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Related Concept Videos

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...
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...
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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...

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Related Experiment Video

Updated: Jun 8, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Computational ecosystems for data-driven medical genomics.

Jonas S Almeida1

  • 1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA. jalmeida@mdanderson.org.

Genome Medicine
|September 22, 2010
PubMed
Summary
This summary is machine-generated.

An integrative bioinformatics infrastructure is key for personalized medicine. Flexible computational ecosystems, not rigid pipelines, are essential for effective multi-omic data analysis, as demonstrated by The Cancer Genome Atlas (TCGA) glioblastoma data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Personalized medicine relies on integrative bioinformatics infrastructure.
  • Large-scale genomic data initiatives like The Cancer Genome Atlas (TCGA) provide opportunities to validate computational infrastructure.
  • Previous assumptions about computational infrastructure effectiveness lacked empirical validation.

Discussion:

  • A recent study analyzed TCGA glioblastoma multiforme multi-omic data using a purpose-built software development environment.
  • The analysis validated design criteria for integrative computational infrastructure.
  • The study highlights the importance of flexible computational ecosystems over rigid pipelines.

Key Insights:

  • Effective bioinformatics infrastructure requires computational ecosystems with high-level abstractions.
  • Rigid, prescriptive pipelines are less effective for complex multi-omic analyses.
  • Iterative infrastructure development guided by sound design principles is crucial.

Outlook:

  • Future bioinformatics infrastructure should prioritize adaptable computational ecosystems.
  • This approach enables greater experimentation in analytical workflows.
  • Such flexibility is vital for advancing biomedical research and personalized medicine.