These are chat archives for dereneaton/ipyrad

Nov 2017
Nov 03 2017 01:27
@dereneaton @isaacovercast I really want to know which step needs the biggest space in a device? For a big dataset I passed the Step3 but failed in Step5!
Isaac Overcast
Nov 03 2017 03:02
@ChaoShenzjs The directories created by steps 2 and 3 probably consumes the most disk (_edits & _clust_*)
Jenny Archibald
Nov 03 2017 19:32

@dereneaton @isaacovercast Hi! Again similar to @tommydevitt, I got this error while attempting to run bpp,

"("bpp: /lib64/ version `GLIBC_2.14' not found (required by bpp)\n",

Following the previous instruction in this forum, I ran conda install conda-build, without issues. That was not enough to fix the problem, and I am guessing from Isaac's comment about running a conda build that I am supposed to do more. However, I'm pretty new to some of these tools, and I don't know exactly what this means "You can fix this by running a conda build on the conda.recipe/bpp recipe from the github repo." Would you have time to give more details on exactly how we do that? I'm afraid I'll mess it up worse if I just muck around with it.

Isaac Overcast
Nov 03 2017 20:43
@jenarch Hi Jenny, try this:
git clone
conda build ipyrad/conda.recipe/bpp/
conda install --use-local bpp
Nov 03 2017 20:57
@isaacovercast Hi Isaac, not sure if you were able to look at those fastq files shared through box drop yet, but they looked normal to me. One thing I have noticed was that when I ran step 1 with the sorted fastqs, in the json file only the first barcode was read: "barcodes":{
"AR56":"CCGAAT", but when I ran step 1 with the same barcode file, but other data, and ipyrad did the demulitplexing from raw data, both barcodes were read: "barcodes":{
"AR56":"CCGAAT+GATCGTTG", I'm not sure why the barcode file is being read properly in one instance but not the other. When I run step 2 with the sorted fastq data the json file updates and shows both barcodes, but no fastq files are created in the edits folder. A stats file is created with this info: Empty DataFrame
Columns: []
Index: [AR14, AR17, AR27, AR31, AR32, AR37, AR51, AR52, AR56, AR63, AR64, AR75, AR78, AR79, AR82, AR83, AR84, AR85, AR87, AR88, AR90, AR91, AR92, AR93, AR97, AR98, MY1014, MY1033, MY1034, MY1038, MY1039, MY1046, MY1056, MY1057, MY1058, MY1059, MY1060, MY1148, MY1149, MY1157, MY1158, MY1160, MY1161, MY1163, MY1164, MY1172, MY1173, MY1317, MY1318, MY1360, MY1361, MY394, MY399, MY405, MY409, MY4203, MY4204, MY4208, MY4209, MY4210, MY4211, MY4213, MY4217, MY4218, MY4221, MY4225, MY4226, MY4230, MY4231, MY4234, MY4235, MY4238, MY4239, MY4246, MY4250, MY4251, MY4258, MY4262, MY4265, MY4366, MY4371, MY4678, MY975, MY977, MY978, MY979, MY985, MY986]