ArchAlign identifies shared chromatin structural patterns from high-resolution chromatin structural datasets derived from next-generation sequencing or tiled microarray approaches for user defined regions of interest. We validated ArchAlign using well characterized functional elements, and used it to explore the chromatin structural architecture at CTCF binding sites in the human genome. ArchAlign is freely available at  The development of protein and DNA sequence alignment algorithms in the 1970s and 1980s revolutionized the functional characterization of unknown proteins and genes . Since tSaccharomyces cerevisiae have a well documented nucleosome-depleted region approximately 50 to 100 bp upstream of the TSS, flanked by a non-canonical acetylated nucleosome containing the histone variant H2A.Z [Currently, the only way to characterize chromatin architecture is to have an accurately mapped functional element in the genome. Functional elements include genes for protein and non-coding RNAs, and regulatory sequences that direct essential functions such as gene expression, DNA replication, and chromosome inheritance. With an accurately mapped functional element, chromatin structural data are aligned by the genomic coordinates and an average profile is created. For example, transcription start sites (TSSs) in nt H2A.Z , intron-nt H2A.Z -11, and nt H2A.Z . All sucInsulator elements are an example of a genomic element that has not been accurately mapped and has not been extensively characterized. Insulators function to restrict transcriptional enhancers from activating unintended promoters, by acting as a barrier between chromatin contexts -15 or byet al.[Identification of shared chromatin architecture at functional sites has recently become an active area of research -25, but et al. used a met al., and canArchAlign has two methods for aligning regions and three similarity/distance metrics for scoring the similarity between two regions Figure . Dependin regions, n independent alignments are generated as described for the single-best-pair approach . The alignment that maximizes the similarity across all regions is then selected as the optimal alignment.The second approach, known as seed sampling, is a more comprehensive search of the possible alignment space. Every region in the alignment is used as one-half of the optimal seed pattern for an independent alignment. Therefore, for a dataset with h Figure . To deteS. cerevisiae [n regions requires n times as much computational time as the single-best-pair approach.ArchAlign was validated by randomizing coordinates for 200 TSSs from revisiae , taking S. cerevisiae. Origins in S. cerevisiae have a well-characterized nucleosome-depleted region surrounded on both sides by an array of nucleosomes [et al.[To ensure that ArchAlign can accurately align chromatin signatures located at various genomic features, we further validated ArchAlign with chromatin data for origins of replications from leosomes . We useds [et al.. The oriTo determine if ArchAlign can uncover novel chromatin architecture, nucleosome occupancy near CTCF binding sites in human CD4+ cells was examined. We selected the top 1,000 CTCF binding sites, which are located at least 2 kb away from a known TSS ,30. The To determine if alignment of the nucleosomes at CTCF sites revealed unique chromatin architecture, we examined the available histone variant and histone modification data for human CD4+ cells at these sites ,30. The ArchAlign was able to uncover the unique chromatin architecture located at CTCF sites using only nucleosome occupancy data. The identified architecture contains a nucleosome-depleted region located near CTCF binding sites with adjacent positioned nucleosomes. Overlaying the histone modification and variant data using the aligned coordinates on top of the nucleosome occupancy showed a strong preference for the presence of the H2A.Z nucleosome variant on the strongly positioned nucleosomes as well as the concurrent presence of the histone tail modifications H3K4me3, H3K4me2, H3K4me1, H3K9me1, and H4K20me1. These results suggest that CTCF is a component of direction-dependent chromatin architecture at the majority of its binding sites, which may be functionally important for its role as an insulator.Insulator elements have been characterized by their ability to act as a barrier between differing chromatin contexts ,16. We fCTCF binding location alone is not capable of providing an accurate alignment because the chromatin architecture at CTCF insulators is likely caused by other associated proteins, the underlying DNA sequence at the region, or a combination of both. At the well-studied H19 imprinting control region nucleosome positioning has been shown to be regulated by the underlying DNA sequence, not CTCF binding . In addiSimilar to DNA and protein sequence alignment algorithms, ArchAlign does not identify the base pair location for a feature of interest. To identify the location of a genomic feature with ArchAlign, example regions containing an experimentally mapped feature need to be included in the alignment. The location of the unknown features can then be inferred from the alignment. The accuracy of the alignment is dependent on both the accuracy of the example regions and the extent to which the chromatin is organized around that feature. As evident from TSSs and CTCF binding sites, histone variants and histone tail modifications help define distinct chromatin architecture for certain genomic features. Future versions of ArchAlign will incorporate these datasets in order to produce an even more biologically relevant alignment.Caenorhabditis elegans are difficult to map due to trans-splicing of the majority of mRNAs, which causes the 5' ends of different messages to have the same leader sequence [Schizosaccharomyces pombe are difficult to map accurately, because S. pombe's origin recognition complex does not bind to specific DNA sequences but to AT-rich regions [ArchAlign is the first tool developed to align chromatin structural data and will prove highly valuable for analyzing chromatin datasets from genomes lacking substantial genomic feature annotation. Currently, there are many genomic features that cannot be accurately mapped by available techniques. For example, TSSs in sequence , and ori regions . ArchAliS. cerevisiae genome-wide nucleosome occupancy maps were downloaded from the Segal Lab and transformed into a log2 ratio for each base pair [ase pair . Mapped ase pair . The 200ase pair . The 156ase pair . The 70 ase pair .2 ratio, as described above, using a tag extension of 120 bp.Genome-wide nucleosome occupancy maps for CD4+ cells were downloaded from NCBI  and tran2 occupancy dataset previously generated. All CTCF sites for which complete data were not available or were within 2 kb of TSSs of a known gene were removed from the original set of sites. The remaining top 1,000 sites by fold enrichment were then selected as CTCF binding sites.CTCF binding data were downloaded from NCBI . The topCD4+ histone modification data were downloaded from NCBI ,30. GenoA random number generator was used to generate the genomic coordinates for 10,000 random non-overlapping regions. The data were then extracted as previously described. The first 1,000 remaining regions after filtering were then selected as the random regions.Usage: Chosen Seed Alignment , where X = Seed Region 1, Y = Seed Region 2, Z = List of Regions, n = Number of Regions, w = Optimal Window of Region, and P = Average Profile.Identify windows of X and Y that maximize the similarityRepeat for i = 3 to nRepeat for j = 0 to Length of Remaining Regionsj that maximizes similarity to PIdentify window of ZIdentify Region j that contained the window that produced the highest similarity to Pj from ZRemove ZOutput Optimal Windows of All RegionsUsage: Single Best-Pair Alignment(Z)Repeat for i = 0 to nRepeat for j = 0 to ni and Zj that maximize similarity out of all possible regions given i ≠ jIdentify windows of Zi and ZjIdentify which two Regions contained the windows that produced the highest similarity to each other for Zi, Zj)Chosen Seed Alignment(Zi = Alignment with a forced seed from Region i.Usage: Seed Selection Alignment(Z), where ARepeat for i = 0 to nRepeat for j = 0 to ni and Zj that maximize similarity out of all possible regions given i ≠ jIdentify windows of Zi and ZjIdentify which two Regions contained the windows that produced the highest similarity to each other for Zi = Chosen Seed AlignmentAi)Post-Alignment Quality Assessment, where A = Alignment.ArchAlign was designed and written in C++ then compiled and run on a 64-bit Linux machine with 8 × 2.76 GHz Xeon X5550 cores, 48 GB RAM, and 80 TB attached disk storage array. The current version of ArchAlign is designed to use only a single CPU core per run.The original (±750 bp) nucleosome profiles of the 200 TSSs were extracted at a resolution of 10 bp. ArchAlign was performed with a sliding window of 1 kb with region reversal disabled. The original (±1 kb) nucleosome of 156 origins of replication were extracted at a resolution of 10 bp. ArchAlign was then performed with a sliding window of 1.5 kb with region reversal disabled.The (±1 kb) nucleosome profiles of the top 1,000 were extracted at a resolution of 10 bp and ArchAlign was run with a sliding window of 1.5 kb with region reversal enabled.The (±1 kb) nucleosome profiles of 1,000 random regions were extracted at a resolution of 10 bp and ArchAlign was run with a sliding window of 1.5 kb with region reversal enabled.Alignment using seed sampling without reversals of 200 2-kb regions at 10-bp resolution with a sliding window of 1.5 kb requires less than 5 minutes of CPU time. Alignment using seed sampling with reversals of 1,000 2-kb regions at 10-bp resolution with a sliding window of 1.5 kb requires approximately 20 hours of CPU time. Increases in number of regions, region size, data resolution, and decreases in window size will result in increases of CPU run time. Alignment using the single-best-pair approach is significantly faster; for a 1,000 2-kb region with reversals at 10-bp resolution with a 1.5-kb window requires less than 10 minutes CPU time.ArchAlign is available at .bp: base pair; ChIP: chromatin immunoprecipitation; CTCF: CCCTC binding factor; MNase: micrococcal nuclease; TSS: transcription start site.The authors declare that they have no competing interests.WKML and MJB conceived and designed the method and wrote the manuscript. WKML implemented ArchAlign.Supplementary figures S1, S2, S3, S4, S5, and S6.Click here for file


A primary component of next-generation sequencing analysis is to align short reads to a reference genome, with each read aligned independently. However, reads that observe the same non-reference DNA sequence are highly correlated and can be used to better model the true variation in the target genome. A novel short-read micro re-aligner, SRMA, that leverages this correlation to better resolve a consensus of the underlying DNA sequence of the targeted genome is described here. Instead, short-read sequence alignment algorithms have been developed to compare each short sequence to a reference genome [Whole-genome human re-sequencing is now feasible using next generation sequencing technology. Technologies such as those produced by Illumina, Life, and Roche 454 produce millions to billions of short DNA sequences that can be used to reconstruct the diploid sequence of a human genome. Ideally, such data alone could be used to question -6. Howeve genome -12. ObseBecause alignment algorithms map each read independently to the reference genome, alignment artifacts could result, such that SNPs, insertions, and deletions are improperly placed relative to their true location. This leads to local alignment errors due to a combination of sequencing error, equivalent positions of the variant being equally likely, and adjacent variants or nearby errors driving misalignment of the local sequence. These local misalignments lead to false positive variant detection, especially at apparent heterozygous positions. For example, insertions and deletions towards the ends of reads are difficult to anchor and resolve without the use of multiple reads. In some cases, strict quality and filtering thresholds are used to overcome the false detection of variants, at the cost of reducing power . Since ede novo assembly are two approaches to reconstruct individual human genomes. Our proposed method has the advantage of utilizing previously developed short-read mapping as the input, coupled with an assembly-inspired approach applied over discrete small windows of the genome whereby multiple reads are used to identify a local consensus sequence. The proposed method overcomes problems specific to alignment and genome-wide assembly, respectively, with the former treating reads independently and the latter requiring nearly error-free data. Unlike de novo assembly, SRMA only finds a novel sequence variant if at least one read in the initial alignment previously observed this variant. De novo assembly algorithms, such as ABySS and Velvet [In this study, a new method to perform local re-alignment of short reads is described, called SRMA: the Short-Read Micro re-Aligner. Short-read sequence alignment to a reference genome and d Velvet -3,5,6,19SRMA uses the prior alignments from a standard sequence alignment algorithm to build a variant graph in defined local regions. The locally mapped reads in their original form are then re-aligned to this variant graph to produce new local alignments. This relies on the presence of at least one read that observes the correct variant, which is subsequently used to inform the alignments of the other overlapping reads. Observed variants are incorporated into a variant graph, which allows for alignments to be re-positioned using information provided by the multiple reads overlapping a given base. We demonstrate through human genomic DNA simulations and empirical data that SRMA improved sensitivity to correctly identify variants and to reduce false positive variant detection.To assess the performance of local re-alignment on a dataset with a known diploid sequence, two whole genome human re-sequencing experiments were simulated  to generate 1 billion 50 base-paired end reads for a total of 100 Gb of genomic sequence representing a mean haploid coverage of 15 × for either Illumina or ABI SOLiD data. SNPs, small deletions, and small insertions were introduced to provide known variants and test improvements of SRMA for their discovery genome-wide, as described in the Materials and methods. The data were initially aligned with BWA (the Burrows Wheeler Alignment tool)  and then-6 for all variants while maintaining >80% power (sensitivity). We note that because inserted bases are directly observed, insertions are more powerfully corrected to the actual sequence relative to deletions. This may help explain the relatively greater improvement in the false positive rate for insertions over deletions at comparable sensitivities.In Figure These simulations assumed ideal conditions: no genomic contamination, a simple error model with a modest uniform error rate, and a simplification that includes only a subset of all possible variants . Nevertheless, the false positive rates achieved after variant calling with no filtering criteria applied is striking and indicates that local re-alignment can be a powerful tool to improve variant calling from short read sequencing. Longer insertions (>5 bp) are not sufficiently examined in the simulation model. However, we note that longer indels are supported by SRMA, but SRMA requires that the initial global alignment permits the sensitive alignment of reads with longer indels to the approximate correct genomic position.To assess the performance of local re-alignment with SRMA on a real-world dataset, a previously published whole-genome human cancer cell line U87MG) was used SRA009912.1) . This daMG was us.1 . ThisIn the case of SNPs called from color space (two-base encoded) data, the decoded reads can be improperly decoded such that SNP positions have a reference allele bias, which is reflected in the original alignments. Thus, in order to assess if SRMA is improving the overall fraction of reads appropriately aligned, we analyzed in aggregate all variant positions to determine if the ratio of reference/variants at heterozygous positions is shifted towards the expected 50%. With respect to heterozygous-called variants, a binomial distribution centered around 0.5 frequency based on sampling/coverage is expected. The observed variant allele frequency after SRMA is substantially shifted towards this expected distribution Figure . SimilarTo further examine the accuracy of the variant calls genome-wide, indels were compared to the known database of common variants found in dbSNP (dbSNP Build ID: 129) . We sougTo further assess the quality of SNP calls, heterozygous genotypes from an Illumina SNP microarray were compared with genotypes called from sequence data before and after application of SRMA to estimate SNP concordance. In Figure The variant calls of SRMA are improved genome-wide by SRMA, and several dramatic examples of sequence improvement can be demonstrated. For instance, a 15-bp deletion flanked by a nearby C-to-T SNP was observed in the coding sequence of ALPK2 in the original BFAST alignments of U87MG and was confirmed by Sanger sequencing. However, a large fraction of the original alignments did not contribute to the calling of this haploid event Figure , insteadw) will increase the number of paths examined. Furthermore, any variant within the graph will lead to a larger branching factor (nodes with multiple neighbors either upstream or downstream) and increase the number of paths examined. Highly polymorphic genomes will also increase the graph's complexity. The complexity of the graph is also influenced by the sequencing technology. For technologies that sequence DNA bases directly, sequencing errors that are indistinguishable from variants will thus be represented in the graph. The two-base encoded data produced by the ABI SOLiD system in practice tends to have fewer spurious variants. With such an encoding, it is more difficult to interpret sequencing error in the encoded color sequence in such a fashion as to produce base changes in the decoded base sequence. Nevertheless, without filtering using the c or p parameters, any observed base difference from an alignment will be included in the graph. Therefore, setting reasonable parameters for c and p beyond removing spurious variants is important to bound the number of search paths and make re-alignment computationally feasible. In practice, the settings used in our evaluations (c = 2 and p = 0.1) work well for human genome re-sequencing experimentsThe running time and memory required by this re-alignment procedure is based on the number of start nodes as well as the complexity of the variant graph. More start nodes (larger SRMA was run in a Map-Reduce framework using a cluster submission script (for Sun Grid Engine (SGE) or Portable Batch System (PBS) systems) provided with the SRMA distribution. The alignments to the reference genome were implicitly split into 1-Mb regions and processed in parallel on a large computer cluster; the re-alignments from each region were then merged in a hierarchical fashion. This allows for the utilization of multi-core computers, with one re-alignment per core, as well as parallelization across a computer cluster or a cloud. The average peak memory utilization per process was 876 Mb (on a single-core), with a maximum peak memory utilization of 1.25 GB. On average, each 1-Mb region required approximately 2.58 minutes to complete, requiring approximately 86.17 hours total running time for the whole U87MG genome. SRMA also supports re-alignment within user-specified regions for efficiency, so that only regions of interest need to be re-aligned. This is particularly useful for exome-sequencing or targeted re-sequencing data.Here we describe a novel local re-alignment algorithm, SRMA, which can significantly reduce the false positive variant detection rate with short-read next generation sequencing technology. While global sequence alignment examines each read independently, multiple reads aligned over a common position are highly correlated especially when a single diploid genome is being sequenced. SRMA uses these correlated alignments to build a limited graph structure that represents these alignments and their differences in compact form such that the alternative allele is more readily observed. The original reads are then re-aligned within a local coordinate window to improve the resulting alignments relative to the target genome rather than a reference genome.Simulations of whole genome human re-sequencing data from both ABI SOLiD and Illumina sequencing technology were used to assess SRMA under simplified conditions in which the variant positions and alleles are known. SRMA was able to improve the ultimate variant calling using a variety of measures on the simulated data from two different popular aligners, BWA and BFAST. These aligners were selected based on their sensitivity to insertions and deletions since a property of SRMA is that it produces a better consensus around indel positions. The initial alignments from BFAST allow local SRMA re-alignment using the original color sequence and qualities to be assessed as BFAST retains this color space information. This further reduces the bias towards calling the reference allele at SNP positions in ABI SOLiD data, and reduces the false discovery rate of new variants. Thus, local re-alignment is a powerful approach to improving genomic sequencing with next generation sequencing technologies.We note as well that while clearly demonstrating improvements in human genomic sequencing, more substantial improvements in variant discovery would be expected when a more distantly related genome is used as the reference. Currently, SRMA does not support enumerating over insertions or deletions caused by homopolymer errors that can be found in 454 data and other flow-based technologies. Nevertheless, similar to utilizing the original color sequence for ABI SOLiD data, the original flow-space data from 454 data could be used during re-alignment and represents future work. Incorporating known variants, for example from dbSNP, into the variant graph as a prior also represents future work. SRMA is publicly available under the GPL license at .de novo assembly (or re-assembly) algorithms report novel sequences without comparing the reads to a reference sequence, this method provides new improved alignments relative to a reference sequence improving downstream consensus calling. Iterative application of SRMA is possible, whereby further rounds of building a variant graph and read re-alignment are performed, but is not examined here.This method relies on short-read alignment algorithms to first align each read to a reference sequence -12. AfteHere we seek to use individual sequence reads to create a series of possible variant options that include the true variants present within the target genome being sequenced. Ultimately, the goal is to distinguish between true variants and sequencing errors genome-wide. Since, in the interest of novel mutation discovery, we must allow for all possible base positions being variant, as well as for an exponentially larger number of possible indels, we opt for an approach that creates a variant graph that includes all aligned reads at a given position in the genome prior to performing re-alignment. This graph is a compact mathematical representation of the initially determined alignments. Each alignment is represented as a path through the graph, although not every path through the graph corresponds to an actual alignment.The variant graph is composed of nodes. Each node represents a DNA base at a specific position relative to the forward strand of the reference genome. Two nodes share an undirected edge if they are adjacent read bases in an existing alignment. For example, the variant graph of the reference sequence that is aligned perfectly to itself consists of one node per reference base, with edges connecting nodes that represent adjacent bases in the reference. In this case, the variant graph has one path. To properly order the nodes in the graph relative to the reference, each node is also assigned a position and an offset. The offset is non-zero only if the node represents an insertion relative to the reference. Insertions relative to the reference are given the reference position of the next non-inserted base with higher physical position on the forward strand, and with its offset set as the number of bases from the beginning of the insertion. Insertions at the same reference position can be combined by merging the paths that represent their longest common prefix and longest common suffix, respectively. A single nucleotide substitution would be annotated to have the same position as its relative reference base. In summary, nodes are described as three distinct types: reference, substitution, and insertion. A node's position, base, type, and offset are unique among all nodes in the graph and define a canonical ordering over all nodes in the graph.Initially, the graph is empty. Bases that match the reference and variants are incorporated into the graph by adding new nodes and edges. Substitutions and insertions are represented as additional nodes in the graph. Deletions, on the other hand, are added as edges that connect nodes that have a positional difference greater than one. An example of creating a variant graph from four alignments is shown in Figure w base positions from the start of the existing alignment are considered as start nodes for a new alignment. A node in the graph is visited at most w times per re-alignment, even though every path reachable from a starting node is examined. Note that the direction of the paths through the graph match the direction implied by the strand of the original alignment. Therefore, the graph is a directed acyclic graph (DAG) during each local re-alignment, with a partial ordering imposed on the nodes as was explained earlier . All valid paths from the starting nodes can be efficiently examined using a breadth-first traversal using a heap data structure.Once the variant graph is constructed from all aligned reads, local re-alignment of the reads proceeds through a series of weighted steps to optimize the final alignments. The variant graph is not modified after re-alignment begins. A dynamic programming procedure is used to compare a read to the variant graph in a similar manner to the Smith-Waterman algorithm -27. EachThe heap stores nodes sorted by their partial order, the current path length, and the current alignment score, in that order; the path length and alignment score are also stored in the heap. Initially, the start nodes are added to the heap with a path length of one and an alignment score based on comparing the read's first base to the base represented by the start node. If the read base matches the start node base, then no penalty is added to the previous re-alignment score. Otherwise, a negative score based on the original base quality of the read is added to the previous re-alignment score to return the current re-alignment score. Other alignment scoring schemes are possible, but mismatched bases are scored using base quality since it has been shown to improve alignment quality .The heap is polled while it is non-empty. Paths to the given node that have the same path length and a smaller alignment score can be removed (from the top of the heap) to remove suboptimal alignment paths. Paths to the same node but with different lengths result from differing start nodes, deletions, and insertions. This pruning step uses a dynamic programming procedure, where the best paths to and from the current node are assumed to be conditionally independent given their respective path lengths (number of read bases examined). Next, if the path length equals the length of the read, all of the bases in the read have been examined. The best  complete path, if any, is compared to the current path and updated accordingly. Otherwise, the path is extended to each child (successor) of the given node. For each child node, the child node's base is compared to the corresponding base in the read (determined by the path length), with the alignment score modified as above. The child node, incremented path length, and updated alignment score are added to the heap. Once the heap is empty, the path with the best score is returned to give a new alignment. This new alignment may match or differ from the original alignment depending on the graph structure.As observed during graph creation, the original alignment is represented as a path through the graph, and therefore will be reconsidered during re-alignment. In fact, the original alignment can be used to set a bound on the minimum re-alignment score. Since the alignment score implemented above decreases monotonically, any path with lower alignment score than the original alignment can be removed from the heap. If the original alignment is likely to be the best alignment after re-alignment, then this bound significantly reduces the practical running time of local re-alignment.The entire variant graph does not need to be constructed before beginning re-alignment, but rather only nodes in the graph that are reachable from the starting nodes need be considered. Therefore, only original alignments that pass through any of these reachable nodes need to be included when creating the variant graph for a specific alignment. Thus, the variant graph can be dynamically built from previous read alignments, with nodes removed from the graph when no longer reachable from the next read re-alignment. This allows only a small local window of the variant graph to be explicitly built and kept in memory, significantly reducing memory requirements.c, only nodes observed in least c original alignments are considered. The minimum edge probability p considers the all edges through non-insertion nodes  at a given genomic position. The total number of observations N across all nodes with the same position (and zero offset) along with the minimum edge probability p is used to bound paths through edges incoming to nodes at that position. Suppose an incoming edge to a node is observed n times, then the edge is pruned if Pr(x ≤ n | N) <p. This probability is modeled using the binomial cumulative distribution function under the assumption that two possible alleles (nodes) are possible at a given position:Two input parameters prune potential alignment paths through the graph: minimum node/edge coverage, and minimum edge probability. Given a minimum node/edge coverage OR logical relationship: a path through a node/edge is included if it passes one or both of the filters. Within high coverage locations, the former filter removes variants that occur by random chance due to sequencing error but is intended to remain sensitive to the detection of alleles that can be obscured by alignment ambiguities. In contrast, within low coverage regions, the former filter will overly penalize variants that were not observed due to insufficient sampling. Thus, the latter filter is designed to include variants in low coverage regions while strictly penalizing variants that do not occur frequently in high coverage regions. These parameters are important for both removing spurious variants and inclusively including potentially real variants in low coverage regions.While this is a valid assumption if the genome has two copies of each chromosome (diploid), deviations from this do not greatly change the pruning strategy as both input parameters are used in conjunction with an Special considerations need to be made to incorporate sequencing reads produced by the ABI SOLiD platform, which are generated in a two-base encoded form. When re-aligning such data to the variant graph, a modified version of the two-base encoded dynamic programming algorithm is used ,21. In tTwo simulated datasets were created to evaluate SRMA, one simulating data from an Illumina sequencer, and one simulating data from an ABI SOLiD sequencer. A uniform 1% per-base error rate was used for the Illumina dataset, while a uniform 5% per-color read error rate was used for the ABI SOLiD dataset. In practice, sequencing error per read is not uniform, tending to be low at the 5' end (the beginning) of the sequence read and higher towards the end of each read, but that is not modeled here. The distance between the two ends of each paired end read was randomly drawn from a normal distribution of mean 500 bases and standard deviation of 50 bases. Polymorphisms were added to the simulated genome at a rate of 1/1000 with a 1/3 probability of being a homozygous variation. Insertions and deletions each accounted for 5% of all polymorphisms. The probability of an insertion or deletion extending beyond one base was 0.3 per extended base to simulate observed in/del distributions in the human genome. The whole-genome simulation program and subsequent accuracy evaluation can be found in the DNA Analysis (DNAA) package .c = 2 and p = 0.1).To empirically test the feasibility and utility of SRMA in a whole genome context, a previously published human whole-genome brain cancer dataset was obtained from the Sequence Read Archive SRA009912.1) . The ori.1 . The SAMtools reports three metrics for each variant position: SNP quality, consensus quality, and base coverage. The SNP quality is the Phred-scaled probability that the consensus is identical to the reference, while the consensus quality is the Phred-scaled likelihood that the called genotype is wrong. Typically, a minimum SNP quality filter can be used to reduce false positives while somewhat reducing sensitivity.ABI: Applied Biosystems Inc.; BFAST: Blat-like Fast Accurate Search Tool; bp: base pair; BWA: Burrows Wheeler Alignment tool; dbSNP: Single Nucleotide Polymorphism Database; SNP: single nucleotide polymorphism; SRA: Sequence Read Archive; SRMA: Short-Read Micro re-Aligner.NH designed and implemented the algorithm, and drafted the manuscript. SFN drafted the manuscript. All authors read and approved the final manuscript.


In structural biology, similarity analysis of protein structure is a crucial step in studying the relationship between proteins. Despite the considerable number of techniques that have been explored within the past two decades, the development of new alternative methods is still an active research area due to the need for high performance tools.In this paper, we present TS-AMIR, a Topology String Alignment Method for Intensive Rapid comparison of protein structures. The proposed method works in two stages: In the first stage, the method generates a topology string based on the geometric details of secondary structure elements, and then, utilizes an n-gram modelling technique over entropy concept to capture similarities in these strings. This initial correspondence map between secondary structure elements is submitted to the second stage in order to obtain the alignment at the residue level. Applying the Kabsch method, a heuristic step-by-step algorithm is adopted in the second stage to align the residues, resulting in an optimal rotation matrix and minimized RMSD. The performance of the method was assessed in different information retrieval tests and the results were compared with those of CE and TM-align, representing two geometrical tools, and YAKUSA, 3D-BLAST and SARST as three representatives of linear encoding schemes. It is shown that the method obtains a high running speed similar to that of the linear encoding schemes. In addition, the method runs about 800 and 7200 times faster than TM-align and CE respectively, while maintaining a competitive accuracy with TM-align and CE.The experimental results demonstrate that linear encoding techniques are capable of reaching the same high degree of accuracy as that achieved by geometrical methods, while generally running hundreds of times faster than conventional programs. Today, biologists are faced with rapidly growing amounts of unknown sequence and structure data related to protein databases. Taking advantage of efficient analysis tools, biologists are highly motivated to derive biological insights from these biomolecules. Sequence comparison tools are commonly used to determine the similarities between proteins with a high degree of similarity, whereas structure comparison methods are essentially utilized to highlight the evolutionary relationships among proteins. Additionally, scientists consider the biological role for these macromolecules as being strongly dependent on their 3D-structure, which has attracted their interest to employ accurate and reliable structure comparison tools with respect to such molecules.Considering the large amount of unknown data which exists in structural biology, efficient powerful tools are needed to investigate, analyze and classify the properties and functionalities of this data. Furthermore, several tools are available for structural analysis and comparison of biological data. The lack of a universal measurement standard for the evaluation of these methods has persuaded biologists to use different tools and scoring functions in their inquiries. This diversity has provided facilities for biologists to extract their required information for query data more efficiently.α backbone atoms in order to find the best optimal correspondence between residue pairs. The techniques include distance matrices comparison (DALI)  where the higher value is better. Accordingly, we used TMscore in order to investigate the alignment optimality, with which the optimal gap penalty is chosen. To this end, we used a randomly selected dataset of 1000 protein structures to test the alignment quality in different gap penalties based on evaluation of the TMscore.where LSkolnick , TMscoreThe above introduced algorithm, called TS-AMIR, was implemented in Microsoft Visual C++ using MS-Windows XP. This section reports the results of experiments in order to assess the performance of the method. The method was subjected to different datasets and its outputs were compared with CE  and TM-aIn order to determine the optimal size of n-gram in the SSEs sequence matching procedure and to balance its accuracy and sensitivity against computational efficiency, an accuracy index of similarity database search was adopted from Receiver Operating Characteristic (ROC) curves . The indTable score was used as a balance between these measures to evaluate the quality of the alignment in this experiment. The test uses a randomly selected set of 1000 protein structures from the PDB with less than 40% sequence identity. In order to choose the optimal gap penalty, all-against-all structural alignment was performed at the following gap penalty values {-1.0, -2.0, -3.0, -4.0, -5.0, -6.0}, and the TMscore was calculated for each alignment.The procedure for the alignment at the residue level requires the optimization to choose an optimum gap penalty. This optimization is based on two contradictory criteria including the length of alignment and RMSD. Therefore, TMscore obtained at different gap penalty values. In this figure, a higher TMscore value indicates a higher degree of precision and/or length in the alignment. As shown in the figure, increasing the negative gap penalty yields a higher TMscore on varying gap penalties ranging from -1.0 to -3.0. Based on the figure, the optimal gap penalty value is -3, where for the higher values, the TMscore decreases slightly. Indeed, the high gap penalty values prevent the alignment from extending along the protein structure, and yet the low value yields numerous gaps in the alignment with a low biological significance. Accordingly, the value of -3.0 seems to be the optimal gap penalty value in our experiment.Figure Additionally, we used the above dataset to test the optimality of the alignment procedure. The experiment was run by choosing different initial sets of fragments to make and apply a rotation matrix based on the alignment procedure. There are only 58 (≈6%) items with an alternative choice for initial fragment with a difference less than 0.01. Accordingly, the procedure is deemed sufficient based on the fast convergence of the rotation matrix after 3-4 repetitions.The benchmark study was performed using a set of 200 non-homologous protein chains collected from the PDB by Zhang and Skolnick  with a pscore in Table The table shows the alignment accuracy by RMSD, the length of alignment, and coverage, which is the fraction of the aligned residues in the target protein. The results produced by CE are included in this table as a basic reference method to evaluate other alignment tools. As seen from the table, TS-AMIR has the largest length of alignment (91.4) and coverage (46.6%) where its accuracy in terms of RMSD (6.17) is ranked second, less than CE and 3D-BLAST. Moreover, TM-align is the best in terms of RMSD (4.99) where it obtains the second rank in the length of alignment (87.4) and coverage (42.0%). Furthermore, comparing the average TMscore values. In order to significantly compare the ability of the methods to match the most similar structure to a given target protein, the averages over only the highest TMscore match for each target protein are computed in Table score (0.502), while positioned lower than TM-align (0.510) showing a slight difference with the first rank.Most of the structures collected in the dataset belong to different folds with low TMprecision and recall parameters as two commonly used measures of information retrieval assessments. The parameters are determined as follows:The retrieval effectiveness of the schemes was investigated using Aung and Tan collected dataset  of 34,05m is the number of correct retrievals from the same SCOP family, n is the total number of retrieved proteins, and N denotes the total number of relevant proteins within the same SCOP family. The experiment uses the above dataset to search 108 query proteins, and the precision and recall values are evaluated for each method. The average precision-recall values calculated for the six different schemes are shown in Figure where The performance of the schemes in terms of running speed was also measured and compared in Table The performance of the methods was evaluated in the determination of structural categories of each query structure from the above 108 proteins. To this end, the false positive rate was computed as a measure of the probability of irrelevant retrieval, where the lower rate indicates a higher degree of efficiency in the retrieval assessment.The average false positive rates were computed for four categories of the SCOP database after the retrieval of 80 proteins for each query structure. Figure Furthermore, a similar experiment was carried out using proteins with missing residues, known as incomplete structures , which cet al. [et al. [The efficiency of the above six methods were examined for homology searching in the low sequence identity. The experiment uses the dataset of 24,337 proteins from ASTRAL SCOP 1.69 collected by Lo et al. . A subse [et al. ).The above dataset was utilized to perform an information retrieval experiment on a subset of query proteins. As shown in Figure The main objective in this study was to develop a rapid protein structure alignment tool by combining two fruitful strategies: geometry-based approach and linear encoding approach. Indeed, TS-AMIR gains the advantages of two different strategies. In the first stage, it uses a linear encoded form of protein secondary structure to reach an initial correspondence map. Linear encoding schemes offer the advantage of fast running speeds. In the second stage, the method utilizes an iterative algorithm to create a rotation matrix using the geometry of aligned residues. Geometrical schemes obtain high performance in terms of accuracy. Accordingly, the method combines the above two schemes to achieve high running speeds as well as a high degree of precision.TS-AMIR has been developed with some key features demonstrating its preference and competence. First of all, the method uses a simplified representation of the secondary structure elements in a topology string in order to match the backbone of two structures. This has a complexity reduction advantage to a one-dimensional problem where it requires a string matching procedure based on language modelling techniques. Moreover, the method adopts the n-gram modelling technique from computational linguistics, making it superior to the other formal language models to capture similarities in the topology strings. As another feature, this representation makes the method free from any parameter setting by the user. Classical structure alignment techniques such as dynamic programming often require a set of optional parameters to reach the best possible match. Moreover, at the residue level, the method applies the Kabsch method to generate the rotation matrix as an efficient well-known solution for the optimal alignment of two structures in an iterative heuristic procedure. The alignment is converged after 3-4 iterations. Consequently, the method obtains a comparable performance with other state of the art programs.Secondary structure encoding in a 1D topology string provides several advantages in structural analysis and alignment of proteins. Firstly, it facilitates the representation of protein backbone structure in a summarized 1D-sequence making an economic pre-calculated database of the structural information. Secondly, the search algorithm to determine the initial overlap between two structures is sped up by choosing an efficient sequence alignment technique to match these strings. Thirdly, classifying the proteins into structural categories within large databases can be done efficiently. Finally, the scheme can be used to simplify computationally complex structure analysis tools by rapidly filtering out irrelevant structures.As mentioned above, TS-AMIR adopts two relatively simple strategies from both linear encoding and geometry-based schemes to be used in secondary structure matching and residue level alignment respectively. Therefore, TS-AMIR obtains a competitive running speed in comparison with the other rapid structure alignment techniques.Comparing the running time of the methods to search for each query protein shows that TS-AMIR runs 3 times faster than YAKUSA, approximately as fast as 3D-BLAST, and more than 30 times slower than SARST method. The lower speed of TS-AMIR against SARST seems to be due to utilizing the iterative procedure for the alignment at the residue level by TS-AMIR in order to make and apply the rotation matrix. However, TS-AMIR is more precise compared to SARST in the information retrieval assessments. Considering the high speed of linear encoding schemes, they are mostly developed as a search tools to run queries in the large structure databases. Therefore, they ignore giving highly accurate results in favour of reaching a high running speed. TS-AMIR improves the low accuracy of the linear encoding approaches by using a geometry-based technique for the alignment at the residue level.Although TS-AMIR did not yield a highly accurate alignment like TM-align, it has shown a competitive accuracy with CE which is known to be a highly accurate method. Moreover, TS-AMIR outperforms the other linear encoding methods such as YAKUSA, 3D-BLAST and SARST in terms of accuracy.score after TM-align. The high efficiency of the method is yielded by the high length of alignment with a relatively low RMSD. This is due to the strategy of the method for the alignment of residues in an iterative step-by-step algorithm. The method first looks for small fragments within matched SSEs to make an initial alignment, and then extends the alignment to the rest of the residues by considering a penalty for each gap. The allowed gaps between the aligned residues provide an opportunity to superpose large and significant coverage between the two structures. Moreover, the algorithm clears the alignment list for each repetition, and looks again for the alignment. This helps the algorithm to determine a more accurate alignment after applying the rotation matrix at the end of each repetition. Due to the convergence of the rotation matrix after 3-4 attempts, these iterations do not critically increase the running time of the algorithm.Looking at the results of the benchmark test, it is clearly visible that TS-AMIR obtains high alignment quality in terms of TMStructure alignment programs using a hierarchical approach ranging from secondary structure matching to the atomic superposition level ,28 are uThe results of assessments for information retrieval make evident that geometry-based methods are preferred to linear encoding methods. Taking advantage of both schemes, TS-AMIR shows improved accuracy in the assessments similar to that of CE, but better than those of other linear encoding programs.Nevertheless, based on the experimental results, the method displays occasional weakness in the retrieval of relevant structures. After subjecting the method to several irrelevant retrievals, it was illustrated that the weakness is due to the repetition of common SSE subsequences shared by different proteins within the whole structure. In fact, TS-AMIR uses n-gram modelling to match the subsequences of SSEs topology strings. Therefore, the method occasionally falls in ambiguity for matching the n-grams, which is in accordance with its multiple occurrences along the topology string, thus leading to the applied heuristic's failure in making decision. However, by setting a relevant n-gram size in the SSEs matching procedure, these irrelevant retrievals are minimized.Additionally, TS-AMIR achieves a fair performance on retrieval of structural categories among the methods where it produces low false positives for different SCOP categories. This is conceptual due to the adoption of an efficient strategy for matching SSEs based on the topology strings. It was expected that the method would produce more false positives due to repetition of alpha helices along with the protein chain as the most abundant regular element of secondary structure. Therefore, the probability of irrelevant matching between SSEs is increased. However, the method overcomes this expectation and yields fair results by choosing the appropriate decreasing sizes of n-gram as mentioned in the methods section.We have developed a rapid protein structure alignment tool called TS-AMIR, a Topology String Alignment Method for Intensive Rapid Protein Structure Comparison, which is a combination of a linear encoding scheme in the first stage and a geometry based technique in the second stage. In terms of speed, the experimental results demonstrate the high performance of the method as it performs as well as linear encoding schemes. In addition, the method obtains results as highly accurate as the geometry based approaches. This high efficiency results from the simple and efficient techniques which are employed by the method.Further studies will focus on the application of the approach for non-sequential structural alignment of proteins, which needs to neglect connectivity of the polypeptide chains. Considering the flexibility of the strategy applied by TS-AMIR for the superposition of the secondary structure elements by topology strings, the approach seems to be relevant for use in the detection of non-sequential structural analogies in the proteins. Moreover, recent studies to fuse theoretical concepts from computational linguistics in structural biology motivate the researchers to conduct further studies in this area.The authors declare that they have no competing interests.JR carried out the design and development of the algorithm, performed the experiments and their statistical analysis, and drafted the manuscript. SD carried out the design and supervision of the study. SP participated in data collection and analysis, and helped to draft the manuscript. All authors read and approved the final manuscript.


The ribosomal DNA comprised of the ITS1-5.8S-ITS2 regions is widely used as a fungal marker in molecular ecology and systematics but cannot be aligned with confidence across genetically distant taxa. In order to study the diversity of Agaricomycotina in forest soils, we designed primers targeting the more alignable 28S (LSU) gene, which should be more useful for phylogenetic analyses of the detected taxa. This paper compares the performance of the established ITS1F/4B primer pair, which targets basidiomycetes, to that of two new pairs. Key factors in the comparison were the diversity covered, off-target amplification, rarefaction at different Operational Taxonomic Unit (OTU) cutoff levels, sensitivity of the method used to process the alignment to missing data and insecure positional homology, and the congruence of monophyletic clades with OTU assignments and BLAST-derived OTU names. The ITS primer pair yielded no off-target amplification but also exhibited the least fidelity to the expected phylogenetic groups. The LSU primers give complementary pictures of diversity, but were more sensitive to modifications of the alignment such as the removal of difficult-to align stretches. The LSU primers also yielded greater numbers of singletons but also had a greater tendency to produce OTUs containing sequences from a wider variety of species as judged by BLAST similarity. We introduced some new parameters to describe alignment heterogeneity based on Shannon entropy and the extent and contents of the OTUs in a phylogenetic tree space. Our results suggest that ITS should not be used when calculating phylogenetic trees from genetically distant sequences obtained from environmental DNA extractions and that it is inadvisable to define OTUs on the basis of very heterogeneous alignments. A large proportion of the world's biogeochemically important terrestrial microorganisms are Fungi. Most of the symbiotic and saprobic fungal taxa that degrade plant-derived carbon compounds (e.g. lignin and cellulose) in forest ecosystems belong to the subphylum Agaricomycotina In general, a sequencing target for phylogenetic reconstruction and for identifying different groups should be orthologous, alignable, and not saturated in the mutations that contain the phylogenetic signal. The extent to which some of the most widely used fungal marker genes satisfy above criteria is debatable. Phylogenetic analyses are tightly connected with alignment, as can also be seen in the development of tools that simultaneously optimize alignment and phylogenies such as POY and SATé-II Previous studies on fungal diversity in environmental samples have already focused on the LSU gene e.g. , which dCompared to the ITS region, the bulk of the LSU sequence exhibits a much lower degree of variability across large taxonomic distances, with the exception of its hypervariable D1–D12 core domains. The considerable length heterogeneity of these domains in eukaryotes There have been no concerted efforts to develop fine-tuned primers for selective amplification of the LSU gene from the higher taxonomic ranks of the Basidiomycota. In order to compare the phylogenetic resolution of the widely used ITS1F/4B primer pair The study described in this paper had two main objectives: a) to design selective primers for amplifying typical Agaricomycotina LSU sequences and compare the potential of ITS and LSU rDNA fragments for assessing fungal diversity in environmental samples; and b) to establish simple pre-phylogenetic data exploration methods based on information entropy and the distance covered by the sequences within an Operational Taxonomic Unit (OTU) that will enable the practising molecular ecologist or mycologist to select appropriate primers and analyses for specific purposes.Oligonucleotide primers were designed to amplify the larger subunit (LSU) of the ribosomal DNA (28S rDNA) of the Agaricomycotina, with particular emphasis on targeting members of the order Agaricales. To cover the sequence variation within these fungi, reference sequences representing distinct species found in temperate forests were retrieved from the NCBI GenBank database . These sequences were aligned using the ClustalX v. 2.0.12 program http://www.operon.com/tools/oligo-analysis-tool.aspx) was used to select primers and pairs of primers with the lowest possible tendency to undergo dimerisation or hairpin formation and with similar melting temperatures to ensure efficient annealing. Subsequently, NCBI-BLAST (BLASTn) New primers were designed to amplify LSU fragments of ca. 800 bp using GeneFisher2 in silico PCR using the ecoPCR program The amplification specificity of the two new LSU primer sets was tested by Saccharomyces cerevisiae gene RDN25-1 ( = LSU) obtained from www.yeastgenome.orgS. cerevisiae is shown in The location of the new primers relative to the D1, D2, and D3 regions of the LSU gene were determined by comparison to the secondary structure models developed by Hassouna et al. Glomus geosporum and G. intraradices) Fagus sylvatica-dominated forest sites in the Hainich-Dün Biodiversity Exploratory in western Thuringia, Germany (www.biodiversity-exploratories.de).The PCR efficiency of the newly-developed 28S rDNA primers was tested using a total of 24 fungal pure cultures  according to the manufacturer's protocol. Soil DNA was extracted from 10 g homogenized samples using the MoBio PowerMax Soil DNA Isolation Kit  according to the manufacturer's instructions. The DNA extracts were quantified with a NanoDrop ND-1000 UV-Vis Spectrophotometer . Where necessary, the template DNA was diluted to a final concentration of 20 ng total DNA µlAll ITS and LSU amplification reactions were performed using a Mastercycler 5333  using GoTaq Green Master Mix  under similar thermocycling conditions see . The PCREscherichia coli from the TOPO TA Cloning Kit . Transformed cells were plated out in three dilutions  and grown overnight at 37°C on LB agar plates. All plates yielded similar clone numbers. At least 48 white colonies from each sample were subjected to colony PCR using the M13F and M13R primers and a PCR program involving an initial 10 min denaturation step at 94°C, followed by 32 cycles of denaturation (40 sec at 94°C), annealing (30 sec at 54°C), and elongation (40 sec at 72°C), with a final elongation step of 4 min at 72°C. Positive PCR products were treated with ExoSAP-IT  at 37°C for 15 min to remove undesirable dNTPs, residual primers, and irrelevant single stranded DNA, and were then incubated at 80°C for 15 min to inactivate the ExoSAP-IT. Approximately 45 cleaned amplicons per library were bidirectionally sequenced using an ABI PRISM 3730xl Genetic Analyzer  and the Big Dye Terminator v. 3.1 Cycle Sequencing Kit (Applied Biosystems). Sequences were edited using Sequencher v. 4.8 for Windows  to construct contigs from the manually corrected forward and reverse DNA sequences after removal of primer residues. A total of 40 high-quality sequences per sample and primer pair were selected randomly and pooled by primer set to create three initial 120-clone databases . The number of sequences per dataset was derived from a simulation, an example of which is given in file S2 and The ITS and LSU amplification products obtained from soil DNA extracts were purified directly with a QIAEX II Gel Extraction Kit (Qiagen). The purified amplicons were ligated into a pCR4-TOPO vector and cloned in TOP10 chemically competent https://biotech.inbre.alaska.edu/fungal_portal/?program=chimera_test). All LSU sequences were also compared to the LSU database made available by the Alaskan Fungal Metagenomics Project and using MOTHUR v.1.16.0 A perl script (File S1) was used to augment sequence names with relevant information from the NCBI taxonomy path and the best hit from a BLASTn search against the DDBJ sequence database excluding environmental sequences. The “best” hit was selected by examining the top ten BLAST hits for each query sequence, excluding those from non-vouchered specimens, and then selecting that with the highest score and an E value equal or close to zero. BLAST was also used to check whether it would be necessary to exclude and replace non-target sequences in order to obtain 40 sequences per soil sample and 120 per primer pair. Four different alignments were prepared: ITS (120 sequences), Mix5 (120 sequences), Mix7 (120 sequences), and the Mix5+7 overlap (240 sequences). These were created using the program MAFFT v. 6.717 (L-INS-i) The online tool FastGroupII i) was calculated as the negative sum of the frequencies  of residues occurring at that position, multiplied by their natural logarithm: i) is the frequency of residue x  at position i in the alignment. We used different feeder formulae to independently determine the frequencies of each residue at a specific position in our alignments, using the IUPAC ambiguity codes  is undefined, partial entropies for non-occurring character states were not added, which also means that different alignment positions have different maximum possible entropy values. However, the product of a value that is close to zero and its own natural logarithm is extremely small, so this did not greatly affect the calculated entropy.Information entropy in silico PCR conditions . Mix5 exhibited similar specificity if no mismatches were allowed; if two mismatches were allowed, two Ascomycota and three Ustilaginomycotina sequences were amplified, representing 0.4% of all virtual amplicons. When tested against Invertebrata and Bacteria databases, neither primer set yielded any amplification even when allowing two mismatches.Virtual PCR tests of new LSU primers using ecoPCR and the GenBank-derived databases confirmed their specificity for Agaricomycotina and demonstrated their robust exclusion of non-fungal rDNA. In tests against the Planta/Fungi database, which features 2 014 974 non-environmental nucleotide sequences, Mix7 always amplified Agaricomycotina sequences exclusively even under very relaxed in vitro, all 19 Basidiomycota  were amplified with both primer pairs (−1), which was considered promising in terms of the potential for robust amplification from environmental extractions. Ascomycota, Glomeromycota, and Zygomycota were not amplified.Of the 26 pure fungal DNA templates tested er pairs . While PRhizoctonia). No non-target sequences that would have to be replaced were detected. 11.7% of the sequences amplified using the Mix5 primers originated from Agaricomycotina orders other than Agaricales, Boletales or Russulales . 6.7% of the Mix7 sequences originated from Sebacinales and Thelephorales.We tested each primer pair for its ability to amplify fungal targets from environmental samples and for evidence of non-target amplification . All tesRussula/Inocybe, Inocybe/Russula, Lactarius/Xerocomus, and Xerocomus/Trechispora). These were removed from the original ITS alignments and excluded from subsequent phylogenetic analyses. The Chimera Test of the University of Alaska did not identify any chimeric sequences in the ITS dataset. Similarly, no potential chimeras were identified in the LSU data using the chimera checker at the Alaskan website or the MOTHUR Chimera Slayer tool.Screening for chimeric sequences with different programs revealed relatively little (ITS) or no (LSU) evidence for chimeras. Using the UNITE PlutoF module in vitro from soil samples by both newly developed LSU primer pairs  included the last third of the D1 expansion domain and the entirety of the D2 and D3 domains. The greatest variation in the amplified LSU sequences was found at and near these divergent regions, which accounted for over 45.3% of the total length of the Mix5+7 alignment. The ClustalX quality scores  and ambiguous character states . As expected, deleting gaps and marginal ambiguous sites from the original ITS alignment  resulted in a major loss of phylogenetic information  had a strong impact in terms of decreasing diversity for all primer pairs  and conventional multitons. For all alignments and treatments, most of the OTUs were real singletons containing only one sequence . Over alVarying the cutoff level had little effect on the number of OTUs for the ITS alignment but had a much larger effect on the LSU datasets. For both the ITS and LSU datasets, the level of sequence diversity decreased on going from the untreated alignments to the T3 alignments, but this trend was much more pronounced for the LSU data. Within the ITS dataset, the T1 and T2 treatments yielded identical cutoff-dependent OTU numbers. For the LSU data, and especially for Mix5, the ratio of singleton OTUs to total OTUs increased rapidly as the cutoff level was reduced (i.e. as the level of sequence similarity required to group multiple sequences into an OTU increased). Thus, the ratio for T1-0.05 was 0.4 (i.e. 8 of 20 OTUs were singletons) while that for T1-0.01 was 0.7 (37 of 52 OTUs). Conversely, for the LSU data, there was no apparent relationship between alignment treatment and OTU number. Out of all 36 different alignments considered, the Mix5 dataset exhibited the greatest variability in OTU numbers and the highest diversity at the 0.01 OTU-cutoff level. Overall, going from T1 to T2 alignments did not change the OTU number for the ITS datasets but had varied effects depending on the cutoff level with the LSU datasets. Conversely, going from T2 to T3 alignments significantly reduced OTU diversity for all datasets.The OTU cutoff affected the universal biodiversity indices for the LSU and ITS datasets in similar ways . As the Different primer sets yielded similar but not identical levels of diversity. The ITS primers showed the greatest affinity for the primary target orders – the Agaricales, Russulales, and Boletales DNA . These three Agaricomycotina orders were heavily represented in all datasets, but LSU primers also efficiently amplified sequences from the Sebacinales , which were not represented in the ITS data.The Agaricales overwhelmingly dominated all datasets, as indicated in Stephanosphora . The most abundant OTU across all datasets, Inocybe cookei (BLAST-derived accession number AM882956 contained both the ITS and the LSU sequences), accounted for 19.8%, 15.8%, and 44.4% of the sequences in ITS, Mix5, and Mix7 data, respectively. Phylogenetic resolution was problematic for Russula-like sequences, indicating that multiple BLAST-derived names may have been assigned to one FastGroupII-derived LSU OTU or that many poorly-supported ITS OTUs represented the same BLAST hit. Only Laccaria and strongly represented Tricholoma sequences were all grouped into one maximum bootstrap supported OTU each but these were not conspecific across all three datasets. These two groups (which were identified as L. montana and T. sejunctum using the ITS data but as L. ochropurpurea and T. apium using the LSU data) were conspecific for both LSU alignments. Some of the non-singleton OTUs identified from the Mix5 sequences using FastGroupII fell within other OTUs. In particular, Lactarius and Russula sequences were mixed even within the same OTU. For example, the Russula I OTU featured 13 Russula sequences and 1 Lactarius sequence. The obvious difference between the ITS- and LSU-derived ME-K2P trees was the large internal distance of the LSU OTUs. ME bootstrap analysis of the ITS data generated only 54.8% interior nodes with over 49% bootstrap support  demonstrated that the BLAST derived OTU names corresponded to MEGA-derived monophyletic groups , 7, 8. Wrelative heterogeneity (h) as the ratio of the actual entropy (Hi) to the cumulative maximum  entropy, Hmax; h = Hi/Hmax. A relative heterogeneity of 0 means that the alignment is very homogenous and that all columns contain only one character state each. This is the easiest alignment to make, but it carries no entropy (i.e. it contains no information and thus no phylogenetic information) on its own. A relative heterogeneity of 1 does not indicate that an alignment is chaotic but that all character states are equally distributed. Therefore, assuming that there is no differential weighting of the various substitution types, no alignment column provides any resolving phylogenetic information that can be used in distance- or likelihood-based methods. The relative heterogeneity correlates with average and median entropy values, which also tend to be low for alignments with little phylogenetic information. However, alignments with relative heterogeneities between 0 and 1 contain information that may be phylogenetically relevant. Low heterogeneity values are typically indicative of large numbers of uni-character alignment columns with no phylogenetic information.We attempted to find a simple parameter that would describe the informativeness of an alignment and could be used to compare whole alignments or subsections thereof to one-another. Starting with the cumulative entropy H , we defined the f = 0.10 and 0.12). The highest relative heterogeneity scores were those for the gap-containing sites of the ITS alignment (hg = 0.77). Notably, the overall median and average entropy of the LSU alignments were relatively small  but those of the ITS alignments were much higher (0.88 and 0.81).Of the three datasets analysed in this work, the cumulative entropy (H = 874.12) and total relative heterogeneity (h = 0.56) were highest for the ITS alignment, for which ca. 59% of all sites contained gaps. Both values were at least three times higher than the corresponding values for the LSU alignments , which h1) are generally much lower than those for pyrimidines (k2). Our results suggest that the Mix7 alignment should yield a higher degree of phylogenetic resolution than either ITS or Mix5. The only indicator of phylogenetic accuracy that can be computed using our data is the number of nodes with high bootstrap support. The values obtained for this parameter were consistent with our expectations: the Mix7 NJ and ME phylogenetic trees had the highest percentage of nodes with the maximum possible bootstrap support value and the highest average bootstrap values when considering only nodes with bootstrap values of 50 or more. This trend was particularly pronounced for the ME trees (data not shown).The removal or inclusion of ambiguously aligned regions and the gap treatment had profound effects on the accuracy of phylogenetic analyses. Similar problems have been encountered in previous phylogenetic studies using non-protein-coding sequences, especially for ITS alignments e.g.  and whenMOTHUR uses the furthest neighbour algorithm to define OTUs for rarefaction analysis, whereas FastGroupII relies on the nearest neighbour algorithm. Consequently, the number of OTUs identified for each alignment using MOTHUR was much lower than with FastGroupII, and the MOTHUR analyses were less sensitive to changes in the way the alignments were processed (data not shown). In the MOTHUR analyses, differences in alignment processing had a much less pronounced influence on curve progression than did the sequence cutoff level. This effectively made most of the MOTHUR rarefaction curves congruent. All of the Mix7 rarefaction curves tracked one-another perfectly and only 9 OTUs were detected among the 120 sequences analyzed. Because of its ease of use and widespread adoption, and to avoid unnecessary duplication of labour, all subsequent analyses were conducted on the basis of the OTUs defined using FastGroupII. The relatively large number of OTUs identified using FastGroupII facilitated analysis of the effects of alignment processing. As expected, the cautious deletion of ambiguously aligned characters (as was done in the T3 treatment) consistently reduced the number of OTUs.The impact of varying the cutoff level on the OTU number was much more apparent for the LSU than for the ITS data. This was attributed to the relatively low level of OTU-defining variation in the LSU alignments. We assume that much of the OTU number stability of the ITS dataset with respect to changes in alignment processing is due to the alignments being somewhat weak to begin with. The Mix5 dataset yielded the highest values for the various diversity indices considered. This is probably due to its relatively high number of singleton OTUs and high degree of species evenness: 71% of its OTUs were singletons, compared to 63% for Mix7. This suggests that when including singleton OTUs, the Mix5 primer pair may be better than Mix7 for assessing fungal biodiversity using LSU rDNA sequences.Singleton OTUs dominated in all alignments and treatments ; this isThe dominant taxonomic orders identified by analysing the ITS and LSU sequences were very similar, i.e. Agaricales, Russulales, and Boletales . Our resThe taxonomic affiliations determined by BLAST searching were compared to those derived from phylogenetic trees. This revealed that while the LSU datasets were less diverse than ITS, some OTUs and some of the higher taxa were found only by LSU primers and vice versa. The names assigned to specific OTUs on the basis of BLAST results generally corresponded to monophyletic clades with a high degree of bootstrap support in the ME-K2P analyses , 7, 8. HOverall the higher-level phylogenetic relationships of Agaricomycotina revealed in the LSU and ITS ME phylograms were in agreement with large-scale phylogenies reported by Matheny et al. The DAMBE plot of observed numbers of transition and transversion versus divergence may indicate substitution saturation if the increase of transversions exceeds that of transitions with increasing genetic distance DAMBE only samples 2, 8, 16, or 32 sequences from each alignment. We extrapolated from these results to estimate the theoretical borderline of phylogenetic accuracy for our ITS and LSU alignments (data not shown). A plot of the difference between the two indices (Iss.c-Iss) against the number of sequences tested yielded some surprising results. For fully asymmetrical trees , the ITS alignment became unsuitable for phylogeny once 18 or more sequences were included. Conversely, for the LSU alignments, the difference between the Iss.c and Iss values did not reach zero until at least 50 sequences were considered (or 55 in the case of Mix7), once again demonstrating the phylogenetic superiority of LSU data.Entropy is a more sophisticated measure of sequence variation and is used to quantify the phylogenetic informativeness of alignments. If a given position within an alignment has high entropy, it is informative but this information may be phylogenetically irrelevant or wrong. For example, if some position within an alignment has the highest possible entropy, all possible character states occur at that position with equal frequencies. As such, it will contribute equally well to any phylogenetic partition . If the only possible character states for the position are the 4 nucleotides A, C, T and G, maximum entropy could thus support a hard polytomy with four branches. A position has zero entropy if it has the same character state in every sequence in the alignment, making it completely uninformative in phylogenetic terms.Analyses of the entropy distributions within the ITS and LSU alignments clearly demonstrated that the highest entropy values were associated with gap-containing sites . Most ofRegions of high entropy were much more dispersed in the two LSU datasets, resulting in relatively thin peaks when entropy values were plotted as a function of alignment position. The Mix5 alignment contained only five clusters featuring two or more successive sites with H>1.5; the longest of these extended over eight sites (412 to 419) with a gap percentage of approximately 77.8% of the nucleotide alignment positions. For Mix7, only one small area (3 ton) with maximal H values was found in the entirety of the alignment . The vast majority of the entropy peaks occurred within the D regions of the LSU rDNA gene , as mighTo describe the effects of changing the cutoff on the number of OTUs identified in an alignment, we created a new type of plot that we have termed an OTU distance area plot see  and someIn theory, an OTU can have a positive internal distance only if it contains more than one sequence (and is therefore not a singleton). The “internal distance” of an OTU is hereby defined as the maximum distance between the sequences of the OTU and their last common ancestor node, which is treated as a part of the OTU for this purpose. Quasi-singleton OTUs contain sequences that are identical, but must be differentiated from other doubletons etc., because they have no measurable internal distance, i.e. they act like singletons. To describe the diversity within an OTU, we created the Internal OTU Distance Area (IODA) parameter, which is defined as the product of the internal distance and the number of sequences in the OTU, expressed as a percentage of the tree's maximum possible distance area . The Realized OTU Distance Area (RODA) is a related factor that incorporates a correction for the actual genetic distance covered by all OTUs together rather than the maximum possible distance. The minimum possible distance area for an OTU is 2 (since the parameter is not defined for singletons) and the maximum is equal to the total number of sequences in the alignment. The parameter cannot be computed if the cutoff value is 100% because by definition this will yield a situation in which all OTUs consist of (quasi)-singletons. The greater the number of distinct sequences in an OTU, the closer it will be to the top of a distance area plot. On moving leftwards within the plot, the OTUs become increasingly like quasi-singletons, i.e. they have an increasing tendency to contain numerous highly similar sequences. Non-quasi-singleton OTUs with only two sequences will be found on the minimum possible distance area line. The plot can be used to get an overview of the genetic diversity space within a sample in a large environmental genomics study, or to determine the optimal level for defining species and identifying correlations with other data in a systematics study.Hygrocybe, Inocybe I, Lactarius, Russula I, and Tremellodendron I) and Mix7  datasets, both of which had multiple OTUs containing 11 to 33 sequences. Only one ITS-derived OTU fell into this category . The maximum RODA value was different for each dataset but was always achieved by the most sequence-rich OTUs. The plot indicated that the Mix7 dataset was the most heterogeneous, with RODA values ranging from 10.68 for Inocybe I to 0.01 for Hygrocybe and 0.02 for Xerocomus, both of which were located on the minimum possible distance area line. The Mix5 OTU pool was very sensitive to the K2P distance: even going from 0.05 to 0.04 resulted in the exclusion of one OTU (Tremellodendron I). The LSU OTUs were distributed across the entire area of the plot. In contrast, the ITS OTUs clustered together and none extended beyond the line corresponding to a K2P distance of 0.02. This suggests that relatively low OTU cutoffs should be used when analyzing ITS data in ecological studies.In our plots , the ITSOTU distance area plots could also be used to monitor the behaviour of individual sequences or groups of sequences in response to changes in the cutoff value, for example by constructing a plot showing all the OTUs obtained at different cutoff values and then drawing lines to connect the OTUs obtained with the higher cutoffs and their derived “children” that are obtained with more stringent cutoffs. This would make it easy to see how OTUs split (or merge) as cutoffs change and to identify OTUs that are robust to changes in the cutoff value. However, it would be necessary to design and develop new software tools to monitor OTU behaviour in this way for situations where changing the cutoff changes the maximum possible IODA value.We have developed simple methods and parameters that can be used to assess the quality of the results obtained with different primer pairs, OTU cutoff values, and alignment treatments when conducting large-scale molecular ecology studies on systems whose true evolutionary history is unknown and for which sampling and sequencing errors cannot be ruled out. We focused on tools for exploring data to determine whether bifurcating phylogenetic trees provide realistic representations of diversity for large-scale molecular ecology. It was assumed that a “correct” alignment would accurately reflect the homology of nucleotide positions and that the methods and models of phylogenetic inference would translate this into a bifurcating phylogenetic tree that could be used to analyze the closeness of relationships within the sample. Quality parameters are needed in order to determine whether alignments and phylogenetic trees accurately reflect the diversity within the sample; one such parameter is the entropy-based relative heterogeneity. Phylogenetic inaccuracies can arise when examining very heterogeneous alignments. The creation of alignments is dependent on the availability of some kind of reference point, i.e. a series of homogenous columns with low entropy. However, once the alignment has been created, it is possible that these reference columns could be cropped without reducing the homological value of the remaining character columns. The heterogeneity of an alignment is largely confined to blocks that typically vary in length, contain numerous gaps, and have many and uncertain positions due to their high nucleotide heterogeneity. An alignment's relative heterogeneity score is thus a simple indicator of its phylogenetic usefulness; ideally, it should be low but non-zero. The heterogeneity score can take values between 0 and 1; it will be necessary to perform simulations with a range of large datasets to determine the optimal range for phylogenetic studies.Overall, our results suggest that for the moment, the ITS and LSU primers should be used together but that when analyzing raw ITS sequence diversity, one should rely on local and not global alignment data and sequence identification based on BLAST searches. Great care should be taken when defining OTUs and constructing phylogenetic trees on the basis of ITS data, as splits tree approaches It is hard to identify an optimal OTU cutoff value if the OTUs are defined in terms of distances that themselves depend on potentially faulty alignments. In the absence of reliable automated methods, it is necessary to rely on taxonomic expertise and an understanding of the species being studied and their sequences when defining appropriate cutoff levels for specific groups of organisms. To this end, it would be useful to develop new software to investigate and visualize the behaviour of OTUs or taxa as a function of their internal distances, using methods such as the OTU distance area plots discussed in this paper.File S1Perl script for condensing BLAST output.(TXT)Click here for additional data file.File S2Simulation of sampling biases.(DOC)Click here for additional data file.Table S1Fungal strains used to test the specificity of the new 28S rDNA primers and the results of the specificity tests.(DOC)Click here for additional data file.Figure S1Resampling profile of 120 of 120 000 items  picked over 100 replicates. Vertical lines correspond to 10 items. Average percentage and S = standard deviations on average percentage are given on the bottom.(TIF)Click here for additional data file.