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In the early phases of an epidemic response, our success will largely be dictated by our preparedness. Our ability to forecast likely scenarios is crucial in cases where responses must be made quickly and with limited information. The first section of the chapter examines several factors to consider when considering using a dataset for artificial intelligence (AI) predictions. These are: relevance, quantity, accuracy and completeness, availability, and bias. Several case studies demonstrate how these data considerations influence the development of AI applications for pandemic preparedness. The second half covers the applications of predictions in the areas of disease spread, population health management, and resource allocation. We examine various applications of AI predictions and how different organizations can put them into practice during a pandemic. In this assessment, we will specifically consider how predictions may be applied to support healthcare providers, first responders, community organizations, governments, and physicians.

COVID-19 is an acute respiratory disease that has been classified as a pandemic by the World Health Organization. Characterization of this disease is still in its early stages; however, it is known to have high mortality rates, particularly among individuals with preexisting medical conditions. Creating models to identify individuals who are at the greatest risk for severe complications due to COVID-19 will be useful for outreach campaigns to help mitigate the disease’s worst effects. While information specific to COVID-19 is limited, a model using complications due to other upper respiratory infections can be used as a proxy to help identify those individuals who are at the greatest risk. We present the results for three models predicting such complications, with each model increasing predictive effectiveness at the expense of ease of implementation.

We present a draft sequence of the genome of Aedes aegypti , the primary vector for yellow fever and dengue fever, which at ∼1376 million base pairs is about 5 times the size of the genome of the malaria vector Anopheles gambiae . Nearly 50% of the Ae. aegypti genome consists of transposable elements. These contribute to a factor of ∼4 to 6 increase in average gene length and in sizes of intergenic regions relative to An. gambiae and Drosophila melanogaster . Nonetheless, chromosomal synteny is generally maintained among all three insects, although conservation of orthologous gene order is higher (by a factor of ∼2) between the mosquito species than between either of them and the fruit fly. An increase in genes encoding odorant binding, cytochrome P450, and cuticle domains relative to An. gambiae suggests that members of these protein families underpin some of the biological differences between the two mosquito species.

As high-throughput screening matures as a discipline, cheminformatics is playing an increasingly important role in selecting new compounds for diverse screening libraries. New visualization techniques such as multi-fusion similarity maps, scaffold trees, and principal moments of inertia plots provide complementary information on compound libraries and enable identification of unexplored regions of chemical space with potential biological relevance. Quantitative metrics have been developed to analyze libraries for properties such as natural product-likeness and shape complexity. Analysis of high-throughput screening results and drug discovery programs identify compounds problematic for screening. Taken together these approaches allow us to increase the diversity of biological outcomes available in compound screening libraries and improve the success rates of high-throughput screening against new targets without making significant increases in the size of compound libraries.

Genetic variation allows the malaria parasite Plasmodium falciparum to overcome chemotherapeutic agents, vaccines and vector control strategies and remain a leading cause of global morbidity and mortality. Here we describe an initial survey of genetic variation across the P. falciparum genome. We performed extensive sequencing of 16 geographically diverse parasites and identified 46,937 SNPs, demonstrating rich diversity among P. falciparum parasites (pi = 1.16 x 10(-3)) and strong correlation with gene function. We identified multiple regions with signatures of selective sweeps in drug-resistant parasites, including a previously unidentified 160-kb region with extremely low polymorphism in pyrimethamine-resistant parasites. We further characterized 54 worldwide isolates by genotyping SNPs across 20 genomic regions. These data begin to define population structure among African, Asian and American groups and illustrate the degree of linkage disequilibrium, which extends over relatively short distances in African parasites but over longer distances in Asian parasites. We provide an initial map of genetic diversity in P. falciparum and demonstrate its potential utility in identifying genes subject to recent natural selection and in understanding the population genetics of this parasite.

Chromosome 18 appears to have the lowest gene density of any human chromosome and is one of only three chromosomes for which trisomic individuals survive to term. There are also a number of genetic disorders stemming from chromosome 18 trisomy and aneuploidy. Here we report the finished sequence and gene annotation of human chromosome 18, which will allow a better understanding of the normal and disease biology of this chromosome. Despite the low density of protein-coding genes on chromosome 18, we find that the proportion of non-protein-coding sequences evolutionarily conserved among mammals is close to the genome-wide average. Extending this analysis to the entire human genome, we find that the density of conserved non-protein-coding sequences is largely uncorrelated with gene density. This has important implications for the nature and roles of non-protein-coding sequence elements.

Although often considered “minimal” organisms, mycoplasmas show a wide range of diversity with respect to host environment, phenotypic traits, and pathogenicity. Here we report the complete genomic sequence and proteogenomic map for the piscine mycoplasma Mycoplasma mobile, noted for its robust gliding motility. For the first time, proteomic data are used in the primary annotation of a new genome, providing validation of expression for many of the predicted proteins. Several novel features were discovered including a long repeating unit of DNA of ∼2435 bp present in five complete copies that are shown to code for nearly identical yet uniquely expressed proteins. M. mobile has among the lowest DNA GC contents (24.9%) and most reduced set of tRNAs of any organism yet reported (28). Numerous instances of tandem duplication as well as lateral gene transfer are evident in the genome. The multiple available complete genome sequences for other motile and immotile mycoplasmas enabled us to use comparative genomic and phylogenetic methods to suggest several candidate genes that might be involved in motility. The results of these analyses leave open the possibility that gliding motility might have arisen independently more than once in the mycoplasma lineage.

We sequenced and annotated the genome of the filamentous fungus Fusarium graminearum , a major pathogen of cultivated cereals. Very few repetitive sequences were detected, and the process of repeat-induced point mutation, in which duplicated sequences are subject to extensive mutation, may partially account for the reduced repeat content and apparent low number of paralogous (ancestrally duplicated) genes. A second strain of F. graminearum contained more than 10,000 single-nucleotide polymorphisms, which were frequently located near telomeres and within other discrete chromosomal segments. Many highly polymorphic regions contained sets of genes implicated in plant-fungus interactions and were unusually divergent, with higher rates of recombination. These regions of genome innovation may result from selection due to interactions of F. graminearum with its plant hosts.

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28., Author Summary The collection and analysis of clinical data has played a key role in providing insights into the diagnosis, prognosis and treatment of disease. However, it is imperative that molecular and genetic data also be collected and integrated into the creation of network models, which capture underlying mechanisms of disease and can be interrogated to elucidate previously unknown biology. Bringing data from the clinic to the bench completes the cycle of translational research, which we demonstrate with this work. We built disease models from genetics, whole blood gene expression profiles and the component clinical measures of rheumatoid arthritis using a data-driven approach that leverages supercomputing. Genetic factors can be utilized as a source of perturbation to the system such that causal connections between genetics, molecular entities and clinical outcomes can be inferred. The existing TNF-α blocker treatments for rheumatoid arthritis are only effective for approximately 2/3 of the affected population. We identified novel therapeutic intervention points that may lead to the development of alternatives to TNF-α blocker treatments. We believe this approach will provide improved drug discovery programs, new insights into disease progression, increased drug efficacy and novel biomarkers for chronic and complex diseases.

We report the discovery and validation of a set of single nucleotide polymorphisms (SNPs) between the reference Neurospora crassa strain Oak Ridge and the Mauriceville strain (FGSC 2555), of sufficient density to allow fine mapping of most loci. Sequencing of Mauriceville cDNAs and alignment to the completed genomic sequence of the Oak Ridge strain identified 19,087 putative SNPs. Of these, a subset was validated by cleaved amplified polymorphic sequence (CAPS), a simple and robust PCR-based assay that reliably distinguishes between SNP alleles. Experimental confirmation resulted in the development of 250 CAPS markers distributed evenly over the genome. To demonstrate the applicability of this map, we used bulked segregant analysis followed by interval mapping to locate the csp-1 mutation to a narrow region on LGI. Subsequently, we refined mapping resolution to 74 kbp by developing additional markers, resequenced the candidate gene, NCU02713.3, in the mutant background, and phenocopied the mutation by gene replacement in the WT strain. Together, these techniques demonstrate a generally applicable and straightforward approach for the isolation of novel genes from existing mutants. Data on both putative and validated SNPs are deposited in a customized public database at the Broad Institute, which encourages augmentation by community users.

The effective control of tuberculosis (TB) has been thwarted by the need for prolonged, complex and potentially toxic drug regimens, by reliance on an inefficient vaccine and by the absence of biomarkers of clinical status. The promise of the genomics era for TB control is substantial, but has been hindered by the lack of a central repository that collects and integrates genomic and experimental data about this organism in a way that can be readily accessed and analyzed. The Tuberculosis Database (TBDB) is an integrated database providing access to TB genomic data and resources, relevant to the discovery and development of TB drugs, vaccines and biomarkers. The current release of TBDB houses genome sequence data and annotations for 28 different Mycobacterium tuberculosis strains and related bacteria. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives. TBDB currently hosts data for nearly 1500 public tuberculosis microarrays and 260 arrays for Streptomyces. In addition, TBDB provides access to a suite of comparative genomics and microarray analysis software. By bringing together M. tuberculosis genome annotation and gene-expression data with a suite of analysis tools, TBDB (http://www.tbdb.org/) provides a unique discovery platform for TB research.

ChemBank (http://chembank.broad.harvard.edu/) is a public, Web-based informatics environment. ChemBank stores and makes freely available data derived from small molecules and small-molecule screens and has resources for relating and studying these data. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays performed at the Broad Institute screening center. Web-based analysis tools are available within ChemBank to study the relationships between small molecules, cell measurements, and cell states. This unit demonstrates the use of ChemBank data to ask and answer questions relating to chemical biology and screening experiments contained within ChemBank.

We present Conrad, the first comparative gene predictor based on semi-Markov conditional random fields (SMCRFs). Unlike the best standalone gene predictors, which are based on generalized hidden Markov models (GHMMs) and trained by maximum likelihood, Conrad is discriminatively trained to maximize annotation accuracy. In addition, unlike the best annotation pipelines, which rely on heuristic and ad hoc decision rules to combine standalone gene predictors with additional information such as ESTs and protein homology, Conrad encodes all sources of information as features and treats all features equally in the training and inference algorithms. Conrad outperforms the best standalone gene predictors in cross-validation and whole chromosome testing on two fungi with vastly different gene structures. The performance improvement arises from the SMCRF’s discriminative training methods and their ability to easily incorporate diverse types of information by encoding them as feature functions. On Cryptococcus neoformans, configuring Conrad to reproduce the predictions of a two-species phylo-GHMM closely matches the performance of Twinscan. Enabling discriminative training increases performance, and adding new feature functions further increases performance, achieving a level of accuracy that is unprecedented for this organism. Similar results are obtained on Aspergillus nidulans comparing Conrad versus Fgenesh. SMCRFs are a promising framework for gene prediction because of their highly modular nature, simplifying the process of designing and testing potential indicators of gene structure. Conrad’s implementation of SMCRFs advances the state of the art in gene prediction in fungi and provides a robust platform for both current application and future research.

A consortium of investigators is engaged in a functional genomics project centered on the filamentous fungus Neurospora, with an eye to opening up the functional genomic analysis of all the filamentous fungi. The overall goal of the four interdependent projects in this effort is to acccomplish functional genomics, annotation, and expression analyses of Neurospora crassa, a filamentous fungus that is an established model for the assemblage of over 250,000 species of nonyeast fungi. Building from the completely sequenced 43-Mb Neurospora genome, Project 1 is pursuing the systematic disruption of genes through targeted gene replacements, phenotypic analysis of mutant strains, and their distribution to the scientific community at large. Project 2, through a primary focus in Annotation and Bioinformatics, has developed a platform for electronically capturing community feedback and data about the existing annotation, while building and maintaining a database to capture and display information about phenotypes. Oligonucleotide-based microarrays created in Project 3 are being used to collect baseline expression data for the nearly 11,000 distinguishable transcripts in Neurospora under various conditions of growth and development, and eventually to begin to analyze the global effects of loss of novel genes in strains created by Project 1. cDNA libraries generated in Project 4 document the overall complexity of expressed sequences in Neurospora, including alternative splicing alternative promoters and antisense transcripts. In addition, these studies have driven the assembly of an SNP map presently populated by nearly 300 markers that will greatly accelerate the positional cloning of genes.

Ustilago maydis is a ubiquitous pathogen of maize and a well-established model organism for the study of plant-microbe interactions. This basidiomycete fungus does not use aggressive virulence strategies to kill its host. U. maydis belongs to the group of biotrophic parasites (the smuts) that depend on living tissue for proliferation and development. Here we report the genome sequence for a member of this economically important group of biotrophic fungi. The 20.5-million-base U. maydis genome assembly contains 6,902 predicted protein-encoding genes and lacks pathogenicity signatures found in the genomes of aggressive pathogenic fungi, for example a battery of cell-wall-degrading enzymes. However, we detected unexpected genomic features responsible for the pathogenicity of this organism. Specifically, we found 12 clusters of genes encoding small secreted proteins with unknown function. A significant fraction of these genes exists in small gene families. Expression analysis showed that most of the genes contained in these clusters are regulated together and induced in infected tissue. Deletion of individual clusters altered the virulence of U. maydis in five cases, ranging from a complete lack of symptoms to hypervirulence. Despite years of research into the mechanism of pathogenicity in U. maydis, no 'true' virulence factors had been previously identified. Thus, the discovery of the secreted protein gene clusters and the functional demonstration of their decisive role in the infection process illuminate previously unknown mechanisms of pathogenicity operating in biotrophic fungi. Genomic analysis is, similarly, likely to open up new avenues for the discovery of virulence determinants in other pathogens.

Chromosome 17 is unusual among the human chromosomes in many respects. It is the largest human autosome with orthology to only a single mouse chromosome1, mapping entirely to the distal half of mouse chromosome 11. Chromosome 17 is rich in protein-coding genes, having the second highest gene density in the genome2,3. It is also enriched in segmental duplications, ranking third in density among the autosomes4. Here we report a finished sequence for human chromosome 17, as well as a structural comparison with the finished sequence for mouse chromosome 11, the first finished mouse chromosome. Comparison of the orthologous regions reveals striking differences. In contrast to the typical pattern seen in mammalian evolution5,6, the human sequence has undergone extensive intrachromosomal rearrangement, whereas the mouse sequence has been remarkably stable. Moreover, although the human sequence has a high density of segmental duplication, the mouse sequence has a very low density. Notably, these segmental duplications correspond closely to the sites of structural rearrangement, demonstrating a link between duplication and rearrangement. Examination of the main classes of duplicated segments provides insight into the dynamics underlying expansion of chromosome-specific, low-copy repeats in the human genome.

Here we present a finished sequence of human chromosome 15, together with a high-quality gene catalogue. As chromosome 15 is one of seven human chromosomes with a high rate of segmental duplication, we have carried out a detailed analysis of the duplication structure of the chromosome. Segmental duplications in chromosome 15 are largely clustered in two regions, on proximal and distal 15q; the proximal region is notable because recombination among the segmental duplications can result in deletions causing Prader-Willi and Angelman syndromes. Sequence analysis shows that the proximal and distal regions of 15q share extensive ancient similarity. Using a simple approach, we have been able to reconstruct many of the events by which the current duplication structure arose. We find that most of the intrachromosomal duplications seem to share a common ancestry. Finally, we demonstrate that some remaining gaps in the genome sequence are probably due to structural polymorphisms between haplotypes; this may explain a significant fraction of the gaps remaining in the human genome.

Here we report a high-quality draft genome sequence of the domestic dog (Canis familiaris), together with a dense map of single nucleotide polymorphisms (SNPs) across breeds. The dog is of particular interest because it provides important evolutionary information and because existing breeds show great phenotypic diversity for morphological, physiological and behavioural traits. We use sequence comparison with the primate and rodent lineages to shed light on the structure and evolution of genomes and genes. Notably, the majority of the most highly conserved non-coding sequences in mammalian genomes are clustered near a small subset of genes with important roles in development. Analysis of SNPs reveals long-range haplotypes across the entire dog genome, and defines the nature of genetic diversity within and across breeds. The current SNP map now makes it possible for genome-wide association studies to identify genes responsible for diseases and traits, with important consequences for human and companion animal health.

The International Human Genome Sequencing Consortium (IHGSC) recently completed a sequence of the human genome. As part of this project, we have focused on chromosome 8. Although some chromosomes exhibit extreme characteristics in terms of length, gene content, repeat content and fraction segmentally duplicated, chromosome 8 is distinctly typical in character, being very close to the genome median in each of these aspects. This work describes a finished sequence and gene catalogue for the chromosome, which represents just over 5% of the euchromatic human genome. A unique feature of the chromosome is a vast region of approximately 15 megabases on distal 8p that appears to have a strikingly high mutation rate, which has accelerated in the hominids relative to other sequenced mammals. This fast-evolving region contains a number of genes related to innate immunity and the nervous system, including loci that appear to be under positive selection--these include the major defensin (DEF) gene cluster and MCPH1, a gene that may have contributed to the evolution of expanded brain size in the great apes. The data from chromosome 8 should allow a better understanding of both normal and disease biology and genome evolution.

Analysis of expression profiling data from the NCI-60 cell line panel, designed to detect transcripts whose expression levels change in response to doxorubicin (Dox) treatment (100nM and 1000nM) for 2, 6 and 24 h, identified peroxiredoxin II (PRDX2) as a modulator of Dox sensitivity. The antitumor activity of Dox is pleiotropic but has been attributed in part to generation of reactive oxygen species which may also be an important factor in myocardial toxicity. Variation in the gene expression data produced by the increasing Dox concentrations together with the variation in response to Dox across the NCI-60 panel was used to construct causal gene expression network models of the mechanism of efficacy of Dox using the REFS™ platform from GNS Healthcare. REFS™ is a scalable, super computer-enabled framework for discovering causal network models directly from experimental data that requires highly optimized machine-learning algorithms run on massively parallel cloud-based supercomputers. REFS™ extracts information in two steps; Reverse Engineering to identify causal relationships followed by Forward Simulation to simulate interventions in silico. In this case, network models were used to simulate the predicted effect of a gene knockdown for each transcript in the NCI-60 cell lines on Dox sensitivity. PRDX2, which encodes for peroxiredoxin 2, an antioxidant enzyme that reduces hydrogen peroxide and has been demonstrated to have a role in protecting against reactive oxygen damage, was identified as a causal driver gene responsible for Dox sensitivity in the renal cell line, ACHN. Validation of the in silico predictions required RNAi to knock down PRDX2 gene expression (75 - 90% inhibition of expression) with 2 siRNA's in the ACHN and HCT-116 cell lines. Replicate experiments (n=3) demonstrated a significant (p Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5663. doi:1538-7445.AM2012-5663

Nature 437, 551–555 (2005) doi:10.1038/nature03983 The name of Keith O'Neill was accidentally omitted from the published author list. He is at the first affiliation in the address list.