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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

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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...
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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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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Updated: Jun 17, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Genomic reproducibility in the bioinformatics era.

Pelin Icer Baykal1,2, Paweł Piotr Łabaj3,4, Florian Markowetz5,6

  • 1Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.

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|August 9, 2024
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Genomic reproducibility ensures consistent bioinformatics tool results across replicates, vital for advancing scientific knowledge and medical applications. This study clarifies definitions and recommends best practices for improving genomic data reliability.

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Area of Science:

  • Biomedical research
  • Genomics
  • Bioinformatics

Background:

  • Reproducibility is crucial for validating scientific discoveries in biomedical research.
  • The definition and implementation of reproducibility in genomics are currently imprecise.
  • Genomic data analysis relies heavily on bioinformatics tools, making their reproducibility a key concern.

Purpose of the Study:

  • To define and emphasize the importance of genomic reproducibility.
  • To clarify the impact of bioinformatics tools on achieving consistent genomic results.
  • To propose methods for evaluating and improving genomic reproducibility.

Main Methods:

  • Reviewing various interpretations of reproducibility within the genomics field.
  • Analyzing the influence of bioinformatics tools on the consistency of experimental outcomes.
  • Exploring evaluation strategies for bioinformatics tools concerning reproducibility.

Main Results:

  • Genomic reproducibility, defined as consistent results from bioinformatics tools across technical replicates, is essential.
  • Current practices lack precise definitions and implementations of reproducibility in genomics.
  • Bioinformatics tools significantly impact the ability to achieve reproducible genomic findings.

Conclusions:

  • Establishing clear definitions and rigorous evaluation methods for genomic reproducibility is critical.
  • Adopting best practices in bioinformatics is necessary to enhance the reliability of genomic research.
  • Improved genomic reproducibility will accelerate scientific discovery and the development of medical applications.