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At least 235 records · Page 13Linked to original sources
PubMed2004

Genomic data support the hominoid slowdown and an Early Oligocene estimate for the hominoid-cercopithecoid divergence.

Several lines of indirect evidence suggest that hominoids (apes and humans) and cercopithecoids (Old World monkeys) diverged around 23-25 Mya. Importantly, although this range of dates has been used as both an initial assumption and as a confirmation of results in many molecular-clock analyses, it has not been critically assessed on its own merits. In this article we test the robusticity of the 23- to 25-Mya estimate with approximately 150,000 base pairs of orthologous DNA sequence data from two cercopithecoids and two hominoids by using quartet analysis. This method is an improvement over other estimates of the hominoid-cercopithecoid divergence because it incorporates two calibration points, one each within cercopithecoids and hominoids, and tests for a statistically appropriate model of molecular evolution. Most comparisons reject rate constancy in favor of a model incorporating two rates of evolution, supporting the "hominoid slowdown" hypothesis. By using this model of molecular evolution, the hominoid-cercopithecoid divergence is estimated to range from 29.2 to 34.5 Mya, significantly older than most previous analyses. Hominoid-cercopithecoid divergence dates of 23-25 Mya fall outside of the confidence intervals estimated, suggesting that as much as one-third of ape evolution has not been paleontologically sampled. Identifying stem cercopithecoids or hominoids from this period will be difficult because derived features that define crown catarrhines need not be present in early members of these lineages. More sites that sample primate habitats from the Oligocene of Africa are needed to better understand early ape and Old World monkey evolution.

Animals↗
PubMed2003

TableView: portable genomic data visualization.

UNLABELLED: TableView is a generalized scientific visualization program for exploration of various biological data, including EST, SAGE, microarray and annotation data. Written in Java, TableView is portable, is easily used together with other software including DBMSs and is versatile enough to be applied to any tabular data AVAILABILITY: TableView is freely available at: http://ccgb.umn.edu/software/java/apps/TableView/.

Computer Graphics↗
PubMed2004

Discovery of meaningful associations in genomic data using partial correlation coefficients.

MOTIVATION: A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. RESULTS: The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. AVAILABILITY: Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software. SUPPLEMENTARY INFORMATION: Supplementary information can be found at: URL to be decided.

Algorithms↗
PubMed2005

Classification and selection of biomarkers in genomic data using LASSO.

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been done on assessing univariate associations between gene expression profiles with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to the consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.

Journal Article↗
PubMed1991

[Analysis of the reassorted influenza virus clone genome: data for use of ordered selection of RNA segments].

Chicken embryonated eggs were coinfected with influenza A/FPV/Rostock and A/FPV/Weybridge strains. 25 plaque isolates were obtained from the mixed yield and their genetic content was analysed by polyacrylamide gel electrophoresis of H3-uridine-labelled vRNA in a modified gel system. At least 18 clones out of 25 plaque isolates proved to be reassortants; however, only one among them contained the homologous RNA-segments belonging to both parents. The results are in agreement with the concept of an ordered recruitment of vRNA-segments into virions.

Animals↗
PubMed2026

Two blind spots in the demographic inference of human origins from genomic data.

Ancient DNA and new inference methods have transformed the study of human origins, but consensus has not followed. Evidence increasingly indicates that hominin populations were pervasively structured and admixed, so complexity rather than simplicity is the appropriate prior. Here I highlight two blind spots that impede resolving that complexity. First, every inference passes through summaries of the data, and those summaries bound what can be recovered. Second, the space of candidate models is vast, yet competing model classes are rarely fit to common data, so a reported best model carries little evidence about untested model classes. This second blind spot reflects practice rather than data. It can be narrowed by testing competing models against withheld summaries and by reporting the models that were tried and rejected rather than only the winner.

Journal Article↗
PubMed2025

Incorporating Epidemiological Data into the Genomic Analysis of Partially Sampled Infectious Disease Outbreaks.

Pathogen genomic data are increasingly being used to investigate transmission dynamics in infectious disease outbreaks. Combining genomic data with epidemiological data should substantially increase our understanding of outbreaks, but this is highly challenging when the outbreak under study is only partially sampled, so that both genomic and epidemiological data are missing for intermediate links in the transmission chains. Here, we present a new dynamic programming algorithm to perform this task efficiently. We implement this methodology into the well-established TransPhylo framework to reconstruct partially sampled outbreaks using a combination of genomic and epidemiological data. We use simulated datasets to show that including epidemiological data can improve the accuracy of the inferred transmission links compared with inference based on genomic data only. This also allows us to estimate parameters specific to the epidemiological data (such as transmission rates between particular groups), which would otherwise not be possible. We then apply these methods to two real-world examples. First, we use genomic data from an outbreak of tuberculosis in Argentina, for which data was also available on the HIV status of sampled individuals, in order to investigate the role of HIV coinfection in the spread of this tuberculosis outbreak. Second, we use genomic and geographical data from the 2003 epidemic of avian influenza H7N7 in the Netherlands to reconstruct its spatial epidemiology. In both cases, we show that incorporating epidemiological data into the genomic analysis allows us to investigate the role of epidemiological properties in the spread of infectious diseases.

Humans↗
PubMed2004

The Rice PIPELINE: a unification tool for plant functional genomics.

The Rice Genome Research Project in Japan performs genome sequencing and comprehensive expression profiling, constructs genetic and physical maps, collects full-length cDNAs and generates mutant lines, all aimed at improving the breeding of the rice plant as a food source. The National Institute of Agrobiological Sciences in Tsukuba, Japan, has accumulated numerous rice biological resources and has already successfully produced a high-quality genome sequence, a high-density genetic map with 3000 markers, 30,000 full-length cDNAs, over 700 expression profiles with a 9000 cDNA microarray and 15,000 flanking sequences with Tos17 insertions in about 3765 mutant lines from about 50,000 transposon insertion lines. These resources are available in the public domain. A new unification tool for functional genomics, called Rice PIPELINE, has also been developed for the dynamic collection and compilation of genomics data (genome sequences, full-length cDNAs, gene expression profiles, mutant lines, cis elements) from various databases. The mission of Rice PIPELINE is to provide a unique scientific resource that pools publicly available rice genomic data for search by clone sequence, clone name, GenBank accession number, or keyword. The web-based form of Rice PIPELINE is available at http://cdna01.dna.affrc.go.jp/PIPE/.

Computational Biology↗