Evidence map›Paper›PMID 36993481›Full record

ArticlebioRxiv : the preprint server for biology2025

Ultra-high throughput mapping of genetic design space.

Kshitij Rai, Ronan W O'Connell, Trenton C Piepergerdes, Yiduo Wang, Lucas B C Brown, Kian D Samra, Jack A Wilson, Shujian Lin, Thomas H Zhang, Eduardo M Ramos and 7 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Ronan W O'ConnellORCID 0000-0002-4689-0307
Trenton C PiepergerdesORCID 0009-0000-2734-2404
Yiduo Wang
Lucas B C Brown
Kian D Samra
Jack A Wilson
Shujian Lin
Thomas H Zhang
Eduardo M Ramos
Andrew Sun
Kristen D Curry

Funding

TRAINING PROGRAM IN COMPUTATIONAL BIOLOGY AND MEDICINET15LM007093 · NLM · RICE UNIVERSITY · PI Lydia E. Kavraki · 1992 to 2026
$20.8M
Synthetic toolkit for precision gene expression control and signal processing in mammalian cellsR01EB029483 · NIBIB · HARVARD UNIVERSITY · PI Ahmad Samir Khalil · 2020 to 2026
$5.0M
MODELING EMERGENT BEHAVIORS IN SYSTEMS BIOLOGY: A BIOLOGICAL PHYSICS APPROACHR35GM119461 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Pankaj Mehta · 2016 to 2026
$3.6M
A synthetic signaling pathway engineering platform for creating precision cell-based sense-and-response devicesR01EB032272 · NIBIB · RICE UNIVERSITY · PI BASHOR, CALEB · 2021 to 2024
$1.4M
NIBIB NIH HHS R01 EB029483NIBIB NIH HHS R01 EB032272NIGMS NIH HHS R35 GM119461NLM NIH HHS T15 LM007093
6 · The paper itself

Abstract

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of multiple sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs and learning "composition-to-function" mappings that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pools of constructs of arbitrary length containing diverse part compositions. We show that CLAS-SIC can measure expression profiles of >10

Identifiers

PMID36993481
PMCPMC10055055

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.