Abstract Motivation: Robust large-scale sequence analysis is a major challenge in modern genomic science, where biologists are frequently trying to characterize many millions of sequences. Here, we describe a new Java-based architecture for the widely used protein function prediction software package InterProScan. Developments include improvements and additions to the outputs of the software and the complete reimplementation of the software framework, resulting in a flexible and stable system that is able to use both multiprocessor machines and/or conventional clusters to achieve scalable distributed data analysis. InterProScan is freely available for download from the EMBl-EBI FTP site and the open source code is hosted at Google Code. Availability and implementation: InterProScan is distributed via FTP at ftp://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/ and the source code is available from http://code.google.com/p/interproscan/. Contact: http://www.ebi.ac.uk/support or [email protected] or [email protected]

Several thousand metagenomes have already been sequenced, and this number is set to grow rapidly in the forthcoming years as the uptake of high-throughput sequencing technologies continues. Hand-in-hand with this data bonanza comes the computationally overwhelming task of analysis. Herein, we describe some of the bioinformatic approaches currently used by metagenomics researchers to analyze their data, the issues they face and the steps that could be taken to help overcome these challenges.

SUMMARY: METIS is a web-based integrated annotation tool. From single query sequences, the PRECIS component allows users to generate structured protein family reports from sets of related Swiss-Prot entries. These reports may then be augmented with pertinent sentences extracted from online biomedical literature via support vector machine and rule-based sentence classification systems. AVAILABILITY: http://umber.sbs.man.ac.uk/dbbrowser/metis/

MOTIVATION: There have been several endeavours to address the problem of annotating sequence data computationally, but the task is non-trivial and few tools have emerged that gather useful information on a given sequence, or set of sequences, in a simple and convenient manner. As more genome projects bear fruit, the mass of uncharacterized sequence data accumulating in public repositories grows ever larger. There is thus a pressing need for tools to support the process of automatic analysis and annotation of newly determined sequences. With this in mind, we have developed PRECIS, which automatically creates protein reports from sets of SWISS-PROT entries, collating results into structured reports, detailing known biological and medical information, literature and database cross-references, and relevant keywords.

There have been several attempts at addressing the problem of annotating sequence data computationally. Annotation generation can be considered a pipeline of processes: first harvesting data from a variety of data sources, then distilling and transforming it into a form more appropriate for the end database. This task is usually performed by human annotators, a solution that is clearly not scaleable. There have been several attempts to mimic some of these pipelines in software. However, these have generally focused on low level annotation, such as database cross-references, or by harvesting data from computational techniques such as gene finding or similarity searches. Higher level annotation such as that seen in the PRINTS database is usually formed from data that is free text, or only partly structured. This presents a much greater computational challenge. Therefore we studied the pipeline that is used to generate annotation for the PRINTS database, and have developed prototype software that reflects and automates this pipeline. As this software operates primarily on data culled from the SWISS-PROT database, we have called it PRECIS (Protein Reports Engineered from Concise Information in SWISS-PROT). This software is currently being used to generate annotation for the prePRINTS database. As the output is a structured report detailing the function, structure and disease associations of a protein, and providing literature references and keywords we believe it will be of more generic use. The software is available on request from [email protected].