Deep Sequencing
Deep Sequencing - Related Videos
09:36
Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
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Cited by 50 •
2016
This protocol describes a rapid and broadly applicable method for unbiased RNA-sequencing of viral samples from human clinical isolates.
07:55
A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe
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Cited by 2 •
2019
We present a simple suppressor screen protocol in fission yeast. This method is efficient, mutagen-free, and selective for mutations that often occur at a single genomic locus. The protocol is suitable for isolating suppressors that alleviate growth defects in liquid culture that are caused by a mutation or a drug.
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.
09:34
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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2021
This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic data.
11:09
Deep Brain Stimulation with Simultaneous fMRI in Rodents
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Cited by 21 •
2014
This protocol describes a standard method for simultaneous functional magnetic resonance imaging and deep brain stimulation in the rodent. The combined use of these experimental tools allows for the exploration of global downstream activity in response to electrical stimulation at virtually any brain target.
FAQs
Greater depth produces more repeated observations of the same genomic or transcript sequence. That increased coverage strengthens confidence in the consensus sequence and raises the chance of observing low-frequency mutations or transcripts. The effect is especially important when a feature is rare and could be missed by conventional sampling.
Repeated reads become informative when they are placed in sequence context. Deep sequencing can align those reads to a reference sequence or assemble them to characterize the sample, allowing the resulting pattern of observations to support consensus sequences and identify genetic features. The selected strategy affects how sequence information is organized and interpreted.
Fragmentation converts nucleic-acid material into pieces that can be processed across many sequencing reactions, while platform-specific adapters prepare those fragments for the sequencing system. Together, these preparation stages connect the original DNA or RNA molecules to parallel read generation. Their purpose is to enable the high-coverage data used during downstream analysis.
Coverage is central because deep sequencing does more than produce a single observation of a sequence. Repeated reads allow abundant features to be characterized while also increasing the possibility of detecting low-frequency mutations or transcripts. Thus, the amount of sequence information influences both consensus confidence and sensitivity to features present at low levels.
For RNA-focused biology, the reads can be used for gene-expression analysis and transcriptome profiling. These applications examine RNA-derived sequence content rather than limiting interpretation to detected DNA variants. The resulting data help characterize which transcripts are represented and support a broader description of the sample’s transcriptome.
Deep Sequencing supports pathogen identification and metagenomic studies by generating extensive sequence information from the sample. In these applications, repeated reads can improve confidence in characterized sequences and increase the chance of detecting genetic features present at low levels. This is useful when limited sampling could fail to represent the biological material under investigation.
The analysis depends on the feature being investigated. Variant detection focuses on sequence differences, gene-expression analysis and transcriptome profiling focus on RNA-related patterns, while pathogen identification and metagenomic studies use sequence information to characterize biological material. This range makes the approach applicable to questions about sequence variation, expression, and sample characterization.