Summary: Data visualization is often regarded as a post hoc step for verifying statistically significant results in the analysis of high-throughput datasets. This common practice leaves a large amount of raw data behind, from which more information can be extracted. However, existing solutions do not provide capabilities to explore large-scale raw datasets using biologically sensible queries, nor do they allow user interaction based real-time customization of graphics. To address these drawbacks, we have designed an open-source, web-based tool called Systems-Level Interactive Data Exploration, or SLIDE to visualize large-scale -omics data interactively. SLIDE's interface makes it easier for scientists to explore quantitative expression data in multiple resolutions in a single screen. Availability and implementation: SLIDE is publicly available under BSD license both as an online version as well as a stand-alone version at https://github.com/soumitag/SLIDE. Supplementary information: Supplementary data are available at Bioinformatics online.

In this paper, a fully automatic method for gridding bright field images of bead-based microarrays is proposed. There have been numerous techniques developed for gridding fluorescence images of traditional spotted microarrays but to our best knowledge, no algorithm has yet been developed for gridding bright field images of bead-based microarrays. The proposed gridding method is designed for automatic quality control during fabrication and assembly of bead-based microarrays. The method begins by estimating the grid parameters using an evolutionary algorithm. This is followed by a grid-fitting step that rigidly aligns an ideal grid with the image. Finally, a grid refinement step deforms the ideal grid to better fit the image. The grid fitting and refinement are performed locally and the final grid is a nonlinear (piecewise affine) grid. To deal with extreme corruptions in the image, the initial grid parameter estimation and grid-fitting steps employ robust search techniques. The proposed method does not have any free parameters that need tuning. The method is capable of identifying the grid structure even in the presence of extreme amounts of artifacts and distortions. Evaluation results on a variety of images are presented.

Bead encoding is a key problem central to all bead based microarrays. Recently a spatially addressable bead encoding technique has been developed ([1], [2]) that alleviates the need for costly hardware while still allowing high-throughput analysis. This paper proposes a pattern matching based scheme that extends this bead encoding technique's usability to uncontrolled environments. A novel affine invariant point pattern matching algorithm is developed to achieve this. The proposed algorithm uses local features to overcome the combinatorial explosion problem encountered in matching corrupted point patterns. The use of efficient data structures is emphasized to make the algorithm fast and scalable. The proposed scheme can decode bead identities in assays involving thousands of beads in a few seconds. Evaluation results using both real and simulated data are presented.

Research done in two aspects of robot localization and mapping are presented. An online fast learning FLANN, capable of learning location specific spatio-temporally stable visual features is developed. A technique for building oriented place maps using vestibular sensory information that can store multiple pose information of objects is investigated. Unlike most localization and mapping techniques ours does not require any depth estimation and can also handle dynamically changing environments. The system is tested in indoor environments, ranging from very simple to extremely cluttered ones. Preliminary research results show good generalization and learning capabilities of the network and improved localization using multiple oriented place maps.