CompStortm

Accelerated Bioinformatic Algorithms and
Analytics Tools

Welcome to
CompStortm

CompStor is a family of solutions that drive accelerated computing using OmniTier’s core technology, applications built around expanded memory storage tiers. Big Data is best processed locally where it is generated and resides. CompStor’s optimized computing clusters make this possible.

We turn servers into supercomputers!

>1,000,000,000
reads in a WGS

High coverage, short-read
whole genome sequencing

>10,000,000 individual
genomes being sequenced

Ongoing global whole genome
sequencing efforts

Computationally Intensive De novo
Assembly Variant Discovery

CompStor Novos ready with novel
bioinformatics on a fast
computational engine

CompStortm Novos Assembly Solution

CompStor Novos Whole Genome Assembly

The promise of de novo assembly is unbiased detection of variants, especially structural ones and indels (insertions-deletions). Alignment based methods using a single known reference do not capture this novel variation. CompStor Novos is a computational platform that enables de novo assembly in a variety of sequence coverage depths for short-read sequence data.

Bringing together
computation & storage

De novo assembly and variant calling

Supercomputer performance from server clusters

4 or 8 node configuration

Optimized data ingress & egress

Analytics & Future Development

Solving equations fundamental to AI, DL/ML, and Scientific Computing

  • Singular Value Decomposition
  • Eigen Analysis
  • Linear Regression
  • Markov Chains
  • Variance Analysis

Pursuing collaborations and product development in

  • Genomic polygenic variant discovery (Beyond GWAS)
  • Genome association with RNA Seq and other omics
  • Deep learning pathology image classification
  • Mass spec proteomics
  • 3D Computed Tomography
  • Robotics & Manufacturing

Big Data Problems

CompStortm Analytics delivers breakthrough run-times
and handles big data problems robustly

Statistical Machine Learning
and Classification



Singular Value Decomposition (SVD)

3-D Tomography with 1B unknowns
and 1.5B equations

Linear Regression