TaxI: a software tool for DNA barcoding using distance methods

DNA barcoding is a promising approach to the diagnosis of biological diversity in which DNA sequences serve as the primary key for information retrieval. Most existing software for evolutionary analysis of DNA sequences was designed for phylogenetic analyses and, hence, those algorithms do not offer appropriate solutions for the rapid, but precise analyses needed for DNA barcoding, and are also unable to process the often large comparative datasets. We developed a flexible software tool for DNA taxonomy, named TaxI. This program calculates sequence divergences between a query sequence (taxon to be barcoded) and each sequence of a dataset of reference sequences defined by the user. Because the analysis is based on separate pairwise alignments this software is also able to work with sequences characterized by multiple insertions and deletions that are difficult to align in large sequence sets (i.e. thousands of sequences) by multiple alignment algorithms because of computational restrictions. Here, we demonstrate the utility of this approach with two datasets of fish larvae and juveniles from Lake Constance and juvenile land snails under different models of sequence evolution. Sets of ribosomal 16S rRNA sequences, characterized by multiple indels, performed as good as or better than cox1 sequence sets in assigning sequences to species, demonstrating the suitability of rRNA genes for DNA barcoding.     

SPInDel: a multifunctional workbench for species identification using insertion/deletion variants

The majority of the available methods for the molecular identification of species use pairwise sequence divergences between the query and reference sequences (DNA barcoding). The presence of multiple insertions and deletions (indels) in the target genomic regions is generally regarded as a problem, as it introduces ambiguities in sequence alignments. However, we have recently shown that a high level of species discrimination is attainable in all taxa of life simply by considering the length of hypervariable regions defined by indel variants. Each species is tagged with a numeric profile of fragment lengths—a true numeric barcode. In this study, we describe a multifunctional computational workbench (named SPInDel for SPecies Identification by Insertions/Deletions) to assist researchers using variable‐length DNA sequences, and we demonstrate its applicability in molecular ecology. The SPInDel workbench provides a step‐by‐step environment for the alignment of target sequences, selection of informative hypervariable regions, design of PCR primers and the statistical validation of the species‐identification process. In our test data sets, we were able to discriminate all species from two genera of frogs (Ansonia and Leptobrachium) inhabiting lowland rainforests and mountain regions of South‐East Asia and species from the most common genus of coral reef fishes (Apogon). Our method can complement conventional DNA barcoding systems when indels are common (e.g. in rRNA genes) without the required step of DNA sequencing. The executable files, source code, documentation and test data sets are freely available at http://www.portugene.com/SPInDel/SPInDel_webworkbench.html .     

A comparison of algorithms for the identification of specimens using DNA barcodes: examples from gymnosperms

In order to use DNA sequences for specimen identification (e.g., barcoding, fingerprinting) an algorithm to compare query sequences with a reference database is needed. Precision and accuracy of query sequence identification was estimated for hierarchical clustering (parsimony and neighbor joining), similarity methods (BLAST, BLAT and megaBLAST), combined clustering/similarity methods (BLAST/parsimony and BLAST/neighbor joining), diagnostic methods (DNA–BAR and DOME ID), and a new method (ATIM). We offer two novel alignment‐free algorithmic solutions (DOME ID and ATIM) to identify query sequences for the purposes of DNA barcoding. Publicly available gymnosperm nrITS 2 and plastid matK sequences were used as test data sets. On the test data sets, almost all of the methods were able to accurately identify sequences to genus; however, no method was able to accurately identify query sequences to species at a frequency that would be considered useful for routine specimen identification (42–71% unambiguously correct). Clustering methods performed the worst (perhaps due to alignment issues). Similarity methods, ATIM, DNA–BAR, and DOME ID all performed at approximately the same level. Given the relative precision of the algorithms (median = 67% unambiguous), the low accuracy of species‐level identification observed could be ascribed to the lack of correspondence between patterns of allelic similarity and species delimitations. Application of DNA barcoding to sequences of CITES listed cycads (Cycadopsida) provides an example of the potential application of DNA barcoding to enforcement of conservation laws. © The Willi Hennig Society 2006.     

The performance of several multiple-sequence alignment programs in relation to secondary-structure features for an rRNA sequence

The performances of five global multiple-sequence alignment programs (CLUSTAL W, Divide and Conquer, Malign, PileUp, and TreeAlign) were evaluated using part of the animal mitochondrial small subunit (12S) rRNA molecule. Conserved sequence motifs derived from an alignment based on secondary structural information were used to score how well each program aligned a data set of five vertebrate and five invertebrate taxa over a range of parameter values. All of the programs could align the motifs with reasonable accuracy for at least one set of parameter conditions, although if the whole sequence was considered, similarity to the structural alignment was only 25%-34%. Use of small gap costs generally gave more accurate results, although Malign and TreeAlign generated longer alignments when gap costs were low. The programs differed in the consistency of the alignments when gap cost was varied; CLUSTAL W, Divide and Conquer, and TreeAlign were the most accurate and robust, while PileUp performed poorly as gap cost values increased, and the accuracy of Malign fluctuated. Default settings for the programs did not give the best results, and attempting to select similar parameter values in different programs did not always result in more similar alignments. Poor alignment of even well-conserved motifs can occur if these are near sites with insertions or deletions. Since there is no a priori way to determine gap costs and because such costs can vary over the gene, alignment of rRNA sequences, particularly the less well conserved regions, should be treated carefully and aided by secondary structure and conserved motifs. Some motifs are single bases and so are often invisible to alignment programs. Our tests involved the most conserved regions of the 12S rRNA gene, and alignment of less well conserved regions will be more problematical. None of the alignments we examined produced a fully resolved phylogeny for the data set, indicating that this portion of 12S rRNA is insufficient for resolution of distant evolutionary relationships.     

Testing the reliability of genetic methods of species identification via simulation

Although genetic methods of species identification, especially DNA barcoding, are strongly debated, tests of these methods have been restricted to a few empirical cases for pragmatic reasons. Here we use simulation to test the performance of methods based on sequence comparison (BLAST and genetic distance) and tree topology over a wide range of evolutionary scenarios. Sequences were simulated on a range of gene trees spanning almost three orders of magnitude in tree depth and in coalescent depth; that is, deep or shallow trees with deep or shallow coalescences. When the query's conspecific sequences were included in the reference alignment, the rate of positive identification was related to the degree to which different species were genetically differentiated. The BLAST, distance, and liberal tree-based methods returned higher rates of correct identification than did the strict tree-based requirement that the query was within, but not sister to, a single-species clade. Under this more conservative approach, ambiguous outcomes occurred in inverse proportion to the number of reference sequences per species. When the query's conspecific sequences were not in the reference alignment, only the strict tree-based approach was relatively immune to making false-positive identifications. Thresholds affected the rates at which false-positive identifications were made when the query's species was unrepresented in the reference alignment but did not otherwise influence outcomes. A conservative approach using the strict tree-based method should be used initially in large-scale identification systems, with effort made to maximize sequence sampling within species. Once the genetic variation within a taxonomic group is well characterized and the taxonomy resolved, then the choice of method used should be dictated by considerations of computational efficiency. The requirement for extensive genetic sampling may render these techniques inappropriate in some circumstances.     

Single nucleotide polymorphism barcoding of cytochrome c oxidase I sequences for discriminating 17 species of Columbidae by decision tree algorithm

DNA barcodes are widely used in taxonomy, systematics, species identification, food safety, and forensic science. Most of the conventional DNA barcode sequences contain the whole information of a given barcoding gene. Most of the sequence information does not vary and is uninformative for a given group of taxa within a monophylum. We suggest here a method that reduces the amount of noninformative nucleotides in a given barcoding sequence of a major taxon, like the prokaryotes, or eukaryotic animals, plants, or fungi. The actual differences in genetic sequences, called single nucleotide polymorphism (SNP) genotyping, provide a tool for developing a rapid, reliable, and high‐throughput assay for the discrimination between known species. Here, we investigated SNPs as robust markers of genetic variation for identifying different pigeon species based on available cytochrome c oxidase I (COI) data. We propose here a decision tree‐based SNP barcoding (DTSB) algorithm where SNP patterns are selected from the DNA barcoding sequence of several evolutionarily related species in order to identify a single species with pigeons as an example. This approach can make use of any established barcoding system. We here firstly used as an example the mitochondrial gene COI information of 17 pigeon species (Columbidae, Aves) using DTSB after sequence trimming and alignment. SNPs were chosen which followed the rule of decision tree and species‐specific SNP barcodes. The shortest barcode of about 11 bp was then generated for discriminating 17 pigeon species using the DTSB method. This method provides a sequence alignment and tree decision approach to parsimoniously assign a unique and shortest SNP barcode for any known species of a chosen monophyletic taxon where a barcoding sequence is available.     

Testing four proposed barcoding markers for the identification of species within Ligustrum L.(Oleaceae)

DNA barcoding is a biological technique that uses short and standardized genes or DNA regions to facilitate species identification. DNA barcoding has been used successfully in several animal and plant groups. Ligustrum (Oleaceae) species occur widely throughout the world and are used as medicinal plants in China. Therefore, the accurate identification of species in this genus is necessary. Four potential DNA barcodes, namely the nuclear ribosomal internal transcribed spacer (ITS) and three chloroplast (cp) DNA regions (rbcL, marK, and trnH-psbA),were used to differentiate species within Ligustrum. BLAST, character-based method, tree-based methods and TAXONDNA analysis were used to investigate the molecular identification capabilities of the chosen markers for discriminating 92 samples representing 20 species of this genus. The results showed that the ITS sequences have the most variable information, followed by trnH-psbA, matK, and rbcL. All sequences of the four regions correctly identified the species at the genus level using BLAST alignment. At the species level, the discriminating power of rbcL, matK, trnH-psbA and ITS based on neighbor-joining (NJ) trees was 36.8%, 38.9%, 77.8%, and 80%,respectively. Using character-based and maximum parsimony (MP) tree methods together, the discriminating ability of trnH-psbA increased to 88.9%. All species could be differentiated using ITS when combining the NJ tree method with character-based or MP tree methods. Overall, the results indicate that DNA barcoding is an effective molecular identification method for Ligustrum species. We propose the nuclear ribosomal ITS as a plant barcode for plant identification and trnH-psbA as a candidate barcode sequence.     

Predicted Secondary Structure for 28S and 18S rRNA from Ichneumonoidea (Insecta: Hymenoptera: Apocrita): Impact on Sequence Alignment and Phylogeny Estimation

We utilize the secondary structural properties of the 28S rRNA D2–D10 expansion segments to hypothesize a multiple sequence alignment for major lineages of the hymenopteran superfamily Ichneumonoidea (Braconidae, Ichneumonidae). The alignment consists of 290 sequences (originally analyzed in Belshaw and Quicke, Syst Biol 51:450–477, 2002) and provides the first global alignment template for this diverse group of insects. Predicted structures for these expansion segments as well as for over half of the 18S rRNA are given, with highly variable regions characterized and isolated within conserved structures. We demonstrate several pitfalls of optimization alignment and illustrate how these are potentially addressed with structure-based alignments. Our global alignment is presented online at (http://hymenoptera.tamu.edu/rna) with summary statistics, such as basepair frequency tables, along with novel tools for parsing structure-based alignments into input files for most commonly used phylogenetic software. These resources will be valuable for hymenopteran systematists, as well as researchers utilizing rRNA sequences for phylogeny estimation in any taxon. We explore the phylogenetic utility of our structure-based alignment by examining a subset of the data under a variety of optimality criteria using results from Belshaw and Quicke (2002) as a benchmark.Access to on-line data: http://hymenoptera.tamu.edu/rna; username, ichs; password, ichzzz     

Plant core DNA barcode performance at a local scale: identification of the conifers of the state of Hidalgo,Mexico

DNA barcoding constitutes a fundamental tool for species identification, especially for highly diverse geographic regions. Here, we characterize and evaluate the plant core barcoding regions matK and rbcL to identify the 25 conifer species from the state of Hidalgo, Mexico, including 10 species in various threat categories. Sequence quality, linguistic complexity, and the presence of the barcode gap were estimated. Two methods were compared for successful species identification: BRONX (Barcode Recognition Obtained with Nucleotide eXposés) and the least inclusive clade. We generated 77 sequences for matK and 88 for rbcL. The matK region had higher haplotype diversity and nucleotide diversity (Π), including six indels. The analysis of 77 specimens with complete sequences (matK + rbcL) resulted in 21 nonspecies-specific unique haplotypes for the 25 conifer species. Higher sequence quality and linguistic complexity were observed in rbcL than in matK. Every diagnosable species had a barcode gap. Ninety-seven specimens were assigned unambiguously to family and genus, regardless of the marker or method employed. The analysis of matK with BRONX produced the highest species level identification success (44%). Despite the low specimen identification success at the specific level, it will be possible to establish local management, conservation, and monitoring projects for at least half of the threatened species even when specimens do not exhibit diagnostic morphological characters. The low divergence between closely related species may result from the slow rate of molecular evolution of the core barcoding markers or from hybridization or incomplete lineage sorting. Similar identification success is expected for groups with comparable life history traits under similar conditions as this study. A reduction in the geographic area will not necessarily translate into higher identification success, especially for high-diversity regions and centres of diversification.     

Improving protein secondary structure prediction with aligned homologous sequences.

Most recent protein secondary structure prediction methods use sequence alignments to improve the prediction quality. We investigate the relationship between the location of secondary structural elements, gaps, and variable residue positions in multiple sequence alignments. We further investigate how these relationships compare with those found in structurally aligned protein families. We show how such associations may be used to improve the quality of prediction of the secondary structure elements, using the Quadratic-Logistic method with profiles. Furthermore, we analyze the extent to which the number of homologous sequences influences the quality of prediction. The analysis of variable residue positions shows that surprisingly, helical regions exhibit greater variability than do coil regions, which are generally thought to be the most common secondary structure elements in loops. However, the correlation between variability and the presence of helices does not significantly improve prediction quality. Gaps are a distinct signal for coil regions. Increasing the coil propensity for those residues occurring in gap regions enhances the overall prediction quality. Prediction accuracy increases initially with the number of homologues, but changes negligibly as the number of homologues exceeds about 14. The alignment quality affects the prediction more than other factors, hence a careful selection and alignment of even a small number of homologues can lead to significant improvements in prediction accuracy.     

A structure-based method for protein sequence alignment

MOTIVATION: With the continuing rapid growth of protein sequence data, protein sequence comparison methods have become the most widely used tools of bioinformatics. Among these methods are those that use position-specific scoring matrices (PSSMs) to describe protein families. PSSMs can capture information about conserved patterns within families, which can be used to increase the sensitivity of searches for related sequences. Certain types of structural information, however, are not generally captured by PSSM search methods. Here we introduce a program, Structure-based ALignment TOol (SALTO), that aligns protein query sequences to PSSMs using rules for placing and scoring gaps that are consistent with the conserved regions of domain alignments from NCBI's Conserved Domain Database. RESULTS: In most cases, the alignment scores obtained using the local alignment version follow an extreme value distribution. SALTO's performance in finding related sequences and producing accurate alignments is similar to or better than that of IMPALA; one advantage of SALTO is that it imposes an explicit gapping model on each protein family. AVAILABILITY: A stand-alone version of the program that can generate global or local alignments is available by ftp distribution (ftp://ftp.ncbi.nih.gov/pub/SALTO/), and has been incorporated to Cn3D structure/alignment viewer. CONTACT: bryant@ncbi.nlm.nih.gov.     

Gaps in structurally similar proteins: towards improvement of multiple sequence alignment

An algorithm was developed to locally optimize gaps from the FSSP database. Over 2 million gaps were identified from all versus all FSSP structure comparisons, and datasets of non-identical gaps and flanking regions comprising between 90,000 and 135,000 sequence fragments were extracted for statistical analysis. Relative to background frequencies, gaps were enriched in residue types with small side chains and high turn propensity (D, G, N, P, S), and were depleted in residue types with hydrophobic side chains (C, F, I, L, V, W, Y). In contrast, regions flanking a gap exhibited opposite trends in amino acid frequencies, i.e., enrichment in hydrophobic residues and a high degree of secondary structure. Log-odds scores of residue type as a function of position in or around a gap were derived from the statistics. Three simple experiments demonstrated that these scores contained significant predictive information. First, regions where gaps were observed in single sequences taken from HOMSTRAD structure-based multiple sequence alignments generally scored higher than regions where gaps were not observed. Second, given the correct pairwise-aligned cores, the actual positions of gaps could be reproduced from sequence more accurately using the structurally-derived statistics than by using random pairwise alignments. Finally, revision of the Clustal-W residue-specific gap opening parameters with this new information improved the agreement of Clustal-W alignments with the structure-based alignments. At least three applications for these results are envisioned: improvement of gap penalties in pairwise (or multiple) sequence alignment, prediction of regions of single sequences likely (or unlikely) to contain indels, and more accurate placement of gaps in automated pairwise structure alignment.     

Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis

The use of some multiple-sequence alignments in phylogenetic analysis, particularly those that are not very well conserved, requires the elimination of poorly aligned positions and divergent regions, since they may not be homologous or may have been saturated by multiple substitutions. A computerized method that eliminates such positions and at the same time tries to minimize the loss of informative sites is presented here. The method is based on the selection of blocks of positions that fulfill a simple set of requirements with respect to the number of contiguous conserved positions, lack of gaps, and high conservation of flanking positions, making the final alignment more suitable for phylogenetic analysis. To illustrate the efficiency of this method, alignments of 10 mitochondrial proteins from several completely sequenced mitochondrial genomes belonging to diverse eukaryotes were used as examples. The percentages of removed positions were higher in the most divergent alignments. After removing divergent segments, the amino acid composition of the different sequences was more uniform, and pairwise distances became much smaller. Phylogenetic trees show that topologies can be different after removing conserved blocks, particularly when there are several poorly resolved nodes. Strong support was found for the grouping of animals and fungi but not for the position of more basal eukaryotes. The use of a computerized method such as the one presented here reduces to a certain extent the necessity of manually editing multiple alignments, makes the automation of phylogenetic analysis of large data sets feasible, and facilitates the reproduction of the final alignment by other researchers.     

How Effective Are DNA Barcodes in the Identification of African Rainforest Trees?

Background

DNA barcoding of rain forest trees could potentially help biologists identify species and discover new ones. However, DNA barcodes cannot always distinguish between closely related species, and the size and completeness of barcode databases are key parameters for their successful application. We test the ability of rbcL, matK and trnH-psbA plastid DNA markers to identify rain forest trees at two sites in Atlantic central Africa under the assumption that a database is exhaustive in terms of species content, but not necessarily in terms of haplotype diversity within species.

Methodology/Principal Findings

We assess the accuracy of identification to species or genus using a genetic distance matrix between samples either based on a global multiple sequence alignment (GD) or on a basic local alignment search tool (BLAST). Where a local database is available (within a 50 ha plot), barcoding was generally reliable for genus identification (95–100% success), but less for species identification (71–88%). Using a single marker, best results for species identification were obtained with trnH-psbA. There was a significant decrease of barcoding success in species-rich clades. When the local database was used to identify the genus of trees from another region and did include all genera from the query individuals but not all species, genus identification success decreased to 84–90%. The GD method performed best but a global multiple sequence alignment is not applicable on trnH-psbA.

Conclusions/Significance

Barcoding is a useful tool to assign unidentified African rain forest trees to a genus, but identification to a species is less reliable, especially in species-rich clades, even using an exhaustive local database. Combining two markers improves the accuracy of species identification but it would only marginally improve genus identification. Finally, we highlight some limitations of the BLAST algorithm as currently implemented and suggest possible improvements for barcoding applications.     

Evaluation of PSI-BLAST alignment accuracy in comparison to structural alignments

The PSI-BLAST algorithm has been acknowledged as one of the most powerful tools for detecting remote evolutionary relationships by sequence considerations only. This has been demonstrated by its ability to recognize remote structural homologues and by the greatest coverage it enables in annotation of a complete genome. Although recognizing the correct fold of a sequence is of major importance, the accuracy of the alignment is crucial for the success of modeling one sequence by the structure of its remote homologue. Here we assess the accuracy of PSI-BLAST alignments on a stringent database of 123 structurally similar, sequence-dissimilar pairs of proteins, by comparing them to the alignments defined on a structural basis. Each protein sequence is compared to a nonredundant database of the protein sequences by PSI-BLAST. Whenever a pair member detects its pair-mate, the positions that are aligned both in the sequential and structural alignments are determined, and the alignment sensitivity is expressed as the percentage of these positions out of the structural alignment. Fifty-two sequences detected their pair-mates (for 16 pairs the success was bi-directional when either pair member was used as a query). The average percentage of correctly aligned residues per structural alignment was 43.5+/-2.2%. Other properties of the alignments were also examined, such as the sensitivity vs. specificity and the change in these parameters over consecutive iterations. Notably, there is an improvement in alignment sensitivity over consecutive iterations, reaching an average of 50.9+/-2.5% within the five iterations tested in the current study.     

Using structure to explore the sequence alignment space of remote homologs

Protein structure modeling by homology requires an accurate sequence alignment between the query protein and its structural template. However, sequence alignment methods based on dynamic programming (DP) are typically unable to generate accurate alignments for remote sequence homologs, thus limiting the applicability of modeling methods. A central problem is that the alignment that is "optimal" in terms of the DP score does not necessarily correspond to the alignment that produces the most accurate structural model. That is, the correct alignment based on structural superposition will generally have a lower score than the optimal alignment obtained from sequence. Variations of the DP algorithm have been developed that generate alternative alignments that are "suboptimal" in terms of the DP score, but these still encounter difficulties in detecting the correct structural alignment. We present here a new alternative sequence alignment method that relies heavily on the structure of the template. By initially aligning the query sequence to individual fragments in secondary structure elements and combining high-scoring fragments that pass basic tests for "modelability", we can generate accurate alignments within a small ensemble. Our results suggest that the set of sequences that can currently be modeled by homology can be greatly extended.     

UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi

Identification of ectomycorrhizal (ECM) fungi is often achieved through comparisons of ribosomal DNA internal transcribed spacer (ITS) sequences with accessioned sequences deposited in public databases. A major problem encountered is that annotation of the sequences in these databases is not always complete or trustworthy. In order to overcome this deficiency, we report on UNITE, an open-access database. UNITE comprises well annotated fungal ITS sequences from well defined herbarium specimens that include full herbarium reference identification data, collector/source and ecological data. At present UNITE contains 758 ITS sequences from 455 species and 67 genera of ECM fungi. UNITE can be searched by taxon name, via sequence similarity using blastn, and via phylogenetic sequence identification using galaxie. Following implementation, galaxie performs a phylogenetic analysis of the query sequence after alignment either to pre-existing generic alignments, or to matches retrieved from a blast search on the UNITE data. It should be noted that the current version of UNITE is dedicated to the reliable identification of ECM fungi. The UNITE database is accessible through the URL http://unite.zbi.ee     

Predicting reliable regions in protein alignments from sequence profiles

For applications such as comparative modelling one major issue is the reliability of sequence alignments. Reliable regions in alignments can be predicted using sub-optimal alignments of the same pair of sequences. Here we show that reliable regions in alignments can also be predicted from multiple sequence profile information alone.Alignments were created for a set of remotely related pairs of proteins using five different test methods. Structural alignments were used to assess the quality of the alignments and the aligned positions were scored using information from the observed frequencies of amino acid residues in sequence profiles pre-generated for each template structure. High-scoring regions of these profile-derived alignment scores were a good predictor of reliably aligned regions.These profile-derived alignment scores are easy to obtain and are applicable to any alignment method. They can be used to detect those regions of alignments that are reliably aligned and to help predict the quality of an alignment. For those residues within secondary structure elements, the regions predicted as reliably aligned agreed with the structural alignments for between 92% and 97.4% of the residues. In loop regions just under 92% of the residues predicted to be reliable agreed with the structural alignments. The percentage of residues predicted as reliable ranged from 32.1% for helix residues to 52.8% for strand residues.This information could also be used to help predict conserved binding sites from sequence alignments. Residues in the template that were identified as binding sites, that aligned to an identical amino acid residue and where the sequence alignment agreed with the structural alignment were in highly conserved, high scoring regions over 80% of the time. This suggests that many binding sites that are present in both target and template sequences are in sequence-conserved regions and that there is the possibility of translating reliability to binding site prediction.     

Scoring profile-to-profile sequence alignments

Sequence alignment profiles have been shown to be very powerful in creating accurate sequence alignments. Profiles are often used to search a sequence database with a local alignment algorithm. More accurate and longer alignments have been obtained with profile-to-profile comparison. There are several steps that must be performed in creating profile-profile alignments, and each involves choices in parameters and algorithms. These steps include (1) what sequences to include in a multiple alignment used to build each profile, (2) how to weight similar sequences in the multiple alignment and how to determine amino acid frequencies from the weighted alignment, (3) how to score a column from one profile aligned to a column of the other profile, (4) how to score gaps in the profile-profile alignment, and (5) how to include structural information. Large-scale benchmarks consisting of pairs of homologous proteins with structurally determined sequence alignments are necessary for evaluating the efficacy of each scoring scheme. With such a benchmark, we have investigated the properties of profile-profile alignments and found that (1) with optimized gap penalties, most column-column scoring functions behave similarly to one another in alignment accuracy; (2) some functions, however, have much higher search sensitivity and specificity; (3) position-specific weighting schemes in determining amino acid counts in columns of multiple sequence alignments are better than sequence-specific schemes; (4) removing positions in the profile with gaps in the query sequence results in better alignments; and (5) adding predicted and known secondary structure information improves alignments.