High Coverage Sequencing
High Coverage Sequencing - Related Videos
08:57
Amplicon Sequencing using the Long-Read Sequencing Technologies
0 Views •
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.
01:21
Solvency Ratios: Interest Coverage Ratio
0 Views •
2024
The interest coverage ratio is crucial in business as it indicates a company's ability to meet its interest obligations, reflecting its financial health and stability. Lenders and creditors use the interest coverage ratio to assess a company's capacity to service its debt. A higher ratio indicates that the company can comfortably meet its interest payments, making it a more attractive candidate for loans and credit. Investors also look at the interest coverage ratio to gauge the risk associated...
08:16
Comparative Lesions Analysis Through a Targeted Sequencing Approach
0 Views •
2019
This article describes a method to identify clonal and subclonal alterations among different specimens from a given patient. Although the experiments described here focus on a specific tumor type, the approach is broadly applicable to other solid tumors.
14:06
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
0 Views •
Cited by 10 •
2012
Pooled DNA sequencing is a fast and cost-effective strategy to detect rare variants associated with complex phenotypes in large cohorts. Here we describe the computational analysis of pooled, next-generation sequencing of 32 cancer-related genes using the SPLINTER software package. This method is scalable, and applicable to any phenotype of interest.
08:38
Targeted DNA Methylation Analysis by Next-generation Sequencing
0 Views •
Cited by 83 •
2015
Bisulfite amplicon sequencing (BSAS) is a method for quantifying cytosine methylation in targeted genomic regions of interest. This method uses bisulfite conversion paired with PCR amplification of target regions prior to next-generation sequencing to produce absolute quantitation of DNA methylation at a base-specific level.
FAQs
Sequencing depth indicates how many independent reads cover a particular DNA base. Greater depth allows researchers to compare repeated observations at that position, helping distinguish a genuine sequence change from an occasional reading error. This is especially important when a variant occurs at low frequency, because repeated supporting reads increase confidence in its detection and subsequent interpretation.
Overlapping short reads provide multiple observations of the same genomic regions. When these reads are aligned, agreement among independent sequences supports the expected base, while an isolated disagreement can be treated cautiously during computational analysis. This redundancy improves confidence in the resulting sequence and strengthens the reliability of variant calling in genomic studies.
Computational analysis either aligns overlapping short reads to a reference genome or assembles them from their overlaps. Researchers then examine the sequence supported at each covered position and identify differences that may represent genetic variants. The resulting calls are more informative when coverage is sufficient to show consistent evidence across independent reads rather than relying on a single observation.
A low-frequency variant may appear in only a small proportion of the reads covering a region, so limited sequencing can miss it or confuse it with an error. High coverage increases the number of observations available at that location, improving the chance of detecting the variant and assessing whether its signal is reproducible across reads.
The analysis begins by aligning overlapping short reads to a reference genome or assembling them computationally when appropriate. Researchers evaluate how many independent reads cover each base, use that evidence to support error correction, and then perform variant calling. These steps transform raw read data into a sequence dataset suitable for studying genetic differences.
In behavioral studies, the method can reveal genetic variation associated with social behavior, learning, stress responses, or susceptibility to neurological conditions. Researchers can compare these molecular findings with measured behavioral phenotypes, helping examine relationships between genomic variation and behavior. The increased confidence from repeated coverage also supports more reliable interpretation of genomic associations.