MOTIVATION: We introduce a novel approach to multiple alignment that is based on an algorithm for rapidly checking whether single matches are consistent with a partial multiple alignment. This leads to a sequence annealing algorithm, which is an incremental method for building multiple sequence alignments one match at a time. Our approach improves significantly on the standard progressive alignment approach to multiple alignment. RESULTS: The sequence annealing algorithm performs well on benchmark test sets of protein sequences. It is not only sensitive, but also specific, drastically reducing the number of incorrectly aligned residues in comparison to other programs. The method allows for adjustment of the sensitivity/specificity tradeoff and can be used to reliably identify homologous regions among protein sequences. AVAILABILITY: An implementation of the sequence annealing algorithm is available at http://bio.math.berkeley.edu/amap/

BACKGROUND: Researchers who use MEDLINE for text mining, information extraction, or natural language processing may benefit from having a copy of MEDLINE that they can manage locally. The National Library of Medicine (NLM) distributes MEDLINE in eXtensible Markup Language (XML)-formatted text files, but it is difficult to query MEDLINE in that format. We have developed software tools to parse the MEDLINE data files and load their contents into a relational database. Although the task is conceptually straightforward, the size and scope of MEDLINE make the task nontrivial. Given the increasing importance of text analysis in biology and medicine, we believe a local installation of MEDLINE will provide helpful computing infrastructure for researchers. RESULTS: We developed three software packages that parse and load MEDLINE, and ran each package to install separate instances of the MEDLINE database. For each installation, we collected data on loading time and disk-space utilization to provide examples of the process in different settings. Settings differed in terms of commercial database-management system (IBM DB2 or Oracle 9i), processor (Intel or Sun), programming language of installation software (Java or Perl), and methods employed in different versions of the software. The loading times for the three installations were 76 hours, 196 hours, and 132 hours, and disk-space utilization was 46.3 GB, 37.7 GB, and 31.6 GB, respectively. Loading times varied due to a variety of differences among the systems. Loading time also depended on whether data were written to intermediate files or not, and on whether input files were processed in sequence or in parallel. Disk-space utilization depended on the number of MEDLINE files processed, amount of indexing, and whether abstracts were stored as character large objects or truncated. CONCLUSIONS: Relational database (RDBMS) technology supports indexing and querying of very large datasets, and can accommodate a locally stored version of MEDLINE. RDBMS systems support a wide range of queries and facilitate certain tasks that are not directly supported by the application programming interface to PubMed. Because there is variation in hardware, software, and network infrastructures across sites, we cannot predict the exact time required for a user to load MEDLINE, but our results suggest that performance of the software is reasonable. Our database schemas and conversion software are publicly available at http://biotext.berkeley.edu.