shhh.flows output is very small! What's wrong?

Hi everybody,

I’m sorry if this might not be a smart question… I’m having troubles with running the shhh.flows command. I’m just using the exact command from the SOP tutorial. It runs quite quickly (only a few hours) and it doesn’t give any error, but then the output …shhh.fasta and …shhh.names are only a few kilobytes which I assume it should not be possible. I have some hundred thousand sequences so they should be much bigger size. Now I realized that also the output of trim.flows, the …flow.files is only 1kb, while the individual flow files of the different samples are around 30Mb each. But I see from my office mate who ran some samples from the same plates (and have more or less the same amount of sequences) that his individual flow files are about 100Mb… What could be going wrong? Is perhaps something going wrong in the trim.flows step (although I did not get any error)? The 454 machine was a FLX Titanium…

Thanks for your help!
Joanito

Can you post the log file of running trim.flows and shhh.flows? Also, could you run summary.seqs using fasta=whatever.shhh.fasta, name=whatever.shhh.names ?

Thanks,
Pat

Dear Pat,

here’s the output of summary seqs on the shhh.fasta:

Start End NBases Ambigs Polymer NumSeqs
Minimum: 1 266 266 0 3 1
2.5%-tile: 1 289 289 0 4 131
25%-tile: 1 299 299 0 5 1307
Median: 1 303 303 0 5 2613
75%-tile: 1 308 308 0 6 3919
97.5%-tile: 1 321 321 0 6 5094
Maximum: 1 347 347 0 9 5224
Mean: 1 303.892 303.892 0 5.07714

of Seqs: 5224

And this is for the shhh.names:

[WARNING]: We found more than 25% of the bases in sequence 3E4NOS02I60VG to be a
mbiguous. Mothur is not setup to process protein sequences.

Start End NBases Ambigs Polymer NumSeqs
Minimum: 1 321414 321414 304273 237 1
2.5%-tile: 1 321414 321414 304273 237 1
25%-tile: 1 321414 321414 304273 237 1
Median: 1 321414 321414 304273 237 1
75%-tile: 1 321414 321414 304273 237 1
97.5%-tile: 1 321414 321414 304273 237 1
Maximum: 1 321414 321414 304273 237 1
Mean: 1 321414 321414 304273 237

of Seqs: 1

Talking to my colleague again, he told me that sometime ago he had problems with shhh.flows too, and you suggested him to use minflows=200 and maxflows=800 in the trim.seqs step, because of the titanium flx platform. Does this make sense?

My data is separated into 5 different sff files, each of which contains many samples but only a few are mine. What I’ve posted here is the output of only one of those 5, for which I’ve now got to the shhh.flows step. I was running the trim.seqs step on those different datasets running mothur in parallel in different terminals. Might have this been the problem and I need to let it go alone for each dataset? (I’ve now seen in the shhh.flows logfile, posted below, that running out of memory might have been the problem).

Also, would you suggest a way to merge the data, should I do it at the beginning, or is it best to merge the fasta files after I’ve denoised each separately? (I don’t know if this would be a problem, but some of my samples coming from different runs have the same barcodes)

Sorry for so many questions, I’m very new to mothur.
Thanks a lot for your kind help,
Joanito

Here’s the log for the trim.flows and shhh.flows (I hope these are the correct ones. They should be… but I was not aware of the production of log files in the mothur folder and now I’ve lost track of which is which, as I’ve run quite many commands by now. Is there an easy way to understand which log belongs to which file?):

mothur > trim.flows(flow=C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.flow, oligos=C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.oligos, pdiffs=2, bdiffs=1, processors=2)

Using 2 processors.
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Output File Names:
C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.trim.flow
C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.scrap.flow
C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.S.amabilisJOA120525-01.flow
C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.S.amabilisJOA120528-05.flow
C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.FungusGardenJOA120528-02.flow
C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.FungusGardenJOA120604-03.flow
C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.flow.files

mothur > shhh.flows(file=C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.flow.files, processors=2)
Unable to open C:\mothur\lookupFiles\LookUp_Titanium.pat. Trying mothur’s executable location C:\mothur\LookUp_Titanium.pat

Using 2 processors.

Processing C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.S.amabilisJOA120525-01.flow (file 1 of 4) <<<<<
Reading flowgrams…
Identifying unique flowgrams…
Calculating distances between flowgrams…

Total time: 8 23.478

Clustering flowgrams…
********************###########
Reading matrix: |||||||||||||||||||||||||||||||||||||||||||||||||||


Denoising flowgrams…
iter maxDelta nLL cycletime
1 284 -62305.6 10 9.589
2 52.0664 4.48308e+006 2 2.793
3 14.2841 4.87232e+006 2 1.607
4 9.74583 5.01151e+006 1 1.497
5 6.97987 5.04193e+006 2 1.482
6 5.23753 5.04795e+006 1 1.467
7 4.0277 5.04009e+006 2 1.45
8 3.15855 5.04954e+006 1 1.436
9 2.50732 5.05754e+006 2 1.45
10 2.02621 5.05942e+006 1 1.404
11 1.71638 5.06783e+006 2 1.404
12 1.59886 5.07636e+006 1 1.389
13 1.4908 5.0744e+006 1 1.373
14 1.39142 5.07298e+006 2 1.404
15 1.30029 5.07958e+006 1 1.372
16 1.2152 5.08187e+006 1 1.373
17 1.13855 5.07907e+006 2 1.357
18 1.12019 5.08575e+006 1 1.388
19 0.995632 5.08127e+006 2 1.373
20 0.936058 5.08863e+006 1 1.373
21 0.880666 5.08904e+006 1 1.357
22 0.829354 5.08626e+006 2 1.357
23 0.781708 5.09417e+006 1 1.342


982 0.000231316 5.10375e+006 1 1.279 983 0.00022947 5.10375e+006 2 1.28 984 0.000227638 5.10375e+006 1 1.279 985 0.000225821 5.10375e+006 1 1.279 986 0.000224019 5.10375e+006 2 1.279 987 0.000222231 5.10375e+006 1 1.28 988 0.000220457 5.10375e+006 1 1.279 989 0.000218698 5.10375e+006 1 1.279 990 0.000216953 5.10375e+006 2 1.279 991 0.000215221 5.10375e+006 1 1.279 992 0.000213504 5.10375e+006 1 1.28 993 0.0002118 5.10375e+006 2 1.279 994 0.000210109 5.10375e+006 1 1.279 995 0.000208432 5.10375e+006 1 1.279 996 0.000206769 5.10375e+006 1 1.28 997 0.000205119 5.10375e+006 2 1.279 998 0.000203482 5.10375e+006 1 1.263 999 0.000201858 5.10375e+006 1 1.264 1000 0.000200247 5.10375e+006 1 1.279

Finalizing…
Total time to process C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.S.amabilisJOA120525-01.flow: 1298 1298.25

Processing C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.S.amabilisJOA120528-05.flow (file 2 of 4) <<<<<
Reading flowgrams…
Identifying unique flowgrams…
Calculating distances between flowgrams…

Total time: 19 32.979

Clustering flowgrams…
********************###########
Reading matrix: |||||||||||||||||||||||||||||||||||||||||||||||||||


Denoising flowgrams…
iter maxDelta nLL cycletime
1 137 -44429.8 16 16.537
2 33.0198 4.61613e+006 2 2.418
3 13.8798 4.68538e+006 3 2.075
4 8.71993 4.69671e+006 2 2.012
5 5.92521 4.69381e+006 2 2.028
6 4.21728 4.69955e+006 2 1.997
7 3.18928 4.69956e+006 2 1.997
8 2.91975 4.69749e+006 2 1.981
9 2.65103 4.69121e+006 2 1.997
10 2.43759 4.69721e+006 2 1.981
11 2.43159 4.69919e+006 2 1.981
12 2.34968 4.70156e+006 1 1.981
13 2.28325 4.6951e+006 2 1.982
14 2.20573 4.69041e+006 2 1.981
15 2.11976 4.69123e+006 2 1.965
16 2.02789 4.69171e+006 2 1.982
17 1.93233 4.69788e+006 2 1.965
18 1.835 4.69637e+006 2 1.966
19 1.73755 4.69302e+006 2 1.981
20 1.64117 4.69304e+006 2 1.981
21 1.54699 4.69129e+006 2 1.966
22 1.4557 4.69116e+006 2 1.981
23 1.3679 4.69576e+006 2 1.966
24 1.28402 4.69251e+006 2 1.965
25 1.20422 4.69149e+006 2 1.966
26 1.12861 4.68333e+006 2 1.966
27 1.05727 4.6969e+006 2 1.965
28 1.02966 4.69356e+006 2 1.966
29 1.01437 4.69372e+006 2 1.965
30 1.00755 4.69125e+006 2 1.966
31 0.994544 4.69001e+006 2 1.966
32 0.977814 4.68886e+006 2 1.965
33 0.95652 4.69412e+006 2 1.966
34 0.93144 4.68987e+006 2 1.95
35 0.903169 4.69104e+006 2 1.965
36 0.87229 4.68805e+006 2 1.966
37 0.839367 4.69096e+006 2 1.966
38 0.820901 4.69681e+006 2 1.965
39 0.866073 4.69184e+006 2 1.95
40 0.909824 4.69302e+006 2 1.966
41 0.951587 4.69297e+006 2 1.95
42 0.990804 4.69666e+006 2 1.981
43 1.02694 4.69429e+006 1 1.95
44 1.0595 4.6943e+006 2 1.966
45 1.08805 4.69419e+006 2 1.95
46 1.11223 4.6969e+006 2 1.965
47 1.13176 4.69928e+006 2 1.95
48 1.14645 4.69078e+006 2 1.966
49 1.15621 4.69945e+006 2 1.95
50 1.16103 4.69945e+006 2 1.966
51 1.16101 4.69946e+006 2 1.965
52 1.15632 4.69093e+006 2 1.95
53 1.14718 4.6917e+006 2 1.966
54 1.13392 4.69156e+006 2 1.95
55 1.11686 4.6966e+006 2 1.95
56 1.0964 4.69672e+006 2 1.965
57 1.07292 4.69225e+006 2 1.95
58 1.04685 4.69363e+006 2 1.95
59 1.01857 4.69647e+006 2 1.966
60 0.988499 4.69466e+006 2 1.95
61 0.956999 4.69466e+006 2 1.95
62 0.924428 4.69103e+006 2 1.965
63 0.891113 4.69226e+006 2 1.95
64 0.85735 4.69226e+006 2 1.95
65 0.823406 4.69227e+006 2 1.966
66 0.789516 4.69668e+006 2 1.966
67 0.755885 4.69668e+006 1 1.95
68 0.72269 4.69516e+006 2 1.965
69 0.69008 4.69516e+006 2 1.95
70 0.658178 4.70216e+006 2 1.95
71 0.627085 4.69585e+006 2 1.95
72 0.596882 4.69586e+006 2 1.95
73 0.56763 4.70151e+006 2 1.966
74 0.539374 4.69615e+006 2 1.965
75 0.512145 4.69616e+006 2 1.95
76 0.485962 4.6979e+006 2 1.95
77 0.460833 4.69791e+006 2 1.935
78 0.436756 4.69791e+006 2 1.95
79 0.413725 4.69791e+006 2 1.95
80 0.391724 4.69792e+006 2 1.95
81 0.370734 4.69792e+006 2 1.95
82 0.35073 4.69792e+006 2 1.95
83 0.331687 4.69793e+006 2 1.966
84 0.313576 4.69793e+006 2 1.95
85 0.296364 4.69793e+006 2 1.95
86 0.28002 4.70227e+006 2 1.95
87 0.264511 4.70445e+006 2 1.966
88 0.249804 4.70461e+006 2 1.965
89 0.235864 4.70461e+006 1 1.966
90 0.222659 4.70496e+006 2 1.965
91 0.210157 4.69679e+006 2 1.95
92 0.198324 4.70396e+006 2 1.966
93 0.187129 4.70615e+006 2 1.966
94 0.176542 4.70762e+006 2 1.95
95 0.166532 4.70977e+006 2 1.965
96 0.157072 4.70977e+006 2 1.95
97 0.148134 4.70977e+006 2 1.981
98 0.13969 4.70977e+006 2 1.95
99 0.131715 4.70977e+006 2 1.95
100 0.124186 4.70977e+006 2 1.95
101 0.120387 4.71025e+006 2 1.95
102 0.118533 4.71025e+006 2 1.951
103 0.116739 4.7062e+006 2 1.965
104 0.114794 4.7062e+006 2 1.95
105 0.112829 4.7062e+006 2 1.95
106 0.110836 4.7062e+006 2 1.95
107 0.10882 4.7062e+006 2 1.935
108 0.106783 4.7062e+006 2 1.935
109 0.10473 4.7062e+006 2 1.95
110 0.102665 4.7062e+006 2 1.95
111 0.100592 4.7062e+006 1 1.95
112 0.0986145 4.71042e+006 2 1.95
113 0.0966191 4.70709e+006 2 1.934
114 0.0945235 4.70709e+006 2 1.95
115 0.0924466 4.70662e+006 2 1.95
116 0.0904771 4.70753e+006 2 1.95
117 0.0884008 4.70753e+006 2 1.95
118 0.0863516 4.70829e+006 2 1.95
119 0.084317 4.70829e+006 2 1.95
120 0.0822992 4.70829e+006 2 1.95
121 0.0803 4.70932e+006 2 1.95
122 0.0783214 4.70932e+006 2 1.95
123 0.0763652 4.70932e+006 2 1.95
124 0.0744328 4.70932e+006 2 1.95
125 0.0725257 4.70932e+006 2 1.95
126 0.0706451 4.70932e+006 2 1.95
127 0.0687923 4.70932e+006 2 1.95
128 0.099824 4.70191e+006 2 1.95
129 0.0651738 4.70191e+006 2 1.95
130 0.0634098 4.70191e+006 2 1.95
131 0.0616769 4.70191e+006 1 1.95
132 0.0599758 4.70191e+006 2 1.95
133 0.0583069 4.70191e+006 2 1.935
134 0.0566706 4.70191e+006 2 1.965
135 0.0550671 4.70191e+006 2 1.95
136 0.0534968 4.70192e+006 2 1.95
137 0.0519598 4.70192e+006 2 1.95
138 0.0504561 4.70192e+006 2 1.935
139 0.0489858 4.70192e+006 2 1.95
140 0.0475488 4.70192e+006 2 1.95
141 0.046145 4.70192e+006 2 1.95
142 0.0447743 4.70192e+006 2 1.95
143 0.0434364 4.70192e+006 2 1.934
144 0.0421311 4.70192e+006 2 1.95
145 0.0408581 4.70192e+006 2 1.95
146 0.039617 4.70192e+006 2 1.95
147 0.0384076 4.70192e+006 2 1.95
148 0.0372294 4.70192e+006 2 1.95
149 0.036082 4.70192e+006 2 1.95
150 0.034965 4.70192e+006 1 1.935
151 0.0339781 4.70222e+006 2 1.95
152 0.0329 4.70222e+006 2 1.95
153 0.0318688 4.70222e+006 2 1.95
154 0.0996921 4.7064e+006 2 1.95
155 0.0298914 4.7064e+006 2 1.965
156 0.028944 4.7064e+006 2 1.95
157 0.0280235 4.70958e+006 2 1.935
158 0.0271294 4.70958e+006 2 1.95
159 0.026261 4.70958e+006 2 1.95
160 0.0254178 4.70958e+006 2 1.95
161 0.0245992 4.70958e+006 2 1.95
162 0.0238048 4.70958e+006 2 1.95
163 0.0230339 4.70958e+006 2 1.95
164 0.022286 4.71348e+006 2 1.95
165 0.0215606 4.71348e+006 2 1.966
166 0.020857 4.71348e+006 2 1.934
167 0.0201748 4.71348e+006 2 1.966
168 0.0195134 4.71348e+006 2 1.934
169 0.0188723 4.71348e+006 2 1.95
170 0.0182509 4.71348e+006 1 1.95
171 0.0176488 4.71348e+006 2 1.95
172 0.0170654 4.71348e+006 2 1.95
173 0.0165002 4.71348e+006 2 1.95
174 0.0159528 4.71349e+006 2 1.95
175 0.0154225 4.71349e+006 2 1.95
176 0.014909 4.71349e+006 2 1.95
177 0.0144119 4.71349e+006 2 1.935
178 0.0139305 4.71349e+006 2 1.95
179 0.0134645 4.71349e+006 2 1.95
180 0.0990421 4.70882e+006 2 1.934
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182 0.0121544 4.70882e+006 2 1.95
183 0.0117455 4.70882e+006 2 1.95
184 0.01135 4.70882e+006 2 1.95
185 0.0109672 4.70882e+006 2 1.935
186 0.010597 4.70882e+006 2 1.95
187 0.0102388 4.70882e+006 2 1.935
188 0.00989239 4.70882e+006 2 1.95
189 0.00955733 4.70882e+006 1 1.95
190 0.00923329 4.70882e+006 2 1.95
191 0.00891993 4.70882e+006 2 1.95
192 0.00861692 4.70882e+006 2 1.934
193 0.00832393 4.70882e+006 2 1.95
194 0.00804066 4.70882e+006 2 1.95
195 0.0077668 4.70882e+006 2 1.95
196 0.00750205 4.70882e+006 2 1.95
197 0.00724612 4.70882e+006 2 1.95
198 0.00699873 4.70882e+006 2 1.95
199 0.00675962 4.70882e+006 2 1.95
200 0.00652851 4.70882e+006 2 1.935
201 0.00630515 4.70882e+006 2 1.95
202 0.00608929 4.70882e+006 2 1.95
203 0.00588069 4.70882e+006 2 1.934
204 0.00567911 4.70882e+006 2 1.95
205 0.00548433 4.70882e+006 2 1.95
206 0.00529612 4.70882e+006 2 1.935
207 0.00511426 4.70882e+006 2 1.965
208 0.00493856 4.70882e+006 1 1.935
209 0.00476881 4.70882e+006 2 1.935
210 0.00460482 4.70882e+006 2 1.95
211 0.00444638 4.70882e+006 2 1.95
212 0.00429333 4.70882e+006 2 1.934
213 0.00414548 4.70882e+006 2 1.95
214 0.00400266 4.70882e+006 2 1.95
215 0.00386471 4.70882e+006 2 1.935
216 0.00373146 4.70882e+006 2 1.95
217 0.00360275 4.70882e+006 2 1.95
218 0.00347843 4.70882e+006 2 1.95
219 0.00335837 4.70882e+006 2 1.934
220 0.0032424 4.70882e+006 2 1.95
221 0.00313041 4.70882e+006 2 1.95
222 0.00302224 4.70882e+006 2 1.95
223 0.00291779 4.70882e+006 2 1.935
224 0.00281691 4.70882e+006 2 1.95
225 0.00271949 4.70882e+006 2 1.965
226 0.00262541 4.70882e+006 1 1.935
227 0.00253457 4.70882e+006 2 1.965
228 0.00244685 4.70882e+006 2 1.951
229 0.00236214 4.70882e+006 2 1.934
230 0.00228034 4.70882e+006 2 1.95
231 0.00220136 4.70882e+006 2 1.95
232 0.00212509 4.70882e+006 2 1.934
233 0.00205146 4.70882e+006 2 1.934
234 0.00198035 4.70882e+006 2 1.95
235 0.0019117 4.70882e+006 2 1.95
236 0.00184542 4.70882e+006 2 1.951
237 0.00178142 4.70882e+006 2 1.934
238 0.00171963 4.70882e+006 2 1.934
239 0.00165998 4.70882e+006 2 1.95
240 0.00160238 4.70882e+006 2 1.95
241 0.00154677 4.70882e+006 2 1.95
242 0.00149309 4.70882e+006 2 1.95
243 0.00144126 4.70882e+006 2 1.934
244 0.00139122 4.70882e+006 2 1.951
245 0.00134291 4.70882e+006 2 1.95
246 0.00129627 4.70882e+006 1 1.934
247 0.00125125 4.70882e+006 2 1.95
248 0.00120778 4.70882e+006 2 1.95
249 0.00116582 4.70882e+006 2 1.95
250 0.00112532 4.70882e+006 2 1.934
251 0.00108621 4.70882e+006 2 1.95
252 0.00104846 4.70882e+006 2 1.951
253 0.00101202 4.70882e+006 2 1.934
254 0.000976842 4.70882e+006 2 1.95
255 0.000942883 4.70882e+006 2 1.95
256 0.000910101 4.70882e+006 2 1.975
257 0.000878456 4.70882e+006 2 1.963
258 0.000847909 4.70882e+006 2 1.966
259 0.000818421 4.70882e+006 2 1.95
260 0.000789957 4.70882e+006 2 1.95
261 0.00076248 4.70882e+006 2 1.95
262 0.000735957 4.70882e+006 2 1.934
263 0.000710355 4.70882e+006 2 1.95
264 0.000685641 4.70882e+006 2 1.934
265 0.000661786 4.70882e+006 2 1.95
266 0.000638759 4.70882e+006 1 1.966
267 0.000616532 4.70882e+006 2 1.966
268 0.000595077 4.70882e+006 2 1.95
269 0.000574368 4.70882e+006 2 1.95
270 0.000554378 4.70882e+006 2 1.935
271 0.000535082 4.70882e+006 2 1.95
272 0.000516457 4.70882e+006 2 1.965
273 0.00049848 4.70882e+006 2 1.95
274 0.000481127 4.70882e+006 2 1.95
275 0.000464378 4.70882e+006 2 1.951
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660 1.54073e-006 4.70882e+006 2 1.935
661 1.52485e-006 4.70882e+006 2 1.934
662 1.50914e-006 4.70882e+006 2 1.95
663 1.49359e-006 4.70882e+006 2 1.95
664 1.4782e-006 4.70882e+006 2 1.95
665 1.46297e-006 4.70882e+006 2 1.95
666 1.4479e-006 4.70882e+006 2 1.95
667 1.43298e-006 4.70882e+006 2 1.934
668 1.41822e-006 4.70882e+006 2 1.951
669 1.4036e-006 4.70882e+006 2 1.95
670 1.38914e-006 4.70882e+006 2 1.95
671 1.37483e-006 4.70882e+006 2 1.95
672 1.36066e-006 4.70882e+006 2 1.95
673 1.34664e-006 4.70882e+006 2 1.95
674 1.33277e-006 4.70882e+006 2 1.934
675 1.31904e-006 4.70882e+006 1 1.95
676 1.30545e-006 4.70882e+006 2 1.95
677 1.292e-006 4.70882e+006 2 1.95
678 1.27868e-006 4.70882e+006 2 1.935
679 1.26551e-006 4.70882e+006 2 1.95
680 1.25247e-006 4.70882e+006 2 1.95
681 1.23957e-006 4.70882e+006 2 1.965
682 1.22679e-006 4.70882e+006 2 1.95
683 1.21415e-006 4.70882e+006 2 1.935
684 1.20165e-006 4.70882e+006 2 1.935
685 1.18926e-006 4.70882e+006 2 1.95
686 1.17701e-006 4.70882e+006 2 1.95
687 1.16488e-006 4.70882e+006 2 1.95
688 1.15288e-006 4.70882e+006 2 1.95
689 1.141e-006 4.70882e+006 2 1.95
690 1.12925e-006 4.70882e+006 2 1.965
691 1.11761e-006 4.70882e+006 2 1.95
692 1.1061e-006 4.70882e+006 2 1.935
693 1.0947e-006 4.70882e+006 2 1.95
694 1.08342e-006 4.70882e+006 2 1.95
695 1.07226e-006 4.70882e+006 1 1.95
696 1.06121e-006 4.70882e+006 2 1.934
697 1.05028e-006 4.70882e+006 2 1.95
698 1.03946e-006 4.70882e+006 2 1.95
699 1.02875e-006 4.70882e+006 2 1.95
700 1.01815e-006 4.70882e+006 2 1.935
701 1.00766e-006 4.70882e+006 2 1.95
702 9.97279e-007 4.70882e+006 2 1.95

Finalizing…
Total time to process C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.S.amabilisJOA120528-05.flow: 1386 1386.19

Processing C:\Users\xtr552\Desktop\MothurAnalysis\ThirdRun\H3E4NOS02.FungusGardenJOA120528-02.flow (file 3 of 4) <<<<<
Reading flowgrams…
Identifying unique flowgrams…
Calculating distances between flowgrams…

Total time: 30 67.19

Clustering flowgrams…
********************###########
Reading matrix: |||||||||||||||||||||||||||||||||||||||||||||||||||


[ERROR]: std::bad_alloc has occurred in the ShhherCommand class function driver. This error indicates your computer is running out of memory. This is most commonly caused by trying to process a dataset too large, using multiple processors, or a file format issue. If you are running our 32bit version, your memory usage is limited to 4G. If you have more than 4G of RAM and are running a 64bit OS, using our 64bit version may resolve your issue. If you are using multiple processors, try running the command with processors=1, the more processors you use the more memory is required. Also, you may be able to reduce the size of your dataset by using the commands outlined in the Schloss SOP, http://www.mothur.org/wiki/Schloss_SOP. If you are uable to resolve the issue, please contact Pat Schloss at mothur.bugs@gmail.com, and be sure to include the mothur.logFile with your inquiry.

Hi everybody,

I’ve now got to repeat the analysis using sff multiple in order to merge all the data that is coming from 5 different runs (I have only a few samples that are mine in each of the file and some of the barcodes overlap between runs). So I have prepared 5 different .oligos files to map the correct samples in the sff files. It all works, but at the end of all the process I get only 8 unique sequences across all my 33 samples! This is impossible as I’m just redoing the analysis with Mothur after I’ve analyzed everything with Qiime and I know there are about 1000 OTUs in my dataset.
I’ve also tried to redo every step singularly and I can see that the output of shhh.flows is very small, the shhh.fasta files are just a few kilobytes and contain 4 or 5 senquences per run. I’m just using the exact commands I find in the SOP tutorial and I get no error message. Now I’m using a very powerful pc with 4 cores so I know it’s not a problem of memory runout like I had before.

What could be going wrong?
I’m sorry if this sounds basic, I hope you can help me.

Thanks!
Joanito

What version of mothur are you running? Can you tell where the number of uniques is dropping? Is mothur scrapping your flows after the trim.flows command or are the sequences being put into the names file in the shhh.flows command?

Can you run this instead…

summary.seqs(fasta=shhh.fasta, names=shhh.names)

Instead of shhh.fasta and shhh.names, use your specific files.

Pat