Daisychain Search and Interactive Visualisation of Homologs in Genome Assemblies

Daisychain is an interactive graph visualisation and search tool for custom-built gene homology databases. The main goal of Daisychain is to allow researchers working with specific genes to identify homologs in other annotation releases. The gene-centric representation includes local gene neighborhood to distinguish orthologs and paralogs by local synteny. The software supports genome sequences in FASTA format and GFF3 formatted annotation files, and the process of building the homology database requires a minimum amount of user interaction. Daisychain includes an integrated web viewer that can be used for both data analysis and data publishing. The web interface extends KnetMaps.js and is based on JavaScript

Boosting Partial Channel Neural Architecture Search with Gradient Projection

Neural Architecture Search has led to the discovery of novel neural network architectures that are capable of outperforming expertly designed architectures with fewer resource requirements at deployment time. This has lead to high performing neural networks that are small enough to fit into embedded systems and mobile devices. Recently, methods have been developed to significantly reduce the computational resources and time required to derive custom neural architectures. Specifically, gradient based methods have leveraged backpropagation to design architectures while a network is being trained, reducing search time from nearly 1400 GPU days to 1. However, differentiable neural architecture search suffers from dominating parameterless operations, steep local minimums, and shallow architectures. A recent multitasking method was able to reduce gradient confliction, dominating gradients, and high curvatures within their domain by projecting conflicting gradients from each task onto each other. We utilize a similar method to project conflicting gradients of edges in a search cell. In this paper we test various methods of gradient projection to determine the best way to avoid the derivation of suboptimal architectures. We show that differentiable neural architecture search can be boosted with the use of gradient projection and partial channel connections. By doing so, we show that parameterless operations and steep local minimum can be related to dominating gradients and high curvatures that are overcome in the multitask setting

BacMap: an interactive picture atlas of annotated bacterial genomes

BacMap is an interactive visual database containing fully labeled, zoomable and searchable chromosome maps from more than 170 bacterial (archaebacterial and eubacterial) species. It uses a recently developed visualization tool (CGView) to generate high-resolution circular genome maps from sequence feature information. Each map includes an interface that allows the image to be expanded and rotated. In the default view, identified genes are drawn to scale and colored according to coding directions. When a region of interest is expanded, gene labels are displayed. Each label is hyperlinked to a custom ‘gene card’ which provides several fields of information concerning the corresponding DNA and protein sequences. Each genome map is searchable via a local BLAST search and a gene name/synonym search. BacMap is freely available at http://wishart.biology.ualberta.ca/BacMap/

Clustering of twitter technology tweets and the impact of stopwords on clusters

Year of 2010 could be termed as the year in which Twitter became completely mainstream. Twitter, which started as a means of communicating with friends, became much more than its beginning. Now Twitter is used by companies to promote their new products, used by movie industry to promote movies. A lot of advertising and branding is now tied to Twitter and most importantly any breaking news that happens, the first place one goes and tries to find is to search it on Twitter. Be it the Mumbai attacks that happened in 2008, or the minor earthquakes that happened in Bay Area in 2010 or the twitter revolution cause of the Iran elections, most of the tech and not so tech savvy viewers were following twitter rather than any main stream news channels. In fact most of the breaking news now comes on Twitter because of the huge number of user base rather than the traditional mainstream media. The focus of this paper is clustering with the TF-IDF weighted mechanism of daily technology news tweets of prominent bloggers and news sites using Apache Mahout and to evaluate the effects of introducing and removing stop words on the quality of clustering. This project restricts itself to only tweets in the English language