Automated Sequencing
Automated Sequencing - Related Videos
06:49
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
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Cited by 28 •
2014
Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca2+ signals with minimal user input and bias.
13:26
Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples
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Cited by 9 •
2015
This manuscript describes an automated gel size selection approach for purifying DNA fragments for next-generation sequencing. The Ranger Technology provides complete automation of the entire process of agarose gel loading, electrophoretic analysis, and recovery of targeted DNA fragments allowing for high-throughput and high quality next-generation sequencing libraries.
08:57
Amplicon Sequencing using the Long-Read Sequencing Technologies
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2025
This protocol was optimized for targeted deep sequencing of 18 drug-resistance regions in Mycobacterium tuberculosis using a long-read sequencing platform, followed by analysis with a tuberculosis-specific bioinformatics pipeline designed for long-read data.
08:06
Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays
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Cited by 1 •
2016
Peptide adsorption to plasticware during traditional tip-based serial dilutions can significantly impact potency determination and confound the understanding of structure-activity relationships used for lead identification and lead optimization phases of drug discovery. Here methods for automated acoustic non-contact serial dilution of peptide samples are described.
07:30
Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Cited by 19 •
2020
This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.
FAQs
Fluorescently labeled chain-terminating nucleotides create detectable stopping points during DNA synthesis. Because each incorporated terminator carries a fluorescence signal, the resulting DNA fragments contain both length and labeling information. The instrument uses these signals after separation to infer nucleotide order, linking a chemical termination event to an automated sequence readout.
Capillary electrophoresis separates the synthesized DNA fragments according to size before detection. Fragments that differ in length reach the detection stage in an ordered pattern, while their fluorescence identifies the terminating nucleotide. This separation-and-detection step converts a mixture of products into a signal pattern that the instrument can interpret as a sequence.
Automation reduces the amount of manual handling between synthesis, fragment separation, and signal interpretation. A standardized instrument-based workflow can make analyses faster and more reproducible than a process dependent on repeated manual intervention. In cancer genomics studies, that consistency strengthens the efficiency and accuracy of genetic analysis across samples.
In tumor samples, the technique can reveal mutations and gene fusions, along with other genomic alterations detectable through genetic analysis. These findings help researchers examine the molecular features of cancers rather than relying only on broader sample characteristics. The resulting data can contribute to biomarker discovery and molecular classification of tumors.
Researchers can use automated sequencing to monitor treatment-related changes in tumor samples. Sequence information provides a way to examine whether detected genomic features differ as a study follows treatment. This application complements mutation and fusion identification, allowing cancer genomics projects to track changes rather than limiting analysis to a single baseline characterization.
Scalable workflows allow cancer genomics studies to extend automated genetic analysis across the samples or analyses required by a project while maintaining a consistent process. That combination of scale, reproducibility, and reduced manual intervention can improve study efficiency and strengthen accuracy. It is particularly relevant when researchers need systematic data for biomarker discovery or molecular classification.