Background Multiple sequence alignment algorithms are very important tools in molecular biology today. Accurate alignment of proteins is central to several areas such as homology modelling, docking studies, understanding evolutionary trends and study of structure-function relationships. In recent times, improvement of existing progressing programs and implementation of new iterative algorithms have made a significant change in this field.
Background
Multiple sequence alignment algorithms are very important tools in molecular biology today. Accurate alignment of proteins is central to several areas such as homology modelling, docking studies, understanding evolutionary trends and study of structure-function relationships. In recent times, improvement of existing progressing programs and implementation of new iterative algorithms have made a significant change in this field.Results We report an alignment algorithm that combines progressive dynamic algorithm, local substructure alignment and iterative refinement to achieve an improved, user-interactive tool. Large-scale benchmarking studies show that this FMALIGN server produces alignments that, aside from preservation of functional and structural conservation, have accuracy comparable to other popular multiple alignment programs.
Results
We report an alignment algorithm that combines progressive dynamic algorithm, local substructure alignment and iterative refinement to achieve an improved, user-interactive tool. Large-scale benchmarking studies show that this FMALIGN server produces alignments that, aside from preservation of functional and structural conservation, have accuracy comparable to other popular multiple alignment programs.Conclusions The FMALIGN server allows the user to fix conserved regions in equivalent position in the alignment thereby reducing the chance of global misalignment to a great extent. FMALIGN is available at
Conclusions
The FMALIGN server allows the user to fix conserved regions in equivalent position in the alignment thereby reducing the chance of global misalignment to a great extent. FMALIGN is available at

Background The advent of large genome projects has led to an explosion of sequence data in public databases. Analysis of protein families, understanding their evolutionary trends and detection of remote homologues are now the primary objectives. Genome annotation and analysis tools like fold prediction, homology modelling, protein-ligand docking and clustering algorithms rely heavily on accurate multiple alignments to provide a genome-wide perspective. The most popular approach for multiple sequence alignment has been the progressive alignment method [ 1 ]. A multiple alignment is built up gradually by aligning the closest sequences first and successively adding in more distant relatives. A number of alignment programs imply this algorithm, for example MULTALIGN [ 2 ], MULTAL [ 3 ] and CLUSTALX [ 4 ]. They employ a global alignment algorithm to construct an alignment over the entire length of the sequences and differ mainly in the procedure employed to determine the order of alignment of the sequences. The most common usage is the sequential branching method to identify two closest sequences first and subsequently align the next closest sequence to those already aligned. MULTALIGN [ 2 ] constructs a guided tree using UPGMA [ 5 ] method. A consensus method is then used to align larger and larger groups of sequences according to the branching order of the tree. CLUSTALX uses the alternative neighbour-joining algorithm [ 6 ] to construct the tree. In contrast to the above global method, PIMA [ 7 ] uses a local dynamic programming algorithm to align only the most conserved motifs. In addition, numerous new alignment algorithms have recently been developed which offer fresh approaches to the multiple alignment problem. A common point of interest has been the application of iterative strategies to refine and improve the initial alignment. A local alignment approach is implemented in the DIALIGN program [ 8 , 9 ] to construct multiple alignments based on segment-to-segment comparison rather than residue-to-residue comparison using an iterative strategy to improve alignment accuracy. Alignment programs like MATCH-BOX [ 10 ] utilize statistical similarity measures to delineate sequentially conserved regions and the final alignments are derived by those of the conserved "box" regions. The regions outside the "boxes" are not aligned. METAMEME [ 11 ] is a motif based search engine that aligns motif regions found in the target sequences. DbClustal [ 12 ] combines the advantages of both local and global alignment algorithm in a traditional tree-based progressive alignment. Starting from ClustalW [ 22 ], which is a global alignment program, local alignment data or anchor points are incorporated in the dataset. The global alignment is then weighted towards, but not constrained, to the locally conserved segments and the alignment is not subject to iterative refinements. MACAW [ 13 ] provides a user interactive interface to select conserved segments from the alignment but these segments are not utilized further to refine the resulting alignment. In this paper, we present an alignment algorithm that combines the properties of both progressive alignment methods and iterative refinement algorithms. This algorithm offers the user the selective advantage of guiding the course of alignment by simultaneous inputs of multiple conserved motif regions that in turn guarantees retention of structure/function in the final alignment. FMALIGN is an alignment server that provides the user to obtain a control over the alignment by providing important conserved regions as input to the alignment program to achieve a more structurally relevant and functionally useful alignment of protein sequences. It employs the sequential branching method to identify the closest pair of sequences and subsequently includes the next closest sequences to generate a guided tree using UPGMA [ 5 ], which in turn dictates the sequential order of the alignment. FMALIGN also considers the local similarity of the sequences in the conserved motif regions; as the name implies, it allows local conserved regions of the sequences to be fixed and aligns the rest based on normal progressive alignment. The chances of global misalignment are thereby reduced and the possibility of obtaining overall better alignment is increased. The FMALIGN server also offers an iterative refinement option where a routine (FINDMOTIF) identifies more conserved regions in the derived alignment and allows the user to provide fresh 'equivalences' to obtain an overall better alignment. Benchmarking studies on difficult alignments, examined in BAliBASE [ 14 ], show promising prospect for the FMALIGN server to be an useful alignment algorithm. Implementation: methodology and description of FMALIGN server The algorithm of FMALIGN (Fixed Motif ALIGNment) program combines three main criteria: (a) Progressive global alignment method, (b) substructure (local) conserved region fixation and (c) iterative refinement of alignment with identification of more conserved regions. The procedure involves three steps: (i) identification and fixing of the specified sequential conserved regions. This alignment method requires specific regions of the sequences to be aligned as anchor and these anchors are generally meant for sequentially conserved parts which do not undergo many changes. (ii) derivation of the progressive multiple sequence alignment guided by the tree. During this step, excluding the fixed anchored regions, the rest of the sequences are divided into several sub-segments that are aligned employing a dynamic alignment algorithm in a sequential order from N to C-terminus. The phylogenetic guide tree has been derived using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) [ 5 ] which is the simplest method of tree construction. The UPGMA method was adopted since the whole length of the protein is divided into several segments considering them as ultrametric After all the sub-segment alignments are performed, the aligned parts as well as the selected, fixed motif regions are combined to produce the full-length alignment for a group of proteins under the hierarchical cluster. This process continues until all the protein sequences are aligned multiply. (iii) The iterative refinement of alignment subsequent to the identification of more motifs. In this step, more conserved regions are identified by observing the amino acid exchanges in the resultant alignment derived from the previous iteration. These conserved regions are then used as anchors together with previously identified motifs and the whole process is repeated until an optimal alignment having maximum number of conserved regions and alignment score is obtained. The algorithm thus combines the progressive dynamic algorithm for global multiple alignment and selected conserved regions or local alignments. Once the primary alignment is derived, the second step can be repeated by including more conserved regions from this alignment as motifs to derive a better alignment. Figure  1  shows a cartoon representation of the methods in a flowchart diagram. FMALIGN server can accept amino acid patterns for multiple motifs provided by the user. It also provides option to the user to search motifs within their sequences using FINDMOTIF routine or to obtain them for the alignment by providing a link to SMoS [ 15 ], a structural motif database for aligned protein superfamilies. The FINDMOTIF routine in the server provides sequential conserved regions for a set of proteins on the basis of sequence similarity and a 20 × 20 substitution matrix by consulting large number of structure-based sequence alignments of homologous families [ 16 ]. Amino acid exchange scores at every alignment position are assigned the same as the element in this matrix for all possible pairs of proteins and averaged over the number of pairs. Contiguous alignment positions with an average amino acid exchange score over 50 (for homologues) or 40 (for superfamilies) are recognised as motifs. FMALIGN also offers an option for the user to refine the derived alignment by generating more sequential conserved regions through FINDMOTIF option. The inter-motif regions are aligned by normal progressive alignment using standard substitution matrices like BLOSUM62. The gap penalties used in this version of FMALIGN are all maintained according to standard multiple alignment parameters. Alignment scores To assess the performance of FMALIGN in comparison to other programs, Sum-of-Pair-score (SPS) and Column-Score (CS) alignment scoring scheme [ 14 ] are applied on FMALIGN derived alignments to assess the quality of alignments compared to BAliBASE reference alignments. SPS and CS are calculated such that the score increases with the number of sequences aligned accurately and is used to determine the extent to which the programs succeed in aligning some, if not all, of the sequences in an alignment correctly [ 14 ]. The scores used to measure the performance of the various alignment programs may not be appropriate for all the datasets. Therefore, for each reference test the most suitable scoring function have been selected according to the nature of the benchmarking.
Background
The advent of large genome projects has led to an explosion of sequence data in public databases. Analysis of protein families, understanding their evolutionary trends and detection of remote homologues are now the primary objectives. Genome annotation and analysis tools like fold prediction, homology modelling, protein-ligand docking and clustering algorithms rely heavily on accurate multiple alignments to provide a genome-wide perspective.The most popular approach for multiple sequence alignment has been the progressive alignment method [ 1 ]. A multiple alignment is built up gradually by aligning the closest sequences first and successively adding in more distant relatives. A number of alignment programs imply this algorithm, for example MULTALIGN [ 2 ], MULTAL [ 3 ] and CLUSTALX [ 4 ]. They employ a global alignment algorithm to construct an alignment over the entire length of the sequences and differ mainly in the procedure employed to determine the order of alignment of the sequences. The most common usage is the sequential branching method to identify two closest sequences first and subsequently align the next closest sequence to those already aligned. MULTALIGN [ 2 ] constructs a guided tree using UPGMA [ 5 ] method. A consensus method is then used to align larger and larger groups of sequences according to the branching order of the tree. CLUSTALX uses the alternative neighbour-joining algorithm [ 6 ] to construct the tree. In contrast to the above global method, PIMA [ 7 ] uses a local dynamic programming algorithm to align only the most conserved motifs. In addition, numerous new alignment algorithms have recently been developed which offer fresh approaches to the multiple alignment problem. A common point of interest has been the application of iterative strategies to refine and improve the initial alignment. A local alignment approach is implemented in the DIALIGN program [ 8 , 9 ] to construct multiple alignments based on segment-to-segment comparison rather than residue-to-residue comparison using an iterative strategy to improve alignment accuracy. Alignment programs like MATCH-BOX [ 10 ] utilize statistical similarity measures to delineate sequentially conserved regions and the final alignments are derived by those of the conserved "box" regions. The regions outside the "boxes" are not aligned. METAMEME [ 11 ] is a motif based search engine that aligns motif regions found in the target sequences. DbClustal [ 12 ] combines the advantages of both local and global alignment algorithm in a traditional tree-based progressive alignment. Starting from ClustalW [ 22 ], which is a global alignment program, local alignment data or anchor points are incorporated in the dataset. The global alignment is then weighted towards, but not constrained, to the locally conserved segments and the alignment is not subject to iterative refinements. MACAW [ 13 ] provides a user interactive interface to select conserved segments from the alignment but these segments are not utilized further to refine the resulting alignment.In this paper, we present an alignment algorithm that combines the properties of both progressive alignment methods and iterative refinement algorithms. This algorithm offers the user the selective advantage of guiding the course of alignment by simultaneous inputs of multiple conserved motif regions that in turn guarantees retention of structure/function in the final alignment. FMALIGN is an alignment server that provides the user to obtain a control over the alignment by providing important conserved regions as input to the alignment program to achieve a more structurally relevant and functionally useful alignment of protein sequences. It employs the sequential branching method to identify the closest pair of sequences and subsequently includes the next closest sequences to generate a guided tree using UPGMA [ 5 ], which in turn dictates the sequential order of the alignment. FMALIGN also considers the local similarity of the sequences in the conserved motif regions; as the name implies, it allows local conserved regions of the sequences to be fixed and aligns the rest based on normal progressive alignment. The chances of global misalignment are thereby reduced and the possibility of obtaining overall better alignment is increased. The FMALIGN server also offers an iterative refinement option where a routine (FINDMOTIF) identifies more conserved regions in the derived alignment and allows the user to provide fresh 'equivalences' to obtain an overall better alignment. Benchmarking studies on difficult alignments, examined in BAliBASE [ 14 ], show promising prospect for the FMALIGN server to be an useful alignment algorithm.Implementation: methodology and description of FMALIGN server The algorithm of FMALIGN (Fixed Motif ALIGNment) program combines three main criteria: (a) Progressive global alignment method, (b) substructure (local) conserved region fixation and (c) iterative refinement of alignment with identification of more conserved regions. The procedure involves three steps: (i) identification and fixing of the specified sequential conserved regions. This alignment method requires specific regions of the sequences to be aligned as anchor and these anchors are generally meant for sequentially conserved parts which do not undergo many changes. (ii) derivation of the progressive multiple sequence alignment guided by the tree. During this step, excluding the fixed anchored regions, the rest of the sequences are divided into several sub-segments that are aligned employing a dynamic alignment algorithm in a sequential order from N to C-terminus. The phylogenetic guide tree has been derived using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) [ 5 ] which is the simplest method of tree construction. The UPGMA method was adopted since the whole length of the protein is divided into several segments considering them as ultrametric After all the sub-segment alignments are performed, the aligned parts as well as the selected, fixed motif regions are combined to produce the full-length alignment for a group of proteins under the hierarchical cluster. This process continues until all the protein sequences are aligned multiply. (iii) The iterative refinement of alignment subsequent to the identification of more motifs. In this step, more conserved regions are identified by observing the amino acid exchanges in the resultant alignment derived from the previous iteration. These conserved regions are then used as anchors together with previously identified motifs and the whole process is repeated until an optimal alignment having maximum number of conserved regions and alignment score is obtained. The algorithm thus combines the progressive dynamic algorithm for global multiple alignment and selected conserved regions or local alignments. Once the primary alignment is derived, the second step can be repeated by including more conserved regions from this alignment as motifs to derive a better alignment. Figure  1  shows a cartoon representation of the methods in a flowchart diagram. FMALIGN server can accept amino acid patterns for multiple motifs provided by the user. It also provides option to the user to search motifs within their sequences using FINDMOTIF routine or to obtain them for the alignment by providing a link to SMoS [ 15 ], a structural motif database for aligned protein superfamilies. The FINDMOTIF routine in the server provides sequential conserved regions for a set of proteins on the basis of sequence similarity and a 20 × 20 substitution matrix by consulting large number of structure-based sequence alignments of homologous families [ 16 ]. Amino acid exchange scores at every alignment position are assigned the same as the element in this matrix for all possible pairs of proteins and averaged over the number of pairs. Contiguous alignment positions with an average amino acid exchange score over 50 (for homologues) or 40 (for superfamilies) are recognised as motifs. FMALIGN also offers an option for the user to refine the derived alignment by generating more sequential conserved regions through FINDMOTIF option. The inter-motif regions are aligned by normal progressive alignment using standard substitution matrices like BLOSUM62. The gap penalties used in this version of FMALIGN are all maintained according to standard multiple alignment parameters. Alignment scores To assess the performance of FMALIGN in comparison to other programs, Sum-of-Pair-score (SPS) and Column-Score (CS) alignment scoring scheme [ 14 ] are applied on FMALIGN derived alignments to assess the quality of alignments compared to BAliBASE reference alignments. SPS and CS are calculated such that the score increases with the number of sequences aligned accurately and is used to determine the extent to which the programs succeed in aligning some, if not all, of the sequences in an alignment correctly [ 14 ]. The scores used to measure the performance of the various alignment programs may not be appropriate for all the datasets. Therefore, for each reference test the most suitable scoring function have been selected according to the nature of the benchmarking.
Implementation: methodology and description of FMALIGN server
The algorithm of FMALIGN (Fixed Motif ALIGNment) program combines three main criteria: (a) Progressive global alignment method, (b) substructure (local) conserved region fixation and (c) iterative refinement of alignment with identification of more conserved regions. The procedure involves three steps: (i) identification and fixing of the specified sequential conserved regions. This alignment method requires specific regions of the sequences to be aligned as anchor and these anchors are generally meant for sequentially conserved parts which do not undergo many changes. (ii) derivation of the progressive multiple sequence alignment guided by the tree. During this step, excluding the fixed anchored regions, the rest of the sequences are divided into several sub-segments that are aligned employing a dynamic alignment algorithm in a sequential order from N to C-terminus. The phylogenetic guide tree has been derived using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) [ 5 ] which is the simplest method of tree construction. The UPGMA method was adopted since the whole length of the protein is divided into several segments considering them as ultrametric After all the sub-segment alignments are performed, the aligned parts as well as the selected, fixed motif regions are combined to produce the full-length alignment for a group of proteins under the hierarchical cluster. This process continues until all the protein sequences are aligned multiply. (iii) The iterative refinement of alignment subsequent to the identification of more motifs. In this step, more conserved regions are identified by observing the amino acid exchanges in the resultant alignment derived from the previous iteration. These conserved regions are then used as anchors together with previously identified motifs and the whole process is repeated until an optimal alignment having maximum number of conserved regions and alignment score is obtained. The algorithm thus combines the progressive dynamic algorithm for global multiple alignment and selected conserved regions or local alignments. Once the primary alignment is derived, the second step can be repeated by including more conserved regions from this alignment as motifs to derive a better alignment. Figure  1  shows a cartoon representation of the methods in a flowchart diagram.FMALIGN server can accept amino acid patterns for multiple motifs provided by the user. It also provides option to the user to search motifs within their sequences using FINDMOTIF routine or to obtain them for the alignment by providing a link to SMoS [ 15 ], a structural motif database for aligned protein superfamilies. The FINDMOTIF routine in the server provides sequential conserved regions for a set of proteins on the basis of sequence similarity and a 20 × 20 substitution matrix by consulting large number of structure-based sequence alignments of homologous families [ 16 ]. Amino acid exchange scores at every alignment position are assigned the same as the element in this matrix for all possible pairs of proteins and averaged over the number of pairs. Contiguous alignment positions with an average amino acid exchange score over 50 (for homologues) or 40 (for superfamilies) are recognised as motifs. FMALIGN also offers an option for the user to refine the derived alignment by generating more sequential conserved regions through FINDMOTIF option. The inter-motif regions are aligned by normal progressive alignment using standard substitution matrices like BLOSUM62. The gap penalties used in this version of FMALIGN are all maintained according to standard multiple alignment parameters.Alignment scores To assess the performance of FMALIGN in comparison to other programs, Sum-of-Pair-score (SPS) and Column-Score (CS) alignment scoring scheme [ 14 ] are applied on FMALIGN derived alignments to assess the quality of alignments compared to BAliBASE reference alignments. SPS and CS are calculated such that the score increases with the number of sequences aligned accurately and is used to determine the extent to which the programs succeed in aligning some, if not all, of the sequences in an alignment correctly [ 14 ]. The scores used to measure the performance of the various alignment programs may not be appropriate for all the datasets. Therefore, for each reference test the most suitable scoring function have been selected according to the nature of the benchmarking.
Alignment scores
To assess the performance of FMALIGN in comparison to other programs, Sum-of-Pair-score (SPS) and Column-Score (CS) alignment scoring scheme [ 14 ] are applied on FMALIGN derived alignments to assess the quality of alignments compared to BAliBASE reference alignments. SPS and CS are calculated such that the score increases with the number of sequences aligned accurately and is used to determine the extent to which the programs succeed in aligning some, if not all, of the sequences in an alignment correctly [ 14 ]. The scores used to measure the performance of the various alignment programs may not be appropriate for all the datasets. Therefore, for each reference test the most suitable scoring function have been selected according to the nature of the benchmarking.Results and discussion Benchmarking at family level FMALIGN derived alignments retain high degree of conservation in secondary structures. Six members from the globin family were selected comprising a wide range of sequence identity between them (7% to 61%). Conserved regions for six aligned globin sequences were identified by the FINDMOTIF routine starting from CLUSTALX [ 4 ] alignment with default parameters and aligned by the FMALIGN server. The resultant alignment shows high degree of secondary structural conservation despite the difficulty of aligning a set of sequences having very wide range of sequence identity (Figure  2 ). In order to evaluate the FMALIGN server, the results have been compared with 10 other alignment programs and objective criteria were employed to assess the quality of an alignment. We selected the BAliBASE benchmark alignment database [ 14 ] to compare the performance of the FMALIGN alignment server. The BAliBASE benchmark alignment database contains 142 reference alignments, divided into five reference sets each containing at least 12 representative alignments. Performance of FMALIGN is checked on all five reference sets provided in BAliBASE datasets. Reference 1 alignments consist of a small number of equidistant sequences of similar length, i.e. the percent residue identity (% ID) between any two sequences is within a specified range and no large extensions or insertions have been introduced. Reference 2 contains alignments of a family of closely related sequences with >25% ID, plus up to three 'orphan' sequences (distant members of the family with <20% ID, sharing a common fold). It is designed to evaluate program accuracy according to two criteria: (i) the stability of the family alignment when orphans are introduced into the sequence set and (ii)the quality of the alignment of the orphan sequences. Reference 3 checks the ability of the programs to correctly align equidistant divergent families into a single alignment. The reference alignments consist of up to four families, with <25% ID between any two sequences from different families. Reference 4 and 5 contain alignments of upto 20 sequences including N/C-terminal extensions (upto 400 residues) and insertions (upto 100 residues), respectively. Reference 1: a small number of approximately equidistant sequences This dataset is designed to study the effect of sequence length and percentage identity on the performance of the alignment program and provides a basis for the remaining tests. The overall performance of FMALIGN server for this dataset is comparable to the two best performing alignment programs, like PRRP [ 17 ] and CLUSTALX [ 4 ], in all three categories (VI, V2, and V3) based on sequence identity and alignment lengths (short, medium and long) as shown in Figure  3 ,  4 ,  5 . Reference 2: a related family with divergent, orphan sequences It is possible to assess the performance of the methods to align divergent 'orphan' sequences (10–20% ID with the family and between orphans) with a family of highly related (>25% ID) sequences using this data set. It is also interesting to observe the disruption of the family alignment due to the introduction of orphans. Figure  6  shows SPS for the alignment of a single orphan against a closely related family. The global alignment programs again perform better than the local ones in this test. However, CLUSTALX and SAGA [ 18 ] now rank above PRRP. The performance of FMALIGN server is significantly better than other programs for all the three length categories. Reference 3: families of related sequences This allows the assessment of the programs to correctly align approximately equidistant divergent families (<20% ID) composed of highly related sequences (>25% ID) into a single multiple alignment. Figure  7  shows the scores for the programs in the order. The iterative strategies of PRRP [ 17 ] and SAGA [ 18 ] perform better in this test than the traditional progressive alignment methods. However, FMALIGN performs better than the other progressive methods, with the global methods generally ranking higher than the local methods. Reference 4: N/C-terminal extensions This dataset includes large N/C-terminal extensions to investigate whether the programs are capable of aligning the core blocks flanking the extensions. No large internal insertions are introduced at this stage. Mostly local alignment strategies out-perform the global methods. PILEUP (Wisconsin Package v.8; Genetics Computer Group, Madison, WI) is the only program based on a global alignment method which does reasonably well compared to other global methods. Performance of FMALIGN is comparable to the best three methods (DIALIGN [ 8 ], SB_PIMA [ 19 ] and PILEUP) as shown in Figure  8 . Reference 5: internal insertions In contrast to reference 4, in this dataset the insertions are internal to the homologous domains and not at the N/C-terminus as overhangs. FMALIGN also performs well in this category and results are comparable to the other better performing global alignment methods like PRRP and CLUSTALX as shown in Figure  8 . Benchmarking at superfamily level Utilization of structural motifs to improve alignment accuracy The performance of FMALIGN has been tested on a dataset of representative superfamilies of proteins belonging to different structural classes in PASS2 [ 20 ] and SMoS [ 15 ] databases. The structural motifs from the SMoS database have been utilized as conserved regions for the member of the superfamily (average sequence identity less than 30%). All the proteins of a superfamily have been aligned. Each alignment is assigned a quality score by averaging the amino acid exchange score of each column over the length of the alignment (see method for details). The alignments derived from FMALIGN have been compared against the CLUSTALX-derived sequence alignment as well as the sequence-structure alignment derived from COMPARER [ 21 ]. Figure  9  shows an equivalent or better accuracy of the FMALIGN server compared to CLUSTALX and COMPARER-derived alignment. This indicates that FMALIGN is particularly efficient for specific sets of sequences for which the degree of conservation is known. The initial results also indicate that FMALIGN can perform very well and provide an alignment which is very similar or better than a structurally derived alignment. Utilization of sequence motifs to improve alignment accuracy Representative superfamily alignments belonging to different structural classes from the PASS2 [ 20 ] superfamily alignment database are taken for a benchmarking test (Table  1 ). These superfamily alignments are of different lengths (short, medium, and long) and possess an average sequence identity that ranges from 10% to 26%. Sequential conserved regions or motifs are identified for each superfamily alignment and utilized in the FMALIGN server to realign the superfamily members. Similarly, the superfamily members are also aligned by the two best performing multiple alignment methods, CLUSTALW [ 22 ] and T-Coffee [ 23 ]. These alignments are then compared against the structure-based COMPARER alignments provided by PASS2 database using the same alignment scoring scheme (Sum-of-Pair score) of the BAliBASE benchmarking database. FMALIGN-derived alignments performed better for most of the cases compared to the other two methods as shown in Figure  10 .
Results and discussion
Benchmarking at family level FMALIGN derived alignments retain high degree of conservation in secondary structures. Six members from the globin family were selected comprising a wide range of sequence identity between them (7% to 61%). Conserved regions for six aligned globin sequences were identified by the FINDMOTIF routine starting from CLUSTALX [ 4 ] alignment with default parameters and aligned by the FMALIGN server. The resultant alignment shows high degree of secondary structural conservation despite the difficulty of aligning a set of sequences having very wide range of sequence identity (Figure  2 ). In order to evaluate the FMALIGN server, the results have been compared with 10 other alignment programs and objective criteria were employed to assess the quality of an alignment. We selected the BAliBASE benchmark alignment database [ 14 ] to compare the performance of the FMALIGN alignment server. The BAliBASE benchmark alignment database contains 142 reference alignments, divided into five reference sets each containing at least 12 representative alignments. Performance of FMALIGN is checked on all five reference sets provided in BAliBASE datasets. Reference 1 alignments consist of a small number of equidistant sequences of similar length, i.e. the percent residue identity (% ID) between any two sequences is within a specified range and no large extensions or insertions have been introduced. Reference 2 contains alignments of a family of closely related sequences with >25% ID, plus up to three 'orphan' sequences (distant members of the family with <20% ID, sharing a common fold). It is designed to evaluate program accuracy according to two criteria: (i) the stability of the family alignment when orphans are introduced into the sequence set and (ii)the quality of the alignment of the orphan sequences. Reference 3 checks the ability of the programs to correctly align equidistant divergent families into a single alignment. The reference alignments consist of up to four families, with <25% ID between any two sequences from different families. Reference 4 and 5 contain alignments of upto 20 sequences including N/C-terminal extensions (upto 400 residues) and insertions (upto 100 residues), respectively. Reference 1: a small number of approximately equidistant sequences This dataset is designed to study the effect of sequence length and percentage identity on the performance of the alignment program and provides a basis for the remaining tests. The overall performance of FMALIGN server for this dataset is comparable to the two best performing alignment programs, like PRRP [ 17 ] and CLUSTALX [ 4 ], in all three categories (VI, V2, and V3) based on sequence identity and alignment lengths (short, medium and long) as shown in Figure  3 ,  4 ,  5 . Reference 2: a related family with divergent, orphan sequences It is possible to assess the performance of the methods to align divergent 'orphan' sequences (10–20% ID with the family and between orphans) with a family of highly related (>25% ID) sequences using this data set. It is also interesting to observe the disruption of the family alignment due to the introduction of orphans. Figure  6  shows SPS for the alignment of a single orphan against a closely related family. The global alignment programs again perform better than the local ones in this test. However, CLUSTALX and SAGA [ 18 ] now rank above PRRP. The performance of FMALIGN server is significantly better than other programs for all the three length categories. Reference 3: families of related sequences This allows the assessment of the programs to correctly align approximately equidistant divergent families (<20% ID) composed of highly related sequences (>25% ID) into a single multiple alignment. Figure  7  shows the scores for the programs in the order. The iterative strategies of PRRP [ 17 ] and SAGA [ 18 ] perform better in this test than the traditional progressive alignment methods. However, FMALIGN performs better than the other progressive methods, with the global methods generally ranking higher than the local methods. Reference 4: N/C-terminal extensions This dataset includes large N/C-terminal extensions to investigate whether the programs are capable of aligning the core blocks flanking the extensions. No large internal insertions are introduced at this stage. Mostly local alignment strategies out-perform the global methods. PILEUP (Wisconsin Package v.8; Genetics Computer Group, Madison, WI) is the only program based on a global alignment method which does reasonably well compared to other global methods. Performance of FMALIGN is comparable to the best three methods (DIALIGN [ 8 ], SB_PIMA [ 19 ] and PILEUP) as shown in Figure  8 . Reference 5: internal insertions In contrast to reference 4, in this dataset the insertions are internal to the homologous domains and not at the N/C-terminus as overhangs. FMALIGN also performs well in this category and results are comparable to the other better performing global alignment methods like PRRP and CLUSTALX as shown in Figure  8 .
Benchmarking at family level
FMALIGN derived alignments retain high degree of conservation in secondary structures. Six members from the globin family were selected comprising a wide range of sequence identity between them (7% to 61%). Conserved regions for six aligned globin sequences were identified by the FINDMOTIF routine starting from CLUSTALX [ 4 ] alignment with default parameters and aligned by the FMALIGN server. The resultant alignment shows high degree of secondary structural conservation despite the difficulty of aligning a set of sequences having very wide range of sequence identity (Figure  2 ).In order to evaluate the FMALIGN server, the results have been compared with 10 other alignment programs and objective criteria were employed to assess the quality of an alignment. We selected the BAliBASE benchmark alignment database [ 14 ] to compare the performance of the FMALIGN alignment server. The BAliBASE benchmark alignment database contains 142 reference alignments, divided into five reference sets each containing at least 12 representative alignments. Performance of FMALIGN is checked on all five reference sets provided in BAliBASE datasets.Reference 1 alignments consist of a small number of equidistant sequences of similar length, i.e. the percent residue identity (% ID) between any two sequences is within a specified range and no large extensions or insertions have been introduced.Reference 2 contains alignments of a family of closely related sequences with >25% ID, plus up to three 'orphan' sequences (distant members of the family with <20% ID, sharing a common fold). It is designed to evaluate program accuracy according to two criteria: (i) the stability of the family alignment when orphans are introduced into the sequence set and (ii)the quality of the alignment of the orphan sequences.Reference 3 checks the ability of the programs to correctly align equidistant divergent families into a single alignment. The reference alignments consist of up to four families, with <25% ID between any two sequences from different families.Reference 4 and 5 contain alignments of upto 20 sequences including N/C-terminal extensions (upto 400 residues) and insertions (upto 100 residues), respectively.Reference 1: a small number of approximately equidistant sequences This dataset is designed to study the effect of sequence length and percentage identity on the performance of the alignment program and provides a basis for the remaining tests. The overall performance of FMALIGN server for this dataset is comparable to the two best performing alignment programs, like PRRP [ 17 ] and CLUSTALX [ 4 ], in all three categories (VI, V2, and V3) based on sequence identity and alignment lengths (short, medium and long) as shown in Figure  3 ,  4 ,  5 .
Reference 1: a small number of approximately equidistant sequences
This dataset is designed to study the effect of sequence length and percentage identity on the performance of the alignment program and provides a basis for the remaining tests. The overall performance of FMALIGN server for this dataset is comparable to the two best performing alignment programs, like PRRP [ 17 ] and CLUSTALX [ 4 ], in all three categories (VI, V2, and V3) based on sequence identity and alignment lengths (short, medium and long) as shown in Figure  3 ,  4 ,  5 .Reference 2: a related family with divergent, orphan sequences It is possible to assess the performance of the methods to align divergent 'orphan' sequences (10–20% ID with the family and between orphans) with a family of highly related (>25% ID) sequences using this data set. It is also interesting to observe the disruption of the family alignment due to the introduction of orphans. Figure  6  shows SPS for the alignment of a single orphan against a closely related family. The global alignment programs again perform better than the local ones in this test. However, CLUSTALX and SAGA [ 18 ] now rank above PRRP. The performance of FMALIGN server is significantly better than other programs for all the three length categories.
Reference 2: a related family with divergent, orphan sequences
It is possible to assess the performance of the methods to align divergent 'orphan' sequences (10–20% ID with the family and between orphans) with a family of highly related (>25% ID) sequences using this data set. It is also interesting to observe the disruption of the family alignment due to the introduction of orphans. Figure  6  shows SPS for the alignment of a single orphan against a closely related family. The global alignment programs again perform better than the local ones in this test. However, CLUSTALX and SAGA [ 18 ] now rank above PRRP. The performance of FMALIGN server is significantly better than other programs for all the three length categories.Reference 3: families of related sequences This allows the assessment of the programs to correctly align approximately equidistant divergent families (<20% ID) composed of highly related sequences (>25% ID) into a single multiple alignment. Figure  7  shows the scores for the programs in the order. The iterative strategies of PRRP [ 17 ] and SAGA [ 18 ] perform better in this test than the traditional progressive alignment methods. However, FMALIGN performs better than the other progressive methods, with the global methods generally ranking higher than the local methods.
Reference 3: families of related sequences
This allows the assessment of the programs to correctly align approximately equidistant divergent families (<20% ID) composed of highly related sequences (>25% ID) into a single multiple alignment. Figure  7  shows the scores for the programs in the order. The iterative strategies of PRRP [ 17 ] and SAGA [ 18 ] perform better in this test than the traditional progressive alignment methods. However, FMALIGN performs better than the other progressive methods, with the global methods generally ranking higher than the local methods.Reference 4: N/C-terminal extensions This dataset includes large N/C-terminal extensions to investigate whether the programs are capable of aligning the core blocks flanking the extensions. No large internal insertions are introduced at this stage. Mostly local alignment strategies out-perform the global methods. PILEUP (Wisconsin Package v.8; Genetics Computer Group, Madison, WI) is the only program based on a global alignment method which does reasonably well compared to other global methods. Performance of FMALIGN is comparable to the best three methods (DIALIGN [ 8 ], SB_PIMA [ 19 ] and PILEUP) as shown in Figure  8 .
Reference 4: N/C-terminal extensions
This dataset includes large N/C-terminal extensions to investigate whether the programs are capable of aligning the core blocks flanking the extensions. No large internal insertions are introduced at this stage. Mostly local alignment strategies out-perform the global methods. PILEUP (Wisconsin Package v.8; Genetics Computer Group, Madison, WI) is the only program based on a global alignment method which does reasonably well compared to other global methods. Performance of FMALIGN is comparable to the best three methods (DIALIGN [ 8 ], SB_PIMA [ 19 ] and PILEUP) as shown in Figure  8 .Reference 5: internal insertions In contrast to reference 4, in this dataset the insertions are internal to the homologous domains and not at the N/C-terminus as overhangs. FMALIGN also performs well in this category and results are comparable to the other better performing global alignment methods like PRRP and CLUSTALX as shown in Figure  8 .
Reference 5: internal insertions
In contrast to reference 4, in this dataset the insertions are internal to the homologous domains and not at the N/C-terminus as overhangs. FMALIGN also performs well in this category and results are comparable to the other better performing global alignment methods like PRRP and CLUSTALX as shown in Figure  8 .Benchmarking at superfamily level Utilization of structural motifs to improve alignment accuracy The performance of FMALIGN has been tested on a dataset of representative superfamilies of proteins belonging to different structural classes in PASS2 [ 20 ] and SMoS [ 15 ] databases. The structural motifs from the SMoS database have been utilized as conserved regions for the member of the superfamily (average sequence identity less than 30%). All the proteins of a superfamily have been aligned. Each alignment is assigned a quality score by averaging the amino acid exchange score of each column over the length of the alignment (see method for details). The alignments derived from FMALIGN have been compared against the CLUSTALX-derived sequence alignment as well as the sequence-structure alignment derived from COMPARER [ 21 ]. Figure  9  shows an equivalent or better accuracy of the FMALIGN server compared to CLUSTALX and COMPARER-derived alignment. This indicates that FMALIGN is particularly efficient for specific sets of sequences for which the degree of conservation is known. The initial results also indicate that FMALIGN can perform very well and provide an alignment which is very similar or better than a structurally derived alignment. Utilization of sequence motifs to improve alignment accuracy Representative superfamily alignments belonging to different structural classes from the PASS2 [ 20 ] superfamily alignment database are taken for a benchmarking test (Table  1 ). These superfamily alignments are of different lengths (short, medium, and long) and possess an average sequence identity that ranges from 10% to 26%. Sequential conserved regions or motifs are identified for each superfamily alignment and utilized in the FMALIGN server to realign the superfamily members. Similarly, the superfamily members are also aligned by the two best performing multiple alignment methods, CLUSTALW [ 22 ] and T-Coffee [ 23 ]. These alignments are then compared against the structure-based COMPARER alignments provided by PASS2 database using the same alignment scoring scheme (Sum-of-Pair score) of the BAliBASE benchmarking database. FMALIGN-derived alignments performed better for most of the cases compared to the other two methods as shown in Figure  10 .
Benchmarking at superfamily level
Utilization of structural motifs to improve alignment accuracy The performance of FMALIGN has been tested on a dataset of representative superfamilies of proteins belonging to different structural classes in PASS2 [ 20 ] and SMoS [ 15 ] databases. The structural motifs from the SMoS database have been utilized as conserved regions for the member of the superfamily (average sequence identity less than 30%). All the proteins of a superfamily have been aligned. Each alignment is assigned a quality score by averaging the amino acid exchange score of each column over the length of the alignment (see method for details). The alignments derived from FMALIGN have been compared against the CLUSTALX-derived sequence alignment as well as the sequence-structure alignment derived from COMPARER [ 21 ]. Figure  9  shows an equivalent or better accuracy of the FMALIGN server compared to CLUSTALX and COMPARER-derived alignment. This indicates that FMALIGN is particularly efficient for specific sets of sequences for which the degree of conservation is known. The initial results also indicate that FMALIGN can perform very well and provide an alignment which is very similar or better than a structurally derived alignment.
Utilization of structural motifs to improve alignment accuracy
The performance of FMALIGN has been tested on a dataset of representative superfamilies of proteins belonging to different structural classes in PASS2 [ 20 ] and SMoS [ 15 ] databases. The structural motifs from the SMoS database have been utilized as conserved regions for the member of the superfamily (average sequence identity less than 30%). All the proteins of a superfamily have been aligned. Each alignment is assigned a quality score by averaging the amino acid exchange score of each column over the length of the alignment (see method for details). The alignments derived from FMALIGN have been compared against the CLUSTALX-derived sequence alignment as well as the sequence-structure alignment derived from COMPARER [ 21 ]. Figure  9  shows an equivalent or better accuracy of the FMALIGN server compared to CLUSTALX and COMPARER-derived alignment. This indicates that FMALIGN is particularly efficient for specific sets of sequences for which the degree of conservation is known. The initial results also indicate that FMALIGN can perform very well and provide an alignment which is very similar or better than a structurally derived alignment.Utilization of sequence motifs to improve alignment accuracy Representative superfamily alignments belonging to different structural classes from the PASS2 [ 20 ] superfamily alignment database are taken for a benchmarking test (Table  1 ). These superfamily alignments are of different lengths (short, medium, and long) and possess an average sequence identity that ranges from 10% to 26%. Sequential conserved regions or motifs are identified for each superfamily alignment and utilized in the FMALIGN server to realign the superfamily members. Similarly, the superfamily members are also aligned by the two best performing multiple alignment methods, CLUSTALW [ 22 ] and T-Coffee [ 23 ]. These alignments are then compared against the structure-based COMPARER alignments provided by PASS2 database using the same alignment scoring scheme (Sum-of-Pair score) of the BAliBASE benchmarking database. FMALIGN-derived alignments performed better for most of the cases compared to the other two methods as shown in Figure  10 .
Utilization of sequence motifs to improve alignment accuracy
Representative superfamily alignments belonging to different structural classes from the PASS2 [ 20 ] superfamily alignment database are taken for a benchmarking test (Table  1 ). These superfamily alignments are of different lengths (short, medium, and long) and possess an average sequence identity that ranges from 10% to 26%. Sequential conserved regions or motifs are identified for each superfamily alignment and utilized in the FMALIGN server to realign the superfamily members. Similarly, the superfamily members are also aligned by the two best performing multiple alignment methods, CLUSTALW [ 22 ] and T-Coffee [ 23 ]. These alignments are then compared against the structure-based COMPARER alignments provided by PASS2 database using the same alignment scoring scheme (Sum-of-Pair score) of the BAliBASE benchmarking database. FMALIGN-derived alignments performed better for most of the cases compared to the other two methods as shown in Figure  10 .Conclusions FMALIGN server provides a web interface for pairwise and multiple sequence alignments of proteins. FMALIGN provides an alignment by combining progressive dynamic algorithm, local substructure alignment and iterative refinement presenting an improved, user interactive alignment procedure. FMALIGN server allows the user to fix conserved regions in equivalent positions amongst the sequences to be aligned leading to alignments that are reliable and biologically more meaningful. Additional options for the users to choose substitution matrices and gap penalty values may be incorporated in future. All the alignments provided in BAliBASE are realigned by FMALIGN and the resulting scores (both SPS and CS) are calculated using a standard program kindly provided by the authors of BAliBASE. The inter-motif regions are aligned in FMALIGN by normal progressive alignment using standard substitution matrices like BLOSUM62. The gap penalties used in this version of FMALIGN are all maintained according to standard multiple alignment parameters. Benchmarking at the superfamily level has also been done utilizing both structural and sequential conserved regions. The dataset is wide enough to include proteins from different structural classes and of different length. The average sequence identity for this dataset was less than 30% since the proteins are related at the superfamily level. The performance of FMALIGN has been tested against one of the best performing multiple sequence alignment (T-Coffee) as well as structure based alignment tool (COMPARER). Studies on large and different datasets revealed an overall better performance of FMALIGN server for all categories in BAliBASE benchmarking database. It works especially well at lower sequence identity range, such as superfamily level, where no two proteins are more than 25% identical to each other in comparison to other popular methods like CLUSTALX and T-Coffee. It is well-known that automatic multiple sequence alignments at poor sequence identity are often subject to careful manual validations and improvements to avoid offsets of critical functionally important residues. FMALIGN can sensitively address this issue to avoid manual intervention subsequent to final alignment. FMALIGN-derived alignments also show a high conservation of secondary structural elements and provide better alignments for comparative modelling. The implementation of this alignment algorithm can be used to include new members into an existing protein superfamily with the help of motif regions that provide a reliable approach to connect protein sequences with their structural homologues within a particular superfamily. FMALIGN is available via the following URL
Conclusions
FMALIGN server provides a web interface for pairwise and multiple sequence alignments of proteins. FMALIGN provides an alignment by combining progressive dynamic algorithm, local substructure alignment and iterative refinement presenting an improved, user interactive alignment procedure. FMALIGN server allows the user to fix conserved regions in equivalent positions amongst the sequences to be aligned leading to alignments that are reliable and biologically more meaningful. Additional options for the users to choose substitution matrices and gap penalty values may be incorporated in future.All the alignments provided in BAliBASE are realigned by FMALIGN and the resulting scores (both SPS and CS) are calculated using a standard program kindly provided by the authors of BAliBASE. The inter-motif regions are aligned in FMALIGN by normal progressive alignment using standard substitution matrices like BLOSUM62. The gap penalties used in this version of FMALIGN are all maintained according to standard multiple alignment parameters. Benchmarking at the superfamily level has also been done utilizing both structural and sequential conserved regions. The dataset is wide enough to include proteins from different structural classes and of different length. The average sequence identity for this dataset was less than 30% since the proteins are related at the superfamily level. The performance of FMALIGN has been tested against one of the best performing multiple sequence alignment (T-Coffee) as well as structure based alignment tool (COMPARER). Studies on large and different datasets revealed an overall better performance of FMALIGN server for all categories in BAliBASE benchmarking database. It works especially well at lower sequence identity range, such as superfamily level, where no two proteins are more than 25% identical to each other in comparison to other popular methods like CLUSTALX and T-Coffee. It is well-known that automatic multiple sequence alignments at poor sequence identity are often subject to careful manual validations and improvements to avoid offsets of critical functionally important residues. FMALIGN can sensitively address this issue to avoid manual intervention subsequent to final alignment. FMALIGN-derived alignments also show a high conservation of secondary structural elements and provide better alignments for comparative modelling. The implementation of this alignment algorithm can be used to include new members into an existing protein superfamily with the help of motif regions that provide a reliable approach to connect protein sequences with their structural homologues within a particular superfamily. FMALIGN is available via the following URLAuthors' contributions S.C. had carried out the benchmarking, was involved in the development of the server and has written the first draft of the manuscript. N.B. had written the initial part of the code and P.A. has written the latter part of the code and developed a web server. R.S. had initiated the idea, was involved in discussions and in the critical reading of the manuscript.
Authors' contributions
S.C. had carried out the benchmarking, was involved in the development of the server and has written the first draft of the manuscript. N.B. had written the initial part of the code and P.A. has written the latter part of the code and developed a web server. R.S. had initiated the idea, was involved in discussions and in the critical reading of the manuscript.List of abbreviations FMALIGN  F ixed  M otif  ALIGN ment SPS Sum-of-Pairs Score
List of abbreviations
FMALIGN  F ixed  M otif  ALIGN mentSPS Sum-of-Pairs Score


Background Personal Digital Assistants (PDAS) are rapidly becoming popular tools in the assistance of managing hospitalized patients, but little is known about how often expert recommendations are available for the treatment of infectious diseases in hospitalized patients.
Background
Personal Digital Assistants (PDAS) are rapidly becoming popular tools in the assistance of managing hospitalized patients, but little is known about how often expert recommendations are available for the treatment of infectious diseases in hospitalized patients.Objective To determine how often PDAs could provide expert recommendations for the management of infectious diseases in patients admitted to a general medicine teaching service.
Objective
To determine how often PDAs could provide expert recommendations for the management of infectious diseases in patients admitted to a general medicine teaching service.Design Prospective observational cohort study
Design
Prospective observational cohort studySetting Internal medicine resident teaching service at an urban hospital in Dayton, Ohio
Setting
Internal medicine resident teaching service at an urban hospital in Dayton, OhioPatients 212 patients (out of 883 patients screened) were identified with possible infectious etiologies as the cause for admission to the hospital.
Patients
212 patients (out of 883 patients screened) were identified with possible infectious etiologies as the cause for admission to the hospital.Measurements Patients were screened prospectively from July 2002 until October 2002 for infectious conditions as the cause of their admissions. 5 PDA programs were assessed in October 2002 to see if treatment recommendations were available for managing these patients. The programs were then reassessed in January 2004 to evaluate how the latest editions of the software would perform under the same context as the previous year.
Measurements
Patients were screened prospectively from July 2002 until October 2002 for infectious conditions as the cause of their admissions. 5 PDA programs were assessed in October 2002 to see if treatment recommendations were available for managing these patients. The programs were then reassessed in January 2004 to evaluate how the latest editions of the software would perform under the same context as the previous year.Results PDAs provided treatment recommendations in at least one of the programs for 100% of the patients admitted over the 4 month period in the 2004 evaluation. Each of the programs reviewed improved from 2002 to 2004, with five of the six programs offering treatment recommendations for over 90% of patients in the study.
Results
PDAs provided treatment recommendations in at least one of the programs for 100% of the patients admitted over the 4 month period in the 2004 evaluation. Each of the programs reviewed improved from 2002 to 2004, with five of the six programs offering treatment recommendations for over 90% of patients in the study.Conclusion Current PDA software provides expert recommendations for a great majority of general internal medicine patients presenting to the hospital with infectious conditions.
Conclusion
Current PDA software provides expert recommendations for a great majority of general internal medicine patients presenting to the hospital with infectious conditions.

PDAs (Personal Digital Assistants) are becoming widely used in medicine. A survey done by the America College of Physician predicted that 67% of physicians would be using PDAs by the end of 2002 [ 1 ]. These devices are used not only by the new generation of residents and physicians, but by all ages and all specialties [ 2 , 3 ]. PDA's are used not only as personal planners and contact lists but also for medical purposes such as patient billing and bedside medical references [ 4 , 5 ]. Physicians who have rapid and easy access to information are increasingly using that data when treating patients. This should ensure appropriate therapy and also reduce medical errors by ensuring proper dosing and treatment choices [ 6 ]. One medical specialty which has focused on PDA's application in medicine is infectious diseases [ 7 ]. There are numerous infectious disease programs which provide both expert recommendations regarding antibiotic choice as well as background information (epidemiology, diagnostic studies, source of pathogens, etc). We sought to determine the how often expert recommendations were available from the PDA infectious disease resources for the infectious conditions seen on our general internal medicine teaching service over a 4 month period of time. A secondary goal was to determine the change in the software over a 15 month period to provide expert recommendations.

PDAs (Personal Digital Assistants) are becoming widely used in medicine. A survey done by the America College of Physician predicted that 67% of physicians would be using PDAs by the end of 2002 [ 1 ]. These devices are used not only by the new generation of residents and physicians, but by all ages and all specialties [ 2 , 3 ]. PDA's are used not only as personal planners and contact lists but also for medical purposes such as patient billing and bedside medical references [ 4 , 5 ]. Physicians who have rapid and easy access to information are increasingly using that data when treating patients. This should ensure appropriate therapy and also reduce medical errors by ensuring proper dosing and treatment choices [ 6 ].One medical specialty which has focused on PDA's application in medicine is infectious diseases [ 7 ]. There are numerous infectious disease programs which provide both expert recommendations regarding antibiotic choice as well as background information (epidemiology, diagnostic studies, source of pathogens, etc). We sought to determine the how often expert recommendations were available from the PDA infectious disease resources for the infectious conditions seen on our general internal medicine teaching service over a 4 month period of time. A secondary goal was to determine the change in the software over a 15 month period to provide expert recommendations.Methods Clinical Setting Miami Valley Hospital is an 827 bed secondary and tertiary referral center, in Dayton, Ohio. It is an urban hospital that averages over three-thousand patient admissions per month. The internal medicine resident teaching service consists of two teams, each team with 2 senior residents and 2 interns who are supervised by an attending physician. There is a senior resident and intern on call in the hospital at all times. Patients were eligible to be admitted to resident's service if they were established within the Medical-Surgical Health Center of Miami Valley Hospital, if they were uninsured or if they had insurance but were without a local physician (a.k.a. private unattached patients). The general medicine teaching service averages approximately eight admissions per twenty-four hour period. Patients and Problems The patients were screened prospectively over a four month period (July 2002 through October 2002). The admitting senior resident prospectively recorded the chief complaint and initial differential diagnosis for each patient. One of us (SDB), as the chief resident during these months, gathered the data during morning report. Patients were considered appropriate for the study if they had a leading diagnosis or active alternatives that suggested an infectious disease at the time of admission. Cases selected were then assigned categories based on the "major clinical syndromes" from  Mandell, Douglass and Bennett's Principles and Practice of Infectious Disease  (PPID) (8) (Table  1 ). Syndromes were categorized by organ system and were considered only if they would commonly require hospitalization (for example, sinusitis was not evaluated). Table 1 Appropriate inpatient clinical syndromes according to PPID (8) and the distribution of patients admitted to the general medicine teaching service from July through October of 2002. Major Clinical Syndromes Patients Fever Fever of Unknown Origin (Neutropenia) 6 (2) Upper Respiratory Pharyngitis 2 Infections of head and neck 1 Pleuropulmonary Acute Bronchitis 16 Chronic Bronchitis 20 Acute Pneumonia 31 Empyema 2 Chronic Pneumonia 1 Cystic Fibrosis 1 Urinary Tract 22 Sepsis Syndrome 3 Peritonitis and Other Intra-abdominal Infections 3 Cardiovascular Endocarditis 2 Central Nervous System Acute Meningitis 6 Encephalitis 1 Brain Abscess 1 Soft Tissue Cellulitis and subcutaneous Tissue Infections 40 Lymphadenitis and Lymphangitis 2 Gastrointestinal Inflammatory Enteritides 20 Abdominal Symptoms and Fever 7 Bone and Joint Infectious Arthritis 1 Osteomyelitis 8 STD Prostatitis, Epididymitis and Orchitis 1 Eye Peri-ocular Infections 2 HIV Pulmonary Manifestations in HIV 5 CNS Manifestations in HIV 1 Searching the PDA A Sony Clie T615C (Palm OS) was the device used to access the software, but all programs evaluated were also available in the Pocket PC format (thus applicable to nearly 100% of PDAs in clinical use). Five PDA programs were initially searched to determine if expert recommendations were available. Software was chosen based on its availability to both the Palm OS and the Pocket PC and at the time of the initial software evaluation were the primary infectious disease programs available (since 2002 numerous other titles have been released to address infectious diseases). The PDA programs included: Sanford's Guide to Antimicrobial Therapy   (SG) [ 9 ], John's Hopkins Antibiotic Guide   (JHABx) [ 10 ], 5-Minute Infectious Diseases Consult  ) (5MID) [ 11 ], 5-Minute Clinical Consult  ) (5MCC) [ 12 ], and Pocket Medicine-Infectious Disease  ) (PMID) [ 13 ]. The initial evaluation was performed with the most current software in October 2002 and reassessed in January 2004 with the latest versions of the available software. In addition, ePocrates ID  ) (QID) [ 14 ] was also evaluated in January 2004 (but was not included in the October 2002 assessment as it was only available for the Palm OS at this junction). A minimum of three attempts were used to locate conditions within each program. Searching was done using either the disease name or clinical problem that had led to the patient's admission. Synonyms were used when appropriate to increase the possibility of locating a condition within each program (for example: "urinary tract infection" was the first term searched and if no results available, then "pyelonephritis" was searched next and if still no results then "kidney infection" was entered into the database). Programs that were organized according to organ system (JHABx, QID only) were searched within the appropriate organ system, while programs that listed diagnoses alphabetically were searched accordingly. Expert recommendations were considered to be present and thus counted as a positive if the software had treatment recommendations present, whether related to antibiotic decision or "supportive care." In order to determine the change in software capabilities over a 14 month period, the same patient data set was used to assess software programs in January of 2004. A comparison of the programs was then performed comparing the availability of expert recommendations. Statistical Analysis The data was collected by a single physician (SDB) and then validated independently by a second physician (TEH). The data was analyzed using descriptive statistics with simple frequencies and the confidence intervals were calculated according to standard formulas.
Methods
Clinical Setting Miami Valley Hospital is an 827 bed secondary and tertiary referral center, in Dayton, Ohio. It is an urban hospital that averages over three-thousand patient admissions per month. The internal medicine resident teaching service consists of two teams, each team with 2 senior residents and 2 interns who are supervised by an attending physician. There is a senior resident and intern on call in the hospital at all times. Patients were eligible to be admitted to resident's service if they were established within the Medical-Surgical Health Center of Miami Valley Hospital, if they were uninsured or if they had insurance but were without a local physician (a.k.a. private unattached patients). The general medicine teaching service averages approximately eight admissions per twenty-four hour period.
Clinical Setting
Miami Valley Hospital is an 827 bed secondary and tertiary referral center, in Dayton, Ohio. It is an urban hospital that averages over three-thousand patient admissions per month. The internal medicine resident teaching service consists of two teams, each team with 2 senior residents and 2 interns who are supervised by an attending physician. There is a senior resident and intern on call in the hospital at all times. Patients were eligible to be admitted to resident's service if they were established within the Medical-Surgical Health Center of Miami Valley Hospital, if they were uninsured or if they had insurance but were without a local physician (a.k.a. private unattached patients). The general medicine teaching service averages approximately eight admissions per twenty-four hour period.Patients and Problems The patients were screened prospectively over a four month period (July 2002 through October 2002). The admitting senior resident prospectively recorded the chief complaint and initial differential diagnosis for each patient. One of us (SDB), as the chief resident during these months, gathered the data during morning report. Patients were considered appropriate for the study if they had a leading diagnosis or active alternatives that suggested an infectious disease at the time of admission. Cases selected were then assigned categories based on the "major clinical syndromes" from  Mandell, Douglass and Bennett's Principles and Practice of Infectious Disease  (PPID) (8) (Table  1 ). Syndromes were categorized by organ system and were considered only if they would commonly require hospitalization (for example, sinusitis was not evaluated). Table 1 Appropriate inpatient clinical syndromes according to PPID (8) and the distribution of patients admitted to the general medicine teaching service from July through October of 2002. Major Clinical Syndromes Patients Fever Fever of Unknown Origin (Neutropenia) 6 (2) Upper Respiratory Pharyngitis 2 Infections of head and neck 1 Pleuropulmonary Acute Bronchitis 16 Chronic Bronchitis 20 Acute Pneumonia 31 Empyema 2 Chronic Pneumonia 1 Cystic Fibrosis 1 Urinary Tract 22 Sepsis Syndrome 3 Peritonitis and Other Intra-abdominal Infections 3 Cardiovascular Endocarditis 2 Central Nervous System Acute Meningitis 6 Encephalitis 1 Brain Abscess 1 Soft Tissue Cellulitis and subcutaneous Tissue Infections 40 Lymphadenitis and Lymphangitis 2 Gastrointestinal Inflammatory Enteritides 20 Abdominal Symptoms and Fever 7 Bone and Joint Infectious Arthritis 1 Osteomyelitis 8 STD Prostatitis, Epididymitis and Orchitis 1 Eye Peri-ocular Infections 2 HIV Pulmonary Manifestations in HIV 5 CNS Manifestations in HIV 1
Patients and Problems
The patients were screened prospectively over a four month period (July 2002 through October 2002). The admitting senior resident prospectively recorded the chief complaint and initial differential diagnosis for each patient. One of us (SDB), as the chief resident during these months, gathered the data during morning report. Patients were considered appropriate for the study if they had a leading diagnosis or active alternatives that suggested an infectious disease at the time of admission. Cases selected were then assigned categories based on the "major clinical syndromes" from  Mandell, Douglass and Bennett's Principles and Practice of Infectious Disease  (PPID) (8) (Table  1 ). Syndromes were categorized by organ system and were considered only if they would commonly require hospitalization (for example, sinusitis was not evaluated).Appropriate inpatient clinical syndromes according to PPID (8) and the distribution of patients admitted to the general medicine teaching service from July through October of 2002.Major Clinical SyndromesPatientsFeverUpper RespiratoryPleuropulmonaryUrinary TractSepsis SyndromePeritonitis and Other Intra-abdominal InfectionsCardiovascularCentral Nervous SystemSoft TissueGastrointestinalBone and JointSTDEyeHIVSearching the PDA A Sony Clie T615C (Palm OS) was the device used to access the software, but all programs evaluated were also available in the Pocket PC format (thus applicable to nearly 100% of PDAs in clinical use). Five PDA programs were initially searched to determine if expert recommendations were available. Software was chosen based on its availability to both the Palm OS and the Pocket PC and at the time of the initial software evaluation were the primary infectious disease programs available (since 2002 numerous other titles have been released to address infectious diseases). The PDA programs included: Sanford's Guide to Antimicrobial Therapy   (SG) [ 9 ], John's Hopkins Antibiotic Guide   (JHABx) [ 10 ], 5-Minute Infectious Diseases Consult  ) (5MID) [ 11 ], 5-Minute Clinical Consult  ) (5MCC) [ 12 ], and Pocket Medicine-Infectious Disease  ) (PMID) [ 13 ]. The initial evaluation was performed with the most current software in October 2002 and reassessed in January 2004 with the latest versions of the available software. In addition, ePocrates ID  ) (QID) [ 14 ] was also evaluated in January 2004 (but was not included in the October 2002 assessment as it was only available for the Palm OS at this junction). A minimum of three attempts were used to locate conditions within each program. Searching was done using either the disease name or clinical problem that had led to the patient's admission. Synonyms were used when appropriate to increase the possibility of locating a condition within each program (for example: "urinary tract infection" was the first term searched and if no results available, then "pyelonephritis" was searched next and if still no results then "kidney infection" was entered into the database). Programs that were organized according to organ system (JHABx, QID only) were searched within the appropriate organ system, while programs that listed diagnoses alphabetically were searched accordingly. Expert recommendations were considered to be present and thus counted as a positive if the software had treatment recommendations present, whether related to antibiotic decision or "supportive care." In order to determine the change in software capabilities over a 14 month period, the same patient data set was used to assess software programs in January of 2004. A comparison of the programs was then performed comparing the availability of expert recommendations.
Searching the PDA
A Sony Clie T615C (Palm OS) was the device used to access the software, but all programs evaluated were also available in the Pocket PC format (thus applicable to nearly 100% of PDAs in clinical use). Five PDA programs were initially searched to determine if expert recommendations were available. Software was chosen based on its availability to both the Palm OS and the Pocket PC and at the time of the initial software evaluation were the primary infectious disease programs available (since 2002 numerous other titles have been released to address infectious diseases). The PDA programs included: Sanford's Guide to Antimicrobial Therapy   (SG) [ 9 ], John's Hopkins Antibiotic Guide   (JHABx) [ 10 ], 5-Minute Infectious Diseases Consult  ) (5MID) [ 11 ], 5-Minute Clinical Consult  ) (5MCC) [ 12 ], and Pocket Medicine-Infectious Disease  ) (PMID) [ 13 ]. The initial evaluation was performed with the most current software in October 2002 and reassessed in January 2004 with the latest versions of the available software. In addition, ePocrates ID  ) (QID) [ 14 ] was also evaluated in January 2004 (but was not included in the October 2002 assessment as it was only available for the Palm OS at this junction).A minimum of three attempts were used to locate conditions within each program. Searching was done using either the disease name or clinical problem that had led to the patient's admission. Synonyms were used when appropriate to increase the possibility of locating a condition within each program (for example: "urinary tract infection" was the first term searched and if no results available, then "pyelonephritis" was searched next and if still no results then "kidney infection" was entered into the database). Programs that were organized according to organ system (JHABx, QID only) were searched within the appropriate organ system, while programs that listed diagnoses alphabetically were searched accordingly. Expert recommendations were considered to be present and thus counted as a positive if the software had treatment recommendations present, whether related to antibiotic decision or "supportive care."In order to determine the change in software capabilities over a 14 month period, the same patient data set was used to assess software programs in January of 2004. A comparison of the programs was then performed comparing the availability of expert recommendations.Statistical Analysis The data was collected by a single physician (SDB) and then validated independently by a second physician (TEH). The data was analyzed using descriptive statistics with simple frequencies and the confidence intervals were calculated according to standard formulas.
Statistical Analysis
The data was collected by a single physician (SDB) and then validated independently by a second physician (TEH). The data was analyzed using descriptive statistics with simple frequencies and the confidence intervals were calculated according to standard formulas.Results Over the four month period, from July through October of 2002, there were 883 patients admitted to the resident service, of whom 212 had syndromes that were suspected on admission to be infectious in etiology (202 of which could be assigned to the pre-defined categories) (See Figure  1  and Table  1 ). Figure 1 Results of patient screened for infectious etiologies over a 4 month period. As shown in Table  2 , treatment recommendations were available in the PDA in at least one program for one-hundred percent of the patients admitted during the software evaluation in January 2004. Expert recommendations were available in all six of the programs for 52% of the patients admitted. The Sanford Guide and ePocrates ID each offered expert recommendations regarding treatment for 100% of the patients, while John's Hopkins Antibiotic Guide, 5 Minute Clinical Consultant and 5 Minute Infectious Diseases both offered expert recommendations in over 95% of the patients. Pocket Medicine-Infectious Disease offered expert recommendations for the fewest number of patients. Table 2 Number and percent of patients with infection- related clinical syndromes covered by the studied PDA programs as of January 2004. Major Clinical Syndromes Patients SG QID JHABx 5MCC 5MID PMID Total % Rec Fever 6 6 6 6 4 4 4 100 Upper Respiratory 3 3 3 3 3 3 3 100 Pleuropulmonary 71 71 71 71 71 69 31 100 Urinary Tract 22 22 22 22 22 22 0 100 Sepsis 3 3 3 3 3 3 0 100 Peritonitis 3 3 3 3 3 3 2 100 Cardiovascular 2 2 2 2 2 2 2 100 Central Nervous System 8 8 8 8 8 8 7 100 Soft Tissue 42 42 42 42 42 42 40 100 Gastrointestinal 27 27 27 27 27 27 7 100 Bone and Joint 9 9 9 9 9 9 8 100 STD 1 1 1 0 0 1 0 100 Eye 2 2 2 2 2 0 2 100 HIV 6 6 6 6 0 0 0 100 TOTALS 202 202 202 201 195 192 106 202 TOTAL % 100 100 99 97 95 52 100 95% Confidence Intervals 100% 100% 97.5% to 100% 94.5% to 99.5% 92% to 98% 45% to 59% In regards to software changes between October 2002 and January 2004, each of the programs evaluated increased the number of patients for whom expert recommendations were available Table  3 . The recommendations were not evaluated for changes in treatment over this time period, only the number of patients for whom recommendations were available was evaluated. The Sanford Guide had minimal improvements to make (initially offering expert recommendations for 96% of patients) while others improved significantly (JHABx improved from approximately 50% to over 96%). PMID, while offering treatment recommendations for the fewest number of patients, did show an improvement of 28% of the 14 month time period assessed. QID was assessed for the first time in 2004 when it was made available for both the Palm OS and Pocket PC and therefore no comparison data is available. Table 3 Improvement in expert recommendations available in October 2002 as compared with January 2004. Programs 2002 2004 Difference SG 94% 100% +6% QID NA 100% NA JHABx 61% 99% +38% 5MCC 64% 97% +33% 5MID 58% 95% +37% PMID 24% 52% +28%
Results
Over the four month period, from July through October of 2002, there were 883 patients admitted to the resident service, of whom 212 had syndromes that were suspected on admission to be infectious in etiology (202 of which could be assigned to the pre-defined categories) (See Figure  1  and Table  1 ).As shown in Table  2 , treatment recommendations were available in the PDA in at least one program for one-hundred percent of the patients admitted during the software evaluation in January 2004. Expert recommendations were available in all six of the programs for 52% of the patients admitted. The Sanford Guide and ePocrates ID each offered expert recommendations regarding treatment for 100% of the patients, while John's Hopkins Antibiotic Guide, 5 Minute Clinical Consultant and 5 Minute Infectious Diseases both offered expert recommendations in over 95% of the patients. Pocket Medicine-Infectious Disease offered expert recommendations for the fewest number of patients.Number and percent of patients with infection- related clinical syndromes covered by the studied PDA programs as of January 2004.Major Clinical SyndromesPatientsSGQIDJHABx5MCC5MIDPMIDTotal % RecFeverUpper RespiratoryPleuropulmonaryUrinary TractSepsisPeritonitisCardiovascularCentral Nervous SystemSoft TissueGastrointestinalBone and JointSTDEyeHIVTOTALSTOTAL %95% Confidence IntervalsIn regards to software changes between October 2002 and January 2004, each of the programs evaluated increased the number of patients for whom expert recommendations were available Table  3 . The recommendations were not evaluated for changes in treatment over this time period, only the number of patients for whom recommendations were available was evaluated. The Sanford Guide had minimal improvements to make (initially offering expert recommendations for 96% of patients) while others improved significantly (JHABx improved from approximately 50% to over 96%). PMID, while offering treatment recommendations for the fewest number of patients, did show an improvement of 28% of the 14 month time period assessed. QID was assessed for the first time in 2004 when it was made available for both the Palm OS and Pocket PC and therefore no comparison data is available.Improvement in expert recommendations available in October 2002 as compared with January 2004.Programs20022004DifferenceSGQIDJHABx5MCC5MIDPMIDDiscussion We were able to find expert recommendations regarding initial treatment of suspected infectious diseases on the PDA for 100% of patients admitted to the general medicine teaching service at Miami Valley Hospital over a 4 month period from July until October of 2002. As far as we know, this is the first ever prospective study of the breadth of coverage provided by PDA software for recommendations in infectious disease. Miller et al provide an excellent review and buyer's guide of many of the PDA software evaluated in this project and did sample the recommendations available for six selected infectious conditions [ 7 ]. This study should be interpreted in light of its potential limitations. First, we may have erred in our selection of which patients had infectious syndromes as the cause for admission. Patients' initial diagnoses might have been mistaken and the cause for admission was non-infectious. We have no information about how often such errors occurred. However, we attempted to assess the frequency of recommendations available for the initial diagnosis by the admitting physician rather than the discharge diagnosis. A second potential limitation to our study is that searching errors occurred. Multiple steps were taken to limit the possibility that recommendations were credited that did not correctly match with the conditions sited or that recommendations were not found that did exist. Explicit criteria were used by an evaluator (SDB) familiar with both the PDA and the aforementioned software looking for management recommendations. This usually included antibiotic recommendations and occasionally recommendations regarding further evaluation. The possibility that we did not find recommendations which were indeed available seems unlikely because of the previously mentioned points as well as a systematic approach using multiple synonyms. Furthermore, the data was independently reviewed by a second physician and no inconsistencies were identified. A third potential limitation is that of reproducibility. Since searching was done by an experienced user, searching errors would be more likely with less experienced user(s). Many of the programs require persistent use to learn the nuances and style of the programs and how to best access information. Therefore, in order for others to duplicate our results, users will need to develop some profiency with the software and be willing to perform searches using multiple synonyms. Lastly, our study may have limits to its applicability. Our patients were admitted to an inpatient general internal medicine teaching service at an urban hospital in the United States. These data probably apply to similar inpatient services elsewhere within North America, but may not apply to either other specialty services (Pediatrics, Surgery, etc) or hospitals in other parts of the world. While the limitations mentioned above apply to this study, they can also be expanded to include limitations in the software. No physician can access a software program for the first time and be expected to take full advantage of the recommendations available. It takes time and effort to maximize the clinical utility that is available on the PDA, which unfortunately, many clinicians do not accomplish. This paper was focused on treatment recommendations and did not evaluate the ability of the individual programs to assist with diagnosis. The diagnostic data and information other than treatment recommendations varies from program to program. This could be a topic for future research or may be available in other formats such as software reviews in PDA magazines. This study demonstrates that these infectious disease programs have improved over time (table  3 ). The reasons for this are several. Many of the programs were newly released in 2002 and where advertised as a "works in progress." As time passed, they had time and resources to add more clinical data to their software (JHABx being the prime example) or they have released a more recent edition of the software (5MCC, 5MID, PMID). Secondly, the researcher (SB) had more time to become familiar with each program and therefore it is possible that part of the increased available recommendations is in part a "false positive" in that it was present in 2002 but not easily located (SG for example claims to have everything in the paperback version available in the PDA version, but accessing the data may be a challenge). This, as discussed previously, is an issue with software (often due to format or search engine) and increased experience with software definitely leads to increased clinical utility (both in regards to time and amount of information located). Table  3  also demonstrates the benefit of the "auto-update" feature that many of these programs possess. As new data is added to database, the device is able to access the central database and import these changes, thus making the software more dynamic rather than static. This allows the user the ability to use the latest recommendations, rather than material which may out of date. These features, allow with the compact size, mobility of the devices and the fact that multiple references may be available on a single device are just several examples of the benefit of using a PDA's in medicine. Despite these potential limitations, our data suggests that PDAs provide expert recommendations for the majority of infectious clinical conditions encountered in practice by non-infectious disease general physicians. Since a majority of patients admitted to the hospital receive care from clinicians who are not specialists in infectious diseases, PDA's have the potential to have an impact on the quality of initial care by guiding not only the choice of antibiotics but also diagnostic testing and when to involve infectious disease specialists. Further research is needed to examine the efficiency of use; evidence supporting the recommendations, and the clinical impact of the use of these resources.
Discussion
We were able to find expert recommendations regarding initial treatment of suspected infectious diseases on the PDA for 100% of patients admitted to the general medicine teaching service at Miami Valley Hospital over a 4 month period from July until October of 2002. As far as we know, this is the first ever prospective study of the breadth of coverage provided by PDA software for recommendations in infectious disease. Miller et al provide an excellent review and buyer's guide of many of the PDA software evaluated in this project and did sample the recommendations available for six selected infectious conditions [ 7 ].This study should be interpreted in light of its potential limitations. First, we may have erred in our selection of which patients had infectious syndromes as the cause for admission. Patients' initial diagnoses might have been mistaken and the cause for admission was non-infectious. We have no information about how often such errors occurred. However, we attempted to assess the frequency of recommendations available for the initial diagnosis by the admitting physician rather than the discharge diagnosis.A second potential limitation to our study is that searching errors occurred. Multiple steps were taken to limit the possibility that recommendations were credited that did not correctly match with the conditions sited or that recommendations were not found that did exist. Explicit criteria were used by an evaluator (SDB) familiar with both the PDA and the aforementioned software looking for management recommendations. This usually included antibiotic recommendations and occasionally recommendations regarding further evaluation. The possibility that we did not find recommendations which were indeed available seems unlikely because of the previously mentioned points as well as a systematic approach using multiple synonyms. Furthermore, the data was independently reviewed by a second physician and no inconsistencies were identified.A third potential limitation is that of reproducibility. Since searching was done by an experienced user, searching errors would be more likely with less experienced user(s). Many of the programs require persistent use to learn the nuances and style of the programs and how to best access information. Therefore, in order for others to duplicate our results, users will need to develop some profiency with the software and be willing to perform searches using multiple synonyms.Lastly, our study may have limits to its applicability. Our patients were admitted to an inpatient general internal medicine teaching service at an urban hospital in the United States. These data probably apply to similar inpatient services elsewhere within North America, but may not apply to either other specialty services (Pediatrics, Surgery, etc) or hospitals in other parts of the world.While the limitations mentioned above apply to this study, they can also be expanded to include limitations in the software. No physician can access a software program for the first time and be expected to take full advantage of the recommendations available. It takes time and effort to maximize the clinical utility that is available on the PDA, which unfortunately, many clinicians do not accomplish. This paper was focused on treatment recommendations and did not evaluate the ability of the individual programs to assist with diagnosis. The diagnostic data and information other than treatment recommendations varies from program to program. This could be a topic for future research or may be available in other formats such as software reviews in PDA magazines.This study demonstrates that these infectious disease programs have improved over time (table  3 ). The reasons for this are several. Many of the programs were newly released in 2002 and where advertised as a "works in progress." As time passed, they had time and resources to add more clinical data to their software (JHABx being the prime example) or they have released a more recent edition of the software (5MCC, 5MID, PMID). Secondly, the researcher (SB) had more time to become familiar with each program and therefore it is possible that part of the increased available recommendations is in part a "false positive" in that it was present in 2002 but not easily located (SG for example claims to have everything in the paperback version available in the PDA version, but accessing the data may be a challenge). This, as discussed previously, is an issue with software (often due to format or search engine) and increased experience with software definitely leads to increased clinical utility (both in regards to time and amount of information located). Table  3  also demonstrates the benefit of the "auto-update" feature that many of these programs possess. As new data is added to database, the device is able to access the central database and import these changes, thus making the software more dynamic rather than static. This allows the user the ability to use the latest recommendations, rather than material which may out of date. These features, allow with the compact size, mobility of the devices and the fact that multiple references may be available on a single device are just several examples of the benefit of using a PDA's in medicine.Despite these potential limitations, our data suggests that PDAs provide expert recommendations for the majority of infectious clinical conditions encountered in practice by non-infectious disease general physicians. Since a majority of patients admitted to the hospital receive care from clinicians who are not specialists in infectious diseases, PDA's have the potential to have an impact on the quality of initial care by guiding not only the choice of antibiotics but also diagnostic testing and when to involve infectious disease specialists. Further research is needed to examine the efficiency of use; evidence supporting the recommendations, and the clinical impact of the use of these resources.