T-bioinfo overview
- 2. Typical Mass-use Pipelines Complex Challenges and Workflows NGS (Next Generation Sequencing) 1. Total-RNA Analysis (RNA-seq, Non-Coding RNA, Repeats) 2. Epigenetics (CHiP-seq and Bisulfate-Seq) 3. Variant Calling 4. Microbiome (Metagenomics) Mass Spec 1. Proteomics 2. Metabolomics Structural Biology 1. Libraries of Small Molecules (Query, Clustering) 2. Docking (Including large molecules) Machine Learning 1. Phenotypic Analysis and Modeling 2. Analysis of visual data 3. Standard Statistical methods 4. Integration of heterogenous data sets CirSeq Mutation Analysis 1. Analysis of viral CirSeq data for precise mutation identification 2. Fitness of mutations reflecting viral adaptation 3. Identification of viral quasi-species Mass Spec 1. Protein-protein Interactions between host and viral proteins 2. Post translational modifications of host proteins Structural Biology 1. Libraries of Small Molecules (Query, Clustering) 2. Docking (Including large molecules) NGS host data 1. Host gene expression variations in response to infectious quasi-species T-BioInfo is a user-friendly computational platform that enables analysis and integration of big data.The challenge of mining -omics data for meaningful patters that can be applied in biomedical and agricultural research as sequencing becomes cheaper and more precise. On the other hand, complex networks of dependencies that define many conditions tend to require integration of huge heterogenous data sets from SNPs, gene expression, epigenetic markers, proteomic and metabolomic profiles, even structural biology data. Our company has developed innovative and user friendly workflows for analysis and integration of these different datasets. Now we are looking to test and commercialize a platform that provides web access to the platform.
- 3. Simple, Flexible and Consistent Interface Across All Sections Integration of analysis types One environment for all types of data and analysis “one-button” approach to most areas of analysis • Flexible analysis pipelines in the pla/orm sec4ons and easy to perform data input • A user is assisted by the pla/orm in construc4ng meaningful algorithmic pipelines for processing data: modules for pipeline con4nua4on are highlighted by black background and yellow 4tle.
- 4. Analysis of Total RNA Concept: raw total transcriptome reads contain informa4on not only about expressed splice variants (isoforms) of genes, but also about expressed transposons and regulatory non-coding RNAs. The complete analysis consists of three steps. First, the reads are mapped on isoforms in order to get isoform expression levels. Second, previously unmapped reads are mapped on known repe44ve elements (RE) and non-coding RNAs in order to get their expression levels. Third, the rest of reads are processed by special clustering (BiClustering) in order to get new expressed RE and non-coding RNAs as well as their expression levels under applied biological condi4ons. On the next stage, data integra4on can be performed: interplay between expressed isoforms, transposons, and regulatory RNAs. 1 Detec4on of expressed isoforms and their expression levels by mapping the reads on constructed transcripts √ 2 For unmapped reads: √ 3 Detec4on of most expressed repeats and regulatory RNA from databases √ 4 BiClustering: associa4ons of kmers and reads as a bicluster, and genera4on of Kchains of biclusters √ 5 Extensions of Kchains ± 6 Mapping of NGS reads on found Kchains: detec4on of most expressed novel transposons and regulatory RNAs √ T-Bioinfo RNA-seq/chip section Example: Expression of RepeatsAlgorithmic Approaches: Analysis of “Junk” RNA
- 5. Epigenetic Analysis: Bisulfite DNA Methylation and CHiP-Seq Bisulfite Concept: bisulfite sequencing shows T instead of C in a read if C of a genomics site (like CpG) is methylated. Thus, detec4on of methylated sites and genome fragments enriched/depleted by methyla4on is based on special type of read mapping, and segmenta4on of the whole genome methyla4on profile. The analysis objec4ves include special mapping algorithms with tolerance of the T-to-C mismatch, sta4s4cal es4ma4on of the per-site methyla4on level, allele specificity of DNA methyla4on, as well as detec4on of the over-methylated and under-methylated genomic regions. CHiP-Seq Concept: detec4on of epigene4c signals such as histone modifica4ons of different types and DNA methyla4on events as well as determining protein/DNA binding sites (TF binding sites) are performed by CHiP-seq and CHiP-chip experiments. Analysis of profiles of these whole genome signals is performed by the genome segmenta4on algorithms. The analysis objec4ves include iden4fying signal enriched genome fragments as puta4ve epigene4c events, and a combina4on of enriched fragments on posi4ve and nega4ve strands with a certain distance between them as the TF binding event. On the next analysis stage, the data integra4on can be performed: interplay between genome muta4ons and epigene4c signals on one side and expressed isoforms, transposons, and regulatory RNAs on the other side. The network of gene regula4on by a transcrip4on factor can be reconstructed from the whole genome TF binding posi4ons and expressions of the down-stream genes. Microarray datasets are transformed into pseudo NGS reads and are analyzed by the same CHiP-seq pipelines. T-Bioinfo CHiP-seq section 1 Preprocessing of raw data √ 2 Mapping of NGS reads by bisulfite mapping algorithms: no penalty for T(read)-to-C(genome) mismatches √ 3 Detec4on of the DNA methylated posi4ons and their scores by the confidence interval method √ 4 Allele specificity of the methyla4on in a posi4on. - 5 Detec4on of over-methylated and under-methylated genomic intervals by the segmenta4on algorithms ± 6 Detec4on of differen4al DNA methyla4ons (individual posi4ons and intervals) between contras4ng condi4ons ±
- 6. Virology Pipeline Mutation Fitness Genome-wide fitness calculations enabled by CirSeq, combined with structural information, can provide high-definition, bias-free insights into structure-function relationships, potentially revealing novel functions for viral proteins and RNA structures, as well as nuanced insights into a viral genome’s phenotypic space. Such analyses have the power to reveal protein residues or domains that directly correspond to viral functional plasticity and may significantly inform our structural and mechanistic understanding of host–pathogen interactions.
- 8. Integration of Heterogenous Data sets Concept: mutual associa4on of features of biological datasets is most substan4al part for integra4on of several analyses of biological projects in one story. We are sugges4ng several techniques for such associa4ons. Matching of metabolite and SNP profiles according to LB’s selection of SNPs
- 9. Patent Pending Technology for Drug Discovery Fast screening and clustering of small molecules based on physico-chemical similarity (70-100 times faster than industry standard) Small Molecule Candidate Identifying a biologically active molecule (Polio) Patent Pending: Ref. P-78368-US | App. No. 14/625,785 entitled SYSTEMS AND METHODS OF IMPROVED MOLECULE SCREENING Computational analysis of small molecules can be roughly divided into three sections: pre- processing analysis, virtual screening methods, and clustering.The aim of the conformer generation process is to build a set of representative conformers that covers the conformational space of a given molecule.There are two main classes of virtual screening methods: similarity- based methods (descriptor-based screening; geometric querying; shape-based querying; fingerprints) and receptor-based methods (docking). One of the greatest challenges of docking software is to consider protein flexibility.These macromolecules are not static objects and conformational changes are often key elements in ligand binding. T-Bioinfo provides a number of proprietary methods that can be combined into pipelines for drug discovery.
- 10. Tauber Bioinformatics Research Center Tauber Bioinformatics Research Center at the University of Haifa has a proven track record in Bioinformatics with scientific collaborations with Hospitals, top US Universities, involvement in government-funded projects, and multiple publications in leading journals such as Science and Nature. 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. Aleph Therapeuticsא Early Adopters and Collaborators: