equator network
http://www.equator-network.org/
Tuesday, October 31, 2017
writing tips
clarity
"I didn't have time to write a short letter, so I wrote a long one instead", Mark Twain.
mSystems
https://www.asm.org/index.php/journal-press-releases/93543-american-society-for-microbiology-to-launch-msystems-new-open-access-journal
Yewno
https://about.yewno.com/
Technology and products that extract meaning at the atomic level, to help you understand deeper
Intelligent Framework
The Yewno Biomedical Intelligent Framework is a true “next generation” research tool. It is the only comprehensive tool available today to model the behavior of complex bio-systems. It uses Computational Linguistics, Neural Networks, Machine Learning, and Graph Theory to provide insight into the world of biomedical work.
With Yewno, you can explore the world’s hard or marginal evidence — from biological pathways and biochemical interactions to pharmacodynamics. With Yewno, you can take your research in directions never thought possible.
Saturday, October 28, 2017
Thursday, October 26, 2017
Wednesday, October 25, 2017
tissue specific gene regulation
How does these affect aging, controllability? Some tissue aging fast and some slow?
http://www.cell.com/cell-reports/abstract/S2211-1247%2817%2931418-3#.We-APbV2phM.facebook
http://www.cell.com/cell-reports/abstract/S2211-1247%2817%2931418-3#.We-APbV2phM.facebook
Highlights
- •Regulatory network connections are more tissue specific than nodes (genes and transcription factors)
- •Tissue-specific function is not solely regulated by transcription factor expression
- •Tissue-specific genes assume bottleneck positions in their corresponding networks
- •Tissue specificity is driven by context-dependent, non-canonical regulatory paths
Tuesday, October 24, 2017
CellMap analysis
Q: Why do I have 'abnormal', 'essential', 'nonessential' genes?
Genetic interactions CellMap exploration
H Qin
10/17/2017
CELLMAP provids genetics interaction in pairwise format. The interactions are provided for strain_ids (alleles). The mapping of strain_id and ORF is in “strain_ids_and_single_mutant_fitness.csv” .
rm(list=ls());
set.seed(2017);
datapath = "~/data/Sce/CellMap/20170626/S1.pairwise/";
debug = 0;
list.files(path=datapath);## [1] "SGA_DAmP.txt"
## [2] "SGA_ExE.txt"
## [3] "SGA_ExN_NxE.txt"
## [4] "SGA_NxN.txt"
## [5] "strain_ids_and_single_mutant_fitness.csv"
## [6] "strain_ids_and_single_mutant_fitness.xlsx"
Load naming lookup tables
dic = read.csv(paste(datapath, "strain_ids_and_single_mutant_fitness.csv", sep=''))
Load essential and non-essential infor that H. Qin generated.
list.files(path="data");## [1] "SummaryRegressionHetHom2015Oct12.csv"
## [2] "SummaryRegressionHetHomFactorized2015Oct13.csv"fit = read.csv("data/SummaryRegressionHetHomFactorized2015Oct13.csv")
Load pairwise interaction data
#Essential X Essential
tb.ee = read.table(paste(datapath,"SGA_ExE.txt", sep=''), header=T, sep="\t");
summary(tb.ee);## Query.Strain.ID Query.allele.name Array.Strain.ID
## YNL308C_tsq2680: 792 mob2-11-supp1: 1419 YCR002C_tsa78 : 1090
## YEL019C_tsq533 : 788 kri1-5001 : 792 YCR002C_tsa79 : 1090
## YNL287W_tsq38 : 788 mms21-1 : 788 YAL041W_tsa410: 1088
## YCL059C_tsq1104: 785 sec21-1 : 788 YAR007C_tsa273: 1088
## YCL059C_tsq326 : 785 cdc10-1 : 785 YAL038W_tsa34 : 1087
## YCR002C_tsq1072: 785 cdc10-2 : 785 YAL041W_tsa412: 1086
## (Other) :813847 (Other) :813213 (Other) :812041
## Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
## cdc10-1: 1090 TSA26:815635 Min. :-0.790500
## cdc10-2: 1090 TSA30: 2935 1st Qu.:-0.034800
## cdc24-2: 1088 Median : 0.005500
## rfa1-m2: 1088 Mean :-0.008011
## cdc19-1: 1087 3rd Qu.: 0.035700
## cdc24-3: 1086 Max. : 0.440300
## (Other):812041
## P.value Query.single.mutant.fitness..SMF. Array.SMF
## Min. :0.00000 Min. :0.13 Min. :0.1137
## 1st Qu.:0.03872 1st Qu.:0.72 1st Qu.:0.7570
## Median :0.19940 Median :0.85 Median :0.8581
## Mean :0.20933 Mean :0.82 Mean :0.8326
## 3rd Qu.:0.35770 3rd Qu.:0.93 3rd Qu.:0.9359
## Max. :1.00000 Max. :1.14 Max. :1.0550
## NA's :109963
## Double.mutant.fitness Double.mutant.fitness.standard.deviation
## Min. :-0.1435 Min. :0.00000
## 1st Qu.: 0.5666 1st Qu.:0.02290
## Median : 0.7123 Median :0.03850
## Mean : 0.6912 Mean :0.04786
## 3rd Qu.: 0.8370 3rd Qu.:0.06120
## Max. : 1.3817 Max. :1.24310
## #EXN and NXE
tb.en = read.table(paste(datapath,"SGA_ExN_NxE.txt", sep=''), header=T, sep="\t");
summary(tb.en);## Query.Strain.ID Query.allele.name Array.Strain.ID
## YJL143W_tsq3031: 3657 prp21-ts : 3657 YAR007C_tsa273 : 2212
## YJL203W_tsq401 : 3657 tim17-5001: 3657 YFR036W_tsa88 : 2212
## YIL021W_tsq1136: 3623 med6-ts : 3646 YAL025C_tsa1066: 2211
## YJL072C_tsq2842: 3623 psf2-5001 : 3623 YAL038W_tsa34 : 2210
## YIL021W_tsq2727: 3615 rpb3-2 : 3623 YFL008W_tsa68 : 2210
## YJR064W_tsq2853: 3585 rpb3-5001 : 3615 YFL009W_tsa334 : 2210
## (Other) :3687193 (Other) :3687132 (Other) :3695688
## Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
## cdc26-1 : 2212 DMA26:1212145 Min. :-1.09550
## rfa1-m2 : 2212 DMA30: 798532 1st Qu.:-0.02740
## mak16-5001: 2211 TSA26:1677715 Median :-0.00060
## cdc19-1 : 2210 TSA30: 20561 Mean :-0.00528
## cdc4-3 : 2210 3rd Qu.: 0.02390
## rsc8-ts16 : 2210 Max. : 1.33530
## (Other) :3695688
## P.value Query.single.mutant.fitness..SMF. Array.SMF
## Min. :0.0000 Min. :0.1 Min. :0.1137
## 1st Qu.:0.1155 1st Qu.:0.7 1st Qu.:0.8393
## Median :0.2746 Median :0.9 Median :0.9577
## Mean :0.2554 Mean :0.8 Mean :0.9072
## 3rd Qu.:0.3950 3rd Qu.:1.0 3rd Qu.:1.0065
## Max. :1.0000 Max. :1.1 Max. :1.1118
## NA's :403475
## Double.mutant.fitness Double.mutant.fitness.standard.deviation
## Min. :-0.1764 Min. :0.00000
## 1st Qu.: 0.6532 1st Qu.:0.02160
## Median : 0.8001 Median :0.03650
## Mean : 0.7680 Mean :0.04677
## 3rd Qu.: 0.9126 3rd Qu.:0.05910
## Max. : 2.1866 Max. :1.19310
## #NXN I do not need consider NxN for my aging modeling project
tb.nn = read.table(paste(datapath,"SGA_NxN.txt", sep=''), header=T, sep="\t");## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec =
## dec, : EOF within quoted string## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec =
## dec, : number of items read is not a multiple of the number of columnssummary(tb.nn);## Query.Strain.ID Query.allele.name Array.Strain.ID
## YJL219W_sn2362: 3708 hxt9 : 3708 YIL173W_dma2391: 1712
## YIR017C_sn2001: 3696 met28 : 3696 YIL165C_dma2396: 1710
## YJL154C_sn371 : 3674 vps35 : 3674 YIL170W_dma2392: 1706
## YJL141C_sn1525: 3673 yak1 : 3673 YIL141W_dma2367: 1700
## YJL056C_sn2004: 3667 zap1 : 3667 YIL145C_dma2366: 1698
## YJR117W_sn882 : 3666 ste24 : 3666 YIL140W_dma2368: 1694
## (Other) :6261409 (Other):6261409 (Other) :6273273
## Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
## vth1 : 1712 : 1 Min. :-1.16160
## yil165c: 1710 DMA26: 203777 1st Qu.:-0.02200
## hxt12 : 1706 DMA30:6079715 Median :-0.00220
## yil141w: 1700 Mean :-0.00389
## pan6 : 1698 3rd Qu.: 0.01780
## axl2 : 1694 Max. : 0.86760
## (Other):6273273 NA's :1
## P.value Query.single.mutant.fitness..SMF. Array.SMF
## Min. :0.0000 Min. :0.2 Min. :0.2265
## 1st Qu.:0.1633 1st Qu.:0.9 1st Qu.:0.9667
## Median :0.3083 Median :1.0 Median :1.0010
## Mean :0.2809 Mean :0.9 Mean :0.9712
## 3rd Qu.:0.4113 3rd Qu.:1.0 3rd Qu.:1.0195
## Max. :1.0000 Max. :1.1 Max. :1.1118
## NA's :1 NA's :479989 NA's :1
## Double.mutant.fitness Double.mutant.fitness.standard.deviation
## Min. :-0.2577 Min. :0.00000
## 1st Qu.: 0.8521 1st Qu.:0.01900
## Median : 0.9695 Median :0.03170
## Mean : 0.9069 Mean :0.04062
## 3rd Qu.: 1.0199 3rd Qu.:0.05050
## Max. : 1.7606 Max. :1.00090
## NA's :1 NA's :1
Columns names in the 3 tables are the same.
rbind( names(tb.en), names(tb.ee), names(tb.nn))## [,1] [,2] [,3]
## [1,] "Query.Strain.ID" "Query.allele.name" "Array.Strain.ID"
## [2,] "Query.Strain.ID" "Query.allele.name" "Array.Strain.ID"
## [3,] "Query.Strain.ID" "Query.allele.name" "Array.Strain.ID"
## [,4] [,5] [,6]
## [1,] "Array.allele.name" "Arraytype.Temp" "Genetic.interaction.score..ε."
## [2,] "Array.allele.name" "Arraytype.Temp" "Genetic.interaction.score..ε."
## [3,] "Array.allele.name" "Arraytype.Temp" "Genetic.interaction.score..ε."
## [,7] [,8] [,9]
## [1,] "P.value" "Query.single.mutant.fitness..SMF." "Array.SMF"
## [2,] "P.value" "Query.single.mutant.fitness..SMF." "Array.SMF"
## [3,] "P.value" "Query.single.mutant.fitness..SMF." "Array.SMF"
## [,10] [,11]
## [1,] "Double.mutant.fitness" "Double.mutant.fitness.standard.deviation"
## [2,] "Double.mutant.fitness" "Double.mutant.fitness.standard.deviation"
## [3,] "Double.mutant.fitness" "Double.mutant.fitness.standard.deviation"
Merge 3 tables into 1 table.
tb.gin = rbind(tb.ee, tb.en, tb.nn);
Double-check the merged results
length(tb.gin[,1]) == sum(length(tb.ee[,1]), length(tb.en[,1]), length(tb.nn[,1]))## [1] TRUE
Remove unused table to free up memory
ls()## [1] "datapath" "debug" "dic" "fit" "tb.ee" "tb.en"
## [7] "tb.gin" "tb.nn"rm(tb.en, tb.ee, tb.nn)
ls()## [1] "datapath" "debug" "dic" "fit" "tb.gin"
Costanzo2016 suggested lenient, intermediate, and stringennt ways for gin quality check.
tb.gin.lenient = tb.gin[ tb.gin$P.value<=0.05, ];
if (debug ==0) { rm(tb.gin); } #freeup memory
Map strain IDs to ORFs, add my essentialFalgs
tb.gin.lenient$ORF1 = dic$Systematic.gene.name[match( tb.gin.lenient$Query.Strain.ID, dic$Strain.ID)]
tb.gin.lenient$ORF2 = dic$Systematic.gene.name[match( tb.gin.lenient$Array.Strain.ID, dic$Strain.ID)]
tb.gin.lenient$essenflag1 = fit$essenflag[ match(tb.gin.lenient$ORF1, fit$orf)]
tb.gin.lenient$essenflag2 = fit$essenflag[ match(tb.gin.lenient$ORF2, fit$orf)]
head(tb.gin.lenient)## Query.Strain.ID Query.allele.name Array.Strain.ID Array.allele.name
## 1 YAL001C_tsq508 tfc3-g349e YBL023C_tsa111 mcm2-1
## 2 YAL001C_tsq508 tfc3-g349e YBL026W_tsa1065 lsm2-5001
## 7 YAL001C_tsq508 tfc3-g349e YBL034C_tsa950 stu1-7
## 13 YAL001C_tsq508 tfc3-g349e YBL076C_tsa275 ils1-1
## 16 YAL001C_tsq508 tfc3-g349e YBL097W_tsa510 brn1-9
## 25 YAL001C_tsq508 tfc3-g349e YBR029C_tsa1063 cds1-5001
## Arraytype.Temp Genetic.interaction.score..ε. P.value
## 1 TSA30 -0.0348 5.042e-03
## 2 TSA30 -0.3529 3.591e-06
## 7 TSA30 -0.1294 1.931e-02
## 13 TSA30 -0.0250 1.301e-04
## 16 TSA30 -0.0808 5.582e-15
## 25 TSA30 -0.1173 8.243e-05
## Query.single.mutant.fitness..SMF. Array.SMF Double.mutant.fitness
## 1 0.8285 0.9254 0.7319
## 2 0.8285 0.9408 0.4266
## 7 0.8285 0.6690 0.4249
## 13 0.8285 0.8097 0.6458
## 16 0.8285 0.5464 0.3719
## 25 0.8285 0.9007 0.6289
## Double.mutant.fitness.standard.deviation ORF1 ORF2 essenflag1
## 1 0.0102 YAL001C YBL023C nonessential
## 2 0.0790 YAL001C YBL026W nonessential
## 7 0.0482 YAL001C YBL034C nonessential
## 13 0.0054 YAL001C YBL076C nonessential
## 16 0.0077 YAL001C YBL097W nonessential
## 25 0.0226 YAL001C YBR029C nonessential
## essenflag2
## 1 essential
## 2 essential
## 7 abnormal
## 13 essential
## 16 essential
## 25 essentialtb.gin.intermediate = tb.gin.lenient[ abs(tb.gin.lenient$Genetic.interaction.score..ε.) >0.08, ];
tb.gin.stringent = tb.gin.lenient[ tb.gin.lenient$Genetic.interaction.score..ε.>0.16 | tb.gin.lenient$Genetic.interaction.score..ε.< -0.12, ]hist(tb.gin.lenient$Genetic.interaction.score..ε., breaks = 100)
summary(tb.gin.lenient);## Query.Strain.ID Query.allele.name Array.Strain.ID
## YJL029C_sn248 : 1364 vps53 : 1364 YIL048W_tsa188: 1368
## YIR033W_sn1943 : 1214 mga2 : 1214 YDL008W_tsa783: 1328
## YIL004C_tsq1171: 1208 bet1-1 : 1208 YDR172W_tsa28 : 1316
## YJR002W_tsq2069: 1124 mpp10-5001: 1124 YFL039C_tsa140: 1315
## YJR045C_tsq2790: 1045 ssc1-2 : 1045 YHR164C_tsa352: 1274
## (Other) :1563516 (Other) :1563516 (Other) :1562870
## NA's : 1 NA's : 1 NA's : 1
## Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
## neo1-2 : 1368 TSA26:523518 Min. :-1.16160
## apc11-13: 1328 TSA30: 5813 1st Qu.:-0.07190
## sup35-td: 1316 DMA26:203445 Median :-0.01780
## act1-125: 1315 DMA30:836695 Mean :-0.01934
## dna2-2 : 1274 : 0 3rd Qu.: 0.05520
## (Other) :1562870 NA's : 1 Max. : 1.33530
## NA's : 1 NA's :1
## P.value Query.single.mutant.fitness..SMF. Array.SMF
## Min. :0.0000000 Min. :0.11 Min. :0.1137
## 1st Qu.:0.0000455 1st Qu.:0.76 1st Qu.:0.8243
## Median :0.0048550 Median :0.92 Median :0.9525
## Mean :0.0125985 Mean :0.86 Mean :0.8981
## 3rd Qu.:0.0229400 3rd Qu.:1.00 3rd Qu.:1.0060
## Max. :0.0500000 Max. :1.14 Max. :1.1118
## NA's :1 NA's :165086 NA's :1
## Double.mutant.fitness Double.mutant.fitness.standard.deviation
## Min. :-0.2577 Min. :0.0000
## 1st Qu.: 0.6044 1st Qu.:0.0090
## Median : 0.8079 Median :0.0163
## Mean : 0.7654 Mean :0.0260
## 3rd Qu.: 0.9619 3rd Qu.:0.0301
## Max. : 2.1866 Max. :0.9147
## NA's :1 NA's :1
## ORF1 ORF2 essenflag1
## YFL039C : 8053 YFL039C : 14881 abnormal : 27510
## YFL034C-B: 3562 YFL034C-B: 7918 essential :493722
## YNL181W : 3529 YHR036W : 4162 nonessential:916926
## YBL105C : 3090 YNL061W : 3851 NA's :131314
## YBR088C : 2778 YJR076C : 3789
## (Other) :1548459 (Other) :1534870
## NA's : 1 NA's : 1
## essenflag2
## abnormal : 25160
## essential : 480824
## nonessential:1009399
## NA's : 54089
##
##
## summary(tb.gin.intermediate);## Query.Strain.ID Query.allele.name Array.Strain.ID
## YNL243W_sn760 : 711 sla2 : 711 YDL008W_tsa783: 1017
## YDR293C_sn729 : 692 ssd1 : 692 YLR078C_tsa199: 940
## YKL154W_tsq1163: 678 srp102-510: 678 YDR172W_tsa28 : 935
## YJR045C_tsq2790: 664 ssc1-2 : 664 YFL039C_tsa140: 883
## YLR275W_tsq2653: 656 smd2-5005 : 656 YER157W_tsa41 : 881
## (Other) :566233 (Other) :566233 (Other) :564978
## NA's : 1 NA's : 1 NA's : 1
## Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
## apc11-13: 1017 TSA26:256208 Min. :-1.16160
## bos1-1 : 940 TSA30: 3153 1st Qu.:-0.15690
## sup35-td: 935 DMA26: 81038 Median :-0.09740
## act1-125: 883 DMA30:229235 Mean :-0.05968
## cog3-1 : 881 : 0 3rd Qu.: 0.09720
## (Other) :564978 NA's : 1 Max. : 1.33530
## NA's : 1 NA's :1
## P.value Query.single.mutant.fitness..SMF. Array.SMF
## Min. :0.0000000 Min. :0.18 Min. :0.1137
## 1st Qu.:0.0000015 1st Qu.:0.76 1st Qu.:0.7400
## Median :0.0016875 Median :0.89 Median :0.8690
## Mean :0.0097757 Mean :0.85 Mean :0.8414
## 3rd Qu.:0.0160700 3rd Qu.:0.99 3rd Qu.:0.9745
## Max. :0.0500000 Max. :1.14 Max. :1.1118
## NA's :1 NA's :87094 NA's :1
## Double.mutant.fitness Double.mutant.fitness.standard.deviation
## Min. :-0.2577 Min. :0.00000
## 1st Qu.: 0.4933 1st Qu.:0.02280
## Median : 0.6851 Median :0.03680
## Mean : 0.6773 Mean :0.04859
## 3rd Qu.: 0.8650 3rd Qu.:0.05780
## Max. : 2.1866 Max. :0.91470
## NA's :1 NA's :1
## ORF1 ORF2 essenflag1
## YFL039C : 4584 YFL039C : 8373 abnormal : 13170
## YNL181W : 2435 YFL034C-B: 4706 essential :221747
## YFL034C-B: 2028 YJR076C : 2437 nonessential:290106
## YBL105C : 1752 YDR182W : 2224 NA's : 44612
## YEL034W : 1522 YNL061W : 1928
## (Other) :557313 (Other) :549966
## NA's : 1 NA's : 1
## essenflag2
## abnormal : 12310
## essential :234519
## nonessential:303111
## NA's : 19695
##
##
## summary(tb.gin.stringent)## Query.Strain.ID Query.allele.name Array.Strain.ID
## YNL243W_sn760 : 574 sla2 : 574 YDL008W_tsa783: 649
## YDR293C_sn729 : 460 ssd1 : 460 YLR268W_tsa121: 629
## YBR049C_tsq1348: 448 reb1-5001 : 448 YFL039C_tsa140: 518
## YLR275W_tsq2653: 425 smd2-5005 : 425 YER157W_tsa41 : 512
## YEL034W_tsq737 : 377 mob2-11-supp1: 380 YJR076C_tsa84 : 494
## (Other) :250780 (Other) :250777 (Other) :250262
## NA's : 1 NA's : 1 NA's : 1
## Array.allele.name Arraytype.Temp Genetic.interaction.score..ε.
## apc11-13: 649 TSA26:124897 Min. :-1.1616
## sec22-3 : 629 TSA30: 1638 1st Qu.:-0.2354
## act1-125: 518 DMA26: 35986 Median :-0.1670
## cog3-1 : 512 DMA30: 90543 Mean :-0.1610
## cdc11-5 : 494 : 0 3rd Qu.:-0.1322
## (Other) :250262 NA's : 1 Max. : 1.3353
## NA's : 1 NA's :1
## P.value Query.single.mutant.fitness..SMF. Array.SMF
## Min. :0.0000000 Min. :0.18 Min. :0.1137
## 1st Qu.:0.0000000 1st Qu.:0.76 1st Qu.:0.7235
## Median :0.0003424 Median :0.89 Median :0.8465
## Mean :0.0072841 Mean :0.86 Mean :0.8220
## 3rd Qu.:0.0092682 3rd Qu.:0.98 3rd Qu.:0.9510
## Max. :0.0500000 Max. :1.14 Max. :1.1118
## NA's :1 NA's :43715 NA's :1
## Double.mutant.fitness Double.mutant.fitness.standard.deviation
## Min. :-0.2577 Min. :0.0000
## 1st Qu.: 0.3829 1st Qu.:0.0310
## Median : 0.5491 Median :0.0536
## Mean : 0.5619 Mean :0.0686
## 3rd Qu.: 0.7233 3rd Qu.:0.0857
## Max. : 2.1866 Max. :0.9147
## NA's :1 NA's :1
## ORF1 ORF2 essenflag1
## YFL039C : 2200 YFL039C : 4416 abnormal : 6379
## YNL181W : 1350 YFL034C-B: 2238 essential :104477
## YFL034C-B: 1068 YJR076C : 1381 nonessential:122575
## YEL034W : 897 YDR182W : 1215 NA's : 19634
## YBL105C : 818 YLR268W : 950
## (Other) :246731 (Other) :242864
## NA's : 1 NA's : 1
## essenflag2
## abnormal : 6140
## essential :113948
## nonessential:124214
## NA's : 8763
##
##
##
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