Freiburg RNA Tools
BrainDead - Results
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BrainDead 4526878

Input and runtime details for job 4526878 (precomputed example)

Training data

? Class-annotated RNAs in FASTA[.fa]

Model parameters

? k-mers of interest
AA,AGA,AGGU,AGU,AGUU,CU,GAA,GAGG,GG,GGG,GU,GUU,UGA,UGU,UU,UUG,UUGU,UUU
? k-mer features reflect
number of occurrences (feature is number >= 0)
? Max. energy ranked stable-3
? Machine learning model
Support Vector Classification using RBF kernel

Candidate data

? Candidate RNAs in FASTA[.fa]

Job ID 4526878 (server version trunk)

?Job Submitted & Queued@ Wed Feb 10 15:00:26 CET 2021
?BrainDead Started@ Wed Feb 10 15:16:53 CET 2021
?BrainDead Finished & Post-Processing@ Wed Feb 10 15:16:53 CET 2021
?Post-Processing Finished@ Wed Feb 10 15:17:07 CET 2021
?Job Completed@ Wed Feb 10 15:17:11 CET 2021
 DIRECT ACCESS: http://rna.informatik.uni-freiburg.de/RetrieveResults.jsp?jobID=4526878&toolName=BrainDead ( 30 days expiry )

Description of the job

miRNAs as ligands for microglia activation

This example summarizes our study that investigates the ability of mature miRNAs to act as immune receptor ligands. The ability of extracellular miRNAs to directly activate receptors is a recently discovered new field of operation of miRNAs beside their classic role in post-transcriptional gene regulation. The small RNAs available within the example's training data were experimentally tested for their potential to activate murine microglia cells in vitro. They are pre-classified as +/-1 when found activating or non-activating, resp., using fold change analyses based TNF-alpha concentration measurements. The example's training data set comprises both the original training data (top group) as well as the experimentally verified candidate sequences (middle and bottom group) from our initial BrainDead main publication (see list of references within Help page). For details on the selected k-mers, please refer to the manuscript. The candidate set covers all mature human miRNAs from mirBase v22.1.

? Output download complete results [zip]

Downloads

Predictions for candidate RNAs

Predicted class

Sort by selecting a column name.
RNA id class rank prob(1)
hsa-miR-5571-3p -1.000 2001.000 0.156
hsa-miR-7152-3p -1.000 2002.000 0.156
hsa-miR-17-5p -1.000 2003.000 0.156
hsa-miR-106a-5p -1.000 2004.000 0.156
hsa-miR-146a-5p -1.000 2005.000 0.156
hsa-miR-3180 -1.000 2006.000 0.156
hsa-miR-4435 -1.000 2007.000 0.155
hsa-miR-585-5p -1.000 2008.000 0.154
hsa-miR-4323 -1.000 2009.000 0.154
hsa-miR-4690-5p -1.000 2010.000 0.154
hsa-miR-708-5p -1.000 2011.000 0.153
hsa-miR-3677-5p -1.000 2012.000 0.153
hsa-miR-23b-3p -1.000 2013.000 0.153
hsa-miR-6798-5p -1.000 2014.000 0.153
hsa-miR-324-5p -1.000 2015.000 0.153
hsa-miR-502-5p -1.000 2016.000 0.153
hsa-miR-134-3p -1.000 2017.000 0.152
hsa-miR-6089 -1.000 2018.000 0.152
hsa-miR-4725-5p -1.000 2019.000 0.152
hsa-miR-4697-5p -1.000 2020.000 0.151
hsa-miR-6857-5p -1.000 2021.000 0.151
hsa-miR-3918 -1.000 2022.000 0.151
hsa-miR-20b-5p -1.000 2023.000 0.151
hsa-miR-4657 -1.000 2024.000 0.150
hsa-miR-331-3p -1.000 2025.000 0.150
hsa-miR-3131 -1.000 2026.000 0.150
hsa-miR-20a-5p -1.000 2027.000 0.150
hsa-miR-6820-5p -1.000 2028.000 0.149
hsa-miR-3190-5p -1.000 2029.000 0.149
hsa-miR-4267 -1.000 2030.000 0.149
hsa-miR-3663-5p -1.000 2031.000 0.149
hsa-miR-4745-3p -1.000 2032.000 0.149
hsa-miR-6872-3p -1.000 2033.000 0.149
hsa-miR-6780a-5p -1.000 2034.000 0.149
hsa-miR-514b-5p -1.000 2035.000 0.149
hsa-miR-6085 -1.000 2036.000 0.149
hsa-miR-6856-5p -1.000 2037.000 0.148
hsa-miR-1914-3p -1.000 2038.000 0.148
hsa-miR-12118 -1.000 2039.000 0.148
hsa-miR-193a-3p -1.000 2040.000 0.148
hsa-miR-4731-3p -1.000 2041.000 0.148
hsa-miR-4536-3p -1.000 2042.000 0.148
hsa-miR-2392 -1.000 2043.000 0.147
hsa-miR-5702 -1.000 2044.000 0.147
hsa-miR-515-5p -1.000 2045.000 0.147
hsa-miR-1324 -1.000 2046.000 0.147
hsa-miR-6733-5p -1.000 2047.000 0.147
hsa-miR-1245b-3p -1.000 2048.000 0.147
hsa-miR-6781-5p -1.000 2049.000 0.147
hsa-miR-4778-5p -1.000 2050.000 0.146
hsa-miR-6865-5p -1.000 2051.000 0.146
hsa-miR-1292-5p -1.000 2052.000 0.146
hsa-miR-4632-5p -1.000 2053.000 0.145
hsa-miR-6883-5p -1.000 2054.000 0.145
hsa-miR-6716-3p -1.000 2055.000 0.145
hsa-miR-1181 -1.000 2056.000 0.145
hsa-miR-8485 -1.000 2057.000 0.145
hsa-miR-328-5p -1.000 2058.000 0.144
hsa-miR-3915 -1.000 2059.000 0.144
hsa-miR-1270 -1.000 2060.000 0.144
hsa-miR-99b-3p -1.000 2061.000 0.144
hsa-miR-6849-5p -1.000 2062.000 0.144
hsa-miR-494-3p -1.000 2063.000 0.144
hsa-miR-1298-3p -1.000 2064.000 0.144
hsa-miR-8082 -1.000 2065.000 0.144
hsa-miR-4688 -1.000 2066.000 0.143
hsa-miR-10394-5p -1.000 2067.000 0.143
hsa-miR-3939 -1.000 2068.000 0.142
hsa-miR-206 -1.000 2069.000 0.142
hsa-miR-2276-3p -1.000 2070.000 0.142
hsa-miR-34b-3p -1.000 2071.000 0.142
hsa-miR-449b-3p -1.000 2072.000 0.142
hsa-miR-3129-3p -1.000 2073.000 0.142
hsa-miR-3145-5p -1.000 2074.000 0.142
hsa-miR-4687-5p -1.000 2075.000 0.142
hsa-miR-6784-3p -1.000 2076.000 0.142
hsa-miR-7847-3p -1.000 2077.000 0.142
hsa-miR-491-3p -1.000 2078.000 0.142
hsa-miR-580-3p -1.000 2079.000 0.142
hsa-miR-4744 -1.000 2080.000 0.141
hsa-miR-8057 -1.000 2081.000 0.141
hsa-miR-24-2-5p -1.000 2082.000 0.141
hsa-miR-5007-3p -1.000 2083.000 0.141
hsa-miR-4506 -1.000 2084.000 0.141
hsa-miR-6821-5p -1.000 2085.000 0.140
hsa-miR-6506-3p -1.000 2086.000 0.140
hsa-miR-3937 -1.000 2087.000 0.139
hsa-miR-4429 -1.000 2088.000 0.139
hsa-miR-6746-5p -1.000 2089.000 0.139
hsa-miR-3167 -1.000 2090.000 0.139
hsa-miR-4493 -1.000 2091.000 0.139
hsa-miR-30a-5p -1.000 2092.000 0.138
hsa-miR-30d-5p -1.000 2093.000 0.138
hsa-miR-675-5p -1.000 2094.000 0.138
hsa-miR-200a-5p -1.000 2095.000 0.138
hsa-miR-6866-5p -1.000 2096.000 0.138
hsa-miR-4788 -1.000 2097.000 0.138
hsa-miR-4514 -1.000 2098.000 0.137
hsa-miR-33b-3p -1.000 2099.000 0.137
hsa-miR-6090 -1.000 2100.000 0.137
Rows: 1-100 101-200 201-300 301-400 401-500 501-600 601-700 701-800 801-900 901-1000 1001-1100 1101-1200 1201-1300 1301-1400 1401-1500 1501-1600 1601-1700 1701-1800 1801-1900 1901-2000 2001-2100 2101-2200 2201-2300 2301-2400 2401-2500 2501-2600 2601-2656 all

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When using BrainDead please cite :

Results are computed with BrainDead version 1.0.1 using IntaRNA 3.1.5 and Vienna RNA package 2.4.14