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Genomics

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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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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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.
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

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

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

Published on: December 7, 2021

Knocking down the obstacles to functional genomics data sharing.

Kaylene J Simpson^1,2, Jennifer A Smith³

¹Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre, Melbourne 3000, Australia.

Scientific Data

|March 2, 2017

Summary

This summary is machine-generated.

Eight new studies present genome-wide functional genomics screen datasets, primarily using RNA interference (RNAi). This research explores host-pathogen interactions and disease pathways, with all data publicly accessible for further analysis and hypothesis generation.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

High-throughput functional genomics screens are crucial for understanding biological processes at a genome-wide scale.
RNA interference (RNAi) is a powerful technique for perturbing gene function and studying cellular mechanisms.
Analyzing large-scale genomics data requires robust computational tools and accessible repositories.

Purpose of the Study:

To present a comprehensive collection of datasets from high-throughput functional genomics screens.
To facilitate research into host-pathogen interactions, innate immunity, and disease pathways.
To enable the development of new analytical methods and scientific hypotheses through open data access.

Main Methods:

Utilizing high-throughput screening, primarily employing RNA interference (RNAi) technology.
Generating and curating large-scale datasets covering various biological aspects.
Ensuring public accessibility of raw and processed data, including high-content screen images.

Main Results:

Publication of eight detailed papers describing functional genomics datasets.
Exploration of host-pathogen dependencies, innate immune responses, and disease pathways at the genome level.
Availability of all data in public repositories like PubChem BioAssay, figshare, Harvard Dataverse, and IDR.

Conclusions:

The presented datasets provide a valuable resource for the scientific community.
Open data access promotes reproducibility, data comparison, and the generation of novel research directions.
Detailed data descriptors enhance the utility of these datasets for advanced analyses, including machine learning.