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Bioinformatics and Computational Biology Program Descriptions - National Institute of General Medical Sciences NIH is moving the launch of its new website to the fall to ensure the best possible experience for everyone who relies on our websites for health information, research, funding opportunities, and other resources. Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits. Tumoral and stromal hMENA isoforms impact tertiary lymphoid structure localization in lung cancer and predict immune checkpoint blockade response in patients with cancer. If plots of expression levels (y axis) against time (x axis) for each gene are overlaid as shown in Figure 13.18 , it is possible to visually compare the expression profiles of genes. One of the primary reasons to carry out such an experiment is to identify the genes that are differentially expressed between the two treatments. This general principle has been used to create clusters of genes that are predicted to have similar or identical functions. Likewise, if the best match to sequence A is B, the best match to B is C, and the best match to C is A, then A, B, and C are likely to have the same function. Suppose that the GenBank protein database is queried with protein sequence A and the result is that its closest match is protein sequence B. At the level of genes, the inference is less accurate, especially if the species involved in the comparison are not closely related, but the approach is nonetheless useful and usually effective. Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulation. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the Human Genome Project and by rapid advances in DNA sequencing technology. This definition placed bioinformatics as a field parallel to biochemistry (the study of chemical processes in biological systems). The first definition of the term bioinformatics was coined by Paulien Hogeweg and Ben Hesper in 1970, to refer to the study of information processes in biotic systems. In structural biology, it aids in the simulation and modeling of DNA, RNA, proteins as well as biomolecular interactions. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms, termed docking algorithms, for studying molecular interactions. Other interactions encountered in the field include Protein–ligand (including drug) and protein–peptide. A variety of methods have been developed to tackle the protein–protein docking problem, though it seems that there is still much work to be done in this field. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms. Furthermore, a protein's crystal structure can be used in simulation of for example ligand-binding studies and in silico mutagenesis studies. Another aspect of structural bioinformatics include the use of protein structures for Virtual Screening models such as Quantitative Structure-Activity Relationship models and proteochemometric models (PCM). The next phase of major data acquisition on the human genome is to discover how differences, both large and small, from individual to individual, result in variation at the phenotypic level. It included roughly 2.9 billion nucleotides, with some 30,000 to 40,000 genes spread across 23 pairs of chromosomes. One limitation of the oligonucleotide approach is that only a single sample can be assayed on a single chip—competitive hybridization is not possible. Fluorescently labeled RNA (rather than cDNA) is collected from the target tissue and hybridized against the oligonucleotide array. For each gene included in the array, approximately 10 to 20 different oligonucleotides of length 20–25 nucleotides are designed and printed onto a chip. Two basic computational approaches will be described, one using comparative genomics and the other using HMMs. Once a genome is sequenced and its genes are found or predicted, the next step in the bioinformatics pipeline is to determine the biological function of the genes. Perhaps most intriguing, as more and more genomes become available, are hybrid methods that integrate, for example, HMMs with comparative genomic data from two or more genomes. The average lengths of introns and exons would be used to estimate the transition probabilities to and from the Exon and Intron hidden states. For example, if a training data set included 150 introns, the observed frequency of C in those intron sequences could be used to come up with the emission probability for C in the hidden state intron. The training step involves taking existing sequences of known genes and estimating all of the transition and emission probabilities for each of the model's hidden states. For artificial intelligence applications in human DNA research , the upstream regions (promoters) of co-expressed genes can be searched for over-represented regulatory elements. Clustering algorithms can be then applied to expression data to determine which genes are co-expressed. In a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). Although both of these proteins have very different amino acid sequences, their protein structures are very similar, reflecting their shared function and shared ancestor. A viable general solution to the prediction of the function of a protein remains an open problem. Data from high-throughput chromosome conformation capture experiments, such as Hi-C (experiment) and ChIA-PET, can provide information on the three-dimensional structure and nuclear organization of chromatin. Microscopic pictures allow for the location of organelles as well as molecules, which may be the source of abnormalities in diseases. For each gene, the change in gene expression level can be plotted against time (Fig. 13.7 ). Microarray data is collected from the same tissues at periodic intervals over some length of time. Two technologies, cDNA and oligonucleotide microarrays, currently dominate the field, and they have opened the door to many exploratory analyses that were previously impossible. For each member in a collection of several thousand genes, the assays provide a quantitative estimate of the number of mRNA copies of each gene being produced in a particular tissue. We present a novel neural network-based approach that uses UK Biobank data to identify known and potentially novel gene associations with family history of Alzheimer's disease (AD), enhancing existing GWAS methods, and highlighting new avenues for understanding AD pathogenesis. Thus, for example, many biomedical research projects involve multiple omics datasets derived from the same sample, so we use integrative machine learning methods to handle such data, an area where some Lab members have previously proposed novel algorithms. In this context, infectious disease models are used to understand the spread of parasites, viruses, and infectious diseases. The scope of topics ranges from -omics, bioimaging, data acquisition using high-throughput technologies, molecular and cellular biology, pharmacology, and populational studies. Databases exist for many different information types, including DNA and protein sequences, molecular structures, phenotypes and biodiversity.

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