galaxy-user January 2014

galaxy-user@lists.galaxyproject.org

32 participants
41 discussions

Help with Summary Statistics
by D. A. Cowart 23 May '14

23 May '14

Hello, I am attempting to use Galaxy to calculate the mean sequence read length and identify the range of read lengths for my 454 data. The data has already been organized and sorted by species. The format of the data is as follows: >HD4AU5D01BHBCQCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC >HD4AU5D01A093MCTCTGTCGCTCTGTCTCTCTTCTCTCTCTCTCTCTCT etc...for each species I have attempted to use the "Summary Statistics" button, however it appears to only be for numerical data and not sequence data. Is this tool/task available via Galaxy? Thank you, Dominique Cowart User name: dac330

6 5

Picrust upload
by Ines 12 Feb '14

12 Feb '14

Hello, I was trying to run a dataset through Picrust on the web-based Galaxy. I did the OTU table in Qiime as explained in http://picrust.github.com/picrust/tutorials/otu_picking.html#otu-picking-tu…, with the command line: pick_closed_reference_otus.py -i $path/file.fasta -r $path/rep-set/97_otus.fasta -o $path/OTU_Table.biom. I uploaded the file in Get Data, and I want to run the Normalize by Copy Number step, but I get the following message History does not include a dataset of the required format / build. I am not sure if when I generate the OTU Table I need to specify a particular format, or what the problem is. I would really appreciate some advice. I attach the biom file generated in case you need to take a look. Thank you very much in advance.

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special character $ gets converted to X in tool
by Ketan Maheshwari 01 Feb '14

01 Feb '14

Hi, In a test tool that I am working on, I need to enter text preceded by a $ sign to be interpreted as an environment variable by the underlying running script. However, it seems that the $ sign gets converted to X when it gets passed to the tool executable. Is there a way to work around this or should I be doing something else to pass environment variables via Galaxy tool UI. Thanks, Ketan

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problem with scientific notation sorting and filtering in galaxy
by Douglas Cavener 31 Jan '14

31 Jan '14

I have found that numbers in scientific notation (e.g 9.8E-9) are incorrectly sorted or filtered by Galaxy. Looks like the negative exponent is ignored and so if you sorting something like P values (see example below) then you find these ordered above P=1 instead at the other end of the list where P is near zero. In the example below a KEGG list is ordered on column 5 in asending order by galaxy. If you scroll to the bottom of the list, however, you will see the smallest numbers all of which are in scientific notation. I should note that I can can copy this into excel and excel will properly sort scientific notation. So this seems to be a specific problem with Galaxy. I found this problem with both filtering and sorting functions of Galaxy. Doug Cavener 41 0.53 26 1.0 0.0000025 cfa04146=Peroxisome 39 0.53 25 1.0 0.0000038 cfa04640=Hematopoietic cell lineage 29 0.59 13 1.0 0.0000044 cfa00980=Metabolism of xenobiotics by cytochrome P450 22 0.63 10 1.0 0.000016 cfa00140=Steroid hormone biosynthesis 28 0.56 19 1.0 0.000027 cfa05204=Chemical carcinogenesis 11 0.85 3 1.0 0.000033 cfa00511=Other glycan degradation 25 0.57 16 1.0 0.000052 cfa00600=Sphingolipid metabolism 9 0.90 2 1.0 0.000074 cfa00740=Riboflavin metabolism 23 0.58 14 1.0 0.000079 cfa00380=Tryptophan metabolism 15 0.68 6 1.0 0.000096 cfa04977=Vitamin digestion and absorption 22 0.56 17 1.0 0.00017 cfa00071=Fatty acid metabolism 22 0.56 17 1.0 0.00017 cfa05150=Staphylococcus aureus infection 26 0.53 27 1.0 0.00017 cfa00982=Drug metabolism - cytochrome P450 49 0.44 42 1.0 0.00018 cfa04142=Lysosome 56 0.42 47 1.0 0.00024 cfa04630=Jak-STAT signaling pathway 34 0.48 35 1.0 0.00026 cfa03320=PPAR signaling pathway 13 0.68 5 1.0 0.00027 cfa00531=Glycosaminoglycan degradation 20 0.56 21 1.0 0.00043 cfa04975=Fat digestion and absorption 22 0.54 24 1.0 0.00043 cfa00830=Retinol metabolism 19 0.56 20 1.0 0.00053 cfa00565=Ether lipid metabolism 14 0.61 11 1.0 0.00092 cfa00591=Linoleic acid metabolism 20 0.53 28 1.0 0.0011 cfa02010=ABC transporters 22 0.50 32 1.0 0.0015 cfa00280=Valine, leucine and isoleucine degradation 33 0.44 44 1.0 0.0020 cfa04974=Protein digestion and absorption 15 0.56 21 1.0 0.0022 cfa03440=Homologous recombination 14 0.56 19 1.0 0.0028 cfa00650=Butanoate metabolism 35 0.42 48 1.0 0.0035 cfa05323=Rheumatoid arthritis 16 0.52 31 1.0 0.0043 cfa00350=Tyrosine metabolism 16 0.52 31 1.0 0.0043 cfa05320=Autoimmune thyroid disease 12 0.57 15 1.0 0.0044 cfa00592=alpha-Linolenic acid metabolism 15 0.52 30 1.0 0.0054 cfa00760=Nicotinate and nicotinamide metabolism 21 0.47 39 1.0 0.0054 cfa05144=Malaria 84 0.36 66 1.0 0.0059 cfa04080=Neuroactive ligand-receptor interaction 16 0.48 33 1.0 0.0091 cfa05143=African trypanosomiasis 12 0.52 29 1.0 0.011 cfa00340=Histidine metabolism 14 0.48 34 0.99 0.015 cfa05330=Allograft rejection 9 0.56 18 1.0 0.015 cfa04614=Renin-angiotensin system 3 1.0 1 1.0 0.021 cfa00780=Biotin metabolism 35 0.38 56 0.99 0.021 cfa00240=Pyrimidine metabolism 15 0.44 43 0.99 0.030 cfa04940=Type I diabetes mellitus 11 0.48 36 0.99 0.032 cfa00563=Glycosylphosphatidylinositol(GPI)-anchor biosynthesis 29 0.38 54 0.98 0.032 cfa04512=ECM-receptor interaction 20 0.41 50 0.98 0.034 cfa04622=RIG-I-like receptor signaling pathway 9 0.50 32 0.99 0.037 cfa00100=Steroid biosynthesis 10 0.48 37 0.99 0.042 cfa04950=Maturity onset diabetes of the young 7 0.54 23 0.99 0.042 cfa03450=Non-homologous end-joining 6 0.55 22 0.99 0.055 cfa00430=Taurine and hypotaurine metabolism 6 0.55 22 0.99 0.055 cfa00790=Folate biosynthesis 22 0.38 58 0.97 0.060 cfa05140=Leishmaniasis 12 0.43 46 0.97 0.062 cfa00512=Mucin type O-Glycan biosynthesis 3 0.75 4 0.99 0.068 cfa00061=Fatty acid biosynthesis 3 0.75 4 0.99 0.068 cfa00232=Caffeine metabolism 23 0.37 62 0.96 0.070 cfa04115=p53 signaling pathway 29 0.36 64 0.96 0.071 cfa00564=Glycerophospholipid metabolism 8 0.47 38 0.98 0.071 cfa00120=Primary bile acid biosynthesis 20 0.38 59 0.96 0.074 cfa00561=Glycerolipid metabolism 19 0.38 57 0.96 0.076 cfa04612=Antigen processing and presentation 48 0.34 74 0.95 0.076 cfa05152=Tuberculosis 18 0.38 53 0.96 0.077 cfa00510=N-Glycan biosynthesis 2 1.0 1 1.0 0.077 cfa00290=Valine, leucine and isoleucine biosynthesis 16 0.39 52 0.96 0.079 cfa00500=Starch and sucrose metabolism 16 0.39 52 0.96 0.079 cfa04621=NOD-like receptor signaling pathway 12 0.41 49 0.96 0.080 cfa00410=beta-Alanine metabolism 7 0.47 39 0.97 0.093 cfa00604=Glycosphingolipid biosynthesis - ganglio series 33 0.34 72 0.94 0.094 cfa05146=Amoebiasis 8 0.44 41 0.96 0.097 cfa00900=Terpenoid backbone biosynthesis 4 0.57 15 0.98 0.099 cfa00460=Cyanoamino acid metabolism 4 0.57 15 0.98 0.099 cfa00750=Vitamin B6 metabolism 12 0.40 51 0.95 0.10 cfa00052=Galactose metabolism 12 0.40 51 0.95 0.10 cfa00983=Drug metabolism - other enzymes 9 0.43 46 0.96 0.10 cfa00040=Pentose and glucuronate interconversions 5 0.50 32 0.97 0.11 cfa04122=Sulfur relay system 26 0.35 70 0.93 0.12 cfa04064=NF-kappa B signaling pathway 10 0.40 51 0.94 0.13 cfa05332=Graft-versus-host disease 17 0.36 63 0.92 0.13 cfa00480=Glutathione metabolism 21 0.35 68 0.92 0.13 cfa05133=Pertussis 7 0.44 45 0.95 0.13 cfa00770=Pantothenate and CoA biosynthesis 12 0.38 61 0.92 0.15 cfa03410=Base excision repair 21 0.34 71 0.90 0.15 cfa04976=Bile secretion 15 0.36 65 0.90 0.16 cfa04978=Mineral absorption 4 0.50 32 0.96 0.16 cfa00072=Synthesis and degradation of ketone bodies 50 0.32 78 0.88 0.16 cfa00230=Purine metabolism 8 0.40 51 0.93 0.16 cfa05310=Asthma 6 0.43 46 0.94 0.17 cfa00603=Glycosphingolipid biosynthesis - globo series 2 0.67 8 0.98 0.19 cfa00785=Lipoic acid metabolism 8 0.38 55 0.90 0.20 cfa00062=Fatty acid elongation 8 0.38 55 0.90 0.20 cfa03430=Mismatch repair 11 0.35 67 0.88 0.22 cfa05340=Primary immunodeficiency 12 0.34 73 0.85 0.25 cfa00260=Glycine, serine and threonine metabolism 40 0.31 81 0.81 0.25 cfa04145=Phagosome 42 0.31 82 0.80 0.25 cfa05164=Influenza A 13 0.33 75 0.83 0.27 cfa00970=Aminoacyl-tRNA biosynthesis 1 1.0 1 1.0 0.28 cfa00472=D-Arginine and D-ornithine metabolism 4 0.40 51 0.89 0.29 cfa00130=Ubiquinone and other terpenoid-quinone biosynthesis 4 0.40 51 0.89 0.29 cfa00920=Sulfur metabolism 8 0.35 69 0.84 0.29 cfa00630=Glyoxylate and dicarboxylate metabolism 36 0.30 85 0.76 0.30 cfa04514=Cell adhesion molecules (CAMs) 10 0.33 75 0.81 0.31 cfa00640=Propanoate metabolism 2 0.50 32 0.93 0.31 cfa00730=Thiamine metabolism 7 0.35 68 0.84 0.31 cfa01040=Biosynthesis of unsaturated fatty acids 14 0.32 77 0.78 0.32 cfa05134=Legionellosis 44 0.30 86 0.74 0.32 cfa05168=Herpes simplex infection 11 0.32 76 0.79 0.33 cfa00860=Porphyrin and chlorophyll metabolism 12 0.32 79 0.77 0.36 cfa00620=Pyruvate metabolism 31 0.30 87 0.70 0.38 cfa05145=Toxoplasmosis 6 0.33 75 0.79 0.38 cfa00670=One carbon pool by folate 12 0.31 81 0.73 0.39 cfa04672=Intestinal immune network for IgA production 12 0.31 81 0.73 0.39 cfa04973=Carbohydrate digestion and absorption 14 0.30 84 0.72 0.40 cfa00520=Amino sugar and nucleotide sugar metabolism 11 0.31 83 0.72 0.41 cfa05219=Bladder cancer 23 0.29 88 0.69 0.41 cfa05222=Small cell lung cancer 8 0.31 81 0.72 0.44 cfa00601=Glycosphingolipid biosynthesis - lacto and neolacto series 8 0.31 81 0.72 0.44 cfa04966=Collecting duct acid secretion 5 0.31 80 0.73 0.47 cfa00533=Glycosaminoglycan biosynthesis - keratan sulfate 1 0.50 32 0.92 0.48 cfa00300=Lysine biosynthesis 10 0.29 89 0.67 0.48 cfa03030=DNA replication 12 0.29 90 0.62 0.51 cfa04930=Type II diabetes mellitus 33 0.28 92 0.57 0.51 cfa04110=Cell cycle 16 0.28 91 0.59 0.53 cfa05416=Viral myocarditis 2 0.33 75 0.79 0.53 cfa00400=Phenylalanine, tyrosine and tryptophan biosynthesis 24 0.28 93 0.57 0.53 cfa04972=Pancreatic secretion 5 0.29 89 0.68 0.53 cfa00360=Phenylalanine metabolism 13 0.27 94 0.53 0.59 cfa00310=Lysine degradation 8 0.27 95 0.54 0.62 cfa00250=Alanine, aspartate and glutamate metabolism 8 0.27 95 0.54 0.62 cfa00514=Other types of O-glycan biosynthesis 4 0.27 95 0.59 0.63 cfa00450=Selenocompound metabolism 4 0.27 95 0.59 0.63 cfa01210=2-Oxocarboxylic acid metabolism 11 0.26 97 0.49 0.64 cfa04330=Notch signaling pathway 14 0.26 96 0.49 0.64 cfa00330=Arginine and proline metabolism 4 0.25 100 0.53 0.69 cfa00053=Ascorbate and aldarate metabolism 8 0.25 100 0.45 0.70 cfa00270=Cysteine and methionine metabolism 9 0.25 100 0.44 0.70 cfa04623=Cytosolic DNA-sensing pathway 18 0.25 98 0.39 0.71 cfa04210=Apoptosis 5 0.24 107 0.45 0.73 cfa04140=Regulation of autophagy 14 0.24 104 0.33 0.77 cfa00010=Glycolysis / Gluconeogenesis 7 0.23 111 0.38 0.77 cfa05020=Prion diseases 8 0.24 109 0.37 0.77 cfa00051=Fructose and mannose metabolism 28 0.25 101 0.28 0.79 cfa04380=Osteoclast differentiation 1 0.20 129 0.58 0.80 cfa00524=Butirosin and neomycin biosynthesis 6 0.22 116 0.35 0.80 cfa05216=Thyroid cancer 31 0.25 102 0.25 0.81 cfa05012=Parkinson's disease 15 0.23 110 0.27 0.82 cfa04920=Adipocytokine signaling pathway 16 0.24 109 0.27 0.82 cfa04350=TGF-beta signaling pathway 23 0.24 105 0.24 0.82 cfa05142=Chagas disease (American trypanosomiasis) 9 0.22 114 0.29 0.82 cfa03420=Nucleotide excision repair 17 0.23 112 0.24 0.83 cfa05410=Hypertrophic cardiomyopathy (HCM) 35 0.24 103 0.22 0.83 cfa05202=Transcriptional misregulation in cancer 10 0.22 116 0.26 0.84 cfa05014=Amyotrophic lateral sclerosis (ALS) 13 0.22 115 0.23 0.85 cfa04370=VEGF signaling pathway 26 0.24 108 0.20 0.86 cfa05160=Hepatitis C 31 0.24 106 0.19 0.86 cfa00190=Oxidative phosphorylation 76 0.25 99 0.17 0.86 cfa04151=PI3K-Akt signaling pathway 7 0.21 127 0.24 0.87 cfa04742=Taste transduction 15 0.22 118 0.18 0.88 cfa05218=Melanoma 14 0.22 122 0.17 0.90 cfa05412=Arrhythmogenic right ventricular cardiomyopathy (ARVC) 17 0.22 120 0.15 0.90 cfa05414=Dilated cardiomyopathy 4 0.18 140 0.23 0.90 cfa00534=Glycosaminoglycan biosynthesis - heparan sulfate / heparin 5 0.19 136 0.20 0.90 cfa00030=Pentose phosphate pathway 21 0.22 117 0.13 0.91 cfa04726=Serotonergic synapse 15 0.21 124 0.13 0.92 cfa03008=Ribosome biogenesis in eukaryotes 4 0.17 145 0.19 0.92 cfa04320=Dorso-ventral axis formation 7 0.19 134 0.16 0.92 cfa04960=Aldosterone-regulated sodium reabsorption 22 0.22 121 0.10 0.93 cfa04066=HIF-1 signaling pathway 24 0.22 119 0.11 0.93 cfa04670=Leukocyte transendothelial migration 28 0.22 116 0.10 0.93 cfa05161=Hepatitis B 43 0.23 113 0.095 0.93 cfa04144=Endocytosis 14 0.20 128 0.11 0.94 cfa05220=Chronic myeloid leukemia 5 0.17 146 0.15 0.94 cfa04744=Phototransduction 6 0.18 142 0.13 0.94 cfa04130=SNARE interactions in vesicular transport 12 0.19 133 0.079 0.96 cfa05211=Renal cell carcinoma 23 0.21 125 0.067 0.96 cfa05162=Measles 4 0.15 154 0.11 0.96 cfa03020=RNA polymerase 10 0.18 141 0.063 0.97 cfa00562=Inositol phosphate metabolism 4 0.14 161 0.079 0.97 cfa04710=Circadian rhythm 10 0.17 146 0.047 0.98 cfa05210=Colorectal cancer 15 0.18 139 0.034 0.98 cfa04914=Progesterone-mediated oocyte maturation 20 0.19 133 0.028 0.98 cfa04270=Vascular smooth muscle contraction 40 0.21 123 0.032 0.98 cfa04510=Focal adhesion 8 0.16 150 0.048 0.98 cfa05213=Endometrial cancer 10 0.15 154 0.015 0.99 cfa05212=Pancreatic cancer 10 0.16 149 0.029 0.99 cfa04260=Cardiac muscle contraction 10 0.16 153 0.021 0.99 cfa05214=Glioma 13 0.16 151 0.012 0.99 cfa05215=Prostate cancer 14 0.17 148 0.016 0.99 cfa04012=ErbB signaling pathway 14 0.18 144 0.025 0.99 cfa04620=Toll-like receptor signaling pathway 15 0.17 146 0.017 0.99 cfa04650=Natural killer cell mediated cytotoxicity 17 0.18 143 0.014 0.99 cfa04114=Oocyte meiosis 24 0.18 137 0.010 0.99 cfa04910=Insulin signaling pathway 32 0.20 130 0.015 0.99 cfa05010=Alzheimer's disease 33 0.20 131 0.010 0.99 cfa05016=Huntington's disease 8 0.15 158 0.020 0.99 cfa04730=Long-term depression 9 0.15 157 0.016 0.99 cfa03018=RNA degradation 9 0.15 157 0.016 0.99 cfa04664=Fc epsilon RI signaling pathway 0 0.0 204 0.38 1.0 cfa00471=D-Glutamine and D-glutamate metabolism 1 0.036 201 0.0014 1.0 cfa00020=Citrate cycle (TCA cycle) 1 0.045 198 0.0076 1.0 cfa03060=Protein export 1 0.053 196 0.018 1.0 cfa04964=Proximal tubule bicarbonate reclamation 1 0.059 194 0.030 1.0 cfa00532=Glycosaminoglycan biosynthesis - chondroitin sulfate / dermatan sulfate 10 0.13 171 0.0017 1.0 cfa04912=GnRH signaling pathway 10 0.13 172 0.0014 1.0 cfa04070=Phosphatidylinositol signaling system 10 0.14 163 0.0053 1.0 cfa05100=Bacterial invasion of epithelial cells 11 0.087 186 1.6E-7 1.0 cfa04310=Wnt signaling pathway 11 0.13 170 0.0012 1.0 cfa04666=Fc gamma R-mediated phagocytosis 11 0.15 156 0.0087 1.0 cfa04970=Salivary secretion 13 0.083 187 1.9E-9 1.0 cfa05034=Alcoholism 13 0.13 167 0.00067 1.0 cfa04660=T cell receptor signaling pathway 15 0.12 173 0.000058 1.0 cfa04360=Axon guidance 15 0.12 173 0.000058 1.0 cfa04530=Tight junction 15 0.12 175 0.000010 1.0 cfa04390=Hippo signaling pathway 15 0.14 164 0.00065 1.0 cfa04722=Neurotrophin signaling pathway 18 0.13 169 0.000042 1.0 cfa03010=Ribosome 18 0.14 165 0.00015 1.0 cfa04120=Ubiquitin mediated proteolysis 18 0.16 152 0.0035 1.0 cfa05322=Systemic lupus erythematosus 2 0.016 203 8.1E-15 1.0 cfa03040=Spliceosome 2 0.028 202 3.3E-8 1.0 cfa03015=mRNA surveillance pathway 26 0.19 135 0.0088 1.0 cfa03013=RNA transport 27 0.15 159 0.000019 1.0 cfa05203=Viral carcinogenesis 28 0.17 145 0.0018 1.0 cfa04141=Protein processing in endoplasmic reticulum 28 0.17 148 0.00087 1.0 cfa04062=Chemokine signaling pathway 29 0.17 147 0.00094 1.0 cfa05169=Epstein-Barr virus infection 3 0.038 200 5.6E-8 1.0 cfa04727=GABAergic synapse 3 0.052 197 0.000014 1.0 cfa04720=Long-term potentiation 3 0.067 189 0.00045 1.0 cfa04961=Endocrine and other factor-regulated calcium reabsorption 3 0.070 188 0.00076 1.0 cfa04962=Vasopressin-regulated water reabsorption 3 0.091 184 0.0090 1.0 cfa05033=Nicotine addiction 30 0.15 155 0.000038 1.0 cfa04810=Regulation of actin cytoskeleton 31 0.18 138 0.0040 1.0 cfa04020=Calcium signaling pathway 34 0.14 160 0.0000011 1.0 cfa04010=MAPK signaling pathway 4 0.061 192 0.000010 1.0 cfa04971=Gastric acid secretion 4 0.098 182 0.0050 1.0 cfa03022=Basal transcription factors 46 0.19 132 0.0017 1.0 cfa05166=HTLV-I infection 5 0.043 199 6.9E-11 1.0 cfa04728=Dopaminergic synapse 5 0.056 195 1.4E-7 1.0 cfa04723=Retrograde endocannabinoid signaling 5 0.062 190 0.0000011 1.0 cfa04540=Gap junction 5 0.11 177 0.0082 1.0 cfa05030=Cocaine addiction 5 0.11 180 0.0066 1.0 cfa03050=Proteasome 5 0.12 175 0.010 1.0 cfa04340=Hedgehog signaling pathway 6 0.059 193 3.4E-8 1.0 cfa04725=Cholinergic synapse 6 0.061 191 7.4E-8 1.0 cfa04724=Glutamatergic synapse 6 0.10 181 0.0012 1.0 cfa05031=Amphetamine addiction 6 0.12 174 0.0084 1.0 cfa05217=Basal cell carcinoma 6 0.12 176 0.0045 1.0 cfa05223=Non-small cell lung cancer 62 0.21 126 0.0038 1.0 cfa05200=Pathways in cancer 66 0.14 162 1.1E-11 1.0 cfa04740=Olfactory transduction 7 0.11 178 0.0016 1.0 cfa04520=Adherens junction 7 0.13 166 0.012 1.0 cfa05221=Acute myeloid leukemia 8 0.090 185 0.000014 1.0 cfa04713=Circadian entrainment 8 0.090 185 0.000014 1.0 cfa04916=Melanogenesis 8 0.095 183 0.000044 1.0 cfa05032=Morphine addiction 8 0.11 179 0.00075 1.0 cfa05132=Salmonella infection 8 0.13 168 0.0068 1.0 cfa04150=mTOR signaling pathway 8 0.13 168 0.0068 1.0 cfa04721=Synaptic vesicle cycle 9 0.14 161 0.0095 1.0 cfa04662=B cell receptor signaling pathway 32 0.68 7 1.0 1.2E-8 cfa03460=Fanconi anemia pathway 34 0.64 9 1.0 4.2E-8 cfa00590=Arachidonic acid metabolism 85 0.45 40 1.0 4.9E-7 cfa04060=Cytokine-cytokine receptor interaction 34 0.60 12 1.0 5.3E-7 cfa04610=Complement and coagulation cascades 408 0.38 60 1.0 8.6E-11 cfa01100=Metabolic pathways -- Douglas R. Cavener Professor and Head Department of Biology 110 Life Science Building Pennsylvania State University University Park, PA 16802 (814) 865-4562 Dept Office (814) 865-9790 Lab Office (814) 865-9131 Fax drc9(a)psu.edu

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GATK - Base recalibrator
by Bolduc, Francois 31 Jan '14

31 Jan '14

Hi all, I'm trying to call variants using GATK best practices workflow. So after performing Realigner Target Creator step, one should jump on Base Recalibrator step. Unfortunatly, I don't see this program under GATK tab. Is there a way to perform this step in Galaxy? There are the Count covariates and Table recalibration steps. Is a combination of these two steps is equivalent to Base recalibrator? Thank you for your help and time. Frank

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February 2014 Galaxy Update Newsletter
by Dave Clements 31 Jan '14

31 Jan '14

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Re: [galaxy-user] visualization through Trackster on local Galaxy installation
by Jennifer Jackson 30 Jan '14

30 Jan '14

Hi Yupu, I'm sorry, I mixed up the processing for custom builds with the processing for installed builds in my initial reply. A .2bit & .len file are appropriate for what you are doing - and the wiki instructions are accurate. Still, I double checked with Jeremy, the scientist that developed the tool, about your issue. He found that the full path to the .len file needs to be specified right now in order for installed builds to function correctly. This deviates from the wiki instructions, but we consider this a bug and plan to correct it in the next release. Please try using the full path and let us know how that works out, Jen Galaxy team On 1/29/14 11:54 AM, Yupu Liang wrote: > Dear Jennifer: > > Hi, I did put 2bit file as suggested by https://wiki.galaxyproject.org/Visualization%20Setup. > > Should I get rid of the len file? and I assume I will also need to take off the len_file_path out of the universe_wsgi.ini file. > > In term of using .fasta file. I have them and I am not clear where I should put them for visualization purpose and also what adjustment i need to make in the ini file. Any help would be appreciated and I am really excited about doing all the visualization within Galaxy. I think the biologists would really love these features. > > Thanks a lot, > Yupu > > > > > On Jan 29, 2014, at 14:21, Jennifer Jackson <jen(a)bx.psu.edu> wrote: > >> Hello Yupu, >> >> Using a .len file is problematic at this time. In the upcoming release this will be corrected. Using a .fasta file is the solution. In many cases, using a .fasta file can be preferred as it will include the reference genome sequence in the visualization, but the choice is yours, once the correction is released to the distribution. >> >> More about this is in a recent post from Jeremy: >> http://lists.bx.psu.edu/pipermail/galaxy-user/2014-January/007106.html >> >> Our apologies for the confusion this caused. The advice from Hans-Rudolf is under normal circumstances the best first-pass route for troubleshooting, and you will want to restart after using the fasta method as well. >> >> Best, >> >> Jen >> Galaxy team >> >> On 1/28/14 11:12 AM, Yupu Liang wrote: >>> Hi, >>> >>> I am trying to set up the visualization functionality on our local Galaxy installation. I followed the instructions on https://wiki.galaxyproject.org/Visualization%20Setup >>> >>> And here is the list of len files: >>> ls -l tool-data/shared/ucsc/chrom/ >>> total 200 >>> -rw-rw-r-- 1 galaxy galaxy 10 Jan 27 15:13 centromeres1.len >>> -rw-rw-r-- 1 galaxy galaxy 821 Jan 27 15:13 hg15.len >>> -rw-rw-r-- 1 galaxy galaxy 713 Jan 27 15:13 hg16.len >>> -rw-rw-r-- 1 galaxy galaxy 792 Jan 27 15:13 hg17.len >>> -rw-rw-r-- 1 galaxy galaxy 855 Jan 27 15:13 hg18.len >>> -rw-rw-r-- 1 galaxy galaxy 202 Jan 27 15:13 hg19Haps.len >>> -rwxr-xr-x 1 galaxy galaxy 1971 Jan 27 15:13 hg19.len >>> -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 hg19LggInv.len >>> -rw-rw-r-- 1 galaxy galaxy 4516 Jan 27 15:13 hg19Patch10.len >>> -rw-rw-r-- 1 galaxy galaxy 1983 Jan 27 15:13 hg19Patch2.len >>> -rw-rw-r-- 1 galaxy galaxy 2844 Jan 27 15:13 hg19Patch5.len >>> -rw-rw-r-- 1 galaxy galaxy 3879 Jan 27 15:13 hg19Patch9.len >>> -rw-rw-r-- 1 galaxy galaxy 11672 Jan 27 15:13 hg38.len >>> -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 hlaRef1.len >>> -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 hlaRef2.len >>> -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 hlaRef3.len >>> -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 repBase0.len >>> -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 repBase1.len >>> -rw-rw-r-- 1 galaxy galaxy 12697 Jan 27 15:13 repeats2.len >>> -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 simHuman.len >>> -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 simHumanMammal.len >>> -rw-rw-r-- 1 galaxy galaxy 362 Jan 27 15:13 venter1.len >>> >>> >>> When I test the visualization through Trackster. I got the following error after seeing a message saying "preparing the data, this can take a while……": >>> >>> Error: The requested genome file (tool-data/shared/ucsc/chrom/hg18.len) could not be opened. Exiting! >>> sort: write failed: standard output: Broken pipe >>> sort: write error >>> Couldn't open tool-data/shared/ucsc/chrom/hg18.len , No such file or directory. >>> >>> >>> It seems something else failed in the middle of visualization as I do have the hg18.len under the default path with the global reading permission…….. >>> >>> Does anybody have similar experience? Is there is way for me to debug? >>> >>> Best, >>> Yupu >>> ___________________________________________________________ >>> The Galaxy User list should be used for the discussion of >>> Galaxy analysis and other features on the public server >>> at usegalaxy.org. Please keep all replies on the list by >>> using "reply all" in your mail client. For discussion of >>> local Galaxy instances and the Galaxy source code, please >>> use the Galaxy Development list: >>> >>> http://lists.bx.psu.edu/listinfo/galaxy-dev >>> >>> To manage your subscriptions to this and other Galaxy lists, >>> please use the interface at: >>> >>> http://lists.bx.psu.edu/ >>> >>> To search Galaxy mailing lists use the unified search at: >>> >>> http://galaxyproject.org/search/mailinglists/ >>> >> -- >> Jennifer Hillman-Jackson >> http://galaxyproject.org >> > -- Jennifer Hillman-Jackson http://galaxyproject.org

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visualization through Trackster on local Galaxy installation
by Yupu Liang 29 Jan '14

29 Jan '14

Hi, I am trying to set up the visualization functionality on our local Galaxy installation. I followed the instructions on https://wiki.galaxyproject.org/Visualization%20Setup And here is the list of len files: ls -l tool-data/shared/ucsc/chrom/ total 200 -rw-rw-r-- 1 galaxy galaxy 10 Jan 27 15:13 centromeres1.len -rw-rw-r-- 1 galaxy galaxy 821 Jan 27 15:13 hg15.len -rw-rw-r-- 1 galaxy galaxy 713 Jan 27 15:13 hg16.len -rw-rw-r-- 1 galaxy galaxy 792 Jan 27 15:13 hg17.len -rw-rw-r-- 1 galaxy galaxy 855 Jan 27 15:13 hg18.len -rw-rw-r-- 1 galaxy galaxy 202 Jan 27 15:13 hg19Haps.len -rwxr-xr-x 1 galaxy galaxy 1971 Jan 27 15:13 hg19.len -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 hg19LggInv.len -rw-rw-r-- 1 galaxy galaxy 4516 Jan 27 15:13 hg19Patch10.len -rw-rw-r-- 1 galaxy galaxy 1983 Jan 27 15:13 hg19Patch2.len -rw-rw-r-- 1 galaxy galaxy 2844 Jan 27 15:13 hg19Patch5.len -rw-rw-r-- 1 galaxy galaxy 3879 Jan 27 15:13 hg19Patch9.len -rw-rw-r-- 1 galaxy galaxy 11672 Jan 27 15:13 hg38.len -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 hlaRef1.len -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 hlaRef2.len -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 hlaRef3.len -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 repBase0.len -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 repBase1.len -rw-rw-r-- 1 galaxy galaxy 12697 Jan 27 15:13 repeats2.len -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 simHuman.len -rw-rw-r-- 1 galaxy galaxy 67 Jan 27 15:13 simHumanMammal.len -rw-rw-r-- 1 galaxy galaxy 362 Jan 27 15:13 venter1.len When I test the visualization through Trackster. I got the following error after seeing a message saying "preparing the data, this can take a while……": Error: The requested genome file (tool-data/shared/ucsc/chrom/hg18.len) could not be opened. Exiting! sort: write failed: standard output: Broken pipe sort: write error Couldn't open tool-data/shared/ucsc/chrom/hg18.len , No such file or directory. It seems something else failed in the middle of visualization as I do have the hg18.len under the default path with the global reading permission…….. Does anybody have similar experience? Is there is way for me to debug? Best, Yupu

3 3

Install tools from Toolshed
by Malik, Shivani 27 Jan '14

27 Jan '14

HI, I am trying to install Deseq from the toolshed. I get the message:Repository installation is not possible due to an invalid Galaxy URL: None. You may need to enable cookies in your browser. I have enabled the cookies and tried both on Firefox and Chrome but could not get it to work. Can you help me on this? Thanks Shivani

2 1

Genome of interest is not listed
by Vikash Yadav 27 Jan '14

27 Jan '14

Dear Sir/Maam I am using Galaxy for analysis of NGS data. I want to map the NGS data, but the genome of my interest (Arabidopsis thaliana) is not listed. So what i can do to mapp my data using galaxy. Waiting for your kind response Thanks & Regards Vikash -- * Vikash Kumar Yadav* *Senior Research Fellow,* *Plant Molecular Biology Lab,* *National Botanical Research Institute,* *Mobile- 090 261 190 92*

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galaxy-user January 2014