New developments in the InterPro database

InterPro is an integrated resource for protein families, domains and functional sites, which integrates the following protein signature databases: PROSITE, PRINTS, ProDom, Pfam, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D and PANTHER. The latter two new member databases have been integrated since the last publication in this journal. There have been several new developments in InterPro, including an additional reading field, new database links, extensions to the web interface and additional match XML files. InterPro has always provided matches to UniProtKB proteins on the website and in the match XML file on the FTP site. Additional matches to proteins in UniParc (UniProt archive) are now available for download in the new match XML files only. The latest InterPro release (13.0) contains more than 13 000 entries, covering over 78% of all proteins in UniProtKB. The database is available for text- and sequence-based searches via a webserver (), and for download by anonymous FTP (). The InterProScan search tool is now also available via a web service at

Leveraging cloud data to mitigate user experience from ‘breaking bad’

Low latency and high availability of an app or a web service are key, amongst other factors, to the overall user experience (which in turn directly impacts the bottomline). Exogenic and/or endogenic factors often give rise to breakouts in cloud data which makes maintaining high availability and deliv-ering high performance very challenging. Although there exists a large body of prior research in breakout detection, existing techniques are not suitable for detecting breakouts in cloud data owing to being not robust in the presence of anomalies. To this end, we developed a novel statistical technique to automatically detect breakouts in cloud data. In particular, the technique employs Energy Statistics to detect breakouts in both application as well as system metrics. Further, the technique uses robust statistical metrics, viz., median, and estimates the statistical significance of a breakout through a permutation test. To the best of our knowledge, this is the first work which addresses breakout detection in the pres-ence of anomalies. We demonstrate the efficacy of the proposed technique using production data and report Precision, Recall and F-measure measure. The proposed technique is 3.5 × faster than a state-of-the-art technique for breakout detection and is being currently used on a daily basis at Twitter. 1