Scientific

MK5 Microarray Data


Affected Genes - Ontology Breakdown - Gene Enrichment - Proteome Analysis

These are the micro-array results of a differential gene expression microarray experiments and the subsequent analysis steps performed on them. The up/down- regulation ratio was obtained by measuring WT cells against MK5 activated cells. See material and methods for technical information as well as the data usage policy.

MK5 Proteome Network Type I Analysis

Type 1 network in which we propagated the absolute log regulation ratio abs(log(r/g)). We used the filtered lowest boundary of the confidence interval for the microarray measurement based and used the high confidence interaction map. Because type-I networks tend to favour highly connected proteins we tested a normalisation scheme in which we would run the network once with real values (the measured column) and once with simulated values where each measured point is set to 1 instead of the actual signal (the expected column). Afterwards we divided the measured value by the expected value. This normalisation scheme works to a certain extend but will lead to a variance that is directly related to the connectivity of each protein. In the end we dropped the use of Type-I networks due to the many technical problems they pose.


Navigation/Query Panel:
Click on the attribute name to hide/unhide it. The green arrows can be used to shift columns left/right. Exact word match is written as =..., regular expressions can be matched with ~.... To select all values larger or equal than use >.... This would be <... for values smaller or equal than. To select all values within a specific range use [...,...].
description Rank Gene measured expected final Hugo
Results: HTML CSV LaTeX Showing element 401 to 450 of 3206 in total
description  :
Rank
measured
expected
final
Hugo
2809 0.124033 0.627474 0.197670342 ZBTB45
2811 0.0949008 0.481532 0.197080983 RPP25
2812 0.121488 0.616542 0.197047403 C9orf23
2822 0.164985 0.852537 0.193522393 ZNF449
2824 0.164486 0.849984 0.193516584 ZNF496
2860 0.0307477 0.162737 0.188941052 ORAI1
2863 0.0372023 0.196899 0.188941031 ORAI3
2867 0.03085 0.163279 0.188940403 ORAI2
2872 0.0371369 0.196598 0.188897649 no value
2880 0.0513128 0.278146 0.184481531 CXorf56
2889 0.0926874 0.515188 0.179909858 C9orf95
2890 0.050962 0.285736 0.178353445 SPTY2D1
2893 0.144225 0.822334 0.175384941 LASS5
2905 0.024304 0.139564 0.174142329 PLEKHG2
2907 0.057982 0.332958 0.174142084 TMEM55A
2908 0.0326222 0.187331 0.174142027 SAP30L
2911 0.0420495 0.241467 0.174141808 ANKS6
2925 0.117871 0.704322 0.167353852 NUP93
2936 0.0831672 0.529029 0.157207261 VPS37C
2938 0.107134 0.682084 0.157068631 VPS37B
2945 0.103715 0.679961 0.152530807 SLC30A9
2959 0.100774 0.689453 0.146165148 FRMPD4
2970 0.104104 0.735452 0.141551046 RIBC2
2981 0.0398119 0.285269 0.139559153 MTERFD1
2987 0.138195 1 0.138195 C9orf68
2990 0.0210454 0.155504 0.135336712 HS3ST4
3002 0.170944 1.33704 0.12785257 C1orf164
3007 0.136179 1.08705 0.125273906 SEPT11
3013 0.0325023 0.266776 0.121833673 FRY
3031 0.128895 1.09557 0.117651086 PTCD1
3032 0.191363 1.62653 0.117651073 UBE2E2
3069 0.0533885 0.517345 0.103197093 no value
3070 0.0365299 0.353982 0.103197055 C1orf156
3087 0.038802 0.397415 0.097635972 FAM20A
3089 0.048624 0.498014 0.097635809 TM4SF19
3091 METTL2A
3119 0.0559756 0.634694 0.088193051 C1orf14
3129 0.0886161 1.29368 0.068499242 CC2D1A
3136 0.066175 1.01213 0.065381917 MAGEB10
3139 1.64337 25.135 0.065381739 NSMCE4A
3154 0.0659071 1.00804 0.065381433 MAGEB18
3167 0.73655 11.3823 0.06471012 C5orf33
3175 0.0184372 0.333333 0.055311655 NIPA2
3176 C6orf130
3184 0.0656147 1.32794 0.049410892 PYCRL
3185 0.0476371 1.01816 0.04678744 CCDC104
3191 0.05032 1.34852 0.037314982 FIBCD1
3194 0.0239451 0.649583 0.036862264 CHMP4C
3196 0.031557 0.85608 0.036862209 CHMP4B
3199 0.00386024 0.104721 0.036862138 CHMP2B

Legend:
- Rank is the rank after comparing the two networks
- Gene is the ensembl human gene identifier measured by 1 or more probes on the microarray
- Hugo is the bloody hugo identifier as demanded by the BMC Bioinformatics idiots

- http://analysis.yellowcouch.org/