A logical functional analysis pipeline Nicos Angelopoulos and - - PowerPoint PPT Presentation

A logical functional analysis pipeline

Nicos Angelopoulos and Giorgos Giamas

WCB, 8/9/2014 – p.1

. . . introduction

. . . it is becoming increasingly clear, that the ability to reason at the highest possible level with the available data has a profound effect on the kind of questions scientists can address. It is indeed the view of experimental data as knowledge which is becoming an important realisation among the scientists in the biological sciences. Traditionally, data analysis has been viewed as an independent exercise it is increasingly becoming apparent though, that data analysis is now taking a more central role with important high-impact papers focusing on evaluating results rather than producing unique datasets.

WCB, 8/9/2014 – p.2

why Prolog?

Clear computational paradigm Knowledge representation and reasoning Robustness Memory management Program and data duality High level of abstraction Calling R functions Indexing Database interactions Operating systems support

WCB, 8/9/2014 – p.3

what is lacking ?

Lack of programmers Language development and stability Critical mass Performance

WCB, 8/9/2014 – p.4

Real - Integrative statistics with R

Run R functions on Prolog data. Reductionist design ? − x ← mean([1, 2, 3]). ? − X ← x. X = 2.0 ? − y ← [1, 2, 3], x ← mean(y).

WCB, 8/9/2014 – p.5

Tyrosine Kinase knockouts

Important molecules in signalling. 65 knock outs

WCB, 8/9/2014 – p.6

dysregulation patterns

ABL2 BTK CSF1R CSK DDR1 EPHA3 EPHA6 EPHA7 EPHB2 EPHB6 ERBB3 FES FGFR1 FLT3 FYN

CA12 CD44 AKR1C2 AGL SQSTM1 TMEM164 PGR JAK1 WDR26 ZNF185 ACTN4 CELSR2 SCARB1 GYS1 SLC38A2 EPB41L1 PYGM ABCA2 SCIN BOP1 ACTN1 ANO6 ARMCX3 ITGB4 PSME3 UACA NEDD4 731788 PLCB3 COBLL1 GALNT7 ITGB4 ASS1 PREX1 MROH1 LIMA1 AGRN HEATR1 RABL6 ADH5 PIK3C2B ASNS OXSR1 IVD PNN BAG6 POLR1A H1F0 DCAF8 ASMTL PHF3 SSRP1 SUPT16H RCN2 UBTF NEDD8 MAP7 DCXR PXDN FHOD1 AKR1A1 PES1 NCOR1 MAP7 HUWE1 MUC5AC CAMSAP2 QSOX1 RHPN2 S100P SQRDL CEACAM5 FTL ERGIC1 TCOF1 BTF3 FTH1 MUC5B SYPL1

1 2 3 4 5 6

WCB, 8/9/2014 – p.7

clusters based on dysregulation

ABL1 ABL2 AXL BTK CSF1R CSK DDR1 EGFR EPHA1 EPHA2 EPHA3 EPHA4 EPHA6 EPHA7 EPHB1 EPHB2 EPHB3 EPHB4 EPHB6 ERBB2 ERBB3 ERBB4 FES FGFR1 F G F R 2 FLT1 F L T 3 F R K F Y N H C K IGF1R INSR J A K 1 JAK2 KDR LCK L M T K 2 LMTK3 LYN M A T K MERTK MET MST1R NTRK1 N T R K 2 N T R K 3 PDGFRB PTK2 PTK2B P T K 6 PTK7 RET R O R 1 ROR2 RYK S R C S T Y K 1 SYK TEC T N K 1 T N K 2 TYK2 TYRO3 YES1 ZAP70

WCB, 8/9/2014 – p.8

cluster populations

5 10 15 1 2 3 4 5 6 7 8 9 10

Clusters Members

Cluster 1 2 3 4 5 6 7 8 9 10

WCB, 8/9/2014 – p.9

level 2 GO biological process

Cell adhesion Biological regulation Apoptosis Cellular component organization Cellular process Development Growth Immune system Transport Metabolic process Reproduction Cell cycle Cell communication 20 40 60 80 1 2 3 4 5 6 7 8 9 10

WCB, 8/9/2014 – p.10

4-carbohydrate metabolic process

GO:0005975, carbohydrate metabolic process. pval:6.531864e-5 count: 32 size: 804. ARFGEF1 DOLPP1 GBA GLYR1 GNS HK1 ISYNA1 NFKB1 OGDH DLAT GPI IDH3A PAPSS2 PGM3 RAE1 SEC24A SLC3A2 CD44 AGRN ENO1 GALK1 GALNT6 GPD2 MUC5B PC PCK2 PDHB PFKL PGK1 PHKA1 SDC1 UGP2

WCB, 8/9/2014 – p.11

doxorubicin

PACSIN2 RNF213 TOM1 PITPNB AHNAK CAPZB BLVRB GOLGA3 H2AFX HIST1H4A ACAA2 PRCC KPNA2 MYH10 RCN2

HCC1143 HCC1395 MDAMB415 MDAMB453 UACC812 EVSAT HCC70 HS578T BT20 MDAMB134VI MDAMB468 HCC1806 HCC1599 HCC2157 CAL851 HCC38 CAL148 CAL51 DU4475 MCF7 Confers resistance sensitivity Clusters3 7 Clusters2 2 3 7 8 Clusters 1 3 4 5 6 7 8 Effect resistant sensitive IC_50 2.35 −4.45 −3 −2 −1 1 2 3

WCB, 8/9/2014 – p.12

knockout effects on TKs

ABL1 ABL2 AXL BTK CSF1R CSK DDR1 EGFR EPHA1 EPHA2 EPHA3 EPHA4 EPHA6 EPHA7 EPHB1 EPHB2 EPHB3 EPHB4 EPHB6 ERBB2 ERBB3 ERBB4 FES FGFR1 FGFR2 FLT1 FLT3 FRK FYN HCK IGF1R INSR JAK1 JAK2 KDR LCK LMTK2 LMTK3 LYN MATK MERTK MET MST1R NTRK1 NTRK2 NTRK3 PDGFRB PTK2 PTK2B PTK6 PTK7 RET ROR1 ROR2 RYK SRC STYK1 SYK TEC TNK1 TNK2 TYK2 TYRO3 YES1 ZAP70 ZAP70 YES1 TYRO3 TYK2 TNK2 TNK1 TEC SYK STYK1 SRC RYK ROR2 ROR1 RET PTK7 PTK6 PTK2B PTK2 PDGFRB NTRK3 NTRK2 NTRK1 MST1R MET MERTK MATK LYN LMTK3 LMTK2 LCK KDR JAK2 JAK1 INSR IGF1R HCK FYN FRK FLT3 FLT1 FGFR2 FGFR1 FES ERBB4 ERBB3 ERBB2 EPHB6 EPHB4 EPHB3 EPHB2 EPHB1 EPHA7 EPHA6 EPHA4 EPHA3 EPHA2 EPHA1 EGFR DDR1 CSK CSF1R BTK AXL ABL2 ABL1

0.5 1 1.5

Value

1000 2000 3000

Color Key and Histogram

Count

WCB, 8/9/2014 – p.13

software

db_facts 44/19 db-tables-as-facts & SQL layer proSQLite 109/33 SQLite interface pubmed 14/3 Access pubmed publication records Real 68/11 Integrative statistics with R mtx working with matrices go_string GO term STRING graphs map_id mapping across nomenclatures func functional analysis pipeline

WCB, 8/9/2014 – p.14