ReviewNature methods2014
Large-scale de novo DNA synthesis: technologies and applications.
Review in Nature methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 353 papers.
What it found
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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.
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Who cites it
353 citing papers in PubMed.
- GenAI-Net: A generative AI framework for automated biomolecular network design.Science advances · 2026Article
- Multichannel genomic recording of biological information with ENGRAM.Nature protocols · 2026Review
- Technologies for engineering repetitive DNA.Quantitative biology (Beijing, China) · 2026Review
- Advances in large DNA fragment assembly for microbial cell factory engineering.Quantitative biology (Beijing, China) · 2026Review
- Towards density-optimal DNA storage with practical bijective coding.Nature communications · 2026Article
- High-throughput synthesis of DNA fragments by molecular self-assembly of overlapping oligonucleotides.Nature biotechnology · 2026Article
- Applications of synthetic biology in biomedicine.Molecular biomedicine · 2026Review
- PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Syrah: a pipeline to maximize spatial transcriptomics data output.G3 (Bethesda, Md.) · 2026Article
- High-Yield Production of Modified DNA Enables Structural Analysis of PARP2 Recognition of Nucleosomal Single-Strand Breaks.Journal of molecular biology · 2026Article
- Photoresponsive DNA steganography for secure information transmission by nanopore.National science review · 2026Article
- Large-Scale Distribution of Physical Data Using DNA-of-Things Technology in Newspaper Printing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Leveraging pre-trained AI models for robust promoter sequence design in synthetic biology.Biophysics reports · 2026Article
- Machine learning-directed massively parallel programmable nucleic acid amplification.Science advances · 2026Article
- Manufacturing-aware generative models enable petascale synthesis of designed DNA.Nature biotechnology · 2026Article
- Comparison of state-of-the-art error-correction coding for sequence-based DNA data storage.Nature communications · 2026Article
- Machine learning-optimized long single-stranded DNA synthesis technology empowers high-precision diagnostic-therapeutic integration in living cells.Nucleic acids research · 2026Article
- Chemical Synthesis of Native ADP-Ribosylated Oligonucleotides Enables Analysis of DNA ADP-Ribosylation Hydrolase Specificity.Journal of the American Chemical Society · 2026Article
- Quantitative Assessment of Randomized DNA Base Sequences Using Multi-Model Physical Analysis for High-Fidelity Data Storage.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DNA Origami-Templated Aptamer Chiral Structures Realize Cellular Enantioselectivity.Advanced materials (Deerfield Beach, Fla.) · 2026Article
293 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For over 60 years, the synthetic production of new DNA sequences has helped researchers understand and engineer biology. Here we summarize methods and caveats for the de novo synthesis of DNA, with particular emphasis on recent technologies that allow for large-scale and low-cost production. In addition, we discuss emerging applications enabled by large-scale de novo DNA constructs, as well as the challenges and opportunities that lie ahead.
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Registered trials
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 OpenQuestion graph.