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  • #91
    Originally posted by Rob Weeks View Post
    I have only just begun to look at RRBS data. I am trying to use trim_galore to quality trim and adaptor trim my sequences. I am doing this in OS X.
    Now when I run 'trim_adaptor filename.fastq.gz' it returns an error due to 'zcat: can't stat: filename.fastq.gz (filename.fastq.gz.Z): No such file or directory".
    This is apparently a problem only in OS X, but it is not clear to me how I can get around this problem.

    Any ideas would be appreciated

    This problem is indeed known and has to do with the version of zcat which is installed on your system. On very old versions it would alter the filename passed to it to append a .Z if there wasn’t one there, so although you’re trying to read a file called Sample1_PE_R1.fastq.gz it’s actually trying to read Sample1_PE_R1.fastq.gz.Z (which doesn’t exist).

    I have changed the way Trim Galore reads from files from using a cat stream to using gunzip -c and it seems to work well. I can send you a copy of this tonight as I am at the Festival of Genomics in London all day if you send me an email. Alternatively you could try to change the filename of your input to end in .gz.Z and try that?
    Good luck, Felix


    • #92
      Never mind, I realised that decompressing my file and then running trim_galore will bypass zcat. It then works.



      EDIT: thanks Felix. I wrote this before I saw your response - I was "never minding" my question not your response!

      I have a solution which works; I will decompress before running trim_galore
      Last edited by Rob Weeks; 01-19-2016, 11:46 PM. Reason: crossed responses


      • #93
        That would work, too, yes. Let me know if you've got any further questions.


        • #94
          *_trimmed vs. *_val

          I am using trim_galore in "--paired" mode. I know that trim_galore runs a second validation step after first running through the first and second reads separately, and is creating *val files as a result. Do these get renamed back to the *trimmed files afterwards, or should downstream analyses use the *val files?
          Thanks for the help!


          • #95
            The temporarily created trimmed.fq files get deleted in the end, leaving only the _val_ files left for further analysis.


            • #96
              Dear Felix and fellow trim_galore users,

              I am having a problem/confusion regarding trimming of Illumina adapters from paired-end bisulfite (and some non-bisulfite) samples of a whole-genome bisulfite sequencing project.
              I am not sure what is going on here, and how I should process my reads.

              First, running FastQC on my raw read pairs, I see both:
              • adapter contamination increasing toward the 3' of both forward and reverse reads (link (forward))
              • K-mer content clearly indicating over-representation of the reverse complemented Illumina adapter sequence in both forward and reverse reads (link (forward))

              When I use trim_galore using either auto-detect or --illumina options:
              • trim_galore detects the adapter sequence in ~53% of the reads, making FastQC extremely happy with a perfect absence of adapter contamination in the trimmed files
              • reverse complemented adapter sequence still show up in the K-mer content

              Basically, trim_galore cleanly removes any trace of the adapter sequence (AGATCGGAAGAGC), and ignores its reverse complement (well, it did its job!)

              Without much surprise, when I told trim_galore to trim the reverse complement of the adapter sequence, the result of FastQC is:
              • trim_galore detects/removes the sequence in ~27% of the reads
              • Adapter contamination still detected toward the 3' of the reads
              • K-mer content does not show any more sign of reverse complemented adapter over-representation (link (forward))

              A final piece of information that might help is that "grep"ing the adapter and rev_comp_adapter sequences in my raw reads, I observed the following:
              • the adapter sequence is typically found a unique time per read (when found), generally toward the 3' end
              • the reverse complemented sequence can be found multiple times within a single read (typically 1-3 times), generally toward the 5' of the read

              Respective examples:

              In a nutshell, my question is: "What procedure is recommended is this case?"

              I tried to do my homework, and saw earlier in this thread that trim_galore cannot be given multiple adapter sequences (I was thinking of giving both adapter and rev_com).

              Any piece of advice would be greatly appeciated !
              Kind regards,


              • #97
                Hi Kevin,

                thanks for providing so many details for your question, you don't get this very often...

                As a general recommendation I would suggest that you simply run Trim Galore in its default mode (which you already did and which seems to have worked just fine), and just ignore the reverse complement of the adapter.

                If you draw out a sketch of how libraries are constructed you will see that both reads (R1 and R2 of a library) should have a single copy of the sequence AGATCGGAAGAGC which then further extends into the rest of either the R1 or R2 adapter sequence (the sequence will then divert for R1 and R2, but for trimming purposes it doesn't matter how the sequence continues). You will further see that no sequence should have the reverse complement of the adapter sequence as a result of read-through adapter contamination. I would expect to find this reverse complemented sequence only as part of some weird processes that happened during library preparation, e.g. adapter or primer dimerisation or concatenation etc. (it should not be present in mammalian genomes at least).

                The only thing that slightly caught me out is the occurrence of 27% which seems very high. This might mean that either you've got quite a lot dimerisation happening, or could it be that this is an aggregate number of all kinds of shorter sequences that have been removed, like G, GC, GCT, GCTC,....? In this case the sequences are probably just some random genomic sequences. If you run
                grep -c GCTCTTCCGATCT your_file
                is the number really that high?

                In any case, I would recommend you just run Trim Galore in default mode and then proceed straight to mapping, I am pretty sure that it is the right thing to do.
                Best wishes, Felix


                • #98
                  Dear Felix,

                  Thanks for the quick answer, always appreciated!

                  I ran "grep -c GCTCTTCCGATCT" on the first million forward reads and got 5597 (0.56%)
                  To be comprehensive, I ran it on the first million reverse reads and got 10258 (1%)

                  By the way, you certainly understood that the 53% and 27% values in my previous post are those reported by trim_galore.

                  Now, I wonder where that the massive difference (27% reported by trim_galore; 0.56% exact matches grep-ed) comes from: is it due to partial rev_comp_adapter detected... although this seems a huge difference (see below for a comparison with the original adapter sequence), especially considering that the reverse complemented sequence tends to appear at the 5' of the read and therefore truncation should not be an issue for detection.
                  Illustration of trim_galore report for the rev-comp sequence:

                  Adapter sequence: 'GCTCTTCCGATCT' ()
                  Maximum trimming error rate: 0.1 (default)
                  Optional adapter 2 sequence (only used for read 2 of paired-end files): 'GCTCTTCCGATCT'
                  Minimum required adapter overlap (stringency): 1 bp
                  Total reads processed: 9,812,098
                  Reads with adapters: 2,662,297 (27.1%)
                  Reads written (passing filters): 9,812,098 (100.0%)

                  Also, running "grep -c AGATCGGAAGAGC" on the first million forward and reverse reads, respectively, returns:
                  243583 (24.4%)
                  238858 (23.9%)
                  In this case, I am confident that the difference between 53% (trim_galore) and 25% (grep) is due to partially adapter sequence.

                  I am looking at TruSeq® DNA Methylation Kit and can clearly how the adapter sequence, ligated to the 3' of ssDNA fragments, can appear by reading through short fragments.
                  Conversely, the rev_comp should clearly not appear in this context. I am not fully familiar with the chemistry of adapters, but the only way I can picture the rev_comp being sequenced is if the adapter gets ligated to the 5' of a fragment (or another adapter).

                  I guess I will do as you suggest:
                  • align the adapter-trimmed paired reads
                  • FastQC the aligned read mates, hoping that the reads showing rev_comp sequences did not map, and therefore do not appear in the K-mer content plot

                  By the way: we were already in touch about this project a while ago, because I had surprisingly low mapping efficiency (link). That might have been the issue. I just had to park the whole project for a while, and only got back to it recently.



                  • #99
                    Hi Kevin,

                    The 27% of sequences that get trimmed is an aggregate value, which is calculated by the sum of sequences that had just anything trimmed, even if it as low as 1, 2, 3 bp etc. The Trim Galore report would give you the whole picture of what has been trimmed, but it normally looks something like this:

                    sequence trimmed
                    G             1000000
                    GC             600000
                    GCT             56671
                    GCTC             4232
                    GCTCTTCCGATCT       4
                    Good luck with resuming your projects!

                    By the way we have started aggregating sequencing related Fails at the QC Fail website:, might be worth visiting every now and then to see if there is anything useful that might explain problems you are seeing. Best, Felix


                    • Dear Dr. Krueger,
                      Thanks for a wonderful piece of stoftware.
                      I am running trim_galore on paired end reads, new version on mac os x, seems that python stops after 30M seqs processed sometimes? i have 64GB of RAM. it will stop and not finish. i get an output for the first fq of the pair but never a second one. i have gotten one set of PE reads to finish and create two trimmed .fq files. so I thought I had it all working. not sure why it may have stopped. maybe just a simple command error. not sure. thanks again.
                      command pasted below
                      Nathan Bowen

                      CHR1:RNASeq_working nbowen$ trim_galore index21_GTTTCG_L001-L002_R1_001.fastq index21_GTTTCG_L001-L002_R2_001.fastq --path_to_cutadapt /Users/nbowen/Library/Python/2.7/bin/cutadapt --paired
                      No quality encoding type selected. Assuming that the data provided uses Sanger encoded Phred scores (default)

                      Path to Cutadapt set as: '/Users/nbowen/Library/Python/2.7/bin/cutadapt' (user defined)
                      Cutadapt seems to be working fine (tested command '/Users/nbowen/Library/Python/2.7/bin/cutadapt --version')

                      AUTO-DETECTING ADAPTER TYPE
                      Attempting to auto-detect adapter type from the first 1 million sequences of the first file (>> index21_GTTTCG_L001-L002_R1_001.fastq <<)

                      Found perfect matches for the following adapter sequences:
                      Adapter type Count Sequence Sequences analysed Percentage
                      Illumina 239964 AGATCGGAAGAGC 1000000 24.00
                      smallRNA 22 TGGAATTCTCGG 1000000 0.00
                      Nextera 12 CTGTCTCTTATA 1000000 0.00
                      Using Illumina adapter for trimming (count: 239964). Second best hit was smallRNA (count: 22)

                      Writing report to 'index21_GTTTCG_L001-L002_R1_001.fastq_trimming_report.txt'

                      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

                      Writing final adapter and quality trimmed output to index21_GTTTCG_L001-L002_R1_001_trimmed.fq

                      >>> Now performing quality (cutoff 20) and adapter trimming in a single pass for the adapter sequence: 'AGATCGGAAGAGC' from file index21_GTTTCG_L001-L002_R1_001.fastq <<<
                      10000000 sequences processed
                      20000000 sequences processed
                      30000000 sequences processed


                      • Hi Nathan,
                        It is difficult so guess what is going on (or rather wrong) because all seems fine and there are no error messages at all. Just generally, Trim Galore would in a first pass generate two _trimmed.fq files, and then validate these afterwards (length constraints etc) and give rise to two val.fq files. Once that has finished the trimmed.fq files should be deleted again.

                        Trim Galore doesn't use a lot of memory so that should not be the problem. Have you checked that you are not running out of disk space? You should be able to gzip the input files, and/or specify --gzip to keep file sizes smaller. And have you checked (maybe run 'top' in another terminal) if Trim Galore is still running or if it has been killed? Maybe it is still running but just very very slowly, e.g. if a network connection or tmp drive is getting full or the like... If it helps I could create you an FTP site and try running it on your files over here to see if there is something unusual? Best, Felix


                        • looks like it does stop using the CPU, it's still running in activity monitor (Python) but no CPU usage. 0% tmp getting full sounds probable. would the tmp dir be on the drive where the script is housed or on the drive where the outputs are being generated? thanks for the help. it's all local so i don't think network would have any issues. also thanks for the offer of FTP, but hopefully i'll get this figured out soon.


                          • here are the open files for the Python process (parent process Perl)


                            • and here's a sample of the process now while it's at CPU 0%.

                              Sampling process 19136 for 3 seconds with 1 millisecond of run time between samples
                              Sampling completed, processing symbols...
                              Analysis of sampling Python (pid 19136) every 1 millisecond
                              Process: Python [19136]
                              Path: /System/Library/Frameworks/Python.framework/Versions/2.7/Resources/
                              Load Address: 0x10ab3c000
                              Identifier: Python
                              Version: ???
                              Code Type: X86-64
                              Parent Process: perl5.18 [19132]

                              Date/Time: 2016-04-14 13:04:45.752 -0400
                              Launch Time: 2016-04-14 10:18:54.276 -0400
                              OS Version: Mac OS X 10.11.4 (15E65)
                              Report Version: 7
                              Analysis Tool: /usr/bin/sample

                              Call graph:
                              2909 Thread_1674560 DispatchQueue_1: (serial)
                              2909 start (in libdyld.dylib) + 1 [0x7fff90e945ad]
                              2909 Py_Main (in Python) + 3137 [0x10abf7011]
                              2909 PyRun_SimpleFileExFlags (in Python) + 698 [0x10abe5634]
                              2909 PyRun_FileExFlags (in Python) + 133 [0x10abe5ae5]
                              2909 ??? (in Python) load address 0x10ab43000 + 0xa2a42 [0x10abe5a42]
                              2909 PyEval_EvalCode (in Python) + 54 [0x10abc5d8c]
                              2909 PyEval_EvalCodeEx (in Python) + 1583 [0x10abc63c1]
                              2909 PyEval_EvalFrameEx (in Python) + 11609 [0x10abc930c]
                              2909 ??? (in Python) load address 0x10ab43000 + 0x894ae [0x10abcc4ae]
                              2909 PyEval_EvalCodeEx (in Python) + 1583 [0x10abc63c1]
                              2909 PyEval_EvalFrameEx (in Python) + 11609 [0x10abc930c]
                              2909 ??? (in Python) load address 0x10ab43000 + 0x894ae [0x10abcc4ae]
                              2909 PyEval_EvalCodeEx (in Python) + 1583 [0x10abc63c1]
                              2909 PyEval_EvalFrameEx (in Python) + 13400 [0x10abc9a0b]
                              2909 ??? (in Python) load address 0x10ab43000 + 0x810af [0x10abc40af]
                              2909 PyFile_WriteObject (in Python) + 338 [0x10ab63fb3]
                              2909 ??? (in Python) load address 0x10ab43000 + 0x39b09 [0x10ab7cb09]
                              2909 ??? (in Python) load address 0x10ab43000 + 0x42a31 [0x10ab85a31]
                              2909 fwrite (in libsystem_c.dylib) + 153 [0x7fff8984f34a]
                              2909 __sfvwrite (in libsystem_c.dylib) + 194 [0x7fff8984edcb]
                              2909 _swrite (in libsystem_c.dylib) + 87 [0x7fff89854202]
                              2909 __write_nocancel (in libsystem_kernel.dylib) + 10 [0x7fff810d5612]

                              Total number in stack (recursive counted multiple, when >=5):

                              Sort by top of stack, same collapsed (when >= 5):
                              __write_nocancel (in libsystem_kernel.dylib) 2909

                              Binary Images:
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                              0x7fff8b64f000 - 0x7fff8b657ffb libsystem_dnssd.dylib (625.40.20) <86A05653-DCA0-3345-B29F-F320029AA05E> /usr/lib/system/libsystem_dnssd.dylib
                              0x7fff8bd80000 - 0x7fff8bda9fff libc++abi.dylib (125) <DCCC8177-3D09-35BC-9784-2A04FEC4C71B> /usr/lib/libc++abi.dylib
                              0x7fff8c2e0000 - 0x7fff8c2e7ff7 libcompiler_rt.dylib (62) <A13ECF69-F59F-38AE-8609-7B731450FBCD> /usr/lib/system/libcompiler_rt.dylib
                              0x7fff8cce5000 - 0x7fff8cce6ffb libremovefile.dylib (41) <552EF39E-14D7-363E-9059-4565AC2F894E> /usr/lib/system/libremovefile.dylib
                              0x7fff8d19f000 - 0x7fff8d216feb libcorecrypto.dylib (335.40.8) <9D300121-CAF8-3894-8774-DF38FA65F238> /usr/lib/system/libcorecrypto.dylib
                              0x7fff8d3c9000 - 0x7fff8d3cafff libDiagnosticMessagesClient.dylib (100) <4243B6B4-21E9-355B-9C5A-95A216233B96> /usr/lib/libDiagnosticMessagesClient.dylib
                              0x7fff8d70d000 - 0x7fff8d716ff7 libsystem_pthread.dylib (138.10.4) <3DD1EF4C-1D1B-3ABF-8CC6-B3B1CEEE9559> /usr/lib/system/libsystem_pthread.dylib
                              0x7fff8d83a000 - 0x7fff8d83aff7 liblaunch.dylib (765.40.36) <1CD7619D-AF2E-34D1-8EC6-8021CF473D9B> /usr/lib/system/liblaunch.dylib
                              0x7fff90b54000 - 0x7fff90b55ffb libSystem.B.dylib (1226.10.1) <CD307E99-FC5C-3575-BCCE-0C861AA63124> /usr/lib/libSystem.B.dylib
                              0x7fff90c9a000 - 0x7fff90cc7fff libdispatch.dylib (501.40.12) <C7499857-61A5-3D7D-A5EA-65DCC8C3DF92> /usr/lib/system/libdispatch.dylib
                              0x7fff90ceb000 - 0x7fff90cf0ff3 libunwind.dylib (35.3) <F6EB48E5-4D12-359A-AB54-C937FBBE9043> /usr/lib/system/libunwind.dylib
                              0x7fff90e91000 - 0x7fff90e94ffb libdyld.dylib (360.21) <8390E026-F7DE-3C32-9486-3DFF6BD131B0> /usr/lib/system/libdyld.dylib
                              0x7fff91bb5000 - 0x7fff9202bfff (6.9 - 1258.1) <943A1383-DA6A-3DC0-ABCD-D9AEB3D0D34D> /System/Library/Frameworks/CoreFoundation.framework/Versions/A/CoreFoundation
                              0x7fff92038000 - 0x7fff9203aff7 libsystem_configuration.dylib (802.40.13) <3DEB7DF9-6804-37E1-BC83-0166882FF0FF> /usr/lib/system/libsystem_configuration.dylib
                              0x7fff920d9000 - 0x7fff9212cff7 libc++.1.dylib (120.1) <8FC3D139-8055-3498-9AC5-6467CB7F4D14> /usr/lib/libc++.1.dylib
                              0x7fff9212e000 - 0x7fff92499657 libobjc.A.dylib (680) <D55D5807-1FBE-32A5-9105-44D7AFE68C27> /usr/lib/libobjc.A.dylib
                              0x7fff9249a000 - 0x7fff924e0ff7 libauto.dylib (186) <999E610F-41FC-32A3-ADCA-5EC049B65DFB> /usr/lib/libauto.dylib
                              0x7fff9283a000 - 0x7fff92863ff7 libxpc.dylib (765.40.36) <2CC7CF36-66D4-301B-A6D8-EBAE7405B008> /usr/lib/system/libxpc.dylib
                              0x7fff92e6d000 - 0x7fff92e7bff7 libbz2.1.0.dylib (38) <28E54258-C0FE-38D4-AB76-1734CACCB344> /usr/lib/libbz2.1.0.dylib
                              0x7fff9335a000 - 0x7fff93383fff libsystem_info.dylib (477.40.5) <6B01C09E-A3E5-3C71-B370-D0CABD11A436> /usr/lib/system/libsystem_info.dylib
                              0x7fff9352b000 - 0x7fff9352bff7 libkeymgr.dylib (28) <8371CE54-5FDD-3CE9-B3DF-E98C761B6FE0> /usr/lib/system/libkeymgr.dylib
                              0x7fff943bc000 - 0x7fff943bffff libsystem_sandbox.dylib (460.40.33) <30671DCC-265F-325A-B33D-11CD336B3DA3> /usr/lib/system/libsystem_sandbox.dylib
                              Sample analysis of process 19136 written to file /dev/stdout


                              • I have to admit that I don't exactly know what is going on, but so far I can't see any indication that (or why) Trim Galore would be failing. Just in case I am attaching the latest development version which you might want to give a whirl.

                                Alternatively there is a chance that Python or Cutadapt are somehow stalling, so not finishing but also not using a noticable chunk of the CPU anymore. Could you try to run Cutadapt on its own on the file with the same command that Trim Galore is invoking to see if that runs to completion?

                                cutadapt -f fastq -e 0.1 -q 20 -O 1 -a AGATCGGAAGAGC -o out.fq index21_GTTTCG_L001-L002_R1_001.fastq
                                Attached Files


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