© 2017 by SeqHub Analytics

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Expertise

What We Do
Skills and Practice Areas

Programming​

  • R

  • Python

  • C++

  • Java

  • Matlab

Other Tech

  • Bioconductor

  • Tensorflow

  • Theano

  • Keras

Bioinformatics Related

  • ChIP-seq

  • RNA-seq

  • ssRNA-seq​

  • Whole Genome-seq

  • CyTOF

With years of data analytics experience in Yale University laboratories, SeqHub understands your research and publication standards. We guarantee clear, publication-worthy analysis delivered in a timely fashion. Our philosophy is to work with you to understand the purpose and field-specific terminology of your research to deliver the most personalized service available.

Math and Statistics

  • Deep learning

  • Signal processsing

  • Sprectral methods

  • Statistical modeling

  • Machine learning

Research Samples

Our team of experts are active academic researchers and contributors to the data science field; the following is a sampling of our publications:

“Ritornello: High fidelity control-free chip-seq peak calling”

http://biorxiv.org/content/early/2015/12/11/034090

“Removal of Batch Effects using Distribution-Matching residual Networks”

https://arxiv.org/abs/1610.04181

“Arpeggio: Harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures”

http://nar.oxfordjournals.org/content/41/16/e161.long

 

“FastPCA” - Random matrix projection approach for very fast principle      components analysis (SVDS in matlab) – publication pending

http://dl.acm.org/citation.cfm?id=3004053

 

“Gating Mass Cytometry by deep learning”

http://biorxiv.org/content/early/2016/05/20/054411

In Situ Quantitative Measurement of HER2mRNA Predicts Benefit from Trastuzumab-Containing Chemotherapy in a Cohort of Metastatic Breast Cancer Patients

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099131

 

Germline competency of parthenogenetic embryonic stem cells from immature oocytes of adult mouse ovary.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3049357/