- Methodology article
- Open Access
LEON-BIS: multiple alignment evaluation of sequence neighbours using a Bayesian inference system
- Renaud Vanhoutreve†1,
- Arnaud Kress†1,
- Baptiste Legrand1,
- Hélène Gass1,
- Olivier Poch1 and
- Julie D. Thompson1Email author
© The Author(s). 2016
Received: 29 March 2016
Accepted: 1 July 2016
Published: 7 July 2016
A standard procedure in many areas of bioinformatics is to use a multiple sequence alignment (MSA) as the basis for various types of homology-based inference. Applications include 3D structure modelling, protein functional annotation, prediction of molecular interactions, etc. These applications, however sophisticated, are generally highly sensitive to the alignment used, and neglecting non-homologous or uncertain regions in the alignment can lead to significant bias in the subsequent inferences.
Here, we present a new method, LEON-BIS, which uses a robust Bayesian framework to estimate the homologous relations between sequences in a protein multiple alignment. Sequences are clustered into sub-families and relations are predicted at different levels, including ‘core blocks’, ‘regions’ and full-length proteins. The accuracy and reliability of the predictions are demonstrated in large-scale comparisons using well annotated alignment databases, where the homologous sequence segments are detected with very high sensitivity and specificity.
LEON-BIS uses robust Bayesian statistics to distinguish the portions of multiple sequence alignments that are conserved either across the whole family or within subfamilies. LEON-BIS should thus be useful for automatic, high-throughput genome annotations, 2D/3D structure predictions, protein-protein interaction predictions etc.
Multiple alignments of protein sequences are a fundamental tool in many areas of molecular biology, including phylogenetic studies, prediction of 2D/3D structure, or propagation of structural/functional information from annotated to non-annotated sequences. All of these applications rely on the identification of the conserved regions in the alignments, suggesting potential homologous relations between the sequences. However, downstream results may be highly sensitive to the alignment used, and ignoring errors or uncertainty in the alignment can lead to significant bias in the subsequent inferences . For example, in evolutionary studies, it has been shown that the accuracy of phylogenetic trees inherently depends on the accuracy of the underlying sequence alignment . Similarly, the CASP comparative modelling experiments  have regularly demonstrated that the quality of sequence alignments is a key factor in comparative modelling of protein 3D structures. Furthermore, many functional predictions are made on the basis of homology with remotely related sequences or structures. In this case, if functional predictions are to be made with confidence, information on the reliability of the alignment at the different sites is critical.
As a consequence, protein MSA is an exceptionally active field of research, and one of the latest developments has been a gradual shift away from the development of more accurate aligners towards the estimation of the reliable regions of an alignment. For example, numerous column scores have been defined that attempt to distinguish the positions that are conserved in all the sequences from the unreliable positions, e.g. [4, 5]. Typically, the unreliable positions are then filtered in the subsequent inference methods, or the methods can be used to identify interesting alignment blocks for phylogenetic studies, e.g. [6, 7]. In , the authors compared 8 state of the art methods of alignment trimming for phylogenetic studies, and used the annotated ‘core blocks’ from the BAliBASE benchmark  as the gold standard definition of reliable positions. However, it has been shown recently that the trees obtained from these filtered MSAs may be worse than those obtained from unfiltered MSAs in some cases .
An alternative approach involves the detection of conserved regions in the aligned sequences, for example using consensus sequences to define conserved alignment blocks [10 11]. More recently, DivA  used four divergence-based parameters and their outlier values to identify very divergent segments in MSAs. The method was used to identify badly annotated introns/exons in sets of orthologous proteins generated by a large-scale avian phylogenomics project. Another recent method, OD-seq , is designed to find outlier sequences by examining the average distance of each sequence to the rest and represents a useful, fast method for checking very large alignments containing thousands of sequences.
In general, these methods work well for multiple alignments of proteins that are homologous over their full lengths and allow the accurate detection of regions that are conserved in all members of a sequence family. However, large multi-domain proteins are becoming more and more prevalent in the sequence databases, with the arrival of numerous new genome sequences, in particular from eukaryotic organisms. In addition, badly predicted sequences mean that there are numerous fragments, spurious insertions/deletions and ‘incoherent’ segments in any set of sequences retrieved from the generalist databases . In the face of these highly complex proteins, new methods are needed to detect local homology corresponding to structural/functional domains or motifs, and in particular those that explain the specificities of certain subfamilies. For example, for detection of binding on different substrates or cofactors and distinct binding affinities , or for residue-level genotype-phenotype correlation studies .
We previously developed LEON  to predict homologous regions in MSAs with respect to a user-defined reference or “query” sequence, and to identify non-homologous or outlier sequences. LEON incorporated sequence clustering  and calculated amino acid frequency profiles  in order to identify locally conserved motifs or ‘core blocks’ within the subfamilies. The conserved blocks for each subfamily were then chained together to form contiguous regions. In large-scale tests, where the conserved regions detected by LEON were compared to known structural or functional domains, the specificity of LEON was shown to be very high (>99 %), although at the expense of some loss of sensitivity (76 %) which meant that some divergent sequences were removed from the alignments even though they were actually related.
Here we introduce a new version of LEON, called LEON-BIS, that replaces the original amino acid frequency profiles by more robust Bayesian statistics based on BILD scores . Bayesian methods provide a natural and principled way of combining prior information with data, within a solid decision theoretical framework. Past information about a parameter can be incorporated to form a prior distribution for future analysis. When new observations become available, the previous posterior distribution can be used as a prior. All inferences logically follow from Bayes’ theorem and they are robust to errors and missing data. When the sample size is large, Bayesian inference often provides results that are very similar to the results produced by frequentist methods. However, because Bayesian analyses do not assume large samples, smaller data sets can be analyzed without losing power but retaining precision. A Bayesian approach has been used previously to construct local pairwise alignments  or to perform a joint analysis of multiple sequence alignments and evolutionary trees , for example. We have also incorporated a similar Bayesian framework in the SIBIS method  to detect inconsistent sequence segments, often corresponding to badly predicted intron/exon structures in protein sequences.
The accuracy of the LEON-BIS method for the detection of conserved sequence segments is evaluated in a large-scale test, using more than 200 multiple sequence alignment from the latest version of the BAliBASE benchmark . These alignments contain examples of many problems encountered in high-throughput projects, including complex multi-domain proteins, with locally conserved regions/core blocks, transmembrane proteins, fragments and badly predicted sequences, etc. In the final LEON-BIS alignment, the sequences that are predicted to be related to the user’s query sequence are ranked according to their similarity to the query sequence. Unrelated sequences containing no conserved regions are filtered from the alignment. More importantly, the conserved regions within the related sequences are delimited and can be thus used for reliable function annotation, fold classification, 2D/3D structure predictions, domain determination etc.
Training and test sets
To test the performance of the program, we used the most recent test set (Reference 10) in the BAliBASE benchmark suite , composed of 218 reference alignments and containing a total of 17,892 protein sequences. These reference alignments are designed to reflect some of the problems specific to aligning large sets of complex sequences. For example, many of the protein families have multi-domain architectures and their members often share only a single domain. In addition, the alignments have a high proportion of sequences with ‘discrepancies’ (unexpected or discordant extensions, insertions or deletions) that may correspond to naturally occurring variants or may be the result of artifacts, including proteins translated from partially sequenced genomes or ESTs, or badly predicted protein sequences. Taken together, this means that only a small proportion (18 %) of the conserved ‘core blocks’ in the alignments are present in most (>90 %) of the aligned sequences, while most of the blocks are only conserved within specific sub-families. These ‘rare’ segments or patterns are often characteristic of context-specific functions, e.g. substrate binding sites, protein-protein interactions or post-translational modification sites.
To create a suitable test set for the LEON-BIS evaluation experiments, we used the unaligned sequence sets corresponding to each reference alignment in BAliBASE Reference 10. The first sequence of each set was arbitrarily defined as the query sequence. For each set, we then added up to four sequences which were considered to be “unrelated” to the query sequence, by selecting sequences from the other reference alignments with two criteria: (i) the selected sequence shared less than 50 % percent residue identity with the query sequence and (ii) no shared domains were identified in the PFAM protein family database . Finally, we aligned each of the reference sets containing both related and unrelated sequences using the MAFFT version 7 multiple alignment program .
Identification of subfamily core blocks
In our method, the prior probability distributions of the amino acids are described by Dirichlet distributions or Dirichlet mixtures. To our knowledge, the only Dirichlet mixture prior parameters for protein sequence alignments have been derived by the team who first proposed such mixtures  and these have been made available at compbio.soe.ucsc.edu/dirichlets/index.html. In these tests, we used a 20 component Dirichlet mixture (recode3.20comp), which was derived from analyses of large numbers of alignments of related protein sequences, and has a relative entropy of 0.61, roughly equivalent to that of the PAM-175 matrix.
A sliding window analysis of the BILD column scores for each subfamily is then performed. As BILD column scores are expressed as probabilities with values ranging between 0 and 1, we can define a threshold above which columns are considered to have significant scores. Here, core blocks are defined as segments with mean BILD score above 0.05.
Estimation of relatedness between core blocks
We extend this to the case of aligning two core blocks by calculating the sum of the substitution scores for each of the aligned pairs of columns. The core blocks are assumed to be related if the sum of the column-column alignment scores is > 0.
Estimation of relatedness between sequence segments and core blocks
In order to avoid including badly predicted or ‘inconsistent’ sequence segments in the predicted regions, the algorithm used in SIBIS  is implemented in LEON-BIS in order to calculate a score for the alignment of a single sequence segment to a core block. Briefly, the posterior distributions ΘM after observing the alignment column x1 to xM are used to calculate the probability of observing a new residue xM+1, under the assumption of relatedness. Then, the score for a segment of length N aligning to the core block (under the simplifying assumption that each position in the protein is generated independently) is equal to the product of the probabilities of aligning each residue to the corresponding column in the core block. In order to estimate the probability of observing a sequence segment under the assumption of unrelatedness, we calculate the score of a random sequence equal to the length of the core block with background amino acid frequencies equal to 1/L. Finally, sequence segments with a score less than that obtained by the random sequence are flagged as inconsistent sequences.
Definition of regions and removal of unrelated sequences
Once the conserved core blocks are defined for all the sequences in the alignment, we chain these core blocks into larger ‘regions’, using the chain_blocks program implemented in the original LEON method. Then, the score for a conserved region is defined as the sum of the scores for the core blocks within the region. Sequences with no regions having a score higher than the cutoff value are removed from the alignment. For comparison purposes, the maximum distance between core blocks and the minimum length of a region are set to 40 and 21 respectively, the same as for the original LEON algorithm.
Sequence-level homology analysis
Accuracy of three methods for the detection of related and unrelated sequences
LEON-BIS predicted slightly more related sequences (15,552 of all 18,682 sequences) compared to LEON (15,227), with higher sensitivity (0.89 versus 0.87), and higher specificity (0.83 versus 0.81). Thus, more sequences with hypothetical relationships are retained in the alignments, although some potential false positives are also included. However, it should be noted that the sequences in these test alignments were aligned using an automatic method, namely MAFFT, and so some of the sequences may be misaligned. Therefore, some of the false negative predictions are in fact true negatives, since the sequences do not contain any regions that are correctly aligned with the query sequence. OD-seq was less successful in these tests, but the authors themselves stated that the method was optimized for very large alignments and was not intended for small, very divergent families . Consequently, the computation time required for the analysis of the 218 multiple alignments was significantly faster for OD-seq (27 s) compared to LEON or LEONII which required 1266 s and 2567 s respectively.
Detection of conserved regions and comparison with PFAM domains
Precision and recall statistics for the identification of known PFAM domains by LEON and LEON-BIS
The above example clearly demonstrates the value of LEON-BIS for the detection of conserved blocks or regions that may represent important new structural or functional features.
The determination of homology is a crucial problem for a wide range of homology-based applications, and poses particular problems in automatic, high-throughput genome analysis and annotation projects. A number of methods exist that estimate homology based on a multiple sequence alignment, but these methods generally look for features shared amongst all or most of the sequences. Alternatively, methods such as OD-seq have been developed to identify outlier sequences and remove them completely from the alignment. Nevertheless, today’s complex alignments require a more precise definition of conserved sequence segments.
Here, we have updated our original method that measured the evolutionary conservation of a set of related sequences using a frequentist approach, based on the observed amino acids in multiple alignment columns. Nevertheless, there are a number of shortcomings in estimating scores for amino acids based only on their observed frequencies, especially when the number of observed sequences is small. First, unseen amino acids are assigned scores based on an amino acid substitution matrix that is generally chosen arbitrarily. Second, an arbitrary threshold for profile scores must be defined to distinguish related from unrelated or erroneous sequences. Therefore, the profile scoring scheme, originally developed in the context of the ClustalW multiple alignment program , has been replaced by a Bayesian statistical framework. This allows the definition of conservation based on background knowledge (amino acid frequencies extracted from alignments and represented by Dirichlet mixture models), combined with observed amino acids in alignment columns. The Bayesian statistics are more robust than the original profiles based on observed amino acid frequencies only, as shown in the tests performed here. The Bayesian framework also means that no parameters need to be fixed by the user. The only parameters in LEON-BIS concern the chaining of blocks to form a region, i.e. the maximum distance between blocks and the minimum length of a region. In these tests, we used the same parameters as LEON for comparison purposes. Modifying these parameters would allow the user to increase either the recall or the precision, as required.
The LEON-BIS method incorporates the SIBIS algorithm, which also uses a Bayesian framework, in order to detect inconsistent or badly predicted sequences. This is an important issue when analyzing eukaryotic genome data, since recent analyses have shown that the complete exon/intron structure is correctly predicted for only about 50-60 % of genes . The situation is further complicated by widespread alternative splicing events, which affect more than 92–94 % of multi-exon human genes . In the presence of these inconsistent or erroneous sequences, the assumptions about amino acid distributions in the Dirichlet mixture models may not be valid. If not addressed, the estimates of amino acid probabilities would be biased, and the true unrelated sequences may not be detected as a result. By delineating the consistent sequence segments from the badly predicted sections, we can avoid excluding too many sequences. The incorporation of SIBIS also means that LEON-BIS is more robust to local misalignments. In the experiments performed here, we used multiple alignments constructed automatically with the MAFFT algorithm, rather than the high quality, manually refined reference alignments, to make the tests more realistic.
In a first large-scale evaluation, the ability of LEON-BIS to distinguish between related and unrelated sequences was compared to existing methods, including the original LEON method and a recent algorithm, OD-seq, for the detection of outlier sequences in multiple alignments. The sensitivity and specificity of LEON-BIS were shown to be slightly higher than LEON. OD-seq had very high sensitivity and detected most of the related sequences, but the specificity was low, meaning that a large number of unrelated sequences were retained in the alignments. It should be noted that OD-seq is designed specifically for very large alignments containing thousands of sequences and it therefore represents a complementary approach to the method developed here. Then, in a second experiment, the regions predicted by LEON-BIS were compared to known domains from the PFAM database and both the precision and recall of LEON-BIS were shown to be about 92 %. Compared to LEON, a significant difference was observed in the prediction of functional domains sharing low percent identity (<30 %). Furthermore, additional conserved regions were also identified in the alignments that were not covered by the existing PFAM annotations. The LEON-BIS homology predictions in combination with known structural/functional information, should therefore provide a powerful tool for the characterisation of new or unknown proteins.
LEON-BIS is a fully automatic method that reliably detects conserved regions in multiple sequence alignments. It can be applied to a wide variety of alignments, including difficult cases such as distantly related sequences, multi-domain sequences, or transmembrane sequences. Incorporating LEON-BIS should therefore improve downstream applications, including phylogenetic studies (although most current methods for phylogenetic tree reconstruction cannot take into account uncertainty within alignment columns) and comparative modeling, as well as detection of sub-family specific regions representing potential specificity-determining motifs. In the future, the reliable blocks detected by LEON-BIS could provide the basis for a multi-level comparative genomics strategy, with homology analysis ranging from complete proteins to the core block level.
We would like to thank the members of the BISTRO and BICS Bioinformatics Platforms in Strasbourg for their support.
This work was supported by the Agence Nationale de la Recherche (BIPBIP: ANR-10-BINF-03-02; ReNaBi-IFB: ANR-11-INBS-0013), the Région Alsace and Institute funds from the CNRS, the Université de Strasbourg and the Faculté de Médecine de Strasbourg.
Availability of data and materials
All of the software, as well as the datasets supporting the conclusions of this article are freely available at http://www.lbgi.fr/~julie/LEON-BIS/. Java code is available for the LEON-BIS algorithm. The method also requires the cluspack software for sequence clustering and the chain_blocks program from the LEON suite, both written in ‘C’. A UNIX shell script is provided that calls all the programs. LEON-BIS takes multiple alignments in either MSF or FASTA format as input and outputs a refined alignment in an XML format file, annotated with the predicted core blocks and the conserved regions. The refined alignment contains only those sequences predicted to contain homologous regions. Software is provided to convert the XML format alignment file to MSF or FASTA formats, if required. The positions of the conserved core blocks and conserved regions are also output to a formatted text file.
The first version of this software was developed by BL, OP and JDT. RV and AK designed and implemented the final version and HG performed the computational experiments. RV and JDT wrote the paper. All authors read and approved the final manuscript.
RV is a PhD student at the University of Strasbourg, AK is a research engineer in the BICS platform of the ICube laboratory, BL and HG are Masters students at the University of Strasbourg, OP is group leader of the Complex Systems and Translational Bioinformatics team of the ICube laboratory and JDT is a member of the Complex Systems and Translational Bioinformatics team of the ICube laboratory.
The authors declare that they have no competing interests.
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