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  • fkrueger
    replied
    Hi there, regarding your question 1: Trim Galore runs Cutadapt with the option -a, which does the following:
    Code:
    -a ADAPTER, --adapter=ADAPTER
                            Sequence of an adapter that was ligated to the 3' end.
                            The adapter itself and anything that follows is
                            trimmed. If the adapter sequence ends with the '$'
                            character, the adapter is anchored to the end of the
                            read and only found if it is a suffix of the read.
    This means that indeed once the adapter is found anywhere within the read anything from that point and further 3' is removed. This is normally what you want to be doing. I guess if you wanted to keep the sequences further 3' to investigate it you would need to write something custom.
    As a side not too should be able to leave out the -a SEQUENCE here completely because Trim Galore should auto-detect your smallRNA adapter sequence. (but since they are the same it won't hurt I guess).

    Regarding your second question the current development version has an option to remove reads with too many Ns but I am afraid it doesn't currently have the option to trim Ns from the ends of reads but if this would really help you it could add it in. Often a single N is not going to make a difference in terms of mapping, and it might in this case also change the length of the small RNA-species. So yea if you think it would be absolutely required I could add it. Best, Felix

    Leave a comment:


  • xuguorong
    replied
    Recently I am using your tool Trim Galore to trim the adapter string from our miRNA sequencing data. It is amazing tool and very fast! Thanks a lot for your great job! When I looked into the resulting file, I found two issues and I could not figure out.

    Question 1:
    1) The raw sequencing is:
    NCCCGTGGTGGAATTCTCGGGTGCCAAGGAACTCCAGTCACGTGGCCATC

    2) And the adapter string:
    TGGAATTCTCGGGTGCCAAGG

    3) After I run the command:
    trim_galore --path_to_cutadapt /path/to/cutadapt --clip_R1 1 --length 5 -q 10 -a TGGAATTCTCGGGTGCCAAGG $inputFile".fastq" $inputFile".trim.fastq"

    4) Then, I got the resulting string:
    CCCGTGG

    I think the trimming algorithm only kept the left short sequence and ignored the right long sequence. I am not sure if Trim Galore can keep the longer sequence by changing the parameters.

    Question 2:
    1) The raw sequencings are:
    read1: NCCCGTGGTGGAATTCTCGGGTGCCAAGGAACTCCAGTCACGTGGCCATC
    read2: TCCCGTGGTGGAATTCTCGGGTGCCAAGGAACTCCAGTCACGTGGCCATC

    If I use the option "--clip_R1 1 ", the first nucleotide "N" in the read1 will be trimmed. But the first nucleotide "T" in the read2 will be also trimmed. Do you have option which can just trim "N" from reads?

    Your response would be really appreciated!

    Leave a comment:


  • fkrueger
    replied
    In that case you might add in the other scenarios and see whether it makes a big difference.

    When you look at non-CG methylation levels in general (such as from the summary report), do you see very high levels that are indicative of conversion problems?

    Frankly we got mixed results from using this method of looking at the filled-in position. Sometimes the values were very low (e.g. around 0.2% for the Booth et al data), but it sometimes came back with 25% methylation at that position which was clearly some sort of artefact since the overall level of non-CG methylation was 1% or so. So yea I would be a little careful with the values you get from looking at this position. If you take more global values such as (non-CG?) methylation levels over CpG islands as a measure, or possibly methylation of chrMT you might get better estimates for non-conversion. Cheers, Felix

    Leave a comment:


  • crazyhottommy
    replied
    Originally posted by fkrueger View Post
    I would say in theory yes, but since we were working in mammalian genomes where you would expect a non-CG methylation of <1% we just assumed that the C before the CG is always converted.

    While looking at the script I noticed that there is another place you need to change, because when we did this back in 2011 our reads were 40 bp long.

    So you need to locate the lines (should be two times) that say
    Code:
    my $poi = 40 - length($rest)-3;
    and change the 40 to your read length, or even better change it to

    Code:
    length($sequence)
    so that this works for any read length.
    Thanks, I noticed that as well and change the length accordingly.

    We are checking the bisulfite conversion rate.
    Although in the human genome, non-CpGs are unmethylated, if there is a bisulfite conversion failure, they will remain as Cs, and not converted to Ts.
    we will miss a lot of sequences with CCG and probably few of the CTG

    Leave a comment:


  • fkrueger
    replied
    I would say in theory yes, but since we were working in mammalian genomes where you would expect a non-CG methylation of <1% we just assumed that the C before the CG is always converted.

    While looking at the script I noticed that there is another place you need to change, because when we did this back in 2011 our reads were 40 bp long.

    So you need to locate the lines (should be two times) that say
    Code:
    my $poi = 40 - length($rest)-3;
    and change the 40 to your read length, or even better change it to

    Code:
    length($sequence)
    so that this works for any read length.

    Leave a comment:


  • crazyhottommy
    replied
    check 4 cases?

    Originally posted by fkrueger View Post
    Here it is. It is looking for an overlap with the adapter from the end, as it stands 5bp (you can change this in this line: my $required_adapter_overlap = 5; ), and should then give you some useful output about the conversion efficiency at the end. You may want to run it with a few different lengths to see if that makes a difference. Let me know if there are any questions. Cheers, Felix
    Thanks for the script.
    what I thought:

    I will need to check CCG + adaptor or TCG + adaptor for unmethylated filled-in Cs.
    and CTG + adaptor or TTG + adaptor for methylated filled-in Cs.

    e.g. a full-length read:

    TGGATGTTGGTTGTGGTTAGTATTCGAGATCGGAAG

    It stats with TGG, so it is not methylated in the genome, but check at the bold part, it start with TCG, so the filed-in C are unmethylated (not converted successfully by bisulfite)

    I only saw in your script you checked TTG + adaptor and TCG+ adaptor.
    Do I need to check CTG and CCG as well?

    Please let me know if I am correct or not RRBS is new for me.

    Leave a comment:


  • fkrueger
    replied
    Here it is. It is looking for an overlap with the adapter from the end, as it stands 5bp (you can change this in this line: my $required_adapter_overlap = 5; ), and should then give you some useful output about the conversion efficiency at the end. You may want to run it with a few different lengths to see if that makes a difference. Let me know if there are any questions. Cheers, Felix
    Attached Files

    Leave a comment:


  • crazyhottommy
    replied
    Originally posted by fkrueger View Post
    Hi Ming,

    I used to have a script that would do this, I can send it over tomorrow if I manage to find it. Best, Felix
    That would be very helpful!
    Thanks!

    Leave a comment:


  • fkrueger
    replied
    Hi Ming,

    I used to have a script that would do this, I can send it over tomorrow if I manage to find it. Best, Felix

    Leave a comment:


  • crazyhottommy
    replied
    most of my samples have a spike-in lambda unmethylated DNA. I mapped them to lambda genome and calculated the efficiency. I do not have spike-in for one sample. I have to check the conversion efficiency for the red unmethylated C (see below) introduced at the 3’ when end-repair was done. for my bismark pipeline, trim_galore will remove this unmehtylated C if there is adaptor contamination. I read it here http://www.bioinformatics.babraham.a...RRBS_Guide.pdf



    How do I calculate it? I need to take the fastqs and trim-off the adaptors, but not the last 2 bases at 3', map with bismark, and check how many Ts are at the end of the each read? Is there any script to do so?

    Thanks, Ming

    Leave a comment:


  • fkrueger
    replied
    Glad that it seems to be working now though! Best, Felix

    Leave a comment:


  • bowen
    replied
    Hi Felix,
    Although this may not be advisable to others, I decided to # out the installed python on my .bash_profile and install python with brew. Things are working fine now. Sorry for the trouble. maybe was an IDLE issue, not sure. again, i appreciate your time and all the best to you.

    Leave a comment:


  • fkrueger
    replied
    To be perfectly honest I really don't know why your Python threads are slowing down to 0%, all individual pieces of software seem to run fine (and to completion apart from this sample). Maybe someone else can chip in here?

    If it just doesn't finished why don't you just modify the Cutadapt command you tried above to run as paired-end sample (this might require you specify and adapter 2, but you can use the same sequence for that). Sorry I can't be of more help, I have never seen such a behaviour before...

    Leave a comment:


  • bowen
    replied
    so now my question is are the processes that are running with 0% still going, and I should just be patient?

    Leave a comment:


  • bowen
    replied
    am running it now. here's the .txt report of one that is hanging. then followed by .txt report of the one that ran correctly:

    hanging:
    SUMMARISING RUN PARAMETERS
    ==========================
    Input filename: index21_GTTTCG_L001-L002_R1_001.fastq
    Trimming mode: paired-end
    Trim Galore version: 0.4.1
    Cutadapt version: 1.9.1
    Quality Phred score cutoff: 20
    Quality encoding type selected: ASCII+33
    Adapter sequence: 'AGATCGGAAGAGC' (Illumina TruSeq, Sanger iPCR; auto-detected)
    Maximum trimming error rate: 0.1 (default)
    Minimum required adapter overlap (stringency): 1 bp
    Minimum required sequence length for both reads before a sequence pair gets removed: 20 bp

    completed: two different .txt files, as it was a paired set

    SUMMARISING RUN PARAMETERS
    ==========================
    Input filename: index23_GAGTGG_L001-L002_R1_001.fastq
    Trimming mode: paired-end
    Trim Galore version: 0.4.1
    Cutadapt version: 1.9.1
    Quality Phred score cutoff: 20
    Quality encoding type selected: ASCII+33
    Adapter sequence: 'AGATCGGAAGAGC' (Illumina TruSeq, Sanger iPCR; auto-detected)
    Maximum trimming error rate: 0.1 (default)
    Minimum required adapter overlap (stringency): 1 bp
    Minimum required sequence length for both reads before a sequence pair gets removed: 20 bp


    This is cutadapt 1.9.1 with Python 2.7.10
    Command line parameters: -f fastq -e 0.1 -q 20 -O 1 -a AGATCGGAAGAGC index23_GAGTGG_L001-L002_R1_001.fastq
    Trimming 1 adapter with at most 10.0% errors in single-end mode ...
    Finished in 850.42 s (30 us/read; 2.01 M reads/minute).

    === Summary ===

    Total reads processed: 28,485,339
    Reads with adapters: 16,948,174 (59.5%)
    Reads written (passing filters): 28,485,339 (100.0%)

    Total basepairs processed: 3,589,152,714 bp
    Quality-trimmed: 6,608,523 bp (0.2%)
    Total written (filtered): 3,299,244,643 bp (91.9%)

    === Adapter 1 ===

    Sequence: AGATCGGAAGAGC; Type: regular 3'; Length: 13; Trimmed: 16948174 times.

    No. of allowed errors:
    0-9 bp: 0; 10-13 bp: 1

    Bases preceding removed adapters:
    A: 17.7%
    C: 32.2%
    G: 32.3%
    T: 17.5%
    none/other: 0.3%

    Overview of removed sequences
    length count expect max.err error counts
    1 3822853 7121334.8 0 3822853
    2 1342103 1780333.7 0 1342103
    3 562083 445083.4 0 562083
    4 338904 111270.9 0 338904
    5 305930 27817.7 0 305930
    6 292726 6954.4 0 292726
    7 293029 1738.6 0 293029
    8 295266 434.7 0 295266
    9 314323 108.7 0 313753 570
    10 314996 27.2 1 308528 6468
    11 294907 6.8 1 288423 6484
    12 301352 1.7 1 294428 6924
    13 306345 0.4 1 298669 7676
    14 308686 0.4 1 301195 7491
    15 292770 0.4 1 285216 7554
    16 297400 0.4 1 289598 7802
    17 293038 0.4 1 285169 7869
    18 289457 0.4 1 281581 7876
    19 299679 0.4 1 291706 7973
    20 297202 0.4 1 289126 8076
    21 300660 0.4 1 292170 8490
    22 286125 0.4 1 278406 7719
    23 269566 0.4 1 261467 8099
    24 274398 0.4 1 266280 8118
    25 264378 0.4 1 257631 6747
    26 255768 0.4 1 248915 6853
    27 256549 0.4 1 249833 6716
    28 257268 0.4 1 250252 7016
    29 244909 0.4 1 238288 6621
    30 234995 0.4 1 229910 5085
    31 229964 0.4 1 224161 5803
    32 224294 0.4 1 219567 4727
    33 205954 0.4 1 201559 4395
    34 201649 0.4 1 197346 4303
    35 197177 0.4 1 192772 4405
    36 166376 0.4 1 162610 3766
    37 164498 0.4 1 160888 3610
    38 154155 0.4 1 150759 3396
    39 149720 0.4 1 146403 3317
    40 147538 0.4 1 144226 3312
    41 139000 0.4 1 135824 3176
    42 117928 0.4 1 115068 2860
    43 150887 0.4 1 147697 3190
    44 74900 0.4 1 73163 1737
    45 85284 0.4 1 83407 1877
    46 84122 0.4 1 82252 1870
    47 79113 0.4 1 77412 1701
    48 71166 0.4 1 69632 1534
    49 71476 0.4 1 69884 1592
    50 64563 0.4 1 63123 1440
    51 62861 0.4 1 61509 1352
    52 53459 0.4 1 52311 1148
    53 50043 0.4 1 48999 1044
    54 46537 0.4 1 45491 1046
    55 41425 0.4 1 40523 902
    56 33841 0.4 1 33125 716
    57 30992 0.4 1 30383 609
    58 27455 0.4 1 26913 542
    59 27536 0.4 1 26969 567
    60 23792 0.4 1 23350 442
    61 21538 0.4 1 21073 465
    62 18972 0.4 1 18504 468
    63 18545 0.4 1 18096 449
    64 15370 0.4 1 14978 392
    65 14415 0.4 1 14016 399
    66 12971 0.4 1 12621 350
    67 11121 0.4 1 10788 333
    68 10333 0.4 1 10010 323
    69 9483 0.4 1 9121 362
    70 8785 0.4 1 8313 472
    71 8295 0.4 1 7621 674
    72 7952 0.4 1 6994 958
    73 8569 0.4 1 6772 1797
    74 11545 0.4 1 6819 4726
    75 40013 0.4 1 7295 32718
    76 23307 0.4 1 21496 1811
    77 4013 0.4 1 3591 422
    78 1490 0.4 1 1251 239
    79 792 0.4 1 628 164
    80 599 0.4 1 448 151
    81 481 0.4 1 342 139
    82 463 0.4 1 314 149
    83 445 0.4 1 281 164
    84 410 0.4 1 248 162
    85 358 0.4 1 212 146
    86 346 0.4 1 180 166
    87 300 0.4 1 138 162
    88 283 0.4 1 137 146
    89 249 0.4 1 108 141
    90 224 0.4 1 100 124
    91 221 0.4 1 89 132
    92 203 0.4 1 64 139
    93 180 0.4 1 61 119
    94 143 0.4 1 36 107
    95 157 0.4 1 34 123
    96 160 0.4 1 30 130
    97 124 0.4 1 25 99
    98 133 0.4 1 24 109
    99 108 0.4 1 17 91
    100 119 0.4 1 6 113
    101 109 0.4 1 15 94
    102 100 0.4 1 4 96
    103 95 0.4 1 8 87
    104 92 0.4 1 6 86
    105 113 0.4 1 1 112
    106 113 0.4 1 5 108
    107 118 0.4 1 5 113
    108 123 0.4 1 2 121
    109 121 0.4 1 2 119
    110 134 0.4 1 2 132
    111 119 0.4 1 5 114
    112 127 0.4 1 11 116
    113 116 0.4 1 14 102
    114 139 0.4 1 13 126
    115 126 0.4 1 5 121
    116 123 0.4 1 7 116
    117 157 0.4 1 5 152
    118 161 0.4 1 2 159
    119 167 0.4 1 2 165
    120 205 0.4 1 6 199
    121 273 0.4 1 8 265
    122 250 0.4 1 7 243
    123 428 0.4 1 10 418
    124 774 0.4 1 2 772
    125 1930 0.4 1 6 1924
    126 3376 0.4 1 4 3372


    RUN STATISTICS FOR INPUT FILE: index23_GAGTGG_L001-L002_R1_001.fastq
    =============================================
    28485339 sequences processed in total

    completed 2:

    SUMMARISING RUN PARAMETERS
    ==========================
    Input filename: index23_GAGTGG_L001-L002_R2_001.fastq
    Trimming mode: paired-end
    Trim Galore version: 0.4.1
    Cutadapt version: 1.9.1
    Quality Phred score cutoff: 20
    Quality encoding type selected: ASCII+33
    Adapter sequence: 'AGATCGGAAGAGC' (Illumina TruSeq, Sanger iPCR; auto-detected)
    Maximum trimming error rate: 0.1 (default)
    Minimum required adapter overlap (stringency): 1 bp
    Minimum required sequence length for both reads before a sequence pair gets removed: 20 bp


    This is cutadapt 1.9.1 with Python 2.7.10
    Command line parameters: -f fastq -e 0.1 -q 20 -O 1 -a AGATCGGAAGAGC index23_GAGTGG_L001-L002_R2_001.fastq
    Trimming 1 adapter with at most 10.0% errors in single-end mode ...
    Finished in 894.72 s (31 us/read; 1.91 M reads/minute).

    === Summary ===

    Total reads processed: 28,485,339
    Reads with adapters: 18,157,389 (63.7%)
    Reads written (passing filters): 28,485,339 (100.0%)

    Total basepairs processed: 3,589,152,714 bp
    Quality-trimmed: 11,062,843 bp (0.3%)
    Total written (filtered): 3,294,805,118 bp (91.8%)

    === Adapter 1 ===

    Sequence: AGATCGGAAGAGC; Type: regular 3'; Length: 13; Trimmed: 18157389 times.

    No. of allowed errors:
    0-9 bp: 0; 10-13 bp: 1

    Bases preceding removed adapters:
    A: 18.4%
    C: 28.5%
    G: 39.5%
    T: 13.3%
    none/other: 0.3%

    Overview of removed sequences
    length count expect max.err error counts
    1 4668833 7121334.8 0 4668833
    2 1551414 1780333.7 0 1551414
    3 711239 445083.4 0 711239
    4 344741 111270.9 0 344741
    5 310825 27817.7 0 310825
    6 297044 6954.4 0 297044
    7 303310 1738.6 0 303310
    8 287034 434.7 0 287034
    9 333133 108.7 0 332495 638
    10 312592 27.2 1 307059 5533
    11 286908 6.8 1 281440 5468
    12 324224 1.7 1 317722 6502
    13 281828 0.4 1 275947 5881
    14 364803 0.4 1 357092 7711
    15 248030 0.4 1 242074 5956
    16 294938 0.4 1 288213 6725
    17 374860 0.4 1 366200 8660
    18 212737 0.4 1 207831 4906
    19 319230 0.4 1 312760 6470
    20 271034 0.4 1 264706 6328
    21 280398 0.4 1 273829 6569
    22 283482 0.4 1 276986 6496
    23 273291 0.4 1 266428 6863
    24 316548 0.4 1 308790 7758
    25 217447 0.4 1 211611 5836
    26 259869 0.4 1 253384 6485
    27 271923 0.4 1 265176 6747
    28 264485 0.4 1 258666 5819
    29 221191 0.4 1 215980 5211
    30 285155 0.4 1 279264 5891
    31 181353 0.4 1 177458 3895
    32 214689 0.4 1 210267 4422
    33 214503 0.4 1 210240 4263
    34 212749 0.4 1 208272 4477
    35 177297 0.4 1 173613 3684
    36 173905 0.4 1 170248 3657
    37 164687 0.4 1 161249 3438
    38 162012 0.4 1 158619 3393
    39 139466 0.4 1 136617 2849
    40 139275 0.4 1 136201 3074
    41 133976 0.4 1 131087 2889
    42 143008 0.4 1 139705 3303
    43 98394 0.4 1 96109 2285
    44 101487 0.4 1 99195 2292
    45 108791 0.4 1 106222 2569
    46 80329 0.4 1 78401 1928
    47 76402 0.4 1 74674 1728
    48 74376 0.4 1 72843 1533
    49 64568 0.4 1 63238 1330
    50 67016 0.4 1 65615 1401
    51 72066 0.4 1 70715 1351
    52 43802 0.4 1 42900 902
    53 51200 0.4 1 50281 919
    54 39798 0.4 1 38959 839
    55 41369 0.4 1 40582 787
    56 34005 0.4 1 33317 688
    57 29637 0.4 1 29067 570
    58 28815 0.4 1 28227 588
    59 25798 0.4 1 25286 512
    60 24621 0.4 1 24062 559
    61 22089 0.4 1 21540 549
    62 20351 0.4 1 19643 708
    63 18922 0.4 1 18103 819
    64 17531 0.4 1 16360 1171
    65 17085 0.4 1 14933 2152
    66 18828 0.4 1 14404 4424
    67 50417 0.4 1 15488 34929
    68 68997 0.4 1 64574 4423
    69 10693 0.4 1 9940 753
    70 3339 0.4 1 3028 311
    71 1922 0.4 1 1694 228
    72 1216 0.4 1 1031 185
    73 910 0.4 1 748 162
    74 824 0.4 1 635 189
    75 688 0.4 1 524 164
    76 631 0.4 1 479 152
    77 556 0.4 1 371 185
    78 518 0.4 1 351 167
    79 403 0.4 1 264 139
    80 412 0.4 1 227 185
    81 359 0.4 1 215 144
    82 325 0.4 1 188 137
    83 279 0.4 1 170 109
    84 259 0.4 1 138 121
    85 198 0.4 1 104 94
    86 180 0.4 1 90 90
    87 174 0.4 1 81 93
    88 176 0.4 1 74 102
    89 152 0.4 1 58 94
    90 132 0.4 1 41 91
    91 136 0.4 1 32 104
    92 120 0.4 1 33 87
    93 98 0.4 1 26 72
    94 111 0.4 1 16 95
    95 112 0.4 1 19 93
    96 84 0.4 1 12 72
    97 100 0.4 1 12 88
    98 78 0.4 1 13 65
    99 75 0.4 1 11 64
    100 101 0.4 1 5 96
    101 79 0.4 1 8 71
    102 59 0.4 1 5 54
    103 81 0.4 1 1 80
    104 78 0.4 1 2 76
    105 78 0.4 1 2 76
    106 103 0.4 1 2 101
    107 74 0.4 1 5 69
    108 76 0.4 1 3 73
    109 104 0.4 1 0 104
    110 64 0.4 1 1 63
    111 98 0.4 1 7 91
    112 81 0.4 1 4 77
    113 89 0.4 1 8 81
    114 100 0.4 1 11 89
    115 73 0.4 1 2 71
    116 115 0.4 1 5 110
    117 112 0.4 1 1 111
    118 125 0.4 1 3 122
    119 131 0.4 1 2 129
    120 124 0.4 1 3 121
    121 137 0.4 1 4 133
    122 166 0.4 1 3 163
    123 230 0.4 1 1 229
    124 422 0.4 1 0 422
    125 1138 0.4 1 2 1136
    126 1931 0.4 1 2 1929


    RUN STATISTICS FOR INPUT FILE: index23_GAGTGG_L001-L002_R2_001.fastq
    =============================================
    28485339 sequences processed in total

    Total number of sequences analysed for the sequence pair length validation: 28485339

    Number of sequence pairs removed because at least one read was shorter than the length cutoff (20 bp): 72318 (0.25%)

    Leave a comment:

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