October 14, 2026 · By DataTecnica

AI CRISPR Screen Analysis: Query Hits, Compare Screens and Plot Results

Use AI to analyse pooled CRISPR screens: rank hits, follow a gene across screens, compare conditions and plot data from CRISPRbrain, CRISPRlipid, CRISPRvirus.

Pooled CRISPR screens produce a lot of data, scattered across many experiments, cell types and conditions. A single question such as "does this gene come up in any other screen?" can mean opening a dozen files. AI CRISPR screen analysis means asking that question in plain language and getting an answer drawn from the data itself, with the numbers and a plot, instead of a recollection.

This post covers what that looks like, using the CRISPR screen Data Commons that codon.bio queries, and is clear about what it does and does not do.

First, what kind of CRISPR is this about

CRISPR screens perturb many genes at once in a pool of cells and measure which perturbations change a phenotype. The main modes are:

CRISPRn cuts the gene to knock it out.
CRISPRi (interference) turns a gene's expression down without cutting it.
CRISPRa (activation) turns a gene's expression up.

Which mode a screen used matters when you interpret a hit: a gene whose knockdown helps a cell survive tells a different story from one whose activation does. A good analysis keeps that context attached to the result.

The data it covers

codon.bio's CRISPR agent queries several Data Commons held in one database:

CRISPRbrain
CRISPRlipid
CRISPRvirus

You can restrict a question to one dataset or compare across them. What exists is discovered at run time from the database itself, so you can ask what screens are available instead of guessing.

What you can ask

In practice, the questions fall into a handful of shapes:

Look up and describe a screen. What was screened, in which cells, with which perturbation mode?
Report and rank hits. Which genes scored most strongly in this screen?
Follow one gene across screens. Everywhere this gene was measured, what did it do?
Compare screens or conditions. Which genes behave differently between two conditions, or between two screens?
Aggregate. Summarise by cell type, lab or treatment.
Differential expression. Report RNA-seq differential expression where it is available alongside the screen.
Plot. Draw volcano, rank, MA and comparison plots and heatmaps, and UMAP views for single-cell (CROP-seq) screens where that data is available.
Export. Take the results out as CSV.

Because it answers from a database query and not from memory, the numbers you see are the numbers in the data. If a question can't be answered from what it holds, it says so.

An example workflow

Suppose you are following up a candidate gene from a genetics study.

Ask what is available. Which screens exist, and how many, and in which cell types?
Follow the gene. Ask how your gene scored in every screen that measured it.
Compare the contexts. Does it score in neurons and not in other cells? Under one treatment and not another?
Plot it. Ask for the volcano or rank plot to see where the gene sits among the hits.
Cross-check with other evidence. In the same project, ask for the human genetic evidence and the literature on the gene, so functional data sits next to the genetics. For a CRISPRbrain hit, that can include full GWAS summary statistics for Alzheimer's, Parkinson's, ALS, FTD and Lewy body dementia.
Export what you need. Pull the table out for the figure or the lab meeting.

For biotech and biomedical teams, the third step is where this pays off: functional evidence from a screen is much easier to weigh when you can see the gene in several contexts at once.

What it does not do

It does not design guide RNAs. It analyses screen data; it is not a guide-design tool.
It only knows the data it holds or that you bring. For data outside the Data Commons, you can upload files to a project and analyse them in a real Python and R environment.
It does not replace statistical judgement. It reports hits as the data provides them. Whether a hit is convincing, given the screen's design and quality, is for you to decide.

Questions people ask

Can AI analyse CRISPR screen data?

Yes, in the sense of querying screen results, ranking and comparing hits and drawing plots from a database. It reads the data; it does not invent results.

Which CRISPR datasets does codon.bio cover?

The CRISPRbrain, CRISPRlipid and CRISPRvirus Data Commons.

Can it explain CRISPRi, CRISPRa and CRISPRn?

Yes, it can explain the concepts, using published text, as well as report the data.

Can I use my own screen data?

You can upload files to a project and analyse them with Python or R. The structured screen queries cover the Data Commons.

Does it design guide RNAs?

No. If you need guide design, you need a dedicated guide-design tool.

Try it on a gene you care about

Open a project in codon.bio and ask how your favourite gene scored across the available screens. Whether the answer matches what you know from the lab is the best test.