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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Bioinformatics | 3/6 | https://en.wikipedia.org/wiki/Bioinformatics | reference | science, encyclopedia | 2026-05-05T14:00:39.573858+00:00 | kb-cron |
The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. Intergenomic maps are made to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Entire genomes are involved in processes of hybridization, polyploidization and endosymbiosis that lead to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectrum of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics, fixed parameter and approximation algorithms for problems based on parsimony models to Markov chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models. Many of these studies are based on the detection of sequence homology to assign sequences to protein families.
=== Pan genomics ===
Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular monophyletic taxonomic group. Although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts: the Core genome, a set of genes common to all the genomes under study (often housekeeping genes vital for survival), and the Dispensable/Flexible genome: a set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.
=== Genetics of disease ===
As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple Mendelian inheritance has been observed for over 3,000 disorders that have been identified at the Online Mendelian Inheritance in Man database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as infertility, breast cancer and Alzheimer's disease), rather than a single cause. There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides. Genome-wide association studies have successfully identified thousands of common genetic variants for complex diseases and traits; however, these common variants only explain a small fraction of heritability. Rare variants may account for some of the missing heritability. Large-scale whole genome sequencing studies have rapidly sequenced millions of whole genomes, and such studies have identified hundreds of millions of rare variants. Functional annotations predict the effect or function of a genetic variant and help to prioritize rare functional variants, and incorporating these annotations can effectively boost the power of genetic association of rare variants analysis of whole genome sequencing studies. Some tools have been developed to provide all-in-one rare variant association analysis for whole-genome sequencing data, including integration of genotype data and their functional annotations, association analysis, result summary and visualization. Meta-analysis of whole genome sequencing studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes.
=== Analysis of mutations in cancer ===
In cancer, the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to single-nucleotide polymorphism arrays identifying point mutations that cause cancer, oligonucleotide microarrays can be used to identify chromosomal gains and losses (called comparative genomic hybridization). These detection methods generate terabytes of data per experiment. The data is often found to contain considerable variability, or noise, and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes. Two important principles can be used to identify cancer by mutations in the exome. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers. Further improvements in bioinformatics could allow for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples. Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.
== Gene and protein expression ==
=== Analysis of gene expression === The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), RNA-Seq, also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.
=== Analysis of protein expression === Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. The former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples when multiple incomplete peptides from each protein are detected. Cellular protein localization in a tissue context can be achieved through affinity proteomics displayed as spatial data based on immunohistochemistry and tissue microarrays.