Nucleic Acids Research, 2006, Vol. 34, Web Server issue W749 for botXminer using the search words ‘botulinum’ and ‘tetanus’. This subset has 26 563 citations, a value that represents <1% of the total number that are available. The ‘Literature’ section of BotDB contains two botXminer-related options. The ‘Search’ option (data not shown) allows the user to query the standard ﬁelds of a MEDLINE output [author, terms in the title or abstract, PubMed identiﬁcation number (PMID) and so on] by entering desired words or terms in the appropriate boxes and by selecting an optional range of publication years. The second option, ‘Group Articles’, is a unique query type. Initially, the user-provided search words or terms are searched for within each MEDLINE XML ﬁle. These results are then grouped by botXminer with terms contained in a select list of MEDLINE ﬁelds: the Medical Subject Headings (MeSH ) qualiﬁer or descriptor names, author, chemical, keyword and gene Symbol. An ‘Advanced search’ feature is also available that allows the user to search the common MEDLINE ﬁelds of journal title, MeSH and chemical in addition to the title and abstract. The option to specify a range of publication years is also applicable to the ‘Group Articles’ and the ‘Advanced search’. Queries may contain common logical operators for literat- ure searches that are supported by Oracle Text: AND (&), NOT ( ), OR ( ) and ACCUM (,) (meaning either term is j acceptable but both are preferred). Other operators use context grammar: NEAR (;) for proximity searches, and MINUS ( ) for a lower preference. Wildcard symbols used include ‘%’ for one or more wildcard characters, and ‘_’ that allows a single wildcard character. A summary list with examples is provided on a help page. Output graphics can presently be viewed in PNG, HTML or SVG formats and have so far been tested using Microsoft Internet Explorer (IE) v.6, Netscape v.8 and the Mozilla Firefox v.1 browsers. The bitmapped PNG format only provides a static representation. The HTML format offers the advantage of being able to hyperlink the graphic points (rectangles and circles) to the citation summary information. Label information is presented as a pop-up label when moving the mouse over the rectangles and circles. For the SVG option, an interactive graphic view of the results is dynamically produced using the aiSee graphics package (http://www.aisee.com). The SVG format also has interactive hyperlinks while its associated plug-in (available for IE and Netscape ) provides the user with a drop-down feature menu to zoom in and zoom out, pan across the image, save the image, and other graph features. In the SVG format under IE, positioning the mouse over the rectangles and circles pro- duces the label information within the ‘window status bar’. At present, the Firefox browser does support the SVG format by using the aiSee software but does not support the drop-down feature menu. For these reasons, it is recommended that, at this time, the botXminer graphics be viewed in the SVG format using either the IE or the Netscape browsers. USAGE As a practical example, a search was conducted to ﬁnd poten- tial interactions between the SNARE proteins (three of which are substrates for the proteolytic botulinum neurotoxins) and other proteins. The substrates of interest [syntaxin-1A, synaptobrevin (VAMP) and SNAP-25] are intimately involved in the evoked release of neurotransmitter from synaptic vesicles. The retrieved citations for SNARE protein–protein interactions may, thus, help to predict what other cellular functions might be affected in addition to the toxin-induced blockade of neurotransmission. A ‘Group Articles’ ‘Advanced search’ was used with the query: near ((snare, interact%), 5) (Figure 1, back panel). This search was designed to locate the search words ‘snare’ and ‘interact%’ (or interacts, interacting, etc.) that are separated by no more than ﬁve words. The ‘abstract’ ﬁeld and the Group term ‘chemical’ were also used in this search. This query resulted in a tabulated listing of 83 Group (chemical) terms that appeared in 18 MEDLINE XML ﬁles (Figure 1, middle panel). Clicking on the highlighted value of ‘18’ produces a list of all the retrieved citations (Figure 1, front panel). Values for the ‘Number of Articles’ for a given term are hyperlinked to a second table that lists citations associated with the selected term (data not shown). The PMID numbers are hyperlinked to their corresponding MEDLINE pages, which are presented in a modiﬁed format, while article titles in this table are hyperlinked to their PubMed abstracts (data not shown). Another way to visualize these data is in a network graph format (Figure 2, upper panel) with a method analogous to that used in ChiliBot (2) and PubNet (3). Before a graph is plotted, numerical characteristics of the graph are displayed. In this example, there are 83 Group terms (chemical names) and 859 connections (circles) between these terms. All pairs of chemical terms are connected by color-coded lines and circles that show the relative ‘Number of Articles’ for two co-occurring chemical terms. Each rectangle is hyperlinked to a list of citations containing the Group (chemical) term, whereas each circle is hyperlinked to a list of citations that contain the pair of terms associated with that connection (data not shown). These graphical views also allow the user to quickly focus on and recognize patterns for the most frequent co-occurring terms. In this example, the cluster of terms near the top of the graph is examined more closely with the ‘Zoom In’ option (accessed through the drop-down menu with the right-hand mouse button) that is used with SVG (Figure 2, lower panel). In this panel, the pairs are only associated with a single paper (gray lines and circles). Nevertheless, from these citations and graphs it is evident that several protein–protein interactions between the SNAREs and other proteins including calmodulin, neuro- transporters and other synaptic proteins were readily identiﬁed using this approach. DISCUSSION One goal in the development of botXminer was to mine the maximum amount of information from the MEDLINE XML ﬁles without doing any natural language processing. This development began with L. C. Wang’s original documentation (http://www.ebi.ac.uk/~lcwang/medline/index.htm) that des- cribed how the whole collection of MEDLINE data can be stored inside Oracle XML DB in object-relational tables. Since