Gene Sequence Comparison
Gene Sequence Comparison - Related Videos
11:52
Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Cited by 15 •
2016
We describe a targeted RNA sequencing-based method that includes preparation of indexed cDNA libraries, hybridization and capture with custom probes and data analysis to interrogate selected transcripts for gene expression, mutations, and gene fusions. Targeted RNAseq permits cost-effective, rapid evaluation of selected transcripts on a desktop sequencer.
10:24
Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
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Cited by 119 •
2014
Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.
04:24
TALEN-Mediated Targeted Gene Integration: Using Engineered Sequence-Specific Nucleases for the Precise Insertion of Fluorescent Protein Gene into hiPSCs
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2025
This video describes a precise genome editing technique in human induced pluripotent stem cells, or hiPSCs, using TALENs to create double-stranded breaks at a targeted locus, inducing homology-directed repair for fluorescence protein gene integration.
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.
12:37
Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
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Cited by 42 •
2016
We describe an efficient, robust, and cost effective method for extracting nucleic acid from swabs for characterization of bacterial communities using 16S rRNA gene amplicon sequencing. The method allows for a common processing approach for multiple sample types and accommodates a number of downstream analytic processes.
FAQs
Sequence alignment places nucleotide sequences in corresponding positions so matching bases and differences can be evaluated systematically. Regions that remain similar across compared sequences may indicate conservation, while scattered or concentrated changes can show where sequences diverged. This positional comparison gives researchers evidence for relating sequence patterns to biological function and relationships among organisms.
Conserved regions are stretches of sequence that show relatively little change among the genes being compared. Their persistence can provide evidence that the region has biological importance, whereas more variable regions may reflect sequence divergence or genetic diversity. Researchers can therefore use conservation patterns to support gene identification, function studies, and comparisons across organisms.
Substitutions show that one base differs from another at a comparable position, while insertions and deletions indicate added or missing sequence relative to the comparison. Recording these changes helps distinguish closely matching regions from more divergent ones. Evaluating their locations and patterns can contribute to studying changing traits, inheritance, and differences among biological sequences.
Computational algorithms organize sequence alignment and calculate how strongly sequences correspond under the selected comparison approach. Statistical measures then help summarize the extent of matching and difference rather than relying only on visual inspection. Together, these tools make large or complex comparisons more systematic, supporting interpretations in genome annotation, evolutionary analysis, and variant investigation.
A typical workflow begins by selecting the DNA or RNA sequences relevant to the biological question. Researchers then align the sequences, inspect matching and conserved regions, and record substitutions, insertions, or deletions. Computational analyses and statistical measures help evaluate the comparison, after which the observed similarities and differences are interpreted in relation to gene identity, function, or biological relationships.
The approach supports gene identification and genome annotation by comparing an uncharacterized sequence with related sequence information. It also contributes to evolutionary and phylogenetic studies, where sequence patterns provide evidence about biological relationships. In medical or trait-focused research, comparing sequences can help detect variants associated with disease or changing traits and clarify patterns of genetic diversity.