Tauber Bioinformatics Research Center at the University of Haifa has a proven track record in Bioinformatics with scientific collaborations with Hospitals, Universities, involvement in government-funded projects, and multiple publications in leading

journals.

Pine Biotech holds an exclusive license for commercialization of tools developed at the TBRC for research, industry applications and education. The startup is located at the BioInnovation Center in New Orleans, LA. In collaboration with TBRC staff, Pine Biotech is completing several pilot projects to validate our approach.

Dr. Leonid Brodsky

Dr. Alfred Tauber

Dr. Baruch Rinkevich

Dr. Hanoch Kaphzan

Bioinformatics

Immunology

Marine Biology

Neurobiology

Rare Genomic Diseases

Data Types

Specific Pipelines

Integration

Education

Machine Learning

Projects and Approaches

Noisy and Complex Heterogenous Datasets

BiAssociation: Integration of different types of –omics data

Identifying hidden patterns in data

BiClustering: Example of Assembly of raw transcriptomic reads from exosomal RNAs and other non aligned reads

Integration and Identification of Key Features

Drug 1 Drug 2 Drug 3

Cell Line 1 IG50 IG51 IG52

Cell Line 2 IG51 IG52 IG53

Cell Line 3 IG52 IG53 IG54

Cell Line 4 IG53 IG54 IG55

Cell Line 1 Cell Line 2 Cell Line 3

Gene 1 Exp. Level Exp. Level Exp. Level

Gene 2 Exp. Level Exp. Level Exp. Level

Gene 3 Exp. Level Exp. Level Exp. Level

Gene 4 Exp. Level Exp. Level Exp. Level

Data Source 1 Data Source 2

Clustering Clustering

Many-to-Many Relationships of clustering results

Key Feature 1Key Feature 2

…

BiAssociation

Identification of predictor genes and mutations for drug efficacy

Selection of tumor and stroma genes as biomarker candidates

cell lines

mut

atio

ns

cell lines

gene

s

cell lines

drug

s

Pres

ence

/Abs

ence

of

SN

P (1

/0)

Expr

essi

on

Valu

es

IC50

Va

lues

drugs

cell

lines IC50

Values

chem

ical

des

crip

tors

IC50 Values

patie

nts

IC50 Values

drugs

drugs

Network of Integrations

Linking clinical conditions with omics data in model experiments

Processed Tables of Raw Expression Data

Samples

Expression levels

Variation

Association

Sequence

Pathway

Variation

Function

Drug-Gene BiAssociation using swRegression

cell lines

gene

s

Expr

essi

on

Valu

es

drugs

cell

lines

IC50

Va

lues

cell line 1

cell line 2

cell line 3

cell line 4

cell line 5

cell line 6

cell line 7

gene expression IC50 value

Detection of gene activation linked to an IC50 value by cell line. Each cell line represents a subtype of cancer, selected by

modeling that biological condition

Predictors:

lnc.LEKR1.3.1

ENST00000426717

ENSG00000138092

ENST00000449399

ENST00000336915

ENSG00000149136

ENST00000456986

ENSG00000115758

ENSG00000100416

ENST00000361219

ENSG00000171793

ENSG00000187741

ENST00000309276

ENSG00000141293

non-coding RNA

Isoform

Gene

Expressed Molecular Features as Predictors

Multivariate Mutation-Expression BiAssociation

cell lines

gene

s

Expr

essi

on

Valu

escell lines

mut

atio

ns

Mut

atio

n Va

lues

(1/0

)

cell line 1

cell line 2

cell line 3

cell line 4

cell line 5

cell line 6

cell line 7

gene expression

mutation isl

ands

island abundance

neighboring gene

DOCK6

DOCK6

DOCK6

DOCK6

DOCK6

DOCK6

DOCK6

OR

2O

R11

OR

5A1

46 cell lines

173 genes of the Olfactory pathway

mutation island vs. SNP

“neighborhood” gene

Applications to Clinical Studies

cell line 1

cell line 2

cell line 3

cell line 4

cell line 5

cell line 6

cell line 7

minus Log (GI50) mutation islands neighbor

ROBO1

ROBO1

ROBO1

ROBO1

ROBO1

ROBO1

ROBO1

46 cell lines

Doxorubicin GI50 Profile

mutation island vs. SNP

ROBO1 receptor

Doxorubicin: standard treatment in eligible patients with advanced/metastatic soft tissue sarcoma

Who? Why? How?

SLIT2 protein

SLIT2 protein

SLIT2 protein

SLIT2 protein

SLT2 protein

ROBO1 receptor

SLIT2 protein

BiAssociation for Hidden Patterns in Omics Data

Lymphoma possibly associated with Epstein-Barr virus Stroma-Specific Sample Identification Small Cell Lung Carcinoma Samples

Lymphomagenesis Samples

Genes deferentially expressed in these outlier samples are enriched with immune processes in the tumor. We hypothesize that these tumors are lymphomas.

One sample from these outlier samples is a chronic lymphocytic leukemia sample and so the B-cell presence in this sample is not surprising. However, the other two samples are lung bronchogenic cancer and lung squamous cancer respectively. Our hypothesis is that these two cancers are lymphoma cancers associated with Epstein-Barr virus

References: Patient-Derived Tumor Xenografts Are Susceptible to Formation of Human Lymphocytic Tumors (2015) and Human Solid Tumor Xenografts in Immunodeficient Mice Are Vulnerable to Lymphomagenesis Associated with Epstein-Barr Virus (2012)

Stroma-Specific Samples

Tumor up-regulated (pVal<0.0001) gene: RBFOX1 Tumor down-regulated: ENT4 and known lincRNA (RP11-1070N10) Stroma up-regulated genes are enriched in the following functional clusters: mitochondrion; zinc-finger H2C2; ion transmembrane transport; metal ion binding; cytoplasm; alternative splicing and transcription factor

Outlier Samples:

Significantly (p-val <0.0001) up-regulated (in Small-cell Carcinoma Lung Cancer samples) tumor genes (491 genes) are enriched by the following functional clusters: Zinc finger C2H2; Kelch repeat; CUB domain; protein phosphatase 2C.

Significantly down-regulated tumor genes (p-val<0.0001; 1056 genes) are enriched by the following functional clusters: connecting peptide; MHC 1; tumor necrosis factor-activated receptor activity; calcium binding

Significantly down-regulated stroma genes (p-val <0.0001; 323 genes) are enriched by the following functional clusters: Interferon regulatory factor; SOCS box; 2'-5'-oligoadenylate synthetase activity

Small Cell Lung Carcinoma

The exosome consists of the following RNAs: mRNA, RNA repeats, rRNA, small RNA (Transfer RNA (tRNA), small interfering (siRNA), small nucleolar RNA (snoRNA), small cytoplasmic RNA (scRNA), small nuclear RNA (snRNA), miRNA lncRNA, snoRNA, piwi-interacting RNA (piRNA), rRNA, viral RNA, bacterial RNA

BiClustering for Exosomal RNA

Consensus

known sequences

known sequences

K-chainsRaw reads Assembly

BiClustering Procedure

Assembly of small RNA, repetitive elements and other transcribed genomic elements via BiClustering.

Thank You!