Help with Summary Statistics
by D. A. Cowart
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
7 years, 5 months
Picrust upload
by Ines
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-...,
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.
7 years, 8 months
special character $ gets converted to X in tool
by Ketan Maheshwari
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
7 years, 8 months
problem with scientific notation sorting and filtering in galaxy
by Douglas Cavener
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
7 years, 8 months
GATK - Base recalibrator
by Bolduc, Francois
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
7 years, 8 months
Re: [galaxy-user] visualization through Trackster on local Galaxy installation
by Jennifer Jackson
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
7 years, 8 months
visualization through Trackster on local Galaxy installation
by Yupu Liang
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
7 years, 8 months
Install tools from Toolshed
by Malik, Shivani
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
7 years, 8 months
Genome of interest is not listed
by Vikash Yadav
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*
7 years, 8 months
