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Related Concept Videos

Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Related Experiment Video

Updated: Jun 14, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information.

Jeanet Mante1, Zach Sents1, Duncan Britt1

  • 1University of Colorado Boulder, Boulder, Colorado 80309, United States.

ACS Synthetic Biology
|September 4, 2024
PubMed
Summary

Synthetic biology advancements are slowed by difficult literature review and poor documentation. SeqImprove, a machine learning tool, assists authors in creating FAIR data, improving reproducibility.

Keywords:
SBOLmachine learningnamed entity normalizationnamed entity recognitionontologies

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Area of Science:

  • Synthetic Biology
  • Bioinformatics
  • Data Curation

Background:

  • Synthetic biology progress is limited by challenges in literature review and replicating documented work.
  • Manual reconstruction of design information is prone to errors and noise.
  • Author involvement in data curation is essential for accuracy.

Purpose of the Study:

  • To develop a machine learning-assisted tool, SeqImprove, to streamline the curation process for synthetic biology data.
  • To encourage author participation in curation without increasing their workload.
  • To improve the findability, accessibility, interoperability, and reusability (FAIR) of biological sequence data.

Main Methods:

  • SeqImprove utilizes named entity recognition (entity normalization) and sequence matching.
  • The tool generates machine-accessible sequence data and metadata annotations.
  • Authors review and edit the generated annotations before final submission.

Main Results:

  • SeqImprove facilitates the creation of machine-readable sequence data and metadata.
  • The tool simplifies the process for authors to submit FAIR data.
  • Reduces noise and errors associated with post hoc data reconstruction.

Conclusions:

  • SeqImprove enhances the efficiency and accuracy of synthetic biology data curation.
  • The tool promotes the submission of FAIR data, accelerating research and development.
  • ML-assisted curation is a viable strategy to overcome current limitations in synthetic biology.