# HG changeset patch # User iuc # Date 1631544656 0 # Node ID cf25b50eff0a34ff450b75f941d26e7a1d22a173 # Parent bc082a79d655786bba18ae6b165a0edff00f693b "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/umi_tools commit bf6a3aa532e8f9d122da4c1e39f3e256ae587b79" diff -r bc082a79d655 -r cf25b50eff0a macros.xml --- a/macros.xml Wed Feb 10 19:31:44 2021 +0000 +++ b/macros.xml Mon Sep 13 14:50:56 2021 +0000 @@ -1,5 +1,43 @@ + + + + 1.1.2 + 0 + 21.01 + + + umi_tools + + + + + + 10.1101/gr.209601.116 + + @misc{githubUMI-tools, + title = {UMI-tools}, + publisher = {GitHub}, + journal = {GitHub repository}, + url = {https://github.com/CGATOxford/UMI-tools}, + } + + + + +
+ +
+
+ + + + @@ -23,90 +61,510 @@ - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + fastqsanger,fastqsanger.gz,fastqillumina,fastqillumina.gz,fastqsolexa,fastqsolexa.gz + + + umi-tools + + - - + + - + + - - - + + + + + - - + + + + - - - 10.1101/gr.209601.116 - - @misc{githubUMI-tools, - title = {UMI-tools}, - publisher = {GitHub}, - journal = {GitHub repository}, - url = {https://github.com/CGATOxford/UMI-tools}, - } - - - - - - umi_tools - - - - 0.5.5 + + + + 'input.bam' && + samtools index -b 'input.bam' && + #set $input_file = 'input.bam' + #else: + ln -sf '${input}' 'input.bam' && + ln -sf '$input.metadata.bam_index' 'input.bam.bai' && + #set $input_file = 'input.bam' + #end if + ]]> + + + + + + + + + + .{8,12})(?PGAGTGATTGCTTGTGACGCCTT)(?P.{8})(?P.{6})T{3}.* + + Where only reads with a 3' T-tail and `GAGTGATTGCTTGTGACGCCTT` in + the correct position to yield two cell barcodes of 8-12 and 8bp + respectively, and a 6bp UMI will be retained. + + You can also specify fuzzy matching to allow errors. For example if + the discard group above was specified as below this would enable + matches with up to 2 errors in the discard_1 group. + + :: + + (?PGAGTGATTGCTTGTGACGCCTT){s<=2} + + Note that all UMIs must be the same length for downstream + processing with dedup, group or count commands]]> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ``, +replacing with e.g ":". + +Alternatively, if your UMIs are encoded in a tag, you can specify this +by setting the option --extract-umi-method=tag and set the tag name +with the --umi-tag option. For example, if your UMIs are encoded in +the 'UM' tag, provide the following options: +``--extract-umi-method=tag`` ``--umi-tag=UM`` + +Finally, if you have used umis to extract the UMI +/- cell barcode, +you can specify ``--extract-umi-method=umis`` + +The start position of a read is considered to be the start of its alignment +minus any soft clipped bases. A read aligned at position 500 with +cigar 2S98M will be assumed to start at position 498.]]> + + + +
+ + + + + + + + + + + +
+
+ + = (2* umi B counts) - 1. Each + network is a read group. + +]]> + + +
+ + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + + + +
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diff -r bc082a79d655 -r cf25b50eff0a test-data/chr19_gene_tags.sam --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/chr19_gene_tags.sam Mon Sep 13 14:50:56 2021 +0000 @@ -0,0 +1,1492 @@ +@HD VN:1.4 SO:queryname +@SQ SN:chr1 LN:248956422 +@SQ SN:chr2 LN:242193529 +@SQ SN:chr3 LN:198295559 +@SQ SN:chr4 LN:190214555 +@SQ SN:chr5 LN:181538259 +@SQ SN:chr6 LN:170805979 +@SQ SN:chr7 LN:159345973 +@SQ SN:chr8 LN:145138636 +@SQ SN:chr9 LN:138394717 +@SQ SN:chr10 LN:133797422 +@SQ SN:chr11 LN:135086622 +@SQ SN:chr12 LN:133275309 +@SQ SN:chr13 LN:114364328 +@SQ SN:chr14 LN:107043718 +@SQ SN:chr15 LN:101991189 +@SQ SN:chr16 LN:90338345 +@SQ SN:chr17 LN:83257441 +@SQ SN:chr18 LN:80373285 +@SQ SN:chr19 LN:58617616 +@SQ SN:chr20 LN:64444167 +@SQ SN:chr21 LN:46709983 +@SQ SN:chr22 LN:50818468 +@SQ SN:chrX LN:156040895 +@SQ SN:chrY LN:57227415 +@SQ SN:chrM LN:16569 +@SQ SN:GL000008.2 LN:209709 +@SQ SN:GL000009.2 LN:201709 +@SQ SN:GL000194.1 LN:191469 +@SQ SN:GL000195.1 LN:182896 +@SQ SN:GL000205.2 LN:185591 +@SQ SN:GL000208.1 LN:92689 +@SQ SN:GL000213.1 LN:164239 +@SQ SN:GL000214.1 LN:137718 +@SQ SN:GL000216.2 LN:176608 +@SQ SN:GL000218.1 LN:161147 +@SQ SN:GL000219.1 LN:179198 +@SQ SN:GL000220.1 LN:161802 +@SQ SN:GL000221.1 LN:155397 +@SQ SN:GL000224.1 LN:179693 +@SQ SN:GL000225.1 LN:211173 +@SQ SN:GL000226.1 LN:15008 +@SQ SN:KI270302.1 LN:2274 +@SQ SN:KI270303.1 LN:1942 +@SQ SN:KI270304.1 LN:2165 +@SQ SN:KI270305.1 LN:1472 +@SQ SN:KI270310.1 LN:1201 +@SQ SN:KI270311.1 LN:12399 +@SQ SN:KI270312.1 LN:998 +@SQ SN:KI270315.1 LN:2276 +@SQ SN:KI270316.1 LN:1444 +@SQ SN:KI270317.1 LN:37690 +@SQ SN:KI270320.1 LN:4416 +@SQ SN:KI270322.1 LN:21476 +@SQ SN:KI270329.1 LN:1040 +@SQ SN:KI270330.1 LN:1652 +@SQ SN:KI270333.1 LN:2699 +@SQ SN:KI270334.1 LN:1368 +@SQ SN:KI270335.1 LN:1048 +@SQ SN:KI270336.1 LN:1026 +@SQ SN:KI270337.1 LN:1121 +@SQ SN:KI270338.1 LN:1428 +@SQ SN:KI270340.1 LN:1428 +@SQ SN:KI270362.1 LN:3530 +@SQ SN:KI270363.1 LN:1803 +@SQ SN:KI270364.1 LN:2855 +@SQ SN:KI270366.1 LN:8320 +@SQ SN:KI270371.1 LN:2805 +@SQ SN:KI270372.1 LN:1650 +@SQ SN:KI270373.1 LN:1451 +@SQ SN:KI270374.1 LN:2656 +@SQ SN:KI270375.1 LN:2378 +@SQ SN:KI270376.1 LN:1136 +@SQ SN:KI270378.1 LN:1048 +@SQ SN:KI270379.1 LN:1045 +@SQ SN:KI270381.1 LN:1930 +@SQ SN:KI270382.1 LN:4215 +@SQ SN:KI270383.1 LN:1750 +@SQ SN:KI270384.1 LN:1658 +@SQ SN:KI270385.1 LN:990 +@SQ SN:KI270386.1 LN:1788 +@SQ SN:KI270387.1 LN:1537 +@SQ SN:KI270388.1 LN:1216 +@SQ SN:KI270389.1 LN:1298 +@SQ SN:KI270390.1 LN:2387 +@SQ SN:KI270391.1 LN:1484 +@SQ SN:KI270392.1 LN:971 +@SQ SN:KI270393.1 LN:1308 +@SQ SN:KI270394.1 LN:970 +@SQ SN:KI270395.1 LN:1143 +@SQ SN:KI270396.1 LN:1880 +@SQ SN:KI270411.1 LN:2646 +@SQ SN:KI270412.1 LN:1179 +@SQ SN:KI270414.1 LN:2489 +@SQ SN:KI270417.1 LN:2043 +@SQ SN:KI270418.1 LN:2145 +@SQ SN:KI270419.1 LN:1029 +@SQ SN:KI270420.1 LN:2321 +@SQ SN:KI270422.1 LN:1445 +@SQ SN:KI270423.1 LN:981 +@SQ SN:KI270424.1 LN:2140 +@SQ SN:KI270425.1 LN:1884 +@SQ SN:KI270429.1 LN:1361 +@SQ SN:KI270435.1 LN:92983 +@SQ SN:KI270438.1 LN:112505 +@SQ SN:KI270442.1 LN:392061 +@SQ SN:KI270448.1 LN:7992 +@SQ SN:KI270465.1 LN:1774 +@SQ SN:KI270466.1 LN:1233 +@SQ SN:KI270467.1 LN:3920 +@SQ SN:KI270468.1 LN:4055 +@SQ SN:KI270507.1 LN:5353 +@SQ SN:KI270508.1 LN:1951 +@SQ SN:KI270509.1 LN:2318 +@SQ SN:KI270510.1 LN:2415 +@SQ SN:KI270511.1 LN:8127 +@SQ SN:KI270512.1 LN:22689 +@SQ SN:KI270515.1 LN:6361 +@SQ SN:KI270516.1 LN:1300 +@SQ SN:KI270517.1 LN:3253 +@SQ SN:KI270518.1 LN:2186 +@SQ SN:KI270519.1 LN:138126 +@SQ SN:KI270521.1 LN:7642 +@SQ SN:KI270522.1 LN:5674 +@SQ SN:KI270528.1 LN:2983 +@SQ SN:KI270529.1 LN:1899 +@SQ SN:KI270530.1 LN:2168 +@SQ SN:KI270538.1 LN:91309 +@SQ SN:KI270539.1 LN:993 +@SQ SN:KI270544.1 LN:1202 +@SQ SN:KI270548.1 LN:1599 +@SQ SN:KI270579.1 LN:31033 +@SQ SN:KI270580.1 LN:1553 +@SQ SN:KI270581.1 LN:7046 +@SQ SN:KI270582.1 LN:6504 +@SQ SN:KI270583.1 LN:1400 +@SQ SN:KI270584.1 LN:4513 +@SQ SN:KI270587.1 LN:2969 +@SQ SN:KI270588.1 LN:6158 +@SQ SN:KI270589.1 LN:44474 +@SQ SN:KI270590.1 LN:4685 +@SQ SN:KI270591.1 LN:5796 +@SQ SN:KI270593.1 LN:3041 +@SQ SN:KI270706.1 LN:175055 +@SQ SN:KI270707.1 LN:32032 +@SQ SN:KI270708.1 LN:127682 +@SQ SN:KI270709.1 LN:66860 +@SQ SN:KI270710.1 LN:40176 +@SQ SN:KI270711.1 LN:42210 +@SQ SN:KI270712.1 LN:176043 +@SQ SN:KI270713.1 LN:40745 +@SQ SN:KI270714.1 LN:41717 +@SQ SN:KI270715.1 LN:161471 +@SQ SN:KI270716.1 LN:153799 +@SQ SN:KI270717.1 LN:40062 +@SQ SN:KI270718.1 LN:38054 +@SQ SN:KI270719.1 LN:176845 +@SQ SN:KI270720.1 LN:39050 +@SQ SN:KI270721.1 LN:100316 +@SQ SN:KI270722.1 LN:194050 +@SQ SN:KI270723.1 LN:38115 +@SQ SN:KI270724.1 LN:39555 +@SQ SN:KI270725.1 LN:172810 +@SQ SN:KI270726.1 LN:43739 +@SQ SN:KI270727.1 LN:448248 +@SQ SN:KI270728.1 LN:1872759 +@SQ SN:KI270729.1 LN:280839 +@SQ SN:KI270730.1 LN:112551 +@SQ SN:KI270731.1 LN:150754 +@SQ SN:KI270732.1 LN:41543 +@SQ SN:KI270733.1 LN:179772 +@SQ SN:KI270734.1 LN:165050 +@SQ SN:KI270735.1 LN:42811 +@SQ SN:KI270736.1 LN:181920 +@SQ SN:KI270737.1 LN:103838 +@SQ SN:KI270738.1 LN:99375 +@SQ SN:KI270739.1 LN:73985 +@SQ SN:KI270740.1 LN:37240 +@SQ SN:KI270741.1 LN:157432 +@SQ SN:KI270742.1 LN:186739 +@SQ SN:KI270743.1 LN:210658 +@SQ SN:KI270744.1 LN:168472 +@SQ SN:KI270745.1 LN:41891 +@SQ SN:KI270746.1 LN:66486 +@SQ SN:KI270747.1 LN:198735 +@SQ SN:KI270748.1 LN:93321 +@SQ SN:KI270749.1 LN:158759 +@SQ SN:KI270750.1 LN:148850 +@SQ SN:KI270751.1 LN:150742 +@SQ SN:KI270752.1 LN:27745 +@SQ SN:KI270753.1 LN:62944 +@SQ SN:KI270754.1 LN:40191 +@SQ SN:KI270755.1 LN:36723 +@SQ SN:KI270756.1 LN:79590 +@SQ SN:KI270757.1 LN:71251 +@SQ SN:ERCC-00002 LN:1061 +@SQ SN:ERCC-00003 LN:1023 +@SQ SN:ERCC-00004 LN:523 +@SQ SN:ERCC-00009 LN:984 +@SQ SN:ERCC-00012 LN:994 +@SQ SN:ERCC-00013 LN:808 +@SQ SN:ERCC-00014 LN:1957 +@SQ SN:ERCC-00016 LN:844 +@SQ SN:ERCC-00017 LN:1136 +@SQ SN:ERCC-00019 LN:644 +@SQ SN:ERCC-00022 LN:751 +@SQ SN:ERCC-00024 LN:536 +@SQ SN:ERCC-00025 LN:1994 +@SQ SN:ERCC-00028 LN:1130 +@SQ SN:ERCC-00031 LN:1138 +@SQ SN:ERCC-00033 LN:2022 +@SQ SN:ERCC-00034 LN:1019 +@SQ SN:ERCC-00035 LN:1130 +@SQ SN:ERCC-00039 LN:740 +@SQ SN:ERCC-00040 LN:744 +@SQ SN:ERCC-00041 LN:1122 +@SQ SN:ERCC-00042 LN:1023 +@SQ SN:ERCC-00043 LN:1023 +@SQ SN:ERCC-00044 LN:1156 +@SQ SN:ERCC-00046 LN:522 +@SQ SN:ERCC-00048 LN:992 +@SQ SN:ERCC-00051 LN:274 +@SQ SN:ERCC-00053 LN:1023 +@SQ SN:ERCC-00054 LN:274 +@SQ SN:ERCC-00057 LN:1021 +@SQ SN:ERCC-00058 LN:1136 +@SQ SN:ERCC-00059 LN:525 +@SQ SN:ERCC-00060 LN:523 +@SQ SN:ERCC-00061 LN:1136 +@SQ SN:ERCC-00062 LN:1023 +@SQ SN:ERCC-00067 LN:644 +@SQ SN:ERCC-00069 LN:1137 +@SQ SN:ERCC-00071 LN:642 +@SQ SN:ERCC-00073 LN:603 +@SQ SN:ERCC-00074 LN:522 +@SQ SN:ERCC-00075 LN:1023 +@SQ SN:ERCC-00076 LN:642 +@SQ SN:ERCC-00077 LN:273 +@SQ SN:ERCC-00078 LN:993 +@SQ SN:ERCC-00079 LN:644 +@SQ SN:ERCC-00081 LN:534 +@SQ SN:ERCC-00083 LN:1022 +@SQ SN:ERCC-00084 LN:994 +@SQ SN:ERCC-00085 LN:844 +@SQ SN:ERCC-00086 LN:1020 +@SQ SN:ERCC-00092 LN:1124 +@SQ SN:ERCC-00095 LN:521 +@SQ SN:ERCC-00096 LN:1107 +@SQ SN:ERCC-00097 LN:523 +@SQ SN:ERCC-00098 LN:1143 +@SQ SN:ERCC-00099 LN:1350 +@SQ SN:ERCC-00104 LN:2022 +@SQ SN:ERCC-00108 LN:1022 +@SQ SN:ERCC-00109 LN:536 +@SQ SN:ERCC-00111 LN:994 +@SQ SN:ERCC-00112 LN:1136 +@SQ SN:ERCC-00113 LN:840 +@SQ SN:ERCC-00116 LN:1991 +@SQ SN:ERCC-00117 LN:1136 +@SQ SN:ERCC-00120 LN:536 +@SQ SN:ERCC-00123 LN:1022 +@SQ SN:ERCC-00126 LN:1118 +@SQ SN:ERCC-00130 LN:1059 +@SQ SN:ERCC-00131 LN:771 +@SQ SN:ERCC-00134 LN:274 +@SQ SN:ERCC-00136 LN:1033 +@SQ SN:ERCC-00137 LN:537 +@SQ SN:ERCC-00138 LN:1024 +@SQ SN:ERCC-00142 LN:493 +@SQ SN:ERCC-00143 LN:784 +@SQ SN:ERCC-00144 LN:538 +@SQ SN:ERCC-00145 LN:1042 +@SQ SN:ERCC-00147 LN:1023 +@SQ SN:ERCC-00148 LN:494 +@SQ SN:ERCC-00150 LN:743 +@SQ SN:ERCC-00154 LN:537 +@SQ SN:ERCC-00156 LN:494 +@SQ SN:ERCC-00157 LN:1019 +@SQ SN:ERCC-00158 LN:1027 +@SQ SN:ERCC-00160 LN:743 +@SQ SN:ERCC-00162 LN:523 +@SQ SN:ERCC-00163 LN:543 +@SQ SN:ERCC-00164 LN:1022 +@SQ SN:ERCC-00165 LN:872 +@SQ SN:ERCC-00168 LN:1024 +@SQ SN:ERCC-00170 LN:1023 +@SQ SN:ERCC-00171 LN:505 +@PG ID:STAR PN:STAR VN:STAR_2.5.2b CL:STAR --runThreadN 8 --genomeDir /data/home/mvanloenhout/Gencode_v25/Star_overhang69/ --readFilesIn /data/home/mvanloenhout/WTF2/scRNA_Analysis/Processed_data/HSC2-I02_S5_R2_001.fastq.gz --readFilesCommand gunzip -c --outFileNamePrefix /data/home/mvanloenhout/WTF2/scRNA_Analysis/Aligned_files/HSC2-I02_S5_R2_001 --outSAMtype BAM SortedByCoordinate --outSAMmultNmax 1 --outFilterType BySJout --outFilterMultimapNmax 20 +@CO user command line: STAR --runThreadN 8 --genomeDir /data/home/mvanloenhout/Gencode_v25/Star_overhang69/ --outSAMtype BAM SortedByCoordinate --outSAMmultNmax 1 --outFilterMultimapNmax 20 --outFilterType BySJout --outFileNamePrefix /data/home/mvanloenhout/WTF2/scRNA_Analysis/Aligned_files/HSC2-I02_S5_R2_001 --readFilesCommand gunzip -c --readFilesIn /data/home/mvanloenhout/WTF2/scRNA_Analysis/Processed_data/HSC2-I02_S5_R2_001.fastq.gz +NS500668:144:H5FCJBGXY:1:11102:10920:18759:CELL_TTCACG:UMI_TTGGGA:SAMPLE_CGATGT:UID_CGATGTTTCACGTTGGGA 0 chr19 812244 255 51M9S * 0 0 CGCTGTGGACTCTGTAGAGGCAGGTTGGCCAGTCTGTACCTGGACTTCGAANNNNNNNNN AAAA/A//EE/AA/EEEA//EE' mode='w' encoding='UTF-8'> -# stdin : <_io.TextIOWrapper name='/tmp/tmpibtvD6/files/000/dataset_5.dat' mode='r' encoding='UTF-8'> -# stdlog : <_io.TextIOWrapper name='/tmp/tmpibtvD6/files/000/dataset_8.dat' mode='a' encoding='UTF-8'> -# stdout : <_io.TextIOWrapper name='' mode='w' encoding='UTF-8'> +# stderr : <_io.TextIOWrapper name='' mode='w' encoding='utf-8'> +# stdin : <_io.TextIOWrapper name='input_read1.gz' encoding='ascii'> +# stdlog : <_io.TextIOWrapper name='/tmp/tmpcx2d26we/files/0/0/8/dataset_008b1843-bfa2-44fb-9d3c-52695bd9ce74.dat' mode='a' encoding='UTF-8'> +# stdout : <_io.TextIOWrapper name='' mode='w' encoding='utf-8'> # subset_reads : 0 # timeit_file : None # timeit_header : None # timeit_name : all +# tmpdir : None # whitelist_tsv : None -2018-02-25 10:50:16,016 INFO Starting barcode extraction -2018-02-25 10:50:16,017 INFO Parsed 0 reads -2018-02-25 10:50:16,019 INFO Starting - whitelist determination -2018-02-25 10:50:17,208 INFO Finished - whitelist determination -2018-02-25 10:50:17,208 INFO Starting - finding putative error cell barcodes -2018-02-25 10:50:17,208 INFO Finished - finding putative error cell barcodes -2018-02-25 10:50:17,208 INFO Writing out whitelist -2018-02-25 10:50:17,208 INFO Parsed 100 reads -2018-02-25 10:50:17,208 INFO 100 reads matched the barcode pattern -2018-02-25 10:50:17,208 INFO Found 23 unique cell barcodes -# job finished in 1 seconds at Sun Feb 25 10:50:17 2018 -- 2.35 0.08 0.00 0.00 -- e78e4e5b-e99e-426a-8a92-c8b3beeadf18 +2021-07-13 15:21:12,587 INFO Starting barcode extraction +2021-07-13 15:21:12,588 INFO Parsed 0 reads +2021-07-13 15:21:12,590 INFO Starting - whitelist determination +2021-07-13 15:21:14,249 INFO Finished - whitelist determination +2021-07-13 15:21:14,249 INFO Starting - finding putative error cell barcodes +2021-07-13 15:21:14,249 INFO building bktree +2021-07-13 15:21:14,249 INFO done building bktree +2021-07-13 15:21:14,249 INFO Finished - finding putative error cell barcodes +2021-07-13 15:21:14,249 INFO Top 1 cell barcodes passed the selected threshold +2021-07-13 15:21:14,249 INFO Writing out whitelist +2021-07-13 15:21:14,249 INFO Parsed 100 reads +2021-07-13 15:21:14,249 INFO 100 reads matched the barcode pattern +2021-07-13 15:21:14,249 INFO Found 23 unique cell barcodes +2021-07-13 15:21:14,249 INFO Found 15 total reads matching the selected cell barcodes +2021-07-13 15:21:14,249 INFO Found 85 total reads which can be error corrected to the selected cell barcodes +# job finished in 1 seconds at Tue Jul 13 15:21:14 2021 -- 7.19 0.62 0.08 0.02 -- ba3841c0-b2d5-4188-88ca-4ee241163293 diff -r bc082a79d655 -r cf25b50eff0a umi-tools_group.xml --- a/umi-tools_group.xml Wed Feb 10 19:31:44 2021 +0000 +++ b/umi-tools_group.xml Mon Sep 13 14:50:56 2021 +0000 @@ -1,115 +1,126 @@ - + Extract UMI from fastq files + macros.xml - samtools + samtools 0: - --gene-tag '$gene_tag' - #end if #if $group_output: --group-out '$group_out' #end if - #if $input.is_of_type("sam"): - --in-sam - #end if --output-bam - -I '$input_file' -S grouped.bam && - samtools sort grouped.bam -@ \${GALAXY_SLOTS:-1} -T "\${TMPDIR:-.}" -o '$output' -O BAM + @GROUPDEDUP_OPTIONS@ + @BARCODE_OPTIONS@ + @UMI_GROUPING_OPTIONS@ + @SAMBAM_OPTIONS@ + @FULLSC_OPTIONS@ + -I '$input_file' -S grouped.bam + @ADVANCED_OPTIONS@ + @LOG@ + ## TODO using samtools sort is a workaround, for the following error that appears when Galaxy + ## compares the generated file with the one in test-data + ## `Converting history BAM to SAM failed: 'samtools returned with error 1: stdout=None, stderr=[main_samview] fail to read the header from "/tmp/tmpd8o61jykdedup_out6.bam".\n'. Will compare BAM files` + ## may be dropped in the future + --no-sort-output + && samtools sort --no-PG grouped.bam -@ \${GALAXY_SLOTS:-1} -T "\${TMPDIR:-.}" -o '$output' -O BAM ]]> - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + - group_out + group_output + - - - - - - + + +
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", -replacing with e.g ":". - -Alternatively, if your UMIs are encoded in a tag, you can specify this -by setting the option --extract-umi-method=tag and set the tag name -with the --umi-tag option. For example, if your UMIs are encoded in -the 'UM' tag, provide the following options: -"--extract-umi-method=tag --umi-tag=UM" - -By default, reads are considered identical if they have the same start -coordinate, are on the same strand, and have the same UMI. Optionally, -splicing status can be considered (see below). - -The start postion of a read is considered to be the start of its alignment -minus any soft clipped bases. A read aligned at position 500 with -cigar 2S98M will be assumed to start at postion 498. - -Methods -------- - -group can be run with multiple methods to identify group of reads with -the same (or similar) UMI(s). All methods start by identifying the -reads with the same mapping position. - -The simpliest method, "unique", groups reads with the exact same -UMI. The network-based methods, "cluster", "adjacency" and -"directional", build networks where nodes are UMIs and edges connect -UMIs with an edit distance <= threshold (usually 1). The groups of -reads are then defined from the network in a method-specific manner. - -Note that the "percentile" method used with the dedup command is not -available with group. This is because this method does not group -similar UMIs as per the network methods. Instead it applies a -threshold for inclusion of the UMI in the output and excluded UMIs are -not assigned to a "true" UMI. - - "unique" - Reads group share the exact same UMI - - "cluster" - Identify clusters of connected UMIs (based on hamming distance - threshold). Each network is a read group - - "directional" - Identify clusters of connected UMIs (based on hamming distance - threshold) and umi A counts >= (2* umi B counts) - 1. Each - network is a read group. +their genomic coordinate and UMI. The group command can be used to create two types of outfile: a tagged BAM or a flatfile describing the read groups @@ -227,138 +182,9 @@ - unique_id The unique id for the group - -Options -------- - ---extract-umi-method (choice) - How are the UMIs encoded in the read? - - Options are: - - - "read_id" (default) - UMIs contained at the end of the read separated as - specified with --umi-separator option - - - "tag" - UMIs contained in a tag, see --umi-tag option - ---umi-separator (string) - Separator between read id and UMI. See --extract-umi-method above - ---umi-tag (string) - Tag which contains UMI. See --extract-umi-method above - ---method (choice, string) - Method used to identify PCR duplicates within reads. All methods - start by identifying the reads with the same mapping position - - Options are: - - - "unique" - Reads group share the exact same UMI - - - "cluster" - Identify clusters of connected UMIs (based on edit distance - threshold). Each network is a read group - - - "directional" - Identify clusters of connected UMIs (based on edit distance - threshold) and umi A counts >= (2* umi B counts) - 1. Each - network is a read group. - ---edit-distance-threshold (int) - For the adjacency and cluster methods the threshold for the - edit distance to connect two UMIs in the network can be - increased. The default value of 1 works best unless the UMI is - very long (>14bp) - ---paired - BAM is paired end - output both read pairs. This will also - force the use of the template length to determine reads with - the same mapping coordinates. - ---spliced-is-unique - Causes two reads that start in the same position on the same - strand and having the same UMI to be considered unique if one is - spliced and the other is not. (Uses the 'N' cigar operation to test - for splicing) - ---soft-clip-threshold (int) - Mappers that soft clip, will sometimes do so rather than mapping a - spliced read if there is only a small overhang over the exon - junction. By setting this option, you can treat reads with at least - this many bases soft-clipped at the 3' end as spliced. +@BARCODE_HELP@ ---multimapping-detection-method (string, choice) - If the sam/bam contains tags to identify multimapping reads, you can - specify for use when selecting the best read at a given loci. - Supported tags are "NH", "X0" and "XT". If not specified, the read - with the highest mapping quality will be selected - ---read-length - Use the read length as as a criteria when deduping, for e.g sRNA-Seq - ---whole-contig - Consider all alignments to a single contig together. This is useful if - you have aligned to a transcriptome multi-fasta - ---subset (float, [0-1]) - Only consider a fraction of the reads, chosen at random. This is useful - for doing saturation analyses. - ---chrom - Only consider a single chromosome. This is useful for debugging purposes - ---per-contig (string) - Deduplicate per contig (field 3 in BAM; RNAME). - All reads with the same contig will be - considered to have the same alignment position. This is useful - if your library prep generates PCR duplicates with non identical - alignment positions such as CEL-Seq. In this case, you would - align to a reference transcriptome with one transcript per gene - ---per-gene (string) - Deduplicate per gene. As above except with this option you can - align to a reference transcriptome with more than one transcript - per gene. You need to also provide --gene-transcript-map option. - This will also add a metacontig ('MC') tag to the reads if used - in conjunction with --output-bam - ---gene-transcript-map (string) - File mapping genes to transripts (tab separated), e.g: - - gene1 transcript1 - gene1 transcript2 - gene2 transcript3 - ---gene-tag (string) - Deduplicate per gene. As per --per-gene except here the gene - information is encoded in the bam read tag specified so you do - not need to supply --gene-transcript-map - ---group-out (string, filename) - Output a flatfile describing the read groups - ---output-bam (string, filename) - Output a tagged bam file to stdout or -S - --i, --in-sam/-o, --out-sam - By default, inputs are assumed to be in BAM format and output are output - in BAM format. Use these options to specify the use of SAM format for - inputs or outputs. - --I (string, filename) input file name - The input file must be sorted and indexed. - --S (string, filename) output file name - --L (string, filename) log file name - -Usage ------ - umi_tools group -I infile.bam --output-bam -S grouped.bam -L group.log -- - +@UMI_GROUPING_HELP@ ]]>