Science Services

Accelerate Discovery with DataXight’s Scientific Services.

Uncover insights faster with expert bioinformatics, data engineering, and AI solutions tailored to life science research. Modern discovery demands more than tools—it needs deep domain expertise, scalable infrastructure, and reproducible methods.

What’s Holding Science Back Today?

Complexity of Multimodal Data

Genomic, clinical, and phenotypic data are hard to unify—and even harder to interpret meaningfully.

Gaps in Internal Capacity and Expertise

Many teams lack the resources or bandwidth to keep up with evolving methods and growing data demands.

Fragmented, Irreproducible Workflows

Disjointed tools and manual handoffs slow progress and limit scientific rigor.

Our Approach

From Complexity to Clarity

We integrate & analyze high-dimensional data with deep scientific context, turning complexity into clear, actionable insights.

Scalable by Design

We architect cloud-native environments that auto-scale while ensuring your workflows remain reproducible and traceable.

Reproducibility by Design

We design reproducible, end-to-end pipelines that streamline analysis, reduce error, and support both publication and regulatory alignment.

Our Services

We help you unlock the full value of your data—at every stage of the scientific journey. To support this, we propose 3 scientific services designed to accelerate your research and innovation.

AI That Understands Biology

We develop machine learning and generative AI tools that supercharge analysis and enable novel discoveries.

Cohort selection and image-based diagnostics

Target identification and drug repurposing

Knowledge graphs and predictive modeling

Benefit: Unlock patterns, predictions, and possibilities beyond human scale.

AI That Understands Biology

We develop machine learning and generative AI tools that supercharge analysis and enable novel discoveries.

Cohort selection and image-based diagnostics

Target identification and drug repurposing

Knowledge graphs and predictive modeling

Benefit: Unlock patterns, predictions, and possibilities beyond human scale.

From Raw Data to Discovery

We design robust, reproducible pipelines that take your raw data to publication-ready results.

Variant calling, annotation, and quantification

Single-cell, spatial, and isoform analysis

Alternative splicing and gene perturbation studies

Benefit: Accelerate research with rigor and clarity—from first base to final figure.

Make Sense of Complexity

We turn high-dimensional data into biologically meaningful insights that inform research and clinical hypotheses.

Biological context for statistical findings

Mechanism-of-action and pathway inference

Translational narratives for publication or regulators

Benefit: Move from results to understanding—and from signals to decisions.

Partner with our experts to turn complex biological data into actionable insights. Talk to our science team or join the waitlist to explore curated workflows and proteomics-ready pipelines.

Real-world Impact

Built a Long-Read Analysis Workflow to Support CAR-T Cell Engineering. We helped deliver AI-ready rAAV data to accelerate gene therapy breakthroughs.

Real-world Impact

Built a Long-Read Analysis Workflow to Support CAR-T Cell Engineering. We helped deliver AI-ready rAAV data to accelerate gene therapy breakthroughs.

We engaged DataXight for a focused project to support our CAR-T program, and their impact was immediate. They developed reproducible analysis pipelines and distilled complex long-read data into clear, actionable reports. Their scientific rigor and ability to communicate findings effectively made the collaboration smooth and valuable.

Saar Gill, PhD,

Associate Professor of Medicine, University of Pennsylvania

Partner with us

Why DataXight

Ø Drama

We operate with full transparency, stress-free collaboration – no surprises, no jargon, just clear communication and a relentless focus on impactful outcomes.

Biological Context, Not Just Code

We focus on meaningful interpretation—delivering not just results, but insights that drive your next research question or clinical hypothesis.

Deep Scientific Expertise

Our team includes experienced biologists, bioinformaticians, computational biologists, data scientists, and software engineers who have led successful scientific initiatives across leading biotech and bioinformatics companies. We speak your language and understand your goals.

Flexible Collaboration and Commercial Models

Whether you need ad hoc support or a long-term scientific partner, we adapt to your business objectives, workflows, timelines, and in-house capabilities.

End-to-End, Reproducible Workflows

From data QC to final report, we build transparent, documented pipelines that support both publication and regulatory alignment.

On-Time Delivery, Every Time

We value your time and timelines. Our project management approach ensures clear milestones, regular updates, and dependable delivery—so you can stay focused on the science.

Science Services
FAQs

Have questions? Find answers.

Any more questions?

From raw data to actionable insights, we bring together software engineering, AI/ML, data science, and domain-specific knowledge to deliver solutions that span the entire lifecycle. Whether you’re capturing complex experimental data, integrating disparate systems, building predictive models, or interpreting outcomes—we have the technical depth and scientific fluency to support every step.

We have deep experience navigating global compliance frameworks. From the start, we design systems with auditability, traceability, and data security in mind. We work closely with your QA, privacy, and governance teams to ensure full alignment with regulatory standards.

Not at all. We offer four engagement models: Staff augmentation, dedicated team, outsourcing, and managed services. We can plug into your team as needed—whether that means working alongside your teams or providing a self-sustaining full-stack support across software, data engineering, ML, and science. We’re experienced working with clients with varying levels of technical and scientific resources.

Absolutely. Our solutions are built on standardized, version-controlled software with rigorous documentation to ensure full reproducibility. We also prioritize explainability through transparent design and interpretable outputs—so stakeholders can trust and understand every decision.

Yes. Our architecture and engineering approach is fully platform-agnostic. Whether you're operating on AWS, Azure, GCP, DNAnexus, an on-prem environment, or a hybrid setup, we design solutions that are portable, and scalable—without locking you into any single vendor.

Find out what’s happening

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Tahoe-100M in Practice: Workflows, Pitfalls, and Pathways to Scalable scRNA Analysis

Single-cell transcriptomics (scRNA) studies now profile millions of cells, revealing identity, state, and tissue heterogeneity, and create unprecedented opportunities to extract biological insights that would be invisible in smaller studies. Tahoe-100M, a groundbreaking resource hosted by Arc Institute, contains 100 million cells covering 379 distinct drugs and 50 cancer cell lines, is one such study. On the other hand, at Tahoe-100M scale, even routine queries pose significant computational ch

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Reproducible Proteomics Pipelines Using Galaxy

{Insight}

7 mins read

Reproducible Proteomics Pipelines Using Galaxy

The Clinical Data Analysis Pipelines (CDAP), originally developed by the NIH Office of Cancer Clinical Proteomics Research (OCCPR), formerly Clinical Proteomic Tumor Analysis Consortium (CPTAC), and now hosted by the NIH Proteomic Data Commons (PDC) standardize proteomics data processing to reduce variability and enable cross-dataset comparisons. Public dissemination of these Galaxy workflows on GitHub is part of the NIH's support of FAIR data principles. While these pipelines represent a promi

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{batch effect}

{scRNA-seq}

10 mins read

Looking at batch effect through scRNA-seq data

Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to explore complex biological systems, but batch effects remain a significant challenge to accurate insights. In this first part of our series, we will delve into what batch effects is and how batch effects can hinder discoveries, illustrated with data visualizations and analysis. Future posts will explore strategies for correcting and visualizing batch effects to ensure reliable and reproducible results across multiomic data.

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Science Services

What’s Holding Science Back Today?

Our Approach