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HybridStat Predictive Analytics

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About HybridStat Predictive Analytics

HybridStat is a young micro-SME located in Athens, Greece, bringing together a team of three highly qualified and motivated young scientists, whose expertise spans a variety of statistics and computer science domains. The company was established in 2014 as a General Partnership under the Greek law.

Our... Show more »

HybridStat is a young micro-SME located in Athens, Greece, bringing together a team of three highly qualified and motivated young scientists, whose expertise spans a variety of statistics and computer science domains. The company was established in 2014 as a General Partnership under the Greek law.

Our mission is to support life-scientists to answer medical or biological questions requiring bioinformatics and biostatistical analyses.

The main offerings of HybridStat are centered on biostatistics, bioinformatics and analytics of high-throughput biological data derived from technologies such as Next Generation Sequencing, DNA/miRNA/protein microarrays and mass spectrometry. Currently, we offer consulting and analytics services as well as custom database and software design to life scientists. In addition, we have developed Geniasis, our cloud-based, next generation bioinformatics medical decision support system. Geniasis offers simplified, affordable and high-quality analysis of high-throughput genomics data with focus on the clinical interpretation of the results.

Our vision is to become a global leader in cloud-based bioinformatics tools for high-throughput genomics data analytics. Our motto is “bioinformatics made simple” and we aim our Geniasis solution to offer life-scientists and clinicians a self-service analytics platform that will simplify, accelerate and optimize the process of analyzing and comprehending clinical genomics data.

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Our Services (24)


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Bioinformatics

Price on request

Advanced bioinformatics analysis, computation and consulting

Apart from the specific and targeted data analysis services described in the previous sections, HybridStat offers custom-tailored advanced bioinformatics analysis services, based on specific needs of the customer. HybridStat also offers consulting services regarding... Show more »

Advanced bioinformatics analysis, computation and consulting

Apart from the specific and targeted data analysis services described in the previous sections, HybridStat offers custom-tailored advanced bioinformatics analysis services, based on specific needs of the customer. HybridStat also offers consulting services regarding bioinformatics data analytics and other issues of statistical issues related to biological and life sciences.

Advanced bioinformatics analysis of -omics data

  • Clustering of gene/protein/metabolite abundance for the identification of common regulation and presence patterns with a variety of clustering algorithms and provision of help on identifying the best performing one for the client’s data. Use of HybridStat’s heuristics algorithms to identify important variables such as the optimal number of clusters for an expression dataset.
  • Classification modeling, supervised and unsupervised learning for the detection of potential signatures characterizing several biological conditions (e.g. healthy vs diseased tissue). Efficient use of popular unsupervised (e.g. k-means clustering) and superior supervised machine learning methodologies (e.g. Random Forests and Support Vector Machines) coupled with feature selection based on information content towards the identification of molecular (gene/protein/miRNA/metabolite) joint signatures able to distinguish between healthy and disease status or between pathologies which are hard to distinguish by macroscopic methods. Sensitivity/specificity and classification accuracy reports for the screening of potential drug targets.
  • Analysis of X-Seq data (other than RNA-, ChIP- and DNA/Exome-, for example FAIRE-Seq) requiring more specialized data handling and statistical modeling.
  • Computational association of putative binding sites derived from ChIP-Seq experiments with gene expression (absolute RNA abundance or deregulated genes). Use of advanced algorithms for the derivation of association scores of TF profiles with gene expression.
  • Scanning for DNA motifs in promoters of genes belonging to similar expression groups, for the identification of common regulatory elements.
  • De novo motif discovery in ChIP-Seq data for the motif enrichment in binding sites and the identification of possible co-factors, using a combination of widely verified motif discovery tools. Motif clustering to identify regulatory element consensuses.
  • Advanced data visualizations and custom analytics upon discussions about the goals of the client.
  • Network visualization of gene and metabolic networks based on public repositories and known protein-protein interactions.
  • Inference of chemical formula for metabolites and/or small molecules that could not be matched against any known database in metabolomics experiments.
  • Screening of public databases for gene/protein/miRNA/metabolite disease associations
  • Custom programming/scripting when existing tools are not sufficient to reach the analysis goals or when the client requires advanced data handling and visualization.

Consulting

As the questions in life sciences, either from focused researchers who study basic mechanisms of biological systems or disease mechanisms in human/animal models or from biotechnology and pharmaceutical companies with focused R&D departments for the discovery of new drug targets and the effective drug design can be endless. HybridStat is dedicated to discussing in details the needs of the client project and offer guidance and custom analysis services and/or software development based on its extensive bioinformatics and bioistatistics expertise. Even if a project’s goals are vague, HybridStat will discuss with the client in an effort to demystify and rationalize the project and design a strategy that has to be followed in terms of statistical designing to make the most out of the anticipated data.

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Genomics Consulting

Price on request

The information required to maintain and reproduce life is encoded in our DNA and organized in genes. The entirety of an organism’s genes (thus its hereditary information) is called a genome. Genomes, control the function (or dysfunction) of organisms according to their “expression” through an essential process called the “central... Show more »

The information required to maintain and reproduce life is encoded in our DNA and organized in genes. The entirety of an organism’s genes (thus its hereditary information) is called a genome. Genomes, control the function (or dysfunction) of organisms according to their “expression” through an essential process called the “central dogma of molecular biology”, an explanation of the flow of genetic information within a biological system leading to proteins, final products responsible for a variety of crucial biological functions. Genomics is a scientific area that concerns the sequencing and analysis of an organism’s genome. Experts in genomics strive to determine complete DNA sequences and perform genetic mapping to help understand disease.

The real revolution in genomics arrived with the introduction of the massively parallel sequencing platforms which represent a new generation of DNA sequencing called Next Generation Sequencing (NGS) and which produces terabytes of sequencing data in short times with continuously decreasing cost. The NGS technology has already proved to have tremendously more applications than originally anticipated. Some of them include the de novo genome sequencing or re-sequencing or the study of chromatin methylations and genome wide protein-DNA interactions, indicating potential drug targets for personalized medicine approaches. Additionally, NGS platforms are gradually replacing genomic methods, such as microarray-based analysis of gene expression, because the data generated in a single run offer wider dynamic range of measurements and additional genomic information. Other applications include the detection of genomic and genome structure variations related to genetic/inherited diseases or cancer. In addition, as NGS applications are maturing and the costs are continuously dropping, the technology is gradually evolving from its presence to the biological lab to a valuable diagnostic tool in personalized medicine as well as a generic prognostic tool which can improve overall healthcare and well-being.

Currently, the meaningful analysis and interpretation of high throughput molecular data comprises the main bottleneck in their exploitation towards the design of novel therapeutic and diagnostic strategies as well as healthcare prognostics in everyday life. The following describe HybridStat’s approaches for the analysis of various types of genomics data.

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RNA-Seq Data Analysis

Price on request

RNA sequencing (RNA-Seq) is a modern approach to transcriptome profiling that uses deep-sequencing NGS technologies and has revolutionized the exploration of gene expression. Studies using this method have already altered the view of the extent and complexity of eukaryotic transcriptomes. Advances in the RNA sequencing workflow,... Show more »

RNA sequencing (RNA-Seq) is a modern approach to transcriptome profiling that uses deep-sequencing NGS technologies and has revolutionized the exploration of gene expression. Studies using this method have already altered the view of the extent and complexity of eukaryotic transcriptomes. Advances in the RNA sequencing workflow, from sample preparation through data analysis, enable rapid profiling and deep investigation of the transcriptome (totality of genes that code for proteins). Next-generation RNA sequencing enables researchers to:

  • Identify and quantify both rare and common transcripts, with over six orders of magnitude of dynamic range

  • Align sequencing reads across splice junctions, and detect isoforms, novel transcripts and gene fusions

  • Perform robust whole-transcriptome analysis on a wide range of samples, including possibly low-quality samples as the technique is very sensitive and as the technology advances, lower quantities of initial biomaterial is required.

  • Obtain high-quality and noise free results from low quantities of input material, as the technique is inherently robust to noise (e.g. there are no cross-hybridization issues like in microarrays).
    In a clinical setting, RNA-Seq technology can be used in a variety of contexts such as:

  • Identification of diagnostic or prognostic biomarkers based on gene expression signatures under disease states

  • Classification of diseases based on genetic signatures which are indistinguishable by less sensitive techniques (e.g. microscopy) and are results of processes difficult to quantitate (e.g. alternative splicing and differential isoform expression).

  • Understand complex and sensitive mechanisms involved in the genesis of disease processes
    create comprehensive and detailed gene expression maps in several tissues and disease states in order to catalog possible drug targets for personalized gene therapies

HybridStat offers RNA-Seq data preprocessing including short sequence mapping to reference genomes, data quality checks and filtering, normalization, statistical analysis, data visualization and more for all major technology platforms (Illumina, SOLiD, Ion Torrent). In addition, HybridStat offers consulting regarding the experimental design for optimal downstream statistical analysis.

Output
Provided with the raw short sequence reads, HybridStat generates friendly reports with:

  • Comprehensive, detailed and annotated (through the usage of related biological databases) gene lists coupled with statistical significance and several confidence metrics for the followed experimental design
  • Lists of biochemical pathways and biological functions where these genes are involved, coupled with statistical significance and several confidence metrics
  • Friendly and established data visualization of the results (gene expression heatmaps, volcano plots)
  • Friendly and established quality diagnostics of the raw data (alignment statistics, sequenced reads qualities) as well the downstream data analysis (boxplots, mean difference plots etc.)
  • Genome browser visualizations for visual inspection of sequencing results and correlation with other annotations
  • Much more RNA-Seq analytics which can be derived upon discussions with the client
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ChIP-Seq Data Analysis

Chromatin immunoprecipitation-sequencing data analysis
Price on request

Transcription factors (TFs) are DNA-associated proteins which are essential in genotype-phenotype mapping. Determining DNA interaction and regulation mechanisms is crucial for unraveling the complexity of many biological processes and disease states. This epigenetic information is complimentary to genotypic and gene expression... Show more »

Transcription factors (TFs) are DNA-associated proteins which are essential in genotype-phenotype mapping. Determining DNA interaction and regulation mechanisms is crucial for unraveling the complexity of many biological processes and disease states. This epigenetic information is complimentary to genotypic and gene expression analysis. Several studies suggest that perturbations in transcriptional networks can promote several diseases including cancer. For example, TF Prox1 has been shown to induce colon cancer progression by promoting phenotype transition from benign to dysplastic and the activity of Cdx2 limits the proliferation of human colon cancer cells by inhibiting the transcriptional activity of the β-catenin – T-cell factor (TCF) bipartite complex. Besides TF activities, other important epigenetic factors of systematic perturbations guiding disease states in cancers are DNA methylations.
ChIP-sequencing, also known as ChIP-Seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins. It can be used to map global binding sites precisely for any protein of interest as well as the study of chromatin methylations. The goal of ChIP-Seq data analyses is to find enriched genomic regions (peaks) in a pool of precipitated DNA fragments and the output of peak-finding methods is usually a list of ‘peak calls’ comprising the genomic locations of sites inferred to be occupied by the protein.
HybridStat offers ChIP-Seq data preprocessing including short sequence mapping to reference genomes, data quality checks and filtering, normalization, enriched region calling and association with closest genomic features of interest – targets, statistical analysis, data visualization and more for all major technology platforms (Illumina, SOLiD, Ion Torrent). In addition, HybridStat offers consulting regarding the experimental design for optimal downstream statistical and biological analysis.

Outcome

Provided with the raw short sequence reads, HybridStat generates friendly reports with:

  • Comprehensive, detailed and quality checked (bioinformatically) signal enriched DNA regions (ChIP-Seq peaks or enriched methylated regions) coupled with statistical significance and several confidence and quality metrics
  • Annotation of the enriched regions with their closest genomic features (e.g. genes) with which they might be functionally interacting
  • Friendly and established data analytics and visualization of the results (genomic distribution of the enriched DNA binding signals, signal profiles across genomic features of interest and more)
  • Friendly and established quality diagnostics of the raw data (alignment statistics, sequenced reads qualities) as well the downstream data analysis (signal-to-noise ratio plots, signal saturation etc.)
  • Genome browser visualizations for visual inspection of sequencing results and correlation with other annotations
  • Much more ChIP-Seq analytics which can be derived upon discussions with the client
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Whole Exome Sequencing Data Analysis

Price on request

DNA variants (variations from a representative, reference consensus genome) are one (Single Nucleotide Polymorphisms –SNPs) or more DNA nucleotides that are responsible for altering the protein product structure, rendering in non-functional or deleterious. When the variant consists of more than one DNA nucleotide, then this group... Show more »

DNA variants (variations from a representative, reference consensus genome) are one (Single Nucleotide Polymorphisms –SNPs) or more DNA nucleotides that are responsible for altering the protein product structure, rendering in non-functional or deleterious. When the variant consists of more than one DNA nucleotide, then this group of nucleotides may be deleted from the reference as a result of a genetic disease (DNA deletion) or be multiplied (DNA insertions). The exome represents less than 2% of the human genome, but contains ~85% of known disease-causing DNA variants, making whole-exome sequencing (Exome-Seq) a cost-effective alternative to whole-genome sequencing. On the other hand, as DNA variants can occur outside known protein-coding regions (for example in gene promoter regions which have strong functional characteristics for the functional integrity of an organism), the detection of DNA mutations in such regions is also importance. The latter can be achieved with whole genome or DNA sequencing (DNA-Seq).
Exome sequencing is a technique for sequencing all the protein-coding genes in a genome (known as the exome). It consists of first selecting only the subset of DNA that encodes proteins (known as exons), and then sequencing that DNA using any high throughput DNA sequencing technology. There are 180,000 exons, which constitute about 1% of the human genome, or approximately 30 million base pairs, but mutations in these sequences are much more likely to have severe consequences than in the remaining 99%.With exome sequencing, researchers can investigate the protein coding regions of the genome when sequencing an entire genome is not practical or necessary. It can efficiently identify variants across a wide range of applications, including population genetics, genetic disease, and cancer studies. Exome sequencing is especially effective in the study of rare Mendelian diseases, because it is the most efficient way to identify the genetic variants in all of an individual’s genes and is one of the most promising technologies to reach the goal of personalized diagnosis and medicine.

Output

Provided with the raw short sequence reads, HybridStat generates friendly reports with:

  • Comprehensive, detailed and annotated (through the usage of related biological databases and reference genome annotations) DNA alteration lists coupled with their exact locations, statistical significance and several confidence metrics.
  • Lists of biochemical pathways and biological functions where the genes with detected DNA alterations are involved, with statistical significance and several confidence metrics
  • Friendly and established data visualization of the results (chromosomal localizations, involved pathways)
  • Friendly and established quality diagnostics of the raw data as well the data analysis
  • Genome browser visualizations for visual inspection of sequencing results and correlation with other annotations
  • Much more analytics which can be derived upon discussions with the client
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Microarray Data Analysis

Price on request

DNA microarray analysis is an established high-throughput technology for monitoring whole genomes in the field of genetic research. Scientists are using DNA microarrays to investigate everything from cancer to pest control. For example, in the case of disease study, microarrays are used by researchers and biotechnology companies... Show more »

DNA microarray analysis is an established high-throughput technology for monitoring whole genomes in the field of genetic research. Scientists are using DNA microarrays to investigate everything from cancer to pest control. For example, in the case of disease study, microarrays are used by researchers and biotechnology companies to:

  • Identify diagnostic or prognostic biomarkers
  • Classify diseases (e.g. tumors with different prognosis that are indistinguishable by microscopic examination)
  • Monitor the response to therapy
  • Understand the mechanisms involved in the genesis of disease processes
  • Catalog potential drug targets of genetic therapies for a variety of diseases

DNA microarray data analysis requires specialized statistical expertise and bioinformatics knowledge to convert raw signals from laser scanned images to biologically meaningful measurements and identify gene biomarker candidates. HybridStat offers microarray data preprocessing, normalization and statistical analysis of gene expression profiling studies for all the major commercial microarray providers (Affymetrix, Illumina, Agilent) as well as custom microarray platforms and exon arrays. Analysis based on several widely used open-source tools as well as proprietary in-house workflows. HybridStat also offers assistance in the experimental design for optimal downstream statistical analysis.

Output

Provided with the raw scanned microarray image signals, HybridStat generates friendly reports with:

  • Comprehensive, detailed and annotated (through the usage of related biological databases) gene lists coupled with statistical significance and several confidence metrics for the followed experimental design
  • Lists of biochemical pathways and biological functions where these genes are involved, coupled with statistical significance and several confidence metrics
  • Friendly and established data visualization of the results (gene expression heatmaps, volcano plots)
  • Friendly and established quality diagnostics of the raw data as well the data analysis (boxplots, mean difference plots etc.)
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Metabolomics

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Metabolomics is the systematic study of chemical processes involving metabolites, that is small molecules present in cellular and body fluids that play key biological roles. Metabolomics studies the unique chemical fingerprints that specific cellular processes leave behind, that is their small-molecule metabolite profiles. The... Show more »

Metabolomics is the systematic study of chemical processes involving metabolites, that is small molecules present in cellular and body fluids that play key biological roles. Metabolomics studies the unique chemical fingerprints that specific cellular processes leave behind, that is their small-molecule metabolite profiles. The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes. Metabolomics aims to compare the relative differences between biological samples based on their metabolite profiles. It can provide an instantaneous snapshot of the entire physiology of an organism. Researchers use metabolomics assays to study a wide variety of problems: disease research, toxicology, environmental analysis, agriculture, biofuel development and nutrition. Metabolomics results can also be combined with gene expression and/or proteomics studies to provide a richer and more comprehensive understanding of the biology. HybridStat offers data analysis services for high-throughput metabolomics data, from raw data preprocessing and normalization, up to statistical analysis and potential identification of small molecules with key roles in biochemical processes.

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Mass Spectrometry Data Analysis

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The rapidly emerging field of metabolomics combines strategies to identify and quantify cellular metabolites using sophisticated analytical technologies with the application of statistical and multi-variant methods for information extraction and data interpretation. Metabolomics research has the potential to provide biomarkers for... Show more »

The rapidly emerging field of metabolomics combines strategies to identify and quantify cellular metabolites using sophisticated analytical technologies with the application of statistical and multi-variant methods for information extraction and data interpretation. Metabolomics research has the potential to provide biomarkers for the detection of disease, for subtyping complex disease populations, for monitoring disease progression and therapy, and for defining new molecular targets for therapeutic intervention. Mass Spectrometry (MS) provides analytical platforms that address the technical barriers to success in metabolomics research and these platforms are utilized for the structural elucidation of unidentified biomolecules and to accurately quantitate biomolecules based upon mass selective detection. Currently, targeted metabolomics analyses of large control and patient populations are used to define both the normal range of a defined metabolite and its potential heterogeneity in complex patient populations.

MS metabolomics experiments, like MS-based proteomics procedures, produce data of high volume and density. In addition, the nature of the small molecules of often unknown chemical structure that make up metabolites, add extra levels of complexity. Thus, the thorough bioinformatics data analysis is a prerequisite for biochemically and biologically meaningful outcomes, from characterization of metabolite molecular mechanisms of action to the disovery of potential disease biomarkers and also potential drug targets. HybridStat offers metabolomics MS data preprocessing, normalization, peak detection and sample clustering for several high-throughput metabolomics technologies, such as LC-MS(/MS), CE-MS, SELDI/MALDI-TOF MS, towards the identification of metabolite abundance and their quantification, as well as differential abundance analysis across different experimental conditions. HybridStat also offers assistance in the experimental design for optimal downstream statistical analysis.

Output

Provided with the raw spectra (mass-to-charge, retention time and raw signals – mass counts in netCDF, mzXML, mzML formats) HybridStat generates friendly reports with:

  • Comprehensive, detailed and annotated (through the usage of related biological databases like HMDB and ChEBI) where possible, metabolite/identified molecule abundance lists coupled with statistical significance (in case of differential abundance measurements across a statistical design) and confidence metrics
  • Lists of biochemical pathways where the identified metabolites are involved, coupled with statistical significance and several confidence metrics where possible
  • Friendly and established data visualization of the results (metabolite abundance heatmaps, volcano plots, aligned spectra etc.)
  • Friendly and established quality diagnostics of the raw data as well the data analysis procedure (normalization assessment boxplots, mean difference plots, spectral alignment assessment etc.)
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Software Development

Price on request

Bioinformatics software plays an important role in modern biology. Although there currently exist several packages either open-source or commercial that perform a variety of bioinformatics analytics tasks, focused on various levels of data analysis, bioinformatics software has a quite peculiar particularity: due to the nature of... Show more »

Bioinformatics software plays an important role in modern biology. Although there currently exist several packages either open-source or commercial that perform a variety of bioinformatics analytics tasks, focused on various levels of data analysis, bioinformatics software has a quite peculiar particularity: due to the nature of biological questions (directly related to research, thus constantly dynamic), there are no golden rules regarding the use of particular software packages for the accomplishment of various bioinformatics tasks. Thus, the realm of bioinformatics software is constantly expanding and so the variety of algorithms that perform generally similar tasks but with little adjustments, particular to the questions to be answered and the biological systems under investigation. The result of this procedure is that bench biologists and bioinformaticians are lost among the provided options, being distracted from the actual scientific work, struggling with endless software parameters to accomplish simple tasks. HybridStat helps remedying these situations by:

  • Designing and implementing analytic workflows and customized software, adjusted to the customer’s needs, after careful discussion and planning
  • Design and implementation of small to medium-scale biological oriented databases, tailored to the client’s needs
  • Organization, storage and management of high-throughput experiments at local level by designing an experiment specific and data-driven dedicated database with customized and easy to use search tools
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Equipment

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Equipment & Software Services

Equipment & Software Services

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Computer Science, Software Development, and IT Services

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Software and Apps Services

Software and Apps Services

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Computational Modeling

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Computational & Statistical Analysis Services

Computational & Statistical Analysis Services

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Data Services

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Data Analysis and Management Services

Data Analysis and Management Services

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Protein Sequencing

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Protein Data Analysis Services

Protein Data Analysis Services

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Biology

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Biology Services

Biology Services

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Omics

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Omics Services

Omics Services

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Biochemistry & Molecular Biology

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Biochemistry & Molecular Biology Services

Biochemistry & Molecular Biology Services

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Nucleic Acid Services

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Nucleic Acid Services

Nucleic Acid Services

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DNA Services

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DNA Services

DNA Services

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DNA Sequencing

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DNA Sequencing Services

DNA Sequencing Services

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Sequencing Data Analysis and Management

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Sequencing Data Analysis Services

Sequencing Data Analysis Services

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Project Management

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Project Management & Consulting Services

Project Management & Consulting Services

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Assay Development

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Experimental Design Services

Experimental Design Services

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Biostatistics & Bioinformatics

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Biostatistics & Bioinformatics Services

Biostatistics & Bioinformatics Services

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Panagiotis Moulos

Bioinformatician

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