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