Large Annotated Datasets Research Articles

WebFlow: A Software Package for High-Throughput Analysis of Flow Cytometry Data

Flow cytometry has emerged as a powerful tool for quantitative, single-cell analysis of both surface markers and intracellular antigens, including phosphoproteins and kinase signaling cascades, with the flexibility to process hundreds of samples in multiwell plate format. Quantitative flow cytometric analysis is being applied in many areas of biology, from the study of immunology in animal models or human patients to high-content drug screening of pharmacologically active compounds. However, these experiments generate thousands of data points per sample, each with multiple measured parameters, leading to data management and analysis challenges. We developed WebFlow (http://webflow.stanford.edu), a web server-based software package to manage, analyze, and visualize data from flow cytometry experiments. WebFlow is accessible via standard web browsers and does not require users to install software on their personal computers. The software enables plate-based annotation of large data sets, which provides the basis for exploratory data analysis tools and rapid visualization of multiple different parameters. These tools include custom user-defined statistics to normalize data to other wells or other channels, as well as interactive, user-selectable heat maps for viewing the underlying single-cell data. The web-based approach of WebFlow allows for sharing of data with collaborators or the general public. WebFlow provides a novel platform for quantitative analysis of flow cytometric data from high-throughput drug screening or disease profiling experiments.

Delivering health care on US$19 per capita

<h3>Abstract</h3> Much of the DNA and RNA sequencing data available is in the form of high-throughput sequencing (HTS) reads and is currently unindexed by established sequence search databases. Recent succinct data structures for indexing both reference sequences and HTS data, along with associated metadata, have been based on either hashing or graph models, but many of these structures are static in nature, and thus, not well-suited as backends for dynamic databases. We propose a parallel construction method for and novel application of the <i>wavelet trie</i> as a dynamic data structure for compressing and indexing graph metadata. By developing an algorithm for merging wavelet tries, we are able to construct large tries in parallel by merging smaller tries constructed concurrently from batches of data. When compared against general compression algorithms and those developed specifically for graph colors (VARI and Rainbowfish), our method achieves compression ratios superior to gzip and VARI, converging to compression ratios of 6.5% to 2% on data sets constructed from over 600 virus genomes. While marginally worse than compression by bzip2 or Rainbowfish, this structure allows for both fast extension and query. We also found that additionally encoding graph topology metadata improved compression ratios, particularly on data sets consisting of several mutually-exclusive reference genomes. It was also observed that the compression ratio of wavelet tries grew sublinearly with the density of the annotation matrices. This work is a significant step towards implementing a dynamic data structure for indexing large annotated sequence data sets that supports fast query and update operations. At the time of writing, no established standard tool has filled this niche.