Most web surfers approach searching haphazardly at best. Enter a few keywords into the little box, and look at whatever results come up. But each search engine makes different default assumptions, so if you enter protein structure into Excite's query field, you are asking for an entirely different search than if you enter protein structure into Google's query field. In order to search effectively, you need to use boolean logic, which is an extremely simple way of stating how a group of things should be divided or combined into sets.

Search engines all use some form of boolean logic, as do the query forms for most of the public biological databases. Boolean queries restrict the results that are returned from a database by joining a series of search terms with the operators AND, OR, and NOT. The meaning of these operators is straightforward: joining two keywords with AND finds documents that contain only keyword1 and keyword2 ; using OR finds documents that contain either keyword1 or keyword2 (or both); and using NOT finds documents that contain keyword1 but not keyword2.

However, search engines differ in how they interpret a space or an implied operator. Some search engines consider a space an OR, so when you type protein structure, you're really asking for protein or structure. If you search for protein structure on Excite, which defaults to OR, you come up with a lot of advertisements for fad diets and protein supplements before you ever get to the scientific sites you're interested in. On the other hand, Google defaults to AND, so you'll find only references that contain protein and structure, which is probably what you intended to look for in the first place. Find out how the search engine you're using works before you formulate your query.

Boolean queries are read from left to right, just like text. Parentheses can structure more complex boolean queries. For instance, if you look for documents that contain keyword1 and one of either keyword2 or keyword3, but not keyword4, your query would look like this: (keyword1 AND (keyword2 OR keyword3)) NOT keyword4.

Many search engines allow you to use quotation marks to specify a phrase. If you want to find only documents in which the words protein structure appear together in sequence, searching for "protein structure" is one way to narrow your results.

Let's say you want to search a literature database for references about computing electrostatic potentials for protein molecules, and you only want to look for references by two authors, Barry Honig and Andrew McCammon. You might structure a boolean query statement as follows:

((protein AND "electrostatic potential") AND (Honig OR McCammon))

This statement tells the search engine you want references that contain both the word protein and the phrase electrostatic potential, and that you require either one or the other of the names Honig and McCammon.

There are many excellent web tutorials available on boolean searching. Try a search with the phrase boolean searching in Google, and see what comes up.

While the purpose of this book isn't to describe exhaustively how search engines work, there are significant differences in how search engines build their databases and rank sites. These differences make some search engines far more useful than others for searching science and technology web sites.

Key features to look at in a web search engine's database building and indexing strategies are free URL submission, full-text indexing, automated, comprehensive web crawling, a fast "refresh rate," and a sensible ranking strategy for results.

Our current favorite search engine is Google. Google is extremely comprehensive, indexing over 1 billion URLs. Pages are ranked based on how many times they are linked from other pages. Links from well-connected pages are considered more significant than links from isolated pages. The claim is that a Google search will bring you to the most well-traveled pages that match your search topic, and we've found that it works rather well. Google caches copies of web pages, so pages can be accessible even if the server is offline. It returns only pages that contain all the relevant query terms. Google uses a shorthand version of the standard boolean search formula, and it allows such specialized services as locating all the pages that link back to a page of interest.

For the neophyte user, however, HotBot is probably the best search engine. HotBot is relatively comprehensive and regularly updated, and it offers form-based query tools that eliminate the need for you to formulate even simple query statements.

PubMed (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi ) is one of the most valuable web resources available to biologists. Over 4,000 journals are indexed in PubMed, including most of the well-regarded journals in cell and molecular biology, biochemistry, genetics, and related fields, as well as many clinical publications of interest to medical professionals.

PubMed uses a keyword-based search strategy and allows the boolean operators AND, OR, and NOT in query statements. Users can specify which database fields to check for each search term by following the search term with a field name enclosed in square brackets.

Additionally, users can search PubMed using Medical Subject Heading (MeSH) terms. MeSH is a library of standardized terms that may help locate manuscripts that use alternate terms to refer to the same concept. The MeSH browser (http://www.nlm.nih.gov/mesh/meshhome.html) allows users to enter a word or word fragment and find related keywords in the MeSH library. PubMed automatically finds MeSH terms related to query terms and uses them to enhance queries.

For example, we searched for "protein electrostatics" in PubMed. The terms protein and electrostatics are automatically joined with an AND unless otherwise specified. The resulting boolean query statement submitted to PubMed is actually:

((("proteins"[MeSH Terms] OR protein[Text Word]) AND ("electrostatics"[MeSH Terms]
 OR electrostatics[Text Word])) AND notpubref[sb])

The results of the search are shown in Figure 6-1.

Figure 6-1. Results from a PubMed search

As you can see in Figure 6-2, PubMed also allows you to use a web interface to narrow your search. The Limits link immediately below the query box on the main PubMed page takes you to this web form.

Figure 6-2. Narrowing a search strategy using the Limits menu in PubMed

The Limits form allows you to add specificity to your query. You can limit your search to particular fields in the PubMed database record, such as the Author Name or Substance Name field. Searches can also be limited by language, content (e.g., searching for review articles or clinical trials only), and date. For clinical research publications, the search can be limited based on the species, age, and gender of the research subjects.

The Preview/Index menu allows you to build a detailed query interactively. You can select a specific data field (for instance, the Author Name field) and then enter a term you want to search for within the specified field only. Clicking the AND, OR, or NOT buttons joins the new term to your previous query terms using the specified boolean operator.

For instance, you might start with a general search for "protein AND electrostatics," then go to the Preview/Index page (Figure 6-3) and specify that you want to search for "Gilson OR McCammon" in the Author Name field only.

Figure 6-3. Building a PubMed query using the Preview/Index form

You can also use the options in the History form to access results from earlier searches, and to narrow a search by adding new terms to the query.

If you want to collect results from multiple queries and save them into one big file, the Clipboard will allow you to do that. To save individual results to the Clipboard, simply click the checkbox next to the result you want to save, then click the Add to Clipboard button in the menu at the top of your results page.^[*]

If you find a search strategy that works for you in PubMed, you can save that strategy in the form of a URL, and repeat the same search at any time in the future by visiting that URL. To save a PubMed URL, click the Details link on your results page, then click the URL link on the Details page. The URL of your search will appear in the Location field at the top of the web browser, so that you can bookmark it.

The "bookmarkable" URL for a PubMed search should look something like this:

http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?cmd=PureSearch&db= 
PubMed&details_term=%28%28%28%28%28%28%22proteins%22%5BMeSH%20Terms 
%5D%20OR%20protein%5BText%20Word%5D%29%20AND%20%28%22electrostatics 
%22%5BMeSH%20Terms%5D%20OR%20electrostatics%5BText%20Word%5D%29%29 
%20AND%20hasabstract%5Btext%5D%29%20AND%20Review%5Bptyp%5D%29%20AND 
%20English%5BLang%5D%29%20AND%20notpubref%5Bsb%5D%29

Spending a few hours developing some detailed PubMed search strategies that work for you, and saving them, can save you a lot of work in the future.

The representation and distribution of biological data is still an open problem in bioinformatics. The nucleotide sequences of DNA and RNA and the amino acid sequences of proteins reduce neatly to character strings in which a single letter represents a single nucleotide or amino acid. The remaining challenges in representing sequence data are verification of the correctness of the data, thorough annotation of data, and handling of data that comes in ever-larger chunks, such as the sequences of chromosomes and whole genomes.

The standard reduced representation of the 3D structure of biomolecule consists of the Cartesian coordinates of the atoms in the molecule. This aspect of representing the molecule is straightforward. On the other hand, there are a host of complex issues for structure databases that are not completely resolved. Annotation is still an issue for structural data, although the biology community has attempted to form a consensus as to what annotation of a structure is currently required.

In the last 15 years, different researchers have developed their own styles and formats for reporting biological data. Biological sequence and structure databases have developed in parallel in the United States and in Europe. The use of proprietary software for data analysis has contributed a number of proprietary data formats to the mix. While there are many specialized databases, we focus here on the fields in which an effort is being made to maintain a comprehensive database of an entire class of data.

Though DNA sequence, protein sequence, and protein structure are in some sense just different ways of representing the same gene product, these datatypes currently are maintained as separate database projects and in unconnected data formats. This is mainly because sequence and structure determination methods have separate histories of development.

The first public molecular biology database, established nearly 10 years before the public DNA sequence databases, was the Protein Data Bank (PDB), the central repository for x-ray crystal structures of protein molecules.

While the first complete protein structure was published in the 1950s, there were not a significant number of protein structures available until the late 1970s. Computers had not developed to the point where graphical representation of protein structure coordinate data was possible, at least at useful speeds. However, in 1971, the PDB was established at the Brookhaven National Laboratory, to store protein structure data in a computer-based archive. A data format developed, which owed much of its style to the requirements of early computer technology. Throughout the 1970s and 1980s, the PDB grew. From 15 sets of coordinates in 1973, it grew to 69 entries in 1976. The number of coordinate sets deposited each year remained under 100 until 1988, at which time there were still fewer than 400 PDB entries.

Between 1988 and 1992, the PDB hit the turning point in its exponential growth curve. By January 1994, there were 2,143 entries in the PDB; at the time of this writing, the PDB has nearly reached the 14,000-entry mark. Management of the PDB has been transferred to a consortium of university and public-agency researchers, called the Research Collaboratory for Structural Bioinformatics, and a new format for recording of crystallographic data, the Macromolecular Crystallographic Information File (mmCIF), is being phased in to replace the antiquated PDB format. Journals that publish crystallographic results now require submission to the PDB as a condition of publication, which means that nearly all protein structure data obtained by academic researchers becomes available in the PDB in a fairly timely fashion.

A common issue for data-driven studies of protein structure is the redundancy and lack of comprehensiveness of the PDB. There are many proteins for which numerous crystal structures have been submitted to the database. Selecting subsets of the PDB data with which to work is therefore an important step in any statistical study of protein structure. As of December 1998, only about 2,800 of the protein chains in the PDB were sufficiently different from each other (having less than 95% of their sequence in common) to be considered unique. Many statistical studies of protein structure are based on sets of protein chains that have no more than 25% of their sequence in common; if this criterion is used, there are still only around 1,000 unique protein folds represented in the PDB. As the amount of biological sequence data available has grown, the PDB now lags far behind the gene-sequence databases.

Sequence databases generally specialize in one type of sequence data: DNA, RNA, or protein. There are major sequence data collections and deposition sites in Europe, Japan, and the United States, and there are independent groups that mirror all the data collected in the major public databases, often offering some software that adds value to the data.

In 1970, Ray Wu sequenced the first segment of DNA; twelve bases that occurred as a single strand at the end of a circular DNA that was opened using an enzyme. However, DNA sequencing proved much more difficult than protein sequencing, because there is no chemical process that selectively cleaves the first nucleotide from a nucleic acid chain. When Robert Holley reported the sequencing of a 76-nucleotide RNA molecule from yeast, it was after seven years of labor. After Holley's sequence was published, other groups refined the protocols for sequencing, even successfully sequencing an 3,200-base bacteriophage genome. Real progress with DNA sequencing came after 1975, with the chemical cleavage method designed by Allan Maxam and Walter Gilbert, and with Frederick Sanger's chain-terminator procedure.

The first DNA sequence database, established in 1979, was the Gene Sequence Database (GSDB) at Los Alamos National Lab. While GSDB has since been supplanted by the worldwide collaboration that is the modern GenBank, up-to-date gene sequence information is still available from GSDB through the National Center for Genome Resources.

The European Molecular Biology Laboratory, the DNA Database of Japan, and the National Institutes of Health cooperate to make all publicly available sequence data available through GenBank. NCBI has developed a standard relational database format for sequence data, known as the ASN.1 format. While this format promises to make locating the right sequences of the right kind in GenBank easier, there are still a number of services providing access to nonredundant versions of the database.

The DNA sequence database grew slowly through its first decade. In 1992, GenBank contained only 78,000 DNA sequences—a little over 100 million base pairs of DNA. In 1995, the Human Genome Project, and advances in sequencing technology, kicked GenBank's growth into high gear. GenBank currently doubles in size every 6 to 8 months, and its rate of increase is constantly growing.

In addition to the Human Genome Project, there are now separate genome project databases for a large number of model organisms. The sequence content of the genome project databases is represented in GenBank, but the genome project sites also provide everything from genome maps to supplementary resources for researchers working on that organism. As of October 2000, NCBI's Entrez Genome database contained the partial or complete genomes of over 900 species. Many of these are viruses. The remainder include bacteria; archaea; yeast; commonly studied plant model systems such as A. thaliana, rice, and maize; animal model systems such as C. elegans, fruit flies, mice, rats, and puffer fish; as well as organelle genomes. NCBI's web-based software tools for accessing these databases are constantly evolving and becoming more sophisticated.

The most important biological activities don't happen by the action of single molecules, but as the orchestrated activities of multiple molecules. Since the early 20^th century, biochemists have studied these functional ensembles of enzymes and their substrates. A few research groups have begun work on intelligently organizing and storing these pathways in databases. Two examples of pathway databases are WIT and KEGG. WIT, short for "What Is There?", was developed at Argonne National Labs. It's a database containing reconstructed metabolic pathways for organisms whose genomes have been entirely sequenced. The Kyoto Encyclopedia of Genes and Genomes (KEGG) stores similar data but links in information from sequence, structure, and genetic linkage databases. Both databases are queryable through web interfaces and are curated by a combination of automation and human expertise.

In addition to these whole genome "parts catalogs," other, more specialized databases that focus on specific pathways (such as intercellular signaling or degradation of chemical compounds by microbes) have been developed.

DNA microarrays (or gene chips) are miniaturized laboratories for the study of gene expression. Each chip contains a deliberately designed array of probe molecules that can bind specific pieces of DNA or mRNA. Labeling the DNA or RNA with fluorescent molecules allows the level of expression of any gene in a cellular preparation to be measured quantitatively. Microarrays also have other applications in molecular biology, but their use in studying gene expression has opened up a new way of measuring genome functions.

Since the development of DNA microarray technology in the late 1990s, it has become apparent that the increase in available gene expression data will eventually parallel the growth of the sequence and structure databases, and that this is another datatype for which public access to raw data will be desirable. Raw microarray data has just begun to be made available to the public in selective databases, and talk of establishing a central data repository for such data is underway. However, formats for delivering this kind of data are still not standardized; often, it's made available in large spreadsheets or tab-delimited text. Two of the most comprehensive resources for microarray data are the National Human Genome Research Initiative's Microarray Project site and the Stanford Genome Resources site. Since many of the early microarray expression experiments were performed at Stanford, their genome resources site has links to both raw data and, in some cases, databases that can be queried using gene names or functional descriptions. Recently, the European Bioinformatics Institute has been instrumental in developing a set of standards for deposition of microarray data in databases. Several databases also exist for the deposition of 2D gel electrophoresis results, including SWISS-2DPAGE and HSC-2DPAGE. 2D-PAGE is a technology that allows quantitative study of protein concentrations in the cell, for many proteins simultaneously. The combination of these two techniques is a powerful tool for understanding how genomes work.

Table 6-1 summarizes sources on the Web for some of the most important databases we've discussed in this section.

NCBI, in cooperation with EMBL and other international organizations, provides the most complete collection of DNA sequence data in the world, as well as PubMed, a taxonomy database, and an alternate access point for protein sequence and structure data. This database, known as GenBank, may be accessed at http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?db=Protein.

NCBI maintains sequence data from every organism, every source, every type of DNA—from mRNA to cDNA clones to expressed sequence tags (ESTs) to high-throughput genome sequencing data and information about sequence polymorphisms. Users of the NCBI database need to be aware of the differences between these datatypes so that they can search the data set that's most appropriate for the work they're doing. The main sequence types that you'll encounter in a full GenBank search include:

There are two ways to search GenBank. The first is to use a text-based query to search the annotations associated with each DNA sequence entry in the database. The second, which we'll discuss in Chapter 7, is to use a method called BLAST to compare a query DNA (or protein) sequence to a sequence database.

Here's a sample GenBank record. Each GenBank entry contains annotation—information about the gene's identity, the conditions under which it was characterized, etc.—in addition to sequence.

LOCUS        AB009351 1412 bp   mRNA   PLN       22-JUN-1999 
DEFINITION   Citrus sinensis mRNA for chalcone synthase, complete cds, clone 
             CitCHS2.
ACCESSION    AB009351 VERSION AB009351.1 GI:5106368
KEYWORDS     chalcone synthase. 
SOURCE       Citrus sinensis young seed cDNA to mRNA, clone:CitCHS2. 
  ORGANISM   Citrus sinensis
             Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
             euphyllophytes; Spermatophyta; Magnoliophyta; eudicotyledons; core 
             eudicots; Rosidae; eurosids II; Sapindales; Rutaceae; Citrus. 
REFERENCE    1 (sites) 
  AUTHORS    Moriguchi,T., Kita,M., Tomono,Y., EndoInagaki,T. and Omura,M. 
  TITLE      One type of chalcone synthase gene expressed during embryogenesis 
             regulates the flavonoid accumulation in citrus cell cultures 
  JOURNAL    Plant Cell Physiol. 40 (6), 651-655 (1999) 
  MEDLINE    99412624 
  [...] 
FEATURES     Location/Qualifiers
  Source     1..1412
             /organism="Citrus sinensis"
             /db_xref="taxon:2711"
             /clone="CitCHS2"
             /dev_stage="young seed"
             /note="Valencia orange"
  CDS        30..1205
             /codon_start=1
             /product="chalcone synthase"
             /protein_id="BAA81664.1"
             /db_xref="GI:5106369" 
             /translation="MATVQEIRNAQRADGPATVLAIGTATPAHSVNQADYPDYYFRIT 
             KSEHMTELKEKFKRMCDKSMIKKRYMYLTEEILKENPNMCAYMAPSLDARQDIVVVEV 
             PKLGKEAATKAIKEWGQPKSKITHLIFCTTSGVDMPGADYQLTKLIGLRPSVKRFMMY 
             QQGCFAGGTVLRLAKDLAENNKGARVLVVCSEITAVTFRGPADTHLDSLVGQALFGDG 
             AAAVIVGADPDTSVERPLYQLVSTSQTILPDSDGAIDGHLREVGLTFHLLKDVPGLIS 
             KNIEKSLSEAFAPLGISDWNSIFWIAHPGGPAILDQVESKLGLKGEKLKATRQVLSEY 
             GNMSSACVLFILDEMRKKSVEEAKATTGEGLDWGVLFGFGPGLTVETVVLHSVPIKA" 
  polyA_site 1412
             /note="18 a nucleotides"
BASE COUNT   331 a    358 c    372 g    351 t 
ORIGIN
  1 aaacatattc attaagggtt caacttgaaa tggcaaccgt tcaagagatc agaaacgctc 
  61 agcgtgccga cggcccggcc accgtcctcg ccatcggtac ggccacgcct gcccacagtg 
  121 tcaaccaggc tgattatccc gactattact tcaggatcac aaagagcgag catatgacgg 
  [...]
  1261 cacagttgag ttattggttg atcgtgtgaa ggtttagttt tgtcaattga gtttaaggca 
  1321 tcgtgccttt tctcttatga cgtcaccaaa cctgggcaac gctttgtgtt tatgcataaa 
  1381 ttcttgggaa tttgagaaag tagtaaattt gt 
//

This sample GenBank record shows the types of fields that can be found in a record from the GenBank Nucleotide database. Everything from the identity of the protein product (in this example, chalcone synthase), the sequence of the protein product, and its starting and ending point within the gene, to the authors who submitted the record and the journal references in which the experiment was described, can be found in the record, and therefore can be used to search the database.

The GenBank search interface is nearly identical to the PubMed search interface. The Limits, Preview/Index, History, and Clipboard features for searching work the same way in the Protein, Nucleic Acid, and Genome databases as they do for PubMed, although the specific fields that can be searched and limits that can be set are somewhat different.

Saving search results

Sequences can be downloaded from NCBI in any of three file formats: the simple FASTA format, which is readable by many sequence analysis programs but contains little information other than sequence; the GenBank flat file format, which is a legacy flat file format that was used at GenBank earlier in its history; and the modern ASN.1 (Abstract Syntax Notation One) format. ASN.1 is a generic data specification, designed to promote database interoperability, that is now used for storage and retrieval of all datatypes—sequences, genomes, structure, and literature—at NCBI. The NCBI Toolkit, a code library for developing molecular biology software, relies on the ASN.1 specification. NCBI, and increasingly, other organizations, rely on the NCBI Toolkit for software development. Learning to use the NCBI Toolkit is a programming challenge well beyond the scope of this book, but there is an excellent tutorial on the Web, developed by Christopher Hogue and his research group at the Samuel Lunenfeld Research Institute.

The casual database user or depositor doesn't have to think too much about file formats, except if database files are to be exported and read by another piece of software. NCBI's forms-based interfaces convert user-entered data into the appropriate format for deposition, and the availability of GenBank files in FASTA format means that most sequence analysis software can handle sequence files you download from NCBI without complicated conversions.

When you save results of a GenBank search, you can choose the format in which to save them. Earlier, you saw what the GenBank sequence record looks like. Many of the computer programs we discuss in the following chapters can read GenBank format sequence files, but some can't. A particularly foolproof format in which to save your sequence files if you're going to process them with other software is the FASTA format. FASTA files have a simple format, a single comment line that begins with a > character, followed by single-character DNA sequence on as many lines as needed to hold the sequence, with no breaks. Of course, some information associated with the gene is lost when you save the data in FASTA format, but if the program you want to use can't read that extra data, it won't be useful to you anyway.

Here's a sample of data in FASTA format:

> gene identifier and comments here
MATVQEIRNAQRADGPATVLAIGTATPAHSVNQADYPDYYFRITKSEHMTELKEKFKRMCDKSMIKKRYM 
YLTEEILKENPNMCAYMAPSLDARQDIVVVEVPKLGKEAATKAIKEWGQPKSKITHLIFCTTSGVDMPGA 
DYQLTKLIGLRPSVKRFMMYQQGCFAGGTVLRLAKDLAENNKGARVLVVCSEITAVTFRGPADTHLDSLV 
GQALFGDGAAAVIVGADPDTSVERPLYQLVSTSQTILPDSDGAIDGHLREVGLTFHLLKDVPGLISKNIE 
KSLSEAFAPLGISDWNSIFWIAHPGGPAILDQVESKLGLKGEKLKATRQVLSEYGNMSSACVLFILDEMR 
KKSVEEAKATTGEGLDWGVLFGFGPGLTVETVVLHSVPIKA

To save your files in FASTA format, simply use the pulldown menu at the top of the results page. When you first see it, it will say "Summary," but you can change it to FASTA, ASN.1, and other formats. Once you've chosen your format, you can click the Save button to save all your sequences into one big FASTA-format file. Figure 6-5 shows you how to change the file formats when doing a GenBank search.

Figure 6-5. Changing the file format to write out your GenBank search results

Unlike NCBI, the Protein Data Bank (http://www.rcsb.org/pdb/) is responsible for only one type of molecular data: molecular structures of molecules and, to a growing extent, the underlying raw data sets from which the molecular structures were modeled.

The PDB web site offers three options for searching the database. You can enter a four-letter PDB identifier directly, or search using the SearchLite or SearchFields interfaces. The SearchLite interface is similar to the other query tools we've discussed. You can enter a term or terms into the query box, joined by the operators AND, OR, and BUTNOT.

The SearchFields interface is an innovative design-it-yourself web form system. As you see in Figure 6-6, when you first go to SearchFields, you can scroll down to the bottom of the web form and select which parts of the form you need. If you're only going to be doing a FASTA search to find similar sequences, you don't need a search form that prompts you for keywords to use in searching the Citation Author field. You might want to add a field that lets you search for proteins with a particular ligand or prosthetic group. With the SearchFields interface, you select the form elements you want for your custom PDB search, and click the "New Form" button to generate the new query form.

Figure 6-6. Customizing the PDB's SearchFields form

Whether you use SearchLite or SearchFields, you'll come to the Query Result browser (Figure 6-7), where you can select options for refining your query, downloading your results as structure or sequence files, and even preparing a tabular report of your search results. These options are straightforward to use and well documented on the PDB web site.

Figure 6-7. Options for using query results at the PDB

The Protein Data Bank makes data available in two formats: the legacy PDB flat- file format, and the newer mmCIF data format. We'll discuss the differences between these two file formats in more detail in Chapter 12. At this point, little of the available structure-analysis and protein-modeling software handles the mmCIF format, so you are not likely to need to download protein structure data in mmCIF format unless you are developing new software.^[†] You can choose to download the complete set of results from your search as a tar archive or a zipped file in either PDB or mmCIF format, as well as in sequence-only FASTA format.

Another convenient way to view protein structure data from the PDB web site is to install a browser plug-in such as RasMol or Chime on your computer. We discuss how to do this in Chapter 9. Once the plug-in is installed and properly configured, you can simply click on a link on the protein's View Structure page and the protein structure is automatically displayed using the plug-in, as shown in Figure 6-8.

Figure 6-8. Viewing a PDB file using a browser plug-in

Deposition of sequences to GenBank has been made extremely simple by NCBI. Users depositing only a few sequences can use the web-based BankIt tool, which is a self-explanatory form-based interface accessible from the GenBank main page at NCBI. Users submitting multiple sequences or other complicated submissions can use NCBI's Sequin software, which is available for all major operating systems. Sequin is well documented on the NCBI site. NCBI has recently established two special submission paths: EST sequences should be submitted through dbEST, rather than to GenBank, and genome survey sequences through dbGSS.

Deposition of structures to the PDB are done using the AutoDep input tool (ADIT). AutoDep is a tool that integrates data validation software with the deposition process so that the user can receive feedback on data quality during the deposition process. AutoDep is tied in with the curation tools the PDB uses to prepare structure data for inclusion in the data bank.

One of the first things to consider when evaluating software, data, or information found on the Internet is the source. Who are the authors? If you don't know the authors presenting the information by reputation, is information about their affiliation and credentials available on the web site? Is their expertise related to the topic or purpose of the web site? Do they make it possible for you to contact them and ask questions?

What is the purpose of the organization sponsoring the information? Is it an academic organization? A government agency? A company? For-profit corporations often have different motivations for offering access to their software and data than nonprofits and academic research groups; usually they are offering a stripped-down version of their software or services to get you to buy a more complete package. An individual academic researcher's site doesn't always have the same need to be all-inclusive as a publicly funded database does. There is nothing inherently wrong with these offerings, but you should be aware of whether or not they are comprehensive, whether all their features are available to the casual user, and why.

Even data and software from national or international public sites are not necessarily entirely correct. It has been estimated that any given sequence in GenBank is likely to contain at least one error. While these errors generally don't render the data meaningless, it's always best to be aware of such issues even when using top-of-the-line public resources. Like any other software you find on the Web, software offered by public agencies such as NCBI and the PDB may still be under development. You can use this software, and much of it is of good quality. If you're basing your research on a beta version (a version still under development) of a software package, just read the documentation carefully so that you know what problems still remain to be worked out.

When you send data off to a web server for processing, do you ever wonder exactly what happens to it? You should. It's OK to use your word processor as a black box, but if you're publishing scientific conclusions based on output that you get from a web server or software package, you should definitely know at least the basics of what's under the hood. Anyone can create a web server, based on any software, whether it's good or just goofy. Creating a web server creates an illusion of authority; after all, the authors know how to build a web server that works, so their other software must work too. But that appearance of authority isn't always well founded.

Ideally, you have access to the source code (the human-readable version of a computer program) for whatever the web server is doing, and you can read the source code and know it's doing what you expect. But you might not know how to read source code, and even if you do, you might not be able to get hold of it. Unfortunately, some bioinformatics software authors don't make their source code publicly available, preferring to set up web servers that are easier to use and maintain. This can incidentally have the effect of hiding the underlying method from close scrutiny by users.

If you can't read the source code, what can you read? Most software or web servers made available by academic researchers or government institutions have online help pages and other documentation, including bibliographic information for publications in refereed journals that describe the methods encoded in the software. Read this documentation and understand the method and its results before you use it, just as you would for an experimental method that is new to you.

If the program or server you want to use has no documentation and doesn't allow you to check the source code, you should seriously consider not using that program, unless you have some way to verify its output (for instance, by comparison with the output of a well-documented program). After all, you're drawing conclusions based on your results; do you want to stake your scientific credibility on an unknown quantity?

One of the most frequently linked biology resource sites on the Web is Pedro's Biomolecular Research Tools (http://www.public.iastate.edu/~pedro/research_tools.html). Sites all over the world still have pointers to this collection of links. And yet, if you click to Pedro's site, you'll find that the collection was last updated in 1996. A funny thing about the Web is that out-of-date sites don't just go away. They remain on the server, looking authoritative. Check web sites for dates. If there's no sign of activity in or reference to the current year, be skeptical.

Timeliness isn't always an issue with software. Software written in 1980 can be as useful and functional now as it was then. What you may encounter are problems compiling software that incorporates proprietary technologies that are no longer supported, or code libraries that have since ceased to be developed.