Dataset Information

Visualizing the genome: techniques for presenting human genome data and annotations.

ABSTRACT: BACKGROUND:In order to take full advantage of the newly available public human genome sequence data and associated annotations, biologists require visualization tools ("genome browsers") that can accommodate the high frequency of alternative splicing in human genes and other complexities. RESULTS:In this article, we describe visualization techniques for presenting human genomic sequence data and annotations in an interactive, graphical format. These techniques include: one-dimensional, semantic zooming to show sequence data alongside gene structures; color-coding exons to indicate frame of translation; adjustable, moveable tiers to permit easier inspection of a genomic scene; and display of protein annotations alongside gene structures to show how alternative splicing impacts protein structure and function. These techniques are illustrated using examples from two genome browser applications: the Neomorphic GeneViewer annotation tool and ProtAnnot, a prototype viewer which shows protein annotations in the context of genomic sequence. CONCLUSION:By presenting techniques for visualizing genomic data, we hope to provide interested software developers with a guide to what features are most likely to meet the needs of biologists as they seek to make sense of the rapidly expanding body of public genomic data and annotations.

SUBMITTER: Loraine AE

PROVIDER: S-EPMC119855 | biostudies-literature | 2002 Jul

REPOSITORIES: biostudies-literature

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Visualizing the genome: techniques for presenting human genome data and annotations.

Loraine Ann E AE Helt Gregg A GA

BMC bioinformatics 20020730

<h4>Background</h4>In order to take full advantage of the newly available public human genome sequence data and associated annotations, biologists require visualization tools ("genome browsers") that can accommodate the high frequency of alternative splicing in human genes and other complexities.<h4>Results</h4>In this article, we describe visualization techniques for presenting human genomic sequence data and annotations in an interactive, graphical format. These techniques include: one-dimensi ...[more]

PMID: 12149135

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Project description:Sensor technologies allow ethologists to continuously monitor the behaviors of large numbers of animals over extended periods of time. This creates new opportunities to study livestock behavior in commercial settings, but also new methodological challenges. Densely sampled behavioral data from large heterogeneous groups can contain a range of complex patterns and stochastic structures that may be difficult to visualize using conventional exploratory data analysis techniques. The goal of this research was to assess the efficacy of unsupervised machine learning tools in recovering complex behavioral patterns from such datasets to better inform subsequent statistical modeling. This methodological case study was carried out using records on milking order, or the sequence in which cows arrange themselves as they enter the milking parlor. Data was collected over a 6-month period from a closed group of 200 mixed-parity Holstein cattle on an organic dairy. Cows at the front and rear of the queue proved more consistent in their entry position than animals at the center of the queue, a systematic pattern of heterogeneity more clearly visualized using entropy estimates, a scale and distribution-free alternative to variance robust to outliers. Dimension reduction techniques were then used to visualize relationships between cows. No evidence of social cohesion was recovered, but Diffusion Map embeddings proved more adept than PCA at revealing the underlying linear geometry of this data. Median parlor entry positions from the pre- and post-pasture subperiods were highly correlated (R = 0.91), suggesting a surprising degree of temporal stationarity. Data Mechanics visualizations, however, revealed heterogeneous non-stationary among subgroups of animals in the center of the group and herd-level temporal outliers. A repeated measures model recovered inconsistent evidence of a relationships between entry position and cow attributes. Mutual conditional entropy tests, a permutation-based approach to assessing bivariate correlations robust to non-independence, confirmed a significant but non-linear association with peak milk yield, but revealed the age effect to be potentially confounded by health status. Finally, queueing records were related back to behaviors recorded via ear tag accelerometers using linear models and mutual conditional entropy tests. Both approaches recovered consistent evidence of differences in home pen behaviors across subsections of the queue.

Dataset Information

Visualizing the genome: techniques for presenting human genome data and annotations.

Publications

Visualizing the genome: techniques for presenting human genome data and annotations.

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