Randomized Sequences
Randomized Sequences - Related Videos
11:14
Primer-Free Aptamer Selection Using A Random DNA Library
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Cited by 20 •
2010
SELEX protocols comprise multiple rounds of selection, each of which require regeneration of bound ligands, which in turn require fixed primer sequences flanking the random library regions. These fixed primer sequences can interfere with the selection process (false positives and negatives). Here we present a primer-free protocol.
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.
12:36
Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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Cited by 2 •
2011
We present a public computational web site for the analysis of genomic sequences. It detects DNA sequence patterns with various non-random nucleotide compositions. This resource also generates randomized sequences with diverse levels of complexity.
07:30
Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Cited by 21 •
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.
02:02
Cis-regulatory Sequences
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2020
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...
FAQs
Design begins by choosing the sequence type and length, then specifying nucleotide or amino acid composition. Researchers can also impose constraints that preserve particular motifs or deliberately exclude them. These choices determine which portions of sequence space are sampled and make the resulting set suitable for a defined comparison rather than an uncontrolled collection of sequences.
Length and composition determine which sequence features are represented in a randomized set. By selecting these properties deliberately, researchers can test whether differences in binding, expression, folding, or function are associated with particular sequence arrangements. Controlled choices also help make comparisons with candidate sequences more interpretable during hypothesis testing.
The comparison helps distinguish effects associated with a candidate’s specific arrangement from effects that could arise from general sequence properties. If candidate and randomized counterparts are evaluated together, researchers can identify functional patterns that appear selectively in the candidate set. This makes randomized sequences valuable for interpreting experimental differences rather than treating every result as sequence-specific evidence.
A basic workflow identifies whether DNA, RNA, or protein sequences are needed, then sets the desired length and composition. Researchers next decide whether particular motifs should be preserved or excluded. The sequences can then be generated computationally or through synthesis and incorporated into an assay, standard, control, or library suited to the research question.
As negative controls, they provide comparison sequences that lack the intended candidate arrangement while retaining a deliberately designed sequence context. As experimental standards, they support consistent assay evaluation. As libraries, they provide collections for testing how sequence features affect binding, expression, folding, or function across many varied arrangements.
Experiments can examine whether sequence arrangements influence molecular binding, expression, folding, or broader function. In biology, the resulting comparisons support assay validation and hypothesis testing, while analysis of candidate and randomized sets can reveal functional patterns. The approach therefore connects sequence-level variation with measurable experimental outcomes without relying only on a single candidate sequence.