Information theory provides an alternative, in the form of mutual information (MI). MI is, in principle, an objective and model independent similarity measure, but it is not widely used in this context and no algorithm for extracting MI from a given alignment (without assuming an evolutionary model) is known. MI can be estimated without alignments, by concatenating and zipping sequences, but so far this has only produced estimates with uncontrolled errors, despite the fact that the normalized compression distance based on it has shown promising results.Existing sequence alignment algorithms use heuristic scoring schemes based on biological expertise, which cannot be used as objective distance metrics. As a result one relies on crude measures, like the p- or log-det distances, or makes explicit, and often too simplistic, global pairwise alignments. Our main result uses algorithmic (Kolmogorov) information theory, but we show that similar results can also be obtained from Shannon theory. For animal mitochondrial DNA our approach uses the alignments made by popular global alignment algorithms to produce MI estimates that are strikingly close to estimates obtained from the alignment free methods mentioned above. We point out that, due to the fact that it is not additive, normalized compression distance is not an optimal metric for phylogenetics but we propose a simple modification that overcomes the issue of additivity. We test several versions of our MI based distance measures on a large number of randomly chosen quartets and demonstrate that they all perform better than traditional measures like the Kimura or log-det  distances.We describe a simple approach to get robust estimates of MI from Several versions of MI based distances outperform conventional distances in distance-based phylogeny. Even a simplified version based on single letter Shannon entropies, which can be easily incorporated in existing software packages, gave superior results throughout the entire animal kingdom. But we see the main virtue of our approach in a more general way. For example, it can also help to judge the relative merits of different alignment algorithms, by estimating the significance of specific alignments. It strongly suggests that information theory concepts can be exploited further in sequence analysis. The algorithm inserts blanks in each of the sequences such that the number of positions at which the two sequences agree is maximized. More precisely, a scoring scheme is used. Each position at which the two sequences agree is rewarded by a positive score, while each disagreement (“mutation”) and each insertion of a blank (“gap”) is punished by a negative one. The best alignment is that with the highest total score. In local alignment, one aligns only subsequences against each other and looks for the highest scores between any pairs of subsequences. Regions that cannot be well-aligned are simply ignored. Existing algorithms use either heuristic scoring schemes or scores derived from explicit probabilistic models In four point conditionneighbor joining, the most popular distance based algorithm for inferring phylogenetic trees paralinearlog-detSimilarities between DNA sequences, e.g. for distance-based phylogenetic tree construction, are typically not based on alignment scores. Instead they use explicit evolutionary assumptions , which takes the amount of information shared between two objects as a measure of their similarity In the above mentioned distances, distinct rates of different substitution types are either taken into account using a model, or are not taken into account at all. This fact stands in stark contrast with algorithmic) “complexity” theory Indeed, there are two variants of information theory: The more traditional Shannon theory, based on a probabilistic interpretation of the sequences, and the less well known Kolmogorov  is defined as the difference vice versa. Indeed, it can be shown that, up to correction terms that become negligible for long sequences  ;  will lead to over-estimation of Shannon entropy, and thus implies no risk of overfitting.edit distanceThe fact that alignment and information theory are closely related has been realized repeatedly. However, most work in this direction has focused on aligning images rather than sequences translation string. The translation string At the heart of our approach is the concept of a if if something is deleted from if transition, i.e. a substitution Aif transversion Aif if An estimate of the conditional complexity In order to obtain an estimate of MI, we have to subtract This is to be compared to the general definition of algorithmic MI, based entirely on concatenation and compression At first sight it might seem paradoxical that translation strings do not give both translations Conceptually our approach is similar to encoding generalized edit strings in For long strings, block or word probability Compared to algorithmic information, Shannon theory is the more widely known version of information theory Eq. (6) is valid under the assumption that correlations between consecutive letters in the string can be neglected. Similarly, additive metric distance, i.e. a non-negative symmetric pairwise function four-point conditionThe value of the MI itself is useful for many purposes: Estimating similarities between different pairs (and thus of finding closest neighbors of a given sequence in a large data set); comparing the qualities of alignments obtained by different algorithms; or assessing the significance of an alignment . But in the case of phylogeny, one wants more. Ideally, one wants an normalized compression distancenormalized Shannon distanceSeveral potential metrics can be derived from MI long branch attraction  DNA compression algorithm To evaluate The complete mtDNA sequences used in our analysis were downloaded from Our first results concern the agreement between the two estimates all of them involve a subspecies of sikka deer , in which a single gene (NADH6) is supposedly on the opposite strand compared to all other mammals.In view of this, it is not obvious that the estimates produced by XM are realistic either. It is thus highly significant that the two estimates shown in Agreement between MI estimates obtained using other global alignment algorithms are similar to those obtained with MAVID; an example is shown in In As Therefore, if the information encoded in gaps is to be taken into account, it is necessary to go beyond the single letter approximation when estimating realistic and absolute sequence similarities. Furthermore, taking into account only pairwise letter correlations would not be sufficient either. This, of course, is not completely new, and the most common way to deal with this problem is to simply ignore indels A very simple but popular distance measure between sequences (both DNA and amino acids) is p-distance. It is defined by first removing all indel positions and then counting the number of positions where the two sequences disagree Our main interest here is to see which of these three neglected aspects  has the biggest effect. In As we had pointed out in the previous subsection, it is widely believed that indels are not very informative. We plan to check this more carefully in a future publication, using a methodology based on a large number of quartets for sequences similar to the one described in the next section.Finally, we want to compare our distance metrics Next we tried to check whether Roughly, the dependence is linear. Thus, to the extent that On a finer scale, one sees several deviations. The most conspicuous, perhaps, is that insects (hexapods) are systematically above the main curve. This is due to the strong compositional bias in most insects, where C/G is underrepresented compared to A/T. This reduces the entropies of individual sequences. At the same time, however, substitution rates involving C and G are not as suppressed. As a consequence, the ratio For intermediate distances  where For each quartet we compute a significance In this way we selected 129 “worst” disagreements between The final results of this are summarized in Before moving on we highlight a few notable observations about our quartet analysis. Previously, we pointed out that the spectacled bear (T. ornatus) is anomalous either in systematically different from the tree constructed using We find that discrepancies between It is well known exactly the same input — the base substitution frequency matrix after removing indels — as the log-det distance. But our tenet is, of course, that compression based estimates should be superior as long as they use the information about indels efficiently. In addition to the log-det distance, there are several measures that are often used. In this subsection we make several pairwise comparisons similar to the one made in the previous subsection. But we restrict ourselves to mammals, as these have the best understood phylogeny, and we expect the least numbers of errors in the literature classification.So far we have only compared in detail quartet classifications based on log-det distances and on single letter Shannon MI. We have used Shannon MI because its estimation is less ambiguous than compression based MI estimation, and because it uses In this subsection we compare MI based distances with the log-det and with both versions of the Kimura distance ) discussed in all possible quartets. Our criteria for identifying the “worst” disagreements is the same as in the previous subsection. Each comparison is based on at least 60 disagreeing quartets. In this table, “KimuraResults are shown in In the present paper we have not presented any detailed application to a specific open phylogenetic problem. We also have not considered larger phylogenetic trees, in view of the imperfections of all existing distance based tree reconstruction algorithms. Instead, we have concentrated on quartets, since there we can obtain high statistics and the inference of the tree from a given distance matrix is trivial. Also, for the most detailed numerical comparison we have concentrated on Shannon information based methods, rather than on compression based methods for estimating MI. The reason is simply that we desired a comparison with other methods (mainly the log-det distance) which is as straightforward and unambiguous as possible. Indeed, it is trivial to replace Eq. (12) by Eqs. (10), (13), (14). In this way we hope to have the best chance to convince even skeptical readers that mutual information based distance measures are useful in sequence analysis.We have also presented similar – but less complete – analyses based on large numbers of random quartets for  compression based algorithms and have demonstrated that distances based on data compression give even better phylogenies. Indeed, from All versions based on MI are better than any version not based on MI.KimuraNevertheless, Within the class of MI based distances, those which do not neglect indels seem systematically better.Among the latter, distances based on systematic errors due to deviations from metric additivity (the Felsenstein phenomenon). On the other hand it amplifies noise. To illustrate this, we discuss in Logarithmic transformation of MI based distances seems to give mixed results. It improves the distances slightly for Shannon MI and for Thus, contrary to wide spread opinion, information about indels can be directly used for phylogenetics, even without any detailed model for how they were generated. A more detailed presentation of these data and their implications will be given elsewhere.We believe that so far we have only scratched the true potential of  information theory for sequence analysis. Several generalizations and improvements are feasible and are listed below:Use more efficient encodings of the translation string. For instance, we only used the letters Use local alignments instead of global ones. In a local alignment between sequences Construct objective measures based on information theory for the quality of multiple alignments. A straightforward measure is the information about sequence At present, biological sequence analysis is heavily based on the concept of alignment. There exist proposals for alignment-free approaches, and it has been suggested that they will become more and more important as more sequence data become available parsimony. In bioinformatics (and in phylogeny in particular) maximal parsimony in dealing with several objects is often taken as synonymous to minimal number of changes needed to go from the description of one object to the description of another. This is most clearly formulated in the so-called “maximum parsimony method” of distance-free phylogenetic tree construction minimum description length principlenumber of changes, one should pay attention to the information needed to encode these changes. We call this “true parsimony”. In this sense, the maximum parsimony method does not really aim for maximal true parsimony. On the other hand, likelihood based and Bayesian methods do aim for true parsimony, but at the cost of depending on explicit models. One goal of the present paper is to show how true parsimony can be measured in less model dependent ways and how maximum true parsimony can be achieved to various degrees of approximation. Moreover, even the crudest approximation – based on MI obtained via single-letter Shannon entropies, with all information about indels discarded – can lead to important practical improvements.From another point of view, the present paper deals with the basic notion of Material S1Additional figures, tables and discussion.(2.82 MB PDF)Click here for additional data file.


One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.O(1) run-time solutions for both local and global dynamic programming pair-wise alignment algorithms on reconfigurable mesh computing model. To align m sequences with max length n, we combining the parallel pair-wise dynamic programming solutions with newly designed parallel components. We successfully reduce the progressive multiple sequence alignment algorithm's run-time complexity from O(m × n4) to O(m) using O(m × n3) processing units for scoring schemes that use three distinct values for match/mismatch/gap-extension. The general solution to multiple sequence alignment algorithm takes O(m × n4) processing units and completes in O(m) time.We design O(m) run-time. We also provide a new parallel algorithm for the Longest Common Subsequence (LCS) with O(1) run-time using O(n3) processing units. This is a big improvement over the current best constant-time algorithm that uses O(n4) processing units.To our knowledge, this is the first time the progressive multiple sequence alignment algorithm is completely parallelized with  The advancement of DNA/RNA and protein sequencing and sequence identification has created numerous databases of sequences. One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. Aligning multiple sequences together provides researchers with one of the best solutions to this task. In general, multiple sequence alignment can be defined as:Definition 1Given: m sequences, , over an alphabet ∑, where each sequence contains up to n symbols from ∑; a scoring function h: and a gap cost function. Multiple sequence alignment is a technique to transform  to where is si ∪ '-' [gap insertions], that optimizes the matching scores between the residues across all sequence columns  as in  and 29]29]. In , the indO(m2n2) to O(n) and step (iii) from O(mn4) to O(m3n) ≈ O(n4), [or O(m4) if n <m]. The overall run-time complexity of the original progressive multiple sequence alignment algorithm is still dominated by step (iii) with an order of O(m3n) regardless of how many processing units are used. The bottle-neck is the pair-wise group alignments must be done in order dictated by the dendrogram (O(m)), and each alignment requires all the column pair-wise scores be calculated (O(m2)). To address these issues, we design our parallel progressive multiple sequence alignment on a reconfigurable mesh (r-mesh) computing model similar to the ones used in , the unary 1UN presentation of x in n-bit is: x = , where bi = 1 for all i ≤ x and bi = 0 for all i > x  can be found in O(c) time on an n × n r-mesh . As a result, a DP r-mesh can be built with 1-bit input components to handle all pair-wise alignments using constant scoring schemes that can be converted to  range. For instance, the scoring scheme for the longest common subsequence rewards 1 for a match and zero for mismatch and gap extension.ci, j loads or computes its symbol matching score for the symbol pair at row i column j, initially. The next step is to configure all the adder/subtractor units based on the loaded values and the gap cost g. Finally, a signal is broadcasted from c0,0 to its neighboring cells c0,1, c1,0, and c1,1 to activate the DP algorithm on the r-mesh. The values coming from cells ci-1, j and ci, j-1 are subtracted with the gap costs. The value coming from ci-1, j-1 is added with the initial symbol matching score in ci, j. These values will flow through the DP r-mesh in one broadcasting step, and cell cn, n will receive the correct value of the alignment.To align two sequences, O(1). Each cell uses 10n processing units (4n for the 1-bit max switch and 2n for each of the three adder/subtrator units). These processing units are bounded by O(n). Therefore, the n × n dynamic programming r-mesh uses O(n3) processing units.In term of time complexity, this dynamic programming r-mesh takes a constant time to initialize the DP r-mesh and one broadcasting time to compute the alignment. Thus, its run-time complexity is k × n adder/subtractor r-meshes and n × n max switches must be used. In addition, to avoid overflow (or underflow) all pre-defined pair-wise symbol matching scores may have to be scaled up (or down) so that the possible smallest (or largest) number can fit in the 1UN representation. With this configuration, the dynamic programming r-mesh takes O(n4) processing units.To handle all other scoring schemes, g = 0).The complication of signed numbers does not exist in the longest common subsequence problem. The arithmetic operation in LCS is a simple addition of 1 if there is a match. The same dynamic programming r-mesh as seen in Figure x and y, each max switch in the DP r-mesh is configured as in Figure ci-1, j-1 is fed into the North-West processing unit, and the other values are fed into the North-East unit. Then, ci, j loads in its symbols and fuses the South-East processing unit (in bold) as NS,E,W if the symbols at row i and column j are different; otherwise, it loads 1 into the adder unit and fuses N,E,SW. These configurations allow either the value from cell ci-1, j-1 or the max value of cells ci-1, j and ci, j-1 to pass through. These are the only changes for the DP r-mesh to solve the LCS problems.To find the longest common subsequence between two sequences O(n3) processing units. However, this is an order of reduction comparing the current best constant parallel DP algorithm that uses an r-mesh of size O(n2) × O(n2)  of the progressive MSA algorithm is following the dendrogram, built in the earlier step, to perform pair-wise dynamic programming alignment on two pre-aligned groups of sequences. The dynamic programming alignment algorithm in this step is exactly the same as the one in step (i); however, quantifying a match between two columns of residues are no longer a simple constant look-up, unless the hierarchical expected probability (HEP) matching scoring scheme is used . The mosf is a column from one pre-aligned group of sequences and g is a column from the other pre-aligned group of sequences. fi and gj are residue symbols from columns f and g, respectively, and s is the matching score between these two symbols fi and gj. For example, to calculate the sum-of-pair of the following two columns f and g:where anda to residue b is the same as the matching between residue b to residue a, these pairs become , 3, , 3, , 3, 3. These redundancies occur since the set of symbols representing the residues is small (1 for gap plus 20 for protein [or 4 for DNA/RNA]). Thus, if we combine the two column symbols with their number of occurrences, the sum-of-pair method can be transformed into a counting problem and can be defined as:we will have to score 15 residue pairs:, , , , , , , , , , , , , , . Since the matching between residue f, g are the two columns, T is the number of different residue symbols (T = 4 for DNA/RNA and T = 20 for proteins), s is the pair-wise matching score, or substitution score, between two residue symbols i and j, and ni and nj are the total count of symbols/types i and j (i.e. the occurrences of residue symbols/types i and j), respectively. Since residues from both column f and g are merged, there is no distinction in which column the residue are from. Since T is constant, the summations in Equation remain constant, regardless how many sequences are involved.where Thus, the sum-of-pair score of the two columns given above will be:m processing units as follows. First, map each residue symbol into a numeric value from 1 to T. Next, m residues from any two aligning columns are assigned to m processing units. Any processing unit holding a residue sends a 1 to processing unit pk, where k is the number represents the residue symbol it is holding. pk sums the 1's it receives. The sum-of-pair score can be computed between the pairs of processing units containing a sum larger than 0 calculated from previous steps. All of these steps are carried out in constant time. There are n2 possible pair-wise column arrangements of two pre-aligned groups of sequences of max length n. Thus, the sum-of-pair column pair-wise matching scores for two pre-aligned groups of sequences can be done in O(1) using m × n2 processing units.This scoring function can be implemented on an array of O(1) using O(m) time using O(m) r-meshes. The latter is preferred since the dendrogram [step (ii)] and the progressive alignment [step (iii)] each takes O(m) time to complete. Then, a dendrogram is built, using the parallel neighbor-joining clustering algorithm described earlier, from all the pair-wise DP alignment scores from step (i). Lastly, [step (iii)], for each pair of pre-aligned groups of sequences along the dendrogram, the sum-of-pair column matching scores are pre-calculated for the DP r-mesh initialization before proceeding with the dynamic programming alignment. There are m - 1 branches in the dendrogram leading to m - 1 pair-wise group alignments to be performed. In terms of complexity, the progressive multiple sequence alignment takes O(m) time using O(n) DP r-meshes to complete all the pair-wise sequence alignments [step (i)] - (or O(1) time using O(m) time using O(m3) processing units. Finally, the progressive step, [step (iii)], takes O(m) time using a DP r-mesh. Therefore, the overall run-time complexity of this parallel progressive multiple sequence alignment is O(m). The number of processing units utilized in this parallel algorithm is bounded by the number of DP r-meshes used and their sizes. The general DP r-mesh uses O(n4) processing units to handle all scoring schemes with affine gap cost. And step (i) needs m of such DP r-meshes resulting in O(mn4) ≈ O(n5) processing units used.Progressive multiple sequence alignment algorithm is a heuristic alignment technique that builds up a final multiple sequence alignment by combining pair-wise alignments starting with the most similar pair and progressing to the most distant pair. The distance between the sequences can be calculated by dynamic programming algorithms such as Smith-Waterman's or Needle-Wunsch's algorithms (step i). The order in which the sequences should be aligned are represented as a guiding and can be calculated via hierarchical clustering algorithms similar to the one described in Section (step ii). After the guiding tree is completed, the input sequences can be pair-wise aligned following the order specified in the tree (step iii). In the previous Sections, we have described and designed several r-meshes to handle individual operations in the progressive multiple alignment algorithm. Finally, a progressive multiple sequence alignment r-mesh configuration can be constructed. First, the input sequences are pair-wise aligned using the dynamic programming r-mesh described previously in Section. These O(mn3) ≈ O(n4) processing units to complete in O(m) time.For alignment problems that use constant scoring schemes without affine gap cost, this parallel progressive multiple sequence alignment algorithm only needs Table m sequences with max length n, we are able to reduce the algorithm run-time complexity from O(m × n4) to O(m) using O(m × n4) processing units. For a scoring scheme that rewards 1 for a match, 0 for a mismatch, and -1 for a gap insertion/deletion, our algorithm uses only O(m × n3) processing units. Moreover, to our knowledge, we are the first to propose an O(1) run-time dynamic programming pair-wise alignment algorithm using only O(n3) processing units.In this study, we have designed various r-mesh components that can run in one broadcasting step, which enabling us to effectively parallelize the progressive multiple sequence alignment paradigm. to align The authors declare that they have no competing interests.KN designed parallel models used in this study. YP and GN participated in designing and criticizing the parallel models and their analysis. All authors read and approved the final manuscript.


In recent years, an exponential growing number of tools for protein sequence analysis, editing and modeling tasks have been put at the disposal of the scientific community. Despite the vast majority of these tools have been released as open source software, their deep learning curves often discourages even the most experienced users.A simple and intuitive interface, PyMod, between the popular molecular graphics system PyMOL and several other tools  has been developed, to show how the integration of the individual steps required for homology modeling and sequence/structure analysis within the PyMOL framework can hugely simplify these tasks. Sequence similarity searches, multiple sequence and structural alignments generation and editing, and even the possibility to merge sequence and structure alignments have been implemented in PyMod, with the aim of creating a simple, yet powerful tool for sequence and structure analysis and building of homology models.PyMod represents a new tool for the analysis and the manipulation of protein sequences and structures. The ease of use, integration with many sequence retrieving and alignment tools and PyMOL, one of the most used molecular visualization system, are the key features of this tool.http://schubert.bio.uniroma1.it/pymod/index.htmlSource code, installation instructions, video tutorials and a user's guide are freely available at the URL  Once confined only to experts in bioinformatics, protein sequence retrieving, aligning and modeling tasks are now being routinely approached by an increasing number of researchers, who can take also advantage of the growing number of structures that are being deposited every day in public databases. Integrating protein sequence and structure information has therefore become an imperative, especially in the field of protein structure prediction from sequence, by means of homology modeling (HM) methodologies.In recent years, a number of valuable tools related to protein sequence analysis and modeling  and PSI-BLAST locally. In the latter case, users are provided with the option to install local sequence databases, while PyMod provides a graphical interface to ease their use. To facilitate template structure search for homology modeling tasks, PyMod will be distributed with a pre-installed PDB sequence database, which will be updated in future releases on a monthly base. A number of (PSI-)BLAST parameters can be controlled by the user from within an apposite PyMod window  BLAST searches of large databases, both locally or remotely, which can be also used as a standalone tool inside the PyMOL framework.Once retrieved sequences from selected databases are loaded in PyMod, they can be used to generate a multiple sequence alignment by means of MUSCLE and ClustalW programs. The choice of two multiple sequence alignment tools is twofold: on the one hand, ClustalW is famous and very popular among people with limited experience in the field; on the other hand, MUSCLE is known to outperform ClustalW in quality and in speed. Future implementation of additional tools  is planStarting from a previously obtained alignment, it is possible to build a homology model of a selected sequence through the PyMod interface to the popular MODELLER program. The satisfaction of spatial restraints algorithm, as implemented in MODELLER, undoubtedly represent one of the most popular homology modelling approaches, and has become the model-building program of choice for several homology modelling servers because of its relative speed and reliability. Several of the strongest performing prediction servers in the CASP8 experiment, such as HHpred , incorpoPyMod represents a new tool for the analysis and the manipulation of protein sequences and structures. The ease of use, integration with many sequence retrieving and alignment tools and PyMOL, one of the most used molecular visualization system, are the key features of this tool. We plan to release future updates of PyMod, including additional tools for secondary structure prediction, sequence retrieving and alignment, as well as other tools suggested by the users' community. Finally, a tighter integration between PyMOL, MODELLER and PyMod will constitute a main issue of future project development plans.Project name: PyModhttp://schubert.bio.uniroma1.it/pymodProject home page: Operating system(s): Windows . Linux (Ubuntu) and Mac OS (10.6) will be supported in the next release.Programming language: PythonLicense: Lesser General Public License (LGPL)Other requirements: PyMOL version 1.1.1 or newer, BioPython version 1.50 or newer, Standalone BLAST 2.2.25+ or newer, Muscle, ClustalW and MODELLER.The authors declare that they have no competing interests.EB wrote the software and helped to draft the manuscript. AP conceived the study, helped to write the program and drafted the manuscript. FB revised the manuscript critically for important intellectual content. SP participated in the study design and coordination and revised the manuscript. All authors read and approved the final manuscript. EB and AP contributed equally to this work.


Phylogeny-aware progressive alignment has been found to perform well in phylogenetic alignment benchmarks and to produce superior alignments for the inference of selection on codon sequences. Its implementation in the PRANK alignment program package also allows modelling of complex evolutionary processes and inference of posterior probabilities for sequence sites evolving under each distinct scenario, either simultaneously with the alignment of sequences or as a post-processing step for an existing alignment. This has led to software with many advanced features, and users may find it difficult to generate optimal alignments, visualise the full information in their alignment results, or post-process these results, e.g. by objectively selecting subsets of alignment sites.de novo alignments, webPRANK can be used for the inference of ancestral sequences with phylogenetically realistic gap patterns, and for the annotation and post-processing of existing alignments. The webPRANK server is freely available on the web at http://tinyurl.com/webprank .We have created a web server called webPRANK that provides an easy-to-use interface to the PRANK phylogeny-aware alignment algorithm. The webPRANK server supports the alignment of DNA, protein and codon sequences as well as protein-translated alignment of cDNAs, and includes built-in structure models for the alignment of genomic sequences. The resulting alignments can be exported in various formats widely used in evolutionary sequence analyses. The webPRANK server also includes a powerful web-based alignment browser for the visualisation and post-processing of the results in the context of a cladogram relating the sequences, allowing (e.g.) removal of alignment columns with low posterior reliability. In addition to The webPRANK server incorporates phylogeny-aware multiple sequence alignment, visualisation and post-processing in an easy-to-use web interface. It widens the user base of phylogeny-aware multiple sequence alignment and allows the performance of all alignment-related activity for small sequence analysis projects using only a standard web browser. When used for evolutionary or phylogenetic analyses, a sequence alignment is meant to represent evolutionary homology and have the characters descended from a common ancestor placed in the same column. Commonly used alignment programs do not consider phylogeny in their placement of gaps, however, and create systematic errors with insertion-deletion events, affecting downstream evolutionary analyses ,2. The phttp://tinyurl.com/prank-msa). We now add an easy-to-use web interface called webPRANK  extensively. The format is capable of storing the full information of the alignment process, including the guide tree and the posterior scores associated with the pair-wise alignments at its internal nodes, in a single, computer-parsable file. This enables advanced analysis and post-processing of newly-generated results as well as full re-processing of earlier results, stored locally in the format, within the associated alignment browser.The webPRANK server uses the XML-based HSAML sequence alignment format  to guide its progressive alignment procedure, or can compute one from the unaligned input sequences. For each alignment task, the full combination of parameters, and the structure model if used, are provided in the output so that the analyses can easily be repeated or recreated with the stand-alone PRANK program.The webPRANK server Figure  supportsO(a2nl2) where a, n and l are the size of the character alphabet  and the number and length of sequences, respectively.  The alignment of 30 DNA sequences of ~1000 nucleotides typically takes 1-2.5 minutes depending on the options chosen; that of 100 DNA sequences of similar length 3.5-20 minutes. The translation of DNA sequences to amino acids or codons decreases sequence lengths but increases alphabet size, requiring computation times similar to (for amino acids) or significantly longer than (for codons) those for untranslated DNA sequences. By default, webPRANK uses alignment anchoring to accelerate analyses of long DNA sequences.The size of alignment tasks is limited to 4 GB of memory and 24 hours of run time. The size and type of data as well as the parameter settings affect the computation time. The PRANK algorithm has time complexity Significant proportions of the longer time estimates for the alignment tasks are spent computing the guide trees and, if a user-defined phylogeny is provided, even larger data sets can be aligned in a reasonable time. With a pre-defined guide tree, the alignment of 1000 simulated DNA sequences of ~1000 nucleotides could be performed in 35 minutes; however, the alignment matrix was 7247 columns wide (the correct width was 7235 columns) and so sparse that it was largely unreadable . For larger alignments , the webPRANK server provides an alternative, light-weight browser that allows basic horizontal and vertical scrolling of the alignment matrices. Alternatively, the full results can be downloaded in HSAML format and analysed locally using the stand-alone PRANKSTER program, or the aligned sequences exported in simpler formats for visualisation with other browser software.The PRANK algorithm can compute column-wise reliability scores for the alignment and, when a structure model is used, provide posterior probabilities for the alignment sites evolving under different evolutionary processes . The relThe PRANK alignment reliability scores provide an objective measure to remove less reliably aligned columns from the data and the webPRANK browser includes advanced functionality to select sets of alignment sites using these scores. The webPRANK filtering is based on the track currently displayed; repeated steps of filtering are accepted and, for convenience, an additional track showing the minimum reliability score across all pair-wise alignments is provided. The current selection of alignment sites is indicated in the browser window using different colouring Figure  and the As a part of the alignment process, the PRANK algorithm reconstructs the sequence history with inferred ancestral nodes. The inferred ancestral sequences, with phylogenetically realistic patterns of character presence vs. absence, can be displayed in the alignment browser or downloaded for further analyses. Ancestral sequences can also be inferred from existing alignments. One should note, however, that non-phylogeny-aware alignment algorithms tend to infer excess deletions  and infede novo alignments: it can be used for visualisation and browsing of any FASTA- or HSAML-formatted alignment, although the full functionality of the browser requires the richer HSAML format. By storing webPRANK-generated alignments in this format, the user can later re-load the results to the webPRANK browser for visualisation and post-processing, and thus perform all alignment-related activity for small sequence analysis projects using a standard web browser only.The webPRANK alignment browser is not limited to the display of http://www.ebi.ac.uk/goldman-srv/prank as well as at the URLs listed above.The webPRANK server incorporates phylogeny-aware multiple sequence alignment, visualisation and post-processing. It widens the user base of phylogeny-aware multiple sequence alignment, which can lead to superior inference of character homology and downstream evolutionary analyses. We encourage the consideration of evolutionary sequence alignment in the context of the tree relating the sequences, and the use of enhanced alignment formats such as HSAML for the exchange of linked phylogenetic and alignment information. Our various alignment-related software and format definitions are all available via Project name: webPRANKhttp://www.ebi.ac.uk/goldman-srv/webPRANK/Project home page: Operating systems: Platform independent (web server)Programming language: C++ , Perl/CGI/SOAP (server interface),HTML/JavaScript Licence: GNU GPL Any restrictions to use by non-academics: noneNG initiated the project and participated in its design, testing and coordination. AL implemented the ideas, wrote the software and drafted the manuscript. Both authors reviewed and approved the final manuscript.The document contains a figure illustrating the difficulty of browsing alignments of many sequences, even when they are closely related, and text explaining this figure.Click here for file


Bacillus subtilis yitF protein. The method was implemented in a computer program, ALAdeGAP , which is available at http://cib.cf.ocha.ac.jp/target_protein/. Proteins 2011; © 2011 Wiley-Liss, Inc.In comparative modeling, the quality of amino acid sequence alignment still constitutes a major bottleneck in the generation of high quality models of protein three-dimensional (3D) structures. Substantial efforts have been made to improve alignment quality by revising the substitution matrix, introducing multiple sequences, replacing dynamic programming with hidden Markov models, and incorporating 3D structure information. Improvements in the gap penalty have not been a major focus, however, following the development of the affine gap penalty and of the secondary structure dependent gap penalty. We revisited the correlation between protein 3D structure and gap location in a large protein 3D structure data set, and found that the frequency of gap locations approximated to an exponential function of the solvent accessibility of the inserted residues. The nonlinearity of the gap frequency as a function of accessibility corresponded well to the relationship between residue mutation pattern and residue accessibility. By introducing this relationship into the gap penalty calculation for pairwise alignment between template and target amino acid sequences, we were able to obtain a sequence alignment much closer to the structural alignment. The quality of the alignments was substantially improved on a pair of sequences with identity in the “twilight zone” between 20 and 40%. The relocation of gaps by our new method made a significant improvement in comparative modeling, exemplified here by the  Most of the proteins perform their function after forming their three-dimensional 3D) structures. Knowledge of protein 3D structure is, therefore, essential for understanding the mechanisms of protein function in atomic detail.D structu7Comparative modeling technique consists of several component methods: a method for finding the best template structure; a method for high-accuracy alignment; and a method for accurately deducing side-chain conformation.Efforts dedicated to improving alignment quality have focused primarily on improving the substitution matrix. Some approaches have attempted to build a general substitution matrix that depends on the protein environment,et al.et al.et al.When aligning amino acid sequences for the purpose of comparative modeling, the 3D structure of the template protein is known by definition; hence, the structural information can be reflected in the gap penalty. Lesk et al.34Even after all these past efforts, the quality of the protein model still falls below the satisfactory level. Kopp http://cib.cf.ocha.ac.jp/target_protein/.Here, we re-evaluated the premise of gap location in protein 3D structures, based on a large protein dataset, and found that the distribution of gaps in protein 3D structures differed from those reported previously. We examined the pure contribution of our new finding to the improvement in sequence alignment by implementing a new gap penalty equation into a simple pairwise alignment method. We found that the new method outperforms most of the conventional alignment methods. Our new program will help to improve the quality of comparative modeling by providing a better alignment between template and target amino acid sequences. The software is available at Gap locations were assigned by superposing a pair of homologous protein structures. The homologous protein pairs were taken from each family in SCOP 1.69 dataset.w with 0.05, and Ni, the number of gaps with a gap accessibility in each bin, was counted. Then gi, the frequency of gaps in each accessibility bin i, was calculated by,“Residue-wise gap accessibility” was defined by the accessibility of each residue aligned in the gap region . We heregi in different bins, the value should be normalized by the frequency of residues in each bin, fi, which is,To compare Ai is the count of residues in accessibility bin i. i. A rule applicable for building a sequence alignment was then deduced as an equation by observing the relationship between the accessibility and the gap odds ratio.where We developed a program for pairwise amino acid sequence alignment based on the assumption that one of the sequences has a known 3D structure (template) and the other does not (target). A pairwise alignment by dynamic programming was implemented as described by Isaev,sI). pI). In this score, a three-residue window is set around the insertion point identified by a structural alignment; if the insertion point identified by the sequence alignment is located in this window, then it is assigned as correct. We used three types of scores to compare the performance of our alignment method with conventional ones. The first is the Q-score, defined by Pei and Grishin,sI and pI is then a prerequisite for modeling. Taking the modeling procedure into account, the accuracy of the model is best measured with correctness and no over-assignment of sI and pI. We quantified this idea in the following equations;From the viewpoint of comparative modeling, alignment can be recognized as a method for gap prediction. Accurate prediction of xs << 0, because correctly assigned insertion segments should outnumber incorrectly assigned segments, and ys ≈ 0 or at least ys ≤ 0, because assignment of too many insertion segments significantly hampers the comparative modeling process. Similar equations can be applied to pI;where We compared the accuracy of alignment methods for comparative modeling based on Q-score and Eqs. In our program, a progressive multiple sequence alignment methodThe relationship between gap accessibility and gap frequency was revisited using a large dataset based on SCOP 1.69,gi/fi=1.0 at a gap accessibility of ∼0.3, which means that an accessibility of 0.3 is the point where gap preference switches: gaps are underrepresented between accessibility of 0.0 and 0.3, and overrepresented at accessibilities greater than 0.3. Go and MiyazawaThe first line crosses y = 2.25x + 0.35 , whereas natural log regression of the plots resulted in log y = 1.55x − 0.50 . The distribution of the frequency of the gap can be reasonably expressed via a logarithmic equation. The gap penalty should be in inverse relation to the gap frequency; hence, we used an exponential equation to deduce the gap penalty for sequence alignment.For the gap penalty calculation in sequence alignment, it is preferable to obtain the gap penalty using a single continuous equation for accessibility. We fit the plot with a linear and logarithmic regression lines. Linear regression of the plots resulted in et al.The logarithmic relationship between gap accessibility and gap odds ratio has not been previously reported. Zhu G;We found the logarithmic relationship between gap location and gap accessibility in the previous section. To reflect this relationship to the gap penalty, which should be the inverse of the gap frequency, we used the following equation for the accessibility-dependent gap opening penalty where accessibility was calculated as shown in The dataset we used for parameter fitting was a subset of the dataset we observed the relationship between gap frequency and accessibility. From the original dataset with 18,019 protein pairs, we selected 1519 pairs. The pairs were selected to avoid multiple appearances of the same protein, and to have lower sequence identity. The distribution of amino acid sequence identity in the selected dataset is shown in ALignment with Accessibility dependent GAp Penalty), and it is freely available at http://cib.cf.ocha.ac.jp/target_protein/.A brute force parameter search was performed in the range 1.0 ≤ α, β ≤ 39.0 with an interval of 1.0. This search revealed that α = 2.0 and β = 33.0 achieved a Q-score of 0.910. Q-score was sensitive to α, because all the parameter sets with a Q-score of 0.91 had α = 2.0. We then further searched for the parameter set in the range 2.0 ≤ α ≤ 3.0 and 31.0 ≤ β ≤ 33.0 with an interval of 0.1 and found that α = 2.1 and β = 32.8 were the best set, with Q-score = 0.911. This score means that 374,784 matches in the alignment are correct, out of the 411,337 matches in 3D structure comparison. The original implementation of the alignment with affine gap penalty resulted in a Q-score of 0.870. We also implemented a gap penalty with a linear relationship with accessibility and obtained a maximum Q-score of 0.902. Hence, the introduction of gap penalty with exponential relation against the accessibility improved the alignment by about 4%. The percentage seems small, but 4% corresponds to ∼15,000 residue matches in 1519 protein pairs. Improvement of the match in ∼10 residues  in one protein pair can coincide with an improvement of loop location by relocating a gap in the alignment. The impact of this is exemplified in the last section. The program with the best parameters was named ALAdeGAP  score (A) and insertion point (pI) score (B), and ran to the upper right in (A) and upper left in (B). The general trends of the both plots are similar. The trend of the plot for PROMALS (dotted black line) is different from the other two: the plot generally stays in the upper left region in (A) and (B). The interpretation of the lower right region  is that the number of false gap assignments exceeds that of true gap assignments, but the number of assigned gaps is less than the number of the real gaps. The interpretation of the upper right region  is that the number of false gap assignments exceeds that of true gap assignments, and the number of assigned gaps is greater than the number of real gaps. The interpretation of the upper left region  is that the number of true gap assignments exceeds the number of false gap assignments, but the number of assigned gaps is greater than the number of real gaps. Note that ALAdeGAP is the only method that goes through or runs close to the  region, where the number of true gap assignments exceeds the number of false gap assignments and the number of assigned gaps is less than the number of real gaps.The plots for ALAdeGAP (solid black line), and ClustalW (broken grey line) started at lower right in both insertion segment  region for pairs in a similar identity range in pI. ALAdeGAP is well suited for alignment for the purpose of comparative modeling of a sequence pair in this range. In the parameter fitting dataset, we put stress on increasing the number of data in this range, because improvement in comparative modeling in this range of sequence identity is mostly in need. The apparent good performance between 20 and 40% identity range may be related to this abundance of aligned sequences in this particular range , but this is because of the difference in the information used by each program. PROMALS incorporates sequence information obtained by PSI-BLAST.Bacillus subtilis genome, there were about 2850 genes (70%) without known functions at the time of genome sequencing.Bacillus genus; homologous proteins in other genera have low amino acid sequence identity, which implies that an accurate multiple sequence alignment is hard to obtain. We modeled the 3D structure of Bacillus subtilis yitF using Escherichia coli D-glucarate dehydratase (GlucD) 3D structureBacillus subtilis yitF was later determined in a structural genomics project  and we can assess the accuracy of the model. The difference in overall Cα root mean square deviations was slight, 4.8 Å for ALAdeGAP model and 5.5Å for ClustalW model. However, due to the inappropriate location of gaps in the alignment, the active site of the protein was covered by a loop in the ClustalW model, and one of the α helices was melted to a loop [Comparative modeling with improved gap location in the template−target alignment is expected to have higher chance of guiding protein function annotation in the right direction. In the o a loop . A prediWe built ALAdeGAP, a new sequence alignment method for comparative modeling. The method is based on the characteristics of protein evolution, namely the gap (insertion and deletion of residues) occurs more frequently on the surface of protein 3D structures. We found that the relation between the frequency and accessibility of gap region is nonlinear. By incorporating this dependency, ALAdeGAP can improve the location of gaps in the alignment when the sequence identity is between ∼20 and ∼40%, a range in which standard methods tend to misplace gaps. We have already implemented our new method to enable multiple sequence alignment. The details of the application of the method will be explained elsewhere. Current threading methods also suffer from precisely locating gaps, namely determination of the precise boundaries of the different elements of secondary structure for the target sequence. Our finding here may indicate a possible benefit, when this gap affinity score could be properly incorporated onto the existing threading algorithms.


Geoglossum but not with Trichoglossum earth tongues, pointing to the significant potential inherent to ecological data mining of environmental samples. Environmental sampling holds the key to many focal questions in mycology, and simultaneous alignment and tree estimation, as performed by SATé, can be a highly efficient companion in that pursuit.An abundance of novel fungal lineages have been indicated by DNA sequencing of the nuclear ribosomal ITS region from environmental samples such as soil and wood. Although phylogenetic analysis of these novel lineages is a key component of unveiling the structure and diversity of complex communities, such analyses are rare for environmental ITS data due to the difficulties of aligning this locus across significantly divergent taxa. One potential approach to this issue is simultaneous alignment and tree estimation. We targeted divergent ITS sequences of the earth tongue fungi (Geoglossomycetes), a basal class in the Ascomycota, to assess the performance of SATé, recent software that combines progressive alignment and tree building. We found that SATé performed well in generating high-quality alignments and in accurately estimating the phylogeny of earth tongue fungi. Drawing from a data set of 300 sequences of earth tongues and progressively more distant fungal lineages, 30 insufficiently identified ITS sequences from the public sequence databases were assigned to the Geoglossomycetes. The association between earth tongues and plants has been hypothesized for a long time, but hard evidence is yet to be collected. The ITS phylogeny showed that four ectomycorrhizal isolates shared a clade with  Some pragmatic attempts to characterize the diversity of these “invisible fungi” from environmental samples have relied on operational taxonomic units (OTUs) that have been delimited as discrete units of sequences that share, e.g., 95–97% pairwise similarity Identification of every environmental sequence to species level, however, may not always be necessary for many ecological and evolutionary questions. To understand the overall fungal diversity and related ecology in some environmental samples, many ecologists would be satisfied with a tree-based genus-level estimation of fungal diversity, which would provide the likely ecology and biology of the unknown lineages. Moreover, as a complement to OTUs, a well-resolved low-level  phylogeny would be a very powerful tool in the characterization of fungal diversity in environmental samples, and it would also be critical for the description of new taxa.One candidate locus for such low-level phylogenies in fungi is the well-studied ITS region. The ITS region is a popular marker for fungal DNA fingerprinting, as it is easily recovered from diverse samples due to its multicopy nature and also because of the relative ease of designing primers to match the conserved neighboring ribosomal genes Geoglossum, Trichoglossum, Sarcoleotia, and Thuemenidium - but the relationships among their species remain understudied emerenciaEarth tongue fungi are ascomycetes that produce large pileate (mushroom-like) fruiting bodies on various substrates, and their unique dark colored and tongue- shaped fruiting bodies are common in temperate regions . AlthougGeoglossum, Trichoglossum, and Thuemenidium, and one ITS sequence (PDD70355) generated from a New Zealand collection was subsequently identified as a species of Microglossum. INSD accession numbers of the newly generated ITS sequences were HQ222862 through HQ222878 , and within this clade, 11 sequences of environmental samples were grouped in five subclades (BPP >0.95) with different Geoglossum, Sarcoleotia, and Trichoglossum species. The reference lineages in Pezizomycetes, Lecanoromycetes, and Lichinomycetes were also strongly supported. The placements and sub-groupings of these clades were not consistent with previously published phylogenies using other gene sequences, however.The whole ITS alignment generated by Clustal W for the reduced data set featured 118 taxa and 1,553 aligned positions, including 328 constant characters, 186 parsimony-uninformative changes, and 1,039 (67%) parsimony-informative positions. No further manual adjustment was made to this alignment. Bayesian analyses using this alignment produced a phylogeny with fairly high support for both the backbone nodes and the internal branches . The GeoPeltula species as the sister group. A total of 39 environmental ITS sequences were assigned to this clade, and 23 sequences were grouped in four subclades (BPP >0.99) with fully identified species of Geoglossum, Trichoglossum, Thuemendidium, and Sarcoleotia. All species of Geoglossum, except for G. glutionosum which is likely to be transferred to Trichoglossum based on other gene phylogenies, were moderately supported as a joint monophyletic lineage (BPP = 0.91). Nine environmental isolates on very long branches were retrieved within the earth tongue clade. Among the reference groups, the Orbiliomycetes was strongly supported as monophyletic with some environmental samples (BPP = 0.99), while the Arthoniomycetes and the Lecanoromycetes were not monophyletic. The overall topology of the best Bayesian tree based on the lower-scored SATé alignment was quite different from the one produced by the best-scored alignment, and all of the reference lineages, except for the Orbiliomycetes, were scattered over more than one clade each  parsimony-informative positions. Bayesian analyses using this data set without manual adjustment produced a phylogeny similar to the automatic tree simultaneously generated by SATé with some few internal branches being well supported . The Geoade each .Geoglossum glutinosum – Trichoglossum – Thuemenidium clade (PBB = 0.95), three isolates shared a clade with Sarcoleotia species (BP = 1.00), and eight isolates were grouped with other Geoglossum species (BP = 1.00). Regarding other groups with reference taxa, a clade including Orbiliomycetes and six environmental isolates was strongly supported (BPP = 1.0), while other reference groups were not monophyletic.The anchored ITS alignment of the data set using SATé featured 296 taxa and 1,094 aligned positions, including 140 constant characters, 75 parsimony-uninformative changes, and 879 (80%) parsimony-informative positions . While mSarcoleotia clade, the Thuemenidium-Trichoglossum-Geoglossum glutinosum clade, and the Geoglossum s. str. clade. A total of 30 environmental isolates grouped in the earth tongue clade, and these isolates shared clades with identified species of Sarcoleotia, Thuemenidium, Trichoglossum, or Geoglossum with strong support (BPP>0.95). A clade including species of Orbiliomycetes and related environmental samples and its subclades received strong support (BPP >0.95) as well, while other reference groups the Arthoniomycetes, the Lecanoromycetes, and the Pezizomycetes were not recovered as monophyletic.Bayesian analysis using the 5.8S re-included ITS alignment of the large data set without manual adjustment produced a phylogeny with moderate to strong support for many internodes . The whoBoth the best-scored whole ITS SATé alignment and the 5.8S re-included SATé alignment produced phylogenetic trees with the highest topological similarity to published multi-gene phylogenies. The phylogenetic tree based on the 5.8S re-included SATé alignment had the largest proportion of well-supported branches and the fewest extremely long branches, although the alignment was longer than for the two above approaches. The lower scored whole ITS SATé alignment produced the most dissimilar trees, and the corresponding multiple alignment was found to be very suboptimal. With the highly conserved 5.8S region excluded, the phylogeny based on the anchored ITS SATé alignment was found to be intermediate, with several reference sequences being correctly grouped.e.g.The difficulties associated with aligning the ITS region across larger fungal lineages has hitherto prevented wide use of ITS data for high-level phylogeny as well as preventing use of ITS data for inference of phylogenetic structure in environmental samples. Our alignments validated these difficulties in that we found ITS sequences to be highly divergent across a wide taxon sampling, with long alignments and comparatively high percentages of parsimony-informative positions. Even so, the performance of SATé in generating large ITS alignments for an accurate estimation of a higher-level phylogeny was surprisingly good, especially since the relationships between the Pezizomycetes and the Orbiliomycetes, and among the earth tongues and the three lichenized groups, have been problematic even with multigene phylogenies e.g.. Indeed,We have shown, with earth tongue fungi and related fungal classes as examples, that a progressive alignment program (SATé) outperformed traditional Clustal W in conveniently, rapidly, and accurately aligning large ITS data sets. However, a careful handling of 5.8S rDNA and residual portions of the 18S and 25S genes often found in ITS sequences is required as a few highly saturated characters can be misleading for inference of events in the initial tree As SATé uses an initial guide tree to assist the alignment and tree building processes, the highly conserved 5.8S gene of the ITS region may potentially mislead alignment in other ITS regions that show lower levels of similarity emerencia provided the most inclusive list of relevant candidate sequences. Emerencia is very useful in searching for closely related sequences for a query sequence or genus, phylogenetic analyses, however, are vital to confirmation of the affinities of these environmental isolates with respect to known fungal groups.Importantly, in this study, we demonstrated that including environmental sequences in our taxon sampling helped us to understand better about the diversity of the Geoglossomycetes. Given that environmental sequencing is becoming cheaper and easier than ever, the finding that data mining provides additional insight are relevant not only to earth tongues but to all fungal lineages. Nevertheless, while environmental sampling will extend our taxon sampling of fungi by no small measure, few of these new lineages will be matched by fully identified sequences in INSD. Brock et al. Geoglossum and Trichoglossum from North America, but recent collections were not available for this study other than five common species and some more representatives from New Zealand in the Southern Hemisphere. The second hotspot of environmental earth tongue diversity was the Duke Forest, Durham, North Carolina sampled by O’Brien et al. Trichoglossum and Geoglossum separately. Interestingly, two ITS isolates (GU256967 and GU256943) from hair root samples of Rhododendron in Sichuan, China, were in a close relationship with Geoglossum species. The region southwest of China hosts many earth tongues species Regarding the diversity of earth tongues in environmental samples, the 30 unidentified sequences that grouped with known earth tongue species in the 5.8S re-included ITS alignment were found to stem from nine scientific studies, including three unpublished ones Rhododendron found in China, an isolate from roots of tanoak in northern California (DQ273321), root samples of oak in a tropical cloud forest (EU624332), and a terrestrial orchid Cephalanthera longifolia (DQ182431). All these putative associations with plants were found in the clade including fully identified species of the genus Geoglossum. Although the Geoglossum clade was strongly supported in the trees based on the 5.8S anchored and 5.8S re-included ITS alignments  were carried out following Wang et al Eighteen recently collected specimens of Geoglossum, Trichoglossum, and Sarcoleotia, which were generated from voucher specimens for the Assembling the Fungal Tree of Life (AFToL) project and other studies, were retrieved from INSD and were combined with the newly generated sequences to build up a core ITS data set for earth tongue fungi. Pairwise alignments were generated and compared among ITS sequences for similarity, which was established by dividing the number of matching nucleotides by the total length of the alignment using the online version of Needle in EMBOSS ITS sequences of European and North American isolates of emerenciaTo investigate the unknown diversity of earth tongue fungi in the public sequence databases, insufficiently identified sequences from environmental samples were compiled for further analyses using three strategies. First, sequence similarity searches using BLAST Four alignment strategies used Clustal W the whole-ITS Clustal W. Clustal W is used for multiple alignment and MrBayes is used for phylogenetic inference. This approach mimics the one adopted by most taxonomists, and we use it as the baseline for our comparison with the more refined approaches below.the whole-ITS SATé. Using the same input sequences as the first approach, this alignment strategy uses SATé for joint estimation of alignment and phylogenetic trees without any prior processing of the alignment.the anchored ITS SATé. Using the same input sequences as the two first approaches, but with the 5.8S gene sequence removed, this approach is designed to examine the impact of the very conserved 5.8S gene sequence on the alignment and phylogenetic estimations of SATé.the 5.8S re-included SATé alignments. This approach relies on the final alignment of the anchored ITS SATé, but with the 5.8S gene sequence re-inserted for the phylogenetic inference. This alignment facilitates examination of the impact of the 5.8S gene sequence upon the alignment stage alone .sans initial guiding tree on single, double, and four-core PCs and Macs separately. One more SATé run for each data set was employed on an 8-core cluster with a 24 hour limitation as well  were obtained from the majority-rule consensus of the remaining trees. Branches with BPP higher than 0.94 were considered as significantly supported. As there is no proper approach to directly assess the quality of alignments of such large sizes, we compared the resulting phylogenies to previously published phylogenies that were based on multilocus data of genes evolving at slower, more appropriate rates. Due to the high rates of evolution of ITS sequences, strong support for deep nodes – such as those observed in the multilocus datasets – were not expected in the ITS trees.All four alignments were analyzed with MrBayes 3.1 under the GTR + Γ +I model of nucleotide evolution by running four chains of 10 million generations. Trees were sampled every 1000Figure S1emerencia; & BLAST of known earth-tongue ITS sequence against INSD; and by # annotation in INSD as unclassified Pezizomycotina environmental samples. Newly generated sequences for this study are presented as taxa without INSD accession numbers while download sequences are listed with their INSD accession numbers.The best tree generated by MrBayes for the second best-scored whole ITS SATé alignment. Bayesian posterior probabilities higher than 0.94 are indicated with bold branches. Taxonomic groups were coded with colors and initials of the classes. Environmental ITS sequences grouped in the earth tongue clades are marked with one or more annotations indicating whether the isolate would be designated an earth tongue by other approaches. Affinity of marked isolates to the earth tongues was suggested by: * the sequence search suite (EPS)Click here for additional data file.Table S1Isolate origin for earth tongue ITS sequences generated for this study.(DOC)Click here for additional data file.Table S2Performance of SATé on different platforms.(DOC)Click here for additional data file.Alignment S1ClustalW alignment of 118 taxa analyzed in this study.(PDF)Click here for additional data file.Alignment S2The best-scored whole ITS SATé alignment for 300 taxa.(PDF)Click here for additional data file.Alignment S3The second best-scored whole ITS SATé alignment.(PDF)Click here for additional data file.Alignment S4The best-scored anchored ITS SATé alignment for 296 taxa.(PDF)Click here for additional data file.Alignment S5The best-scored 5.8S re-introduced ITS SATé alignment for 296 taxa.(PDF)Click here for additional data file.


Detection of sequences that are homologous, i.e. descended from a common ancestor, is a fundamental task in computational biology. This task is confounded by low-complexity tracts (such as atatatatatat), which arise frequently and independently, causing strong similarities that are not homologies. There has been much research on identifying low-complexity tracts, but little research on how to treat them during homology search. We propose to find homologies by aligning sequences with “gentle” masking of low-complexity tracts. Gentle masking means that the match score involving a masked letter is  The problem of false homology prediction due to low-complexity sequences is sufficiently severe that it has been addressed since the early days of computational biology. Methods to avoid this problem can be classified into three approaches:The first approach is to identify low-complexity regions by some means, and then replace each letter in these regions with a dummy letter, typically X for proteins and N for DNA. During alignment, the dummy letter receives a negative match score when aligned to anything. For example, in the NCBI blosum matrices, X receives a score of −1. This prevents low-complexity regions from getting high alignment scores.This approach obviously depends on the means of identifying low-complexity regions. We recently showed that standard methods such as SegMasker and DustMasker are not perfect: they fail to mask some low-complexity sequences, which then produce strong  This method is used by blastz and lastz, which are employed to construct the widely-used UCSC genome alignments Unfortunately, excluding low-complexity regions from seeding but not extension fails to prevent spurious alignments It might be objected that masking at all stages of homology search will mutilate alignments of genuinely homologous sequences. It may break them into smaller alignments, and prevent alignment of mildly low-complexity regions whose homology is supported by surrounding high-complexity regions. We can avoid this mutilation as follows. After identifying homologous regions, re-align them with masking turned off, allowing the re-alignments to extend beyond the regions and merge nearby alignments. This re-alignment is naturally achieved by repeating the extend step of a seed-and-extend algorithm. We know of only two alignment tools that perform this careful variant of soft masking: fasta and last blastp  against the nr database, using “conditional compositional score matrix adjustment” (the default setting as of 2011-06-20). The search found 221 hits, including 21 with E-value less than For example, In summary, the only reliable way to avoid false homology predictions is to mask the sequences with tantan, and apply this masking at all stages of the homology search algorithm.  are lowercase is: ers e.g. . This meNUMTs: There have been many studies of NUMTs, which are copies of mitochondrial DNA in nuclear genomes C. elegansWe looked for NUMTs in several nuclear genomes, using either harsh masking or gentle masking of low-complexity regions identified by tantan. The difference is not great, but we found a few extra NUMTs using gentle masking . In partC. elegans NUMT is not trivial to find, because large parts of it are deemed low-complexity by tantan  and then aligned them to the genomes. With harsh masking 7,907 were aligned, and with gentle masking 7,910 were aligned. This suggests that gentle masking decreases the specificity of the search by less than it increases the sensitivity.Protein-coding homology: It is useful to identify segments of a genome that are homologous to protein-coding DNA. Such segments are usually either protein-coding exons, or pseudogene fragments. One reason this is useful is to explain evolutionarily-conserved segments of the genome. Searches for conserved non-protein-coding segments tend to hit recent pseudogenes, because they have indeed been conserved for much of their evolutionary history. This is not always obvious, because comprehensive pseudogene annotation is lacking.We identified segments of the human genome with protein-coding homology, by finding local alignments between the genome and known proteins in the UniRef90 database. We used “three-frame alignment”, which translates the DNA in all reading frames and aligns the amino acids allowing frame-shifts PLEKHN1. The lower alignment in As an example, the upper alignment in blosum80), whereas, for example, blast uses a score of −1 for X residues. To address this concern, we repeated the search after replacing masked letters with X, with score −1. To make a fair comparison, we also repeated our gentle masking search without the final step of realignment without masking. In this case, with gentle masking we found 227,018 segments . With X masking, we failed to find 1,640 of these, and found 43 segments not found with gentle masking. So gentle masking still improves the sensitivity.It might be argued that our harsh masking is a straw man, because we used a mask score of −6  which misses some alignments. In any case, spurious alignments were thoroughly suppressed.In the first test, we compared the h tantan . The numWe obtained similar results when comparing five other pairs of DNA or protein datasets . The resThe DNA alignments of Finally, we compared DNA sequences to reversed protein sequences . In ordeblast-like algorithms slow because they would explore alignment extensions across the whole length of every masked region that they encounter. It also complicates the final step of realignment without masking, because optimal unmasked alignments might be multiple fragments of an optimal masked alignment. Our gentle (and harsh) masking method avoids this problem by guaranteeing that the masked score cannot exceed the unmasked score.An alternative to gentle masking is to assign a score of zero for a match between a masked letter and any other letter. This idea has several problems, however: it leads to over-extended alignments such as that in A more sophisticated way to avoid false homology predictions would be to use probabilistic models. The standard approach to sequence alignment, using a score matrix, can be interpreted as comparing a model of related sequences to a model of independent sequences last enlarges the score matrix as in blast. This would hamper its adoption by the bioinformatics community.The main advantage of gentle masking is that it actually exists now.  remains frequently useful, as illustrated by the three examples in this study. Moreover, pairwise sequence comparison is needed for gathering homologs to construct profiles in the first place. In this homolog-gathering step, it is often particularly important to avoid contamination by non-homologous sequences: our masking approach should be very useful here.Interspersed repeats and low-complexity sequences are often lumped together as “repeats”, so we must constantly point out the differences between them. Interspersed repeats (such as LINEs and SINEs) do not cause false homology predictions, because, for example, every LINE-1 element is genuinely homologous to every other LINE-1 element. They may cause other problems, such as making sequence comparison algorithms run too slowly and produce too much output. So it can be useful to lowercase-mask both low-complexity regions (using tantan) and interspersed repeats . Gentle masking would then operate on both types of repeat, which seems harmless. Low-complexity masking is important for accurate homology search, but its application to orthology search is less clear. Prominent examples of orthology search include: comparing two whole genomes, and aligning human DNA reads to a reference human genome. In these cases, we usually wish to avoid paralogous alignments. The danger is that we might mask an ortholog but not a paralog, and thus increase the rate of paralog alignments. This seems especially likely with short sequences, where any masking is more likely to cover the whole ortholog. On the other hand, if we have DNA reads with contaminants , there is a risk of spurious low-complexity matches between the contaminants and the genome. We speculate that it might be useful to apply low-complexity filtering as a final step, after identifying orthologs.Homology search has two somewhat different aspects: 1) finding homologous regions, and 2) aligning homologous letters within those regions. This study addresses only the first aspect. There have been several studies on the accuracy of letter alignment e.g. , but to Gentle masking is an extremely simple but useful way to treat lowercase-masked low-complexity tracts during sequence alignment. In tests with reversed sequences, gentle masking with tantan suppressed spurious alignments in a practically identical manner to harsh masking with tantan. On the other hand, in three tests using real (non-reversed) sequences, gentle masking resulted in slightly but noticeably more alignments than harsh masking. Since both methods suppress spurious alignments equally well, we infer that these extra alignments are largely true homologies. In support of this conclusion, some of the extra alignments have highly significant E-values (which are never observed for reversed sequences), and some of the putative protein-coding homologs are supported by neighbouring exons.To obtain the results shown in All: In all of these tests, we masked both sets of sequences (query and reference) using tantan version 4 In an abundance of caution, we made sure that the DNA strands were treated symmetrically, despite tantan's directional asymmetry. We first compared forward strands only, using lastal option -s1. We then reverse-complemented the original (un-tantan'd) query sequences, ran tantan on these, and fed them to lastal using option -s1 again.This cautious treatment of strands is the reason why our results with the metagenome data are not identical to the results we obtained earlier NUMTs: We downloaded these genomes from the UCSC genome database: cb3, ce6, dm3, hg19, mm9 C. elegans was 36. We set the alignment score threshold to the minimum score with E-valueWe obtained a count of NUMTs as follows. We recorded the segment of the nuclear genome covered by each alignment, discarding the mitochondrial coordinates and all strand information. We then merged overlapping and touching segments. Finally, NUMTs found by one method but not the other are defined to be segments found by one method that have no overlap with segments found by the other.Metagenomic DNA reads: One of the genomes that we downloaded (Acidaminococcus_D21) has other genomes spuriously appended to it, so we used only the first 2,238,973 bases of this sequence.We ran lastal with options -d20 -e25. This means that we used a score threshold of 25, for a E-value of about 0.0188 per read.In the test with reversed reads, masking was done before reversal.Protein-coding homology: We obtained UniRef90 from UniProt release 2011_05 The proteins were masked using tantan options -p -r0.02, as recommended for DNA-versus-protein alignment We aligned the DNA and the proteins using lastal with options -pBLOSUM80 -F15 -e137. The score threshold of 137 corresponds to an E-value of about 0.01.blosum80 instead of the more standard blosum62 for two reasons. First, blosum80 is more powerful at discriminating short, strong homologies from chance matches (e.g. blosum62 is more prone to over-extending alignments (e.g. We used hes e.g.  [18]. Sents e.g.  [21].We counted protein-coding segments in the same way that we counted NUMT segments, with one difference: this time, we treated segments on opposite strands of a chromosome as distinct.


BLAST  are quite abstract and are best communicated with examples . It is pMolecular evolution Gene alignmentDomain structure of proteinsBiochemical nature of amino acids: “frequent” and “infrequent” substitutionsConserved versus divergent regions of genesAmino acid identity matricesQuantification of sequence identity and similarityDatabase queries and their tradeoffs (speed versus completeness)Large dataset managementLess obviously, another benefit of teaching students how the BLAST algorithm works is that it provides an opportunity to illustrate how mathematics functions as a language of biology. For example, given that BLAST has been designed to retrieve homologs, there are several steps in the BLAST program that incorporate molecular evolution concepts to maximize the possibility of finding sequences with a shared evolutionary history. More basically, understanding the steps in the calculation of an E-value provides an opportunity to show the relationship between how the algorithm works and fundamental principles of biochemistry and evolution. Here, we provide an approach to teaching the basics of BLAST to students in order to emphasize how the algorithm translates fundamental biological principles into numerical terms culminating in an E value. Acquiring a feel for the algorithm and exploring genomic sequence data has the potential to inform a student's grasp of biomedical, biochemical, and biogeochemical concepts, presenting an excellent opportunity for multidisciplinary integration.To begin a BLAST search of a database, the user provides a query sequence, which is a nucleotide (nt) or amino acid (aa) sequence for which they are interested in finding homologs. The BLAST algorithm begins by fragmenting the sequence into “words”  interface, 16–256 nt, or 2–3 aa), and, from each word, creating a set of acceptable “synonyms” that represent possible changes in sequence due to mutation . Words aS. A pairwise alignment between a subject sequence and query sequence in which the two have a high degree of sequence similarity will have a high S value. S is calculated from the alignment of the two sequences by adding the scores from each pair ij of residues in the alignment: M is the score from a similarity matrix  for a particular pair of amino acids, c is the number of gaps, O is the penalty for the existence of a gap, d is the total length of the gaps, and G is the per-residue penalty for extending the gap.The extent of the sequence similarity between the subject and query sequences is reported as a raw score, O is 11, while for G it is 1. It is useful to ask students to consider why the gap existence penalty is larger than the gap extension penalty. The answer is that it is assumed that that a gap corresponds to either an insertion or deletion event, regardless of the size of the gap. Hence, a larger penalty is imposed for the existence of the gap whereas the length of that insertion or deletion is relatively less significant. Furthermore, these insertion and deletion events are likely rare and so more heavily penalized. If they were not, and the gap opening penalty correspondingly lowered, numerous gaps would be included in alignments; taken to the extreme, this would allow any two sequences to be aligned.Gaps are spaces introduced into either the query or subject sequence to optimize alignments. In terms of the underlying biology of the two aligned sequences, gaps correspond to regions in which there is an insertion or a deletion in one of the amino acid sequences relative to the other. In a protein BLAST, the default setting for Alignments are extended position by position with concomitant scoring until the match falls below a threshold score, at which point it is terminated. It is important to note that alignment between the query and subject sequences does not have to cover the full length of the two sequences. Therefore, retrieved subject sequences commonly align with only a portion of the query sequence—it is this “local” rather than global quality that is more than nominally BLAST's strength. Underscoring this distinction for students provides another opportunity to discuss protein evolution. In considering their results, they may need to evaluate if they have found homology in one domain of a multidomain protein. BLAST provides a list of potential homologs to the query sequence that is based on regions of sequence similarity. Matches that occur over limited regions of these sequences provide an illustration of both the modular nature of protein structure (domains), as well as the modular nature of protein evolution (new domain combinations). Protein domains with a common ancestry are shared among many protein families whose members have divergent functions. For example, flavin reductase domains occur in the many enzymes that transfer electrons from a reduced compound  to FMN or FAD. If two amino acid sequences have a flavin reductase domain but do not share other domains with common ancestry, the two sequences will align with a high score at the flavin reductase domain, but will not align meaningfully or significantly elsewhere .Sometimes it is helpful to “mask” parts of the query sequence to prevent them from being aligned with subject sequences. Masking is helpful when the query sequence has “low-complexity” regions, such as stretches of small hydrophobic amino acids that are commonly present in transmembrane helices of integral membrane proteins. Because these features arose from convergent evolution, and their inclusion in BLAST searches could result in spurious hits, it is best to set the BLAST search parameters to eliminate these sorts of regions from word generation, as well as alignment scoring.S' values (bit scores) are calculated:λ and K reflect the matrices and penalties used. If analyses were to stop here, one would have a list of sequences sorted by bit scores that would reflect the degree of similarity to the query sequence. The question of whether the sequences were homologs to the query sequence would not yet be directly addressed: how high does a bit score have to be to suggest shared ancestry? Larger databases are more likely to include sequences with matches to the query that are due to chance, not homology.Because one has the option of using different parameters  in different BLAST experiments, it is ideal to report results in such a way as to be able to compare alignments made with different scoring matrices or gap penalties. To do this, n is the total number of residues (amino or nucleic acids) in the database, and m is the length of the query sequence. E-values are the number of subject sequences that can be expected to be retrieved from the database that have a bit score equal to or greater than the one calculated from the alignment of the query and subject sequence, based on chance alone. E-values for subject sequences that are very similar to the query sequence will be quite small, and are widely used as a means to assess the confidence with which one should claim the subject sequence(s) and the query sequence as homologs. Collectively, the parameters for a BLAST search are given at the bottom of the output; going through these with students provides another opportunity to reinforce these concepts and explain why an E-value for BLAST searches using the same protein sequence will change over time.To address this issue, E-values are calculated from bit scores as Substitution matrices are critical at two steps in the BLAST process: 1) in the evaluation of “words” with which to tag subject sequences for further scrutiny, and 2) in the extension and evaluation of alignments between the query sequence and a subject sequence. These matrices are used to calculate scores for alignments, which are used to gauge the strength of the match between two sequences. Scores are calculated by comparing residues at each position in the alignment. This series of scores is summed over the length of the alignment and used to calculate the raw score.It is intuitive that having an exact match in residues at many positions in an alignment should result in a high alignment score; it is less obvious how these matches should be weighted among different residues (is a leucine–leucine match as informative as a tryptophan–tryptophan match?), as well as how severely residue substitutions should be penalized .Substitution matrices were created to resolve these “gray areas”; they consist of factors to use to weigh residue matches and substitutions. BLOSUM matrices are commonly used to compare amino acid sequences and were constructed from curated alignments of amino acid sequences Different levels of overall sequence divergence also affect amino acid substitution frequencies . To account for this effect, BLOSUM matrices were constructed from alignments with different degrees of divergence. BLOSUM62 was created from an alignment of sequences with at least 62% sequence identity, while BLOSUM80 used an alignment with 80% Examining and comparing substitution matrices with students provides an opportunity to consider not only the consequences of residue substitution but also the reason(s) for the different substitution penalties, allowing them to apply what they know about amino acid chemistry. When comparing two BLOSUM matrices, students will need to consider the types of amino acid replacements expected to accumulate between recently versus deeply diverging sequences.Students (and researchers as well) tend to draw an arbitrary line below which they consider E-values to provide convincing evidence that two sequences are homologs . It is informative to scrutinize this assumption, and ask the students to consider whether and when more stringent E-values might be appropriate , or when larger E-values do not provide definitive evidence of evolutionary independence (as is the case when two sequences share an ancient ancestor).It is also informative for the students to discuss what their alignments “mean”, and whether the pairwise alignments between their query sequence and the subject sequences “prove” whether the sequences are homologs. Indeed, it can catalyze a larger discussion of whether it is possible to “prove” that two sequences are homologs, and what other approaches  might be used to strengthen or refute such an assertion.A meaningful alignment will facilitate the comparison of two sequences with a shared evolutionary history by maximizing the juxtaposition of similar and identical residues. Sequences with a recent shared ancestry will have a high degree of similarity; their alignments will have many identical residues, few substitutions and gaps, and tiny E-values. Conversely, sequences with an ancient common ancestor will be deeply divergent, with few shared sequence identities, many gaps, and larger E-values. Furthermore, an alignment of two sequences can clarify which portions are conserved , and which are divergent, which helps cultivate students' understanding of protein structure and function.In summary, deconstructing the BLAST algorithm and manipulating parameters systematically and evaluating the results with students helps them understand not only what the scores mean but also how to manipulate parameters to optimize their searches. There is a wealth of additional resources available . FinallyPowerpoint slides for teaching BLAST http://www.ncbi.nlm.nih.gov/guide/National Center for Biotechnology Information (NCBI): BLAST interface to query vast protein and nucleotide sequence databases, and protein structure databasesLinks to PubMedTutorials for resourceshttp://www.ebi.ac.uk/2can/tutorials/index.htmlBioinformatics tutorials at the European Bioinformatics Institute (EBI): http://www.ebi.ac.uk/2can/tutorials/protein/blast.htmlBLAST background http://img.jgi.doe.gov/cgi-bin/pub/main.cgiIntegrated Microbial Genomes (IMG): BLAST interface to query sequenced microbial genomesSimple tools for genome: genome comparisonhttp://pfam.sanger.ac.uk/PFAM: BLAST interface to query curated protein familiesComprehensive background information and literature about protein familiesWhat is a homolog?How can you tell if two genes are likely to share a common ancestor?How can you tell if two proteins share a common domain?Is the degree of harm of a mutation in a gene sensitive to its position in a gene?Can you ever prove that two genes share a common ancestor? Why or why not?How could you build a strong argument that two genes have the same or similar function in two different organisms?Can you, in your own words, describe the process of a BLAST search?Which steps in a BLAST search help to make it happen so quickly?Supporting Information S1Powerpoint file--Using BLAST To Teach “E-value-tionary” Concepts.(PPT)Click here for additional data file.Text S1Acquiring a feel for the algorithm: manipulating the parameters of BLAST and other experiments.(DOCX)Click here for additional data file.