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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: May 18, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Inferring correlation networks from genomic survey data.

Jonathan Friedman1, Eric J Alm

  • 1Computational & Systems Biology Initiative, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Plos Computational Biology
|October 3, 2012
PubMed
Summary
This summary is machine-generated.

Analyzing microbial community data using correlation analysis can yield artifactual results due to compositional effects. A new method, SparCC, accurately estimates correlations and reveals true ecological networks, especially in low-diversity environments.

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Last Updated: May 18, 2026

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Microbial ecology
  • Bioinformatics
  • Computational biology

Background:

  • High-throughput sequencing, like 16S rRNA gene profiling, reveals microbial community structures.
  • Correlation analysis is crucial for identifying inter-species dependencies in these communities.
  • Compositional data, presented as relative fractions, can lead to unreliable correlation analyses.

Purpose of the Study:

  • To investigate the impact of compositional effects on correlation analyses in microbial community data.
  • To develop a robust method for estimating correlations from compositional data.
  • To infer ecological networks and assess the accuracy of standard correlation methods.

Main Methods:

  • Utilized simulated and real data from the Human Microbiome Project.
  • Developed SparCC, a novel approach for correlation estimation from compositional data.
  • Inferred ecological networks using SparCC and compared results with standard methods.

Main Results:

  • Compositional effects in standard correlation analyses are widespread and can be severe, creating artifactual correlations and masking true interactions.
  • Community diversity significantly modulates the severity of compositional effects.
  • SparCC accurately estimates correlations and infers ecological networks, identifying a high rate of spurious interactions in standard methods.

Conclusions:

  • Standard correlation analyses are unreliable for compositional microbial data, especially in low-diversity environments.
  • SparCC provides a more accurate method for analyzing microbial community interactions and constructing ecological networks.
  • The findings highlight the importance of accounting for data composition in microbiome research.