than through physical taxonomy or physiological observations alone, compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important in bacterial speciation, build complex computational population genetics models to predict the outcome of the system over time track and share information on an increasingly large number of species and organisms future work endeavours to reconstruct the now more complex tree of life. the area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are not necessarily related. comparative genomics the core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. it is these intergenomic maps that make it possible 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. ultimately, whole genomes are involved in processes of hybridization, polyploidization and endosymbiosis, often leading 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 which eventually took root in bioinformatics. pan genome is the complete gene repertoire of a particular 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: set of genes common to all the genomes under study (these are often housekeeping genes vital for survival) and the dispensable/flexible genome: 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 main article: genome-wide association studies with the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases including infertility, breast cancer or alzheimer's disease. genome-wide association studies are a useful approach to pinpoint the mutations responsible for such complex diseases. through these studies, thousands of dna variants have been identified that are associated with similar diseases and traits. furthermore, the possibility for genes to be used at prognosis, diagnosis or treatment is one of the most essential applications. many studies are discussing both the promising ways to choose the genes to be used and the problems and pitfalls of using genes to predict disease presence or prognosis. 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 main article: oncogenomics in cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways. massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer. bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of human genome sequences and germline polymorphisms. new physical detection technologies are employed, such as oligonucleotide microarrays to identify chromosomal gains and losses (called comparative genomic hybridization), and single-nucleotide polymorphism arrays to detect known point mutations. these detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate terabytes of data per experiment. again the massive amounts and new types of data generate new opportunities for bioinformaticians. 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 in the analysis of cancer genomes bioinformatically pertaining to the identification of 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. with the breakthroughs that this next-generation sequencing technology is providing to the field of bioinformatics, cancer genomics could drastically change. these new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably. this could create a more flexible process 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. microarray vs rna-seq 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. bioinformatics is very much involved in making sense of protein microarray and ht ms data; 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 where multiple, but 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. analysis of regulation gene regulation is the complex orchestration of events by which a signal, potentially an extracellular signal such as a hormone, eventually leads to an increase or decrease in the activity of one or more proteins. bioinformatics techniques have been applied to explore various steps in this process. for example, gene expression can be regulated by nearby elements in the genome. promoter analysis involves the identification and study of sequence motifs in the dna surrounding the coding region of a gene. these motifs influence the extent to which that region is transcribed into mrna. enhancer elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. these interactions can be determined by bioinformatic analysis of chromosome conformation capture experiments. expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. in a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). one can then apply clustering algorithms to that expression data to determine which genes are co-expressed. for example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented regulatory elements. examples of clustering algorithms applied in gene clustering are k-means clustering, self-organizing maps (soms), hierarchical clustering, and consensus clustering methods. analysis of cellular organization several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells. this is relevant as the location of these components affects the events within a cell and thus helps us to predict the behavior of biological systems. a gene ontology category, cellular component, has been devised to capture subcellular localization in many biological databases. microscopy and image analysis microscopic pictures allow us to locate both organelles as well as molecules. it may also help us to distinguish between normal and abnormal cells, e.g. in cancer. protein localization the localization of proteins helps us to evaluate the role of a protein. for instance, if a protein is found in the nucleus it may be involved in gene regulation or splicing. by contrast, if a protein is found in mitochondria, it may be involved in respiration or other metabolic processes. protein localization is thus an important component of protein function prediction. there are well developed protein subcellular localization prediction resources available, including protein subcellular location databases, and prediction tools. nuclear organization of chromatin data from high-throughput chromosome conformation capture experiments, such as hi-c (experiment) and chia-pet, can provide information on the spatial proximity of dna loci. analysis of these experiments can determine the three-dimensional structure and nuclear organization of chromatin. bioinformatic challenges in this field include partitioning the genome into domains, such as topologically associating domains (tads), that are organised together in three-dimensional space. structural bioinformatics 3-dimensional protein structures such as this one are common subjects in bioinformatic analyses. protein structure prediction is another important application of bioinformatics. the amino acid sequence of a protein, the so-called primary structure, can be easily determined from the sequence on the gene that codes for it. in the vast majority of cases, this primary structure uniquely determines a structure in its native environment. (there are exceptions, such as the bovine spongiform encephalopathy (mad cow disease) prion.) knowledge of this structure is vital in understanding the function of the protein. structural information is usually classified as one of secondary, tertiary and quaternary structure. a viable general solution to such predictions remains an open problem. most efforts have so far been directed towards heuristics that work most of the time. one of the key ideas in bioinformatics is the notion of homology. in the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene a, whose function is known, is homologous to the sequence of gene b, whose function is unknown, one could infer that b may share a's function. in the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. in a technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known. until recently, this remained the only way to predict protein structures reliably. however, a game-changing breakthrough occurred with the release of new deep-learning algorithms-based software called alphafold, developed by a bioinformatics team within google's a.i. research department deepmind. alphafold, during the 14th critical assessment of protein structure prediction (casp14) computational protein structure prediction software competition, became the first contender ever to deliver prediction submissions with accuracy competitive with experimental structures in a majority of cases and greatly outperforming all other prediction software methods up to that point. alphafold has since