Phronetik provides Next-Generation Sequencing (NGS) services for genetic testing, analysis, and research support. We take structured and unstructured data, clean it, standardize it, integrate it, then analyze it. Data is the foundation of what we do, allowing us to deepen insight into health and disease.
Our bioinformaticians and scientists offer expertise in:
Whole-genome, whole-exome and targeted sequencing allows mapping and studying genetic variants or mutations. Our genome variation analysis identifies SNPs, indels, gene copy numbers, and genomic rearrangements from the various types of DNA-sequencing and microarray data. Our bioinformatics analysis of variants and mutations coupled with phenotypic data enables the discovery of novel associations.
Using microarray analysis, our staff provides bioinformatics analysis of RNA-sequencing to allow pinpointing molecular mechanisms between genotype and phenotype. We also provide transcriptomics, or analysis of gene expression on both single genes and pathways, as well as transcriptome assembly and transcriptome annotation.
Epigenetics is the study of dynamic change in gene expression that do not affect the underlying DNA (change in phenotype, not genotype). Using a variety of microarray analyzing techniques, we offer:
DNA methylation (MeDIP-seq, BiS-seq)
Chromatin state (ChIP-seq)
DNA binding (ChIP-seq)
Our bioinformatics analysis of the various epigenomic NGS data associates the identified genomic sites to phenotypic attributes. These sites can be annotated with public domain database information to help in interpreting biological meaning.
Biomarkers can help detect the early presence of disease and help determine a patient's response to treatment. Our team can aid in identifying particular data points, genetic or other types, and statistically compare samples of interest with a control group. We can use genomic, transcriptomic and epigenomic data with metadata to find a biomarker or a combination of biomarkers that can be used to classify future samples into relevant categories, such as patients likely responding to a treatment versus non-responders.