Altavera Analytics

A collaborative bioinformatics consulting company

Altavera works with clients to develop an analytical strategy customized to client needs.


Consultation begins with developing an understanding of the driving questions and needs of the client.

Best-practice workflows of standard and emerging genomic data are combined with innovative analytical approaches.


Altavera specializes in genomic data, including bioinformatics processing and statistical analyses.

A collaborative approach allows companies and clients to derive actionable insight from their data.


Biological interpretation, together with visualization, results in impactful delivery of key insights.


Altavera provides custom solutions for bioinformatics, utilizing cloud-based workflows and statistical analysis of genomic data.

Bioinformatics Processing

Altavera offers processing of numerous ‘omics data types, including RNA-Seq, Single-Cell RNA-Seq, Exome-Seq, Methyl-Seq, ChIP-Seq, metabolomics, proteomics, and microbiome.

Analyses & Visualization

Typical analyses include expression analysis, integrative analysis of multiple genomic data sets, biomarker identification via machine learning, single-cell RNA-Seq annotation and expression analysis, microbiome analysis, and small genome assembly.

Scientific Consultation

Altavera specializes in biomedicine, including immunology and neurology, as well as agricultural applications, including plant molecular biology and CRISPR screens. Applications of expertise span animal and plant model and non-model organisms.

Custom Software

Altavera provides client-focused custom solutions, including containerized workflows for cloud deployment, as well as analytical and visualization tools.


Deriving biological insight from genomic data



Jennifer Modliszewski, Ph.D.

I am a broadly trained bioinformatician with over fifteen years of experience in the fields of quantitative genetics, molecular biology, and bioinformatics. My expertise in genomics includes bioinformatics processing and statistical analysis of numerous data types: transcriptomic, epigenetic, proteomic, metabolomic, whole genome sequencing with short and long-read technologies, variant calling, and microbiome analysis.