Genomic Data
Genomic Data - Related Videos
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Cited by 6 •
2012
Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.
08:31
Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'
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Cited by 2 •
2013
Single Virus Genomics (SVG) is a method to isolate and amplify the genomes of single virons. Viral suspensions of a mixed assemblage are sorted using flow cytometry onto a microscope slide with discrete wells containing agarose, thereby capturing the virion and reducing genome shearing during downstream processing. Whole genome amplification is achieved using multiple displacement amplification (MDA) resulting in genomic material that is suitable for sequencing.
08:31
Genome Editing
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2023
A well-established technique for modifying specific sequences in the genome is gene targeting by homologous recombination, but this method can be laborious and only works in certain organisms. Recent advances have led to the development of “genome editing”, which works by inducing double-strand breaks in DNA using engineered nuclease enzymes guided to target genomic sites by either proteins or RNAs that recognize specific sequences. When a cell attempts to repair this damage, mutations can be...
11:59
Competitive Genomic Screens of Barcoded Yeast Libraries
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Cited by 44 •
2011
We have developed comprehensive, unbiased genome-wide screens to understand gene-drug and gene-environment interactions. Methods for screening these mutant collections are presented.
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.
FAQs
Aligning sequencing reads to a reference genome provides a basis for locating the DNA segments represented in the data and comparing them with an established genomic sequence. This comparison helps researchers identify differences, including single-nucleotide variants, insertions, deletions, and copy-number changes. The alignment step therefore connects raw sequencing output with interpretable genomic variation.
Variant categories capture different kinds of genomic change and can support different biological interpretations. A single-nucleotide variant affects one DNA base, whereas insertions and deletions alter the presence or length of a sequence segment; copy-number changes affect the number of copies of a genomic segment. Distinguishing these patterns helps analyses describe genetic diversity and investigate links to traits or disease mechanisms.
Quality control checks whether generated data are suitable for downstream analysis, while annotation adds biological information to detected variants. Together, they help separate reliable results from questionable observations and place sequence differences in an interpretive context. These steps matter because identifying a variant alone does not establish what it means for gene function, disease mechanisms, or an observable trait.
Once sequence reads have been generated and differences identified, researchers use quality control and annotation to support interpretation. They can then organize the results around a biological question, such as comparing populations, examining disease mechanisms, or relating variation to additional transcriptomic or clinical information. This progression turns computational outputs into evidence relevant to a defined study.
Combining genomic data with transcriptomic or clinical information can connect a DNA-level difference with its biological or medical context. Transcriptomic information can help relate variation to patterns in gene activity, while clinical information can connect genetic patterns with observed disease-related features. This integration strengthens interpretation because researchers can examine how variation relates to traits rather than studying sequence differences in isolation.
In biology, these datasets support questions about gene function, evolution, population diversity, disease mechanisms, and personalized medicine. The appropriate use depends on the research question: researchers may examine genetic differences among populations, investigate how variation relates to disease, or connect sequence information with medical context. Thus, the same data can support basic, evolutionary, and clinically oriented research.
Genomic analyses require careful management because the data represent genetic material and may be connected with clinical information. Privacy practices help limit inappropriate exposure, reproducibility allows others to understand and repeat the analysis, and computational management keeps datasets organized for reliable use. Together, these considerations support responsible interpretation and strengthen confidence in biological conclusions.