341403167 0.325476259419332 ZFYVE28;NM_001172658 0.0756698200613701 0 0.04683402799969 0.0957049821768377 ZFYVE28;NM_001172657 0 0.0477646338330584 0.0513391122314321 0.021048961825678 PAQR3;NM_001040202 1 1 1 1 NELFA;NM_005663 1 1 1 1 CASP6;NM_001226 1 1 1 0.925227306726949

head ctrl.tpm A001 A002 A003 A004 NM_001040071 0.0474572 0.107048 0.0377436 0 NM_001077637 0 0 0.00283034 0 NM_001242750 0.0264464 0.0357259 0.0518425 0.0465723 NM_001195272 0 0 0 0.00316265 NR_002826 0 0.00712944 0.00387732 0 NR_002144 0 0 0 0.00587538 NR_003665 0 0 0.00910444 0.00825432 NR_027761 0 0 0 0 NR_033267 0.156549 0.13652 0.197628 0.176897

head ctrl.psi A001 A002 A003 A004 ZFYVE28;NM_001172659 0 0.379733980683996 0 0 ZFYVE28;NM_001172656 0.0527966941612684 0 0.209271105102184 0 ZFYVE28;NM_020972 0.576730352266039 0.282879201974376 0.417800975234592 0.663815782136577 ZFYVE28;NM_001172660 0.285903158079492 0.215129882269024 0.296090877264047 0.229055891297946 ZFYVE28;NM_001172658 0.0845697954932001 0.0606514127663197 0.0768370423991778 0.0579840256290471 ZFYVE28;NM_001172657 0 0.0616055223062837 0 0.0491443009364301 PAQR3;NM_001040202 1 1 1 1 NELFA;NM_005663 1 1 1 1 CASP6;NM_001226 1 0.963539048926798 0.974659104153931 1

head refGene_SE_strict.ioe seqname gene_id event_id alternative_transcripts total_transcripts chr4 ZFYVE28 ZFYVE28;SE:chr4:2321998-2337432:2337521-2339134:- NM_001172659,NM_020972 NM_001172659,NM_020972,NM_001172656 chr4 ZFYVE28 ZFYVE28;SE:chr4:2343342-2355660:2355800-2420012:- NM_001172656,NM_020972,NM_001172657 NM_001172656,NM_020972,NM_001172657,NM_001172658 chr4 ZFYVE28 ZFYVE28;SE:chr4:2330869-2337432:2337521-2339134:- NM_001172660 NM_001172660,NM_001172658 chr4 CAMK2D CAMK2D;SE:chr4:114375671-114376882:114376977-114378491:- NM_001221 NM_001221,NM_172128 chr4 CAMK2D CAMK2D;SE:chr4:114421667-114424092:114424133-114430794:- NM_172114 NM_172114,NM_001221,NM_172128,NM_172127,NM_172115 chr4 CAMK2D CAMK2D;SE:chr4:114421667-114429392:114429424-114430794:- NM_172129 NM_172129,NM_001221,NM_172128,NM_172127,NM_172115 chr4 HPSE HPSE;SE:chr4:84232043-84234267:84234440-84240497:- NM_001098540,NM_001166498,NM_006665 NM_001098540,NM_001166498,NM_006665,NM_001199830 chr4 FBXL5 FBXL5;SE:chr4:15642506-15643147:15643250-15646116:- NR_036464 NR_036464,NM_001193535,NM_001193534,NM_012161 chr4 ABLIM2 ABLIM2;SE:chr4:8010829-8021345:8021398-8031383:- NM_001286688 NM_001286688,NM_001130087,NM_032432,NM_001130086,NM_001130088

tail ctrl.psi LOC100288966;NM_001257362 NA NA NA NA MIR3118-1;NR_036061 NA NA NA NA FRG2;NM_001286820 1 1 1 1 DUX4;NM_001306068 0 0 0 0 DUX4;NM_001293798 1 1 1 1 FAM230C;NR_027278 1 1 1 1 MGC70870;NR_003682 1 1 1 1 FLJ43315;NR_033856 1 1 1 1 MGC39584;NR_038377 1 1 1 1 LOC283788;NR_027436 1 1 1 1 tail refGene_SE_strict.ioe chr19_gl000209_random KIR3DS1 KIR3DS1;SE:chr19_gl000209_random:70141-71141:71176-73694:+ NM_001282170 NM_001282170,NM_001282171 chr19_gl000209_random KIR3DS1 KIR3DS1;SE:chr19_gl000209_random:71176-71921:72205-73694:+ NM_001083539 NM_001083539,NM_001282170 chr19_gl000209_random KIR2DS2 KIR2DS2;SE:chr19_gl000209_random:132329-134766:135065-136584:+ NM_012312,NM_001291695,NM_001291701 NM_012312,NM_001291695,NM_001291701,NM_001291696 chr19_gl000209_random KIR2DS2 KIR2DS2;SE:chr19_gl000209_random:136877-140046:140096-144361:+ NM_012312,NM_001291695,NM_001291696 NM_012312,NM_001291695,NM_001291696,NM_001291701 chr19_gl000209_random KIR2DS2 KIR2DS2;SE:chr19_gl000209_random:132329-136584:136877-140046:+ NM_001291696 NM_001291696,NM_001291700 chr19_gl000209_random KIR2DL4 KIR2DL4;SE:chr19_gl000209_random:62486-66730:66834-67296:+ NM_002255_2 NM_002255_2,NM_001080770_2 chr19_gl000209_random KIR2DL4 KIR2DL4;SE:chr19_gl000209_random:62486-66730:66833-67296:+ NM_001080772_2 NM_001080772_2,NM_001080770_2 chrUn_gl000223 ZNF26 ZNF26;SE:chrUn_gl000223:95942-98546:98641-98910:- NM_019591_2,NM_001256279_2 NM_019591_2,NM_001256279_2,NM_001256280_2 chrUn_gl000223 ZNF26 ZNF26;SE:chrUn_gl000223:99036-104347:104445-119205:- NM_001256279_2 NM_001256279_2,NM_019591_2,NM_001256280_2 chrUn_gl000222 LINC00960 LINC00960;SE:chrUn_gl000222:74835-77483:77643-77731:- NR_040004_2 NR_040004_2,NR_040005_2

Cheers,

Ali

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ali0010 commented 7 years ago

Hi again Eduardo (@ EduEyras )! Yes. It worked! Thank you for your help. I hadn't generated the ioi files!

Cheers,

Ali

cym0304 commented 7 years ago

Hi, EduEyras (@EduEyras ) I used SUPPA to do the DTU analysis. I noticed that the result didn't contain the AS information. And there are some gene_ids different with the gtf file.

This is an example: ARADU.0J8PV_locus1;ARADU.0J8PV_novel03 0.22818357786993657 0.3278965728710294 0.37491187750042154 ARADU.0J8PV_locus1;ARADU.0J8PV_novel02 0.021189351659732975 0.536531974986597 0.08838313168723855 ARADU.0J8PV_locus1;ARADU.0J8PV_novel01 0.7506270704703304 0.13557145214237348 0.5367049908123398

In the gtf file, the record is like this: aradu.a05 protein_coding exon 4920476 4921137 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel01"; exon_number "1"; aradu.a05 protein_coding exon 4921237 4921332 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel01"; exon_number "2"; aradu.a05 protein_coding exon 4921536 4921702 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel01"; exon_number "3"; aradu.a05 protein_coding exon 4922325 4923005 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel01"; exon_number "4"; aradu.a05 protein_coding exon 4920429 4921137 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel02"; exon_number "1"; aradu.a05 protein_coding exon 4921222 4921332 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel02"; exon_number "2"; aradu.a05 protein_coding exon 4921536 4921699 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel02"; exon_number "3"; aradu.a05 protein_coding exon 4922325 4923007 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel02"; exon_number "4"; aradu.a05 protein_coding exon 4920429 4921137 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel03"; exon_number "1"; aradu.a05 protein_coding exon 4921222 4921332 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel03"; exon_number "2"; aradu.a05 protein_coding exon 4921536 4923005 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel03"; exon_number "3"; aradu.a05 protein_coding exon 4924680 4925248 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "1"; aradu.a05 protein_coding exon 4925527 4925746 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "2"; aradu.a05 protein_coding exon 4925879 4925989 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "3"; aradu.a05 protein_coding exon 4926079 4926245 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "4"; aradu.a05 protein_coding exon 4927003 4927522 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "5"; aradu.a05 protein_coding exon 4924774 4925248 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel05"; exon_number "1"; aradu.a05 protein_coding exon 4925527 4925746 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel05"; exon_number "2"; aradu.a05 protein_coding exon 4925879 4925989 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel05"; exon_number "3"; aradu.a05 protein_coding exon 4926079 4927533 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel05"; exon_number "4"; aradu.a05 protein_coding exon 4924774 4925248 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel06"; exon_number "1"; aradu.a05 protein_coding exon 4925527 4925746 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel06"; exon_number "2"; aradu.a05 protein_coding exon 4925879 4927667 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel06"; exon_number "3";

The gene ID changed. Could you tell me the reasons? And will it effect the final results? Best Yunmei Chen

EduEyras commented 7 years ago

Hi Yunmei,

did you run the analysis with the -p option (pool genes)? I could be that it's changing the ID by default.

I don't understand the other question about not producing the splicing information. In the file you will see the gene;isoform and the PSI per sample. Didn't you get the file with the deltaPSIs?

cheers

Eduardo

On Tue, Nov 7, 2017 at 8:55 AM, YunmeiChen notifications@github.com wrote:

Hi, EduEyras (@EduEyras https://github.com/edueyras ) I used SUPPA to do the DTU analysis. I noticed that the result didn't contain the AS information. And there are some gene_ids different with the gtf file.

This is an example: ARADU.0J8PV_locus1;ARADU.0J8PV_novel03 0.22818357786993657 0.3278965728710294 0.37491187750042154 ARADU.0J8PV_locus1;ARADU.0J8PV_novel02 0.021189351659732975 0.536531974986597 0.08838313168723855 ARADU.0J8PV_locus1;ARADU.0J8PV_novel01 0.7506270704703304 0.13557145214237348 0.5367049908123398

In the gtf file, the record is like this: aradu.a05 protein_coding exon 4920476 4921137 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel01"; exon_number "1"; aradu.a05 protein_coding exon 4921237 4921332 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel01"; exon_number "2"; aradu.a05 protein_coding exon 4921536 4921702 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel01"; exon_number "3"; aradu.a05 protein_coding exon 4922325 4923005 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel01"; exon_number "4"; aradu.a05 protein_coding exon 4920429 4921137 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel02"; exon_number "1"; aradu.a05 protein_coding exon 4921222 4921332 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel02"; exon_number "2"; aradu.a05 protein_coding exon 4921536 4921699 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel02"; exon_number "3"; aradu.a05 protein_coding exon 4922325 4923007 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel02"; exon_number "4"; aradu.a05 protein_coding exon 4920429 4921137 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel03"; exon_number "1"; aradu.a05 protein_coding exon 4921222 4921332 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel03"; exon_number "2"; aradu.a05 protein_coding exon 4921536 4923005 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel03"; exon_number "3"; aradu.a05 protein_coding exon 4924680 4925248 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "1"; aradu.a05 protein_coding exon 4925527 4925746 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "2"; aradu.a05 protein_coding exon 4925879 4925989 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "3"; aradu.a05 protein_coding exon 4926079 4926245 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "4"; aradu.a05 protein_coding exon 4927003 4927522 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel04"; exon_number "5"; aradu.a05 protein_coding exon 4924774 4925248 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel05"; exon_number "1"; aradu.a05 protein_coding exon 4925527 4925746 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel05"; exon_number "2"; aradu.a05 protein_coding exon 4925879 4925989 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel05"; exon_number "3"; aradu.a05 protein_coding exon 4926079 4927533 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel05"; exon_number "4"; aradu.a05 protein_coding exon 4924774 4925248 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel06"; exon_number "1"; aradu.a05 protein_coding exon 4925527 4925746 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel06"; exon_number "2"; aradu.a05 protein_coding exon 4925879 4927667 . + . gene_id "ARADU.0J8PV"; transcript_id "ARADU.0J8PV_novel06"; exon_number "3";

The gene ID changed. Could you tell me the reasons? And will it effect the final results? Best Yunmei Chen

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cym0304 commented 7 years ago

Hi, EduEyras (@EduEyras ), Thanks for your reply. I didn't try the -p option. And I can get the psi-values file. I will do more statistic analysis based on the results, so the changed gene ID may give rise to another question. But I will try to do it anyway. Maybe I can find the solution. Best Yunmei chen

EduEyras commented 7 years ago

We'll look it up. I don't think the gene name should change if you didn't use the -p option.

For isoforms, the only thing we do is to create an id of the form gene_id;isoform_id to keep track of the "isoform event"

Let me know if there is anything strange happening

best

Eduardo

On Thu, Nov 9, 2017 at 6:47 AM, YunmeiChen notifications@github.com wrote:

Hi, EduEyras (@EduEyras https://github.com/edueyras ), Thanks for your reply. I didn't try the -p option. And I can get the psi-values file. I will do more statistic analysis based on the results, so the changed gene ID may give rise to another question. But I will try to do it anyway. Maybe I can find the solution. Best Yunmei chen

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http://scholar.google.com/citations?user=LiojlGoAAAAJ http://www.researcherid.com/rid/L-1053-2014 http://regulatorygenomics.upf.edu/

EduEyras commented 6 years ago

Hi Yunmei,

I did not hear back from you. I hope you solved the issue. I will close this for now. Please let us know if you have any other problems. Best

Eduardo

ghost commented 5 years ago

Dear SUPPA developers, I use the diffSplice to calculate the p-value of isoforms, but the SUPPA always note me the message: "ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f7a50fa2d08>)". This is my command: python3 /software/SUPPA-2.1/suppa.py diffSplice -m empirical -i ./transcript_out/LR.isoforms.ioi -p ./split/L2_1_SE_isoform.psi ./split/L2_2_SE_isoform.psi -e ./split/L2_1_simulate.tpm ./split/L2_2_simulate.tpm -gc -o ./transcript_out/isoform_dpsi_1_2 Could you give me some adavises please?

Dear SUPPA users and developers,

I've been struggling for days with the IndexError mentioned above. @cym0304 says he solved this issue and I'm wondering how? I tried a lot, followed the SUPPA2 tutorial, but can't spot the difference between my files and the ones used there. I'm trying to run the diffSplice class from SUPPA2, but get this error: ERROR:__main__:Unknown error: (<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f0c4adbcf48>)

Can someone help me resolve this issue?

Code used: sudo python3 SUPPA-2.3/suppa.py diffSplice -m empirical -i test3/ensembl_hg38.events.ioe -e test3/R30DM_formatted.tpm test3/CEMWT_formatted.tpm -p test3/R30DM_events.psi test3/CEMWT_events.psi -o CEMWT_R30DM_diffSplice Files used:

test@imagetest:~/test3$ head CEMWT_formatted.tpm
CEMWT
ENST00000631435 0
ENST00000415118 0
ENST00000448914 0
ENST00000434970 0
ENST00000439842 0
ENST00000390567 0
ENST00000452198 0
ENST00000390569 0
ENST00000437320 0
test@imagetest:~/test3$ head R30DM_formatted.tpm
R30DM
ENST00000631435 0
ENST00000415118 0
ENST00000448914 0
ENST00000434970 0
ENST00000439842 0
ENST00000390567 0
ENST00000452198 0
ENST00000390569 0
ENST00000437320 0
test@imagetest:~/test3$ head CEMWT_events.psi
CEMWT
ENSG00000000003;A5:X:100635746-100636191:100635746-100636608:-  0.0
ENSG00000000003;A5:X:100635746-100636608:100635746-100636793:-  1.0
ENSG00000000003;AF:X:100635746-100636191:100636689:100635746-100636793:100637104:-      nan
ENSG00000000003;SE:X:100630866-100632485:100632568-100633405:-  1.0
ENSG00000000419;A3:20:50940955-50941105:50940933-50941105:-     0.0
ENSG00000000419;A3:20:50940955-50942031:50940933-50942031:-     0.04344453071293822
ENSG00000000419;A5:20:50940933-50941105:50940933-50941129:-     0.5493052173899757
ENSG00000000419;MX:20:50940933-50941129:50941209-50945737:50940933-50942031:50942126-50945737:- 0.06658547156394101
ENSG00000000419;SE:20:50940933-50941105:50941209-50942031:-     0.07998880844284445
test@imagetest:~/test3$ head R30DM_events.psi
R30DM
ENSG00000000003;A5:X:100635746-100636191:100635746-100636608:-  0.0
ENSG00000000003;A5:X:100635746-100636608:100635746-100636793:-  1.0
ENSG00000000003;AF:X:100635746-100636191:100636689:100635746-100636793:100637104:-      nan
ENSG00000000003;SE:X:100630866-100632485:100632568-100633405:-  1.0
ENSG00000000419;A3:20:50940955-50941105:50940933-50941105:-     0.0
ENSG00000000419;A3:20:50940955-50942031:50940933-50942031:-     0.025566595841930585
ENSG00000000419;A5:20:50940933-50941105:50940933-50941129:-     0.7643558394483768
ENSG00000000419;MX:20:50940933-50941129:50941209-50945737:50940933-50942031:50942126-50945737:- 0.0
ENSG00000000419;SE:20:50940933-50941105:50941209-50942031:-     0.02776222115903203
test@imagetest:~/test3$ head ensembl_hg38.events.ioe
seqname gene_id event_id        alternative_transcripts total_transcripts
1       ENSG00000237491 ENSG00000237491;A3:1:784493-803919:784493-803922:+      ENST00000591440,ENST00000593022,ENST00000588951 ENST00000589531,ENST00000593022,ENST00000588951,ENST00000591440
1       ENSG00000237491 ENSG00000237491;A3:1:803667-803919:803667-803951:+      ENST00000587530 ENST00000587530,ENST00000586288
1       ENSG00000237491 ENSG00000237491;A3:1:807323-809622:807323-809658:+      ENST00000412115 ENST00000588951,ENST00000412115
1       ENSG00000228794 ENSG00000228794;A3:1:849602-850178:849602-850181:+      ENST00000609009 ENST00000609009,ENST00000608189
1       ENSG00000187634 ENSG00000187634;A3:1:939129-939272:939129-939275:+      ENST00000618181,ENST00000622503,ENST00000616125 ENST00000618181,ENST00000342066,ENST00000617307,ENST00000341065,ENST00000420190,ENST00000622503,ENST00000616125,ENST00000616016
1       ENSG00000187961 ENSG00000187961;A3:1:962917-963032:962917-963109:+      ENST00000466300 ENST00000338591,ENST00000466300,ENST00000622660
1       ENSG00000187583 ENSG00000187583;A3:1:971006-971077:971006-971113:+      ENST00000379409,ENST00000379407,ENST00000480267 ENST00000379410,ENST00000379407,ENST00000379409,ENST00000480267
1       ENSG00000217801 ENSG00000217801;A3:1:1064589-1065830:1064589-1065949:+  ENST00000433695 ENST00000451054,ENST00000433695
1       ENSG00000162571 ENSG00000162571;A3:1:1173926-1174266:1173926-1174285:+  ENST00000379290 ENST00000379289,ENST00000379290

EduEyras commented 5 years ago

Hi Michel,

I cannot see any obvious issue. You said you tried the commands in the tutorial and those work well?

Have you tried running it in the same directory where the input files are?

I cc JC and JL in case they can spot anything

best

E.

On Mon, 22 Jul 2019 at 19:52, Michel van Elswijk notifications@github.com wrote:

Dear SUPPA developers, I use the diffSplice to calculate the p-value of isoforms, but the SUPPA always note me the message: "ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f7a50fa2d08>)". This is my command: python3 /software/SUPPA-2.1/suppa.py diffSplice -m empirical -i ./transcript_out/LR.isoforms.ioi -p ./split/L2_1_SE_isoform.psi ./split/L2_2_SE_isoform.psi -e ./split/L2_1_simulate.tpm ./split/L2_2_simulate.tpm -gc -o ./transcript_out/isoform_dpsi_1_2 Could you give me some adavises please?

Dear SUPPA users and developers,

I've been struggling for days with the IndexError mentioned above. @cym0304 https://github.com/cym0304 says he solved this issue and I'm wondering how? I tried a lot, followed the SUPPA2 tutorial, but can't spot the difference between my files and the ones used there. I'm trying to run the diffSplice class from SUPPA2, but get this error: ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f0c4adbcf48>)

Can someone help me resolve this issue?

Code used: sudo python3 SUPPA-2.3/suppa.py diffSplice -m empirical -i test3/ensembl_hg38.events.ioe -e test3/R30DM_formatted.tpm test3/CEMWT_formatted.tpm -p test3/R30DM_events.psi test3/CEMWT_events.psi -o CEMWT_R30DM_diffSplice Files used:

test@imagetest:~/test3$ head CEMWT_formatted.tpm CEMWT ENST00000631435 0 ENST00000415118 0 ENST00000448914 0 ENST00000434970 0 ENST00000439842 0 ENST00000390567 0 ENST00000452198 0 ENST00000390569 0 ENST00000437320 0 test@imagetest:~/test3$ head R30DM_formatted.tpm R30DM ENST00000631435 0 ENST00000415118 0 ENST00000448914 0 ENST00000434970 0 ENST00000439842 0 ENST00000390567 0 ENST00000452198 0 ENST00000390569 0 ENST00000437320 0 test@imagetest:~/test3$ head CEMWT_events.psi CEMWT ENSG00000000003;A5:X:100635746-100636191:100635746-100636608:- 0.0 ENSG00000000003;A5:X:100635746-100636608:100635746-100636793:- 1.0 ENSG00000000003;AF:X:100635746-100636191:100636689:100635746-100636793:100637104:- nan ENSG00000000003;SE:X:100630866-100632485:100632568-100633405:- 1.0 ENSG00000000419;A3:20:50940955-50941105:50940933-50941105:- 0.0 ENSG00000000419;A3:20:50940955-50942031:50940933-50942031:- 0.04344453071293822 ENSG00000000419;A5:20:50940933-50941105:50940933-50941129:- 0.5493052173899757 ENSG00000000419;MX:20:50940933-50941129:50941209-50945737:50940933-50942031:50942126-50945737:- 0.06658547156394101 ENSG00000000419;SE:20:50940933-50941105:50941209-50942031:- 0.07998880844284445 test@imagetest:~/test3$ head R30DM_events.psi R30DM ENSG00000000003;A5:X:100635746-100636191:100635746-100636608:- 0.0 ENSG00000000003;A5:X:100635746-100636608:100635746-100636793:- 1.0 ENSG00000000003;AF:X:100635746-100636191:100636689:100635746-100636793:100637104:- nan ENSG00000000003;SE:X:100630866-100632485:100632568-100633405:- 1.0 ENSG00000000419;A3:20:50940955-50941105:50940933-50941105:- 0.0 ENSG00000000419;A3:20:50940955-50942031:50940933-50942031:- 0.025566595841930585 ENSG00000000419;A5:20:50940933-50941105:50940933-50941129:- 0.7643558394483768 ENSG00000000419;MX:20:50940933-50941129:50941209-50945737:50940933-50942031:50942126-50945737:- 0.0 ENSG00000000419;SE:20:50940933-50941105:50941209-50942031:- 0.02776222115903203 test@imagetest:~/test3$ head ensembl_hg38.events.ioe seqname gene_id event_id alternative_transcripts total_transcripts 1 ENSG00000237491 ENSG00000237491;A3:1:784493-803919:784493-803922:+ ENST00000591440,ENST00000593022,ENST00000588951 ENST00000589531,ENST00000593022,ENST00000588951,ENST00000591440 1 ENSG00000237491 ENSG00000237491;A3:1:803667-803919:803667-803951:+ ENST00000587530 ENST00000587530,ENST00000586288 1 ENSG00000237491 ENSG00000237491;A3:1:807323-809622:807323-809658:+ ENST00000412115 ENST00000588951,ENST00000412115 1 ENSG00000228794 ENSG00000228794;A3:1:849602-850178:849602-850181:+ ENST00000609009 ENST00000609009,ENST00000608189 1 ENSG00000187634 ENSG00000187634;A3:1:939129-939272:939129-939275:+ ENST00000618181,ENST00000622503,ENST00000616125 ENST00000618181,ENST00000342066,ENST00000617307,ENST00000341065,ENST00000420190,ENST00000622503,ENST00000616125,ENST00000616016 1 ENSG00000187961 ENSG00000187961;A3:1:962917-963032:962917-963109:+ ENST00000466300 ENST00000338591,ENST00000466300,ENST00000622660 1 ENSG00000187583 ENSG00000187583;A3:1:971006-971077:971006-971113:+ ENST00000379409,ENST00000379407,ENST00000480267 ENST00000379410,ENST00000379407,ENST00000379409,ENST00000480267 1 ENSG00000217801 ENSG00000217801;A3:1:1064589-1065830:1064589-1065949:+ ENST00000433695 ENST00000451054,ENST00000433695 1 ENSG00000162571 ENSG00000162571;A3:1:1173926-1174266:1173926-1174285:+ ENST00000379290 ENST00000379289,ENST00000379290

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ghost commented 5 years ago

Hi Eduardo Eyras,

Thanks for your quick reply! I tried running SUPPA2 diffSplice in the same directory as the input files but without success:

test@testimagetest:~$ sudo python3 SUPPA-2.3/suppa.py diffSplice -m empirical -i ensembl_hg38.events.ioe -e R30DM_tpm_formatted.txt CEMWT_tpm_formatted.txt -p R30DM_events.psi CEMWT_events.psi -o CEMWT_R30DM_diffSplice

Calculating differential analysis between conditions: R30DM_events and CEMWT_events

ERROR:__main__:Unknown error: (<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f91ba73dfc8>)

I tried the tutorial with hg19 successfully, but now with hg38 and some of my own samples, it suddenly starts giving me this "IndexError".

EduEyras commented 5 years ago

Hi Michel,

we have been using SUPPA2 with hg38 without any issues.

Have you checked whether there might be an issue with the haplotypes or duplicated annotations? hg38 contains haplotypes with the same gene ids and transcript ids. We usually do not include them in the analysis because of this ambiguity.

cheers

E.

On Tue, 23 Jul 2019 at 01:10, Michel van Elswijk notifications@github.com wrote:

Hi Eduardo Eyras,

Thanks for your quick reply! I tried running SUPPA2 diffSplice in the same directory as the input files but without success:

test@testimagetest:~$ sudo python3 SUPPA-2.3/suppa.py diffSplice -m empirical -i ensembl_hg38.events.ioe -e R30DM_tpm_formatted.txt CEMWT_tpm_formatted.txt -p R30DM_events.psi CEMWT_events.psi -o CEMWT_R30DM_diffSplice

Calculating differential analysis between conditions: R30DM_events and CEMWT_events

ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range',), <traceback object at 0x7f91ba73dfc8>)

I tried the tutorial with hg19 successfully, but now with hg38 and some of my own samples, it suddenly starts giving me this "IndexError".

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ghost commented 5 years ago

Hi Eduardo Eyras,

Last week I dug in the code and came to the discovery that my .tpm and .psi files had some issues. Fixed the file issues then the code worked just fine 👍 Thnx for the help!

cheers

Michel

EduEyras commented 5 years ago

Hi Michel,

Great to hear that you made it work. Thanks for letting me know.

cheers

Eduardo

On Mon, 29 Jul 2019 at 18:34, Michel van Elswijk notifications@github.com wrote:

Hi Eduardo Eyras,

Last week I dug in the code and came to the discovery that my .tpm and .psi files had some issues. Fixed the file issues then the code worked just fine 👍 Thnx for the help!

cheers

Michel

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yazuo0204 commented 4 years ago

Dear SUPPA Members,

I have the same issue as above.

The things is, I have four samples in different 3 groups, I need to compare between groups. This command works fine with the group contains 2 samples and 1 samples. But it would report index error while I tried 1 vs 1 compare.

suppa.py diffSplice --method empirical --input 02.Diff_AS/NC_vs_M7B_OV/AS_events/transcript_events.ioi --psi 02.Diff_AS/NC_vs_M7B_OV/isoform_CONTROL.psi 02.Diff_AS/NC_vs_M7B_OV/isoform_EXPERIMENT.psi --tpm 02.Diff_AS/NC_vs_M7B_OV/quantification/CONTROL.tsv 02.Diff_AS/NC_vs_M7B_OV/quantification/EXPERIMENT.tsv --area 1000 -gc --lower-bound 0.00 --gene-correction --output Delta_psi_isoform

Could you help me.

Thank you

ghost commented 4 years ago

Dear SUPPA Members,

I have the same issue as above.

The things is, I have four samples in different 3 groups, I need to compare between groups. This command works fine with the group contains 2 samples and 1 samples. But it would report index error while I tried 1 vs 1 compare.

suppa.py diffSplice --method empirical --input 02.Diff_AS/NC_vs_M7B_OV/AS_events/transcript_events.ioi --psi 02.Diff_AS/NC_vs_M7B_OV/isoform_CONTROL.psi 02.Diff_AS/NC_vs_M7B_OV/isoform_EXPERIMENT.psi --tpm 02.Diff_AS/NC_vs_M7B_OV/quantification/CONTROL.tsv 02.Diff_AS/NC_vs_M7B_OV/quantification/EXPERIMENT.tsv --area 1000 -gc --lower-bound 0.00 --gene-correction --output Delta_psi_isoform

Could you help me.

Thank you

Dear yazuo0204,

I think you're struggling with the same issue I was a while back. If I remember correctly SUPPA expects replicates. So when given a 1 to 1 comparison it overextends a for loop somewhere in the code. So you will have to give it 2 or more replicates. It's a while back for me so I'm not 100% sure, correct me if I'm wrong.

Kind Regards, Michel

EduEyras commented 4 years ago

Hi,

unfortunately, SUPPA will need at least 2 samples per condition. This is probably why it is not working. I hope this helps E.

On Wed, 26 Aug 2020 at 19:10, yazuo0204 notifications@github.com wrote:

Dear SUPPA Members,

I have the same issue as above.

The things is, I have four samples in different 3 groups, I need to compare between groups. This command works fine with the group contains 2 samples and 1 samples. But it would report index error while I tried 1 vs 1 compare.

suppa.py diffSplice --method empirical --input 02.Diff_AS/NC_vs_M7B_OV/AS_events/transcript_events.ioi --psi 02.Diff_AS/NC_vs_M7B_OV/isoform_CONTROL.psi 02.Diff_AS/NC_vs_M7B_OV/isoform_EXPERIMENT.psi --tpm 02.Diff_AS/NC_vs_M7B_OV/quantification/CONTROL.tsv 02.Diff_AS/NC_vs_M7B_OV/quantification/EXPERIMENT.tsv --area 1000 -gc --lower-bound 0.00 --gene-correction --output Delta_psi_isoform

Could you help me.

Thank you

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EduEyras commented 4 years ago

Yes, that's right. Suppa will expect replicates, at least 2, per condition Thanks E.

On Wed, 26 Aug 2020 at 20:21, Michel van Elswijk notifications@github.com wrote:

Dear SUPPA Members,

I have the same issue as above.

The things is, I have four samples in different 3 groups, I need to compare between groups. This command works fine with the group contains 2 samples and 1 samples. But it would report index error while I tried 1 vs 1 compare.

suppa.py diffSplice --method empirical --input 02.Diff_AS/NC_vs_M7B_OV/AS_events/transcript_events.ioi --psi 02.Diff_AS/NC_vs_M7B_OV/isoform_CONTROL.psi 02.Diff_AS/NC_vs_M7B_OV/isoform_EXPERIMENT.psi --tpm 02.Diff_AS/NC_vs_M7B_OV/quantification/CONTROL.tsv 02.Diff_AS/NC_vs_M7B_OV/quantification/EXPERIMENT.tsv --area 1000 -gc --lower-bound 0.00 --gene-correction --output Delta_psi_isoform

Could you help me.

Thank you

Dear yazuo0204,

I think you're struggling with the same issue I was a while back. If I remember correctly SUPPA expects replicates. So when given a 1 to 1 comparison it overextends a for loop somewhere in the code. So you will have to give it 2 or more replicates. It's a while back for me so I'm not 100% sure, correct me if I'm wrong.

Kind Regards, Michel

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ouyang-lang commented 3 years ago

Hi Eduardo Eyras,

Last week I dug in the code and came to the discovery that my .tpm and .psi files had some issues. Fixed the file issues then the code worked just fine 👍 Thnx for the help!

cheers

Michel

I also have the same problem. I would like to ask you how this problem was solved, and how should the tpm and psi files be handled? hope to get your reply

ouyang-lang commented 3 years ago

Excuse me, can suppa2 be able to calculate the variable shear events between a control group and an experimental group, but multiple groups cannot be calculated?

my run commmand suppa.py diffSplice -m empirical -gc -i /data/gs/data/data1/SUPPA2/ref/gencode.vM24.all.events.ioe --save_tpm_events -p treat.psi control.psi treat1.psi control1.psi -e treat.tpm control.tpm treat1.tpm control1.tpm -o project1_diffSplice

The result of this command was normal for the first two times, and an error was reported the third time.

EduEyras commented 3 years ago

Thanks for your message.

I am not sure I understand the issue that you indicate.

Are you comparing the sample groups pairwise? i.e. a pair of groups at a time?

Since you can only compare two groups at the time, one must group the expression files into the same file for each group, so that expressions from the two groups are not mixed. In your command line they are intercalated.

Could you please provide more details about what you're trying to do? That will help me identify where the problem might be

Thank you

Eduardo

On Fri, 28 May 2021 at 22:59, ouyang-lang @.***> wrote:

Excuse me, can suppa2 be able to calculate the variable shear events between a control group and an experimental group, but multiple groups cannot be calculated?

my run commmand suppa.py diffSplice -m empirical -gc -i /data/gs/data/data1/SUPPA2/ref/gencode.vM24.all.events.ioe --save_tpm_events -p treat.psi control.psi treat1.psi control1.psi -e treat.tpm control.tpm treat1.tpm control1.tpm -o project1_diffSplice

The result of this command was normal for the first two times, and an error was reported the third time.

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ouyang-lang commented 3 years ago

Thanks for your message. I am not sure I understand the issue that you indicate. Are you comparing the sample groups pairwise? i.e. a pair of groups at a time? Since you can only compare two groups at the time, one must group the expression files into the same file for each group, so that expressions from the two groups are not mixed. In your command line they are intercalated. Could you please provide more details about what you're trying to do? That will help me identify where the problem might be Thank you Eduardo … On Fri, 28 May 2021 at 22:59, ouyang-lang @.***> wrote: Excuse me, can suppa2 be able to calculate the variable shear events between a control group and an experimental group, but multiple groups cannot be calculated? my run commmand suppa.py diffSplice -m empirical -gc -i /data/gs/data/data1/SUPPA2/ref/gencode.vM24.all.events.ioe --save_tpm_events -p treat.psi control.psi treat1.psi control1.psi -e treat.tpm control.tpm treat1.tpm control1.tpm -o project1_diffSplice The result of this command was normal for the first two times, and an error was reported the third time. — You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub <#2 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/ADCZKBYDTEPLVFOFAK44YQLTP6HU3ANCNFSM4C7G7KEQ . -- Prof. E Eyras EMBL Australia Group Leader The John Curtin School of Medical Research - Australian National University https://github.com/comprna http://scholar.google.com/citations?user=LiojlGoAAAAJ

Thank you for your reply. What I want to ask is whether suppa2 can compare multiple groups. At present, the command I run is to compare only one experimental group and one control group. If I add another experimental group to the control group, It will report an error, so suppa2 can only compare the difference between a group of variable cutting events at a time.

my command and error： suppa.py diffSplice -m empirical -gc -i /data/gs/data/data1/SUPPA2/ref/gencode.vM24.all.events.ioe --save_tpm_events -p treat.psi control.psi treat1.psi control1.psi -e treat.tpm control.tpm treat1.tpm control1.tpm -o project1_diffSplice

Calculating differential analysis between conditions: treat and control Saving between_conditions_avglogtpm... Saved project1_diffSplice_avglogtpm.tab Calculating events empirical p-value: 10447 / 10446. 100.00% completed. Done!

Calculating differential analysis between conditions: control and treat1 Saving between_conditions_avglogtpm... Saved project1_diffSplice_avglogtpm.tab Calculating events empirical p-value: 10447 / 10446. 100.00% completed. Done!

Calculating differential analysis between conditions: treat1 and control1 ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range'), <traceback object at 0x7f43b2dab940>)

thank you

EduEyras commented 3 years ago

Hi,

the diffSplice option will calculate the differential splicing between adjacent conditions by default.

You can use -c to test the diff splicing between all pairs of conditions

Try to run the comparison between treat1 and control1 separately to see whether it works. It could be that there is something failing in this specific comparison.

You should have at least two replicates per condition, and have the same formats for all of the files, and event IDs in the .ioe and .psi files.

It is strange that it fails only in one of the comparisons, so there might be something different in control1 files that makes it fail.

I hope this helps

best

Eduardo

On Sat, 29 May 2021 at 12:19, ouyang-lang @.***> wrote:

Thanks for your message. I am not sure I understand the issue that you indicate. Are you comparing the sample groups pairwise? i.e. a pair of groups at a time? Since you can only compare two groups at the time, one must group the expression files into the same file for each group, so that expressions from the two groups are not mixed. In your command line they are intercalated. Could you please provide more details about what you're trying to do? That will help me identify where the problem might be Thank you Eduardo … <#m-373163937313398440> On Fri, 28 May 2021 at 22:59, ouyang-lang @.***> wrote: Excuse me, can suppa2 be able to calculate the variable shear events between a control group and an experimental group, but multiple groups cannot be calculated? my run commmand suppa.py diffSplice -m empirical -gc -i /data/gs/data/data1/SUPPA2/ref/gencode.vM24.all.events.ioe --save_tpm_events -p treat.psi control.psi treat1.psi control1.psi -e treat.tpm control.tpm treat1.tpm control1.tpm -o project1_diffSplice The result of this command was normal for the first two times, and an error was reported the third time. — You are receiving this because you modified the open/close state. Reply to this email directly, view it on GitHub <#2 (comment) https://github.com/comprna/SUPPA/issues/2#issuecomment-850401589>, or unsubscribe https://github.com/notifications/unsubscribe-auth/ADCZKBYDTEPLVFOFAK44YQLTP6HU3ANCNFSM4C7G7KEQ . -- Prof. E Eyras EMBL Australia Group Leader The John Curtin School of Medical Research - Australian National University https://github.com/comprna http://scholar.google.com/citations?user=LiojlGoAAAAJ

Thank you for your reply. What I want to ask is whether suppa2 can compare multiple groups. At present, the command I run is to compare only one experimental group and one control group. If I add another experimental group to the control group, It will report an error, so suppa2 can only compare the difference between a group of variable cutting events at a time.

my command and error： suppa.py diffSplice -m empirical -gc -i /data/gs/data/data1/SUPPA2/ref/gencode.vM24.all.events.ioe --save_tpm_events -p treat.psi control.psi treat1.psi control1.psi -e treat.tpm control.tpm treat1.tpm control1.tpm -o project1_diffSplice

Calculating differential analysis between conditions: treat and control Saving between_conditions_avglogtpm... Saved project1_diffSplice_avglogtpm.tab Calculating events empirical p-value: 10447 / 10446. 100.00% completed. Done!

Calculating differential analysis between conditions: control and treat1 Saving between_conditions_avglogtpm... Saved project1_diffSplice_avglogtpm.tab Calculating events empirical p-value: 10447 / 10446. 100.00% completed. Done!

Calculating differential analysis between conditions: treat1 and control1 ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range'), <traceback object at 0x7f43b2dab940>)

thank you

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Xtest2000 commented 3 years ago

Dear SUPPA users and developers. I have the same issue as ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range'), <traceback object at 0x7f3dc9f574c0>)

this is my code please help me. ` suppa.py diffSplice --method classical --input ./test_suppa/suppa_ioi_ioe/test_suppa_re_zea_gtf.ioi --psi ./test_suppa/out_psi/142/B73_isoform.psi ./test_suppa/out_psi/147/Mon_isoform.psi --tpm ./total_tpm/total_B.tpm ./total_tpm/total_M.tpm --area 1000 --lower-bound 0 -pa -gc -o ./test_suppa/compare_out/b_m

./test_suppa/out_psi/147/Mon_isoform.psi 147_sample147 145_sample145 Zm00001d022649;Zm00001d022649_T001 1.0 1.0 Zm00001d022650;Zm00001d022650_T001 1.0 1.0 Zm00001d027269;Zm00001d027269_T001 1.0 nan Zm00001d027275;Zm00001d027275_T001 1.0 1.0 Zm00001d027283;Zm00001d027283_T001 1.0 1.0 Zm00001d027286;Zm00001d027286_T001 1.0 1.0 Zm00001d027290;Zm00001d027290_T001 1.0 1.0 Zm00001d027292;Zm00001d027292_T001 1.0 1.0 Zm00001d027323;Zm00001d027323_T001 1.0 1.0 Zm00001d027338;Zm00001d027338_T001 1.0 1.0 Zm00001d027343;Zm00001d027343_T001 1.0 1.0 Zm00001d027348;Zm00001d027348_T001 1.0 1.0 Zm00001d027365;Zm00001d027365_T001 1.0 1.0 Zm00001d027368;Zm00001d027368_T001 1.0 1.0 Zm00001d027375;Zm00001d027375_T001 1.0 1.0 Zm00001d027377;Zm00001d027377_T001 1.0 1.0 Zm00001d027393;Zm00001d027393_T001 1.0 1.0 Zm00001d027396;Zm00001d027396_T001 nan 1.0 ENSRNA049440088;ENSRNA049440088-T1 nan 1.0

./test_suppa/out_psi/142/B73_isoform.psi 143_sample143 142_sample142 Zm00001d027250;Zm00001d027250_T001 1.0 1.0 Zm00001d022649;Zm00001d022649_T001 1.0 1.0 Zm00001d022650;Zm00001d022650_T001 1.0 1.0 Zm00001d027269;Zm00001d027269_T001 1.0 1.0 Zm00001d027275;Zm00001d027275_T001 1.0 1.0 Zm00001d027283;Zm00001d027283_T001 1.0 1.0 Zm00001d027286;Zm00001d027286_T001 1.0 1.0 Zm00001d027290;Zm00001d027290_T001 1.0 1.0 Zm00001d027292;Zm00001d027292_T001 1.0 1.0 Zm00001d027323;Zm00001d027323_T001 1.0 1.0 Zm00001d027338;Zm00001d027338_T001 1.0 1.0 Zm00001d027343;Zm00001d027343_T001 1.0 1.0 Zm00001d027348;Zm00001d027348_T001 1.0 1.0 Zm00001d027365;Zm00001d027365_T001 1.0 1.0 Zm00001d027368;Zm00001d027368_T001 1.0 1.0 Zm00001d027375;Zm00001d027375_T001 1.0 1.0 Zm00001d027377;Zm00001d027377_T001 1.0 1.0 Zm00001d027393;Zm00001d027393_T001 1.0 1.0 Zm00001d027409;Zm00001d027409_T001 1.0 1.0

./total_tpm/total_B.tpm 143_sample143 142_sample142 Zm00001d027250_T001 2.932164 2.059065 Zm00001d022649_T001 5.492334 2.695062 Zm00001d022650_T001 0.810536 1.859295 Zm00001d027269_T001 0.209009 0.006398 Zm00001d027275_T001 3.312964 2.479482 Zm00001d027283_T001 39.815929 76.066856 Zm00001d027286_T001 6.87519 8.410566 Zm00001d027287_T001 0.894176 0.511115 ENSRNA049464979-T1 5.87035 5.163274 Zm00001d027290_T001 981.341797 1009.847595 Zm00001d027291_T001 6.661369 7.210714 Zm00001d027292_T001 8.410386 10.049859 Zm00001d027323_T001 82.071609 76.822533 Zm00001d027332_T001 3.458629 4.820723 Zm00001d027338_T001 39.187923 18.885529 Zm00001d027341_T001 1.276371 1.791971 Zm00001d027343_T001 2.105327 2.358803 Zm00001d027345_T001 18.746349 19.943068 Zm00001d027348_T001 35.370434 41.931633

./total_tpm/total_M.tpm 147_sample147 145_sample145 Zm00001d022649_T001 6.316686 7.814531 Zm00001d022650_T001 1.429051 1.507863 Zm00001d027269_T001 0.04071 nan Zm00001d027275_T001 1.773664 1.24462 Zm00001d027283_T001 92.60096 59.442051 Zm00001d027286_T001 27.008678 23.503626 Zm00001d027287_T001 0.979461 0.987105 ENSRNA049464979-T1 6.103252 3.267408 Zm00001d027290_T001 4407.647949 2952.158936 Zm00001d027291_T001 35.534882 22.6513 Zm00001d027292_T001 12.432271 14.08917 Zm00001d027323_T001 101.630959 104.392921 Zm00001d027332_T001 29.724955 16.394369 Zm00001d027338_T001 9.048421 19.162205 Zm00001d027341_T001 1.42377 1.373371 Zm00001d027343_T001 2.431063 2.64971 Zm00001d027345_T001 17.310293 19.110027 Zm00001d027348_T001 42.347343 31.663141 Zm00001d027365_T001 56.368389 60.761173

head -n 10 ./test_suppa/suppa_ioi_ioe/test_suppa_re_zea_gtf.ioi seqname gene_id isoform_id inclusion_transcripts total_transcripts 1 Zm00001d027230 Zm00001d027230;Zm00001d027230_T001 Zm00001d027230_T001 Zm00001d027230_T001 1 Zm00001d027235 Zm00001d027235;Zm00001d027235_T001 Zm00001d027235_T001 Zm00001d027235_T001 1 Zm00001d027245 Zm00001d027245;Zm00001d027245_T001 Zm00001d027245_T001 Zm00001d027245_T001 1 Zm00001d027250 Zm00001d027250;Zm00001d027250_T001 Zm00001d027250_T001 Zm00001d027250_T001 1 Zm00001d027253 Zm00001d027253;Zm00001d027253_T001 Zm00001d027253_T001 Zm00001d027253_T001 1 Zm00001d027254 Zm00001d027254;Zm00001d027254_T001 Zm00001d027254_T001 Zm00001d027254_T001 1 Zm00001d027256 Zm00001d027256;Zm00001d027256_T001 Zm00001d027256_T001 Zm00001d027256_T001 1 Zm00001d022649 Zm00001d022649;Zm00001d022649_T001 Zm00001d022649_T001 Zm00001d022649_T001 1 Zm00001d027258 Zm00001d027258;Zm00001d027258_T001 Zm00001d027258_T001 Zm00001d027258_T001 `

EduEyras commented 3 years ago

Dear Xiu,

thanks for your email.

It seems that all your genes have one single transcript. As a consequence, all PSIs are equal to 1, and there is no way to study differential splicing, at event or isoform levels.

Try using the option "-p | --pool-genes:" for generateEvents so that if transcripts overlap each other in gene locus and exon content, they will be grouped together into the same gene locus. This will provide the information to have multiple transcripts per gene, and calculate changes in PSI values.

I hope this helps

Eduardo

On Fri, 15 Oct 2021 at 17:07, xiu li @.***> wrote:

Dear SUPPA users and developers. I have the same issue as ERROR:main:Unknown error: (<class 'IndexError'>, IndexError('list index out of range'), <traceback object at 0x7f3dc9f574c0>)

this is my code please help me. ` suppa.py diffSplice --method classical --input ./test_suppa/suppa_ioi_ioe/test_suppa_re_zea_gtf.ioi --psi ./test_suppa/out_psi/142/B73_isoform.psi ./test_suppa/out_psi/147/Mon_isoform.psi --tpm ./total_tpm/total_B.tpm ./total_tpm/total_M.tpm --area 1000 --lower-bound 0 -pa -gc -o ./test_suppa/compare_out/b_m

./test_suppa/out_psi/147/Mon_isoform.psi 147_sample147 145_sample145 Zm00001d022649;Zm00001d022649_T001 1.0 1.0 Zm00001d022650;Zm00001d022650_T001 1.0 1.0 Zm00001d027269;Zm00001d027269_T001 1.0 nan Zm00001d027275;Zm00001d027275_T001 1.0 1.0 Zm00001d027283;Zm00001d027283_T001 1.0 1.0 Zm00001d027286;Zm00001d027286_T001 1.0 1.0 Zm00001d027290;Zm00001d027290_T001 1.0 1.0 Zm00001d027292;Zm00001d027292_T001 1.0 1.0 Zm00001d027323;Zm00001d027323_T001 1.0 1.0 Zm00001d027338;Zm00001d027338_T001 1.0 1.0 Zm00001d027343;Zm00001d027343_T001 1.0 1.0 Zm00001d027348;Zm00001d027348_T001 1.0 1.0 Zm00001d027365;Zm00001d027365_T001 1.0 1.0 Zm00001d027368;Zm00001d027368_T001 1.0 1.0 Zm00001d027375;Zm00001d027375_T001 1.0 1.0 Zm00001d027377;Zm00001d027377_T001 1.0 1.0 Zm00001d027393;Zm00001d027393_T001 1.0 1.0 Zm00001d027396;Zm00001d027396_T001 nan 1.0 ENSRNA049440088;ENSRNA049440088-T1 nan 1.0

./test_suppa/out_psi/142/B73_isoform.psi 143_sample143 142_sample142 Zm00001d027250;Zm00001d027250_T001 1.0 1.0 Zm00001d022649;Zm00001d022649_T001 1.0 1.0 Zm00001d022650;Zm00001d022650_T001 1.0 1.0 Zm00001d027269;Zm00001d027269_T001 1.0 1.0 Zm00001d027275;Zm00001d027275_T001 1.0 1.0 Zm00001d027283;Zm00001d027283_T001 1.0 1.0 Zm00001d027286;Zm00001d027286_T001 1.0 1.0 Zm00001d027290;Zm00001d027290_T001 1.0 1.0 Zm00001d027292;Zm00001d027292_T001 1.0 1.0 Zm00001d027323;Zm00001d027323_T001 1.0 1.0 Zm00001d027338;Zm00001d027338_T001 1.0 1.0 Zm00001d027343;Zm00001d027343_T001 1.0 1.0 Zm00001d027348;Zm00001d027348_T001 1.0 1.0 Zm00001d027365;Zm00001d027365_T001 1.0 1.0 Zm00001d027368;Zm00001d027368_T001 1.0 1.0 Zm00001d027375;Zm00001d027375_T001 1.0 1.0 Zm00001d027377;Zm00001d027377_T001 1.0 1.0 Zm00001d027393;Zm00001d027393_T001 1.0 1.0 Zm00001d027409;Zm00001d027409_T001 1.0 1.0

./total_tpm/total_B.tpm 143_sample143 142_sample142 Zm00001d027250_T001 2.932164 2.059065 Zm00001d022649_T001 5.492334 2.695062 Zm00001d022650_T001 0.810536 1.859295 Zm00001d027269_T001 0.209009 0.006398 Zm00001d027275_T001 3.312964 2.479482 Zm00001d027283_T001 39.815929 76.066856 Zm00001d027286_T001 6.87519 8.410566 Zm00001d027287_T001 0.894176 0.511115 ENSRNA049464979-T1 5.87035 5.163274 Zm00001d027290_T001 981.341797 1009.847595 Zm00001d027291_T001 6.661369 7.210714 Zm00001d027292_T001 8.410386 10.049859 Zm00001d027323_T001 82.071609 76.822533 Zm00001d027332_T001 3.458629 4.820723 Zm00001d027338_T001 39.187923 18.885529 Zm00001d027341_T001 1.276371 1.791971 Zm00001d027343_T001 2.105327 2.358803 Zm00001d027345_T001 18.746349 19.943068 Zm00001d027348_T001 35.370434 41.931633

./total_tpm/total_M.tpm 147_sample147 145_sample145 Zm00001d022649_T001 6.316686 7.814531 Zm00001d022650_T001 1.429051 1.507863 Zm00001d027269_T001 0.04071 nan Zm00001d027275_T001 1.773664 1.24462 Zm00001d027283_T001 92.60096 59.442051 Zm00001d027286_T001 27.008678 23.503626 Zm00001d027287_T001 0.979461 0.987105 ENSRNA049464979-T1 6.103252 3.267408 Zm00001d027290_T001 4407.647949 2952.158936 Zm00001d027291_T001 35.534882 22.6513 Zm00001d027292_T001 12.432271 14.08917 Zm00001d027323_T001 101.630959 104.392921 Zm00001d027332_T001 29.724955 16.394369 Zm00001d027338_T001 9.048421 19.162205 Zm00001d027341_T001 1.42377 1.373371 Zm00001d027343_T001 2.431063 2.64971 Zm00001d027345_T001 17.310293 19.110027 Zm00001d027348_T001 42.347343 31.663141 Zm00001d027365_T001 56.368389 60.761173

head -n 10 ./test_suppa/suppa_ioi_ioe/test_suppa_re_zea_gtf.ioi seqname gene_id isoform_id inclusion_transcripts total_transcripts 1 Zm00001d027230 Zm00001d027230;Zm00001d027230_T001 Zm00001d027230_T001 Zm00001d027230_T001 1 Zm00001d027235 Zm00001d027235;Zm00001d027235_T001 Zm00001d027235_T001 Zm00001d027235_T001 1 Zm00001d027245 Zm00001d027245;Zm00001d027245_T001 Zm00001d027245_T001 Zm00001d027245_T001 1 Zm00001d027250 Zm00001d027250;Zm00001d027250_T001 Zm00001d027250_T001 Zm00001d027250_T001 1 Zm00001d027253 Zm00001d027253;Zm00001d027253_T001 Zm00001d027253_T001 Zm00001d027253_T001 1 Zm00001d027254 Zm00001d027254;Zm00001d027254_T001 Zm00001d027254_T001 Zm00001d027254_T001 1 Zm00001d027256 Zm00001d027256;Zm00001d027256_T001 Zm00001d027256_T001 Zm00001d027256_T001 1 Zm00001d022649 Zm00001d022649;Zm00001d022649_T001 Zm00001d022649_T001 Zm00001d022649_T001 1 Zm00001d027258 Zm00001d027258;Zm00001d027258_T001 Zm00001d027258_T001 Zm00001d027258_T001 `

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comprna / SUPPA

Differential isoform usage #2

Dear SUPPA users and developers,