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During the past 15 years, we are experiencing an unparalleled boom in the data available in modern biological sciences. The completion of the sequencing of the human genome and the pioneering techniques that were developed to achieve this, led to an unprecedented emergence of new high-throughput biotechniques producing huge amount of various biological data. All the above have given rise to a new field, bioinformatics, which combines elements of biology, computer science and statistics in order to extract meaningful outcome from huge and complex biological datasets and answer the associated biological questions.
Bioinformatics is nowadays an inherent part of research in molecular biology. It helps bioscientists to┬áuse of computers to study and manage┬ábiological data, develop computational methods to study the┬ástructure, function, and evolution of genes,┬áproteins and genomes and unravel biological mechanisms of action.
In the area of medicine, bioinformatics tackles problems of identifying disease genes both from the huge amount of DNA sequence now available, and from population studies.┬á Using these techniques and more, bioinformatics has already opened new avenues for identifying useful drugs.┬á Practically, the identification of disease genes has allowed, in some instances, the replacement of the affected proteins.┬á And last, bioinformatic comparison of infectious viral and bacterial genomes have given indications of what makes a particular strain virulent.
Applied to biology, these techniques have led to a better understanding of how extent animals are related evolutionally. Comparison of homologous genes across organisms has and continues to clarify what are the important nucleotides in a gene.┬á┬áIn silico┬áprotein folding has provided information about the chemistry of catalysis, giving insights into how genes control biological processes.┬á Despite these successes many fascinating problems remain.┬á Especially intriguing are problems of the control of genes and the mechanisms by which this control leads to development of organisms.┬á Much of this will be done in the laboratory, but bioinformatics will be an integral part.
A major part of HybridStat services specialize in Bioinformatics. HybridStat can offer consulting services for all your bioinformatics needs, statistical experimental designs and data analysis. Specifically, HybridStat can help you treat your data in the following domains:
- Analysis of high-troughput genomic experiments
- DNA amd miRNA microarrays: preprocessing, normalization and statistical analysis of gene expression profiling studies┬á┬áfor 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 algorithms.
- Genotype analysis:┬áSNP genotyping data processing including SNP filtering, haplotype analysis, genotype calls, Genome-Wide Association Studies (GWAS) and Copy Number Variation (CNV) analysis.
- Next Generation Sequencing:┬á┬ádata preprocessing (mapping, filtering, genome browser sesstions) for all of the major technology platforms (Illumina, SOLiD, 454) for a wide range of applications including RNA-Seq (whole transcriptome as well as de novo assembly analysis for differential expression analysis), ChIP-Seq (normalization and putative binding site detection) for the analysis of protein-DNA interactions, Methyl-Seq (enriched methylated site detection) for the analysis of epigenetic events.
- Meta-analysis of high-throughput genomic experiments
- Gene expression clustering, classification modeling, supervised and unsupervised learning, functional analysis and pathway mapping of gene lists and putative binding sites derived grom ChIP-Seq experiments
- Motif discovery
- DNA motifs detection in promoters of similar expression gene groups, for the identification of common regulation 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 and querying against several motif family databases.
- Design and implementation of biological oriented relational databases
- Make the most out of a series of high-throughput experiments by designing an experiment specific and data-driven dedicated database with customized and easy to use search tools.
- Analysis of high-troughput proteomic experiments
- Antibody microarrays: preprocessing, normalization and statistical analysis of protein profiling studies for several commercial microarray providers (e.g. Agilent) as well as custom antibody array platforms. Analysis based on several widely used open-source tools as well as proprietary in-house algorithms.
- Mass spectrometry: preprocessing, normalization, peak detection and sample clustering for several┬áhigh-throughput proteomics technologies, such as LC-MS(/MS), CE-MS, SELDI/MALDI-TOF MS, towards the identification of biomarkers, protein identification and quantification, differential abundance analysis.
- Meta-analysis of high-troughput proteomic experiments
- Abundance clustering, classification modeling, supervised and unsupervised learning, functional analysis and pathway mapping of protein lists.
- Analysis of high-troughput metabolomic experiments
- Mass spectrometry: preprocessing, normalization, peak detection and sample clustering for several┬áhigh-throughput metabolomics technologies, such as LC-MS(/MS), CE-MS, SELDI/MALDI-TOF MS, towards the identification of biomarkers, hormones and lipids. Metabolite identification and quantification, differential abundance analysis.
- Meta-analysis of high-troughput proteomic experiments
- Abundance clustering, classification modeling, supervised and unsupervised learning, exhaustive and analytic database search for the identification of molecules.