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MAVERIX ADDS SIGNIFICANT ENHANCEMENTS TO GENOMIC ANALYSIS PLATFORM FOR RNA-SEQ RESEARCH

June 18, 2014

Enhanced design allows comparison of side-by-side results from HTSeq/DESeq and Cufflinks

SAN MATEO, CA - Jun 18, 2014 - MAVERIX ADDS SIGNIFICANT ENHANCEMENTS TO

GENOMIC ANALYSIS PLATFORM

FOR RNA-SEQ RESEARCH

Enhanced design allows comparison of side-by-side results

from HTSeq/DESeq and Cufflinks

Maverix Biomics Inc., a leading genomic analysis software company,

today announced improved functionality for RNA-seq differential expression analysis on

the Maverix Analytic Platform, an industry leading cloud-based solution that enables life

science researchers to directly analyze, visualize, interrogate, and manage biological

sequence data in order to accelerate exploration and discovery. The enhancements will

enable scientists engaged in RNA-seq studies to better analyze differential expression

results through improved visualization for quality assessment of the data and for

identifying highly-confident genes uncovered by next generation sequencing of the

information-dense transcriptome.

The Maverix Analytic Platform now supports the latest version of Cufflinks, a popular

open source software application which assembles transcripts, estimates their quantity,

and tests for differential expression and regulation in RNA-seq samples. This new

version on the Maverix Analytic Platform allows multiple samples to be compared more

efficiently, resulting in enhanced functionality for the platform’s turnkey RNA-seq

analysis.

Additionally, inherent in the design of the Maverix Analytic Platform is the ability to

easily launch two proven RNA-seq methods, Cufflinks and DESeq, and compare the

results side-by-side with the same ease-of-use, cost, flexibility and visualizations to

answer the most pressing questions about the transcriptome.

The platform organizes all-vs-all comparisons provided by Cufflinks into a dynamicallycontrollable

heat map, providing researchers an optimal way to select desired sample

comparisons, filter and sort by common parameters like p-value, fold-change, and

abundance, and quickly zoom in on a shortlist of targeted genes. The Maverix platform

also provides seamless integration with a secure, private implementation of the UCSC

Genome Browser, enabling alignment and coverage data from user experiments to be

viewed in context with hundreds of existing annotation tracks, and public datasets.

Dr. Jeanne Loring, Professor of Developmental Neurobiology, and Director of the Center

of Regenerative Medicine at the Scripps Research Institute, is advancing the study of

human stem cell research by applying powerful new cutting edge technologies, such as

next-generation sequencing.

Dr. Loring's group, including graduate student Kit Nazor, is building a repository of

comprehensive genomic and epigenomic data on human pluripotent stem cells and the

wide range of cell types that they can generate. They are focusing on neurological

diseases, including Parkinson's disease, Alzheimer disease, multiple sclerosis, and

autism. The data resulting from these studies is enabling breakthroughs in understanding

the molecular basis of human disease and development of safe, effective stem cell-based

therapies.

"The Maverix RNA-Seq Analysis Kit provides us with a user-friendly means of quality

control and the ability to process data from large RNA sequencing experiments. What

really sets Maverix apart is that their platform simultaneously processes the data using

not just one but two of the most well accepted methods,” said Nazor. “There is no

mystery about how the data is processed, as the underlying code that is being used to

process the data is also provided at each stage. This has not only allowed us to understand

the intricacies of how the data was analyzed, but also provides a unique opportunity to

learn the basics of bioinformatics.”

“The Maverix Analytic Platform’s filtering functions allow for an easy way to reduce the

data to a set of genes of interest based on significance and fold change values, and

provide contextualization of the identified significant transcripts with previously curated

data sets,” said Dr. Loring.

Dr. Jeff Brockman is a Principal Scientist in the commercial animal heath and nutrition

field. He has been using cutting edge technologies such as next generation sequencing to

understand disease and the aging processes in companion animals, specifically dogs

(Canis lupus familiaris). By transitioning his organization from microarray platforms to

high throughput sequencing, Dr. Brockman has developed a cutting edge translational

genomics program to identify susceptibility biomarkers and candidate genes for

therapeutic intervention in multiple areas of animal health and nutrition.

“The increased functionality from both the HTSeq/DESeq and Cufflinks pipelines

combined with the ease and convenience of analyzing large data sets in multiple ways is

what attracted me to the Maverix Analytic Platform,” said Dr. Brockman. “It’s

incredibly seamless to identify differentially expressed genes in my canine samples and

dynamically compare differential expression results with alternate samples, such as those

from time-course studies. Instead of managing a multitude of files required by

competitive platforms, I can spend my time where it’s most critical -- exploring my

results to make new discoveries.”

About the Maverix Analytic Platform

Maverix Analytic Platform is a cloud-based solution designed for use directly by life

sciences researchers who may not have software or bioinformatics expertise. It leverages

proven, open-source algorithms and applications developed at leading academic and

research centers. After loading sequence data from any organism (human, animal, plant,

or microbe), researchers are able to immediately perform analysis with reliable,

scientifically recommended configurations, as cited in peer- reviewed journal

publications. Visualization is provided through a variety of integrated graphical tools,

including the UCSC Genome Browser, the world’s most widely used genome browser.