Aligning similar molecular structures is an important step in the process of bio-molecular structure and function analysis. Molecular surfaces are simple representations of molecular structure that are easily constructed from various forms of molecular data such as 3D atomic coordinates (PDB) and Electron Microscopy (EM) data.We present a Multi-Scale Morse-Smale Molecular-Surface Alignment tool, MS3ALIGN, which aligns molecular surfaces based on significant protrusions on the molecular surface. The input is a pair of molecular surfaces represented as triangle meshes. A key advantage of MS3ALIGN is computational efficiency that is achieved because it processes only a few carefully chosen protrusions on the molecular surface. Furthermore, the alignments are partial in nature and therefore allows for inexact surfaces to be aligned.The method is evaluated in four settings. First, we establish performance using known alignments with varying overlap and noise values. Second, we compare the method with SurfComp, an existing surface alignment method. We show that we are able to determine alignments reported by SurfComp, as well as report relevant alignments not found by SurfComp. Third, we validate the ability of MS3ALIGN to determine alignments in the case of structurally dissimilar binding sites. Fourth, we demonstrate the ability of MS3ALIGN to align iso-surfaces derived from cryo-electron microscopy scans.http://vgl.serc.iisc.ernet.in/ms3align.We have presented an algorithm that aligns Molecular Surfaces based on the topology of surface curvature. A webserver and standalone software implementation of the algorithm available at The online version of this article (doi:10.1186/s12859-015-0874-8) contains supplementary material, which is available to authorized users. MUSTANG and DALI . Landmark points p∈P and q∈Q are declared as a corresponding pair  if the absolute difference between their mean curvatures at every scale is bounded by a threshold Tms. The threshold Tms is specified as a fraction similar to Ts.In the third stage, correspondences between pairs of landmark points on either surface P and Q are collected into maximal sets of correspondences. A maximal set pi,qi) and , the two landmark points pi and pj in P have relative pairwise geometric properties similar to that of the two landmark points qi and qj in Q. We use two geometric properties to establish relative pairwise similarity. First, we ensure that the absolute difference between the distances of the two landmarks on either surface is less than a threshold Tmrd, referred to as the maximum relative distance threshold. Second, we ensure that the absolute difference in the angles between the surface normals of two landmarks on either surface is less than π/2. In other words, for a given maximal set In the fourth stage, correspondences between landmark points in N(p) represents the surface normal at point p. The constructed sets are maximal in the sense that no other correspondence may be added without violating conditions . Edges exist between pairs of correspondences  and  if they satisfy conditions   p,q). EdWe use the greedy pivot heuristic modification of the Bron-Kerbosch algorithm  by Koch Tmrd, as well as the number of correspondences, captured by Tms. Choosing a very small Tms and Tmrd causes the algorithm to demand near exact matches in the mean curvature as well as the relative positions of the landmarks, whereas higher values allow larger variations.Indeed, the total number of maximal correspondence sets may be exponential in the number of landmark points. The number of maximal correspondence sets depends on the tolerance for positional uncertainty of the landmark points, captured by  from P to Q via a least squares minimization [In the final stage, each maximal correspondence set is first used to determine a rigid body transformation mization .p2,q2),…,}, we compute a measure R,t), given by Next, for each correspondence set AP(pi) is the area of the descending manifold of pi, P in AP is the area of P. Also, P and Q in R,t) is again applied only to landmarks in P.Here, P in P. Due to the denominator, correspondence sets having larger area fractions are preferred.The numerator of ) in Eq.  represen% of its total area.Maximal correspondence sets that result in the least values of this measure are reported along with their transformations. Computing this measure is efficient even with a large numbers of maximal correspondence sets since it only requires landmarks and not all points on the surface. It is possible for some spurious correspondence sets consisting of a few landmarks to align nearly perfectly. Hence, we consider only those correspondence sets so that the area of the corresponding regions of either surface is at least 15 Rc, Ts, Tms and Tmrd control the performance of the MS3ALIGN algorithm. While the parameters Rc and Ts control the quality of the landmarks, the parameters Tmrd and Tms control how these landmarks are aligned. The expected size/scale of features must have direct bearing on the choice of Rc and Tmrd because they are distance values (specified in Å). Hence, this choice depends on the dataset being studied. By default, we assume Tmrd to be equal to Rc. If this results in an insufficient number of landmark alignments, then Tmrd may be set to a higher value. Conversely, if we expect or desire less relative movement of feature points, then it is set o a lower value. The parameters Ts and Tms are specified in a normalized scale of positive curvature values. Hence, we explicitly conducted performance experiments  added to the atom locations. The mean-curvature computed is shown using a color map. Comparing two panels directly, the effect of Rc is evident in the density of the landmarks. The choice of Rc=1.2 is inappropriate here as significant protrusions are not captured effectively. Raising the simplification threshold to Ts=0.1 does not resolve the issue. Furthermore, with the addition of noise, the corresponding change in landmarks is hard to distinguish. In contrast, in the right panel where Rc=3.0, landmarks representing significant protrusions are retained across simplification thresholds of 0.001 and 0.1 Furthermore, it is possible to visually establish the correspondences between the landmarks across the noiseless and noisy versions, particularly with Ts is set to 0.1.To illustrate the consequence of these choices, we present Fig. MS3ALIGN. We begin with a discussion on the molecular surfaces used in our experiments  that interact with the ligand. These residues are referred to as the pocket residues or just the pocket. A subset of the molecular surface is extracted so that all vertices of the subset are within the van der Waals sphere of at least one of the atoms of the pocket. An additional 0.5Å is added to the van der Waals sphere to account for the possible error introduced when extracting the skin surface, because NANOSHAPER uses a structured grid with edge length 0.5Å. This subset surface may be disconnected and/or contain holes. This topological noise is repaired using a variant of the dilation-erosion operation applied to triangle meshes [Å, the radius of the hydrogen atom.The pocket surface is extracted as a subset of the molecular skin surface of the protein. This subset is the part of the surface that belongs to the residues  for each comparison for their small ligand datasets using a 2.4 GHz Intel Xeon Processor. Baum and Hege [MOLOC takes between few seconds to minutes for medium sized binding surface areas.Next, we present the runtime breakup of the various stages of and Hege  report aand Hege  report tMS3ALIGN with the SURFCOMP tool [SURFCOMP validate using the ligand surfaces from two datasets. The first dataset consists of thermolysin inhibitor ligands of two kinds, the first containing tryptophan and the second with an aliphatic residue at the C-terminal end. The second dataset consists of ligands bound with the Dihydrofolate Reductase (DHFR) enzyme.In this experiment, we compare OMP tool . The autIn both experiments, surfaces obtained from ligand molecules are aligned. Hofbauer et al. consider two types of physico-chemical properties on molecular surfaces to determine alignments.Rc parameter is set to 1.2Å since we wish to study alignments at the scale of a single atom. The Ts parameter is set to 0.06 for the first dataset and 0.1 for the second dataset. The Tms parameter is set to 0.09 and 0.15 respectively. The Tmrd parameter is set equal to Rc. In the following paragraphs, we discuss the alignments determined by MS3ALIGN for each of the datasets in detail and compare them with SURFCOMP.Since they conclude that electrostatic potential (ESP) results in better alignments, we only compare against these alignments. The chemical structures of the ligands are presented in the appendix. For these experiments, the SURFCOMP compares eight thermolysin inhibitor ligands considered in two groups. The first group consists of ligands from PDBs 1THL, 1TLP, 1TMN and 3TMN. In our experiment, we were unable to extract the ligand structure from 3TMN because of a failure in the PDB2PQR tool which is used in a preprocessing step to compute the molecular skin surface. Hence, we remove 3TMN from this list. The second group consists of ligands from PDBs 4TLN, 5TLN, 5TMN, and 6TMN. For consistency of labeling of datasets with respect to Hofbauer et al., we use the PDB id to reference the ligand considered.In the thermolysin dataset, SURFCOMP for the first group. However, in the case of 1THL-1TMN our sixth best alignment corresponds to the SURFCOMP’s best alignment. For the same pair 1THL-1TMN, SURFCOMP reports an alignment of the tryptophan moiety of both ligands. MS3ALIGN reports it as the sixth best alignment with other meaningful alignments being detected as better. Figure MS3ALIGN. Since the ligand 0PI (6TMN) is structurally very similar to the ligand 0PJ (5TMN), the same alignments with respect to other ligands were also found, and thus images of these alignments are omitted. Thus, we find that MS3ALIGN aligns relevant portions of the surfaces of the ligand BAN in 5TLN with all the other considered ligands. For many pairs, we determine partial alignments of other substructures. Figure We were able to determine the same alignments as In the DHFR dataset, alignments of surfaces of four ligands interacting with DHFR are analyzed. The ligands used are Folic acid (FOL), Methotrexate (MTX), Trimethoprim (TMP), and BR-WR99210 (WRB) from PDBs 1DHF, 1DF7, 1DG5 and 1DG7 respectively.SURFCOMP reports only two good alignments of FOL with MTX and WRB. We were able to recover a similar alignment of FOL and MTX. MS3ALIGN does not determine the same alignment reported by SURFCOMP for the case of FOL and WRB. Figure 11MS3ALIGN where the alignment is along the C =N−C= N moieties with amino groups at the two and four positions from triazine and pteridine of WRB and MTX respectively. SURFCOMP reports an alignment where the brominated benzyl group of WRB aligns with the central benzyl group of FOL which is different from the best alignment we determine.We note that MS3ALIGN for the pairs MTX-TMP, MTX-WRB, and FOL-WRB, that were not determined by SURFCOMP are shown in Fig. Relevant alignments determined by MS3ALIGN and SURFCOMP. We note here that small RMSD values (<1.5Å) are indicative of successful alignments whereas larger RMSD values (1.5−5) do not necessarily indicate failure of the alignment. This is particularly true when the reported alignment is a partial alignment of surfaces where a small fraction of the surface is aligned. Table MS3ALIGN and SURFCOMP. In conclusion, we were able to replicate all alignments, except one, reported by Hofbauer et al. in their evaluation of SURFCOMP. Additionally, we were able to obtain relevant alignments of similar chemical moieties.Figure  values <.5Å are iMS3ALIGN against those generated using PYMOL [PYMOL’s “super” command. This command aligns proteins using a dynamic programming approach followed by multiple refinement cycles that improve the fit by eliminating pairings with high relative variability. We curate a dataset of thirty one protein structures from the PDB [POCKETMATCH [POCKETMATCH generates 90 sorted lists of distances from the three dimensional coordinates and chemical properties of the site. For a pair of sites, a normalized score based on the similarity of the pair of 90 lists is computed. Sites having a POCKETMATCH Pmax score greater than 0.6 are statistically shown to be structurally similar, with a score of 1 indicating identical sites. A variety of ligands ranging from small sugars such as glucose to molecules containing substituted sugars such as NDP, fatty acids such as ACD (arachidonic acid), vitamins such as Biotin (BTN) and Retinoic Acid (REA) are chosen. In the Additional file In this experiment, we validate alignments generated using ng PYMOL . Here, t the PDB , each inKETMATCH . For eacMS3ALIGN with Rc=1.2Å, Ts=0.1, Tms=0.15, Tmrd=1.2Å. In Fig. PYMOL’s and MS3ALIGN’s alignment is plotted for the set of considered pocket pairs. The POCKETMATCH score between each pair is also shown for comparison. When the RMS distance between the pockets transformed by PYMOL’s and MS3ALIGN’s alignment is less than 1.2Å, we conclude that both alignments are equivalent. In Fig. Å. We conclude that we were able to successfully determine alignments of pocket surfaces whose POCKETMATCH score is greater than 0.7 and in some cases even 0.6.For this experiment, the binding surfaces of each ligand type is aligned with each other using Å. Table In this experiment, we consider two sets of related iso-surfaces generated from cryo electron microscopy scans obtained from the EMDataBank . The firRc=30Å, Ts=0.05, Tms=0.1, Tmrd=30Å, and computed alignments using MS3ALIGN. Figure MS3ALIGN. Here the RMSD is computed after alignment as the root mean square of the closest distance from every point of the first surface to the second surface and vice versa. Thus, we conclude that the alignments were successful.For both datasets, we set the parameter MS3ALIGN, a tool that implements this method.In this paper, we present a method to align molecular surfaces by identifying and establishing correspondences between significant protrusions on the surface. We also present SURFCOMP, PBSALIGN, and PYMOL, which rely on protein sequence data and other derived scalar values such as the electrostatic potential, which are often not directly available/computable. In the future, we plan to expand MS3ALIGN to align surfaces by including other geometric properties such as spherical harmonics and Zernike coefficients of individual segments. These global properties may be applied to smaller segments resulting in a method for determining alignments using a blend of local and global properties. Currently, we use only the local curvature property. Another possible direction for future work is an extension towards a framework that harmoniously incorporates additional physico-chemical properties that may be available.A key advantage of our method is robust segmentation of the surface into segments that can be individually evaluated for correspondences. Furthermore, due to its purely geometric design, it is applicable to molecular surfaces arising from various sources such as the PDB and Electron-Microscopy scans. This is a key advantage over existing methods such as Additional file 1Supplementary material for “MS3ALIGN: An efficient molecular surface aligner using the topology of surface curvature”. (PDF 629 kb)


Although the binding of transcription factors is sequence-specific, the identification of functionally similar enhancers is very difficult and it cannot be carried out with traditional alignment-based techniques.Enhancers are stretches of DNA (100–1000 bp) that play a major role in development gene expression, evolution and disease. It has been recently shown that in high-level eukaryotes enhancers rarely work alone, instead they collaborate by forming clusters of k. Here we propose an alignment-free statistic, called The use of fast similarity measures, like alignment-free measures, to detect related regulatory sequences is crucial to understand functional correlation between two enhancers. In this paper we study the use of alignment-free measures for the classification of CRMs. However, alignment-free measures are generally tied to a fixed resolution EP-sim that is freely available: http://www.dei.unipd.it/~ciompin/main/EP-sim.html.We evaluate several alignment-free statistics on simulated data and real mouse ChIP-seq sequences. The new statistic,  How to measure the degree of similarity between biological sequences is one of the foremost questions on the mind of bioinformaticians. This problem relates to the identification of homologous sequences like proteins and, to this end, the use of tools like BLAST is nowadays a standard procedure. In this paper we study the same question but for regulatory sequences such as promoters or enhancers of genes. The detection of similarities between coding sequences is a widespread approach to estimate functional correlations. Indeed, there is a general belief that similar binding site contents in regulatory sequences are expected to drive similar expression patterns . MoreoveMany articles  discuss Here we summarize the main features of CRMs. First, they contain several short (6–15 bp) DNA motifs that act as binding sites for transcription factors (TFBSs) and often allow different nucleotides at some of the binding positions. In other words, there may be mutations on TFBSs. Second, these TFBSs act seemingly independently of the distance and orientation to their target genes as a consequence of looping. It follows that the strand to which a CRM under study belongs is unknown so both cases need to be considered. Third, they maintain their functions independently of the sequence context, are modular and contribute additively and partly redundantly to the overall expression pattern of their target genes. Finally, enhancers with similar transcription factors binding sites content have a high probability of bearing a similar function. This is why predictions and classifications of enhancers can be addressed by similarity searches. However, the presence of multiple binding sites, with different spacing between them, can make the comparison of two CRMs very difficult. For these reasons biologists need first to screen ChIP-seq datasets to select cell-specific regulatory sequences on the basis of common contents.A similarity measure for regulatory sequences is crucial to detect and understand functional similarities between two enhancers and will facilitate large-scale analyses like clustering, prediction and classification. As opposed to traditional methods that output a list of putative TFBSs, alignment-free methods –7 do notThe comparison of sequences can be carried out without the need of costly alignments. A sequence can be represented by its word distribution. It has been shown that the word content and distribution can be effectively used to compare sequences in a number of applications . This rek. The problem with these methods is that the performance depends dramatically on the choice of the resolution k =μ(w) and its variance is Var[Yi(w)]=μ(w)[1−μ(w)]. This indicator provides a way to define the number of occurrences cw of word w: For each Yi(w), the expected entropy E[SEw] of the word w can be defined as in the following: Now, based on the variables w is estimated as (l−k+1)μ(w)  is the asymmetric overlap indicator t=d−k+1 and πt is the probability that the last letter of w is separated from an occurrence of w[1] by t−1 letters.w′. This assumption is without loss of generality because of the symmetry of the covariance, Yi(w), and developed by applying its well-known properties: In this second case, Yi(w′) are all zero since there are not enough letters to make the word w′. The two terms in Formula the former stands for all the terms due to two words of different length that do not start at the same position;the latter stands for all the terms due to two words of different length that start at the same position  and move d be the shift of the moving word w′. A summary of the possible overlaps between w′ and To reformulate the former and to study overlaps, we can always fix the first mentclass2pt{minimi and d. This way, i is fixed and d varied in order to consider the positions before i (left overlap) and after (right overlap). d=0.Part 1 of Eq. Yi(w′) and k′ letters away from each other ..5]. To aThese regulatory sequences can be further compared pairwise. Following the same setup as above, the pairwise comparison of all tissue-specific enhancers are shown in Table N2. For a given word w, both methods need to count not only the occurrences of w, but N2 considers also all words at Hamming distance 1 from w, whereas w. In the following experiments both methods include reverse complements as part of the occurrence counts. We create a dataset composed by 20 sequences taken at random from the mouse genome. All sequences have the same length and we test the running time while increasing the sequence length. The platform used for these experiments is a common laptop with Intel i7 and 4 GB of RAM. The results are summarized in Fig. N2. This advantage is due to the fact that suffix counts can be easily recovered by exploiting word hashing properties.In this section we assess the performance, in terms of running time, of the two measures In this paper we studied the use of alignment-free measures to detect functional or evolutionary similarities among regulatory sequences. We introduced a multiple resolution alignment-free method based on Entropic Profiles that is designed around the use of variable-length words combined with statistical properties. To evaluate the performance of several alignment-free methods, we devised a series of tests on both synthetic and real data. In almost all simulations our method As a byproduct we provide a formula to compute the exact variance of variable length word counts, a result that can be of general interest also in other applications, e.g. the discovery of surprising patterns. As a future direction we plan to implement different methods to incorporate reverse complements. Another context where the these statistics can be of help is the comparison of viral sequences.1http://www.dei.unipd.it/~ciompin/main/EP-sim.html2 FlyBase, http://flybase.org/3ftp://ftp.ensembl.org/pub/release-84/variation/VEP/mus_musculus_vep_84_GRCm38.tar.gz


Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference.Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment.We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures.We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools. Even thcolinear alignment. Structural changes involve longer genomic segments, thereby affecting the structure and order of genomic sequences. They include non-colinear changes like inversions, translocations and duplications in addition to insertions and deletions of longer segments.Evolutionary events that change genomic sequences are often classified into small changes and large structural changes . Small cgenome alignments for short, integrate as much sequence similarity information as is available.While colinear multiple sequence alignment has been studied extensively for a long time -16, the segmentation of the genomes originating from large structural changes. Depending on the similarity of genomes, segments can be shorter or span several genes and reveal local colinearity. Rearrangement studies =v on each alignment graph vertex v′∈V′. The label indicates the A-Bruijn graph vertex v that represents the connected component containing v′.As a first step, compute all block edge connected components e= to the set of A-Bruijn graph edges E for each edge e′= from the set of alignment graph adjacency edges u=m[ u′] and v=m[ v′].The remaining task is to transfer adjacency edges from the alignment graph to the A-Bruijn graph. Using the mapping, add an edge G= into an alignment graph structure ℓdup for the edges of G in addition to G. In accordance with the transformation from G′ to G, we successively create a one-to-many mapping from A-Bruijn graph vertices to alignment graph vertices during the transformation. The mapping m labels each A-Bruijn graph vertex v∈V with a set of alignment graph vertices from V′. At the beginning m[ v]={} for all v∈V. At the end, a label b represented by v in the A-Bruijn graph.We describe the transformation of an A-Bruijn graph structure ℓdup(e1)<ℓdup(e2)<⋯<ℓdup(eE||). Initially, add for each genome a new vertex u′ to the set of alignment graph vertices V′. If the source vertex u of the A-Bruijn graph edge e1= is labeled with a non-empty set of vertices u′ and all vertices u′ to the set of vertices mapped to u. Repeat these three steps for the target vertex v of e1: add a vertex v′, add block edges, and add v′ to the mapping. In addition, add a directed edge from u′ to v′ to the set of alignment graph adjacency edges We transform the A-Bruijn graph by following each genome separately and assume that the edges are given in increasing order of labels: G is successively built up.Iterate over the A-Bruijn graph edges in increasing order of labels and repeatedly add for the target vertex a new vertex, add block edges, add the new vertex to the mapping, and add an adjacency edge from the previous to the new vertex. This way, the genomes are threaded through the A-Bruijn graph and the alignment graph structure G= be an Enredo graph structure and MG= be an Enredo graph model. In an Enredo graph, the set of edges E=EA∪EB decomposes again into a set of directed adjacency edges EA and a set of undirected block edges EB. We define ℓ as a labeling function of the block edges EB.In this section, let EB of G represent blocks, and vertices V of G represent the ends of blocks. In contrast to alignment graphs, a single block edge represents an entire block. For every block V connected by an undirected block edge eb∈EB =b. By choosing one of the two possible block representations as label, the two vertices that are connected by eb are implicitly labeled as head and tail of the block. Note that they are not labeled as head or tail in G. Given the block labels, recovering MG is again straightforward.The block edges EA of G =b1 and ℓ(e2)=b2, there is a directed edge e∈EA from an endpoint of e1 to an endpoint of e2 for every two adjacent segments s1= and s2= with s1∈b1 and s2∈b2. In contrast to alignment graphs and A-Bruijn graphs, the endpoints of adjacency edges in G indicate in relation to other adjacency edges the orientation of segments in a block. Given labels, one endpoint of each block edge is a head vertex and the other a tail vertex. If p1<q1, then the adjacency edge e starts at the head vertex uh, and if p1>q1, e starts at the tail vertex ut. If p2<q2, then e points to the tail vertex vt, and if p2>q2, e points to the head vertex vh. In other words, e= if q1=p2, e= if q1=q2, e= if p1=p2, and e= if p1=q2. Again, there may be several adjacency edges connecting the same two vertices. Thus, the Enredo graph is also a multi-graph.Directed adjacency edges G reflects the relative orientation of blocks as opposed to the alignment graph structure and the A-Bruijn graph structure  into an Enredo graph structure G= using the labeling function ℓinv:E′→{+,−}×{+,−}. Then, we describe the transformation of blocks of size 1 to labeled adjacency edges in the Enredo graph given full block information by the function ℓ for transferring labels.First, we describe the transformation of an A-Bruijn graph structure G′ into G, add for each A-Bruijn graph vertex v′∈V′ a tail vertex vt and a head vertex vh to the set of Enredo graph vertices V. Additionally, add an undirected edge eb between vt and vh to the set of Enredo graph block edges EB. We obtain a one-to-one mapping of A-Bruijn graph vertices and Enredo graph block edges, which we keep as separate labels m:V′→EB on A-Bruijn graph vertices such that m[ v′]=eb.To transform ℓinv and the mapping m, we can unambiguously transfer adjacency edges to the Enredo graph. For each edge e′= in the set of A-Bruijn graph edges E′ where m[ u′]=eu and m[ v′]=ev, add an edge e= to the set of Enredo graph adjacency edges EA where ux is an endpoint of eu and vy is an endpoint of ev. The vertex ux is the head vertex of eu if the first bit in ℓ(e′) is +, and otherwise the tail vertex. The vertex vy is the tail vertex of eu if the second bit in ℓ(e′) is +, and otherwise the head vertex.Using the labeling function eb={vt,vh} representing blocks of size 1 into adjacency edges. Since the size of ℓ(eb) is 1, the corresponding vertices vt and vh are incident to exactly one adjacency edge each, e1= and e2=. Replace such sets of two vertices vt, vh and three edges e1, e2, eb by a new adjacency edge e=. Finally, transfer the label of the block edge ℓ(eb)={s} to the adjacency edge such that ℓA(e)=s.In another step, we can transform all block edges ℓA in an Enredo graph structure G=. Afterwards, we describe the transformation from G to an A-Bruijn graph structure G′=, which is possible without additional labels.We start by describing how to recover block edges for blocks of size 1 from adjacency edges that are labeled with segments by e= from the set of Enredo graph adjacency edges EA, where ℓA(e)=s, by two vertices vt and vh and a block edge eb=, and set ℓ(eb)={s}. Further, add e1= and e2= to the set of Enredo graph adjacency edges EA.Replace each edge eb= in the set of Enredo graph block edges EB, a vertex v′ to the set of A-Bruijn graph vertices V′. Again, we obtain a one-to-one mapping of Enredo graph block edges and A-Bruijn graph vertices, which we keep this time as labels m:EB→V′ on Enredo graph block edges such that m[ eb]=v′. Finally, add for each edge e= in the set of Enredo graph adjacency edges EA where ux is incident to the block edge eu and vy is incident to the block edge ev, an edge e′= to the set of A-Bruijn graph vertices, where m[ eu]=u′ and m[ ev]=v′. In this last step, we lose inversion information in the graph’s structure.For the transformation to an A-Bruijn graph, add for each edge G= be a cactus graph structure and MG= be a cactus graph model. Cactus graphs have only one type of edges. We define ℓ as a labeling function of the edges E. The cactus graph structure G stands out from the other graph structures by fulfilling well-defined structural properties: Every edge e∈E is part of at most one simple cycle, which makes G a cactus graph =v.To transform an Enredo graph structure ribed in ,40. Firsadjacency-edge connected componentsG′. Each component V′. For each V. Assuming that the start and end of all genomes are connected, add only one origin vertex for all of them to V. We obtain the many-to-one mapping that indicates the cactus vertex representing the adjacency edge connected component of any Enredo graph vertex. Given this mapping, transfer the Enredo graph block edges EB to the cactus graph: For each edge e′={u′,v′} in the set of Enredo graph block edges EB, where m[ u′]=u and m[ v′]=v, add an edge e={u,v} to the set of cactus graph edges E. It is possible that u=v even if u′≠v′. This yields the precursor cactus graph in Figure First, compute all V that are 3-edge-connected and add instead a single vertex v to V  to an Enredo graph structure ℓadj to separate the sets of adjacencies represented by cactus graph vertices V and to add edges e∈E with an Enredo graph block edge u′ from the head vertex v′ such that m[ e]=. The direction is not present in the Enredo graph structure G′. The transformation proceeds by threading the genomes G.In the transformation from a cactus graph structure u∈V the edge e0={u,v} whose label contains the smallest number n0∈ℓadj(e0) where n0<n and n∈ℓadj(e) with e={u,x}. Add two vertices u′ and v′ to the set of Enredo graph vertices V′ and an edge m[ e0]=. Keep a reference to v′ for the next step.Initially, identify among all cactus graph edges incident to the origin vertex v, identify the next edge e1= whose label contains the next larger number n1∈ℓadj(e1) such that n1>n0 but n1<n where n≠n0 and n∈ℓadj(e) with e={v,x}. If the mapping for e1 is undefined, add two vertices v′′ and w′ to the set of Enredo graph vertices V′ and an edge m[ e1]=. Further, add an edge EA and keep w′ for the next step. If the mapping for e1 is already defined with m[ e1]=, only add an adjacency edge: If the orientation bit in ℓadj(e1) is +, add the edge EA and keep w′ for the next step. If the orientation bit is −, add an edge v′′ for the next step.Among all edges incident to w. Proceed like this until reaching the end of all genomes to obtain the full Enredo graph structure G′.Next, repeat the same for incident edges of All in all, the need for labels shows that the four graphs markedly differ in the information represented in their structures. Complete duplication information (dup) is only present in alignment graph structures, and only the structure of Enredo graphs reveals inversion information (inv). A-Bruijn graphs are a compact and intuitive representation but lack both inversion and duplication information. Finally, cactus graph structures do not represent parts of the adjacency information (adj). Despite these structural differences, all graph models, which include labels, can be transformed into each other.Based on these observations, some advantages or disadvantages of the graph structures become apparent. For example, for a genome aligner intended to reveal inversions, an Enredo graph structure appears to be more suitable, whereas a more general analysis of the genetic content of genomes will work well with the more compact A-Bruijn graph structure. Duplications are best visible in an alignment graph structure. The advantage and information provided by cactus graphs subdivides genomes into independent regions revealing specific and unique substructures as described in the following.We collected substructures from graph-based genome alignment approaches and classify them here into four types: colinear paths, visiting blocks, short cycles, and cactus groups. Substructures are useful for deriving a meaningful genome segmentation or they indicate misalignment, i. e., the alignment of non-homologous segments. Furthermore, they pinpoint parts of genome alignments that can be improved through modification.models. If the time complexity for detecting occurrences of the substructures was the same in all graphs models, they could be used interchangeably. Here, our aim is to analyze abilities of the graph structures to reveal potential misalignments without additional information from labels.Some substructures have been described for several graph-based approaches, while others are unique to only one approach. We conjecture that it is possible to identify all substructures in all graph colinear paths. Colinear paths are sets of blocks that appear in one or more genomes consecutively in the same orientation and without breakpoints in between. A sequence of blocks b1,…,bk forms a colinear path if there is an adjacency but no breakpoint between bi and bi+1 for all i=1,…,k−1. Consequently, all blocks along a colinear path have the same size and there are segments s1∈b1 and sk∈bk with s1= and sk= such that s= is a consecutive genome segment that concatenates one segment from each block b1,…,bk. We also consider a single block as a colinear path.We refer to the first type of substructures as maximal if it cannot be further extended by other adjacent blocks, but is bounded by breakpoints. Note that alignment modifications often remove bounding breakpoints such that a colinear path can again be further extended. The set of maximal colinear paths of a genome alignment determines the final genome segmentation. Independent from the underlying graph structure, all graph-based genome alignment methods have the common aim to maximize colinear paths both in terms of size (number of genome segments) and length .A colinear path is In Enredo graphs, simple non-branching paths are colinear paths. Similarly, colinear paths appear as non-branching paths in the A-Bruijn graph structure, but here a non-branching path is not necessarily a colinear path. Along a non-branching path in A-Bruijn graphs, one or more blocks can be inverted in a subset of the genomes. The structure of A-Bruijn graphs provides no information about inversions : For all segments s= of the colinear path that are adjacent at position p1 to a segment s0∈b0, there is a segment sk+1∈bk+1 adjacent to s at position qk; and for all segments s= that are adjacent at position qk to a segment sk+1∈bk+1, there is a segment s0∈b0 adjacent to s at position p1. The important property is that all segments from block b0 that are adjacent to segments of the colinear path, continue in the same block bk+1 at the other end of the colinear path and vice versa.We name the second type of substructure A visiting block arises from merging blocks from within a colinear path with other blocks. If the merged blocks are short, they often only have spurious similarity. Hence, they break colinearity at two positions without providing much evidence for a large structural change. This is a reason why genome aligners address visiting blocks and separate the otherwise colinear paths.In A-Bruijn and Enredo graphs, visiting blocks appear as simple non-branching paths bounded by branching vertices. In Enredo graphs, the path always starts and ends with a block edge. In both graphs, at least one branch which enters the visiting block at one end must be formed by a set of segments that leaves the visiting block as its own separate branch at the other end  simple cycles, too, but there is no one-to-one correspondence: The alignment graph structure can have more than one cycle for a single genome alignment cycle  or by determining breakpoint positions that will be part of the final segmentation (“merging consecutive blocks” and “cutting adjacencies”).These four modifications derive from the mentioned graph-based genome alignment approaches, but they match the operations described for the approaches only in part. Some genome alignment approaches clearly separate block modification and segmentation, other approaches do both tasks together. Similarly, some approaches apply compound operations consisting of several of the modifications described here. Our intention is to provide small modification entities from which it is possible to assemble more complex operations.We describe every modification on the set of blocks . Furthermore, we mention effects of the modifications in the graph structures although they can be applied to a genome alignment independently from a graph structure. We explain how these modifications correspond to operations in the graph-based genome alignment approaches, especially if the correspondence is not obvious. For example, this is the case for DRIMM-Synteny , which sb={s1,…,sn}, where n≥2 is the size of b, by two blocks b1={s1,…,sk} and b2={sk+1,…,sn}, 1≤k<n. The new blocks may have size 1, thus may consist of a single segment. Transferred to the original set of local alignments from which the blocks were formed, this modification corresponds to removing local alignments. In some cases, it is enough to remove a single pairwise local alignment to split a block into two blocks. In other cases, a particular subset of the local alignments needs to be removed simultaneously.The most prevalent modification is splitting a block by dividing its set of segments into subsets that form new smaller blocks. Formally, the modification replaces a block Splitting blocks has different effects on the genome alignment graphs  where i=1,…,n and with a breakpoint at the tail of b, the modification cuts the genomes at all positions qi. The modification does not affect the set of blocks but rather the set of genomes. Thus, it is part of the genome segmentation process.As opposed to merging consecutive blocks, the last modification fixes a breakpoint in the genome alignment by cutting genomes into several segments. For example, given a block Cutting adjacencies corresponds to removing a single edge from an A-Bruijn graph structure, a single adjacency edge from an Enredo graph structure, or a set of adjacency edges from an alignment graph structure. Again, there are multiple possible effects in a cactus graph structure. In the simplest case, the cactus graph structure remains unchanged. In all graphs, the removal of edges can disconnect the graph structures, generating several components that correspond to disjoint sets of genome segments. Thus, it can become impossible to thread the genomes through the graphs without additional effort ,38.Cutting adjacencies is used in various ways by genome alignment approaches. The ABA method cuts adjacencies for eliminating bulges from A-Bruijn graphs and the Enredo method for eliminating small cycles in general. In addition, the segmentation processes in A-Bruijn and Enredo graphs implicitly use this modification: In DRIMM-Synteny, segmentation is realized by coloring the graph. In Enredo, it is realized by excluding adjacencies shorter than a given length threshold. Genome segmentation in alignment graphs and cactus graphs has not been described explicitly.We compared four graph data structures and their usage for genome alignment. Our comparison identified that essential pieces of information about duplication and inversion are only present in the structures of some graphs. In addition, we examined substructures in the graph structures that are subject to elimination in various genome alignment approaches, and determined four classes of substructures. We found that information about duplications or information about inversions or even both are necessary for distinguishing any type of substructure in the graphs. Thus, it is indeed essential to keep additional information in labels of the vertices or edges, though the different graphs depend on the labels to a lesser or greater extent. Finally, we reduced the set of operations applied for eliminating substructures from the graphs to four elementary modifications. Overall, it became apparent that many ideas are shared by all graph-based approaches.These shared ideas allow us to derive a framework for graph-based genome alignment  from pairwise alignments may pose a problem. There are only few exceptions of genome aligners that avoid the problem by directly computing local multiple alignments ,47. If wFurther, we have not covered all aspects of the framework in this paper and left out details on the detection of substructures. For example, ABA and DRIMM-Synteny detect small cycles by efficiently computing a maximum spanning tree before heuristically inspecting the remaining edges that create cycles. Different detection methods clearly have an influence on the time complexity of an approach and, depending on their sensitivity, also on the accuracy of a genome aligner. Thus, a thorough analysis of detection methods is certainly interesting but beyond the scope of this work.Similarly, we have not addressed algorithms for eliminating substructures. These algorithms determine the order in which modifications are applied. The elimination of one type of substructures can create other substructures, which again can create the first type of substructures upon elimination. For this reason, iterative elimination strategies are prevalent in graph-based genome aligners. End criteria for iteration are typically given as parameters of the method, e. g., a maximal length of cycles or an explicit number of iterations.The parameters usually require customized values for every new input set of genomes. Usually, this inhibits broad usage of tools if automatic parameter selection is not offered. A genome aligner has to find a trade-off between size and length of blocks. Very similar genomes will have long blocks conserved across many genomes, whereas more diverged genomes show fewer long blocks and conservation across fewer genomes. Hence, a factor to consider for parameter selection is genome divergence in addition to genome lengths. Given the initial set of local alignments, automatic parameter selection seems possible. It will be necessary to carefully study the influence of all factors to be able to automate the selection, but consequently it will enable a larger community to benefit from graph-based genome aligners.Finally, graph-based genome aligners, just as other genome aligners, have to decide between positional homology alignment  or alignIn conclusion, our framework demonstrates shared aspects of graph-based genome aligners. It contributes to developing a common view on graph-based genome alignment, an active field of research with currently at least two graph-based tools for genome alignment being actively developed ,51. In tThe authors declare that they have no competing interests.BK and KT participated in the design of the study and drafted the manuscript. MH participated in the design of the study and helped editing the manuscript. KR conceived of the study and participated in its design. All authors read and approved the final manuscript.


Tissue inhibitors of metalloproteinases (TIMPs) help regulate the extracellular matrix (ECM) in animals, mostly by inhibiting matrix metalloproteinases (MMPs). They are important activators of mutable collagenous tissue (MCT), which have been extensively studied in echinoderms, and the four TIMP copies in humans have been studied for their role in cancer. To understand the evolution of TIMPs, we combined 405 TIMPs from an echinoderm transcriptome dataset built from 41 specimens representing all five classes of echinoderms with variants from protostomes and chordates. We used multiple sequence alignment with various stringencies of alignment quality to cull highly divergent sequences and then conducted phylogenetic analyses using both nucleotide and amino acid sequences. Phylogenetic hypotheses consistently recovered TIMPs as diversifying in the ancestral deuterostome and these early lineages continuing to diversify in echinoderms. The four vertebrate TIMPs diversified from a single copy in the ancestral chordate, all other copies being lost. Consistent with greater MCT needs owing to body wall liquefaction, evisceration, autotomy and reproduction by fission, holothuroids had significantly more TIMPs and higher read depths per contig. Ten cysteine residues, an HPQ binding site and several other residues were conserved in at least 70% of all TIMPs. The conservation of binding sites and the placement of echinoderm TIMPs involved in MCT modification suggest that ECM regulation remains the primary function of TIMP genes, although within this role there are a large number of specialized copies. Early sC-tensilin) [H-tensilin [Echinoderms are known for rapidly altering the tensile strength of certain collagenous tissues after various stimuli ,7,8, a pensilin)  and H-tetensilin —have shotensilin ,15. Thestensilin ,15.FiguTIMPs have also been identified as important inhibitors of metastasis and tumour growth, mostly because of their role as inhibitors of MMPs, which renew and modify the ECM . The proWe saw an opportunity to significantly advance our understanding of TIMPs by generating a phylogeny from echinoderm TIMP transcripts; this would test the importance of TIMPs in the development of MCT in echinoderm evolution and identify which echinoderm TIMPs are most closely related to those from vertebrates. Because, unlike other echinoderms, most holothuroids rely on MCTs to maintain body turgor , and the2.2.1r , and RNA extractions were quantified using an Agilent 2100 BioAnalyzer . RNA was purified using the standard protocols of Qiagen miRNeasy kits. The specimens represented at least 40 species, 24 orders and 37 families in all five extant classes  were preserved in Qiagen RNAlater classes . One PsoCheiraster sp.) to 88 987 394 (Pisaster ochraceus). Following trimming and adapter removal, 2 101 192 636 reads remained. This was a reduction of approximately 11% in total, and ranged per sample from 3.65% (Gephyrocrinus messingi) to 19% (Promachocrinus kerguelensis). A correlation between taxonomic class and read count was not observed. De novo assembly of contigs was then performed using Trinity [ransdecoder (http://transdecoder.sourceforge.net) and the PFAM-B protein family database [RNA-seq produced a total of 2 360 841 332 raw reads and ranged per sample from 30 190 658 , the underlying coding sequence (‘cds’) and the original raw nucleotide sequence (‘nuc’). Each putative protein was annotated using InterProScan [Strongylocentrotus purpuratus  and tensilin from the holothuroid Cucumaria frondosa (GenBank accession number AY033934). GenBank was also searched for non-echinoderm TIMP genes, and for each of those 71 unique entries the full nucleotide, coding and amino acid sequences were downloaded and added to those from the echinoderm transcriptome database. At this stage, each of the aa, cds and nucalignments had the same 481 terminals.An AccessrProScan ,28, whicrProScan , as wellrProScan ,31, PfamrProScan  and PROSrProScan  database2.3aa, cds and nuc sequences; electronic supplementary material, figure S1). Despite having some subregions identified as TIMP genes when analysed by protein databases, we questioned whether highly divergent contigs were homologous to our core dataset. With no other tests of homology available to us, highly dissimilar contigs were considered possible artefacts and thus removed. Highly divergent sequences, especially those with large indels, also had the potential to be disruptive to alignment algorithms, even if truly homologous.We then began a process of recursively aligning, examining and culling our collection of TIMPs , and a distance matrix was generated in BioEdit [nuc alignments  and removed them from the aa, cds and nuc alignments.The data were aligned in MAFFT   ‘residue overlap’, which is the proportion of an alignment column occupied by residues (not gaps or missing data) and (ii) ‘sequence overlap’, which is the percentage of positions with residues (not gaps or missing data) in an aligned sequence. If a sequence does not fulfil both of the user-set thresholds for these parameters, it is removed from the alignment and the data realigned and evaluated. This process was performed using six settings  for each overlap parameter, giving us 36 alignments from which to continue. We chose the alignment for each of the three sequence types that had the largest reduction in gaps while also retaining at least one of the sequences downloaded from GenBank for Branchiostoma floridae, a cephalachordate and a possible source of important transitional sequences between echinoderm and the vertebrate TIMP sequences. The chosen alignments from Boxer had the following total number of terminals: aa 373 (294 echinoderm), cds 406 (327 echinoderm) and nuc 319 (246 echinoderm).To increase the quality of the alignments prior to tree-searching, we used a two-step process that employed the program trimAl  and a cuxML [Crassostrea gigas (GenBank accession number AF321279). We then uploaded bootstrap trees and the best tree to RogueNaRok [cds and 12 from nuc. The gastropod Haliotis diversicolor (GenBank EU244343) was removed from all three alignments, but most of the other removed rogue terminals were unique to the different alignments.For each of the three sequence types, we then did another round of terminal reduction based on stability in trees. We conducted rapid bootstrapping tree-searches in RAxML , rootinggueNaRok , an onlilocalpair strategy for the aa and cds alignments and globalpair for the nuc alignment (which is better at handling large indels). They were then subjected to rapid bootstrap maximum-likelihood searches in RAxML. The alignments output by Boxer, chosen using the B. floridae rule and then reduced further in RogueNaRok, included four protostomes , which were all in the aa, cds and nuc alignments. Final trees were read into R [ape and dendextend [The newly reduced sequence sets were aligned again in MAFFT, this time using the d into R  and compndextend ; the ‘taDrosophila melanogaster sequences (culled earlier). We considered the latter two important sequences to be analysed in this study , but we could not find a reasonable alignment for the TIMP gene from Caenorhabditis elegans and did not include it. Sequences were more likely to be eliminated  during culling if they had large read depths, so we also returned a holothuroid sequence culled previously from aa, cds and nuc datasets with the second highest read depth (a Synapta maculata contig based on 240 066 reads) to examine its behaviour in tree searches. This sequence was actually conserved at key TIMP positions, but it also had a long C-terminal chain (over 100 amino acids) not shared by other sequences. We also removed an asteroid (sea star) sequence that had been recovered among vertebrate TIMP-2 sequences; it was identical to mouse TIMP-2 for part of the sequence, and given its low read depth (18) we considered it a contaminant. This final alignment of coding sequences, consisting of 180 echinoderm and 46 non-echinoderm sequences, was then subjected to rapid bootstrapping tree searches in RAxML.Finally, we chose a representative sequence for each taxon in each of the many small clades of similar sequences, avoiding those with small read depths, and we added back the human TIMP-2 sequence and 2.4S. maculata, we analysed lineages-through-time (LTT) plots using the R package ape. To perform this analysis, we first took the alignment of coding sequences from Boxer which had the largest reduction in terminals while still retaining 10 or more S. maculata sequences. This was the alignment of 463 copies, 392 of which were echinoderm. We then found the most likely tree by using RAxML with the same settings described above, and we made the resulting tree ultrametric with the program PATHd8 [d8 is used to date phylogenies by adjusting node heights based on user-input date parameters, and it is considered particularly effective for large phylogenies. We fixed the root to ‘100’ . The ultrametric tree was then read into R, and all terminals but those of interest (those specimens with 10 or more TIMPs) and the two C. gigas outgroups were pruned using the ‘drop.tip’ command. Each of the new trees (one for each taxon of interest) was then used to make LTT plots, and a gamma statistic [p-Values for gamma statistics were calculated using the command ‘2*(1-pnorm(abs(gammaStat(phylo))))’, where ‘phylo’ is the name of the tree read into the memory. LTT plots with larger gamma statistics have diversification patterns more divergent from a constant rate of growth.To examine diversification rates of TIMPs in different species, especially the apodan holothuroid m PATHd8 . PATHd8 tatistic  was calcTo examine the conserved domains across animal TIMPs, we took the final alignment of 226 TIMP amino acids and made consensus sequences showing those residues found in at least 90%, 80%, 70%, 60% and 50% of all sequences, counting gaps as sites. For the protostomes, chordates and each of the five classes of echinoderms, we made consensus sequences at the 70% threshold for comparison.3.p=0.0001), using our randomly selected, single exemplars of the chosen species from each class. The average proportion in holothuroids was about three to eight times higher than in the other classes (p=0.003), with the average proportion in holothuroids being 4–39 times this proportion in the other classes. Ophiuroids had the lowest proportion of TIMP genes per taxon (0.012%), and echinoids had the lowest proportion of reads per TIMP gene (0.006%).The number of TIMP genes per sampled taxon, as a proportion of all contigs from that taxon, was significantly different among the five classes . The effect was more pronounced in holothuroid sequences (culled=11 469.4±9712.9 versus kept=591.00±225.16). There was no difference in read depths between culled and kept amino acid and coding sequences.The average read depth of full nucleotide sequences which were culled (7446.4±95% CI: 4628.4) was greater than those kept through the process  did not have fewer TIMPs than other holothuroids. The proportion of TIMP contigs in S. maculata was 0.117%, second only to the dendrochirotids , and the proportion of reads in TIMP contigs was higher in S. maculata (1.124%) than all other taxa, including those in other classes. Indeed, two S. maculata TIMP contigs had depths of over 200 000 reads.The proportions of TIMP genes and read depth varied among holothuroid taxa, but our apodan  consistently recovered the vertebrate TIMPs as monophyletic, and within that clade each of the four TIMP types (1–4) also as monophyletic . Sequences characterized as TIMP-1 or -2 outside of vertebrates  appear to have no relationship with vertebrate TIMP-1 or -2 genes. The cephalochordate sequences included (i.e. from B. floridae) were recovered as sister to the vertebrate ones with the aa and nuc terminal sets, and separated from the vertebrate clade by three unstable echinoderm ones in the cds terminal set. Crinoid sequences consisted mostly of the older TIMP lineages, and holothuroid sequences dominated more recent diversifications. All trees had low bootstrap support for relationships among the different echinoderm and chordate lineages, but increasing support among the more derived clades.Optimal trees from the D. melanogaster sequence was recovered among the earliest diverging echinoderm lineages (which were mostly asteroid), and the human TIMP-2 sequence was recovered among other vertebrate TIMP-2 sequences. The re-added holothuroid sequence with the second highest read depth was recovered within a recent diversification of holothuroid sequences and sister to a sequence from the same taxon. This clade of holothuroid sequences was recovered as sister to echinoid sequences. The translation of the final coding sequence alignment recovered a very similar tree (−lnl=75 400.75), although it placed the chordate TIMPs (which had bootstrap support >50%) earlier in the tree than the coding sequences —culled of redundant clade representatives and with human TIMP-2 and other important sequences re-added—showed the same patterns as the more inclusive trees: monophyletic chordate, vertebrate and TIMP-1–4 sequences, an early diversification of TIMPs, a diversification after vertebrates (mostly in holothuroids), and low bootstrap support among constituent lineages . The D. equences .Figure Psolus sp. 11, Psolus sp. 41, Abyssocucumis sp. and Peniagone sp.) and one crinoid  but had acquired them through a steady accumulation of copies over time. The remaining taxa with 10 or more TIMPs in this alignment (the asteroids Pteraster tesselatus and P. ochraceus and the holothuroids Pannychia moseleyi and S. maculata) had the smallest number of TIMPs (between 10 and 13) and showed no significant change in the rate of their accumulation over time.Lineages-through-time plots showed a significant recent acceleration in the accumulation of TIMP copies in four holothuroids (Labidiaster annulatus), 10 crinoid copies  and two holothuroid copies (both from S. maculata). An SA-binding motif (HPQ) is highly conserved across all TIMPs (including chordates), found in at least 70% of all copies in our final alignment. Other residues conserved in 70% of all TIMPs, located across positions 320–596 of our final, 833-position alignment, are four glycines (G), three tyrosines (Y) and one each of lysine (K), threonine (T), leucine (L), glutamine (Q) and tryptophan (W)  .Table 24.TIMP genes are far more numerous and diverse in structure than was previously known , and ourS. maculata, which was hypothesized to have the fewest TIMPs among holothuroids owing to its extremely thin body wall [S. maculata is the only holothuroid we found to have any TIMPs with the VIRAK domain conserved in chordate copies, and S. maculata did not acquire its TIMP diversity through a recent radiation , but this is an artefact of the alignment; all copies that had this domain had the three elements in tandem. If vertebrate TIMPs do have unique functions in deuterostomes, it may perhaps be due to their VIRAK domain, but even this has been conserved in some echinoderm copies.The echinoderm TIMPs identified here offer us a large, diverse pool of naturally occurring TIMPs that can inform theoretical and practical studies of these important genes. The goal now is to bridge the gap between fine-scale studies of specific tissues and molecules and our new understanding of how abundant and varied TIMPs really are. The former classification of TIMPs into four types is clearly applicable only to vertebrates, and what is desired now is a much broader functional classification that will assist in understanding the roles of various TIMPs in the many different processes they influence. For example, do certain domains indicate direct interaction with ECM proteins versus MMP inhibition? Are there consistent characteristics among those TIMPs involved in tumour suppression, and what are they? General principles of TIMP functioning would emerge from the building of a more comprehensive classification system, and this would be a key prerequisite to fabricating TIMP-like molecules useful in tumour suppression or the activation of MCT-like materials.


Multiple sequence alignments (MSAs) are a prerequisite for a wide variety of evolutionary analyses. Published assessments and benchmark data sets for protein and, to a lesser extent, global nucleotide MSAs are available, but less effort has been made to establish benchmarks in the more general problem of whole-genome alignment (WGA). Using the same model as the successful Assemblathon competitions, we organized a competitive evaluation in which teams submitted their alignments and then assessments were performed collectively after all the submissions were received. Three data sets were used: Two were simulated and based on primate and mammalian phylogenies, and one was comprised of 20 real fly genomes. In total, 35 submissions were assessed, submitted by 10 teams using 12 different alignment pipelines. We found agreement between independent simulation-based and statistical assessments, indicating that there are substantial accuracy differences between contemporary alignment tools. We saw considerable differences in the alignment quality of differently annotated regions and found that few tools aligned the duplications analyzed. We found that many tools worked well at shorter evolutionary distances, but fewer performed competitively at longer distances. We provide all data sets, submissions, and assessment programs for further study and provide, as a resource for future benchmarking, a convenient repository of code and data for reproducing the simulation assessments. Here, we are interested in the relationship of evolutionary homology. In other contexts, residues may be aligned with a different aim, as in structural alignments, where residues are aligned if located at the same point in a shared crystal structure . MSA is elements , enhanceThe lack of accepted gold standard reference alignments has made it hard to objectively assess the relative merits of WGA methods. Previous evaluations of MSAs can be split into roughly four types: those using simulation, those using expert information, those using direct statistical assessments, and finally those that assess how well an alignment functions for a downstream analysis. We briefly describe and review these approaches , based upon a subset of hg19/GRCh37  (see Methods). The first simulated data set models a great ape phylogeny consisting of genomes with the same evolutionary relationships as humans, chimpanzees, gorillas, and orangutans . The secDrosophila melanogaster  to fragmentary in the case of D. rhopaloa   .The initial data sets were released in December 2011 and teams were given until February 2012 to submit their entries. The initial simulated data sets included the truths and information on where to obtain  the analysis software. As in the Assemblathons, none of the teams had access to the data sets until their initial release. The Alignathon received 35 submissions, 13 for the primate simulation, 13 for the mammal simulation, and nine for the fly data set ; MULTIZ http://genome.ucsc.edu/FAQ/FAQformat.html#format5). A suite of MAF comparison tools was developed for the project (mafTools) (see Methods), including a comparator tool, so-called because it compares two alignment files. We call the set of aligned pairs of residues within an alignment its alignment relation. The comparator tool works by taking two input MAF files, A and B, and comparing their alignment relations. For the simulated data sets, if A is the predicted alignment created by a tool and B is the simulated truth, then the ratio of the number of pairs in the intersection of A and B to the number of pairs in A is the precision of the prediction. Conversely, the ratio of the number of pairs in the intersection of A and B to the number of pairs in B is the recall of the prediction. One standard method for combining precision and recall into a single value is the balanced F-score, which is simply the harmonic mean of precision and recall   a subset of the alignment relation of one and checked if any or all elements of the subset were present in the alignment relation of the other. Ten million pairs were sampled for each direction of a MAF pair comparison, and variance between sampling runs was negligible (data not shown).For the simulated data sets we performed analyses both with respect to the entire genome and to areas of the genome subsetted by annotation type  (see Methods). Results are shown in For the mammal simulations we found a much wider spread of results, both between aligners and within different annotation classes. The strongest submission, Cactus, had an F-score 0.081 points higher than its nearest competitor. Looking at the mammal results by annotation type, generally (and predictably) submissions performed the best in genic regions, where simulated selection, which led to strong conservation, was presumably highest. Performance was intermediate in neutral regions and submissions generally performed most poorly in repetitive regions. Generally, submissions retained their ranking across annotation regions, that is to say, the submissions ranked 1 and 2 overall were also ranked 1 and 2 in genic regions. However, this trend did not hold for repetitive regions; and surprisingly, several submissions performed slightly better in repetitive regions than in the neutral regions .As phylogenetic distance between species grows the number of unobserved mutation events increases, and the alignment problem naturally becomes more difficult . To see PSAR pair score, for every pair of matched residues in the alignment. Each PSAR pair score is similar to the posterior probability that a given pair of residues in the input alignment are aligned . Subregion alignments were converted to make them appropriate for PSAR  (see Methods). For a pair of genomes we define the coverage of B on A. The overall coverage  is the average of coverages for all pairs of distinct species. Hypothesizing that PSAR-precision can be used to approximate precision and that coverage can be used as an estimate of recall, the natural statistical analog to F-score is the harmonic mean of PSAR-precision and coverage, which we call the pseudo F-score.To complement our proxy to precision we used a simple proxy to recall: coverage. For a pair of genomes A and B, the proportion of residues in A aligned to a residue from B is the To see how consistent our statistical measures were to the measures derived from the simulations, we calculated them for the user-generated simulated primate and mammal alignments (r2 = 0.984), but no linear correlation between PSAR-precision and precision. In particular, PSAR reports relatively consistent, high scores for all the different alignment programs, suggesting that at a local, residue level, on aggregate the alignments look equivalently reasonable between alignment programs. Despite the lack of linear correlation between precision and PSAR-precision, we find that because of the excellent recall and coverage correlation, the F-score and pseudo F-score results linearly correlate strongly . This appears to be because the more limiting factor in many of the alignments’ performance was not precision, but rather a lack of relative recall/coverage, something particularly affecting the GenomeMatch, Mugsy, and to a lesser extent, Pecan, EPO, and Robusta submissions.In the simulated primates all the values were uniformly high, and hence saturated. However, looking at the simulated mammals, we find a very good, linear correlation between recall and coverage (r2 = 0.671), indicating that pseudo F-score can be used as a reasonable proxy to F-score at this regional level of resolution . It should be noted that the correlation is imperfect; in particular, it appears that the pseudo F-scores saturate at high values, whereas the corresponding F-scores still discriminate alignment quality, i.e., pseudo F-scores do not always discriminate between good and very good alignments.We have demonstrated that the performance of alignments varied regionally according to the simulation of annotation types. Having developed scoring metrics that can be applied across simulated and nonsimulated genomes, we corroborated this analysis by visualizing how the scores vary across the sampled subregions. To view a complete subregion at approximately this level of resolution, we binned the reference sequence into 1-kb nonoverlapping intervals and calculated the F-score (for the simulated data sets) and pseudo F-score for each bin, calculating the score for a bin as if it represented the complete alignment, and for the simulated comparison, restricting the true alignment to just those pairs involving residues in the reference interval that defined the bin. Several of the pipelines used some of the same underlying programs. To see how these commonalities affected the alignments, for each data set we calculated the Jaccard distance between the alignment relations of each of the submissions . As predx is aligned to a residue y and y is aligned to a residue z, then x and z should be aligned, because it is not possible for x and y to share a common ancestor and for y and z to share a common ancestor, whereas x and z do not share a common ancestor. Comparing two alignments, one of which is transitively closed and one of which is not, based only on the aligned pairs contained in a MAF potentially gives an advantage to the nontransitively closed submission. This is because the transitively closed submission must align all residues transitively connected by alignments, which may lower the overall precision of the set of aligned pairs. To see how different the results would be if we were to have enforced transitive closure—and therefore a strict homology assumption—we created a tool (mafTransitiveClosure) that computes the transitive closure of a MAF (a linear time operation). Supplemental Figure S8 shows the results for the simulated data sets. We found that the progressiveMauve, Cactus, Pecan, Robusta, EPO, and Mugsy programs produced WGAs that were transitively closed and therefore unaffected by transformation. As predicted, those submissions that were not initially transitively closed, such as the pairwise and reference-based alignments, all saw their precision performance decline, in some cases very substantially, and no submission saw a large boost in recall.The MAF specification allows sequence residues to appear in one or more columns of the file. This allows a program to report sequence similarity, which is not a transitive property, but allows inconsistency if the intention is to report homology. For example, if a residue To find duplications within the alignments, we used a simple metric, duplicative coverage. For a pair of genomes A and B, the duplicative coverage of B on A is the proportion of residues in A aligned to two or more distinct residues in B. This assessment is complicated by the lack of transitive closure in some submissions, because a single residue may align to two or more residues in a genome, but in separate columns of the file. To avoid this complexity, we assessed the submissions after computing the transitive closure . To avoid misrepresenting submissions, we dropped submissions from the assessment for which the transitive closure adversely (>0.05 change) affected the F-score or pseudo F-score. Supplemental Figure S9 shows the results; in short, we find that only Cactus had significantly nonzero duplicative coverages, e.g., just over 3% of all fly genome bases were found to be duplicated, on average, when looking at any other genome.http://compbio.soe.ucsc.edu/alignathon/). Unfortunately the PSAR analysis involved using a compute cluster, making it expensive for outside groups to repeat this assessment. However, given a MAF file of one of the simulated data sets, the benchmarking pipeline can be used to make a performance assessment. The user can download the analysis repository, compile the necessary software, download the requisite data, place their alignment in a specified subdirectory and type “make” in the terminal window to launch the analysis. This approach will hopefully spur future development and assessment upon this resource.To aid future assessment, we have created an easy to evaluate benchmarking pipeline , but we did find a very strong correlation between recall and coverage. Importantly, for the submissions we received on both flies and simulated mammals, differences in recall were overall greater than differences in precision, and therefore more critical in determining the observed performance differences. We speculate, given the various overlaps in the tools used between the pipelines and the resulting similarities between the submissions, that the larger differences in recall were largely due to differences in synteny mapping, and that this is one area where there is clear room for improvement. Visualizing the data regionally, we were able to observe local differences in performance that fit well with these overall results.For simplicity of interpretation in the simulations, homologies that predated the MRCA of the extant genomes were not included in the “true” simulated alignments; therefore, some ancient homologies captured by the aligners are considered false by the benchmarking pipeline. Additionally, EVOLVER does not track the alignments it generates when creating simulated mobile element  insertions; thus two highly similar transposon copies from separate insertion events are not considered homologous in the true alignment. For these reasons, the reported simulated precision values may be considered a lower bound that may, for some purposes , underestimate the accuracy of the alignments. This may partly explain the lack of correlation between precision and PSAR-precision, because such repeat regions might appear to be reasonably alignable to PSAR, but false homologies according to the simulation.There were some dependencies between the assessments and the assessed programs. EVOLVER simulations were used to benchmark the Cactus aligner in its initial publication, although at ∼1/250th the scale used here , and as The use of the MAF format for submissions made an apples-to-apples comparison somewhat difficult because the format does not force transitivity of homology. However, this permissiveness in format allowed us to assess a variety of WGAs, some of which are naturally not transitively closed, such as the reference-based MULTIZ aligners and the GenomeMatch pairwise submissions. To make comparisons under a strict homology assumption, we tried taking the transitive closure of such alignments, but this does not generally result in a reasonable WGA. In general, when performing consistent evolutionary analyses the nature of the alignment relationship—similarity or homology—bears consideration.In theory, several of the tools should have been able to align duplicated regions together. Unfortunately, we received submissions only for the simulation and not fly data sets for some duplication-aware tools, such as EPO and Mugsy, but across the pipelines the lack of aligned duplications indicates there is likely significant room for WGA tools to improve in detection of duplicative homologies.Some individual results of the simulations were surprising. For example, the EPO results had particularly low coverage on the simulations—substantially lower than that pipeline achieved in the genome alignments available from Ensembl . One posRobust WGA tools are critical for the future of comparative genomics, but to make objective progress we must agree on assessments or risk not knowing when a genuine advance has been made. As is typical in bioinformatics, much prior assessment of WGAs has been made as part of the publication of a novel tool. Naturally, these assessments tend to present results that favor the new tool. The few independent assessments that have been made of WGA, although useful, are several years old and assessed only a fraction of the available methods . In compJust as in any area where there is no accepted ground truth, comparing WGAs is hard, and each of the assessment types categorized in the introduction has flaws. Here we put an emphasis on two independent methods for assessment and showed some consistency between them across data sets. For this reason, and because many of the results met with our prior expectation, we have some confidence in the results. Indeed, it is possible to compare the various F-scores and pseudo F-scores . Specific parameter files used to create the simulations are available on the project website. EVOLVER has a model for proteins, genes, and base-level evolutionary constraints. EVOLVER uses a two-step process for simulating a single forward step in a simulation: The first step is an intrachromosomal evolution step, and the second is an interchromosomal step. The intrachromosomal step allows events such as substitutions, insertions and deletions, duplications, translocations, and inversions, according to rates distributed according to the length of the event. The interchromosomal step allows chromosome fusions, fissions, segment copying, segment movement, reciprocal translocations, and nonreciprocal translocations. Additionally, EVOLVER keeps a separate mobile element library that can insert mobile element DNA into the simulated genome; this library is itself also undergoing simulated evolution. EVOLVER logs all evolutionary events that take place during a cycle and keeps track of the relationships between residues in the parent and child genomes.As in the Assemblathon 1 project , simulathttps://github.com/dentearl/evolverSimControl and https://github.com/dentearl/evolverInfileGeneration/, respectively. These extra tools, along with mafJoin (https://github.com/dentearl/mafJoin/) were used to construct MAF files containing the entire simulated true evolutionary relationships of all of the genomes: leaves, internal nodes, and the root.EVOLVER as distributed is only capable of performing a single cycle of evolution. In order to run the arbitrary phylogenies necessary for this project, we used the evolverSimControl and evolverInfileGeneration tools available at As in the Assemblathon project, we initiated the simulation using a subset of the well-annotated human genome, hg19/GRCh37. Complete chromosome sequences for chromosomes 20, 21, and 22 along with annotations for those chromosomes from the UCSC Genome Browser tracks mgcGenes, knownGene, knownGeneOld5, cpgIslandExt, and ensGene, were obtained from the UCSC Golden Path download site. The tool suite evolverInfileGeneration was used to take the raw data and make it into an EVOLVER infile data set. This starting data set was then put through the EVOLVER simulator for a distance of 1.0 neutral substitutions per site, an evolutionary time of ∼500 million years of vertebrate evolution . This prEVOLVER rediscovers the tandem repeat sequence annotation at every step of a simulation by calling tandem repeats finder , v4.0. RThe primate simulation was described by the phylogenetic tree (in newick format) :  courtesy of Artyom Kopp (UC Davis) and the phylogeny used by UCSC for the 15-way insect alignment. The Kopp tree lacked droSim1 and droSec1 which were added by normalizing the branch lengths between the dm3 branches on the two trees. Extraneous species were trimmed using tree_doctor from PHAST. This tree was provided for progressive aligners that need a guide tree. This phylogeny corresponds to the newick tree  and eight flies from NCBI on January 25, 2012 .https://github.com/dentearl/mafTools/) to perform the majority of transformations, manipulations, and analyses. Scripts to perform the analyses described can be found in the analysis repository.Participants submitted their predictions of alignments in MAF files. To process the submissions, we wrote a suite of open-source tools called mafTools  to see how many (if any) pairs to sample from that column. If x many pairs are to be sampled, we then sample x times from a discrete uniform  decrementing the range of the distribution with each sample, without replacement, and then map those integers to pairs using a bijective function. This allows us to efficiently sample pairs without iterating through each and every pair.During the second pass we iterate over every block in the MAF and then every column in the block. We calculate the number of pairs present in the block, call this To accommodate PSAR, which processes global MSAs in which the alignment is represented as a 2D matrix where the aligned sequences, interspersed with gaps, are the rows and the columns represent the equivalence classes of aligned bases, we constructed subalignments of sampled regions.D. melanogaster, dm3, and for the simulations, simHuman). For each of the three test sets, regional intervals were selected by sampling five different starting values from a discrete uniform distribution , where g is the total length of the reference genome and 500,000 is the length of the interval. Sampled values were then mapped back to individual chromosomes. All alignments containing any positions of the reference within these intervals were extracted from the submitted alignments. Although this model of sampling does not prevent overlapping regions, no overlapping regions were sampled. Likewise this model of sampling does not prevent regions that cross between chromosomes, but no such bridged regions were sampled.Regions were randomly sampled by picking intervals of a chosen reference genome (for the flies Details of how we adjusted each submission for regional analysis with PSAR are in the Supplemental Material.http://compbio.soe.ucsc.edu/alignathon/. This website links to all the data sets, submissions, and benchmarking code.The project website is available at


A number of alignment tools have been developed to align sequencing reads to the human reference genome. The scale of information from next-generation sequencing (NGS) experiments, however, is increasing rapidly. Recent studies based on NGS technology have routinely produced exome or whole-genome sequences from several hundreds or thousands of samples. To accommodate the increasing need of analyzing very large NGS data sets, it is necessary to develop faster, more sensitive and accurate mapping tools.HIA uses two indices, a hash table index and a suffix array index. The hash table performs direct lookup of a q-gram, and the suffix array performs very fast lookup of variable-length strings by exploiting binary search. We observed that combining hash table and suffix array (hybrid index) is much faster than the suffix array method for finding a substring in the reference sequence. Here, we defined the matching region (MR) is a longest common substring between a reference and a read. And, we also defined the candidate alignment regions (CARs) as a list of MRs that is close to each other. The hybrid index is used to find candidate alignment regions (CARs) between a reference and a read. We found that aligning only the unmatched regions in the CAR is much faster than aligning the whole CAR. In benchmark analysis, HIA outperformed in mapping speed compared with the other aligners, without significant loss of mapping accuracy.Our experiments show that the hybrid of hash table and suffix array is useful in terms of speed for mapping NGS sequencing reads to the human reference genome sequence. In conclusion, our tool is appropriate for aligning massive data sets generated by NGS sequencing.The online version of this article (doi:10.1186/s13015-015-0062-4) contains supplementary material, which is available to authorized users. Recent studies based on next-generation sequencing (NGS) technology have produced hundreds or thousands of exome or whole genome sequences with decreasing cost of NGS experiments . As the All HT-based alignment tools use the same seed-and-extend strategy , 12, whiSupport of long-read alignment, high speed, accuracy, and sensitivity are essential features of the current NGS mapping tools. Here, we tried to merge the advantages of HT-based and suffix tree-based alignment in a tool that satisfies these requirements. To this end, we developed a genome mapper using hybrid index-based sequence alignment (HIA).In this article, we describe the HIA tool, and show the results of comparisons of performance on simulated and real read data between HIA and the other four popular alignment tools including BWA, Bowtie2, SOAP2 and SeqAlto. The results of the benchmark analysis demonstrate that HIA outperforms the other aligners, especially in speed.0s1 … sm−1 be a string over Σ. Let |S| = m be the length of S, S[i] = si be the i-th symbol of S, S = si … sj be a substring, and Si = S be a suffix of S. We define a q-gram as a substring of S with length q. In the context of DNA sequence, the alphabet Σ consists of the four nucleotides A, C, G, and T, i.e., Σ = {A, C, G, T}. We assign A, C, G, and T to the numbers 0, 1, 2, and 3, respectively. Thus, each q-gram is encoded as an unsigned integer with two bits per nucleotide. However, most reference genome sequences contain a nucleotide other than {A, C, G, T}, such as ‘N’. This occasionally happens with NGS read sequences as well. We replace ‘N’ with a uniform random nucleotide such as BWA and many other tools do.Let Σ be an alphabet and S = sq + 1. Given a q-gram, HT[x] is the first position of the q-gram in SA, where x is the numeric value of the q-gram. We define the range (R) of the q-gram in SA by  is empty.In terms of a hybrid index, SOAP2 implements a hash table on the FM-index which is a compressed SA. On the other hand, our hybrid index consists of a reference sequence, a suffix array (SA), and a hash table (HT), as in Fig. The procedure for constructing the hybrid index consists of four processes, as follows: 1) packing the reference sequence in a 2-bits per base format (Sequence); (2) counting each q-gram in the sequence and set the range of q-grams in SA (HT); (3) inserting the sequence positions of each q-gram into its range of SA; and (4) sorting the range for each q-gram (SA) lexicographically. Based on the heuristic determining the final positions of most of the suffixes using only the first few symbols of each suffix , the SA  packing Retrieving positions of a query Q in the sequence is implemented in two steps: HT lookup and binary search of the SA. If the prefix of Q of length q (q-gram) is a substring of the sequence, we find the range (R) in which the q-gram belongs in the SA, using Eq. . OtherwiHybrid mapping follows the same seed-and-extend approach used by all HT-based tools. A MR (matching region) is a common substring between the reference sequence and the read. Let ‘sp’ be the starting position in the reference sequence where the MR occurs. We will indicate each MR as 3-tuple <dv, ro, L>, where ‘ro’ (read offset) is the starting position in the read where the MR occurs, ‘dv’  is defined as dv = sp − ro, and ‘L’ is the length of the MR. Given a length-m′ MR, there are (m′ − q + 1) q-grams having the same diagonal values and consecutive read offsets. Diagonal values having same values implies that the corresponding MRs are close each other in the reference sequence. A CAR  is a list of MRs, which are close each other and ordered by ‘ro’. We define a CAR as a seed and align only the unmatched regions in CAR.I.Retrieve range of SA of each q-gram using HT and SAThe procedure for finding MRs and CARs of a read is as follows: 1) retrieving range of SA of each q-gram using HT and SA; (2) computing diagonal values; (3) sorting by diagonal value and offset; (4) grouping MRs with same diagonal value and successive offsets; (5) merging the adjacent MRs into CARs; (6) sorting the CARs by matched bases in descending order. For example, given a read (r = GCCATG) and q-gram length (q = 2) and the hybrid index constructed in Fig.  retrievi0-th q-gram positions: 1-th q-gram positions: (CC: SA[8])2-th q-gram positions: 3-th q-gram positions: II.Compute diagonal values4-th q-gram positions: (TG: SA[15])III.Sort by diagonal value and offset, , , , , , , IV.Group MRs with same diagonal value and successive offsets, , , , , , , MR0:  ← MR1:  ← , , MR2:  ← V.Merge the adjacent MRs into CARs and set matched basesMR3:  ← , , CAR0: CAR1: VI.Sort the CARs by matched basesCAR2: CAR1: CAR0: CAR2: .Although diagonal values of two adjacent MRs are different, they could be located in a same CAR if there were inserted or deleted bases between them. In the case of CAR1, the difference value between diagonal value of MR1 and diagonal value of MR2 is 1 and there is one inserted base (C) between MR1 and MR2. We refer to this value as adjacency and we use the value in order to set the permitted size of insertion and deletion between MRs.In order to find MRs and CARs efficiently, we apply three heuristics. Let the read length and the error rate be m and ε, respectively. The first heuristic is that there is a common substring of length at least m/(k + 1) between two reads of length m with k differences . Let λ =The formula  is the cSecondly, since a q-gram that occurs in many regions in the sequence is not a good discriminator, such q-gram was given less weight than one that occurs in few regions. This heuristic is based on the inverse document frequency (IDF) commonly used in the field of information retrieval. The IDF is a measure of whether or not the term is common across all documents . ApplyinFinally, given a read (r) and two strings, S1 and S2, both of length m, if the number of matched bases between r and S1 is greater than the number of matched bases between r and S2, then the number of differences between r and S1 is smaller than the number of differences between r and S2. This heuristic can be used to rank CARs by the number of matched bases and to filter out the lower-ranked CARs.To align the top-ranked CARs, we simply align the unmatched regions in each CAR, because the MRs are already aligned (matched). We classify the unmatched regions into three groups: leftmost unmatched region (LMUR), which is the left unmatched region of the first MR in a CAR; rightmost unmatched region (RMUR), which is the right unmatched region of the last MR in a CAR; and unmatched regions between two MRs (MRURs). The matched and unmatched region can be separated because of mismatch, insertion and deletion. We analyze these split causes such as Fig. There are also many split causes of MRUR like as Fig. Hybrid mapping might miss some CARs and result in the failure of mapping (unmapped). The main reasons for missed CARs might be one of following reasons: (1) too many errors in a read; (2) too many highly frequent q-grams in a read; and (3) many high-ranked CARs. In the first case, a long q-gram based on the first heuristic approach results in empty q-gram ranges. In the second case, the second heuristic approach causes highly frequent informative q-grams to be missed. In the third case, the lower-ranked but informative CARs are lost because of the third heuristic approach. We applied extended hybrid mapping to the unmapped, which uses shorter q-grams including some highly frequent q-grams, and extends ranked CARs further. Together, these modifications increase the sensitivity of the technique.We implemented HIA on Java to support multiple platforms , Unique (%), Q10 (%) and Time (s). The Aligned (%) denotes the percent of aligned reads over total reads and indicates the overall mapping rate. The Unique (%) measures the percent of uniquely aligned reads over total reads and refers to MAPQ ≥ 1. The Q10 (%) measures the fraction of mapped reads MAPQ ≥ 10. The Time is the elapsed time (seconds) including both the index loading time and the alignment time. In the case of the simulated datasets, the %Err measures the percent of wrong aligned reads over the reads satisfying Unique (%) or Q10 (%). We adopt the concept of a correct alignment from Langmead and Salzberg , who detTo evaluate the performance of HIA, we compared HIA to BWA, Bowtie2, SOAP2 and SeqAlto on six simulated datasets and two real datasets. In all tests, we used the GRCH37 build of the human genome as the reference sequence used in alignment. We performed the alignments using a computer with two Intel Xeon 6-Core X5670 2.93-GHz processors and 48 GB RAM. All alignment tools were run with a single thread for alignment except for multi-thread tests.Table In the perspective of SA construction, we performed several tests of index generation and compared the results from our index generation algorithm and the divsufsort that is one of the best SA construction algorithms . It was We ran six of the aligners with various parameter settings for 100, 150, 250 and 400 bp single-end reads. All results can be found in the Additional file Table We also ran six of the aligners with the same parameter settings as in the single-end reads for 100 and 150 bp paired-end reads. All results can be found in the Additional file Table We ran six of the aligners with various parameter settings for one paired-end reads and two single-end reads. All results can be found in the Additional file Table We ran six of the aligners with 6 threads and 12 threads modes for the whole of the paired-end HiSeq reads. All results can be found in the Table We developed a new sequence alignment tool for aligning short and long reads to a reference genome. HIA has two indexes, a HT index and a SA index. The HT is capable of direct lookup of a q-gram, and the SA can very rapidly look up q-grams of variable length. Our experiments show that the hybrid of HT and SA is useful, from the perspective of speed, for mapping NGS sequencing reads to a reference genome sequence. HIA also supports the multithreading of mapping. In particular, HIA is much faster than all of the other aligners; therefore, our tool is appropriate for aligning massive data sets generated by NGS sequencing.The accuracy of alignment is very important in re-sequencing because the main purpose of alignment is to discover the variants relative to a reference genome. Although these variants (or sequencing errors) cause sequencing reads within them to match inexactly to the reference, alignment tools should nonetheless correctly map these reads to the reference. Considering the results of the benchmark analysis, we can conclude that HIA performs comparably to four popular alignment tools.Project name: HIAhttp://biomi.cdc.go.kr/hia/Project home page: Operating systems: Platform independentProgramming languageLicense: GNU GPLAny restrictions to use by non-academics: none.