The historically developed practice of learning to play a music instrument from notes instead of by imitation or improvisation makes it possible to contrast two types of skilled musicians characterized not only by dissimilar performance practices, but also disparate methods of audiomotor learning. In a recent fMRI study comparing these two groups of musicians while they either imagined playing along with a recording or covertly assessed the quality of the performance, we observed activation of a right-hemisphere network of posterior superior parietal and dorsal premotor cortices in improvising musicians, indicating more efficient audiomotor transformation. In the present study, we investigated the detailed performance characteristics underlying the ability of both groups of musicians to replicate music on the basis of aural perception alone. Twenty-two classically-trained improvising and score-dependent musicians listened to short, unfamiliar two-part excerpts presented with headphones. They played along or replicated the excerpts by ear on a digital piano, either with or without aural feedback. In addition, they were asked to harmonize or transpose some of the excerpts either to a different key or to the relative minor. MIDI recordings of their performances were compared with recordings of the aural model. Concordance was expressed in an audiomotor alignment score computed with the help of music information retrieval algorithms. Significantly higher alignment scores were found when contrasting groups, voices, and tasks. The present study demonstrates the superior ability of improvising musicians to replicate both the pitch and rhythm of aurally perceived music at the keyboard, not only in the original key, but also in other tonalities. Taken together with the enhanced activation of the right dorsal frontoparietal network found in our previous fMRI study, these results underscore the conclusion that the practice of improvising music can be associated with enhanced audiomotor transformation in response to aurally perceived music. From a global perspective, however, score-dependence may be considered to be the exception. All over the world, both in the past and in the present, instrumental music was and is generally learned by imitation and improvisation  (n = 8): subjects were instructed to play together (simultaneously) with two consecutively presented recordings of the excerpt, without aural feedback. The tonality, which was the same for all excerpts, was announced before the task started.Replicate [no aural feedback] (n = 5): subjects were instructed to listen to the excerpt twice and then play it themselves, without aural feedback. The tonality, which was the same for all five examples, was announced before the task started.Replicate and then transpose to the relative minor [aural feedback] (n = 5): subjects were instructed to listen to the excerpt twice, a) play it once in the same (major) key and then b) transpose it to the relative minor.Replicate, adding inner voices [aural feedback] (n = 5): subjects were instructed to listen to the excerpt twice and then play it, adding inner voices belonging to the harmony.Replicate [aural feedback] (n = 7): subjects were instructed to listen to the excerpt twice and then play it in the same key. Subjects were informed that the excerpts used in this task each contained a sequential repetition. For an example, see excerpt 5 : subjects were instructed to listen to the excerpt twice, a) play it once in the same key  and then b) transpose it to e minor.While all tasks and conditions involved a form of replication of the aural model, they were also designed to promote recruitment of implicit knowledge of music and music syntax. Performance without aural feedback, for example, was designed to elicit top-down recruitment of procedural knowledge of the tonality. The two-part style used in all tasks would induce disambiguation of the harmony based on prior experience, and the obligation to add inner voices in block 4 might enhance this effect. The sequential repetition found in all excerpts in block 5 was designed to recruit knowledge of both harmony and tonality, just as the transposition tasks in blocks 3 and 6.The music excerpts were performed by one of the researchers (RH), a professional pianist, on an AKAI piano-action MPK88 MIDI  controller, without pedal, using the Steinberg ‘The Grand 3’ Yamaha C7, and recorded as midi sequences in Cubase AI5 using a Steinberg CI2 audio interface. Instead of recording the audio signal, MIDI registers key depression and velocity, allowing digital analysis. The choice not to use pedal was motivated by the fact that it might compromise the independence of the voices in the two-part tonal style and/or confound the analysis of the midi sequences. Every effort was made to achieve an ecologically valid concert performance despite the use of an electronic instrument.Data acquisition made use of the same instrument used to record the aural model. Audio was presented with Stagg SHP-2300 stereo headphones. Subjects familiarized themselves with the keyboard prior to acquisition and adjusted the volume themselves. Before the experiment started, the protocol was explained in detail, block by block, making use of printed instruction material. Subjects then rehearsed the first excerpt from each block. Subjects were instructed that each of the six blocks would consist of at least five examples, one previously rehearsed excerpt and four or more unfamiliar excerpts.During acquisition, the instructions for each block were repeated before it began, and the subject was reminded that the first example had already been rehearsed. An oral prompt announced that the recording was about to begin. Subjects then heard the pitches from the first beat of the ensuing recording, or if the example began with an upbeat, both the upbeat and the first beat. After a few seconds a woodblock (tuned to the first beat) would indicate the tempo by playing a full bar in the tempo of the recording, one note to a beat. Then the presentation of the music would begin. Subjects were allowed to hear each example twice, with an empty bar between presentations. During the empty bar, the woodblock kept time, playing on every beat. The amount of time given to perform each task was three times the length of the aural model.Analysis consisted of a comparison between the original midi sequences used as the aural model and the midi sequences produced by the subjects during acquisition. The original midi sequences of the aural model were edited into a separate treble and bass midi sequence in Cubase. In addition, for the transposition tasks, the sequences were transposed to the new key in Cubase and not re-recorded, in order to preserve timing and expression. The midi sequences produced during acquisition were also edited into a separate treble midi sequence and a bass sequence. The ‘finding of the right key’ was edited out of the midi sequence, as well as false starts: subjects playing the first few beats, stopping and then beginning again. In a few cases, subjects did not respond to the aural model and in a few cases, only one voice was played, usually the treble. The inner voices from block 4 were edited out of the midi sequences as well as all other extra improvised voices.In block 1, a large number of subjects did not play along with the first presentation of each excerpt. To avoid a bias, this first presentation was discarded before analysis, not only for the subjects who did not respond, but also for the subjects who immediately played along with the first presentation. The rehearsed excerpt from each block was also discarded before analysis. Therefore, the total number of midi sequences came to a maximum of thirty-seven per subject, per voice. After editing, the average number of midi sequences was 35.8 (±1.5) for the treble voice and 35.4 (±1.7) for the bass. If no midi sequence was acquired, it was treated as a missing value.The similarity of the aural model and the performance of the subject was determined by the construction of an alignment. This approach has often been used in musicology, especially in folk song research where it has been used to study the variability of melodies in oral transmission . Algoritx1,…,xi,…,xn, and y: y1,…,yj,…,ym are the sequences to be aligned, S is a similarity measure for arbitrary symbols, and γ is the (fixed) gap score . D = 0, D = iγ and D = jγ. D contains the score of the optimal alignment up to symbols xi and yj of sequence x and y respectively and therefore D contains the score of the optimal alignment of the complete sequences. The algorithm has both time and space complexity O(nm), which is quadratic. This algorithm is known as the Needleman-Wunsch algorithm [In recent years, alignment algorithms have often been employed in Computational Musicology and Music Information Retrieval –31. The lgorithm . For furS and the gap score γ. The intuitive meaning of the substitution score function is: the higher the substitution score of two symbols, the more we want them to be aligned. In general, this implies that the substitution score function will be defined as a similarity measure for symbols. To define the function, we can of course use different properties of the notes. For the present study, we used exact pitch and IOR . Both are available in, or computable from, the MIDI-input. To represent pitch, we used the MIDI-representation, which basically corresponds to the indices of the keys of the keyboard in which a1 (A440) = 69.To apply this algorithm to melodies, or in this case midi sequences, the abstract elements of the algorithm that need to be defined are 1) the symbols, 2) the substitution score function, and 3) the gap score γ. In the present study, as we were dealing with MIDI, we took each element from the midi sequence  as a symbol. We subsequently determined a substitution score function The IOR of a given note is the ratio between the IOI  of the note and the IOI of the previous note, where the IOI of a note is defined as the difference in time between the onset of one note and the onset of the next. The IOR can be considered to be the relative duration of a given note with respect to the previous note. For the last note in the sequence we defined the IOI as the duration of that note. For the first note in the sequence, we set IOR to 1.p(s) is the pitch of symbol x in MIDI encoding, andior(xi) = ioi(xi)/ioi(xi-1), in which ioi(xi) is the time difference between the onsets of xi and xi + 1. We defined the gap score as γ = -0.5 for exact pitch and γ = 0 for IOR. In the event we used Spitch we obtained a value for the similarity of the aural model and the recorded midi sequence with respect to the sequence of pitches, and when we used Sior we got a value for the similarity of the aural model with the recorded midi sequence with respect to the sequence of IORs, which reflects rhythmic similarity.We defined two substitution score functions:Since the score of an alignment depends on the length of the midi sequences, normalization is needed to compare different alignment scores. Otherwise, the alignment of two similar long sequences would result in a much higher score than the alignment of two short sequences. We therefore divided the alignment score by the length of the alignment, which is the length of sequence x increased with the number of gaps inserted in x (or the length of sequence y increased with the number of gaps inserted in y). Thus, an exact match resulted in a score of 1, as the maximum value of our substitution score functions is 1, and no gaps are needed, causing the score of the alignment to equal the length of the sequences. Anything less than an exact match resulted in a score lower than 1. The scores that are reported in this paper are the normalized alignment scores.Our main goal was assessment of the differing ability of improvising and score-dependent musicians to replicate aurally perceived music at the piano. Accordingly, mean alignment scores for the four variables , bass exact pitch, and bass IOR) were subjected to a one-way multivariate analysis of variance (MANOVA) to determine significance of the difference between groups. Subsequently, differences of means were tested for each of the four variables using one-way ANOVA. Interactions between the factors group , voice , and parameter  were studied using a three-way mixed (between-subjects/within-subjects/within-subjects) analysis of variance (ANOVA).For the comparison of replication and transposition, the replication tasks from block 3 and 6 (3a and 6a) were contrasted with the transposition tasks from the same blocks (3b and 6b), based on identical stimuli. Two-way mixed ANOVA was used to investigate the interaction between group (between-subjects) and task (within-subjects) for each of the four variables. Similarly, for the comparison of performance with and without aural feedback, the replication tasks from block 1 and 2 without feedback were contrasted with all replication tasks from blocks 3–6 with aural feedback . Again, two-way mixed ANOVA was used to investigate the interaction between group (between-subjects) and condition (within-subjects) for each of the four variables.Supporting Information  playing along with the excerpt, 2) listening and then replicating it in the same key, 3) listening and replicating a major-key excerpt, first in the same key and then in the relative minor, 4) listening and replicating the excerpt while adding inner voices, 5) listening to an excerpt containing a sequential repetition and replicating it in the same key, or 6) listening and replicating the excerpt, first in the same key and then in a different key. Tasks were performed under two contrasting conditions: with aural feedback (blocks 3–6) or without (blocks 1 and 2). Alignment scores were computed for exact pitch and IOR (which reflects rhythmic similarity) for the treble and bass voices separately. Mean alignment scores of the participants are presented in the ormation . MIDI seormation .F = 3.309, p = 0.035. Subsequent one-way ANOVA indicated that improvising musicians’ mean treble alignment scores were significantly higher than those of score-dependent musicians, both for exact pitch and IOR  revealed a significant difference between improvising and score-dependent musicians based on their combined audiomotor alignment scores, F = 102.636, p < 0.0001  ANOVA was conducted to investigate interaction between group, voice, and parameter which, however, was not observed. A significant two-way interaction was observed between parameter and voice, 0001 see .F = 116.508, p < 0.0001 and parameter (IOR > exact pitch), F = 66.584, p < 0.0001 was observed. The latter was not further explored as we considered a pitch-rhythm comparison to be conceptually non-informative. One-way ANOVA indicated that the effect of voice (treble > bass) pertained to both exact pitch and IOR and was significant for both groups , h groups .F  = 4.483, p = 0.047  = 121.364, p < 0.00001. As can be seen in In blocks 3 and 6, the same stimulus was used for two different tasks, enabling a direct comparison between replication  and transposition (to a different key or to the relative minor). Two-way mixed ANOVA revealed significant interaction between group (between-subjects) and task (within-subjects), but only for treble exact pitch,  = 0.047 . A signiF = 6.254, p = 0.021  and condition (within-subjects), but only for treble IOR,  = 0.021 . Subsequ = 0.021 .s = 0.02; treble IOR: rs = 0.19; bass exact pitch: rs = -0.22; bass IOR: rs = -0.08) or years of professional experience, expressed in number of years since completion of the propadeutic exam . One of the highest scoring organists was actually still completing his bachelor in organ performance at the time of the study.No significant correlations were found between mean alignment scores and either age . With the exception of treble IOR, aural feedback did not contribute significantly to higher alignment scores in improvising musicians, however, a possible effect of aural feedback on transposition was not assessed. The higher audiomotor alignment scores found here can be seen as evidence of enhanced audiomotor transformations, a notion that is supported by the significantly larger activation of the right dorsal parietal-premotor network identified with fMRI in improvising musicians while the participants imagined playing along with a recording or covertly assessed the quality of the performance .The superior ability to replicate aurally perceived music ‘by ear’, particularly the ability to do so in different contexts, for example during aural transposition, may possibly be associated with enhanced employment of procedural knowledge of music syntax during performance. SRT  studies have demonstrated that implicit knowledge of low-level action syntax can be acquired non-consciously during the mere repetition of motor sequences –35. SRT The apparent distinction between low- and high-level syntax is reflected by the existence of specific processing networks in the brain dedicated to low- and high-level syntax. Imaging studies indicate that processing of low-level syntax activates a ventral network comprised of the frontal operculum and anterior temporal cortex  while co‘String parsing’ has been proposed as the mechanism by which ‘program-level imitation’ of behavior leads to the acquisition of hierarchically complex syntax . It alsoThe large individual differences in alignment scores found in the score-dependent group suggest that practice strategies in classical music might not be as uniform as one would think. It would seem that practice methods fostering implicit, non-conscious audiomotor learning are actually employed by a minority of score-dependent musicians and can be said to have a beneficial effect on the implicit acquisition of hierarchical music syntax. Although improvisation, like immersion in language acquisition , is a feTreble alignment scores for replication were significantly higher than for transposition in both groups, but only for exact pitch, suggesting that transposition may not be just another form of pitch replication. Recent studies have implicated the right intraparietal sulcus (IPS) in transposition , retrogrThe observation that treble alignment scores were significantly higher than bass alignment scores, despite the use of two-part polyphonic excerpts, corroborates the high-voice superiority effect found in both behavioral and neural studies. A higher-pitch advantage for melody recognition was found in infants  as well The high-voice superiority effect has been shown to be subject to neuroplasticity. MMN caused by pitch deviants in the bass has been found to be equal (but not larger) to that elicited by the treble voice in double bass players . In addiAn advantage of aural feedback was observed for the replication of treble IOR, but only for improvising musicians. At first sight, this might seem to conflict with studies that have demonstrated that musicians are largely independent of aural feedback . PerformWhile musicians are quite able to perform without aural feedback, asynchronously altered feedback (AAF) may compromise performance during both singing and playing. Delaying feedback until the next tone is being sung or played , however, compromises performance in singers but not in score-dependent pianists . While tThe present study has found behavioral evidence for superior audiomotor transformation during the replication and particularly the transposition of aurally perceived music in improvising musicians. These results are consistent with the associated fMRI study , providiS1 Alignment Scores(ZIP)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S1 Research Data(ZIP)Click here for additional data file.S1 Transcriptions(PDF)Click here for additional data file.


Since the last major release of MUMmer version 3 in 2004, it has been applied to many types of problems including aligning whole genome sequences, aligning reads to a reference genome, and comparing different assemblies of the same genome. Despite its broad utility, MUMmer3 has limitations that can make it difficult to use for large genomes and for the very large sequence data sets that are common today. In this paper we describe MUMmer4, a substantially improved version of MUMmer that addresses genome size constraints by changing the 32-bit suffix tree data structure at the core of MUMmer to a 48-bit suffix array, and that offers improved speed through parallel processing of input query sequences. With a theoretical limit on the input size of 141Tbp, MUMmer4 can now work with input sequences of any biologically realistic length. We show that as a result of these enhancements, the nucmer program in MUMmer4 is easily able to handle alignments of large genomes; we illustrate this with an alignment of the human and chimpanzee genomes, which allows us to compute that the two species are 98% identical across 96% of their length. With the enhancements described here, MUMmer4 can also be used to efficiently align reads to reference genomes, although it is less sensitive and accurate than the dedicated read aligners. The nucmer aligner in MUMmer4 can now be called from scripting languages such as Perl, Python and Ruby. These improvements make MUMer4 one the most versatile genome alignment packages available.The MUMmer system and the genome sequence aligner  PLOS Computational Biology Software paper.This is a Since the 2004 publication of the MUMmer3 sequence alignment package , the bioe.g., BWA and Bowtie2 are best suited for aligning large numbers of relatively short sequences (50-300 bp) to a reference genome; and BLASR is designed to align long high-error-rate (15-20%) sequences to a reference. MUMmer and its sequence alignment component nucmer were originally developed [ad hoc parallelization is both inefficient and inconvenient, requiring additional steps to combine and process the resulting multiple outputs.Many DNA and protein sequence alignment software packages are available today, including BLAST , Bowtie eveloped  for alignucmer sequence aligner that is part of MUMmer4, and most of our discussion will focus on these. Although the executables in both MUMmer3 and MUMmer4 are called “nucmer”, for clarity we will use nucmer3 to refer to the nucmer program in MUMmer3, and nucmer4 to refer to the MUMmer4 version. Unlike nucmer3, which has limits of ∼500 Mb on the reference sequence size and ∼4 Gb on the query sequence size, nucmer4 can process sequences of essentially any size, with a theoretical limit of 141 trillion bases (Tbp), due to its use of a new 48-bit long suffix array instead of a 32-bit suffix tree. It is unlikely that this limit, which is 1000 times larger than the largest known genome, will be exceeded in any current practical scenario. Furthermore, nucmer4 can use multiple cores on the same computer, resulting in much faster run times. In addition, while nucmer3 computes the suffix tree on the fly–at the time the program is invoked–and discards it after each use, nucmer4 offers a more efficient two-step mode of operation, similar to short read aligners, where the suffix array of the reference can be created and saved to disk, and then loaded repeatedly to align sets of query sequences. Nucmer4 produces output that is identical to nucmer3, except for the order in which the alignments are output. In the results below, we demonstrate nucmer4’s improvements in speed by comparing it to nucmer3 and to other aligners, showing that this multi-purpose sequence aligner compares favorably even with aligners that have been optimized to handle more specialized alignment tasks.In this paper, we describe MUMmer4, a major new release that has been re-engineered and extended based on MUMmer3, providing full backward compatibility of inputs, outputs and options. The biggest changes affect the The input and output formats of nucmer4 have also been changed to make it compatible  with software pipelines that are designed to operate on next-generation sequencing (NGS) reads. On the input side, nucmer4 now accepts both FASTA and FASTQ sequence formats. On the output side, nucmer3 only provided output in a custom format known as a delta file, which can be used by other MUMmer utilities to produce alignments, SNP reports, and plots. Nucmer4 can now produce either a delta file or a SAM file, the most common output format for short read aligners. SAM output is compatible with many other packages, including SAMtools .nucmer Perl program: (1) preprocessing of the input sequences (the prenuc program), (2) finding exact seed matches (the mummer program), (3) clustering of matches (the mgaps program), and (4) extending and joining matches (the postnuc program).The main pipeline of MUMmer is NUCmer, to align nucleotide sequences. The NUCmer pipeline consists of 4 main programs driven by the prenuc program transforms the multi-fasta file of the reference sequence into a valid format for the mummer program: a single string using x characters as a separators.The mummer program finds exact matches between pair of sequences that are maximal in length and at l bases long. By default, mummer finds all matches between a query sequence and a reference sequence that are unique in the reference, called Maximal Unique Matches (MUMs). A command-line option allows mummer to instead find all matches that are unique in both the query and the reference, or to find all Maximal Exact Matches (MEMs) regardless of uniqueness. The “MEM” option should be used with care, as it can produce very large amounts of output when the input sequences contain numerous repeats. In MUMmer3 and earlier versions, this exact match step is accomplished using a suffix tree data structure [The tructure .mgaps program which groups the exact matches into clusters based on their proximity to one another. In a typical alignment run, many of these exact matches will become part of longer inexact matches, which will be computed in the next step. mgaps writes its output to a file.The MUMs or MEMs are then piped into the postnuc program computes longer alignments from the clustered exact matches. It first re-reads the reference sequence to record the names as well as the beginning and ending positions of the original reference subsequences in S.Finally, the postnuc program uses a banded Smith-Waterman alignment algorithm [S back to positions in the reference subsequences.Then the lgorithm  to find If this implementation has the advantage of being modular, it suffers from a number of inefficiencies and limitations: (1) the reference sequence is read twice, which creates more I/O, duplicated work, and prevents streaming in the reference; (2) multiple intermediary steps write their results to disk, creating extra I/O; (3) the pipeline is hard to use from another program, written in C or script language.prenuc and postnuc, parsing the reference sequence only once, thus allowing it to take input from a pipe. It also avoids the use of the file system or pipes to pass information between stages by instead passing C++ objects representing the matches and clusters. This re-design not only provides speed advantages, but it makes it far easier to incorporate nucmer4 into other software packages.We have designed the nucmer4 program to overcome many of the limitations of the nucmer3 pipeline. It avoids the redundant work done by both libumdmummer. Each step in the original nucmer3 pipeline is now represented by a C++ class. Although the individual programs from the pipeline are still available in the MUMmer4 software distribution, the nucmer program is now a unified, multi-threaded C++ binary that uses the MUMmer library.In nucmer4, we implemented the primary computational steps of each component program into a C++ library, named essaMEM program [The biggest internal change to MUMmer4 is the use of a suffix array instead of a suffix tree to index the reference sequences. Although both suffix trees and suffix arrays require a linear amount of memory to represent the reference sequences, the constant factors are smaller with (carefully implemented) suffix arrays, reducing the total memory requirement. To make the switch, we utilized the  program , which wqsufsort [essaMEM by the faster library divsufsort [postnuc in nucmer3) for its single query sequence.In addition, we replaced the qsufsort  suffix avsufsort . After bstdout, again using a lock-free FIFO queue to synchronize writing by the different threads. The default output appears in delta format and is designed to be identical to the output of Nucmer3. Because of the multi-threaded operation, the order of the sequences in the output file is not strictly preserved, and may be different from run to run. Thus, although the alignments of nucmer4 and nucmer3 are identical, the order in which they appear in the output files might differ.The results are output to disk or to e.g., aligning a large set of reads to the human genome. In MUMmer4, we introduce parallelism at the level of these query sequences, allowing many of them to be aligned simultaneously against a reference. When aligning large numbers of query sequences, nucmer can achieve a high level of concurrency, limited only by the number of processing cores. On the other hand, if the query contains a small number of large sequences , the concurrency is limited by the number of sequences in the query. In the limiting case of aligning a single query to the reference, query parallelism provides no benefit.One of the most common alignment tasks today is to align many sequences against a reference genome; i.e., the length of the concatenated string S—to ∼536 Mb. This is large enough for all bacterial genomes and for some small eukaryotes, such as Drosophila melanogaster; however, it is not sufficient for larger plant and animal genomes, which often exceed 1 Gb and can be as large as 32 Gb as seen, for example, in the sugar pine, Pinus lambertiana [32 − 1 ≈ 4 Gb.One of the built-in limitations in MUMmer3 is a strict upper bound on the lengths of the input sequences. The implementation limits the total reference sequence—bertiana . MUMmer347 − 1 ≈ 141 Tb. The available computer hardware provides a more practical limit; MUMmer4 uses 15 bytes per base of the reference sequence to store the index. Thus for example, if the reference sequence were 66 Gb , the suffix array would require about 1Tb of computer RAM. To accommodate computers with limited RAM, MUMmer4 includes a new “--batch X” option that automatically splits long reference sequences and processes them in smaller batches. Specifically, it loads X bases of reference sequence, matches all query sequences against the first batch, then loads the next X bases of reference and so on. In practice this eliminates any restrictions on the size of the input reference sequence and also allows the user to tune the alignment runs to the total available computer memory. MUMmer4 has no absolute limit on the total query sequence size. Also, as a minor improvement, MUMmer4 removes the previous 128-character limit on the length of the names of reference and query sequences.MUMmer4 significantly raises these size limits, making it possible to align even the largest genomes. The length of the reference sequence for each alignment job is limited in principle to 2e.g., the human genome, the mouse genome, and other model organisms—are essentially static, with periodic updates and controlled versions. Thus there is no need to build the suffix array on the fly  if one intends to use the same reference repeatedly, e.g., to align Illumina data to a human reference genome. Using the new MUMmer4 option, the suffix array can be built once, saved and then loaded for each run.MUMmer4 now includes options to save and load the suffix array for a given reference. The most popular systems for aligning reads to a reference genome, Bowtie2  and BWA For a large reference genome, MUMmer4 uses approximately 13-14 bytes/base for the index . For example, the size of the suffix array for the human genome is approximately 39 GB. Loading the suffix array from memory is much faster than constructing it from scratch, and on a typical server with 200 MB/sec input speed, a 39 GB suffix array will load in approximately 3 minutes.Nucmer4 requires additional memory in scenarios when running with multiple threads on query sequences that are large. Our parallelization routine distributes multiple query sequences into multiple threads, one sequence per thread, and query sequences have to be loaded into memory. The step of loading multiple query sequences into memory at the same time increases peak memory usage in such scenarios, proportional to the number of threads used. With only one thread, memory usage is similar between nucmer3 and nucmer4.nucmer, the delta format, contains only the minimum information necessary to quickly recreate the alignment. It contains the name of the matching sequences, the length of the match, number of errors and positions of indels. An important addition in MUMmer4 is option to produce output in SAM format, one of the most widely used formats for alignments of NGS data [--sam-short, nucmer4 reports only the name of the matching sequence, length, and CIGAR string (which reports the indel positions). With --sam-long, it additionally reports the MD string (which specifies the mismatching positions), the sequence and, if applicable, the quality values of the matching sequence. The long format is more expensive to compute and it generates larger output files, but this option allows nucmer4 to match the behavior of other aligners such as Bowtie2 or BWA.The original output format of NGS data . This allibumdmummer to instantiate multiple aligner objects concurrently, for example in a multi-threaded program. The new library is also usable from the scripting languages Python, Perl or Ruby, making MUMmer4 much more flexible. We used the SWIG [We transformed the global variables in the original code to object instance variables. As a result, it is possible for an application using the SWIG  tool to The enhancements in MUMmer4 allow it to align (1) a pair of genomes to each other, (2) two large sets of sequences to one another, or (3) a set of reads to a reference genome. This functionality stands in contrast to specialized NGS alignment tools such as Bowtie2, BWA or BLASR, which are designed for aligning reads to a reference genome. BWA and Bowtie2 are designed primarily for alignment of short Illumina sequences, while BLASR is designed for alignment of long high-error-rate sequences, such as those produced by the Pacific Biosciences SMRT and Oxford Nanopore MinION technologies. MUMmer4’s suffix array data structure ensures that alignment time is a linear function of the read length and is independent of the size of the reference genome, similar to NGS aligners that use the Burrows-Wheeler transform as their principal data structure.Although specialized read aligners are more accurate and sensitive (and thus likely preferable) for that task, we show below that when run with default settings, the speed of MUMmer4 is comparable to Bowtie2, BWA and BLASR for the alignment of both short low-error-rate (Illumina) and long high-error-read (PacBio) sequences to a reference genome. MUMmer4 is much faster than Mauve  and LASTWe also show substantial improvements in speed and versatility compared to the MUMmer3 package. In the Supplementary material, we report all settings and command line parameters used for generating the results shown here. All timings were measured on a dual-CPU, 32-core AMD Opteron 6276 computer with 256 GB of DDR3 PC3-12800 RAM.Arabidopsis thaliana Col-0 reference genome [For the comparisons shown here, we used two different organisms as reference genomes. For mapping reads to a genome, we used the e genome  and the e genome . We remoPan troglodytes) genome [Arabidopsis lyrata assembly 1.0 [A. thaliana reference. Below we describe MUMmer4’s performance statistics on these tasks as well as summaries of the resulting alignments.As a test of MUMmer’s performance on aligning large genomes, we aligned the reference assemblies of the human genome and the chimpanzee  genome  MUMmer had 2.782 Gb of the sequence in mutual best alignments, where each location in the chimp was aligned to its best hit in human and vice versa, with an average identity of 98.07%. The 1.93% nucleotide-level divergence found here is higher than the 1.23% reported in the original chimpanzee genome paper . Our higA. lyrata genome to the 120 Mb A. thaliana reference genome with nucmer4 took 3m43s on our test system with default parameters using 32 threads. The same alignment performed with nucmer3—which is single threaded— took 17m32s, about 4.7 times slower. These times include the time taken to build the suffix tree or array. We checked the alignments and they were identical between the two programs, as expected. The alignment determined that there are 68.3 Mb in 48,852 1-to-1 best alignments between these two genome sequences with average identity of 88.2%. Overall there are 83 Mb of sequence in 70,044 many-to-many alignments with the same average identity of 88.2%.The alignment of the 207 Mb Hypsibius dujardini). The first study described a 212 Mb assembly (here designed Hd-Boothby) and used the results to argue for extensive horizontal gene transfer from unrelated species [dnadiff program, which is part of the MUMmer package , a new feature in MUMmer4. The suffix array is considerably larger than the compressed Burrows-Wheeler transform used by Bowtie2 and BWA, but the alignment speed is comparable and sometimes faster.Arabidopsis thaliana genome. Timings were measured on the 32-core AMD Opteron system mentioned above. Default parameters were used for most alignments, except that “-x pacbio” was used for BWA mem with PacBio data (as instructed by the BWA-MEM documentation), and “-l 15 -c 31” was used for nucmer4. We recommend these modifications to the default nucmer4 parameters for long reads with error rates above 10%.--maxmatch” switch at the expense of run time. Nucmer4 also has marginally higher false alignment rate. The sensitivity numbers are consistent with the results on real data.To align 481,000 PacBio reads to the Arabidopsis genome, nucmer4 took 24 minutes versus 49 minutes for BWA-MEM and 95 minutes for BLASR , althougFor aligning Illumina reads to Arabidopsis , nucmer4The creation of a Burrows-Wheeler index or a suffix array is costly for a large genome such as the 3 Gb human genome. Thus it is common practice to precompute the index and simply load it into memory at the time of alignment. For alignments of reads to the human genome, therefore, we report separately the time to create the index and to align the reads.--maxmatch” switch, which will force nucmer4 to use all seeds at the cost of longer run times.http://mummer4.github.io.The MUMmer4 software including source code is available under an open source license at In this paper we described MUMmer4, the successor to MUMmer3, a versatile and efficient genome alignment system. Nucmer4, the primary DNA sequence aligner in the MUMmer4 package, can be used for a variety of tasks ranging from simple alignment of two genome sequences to alignment of large, complex draft genomes with thousands of contigs. With the performance enhancements in this new system, Nucmer4 can align two mammalian genomes in about three hours on a 32-core server; we illustrated this by using it to compute that almost 90% of the human genome is about 98% identical to the chimpanzee genome.Our future plans include support and maintenance of the software and improvements to other useful parts of MUMmer such as Promer, the protein sequence aligner.S1 File(DOCX)Click here for additional data file.


High-accuracy alignment of sequences with disease information contributes to disease treatment and prevention. The results of multiple sequence alignment depend on the parameters of the objective function, including gap open penalties (GOP), gap extension penalties (GEP), and substitution matrix (SM). Firstly, the theory parameter formulas relating to GOP, GAP, and SM are inferred, combining unaligned sequence length, number, and identity. Secondly, we tested the rationality of the theory parameter formulas, with experiment on the ClustalW and MAFFT program. In addition, we obtained a group of MAFFT program parameters according to the formulas proposed. The results of all experiments show that the SPS (sum-of-pair score) obtained from theory parameters is better than the SPS obtained from the default parameters of ClustalW and MAFFT. In both theory and practice, our method to determine the parameters is feasible and efficient. These can provide high-accuracy alignment results for precision medicine. In 2015, US President Barack Obama stated his intention to fund a United States national “Precision Medicine Initiative” . The number of gaps inserting the shortest sequence is numgap = lenalign − lenmin = 25 − 7 = 18, and the number of gaps in sequence is numgap ≤ int[0.2 · lenmax] + lenmax − lenmin. The number of gaps in other sequences is numgap ≤ int[0.2 · lenmax] + lenmax − lenmin.The following parameter formulas are inferred according to information obtained from According to , the SP .That is,mm−12·lenquation  · numgap and NGEP = m · (1 − 1/λ) · numgap. Because GOP > GEP, we accept that GOP = n · GEP, where λ, n is the positive integer, soBased on the affine penalty, numteger, so∑penalty=rding to  can be eding to /2) · lenmin · iden, numgap = int(0.2 · lenmax) + lenmax − lenmin, and int is a rounding function. lenmin is the length of the shortest sequence in the unaligned sets, and iden is the mean identity of unaligned sets.If the upper limit of GOP is multiplied by weight coefficient λ, n, ω, α, and β in (The estimation formula of GEP isand β in  and 15)(15)GEP=GIn order to test the rationality of the parameter formulas and determine the optimal value of each weight coefficient, we designed the following experiments on the BAliBASE 2.0 and BAliBASE 3.0.BAliBASE version 2.0  is an imSPS (sum-of-pair score) works as an objective function, which can determine score increases if sequences are correctly aligned. If the SPS is higher, the results of alignment are close to the reference alignment and can be even better than the reference alignment . To testIn our experiment, 1 ≤ GOP ≤ 20,0 ≤ GEP ≤ GOP/2. The GOP step is 1, the GEP step is 0.2, and the substitution matrices are BLOSUM30, BLOSUM45, and BLOSUM62. For each group of sequences, through batch processing, the number of alignment results is 1,590 because there are 1,590 different combined parameter patterns.This section shows how the rationality of the substitution matrix was established (see ).  FigurBased on the SPS and MAFFT program (MAFFT-7.220-WIN64 version), we tested the rationality of  and 15)15). The λ = 3, α = 0.2, β = 0.9, and n = 5, the number of sequences is greater than λ = 3, α = 0.2, β = 0.9, and n = 10. The best result is indicated in Blosum45, num 19, with an SPS of 0.8003  + lenmax − lenmin. The number of GOP is limited and it will not increase too much, while the distribution of GEP is more concentrated. These parameters are more consistent with the biological characteristics of multiple sequence alignment.From the aforementioned experiments, we can determine the substitution matrix and and ω in . The othOptimal parameters and the SPS value are listed in This paper clearly shows that the parameters of MSA tools influence MSA results. These parameters not only include substitution matrices, GOP, and GEP but also include the length, number, and identity of sequences. Our goal was to find a group of combined optimal parameters. Based on the SP function, we established a series of formulas which can determine the value of substitution, GOP, and GEP. In order to test the rationality of the formulas, our experiments were conducted in the MAFFT program base or in the BAliBASE 2.0 and BAliBASE 3.0 (RV11 and RV12) database. Moreover, we obtained the optimal value of the substitution matrices, GOP and GEP, and these values proved to be better than the default values of the MAFFT program. After the theory analysis and experimental analysis, we can conclude that the proposed method can effectively solve the MSA parameter problems and improve MSA accuracy, which can provide more accuracy information for precision medicine in disease analysis and prediction.


These servers make calculations simpler, but largely exclude users from the process. In this study, we present the PRotein Interactive MOdeling (PRIMO) pipeline for homology modeling of protein monomers. The pipeline eases the multi-step modeling process, and reduces the workload required by the user, while still allowing engagement from the user during every step. Default parameters are given for each step, which can either be modified or supplemented with additional external input. PRIMO has been designed for users of varying levels of experience with homology modeling. The pipeline incorporates a user-friendly interface that makes it easy to alter parameters used during modeling. During each stage of the modeling process, the site provides suggestions for novice users to improve the quality of their models. PRIMO provides functionality that allows users to also model ligands and ions in complex with their protein targets. Herein, we assess the accuracy of the fully automated capabilities of the server, including a comparative analysis of the available alignment programs, as well as of the refinement levels used during modeling. The tests presented here demonstrate the reliability of the PRIMO server when producing a large number of protein models. While PRIMO does focus on user involvement in the homology modeling process, the results indicate that in the presence of suitable templates, good quality models can be produced even without user intervention. This gives an idea of the base level accuracy of PRIMO, which users can improve upon by adjusting parameters in their modeling runs. The accuracy of PRIMO’s automated scripts is being continuously evaluated by the CAMEO  project. The PRIMO site is free for non-commercial use and can be accessed at  Studying the three-dimensional 3D) structure of a protein is crucial to gaining insights into its function, which is one of the driving principles behind structural biology  and struD structuin silico prediction of protein structures has become an important means of studying proteins in spite of this sequence-structure gap [ab initio techniques [In the absence of experimental data, ture gap . There achniques –8. In thchniques ,9–11.Homology modeling involves predicting the structure of a protein using homologous protein(s), with known structure(s), as template(s). This approach works due to the principle that the structure of a protein is far more conserved than its primary amino acid sequence . The mosWhile the software for MODELLER can be downloaded and used locally for protein structure prediction, there are many users with little to no experience in structural bioinformatics who rather turn to one of many automated modeling servers. Examples of these include the automated server, ModWeb , SWISS-Mab initio modeling and 3DLigandSite [ModWeb is a modeling server that forms part of ModBase . It is agandSite  to prediWhile automated servers are useful, they do exclude users from the modeling process. This lack of engagement limits their understanding of what is happening in the background, making it difficult to critically evaluate their models, as well as make alterations and improvements. An example of a server which does allow for this is the HHpred homology detection server , which pab initio techniques as part of its template-based modeling protocol. While this server is well suited to challenging targets it is not ideal for more standard modeling jobs as it can be time consuming, often limiting users to a single modeling run at a time, spanning over a number of days.Apart from homology modeling servers, some other techniques and servers have also been developed which work well especially on more challenging protein targets, i.e. those with no homologous templates of known structure. The most successful of these servers is probably I-TASSER  which inConsidering all the above points, we have developed the PRotein Interactive MOdeling (PRIMO) pipeline to provide a user-inclusive online modeling resource. It incorporates a user-friendly interface that has been designed to guide users through each stage in the homology modeling process. Keeping novice users in mind, the interface is simple and easy to learn, while allowing more experienced users to alter parameters and exhibit control over their modeling jobs. Multiple options are provided for both template identification and template-target sequence alignment. Additionally, PRIMO allows users to alter parameters specific to MODELLER, such as refinement level and number of models produced, as well as allowing users to model specific ligands and ions found within template PDB files.PRIMO is being developed as part of H3ABioNet  for use Here we describe the features of the PRIMO web interface and assess the backend scripts of PRIMO to demonstrate the accuracy of the pipeline when choosing fully automated options for modeling protein targets of interest.The backend functionality of PRIMO was written in Python, presented as three separate tools in a local version of the Job Management System (JMS) . The PRIThe PRIMO modeling algorithm is displayed in ftp://ftp.ncbi.nlm.nih.gov/blast/db. Output from BLAST is parsed to extract information about each template, including the PDB ID and chain, template-target sequence identity, query coverage and the alignment produced when running BLAST. Alternatively, HHsearch can be run if distant homologs need to be identified. This option incorporates various programs from the HHsuite package. HHblitz is run to search the target sequence against the HHsuite uniprot20 database. Secondary structure is added to the A3M alignment, an alignment format generated by HHblitz and used by HHsearch, before converting it to hidden Markov model using HHmake. HHsearch is then used to search against the HHsuite pdb70 database to identify templates. The resulting hhr file is parsed to extract the same information obtained if BLAST was run.This step involves helping the user find suitable templates for modeling. PRIMO allows users to select either HHsearch of HHsuite or protein BLAST to search for templates. BLAST is set as a default search option as it runs substantially faster than HHsearch, and identifies closely related templates if any are present in the PDB. A local version of BLAST is used to query the target sequence against a National Center for Biotechnology Information (NCBI) database of PDB files downloaded from For each template selected, the PDB file is parsed to extract its sequence. Both missing residues and non-standard amino acids are replaced with an “X” character, so this information may be included in the alignment. PRIMO performs the alignment using MAFFT, MUSCLE, Clustal Omega or T-Coffee. The template-target alignment provided by protein BLAST or HHsearch may also be used if one of these was run for template identification.The final step in the modeling process involves using the target-template sequence alignment and the template PDB file(s) to generate a PIR file and modeling script. The alignment undergoes some preprocessing before being converted to PIR format. Primarily this involves replacing the missing residue characters with gap characters (“-“) and modified residues with period (“.”) characters, since this is how MODELLER recognizes modified residues. The sequences also undergo trimming at each end to ensure that the parts of the target sequence being modeled have a corresponding template section at each end. Finally, each template sequence is checked against the sequence extracted from its PDB file to ensure that it is correct. The starting and ending residues in each template, which is required by MODELLER is also determined, and added to the PIR file. The PIR file is required by MODELLER to link the template-target alignment to the specific segments of each template PDB file used in modeling. Once the PIR file has been created, the modeling script is prepared, then run using MODELLER. After modeling has completed, the models are evaluated by MODELLER’s normalized DOPE function (DOPE Z-score) , as wellIf ligands are specified for modeling, an additional set of steps is taken to prepare the PIR file before modeling can begin. In this context, “ligands” include any HETATM record found in the template PDB, excluding non-standard amino acids; for example substrates, ions, inhibitors and solvent molecules. All ligands specified are identified within their respective template PDB file. The position of the ligand that occurs last in the coordinate section is noted and becomes the ending residue for that template in the PIR file. All residues and ligands that occur up to this position are then appended to the template entry in the original PIR file. In the target sequence, gap characters are added to match the length of the template. Gaps are then replaced with period characters to match the positions of ligand molecules of interest that occur within the template, since MODELLER recognizes these characters as ligands as well. In addition to PIR file modifications, additional parameters are given to the modeling script to instruct MODELLER to read in ligands or solvent molecules where applicable.http://msa.biojs.net/) is a JavaScript component used to visualize multiple sequence alignments. Although useful in their own right, the need to view a structure in conjunction with its sequence often arises in bioinformatics. In addition, these tools can be difficult to use as their application programming interfaces (API) are fairly unintuitive. To cater for this, we have developed PV-MSA, a wrapper that combines the functionality of PV and MSA in a single JavaScript plugin. PV-MSA also provides a simplified API that makes a fair amount of the functionality of both PV and MSA available. For functionality that has not been included yet, PV-MSA provides direct access to the underlying PV and MSA objects.PV  is a widOver and above simply wrapping the two plugins, PV-MSA links the selection functionality. For example, a user can select a residue in the protein structure and it will automatically be highlighted in the alignment. The alignment is automatically scrolled to the selected position. Similarly, if a residue is selected in the alignment, its location is highlighted on the corresponding structure. PV-MSA also allows structures to be superposed. In such cases, selecting a residue in one structure will also highlight the aligned residue in the superposed structure. This selection is based on the alignment, and as such, gaps and missing residues are taken into account.https://github.com/davidbrownza/PV-MSA.Multiple structures and their sequences can be loaded into the plugin at once and structures and sequences can be hidden and shown independently. PV-MSA also allows users to visualize and select ligands and ions in a structure. Selecting a ligand displays a label over the ligand with the ligand name. Functionality has also been included to resize both the PV and MSA plugins together and independently as the user needs. The PV-MSA plugin can be downloaded from In order to evaluate the performance and reliability of the PRIMO modeling scripts, tests were run which involved modeling target proteins from the PDB with known structures. The process followed to test the PRIMO scripts is shown in For each entry in each bin, the targets were aligned to the templates using the four sequence alignment programs provided by PRIMO, as well as the HHsearch alignment calculated during template identification.For MAFFT, a script was written to mimic the MAFFT-homologs alignment option. This firstly runs a local version of protein BLAST  on both For each alignment produced, modeling jobs were run using MODELLER, producing 10 models per run using very slow refinement. Due to sequence trimming of the PIR preparation step, not all models from the same target-template set were the same size when modeled using different alignment options. To normalize the models, the PDB files in each modeling set were trimmed to the longest common segment of all models in that set.Models also went through a series of filtering steps . After phttp://zhanglab.ccmb.med.umich.edu/TM-score/). Software to calculate lDDT score was downloaded from http://swissmodel.expasy.org/lddt.After filtering, the models were evaluated. DOPE Z-score calculations were performed on each model produced, to select the top model from each set. The top model and the target PDB structure were then compared by calculating RMSD, Global distance test–high accuracy (GDT-HA) score, template modeling (TM) score  and LocaA PDB remodel set was also produced and evaluated for each target. Each of the targets was modeled using its own PDB structure as a template, representing ideal modeling conditions and giving an indication of the error produced by MODELLER itself.In addition to testing the different alignment options provided by PRIMO, some tests were performed to evaluate the different refinement options provided when modeling using MODELLER. These were done using the MAFFT modeling set, with the same PIR files as calculated for the MAFFT alignments. These were also only done using the final set of models evaluated after filtration steps were carried out. The only parameter altered was the refinement level option in the modeling script. The additional refinement levels tested included none and fast. These were compared with the very slow option used in the alignment studies. Models were evaluated by DOPE Z-score and RMSD, as in the other tests.Plasmodium falciparum  as monomer and X-linked tyrosine kinase from Homo sapiens , modeling with ligands. Online modeling servers tested included SWISS-MODEL [In order to demonstrate the performance of PRIMO when compared to other modeling options, two case studies were performed. These included the modeling of heat shock protein 70-x from SS-MODEL , Phyre2 SS-MODEL , ModWeb SS-MODEL , HHpred SS-MODEL  and I-TASS-MODEL . SWISS-MSS-MODEL , Verify3SS-MODEL ,39, PROCSS-MODEL , the QMESS-MODEL  and DOPEhttp://cameo3d.org/.As an additional validation step, PRIMO has been registered to participate in the CAMEO  project . CAMEO pThe PRIMO website acts as a frontend to link users to the modeling scripts integrated into the JMS . The iniThis provides an overview of the modeling job. Users can simply enter in a target sequence and begin the modeling process. PRIMO utilizes MODELLER , so userIf automatic template identification is run, the templates identified are displayed, including information about sequence identity and query coverage. Templates can be selected through simple check boxes to be included in the target-template alignment stage. Users can also click on the ID of any template, which links directly to its entry in the PDB, allowing users to further assess the quality of each template. The templates can be individually selected and displayed to assess their conformations for multiple-template modeling. The alignment produced by HHsearch or BLAST (whichever was run) is also displayed in order for the user to assess the suitability of each template as well as inspect query coverage. The interface also provides options to allow the modeling of ligands found within any of the templates. A drop down list appears for each template returned, which details ligands that can be included in the modeling run.Sequences are extracted from the templates and aligned to the target sequence, using the alignment option selected. The alignment is displayed in an integrated alignment viewer and can be inspected and edited manually by the user before moving on to the modeling stage. The alignment editor validates changes that the user makes in order to prevent edits, which would cause the modeling stage to fail. In template sequences, gaps can be added anywhere, but the sequences can only be trimmed from the outsides. If the edited sequence cannot be found within the original sequence (excluding gaps), it is invalid. The target sequence can be edited in just about anyway, so long as valid characters for amino acids and gaps (‘-‘) are used.The sequence alignment is utilized to prepare a PIR file, which is used by MODELLER. Modeling is performed using the parameters specified in the input page and the models are assessed by DOPE Z-score calculations. The top models are listed and can be visualized using the integrated PV-MSA PDB viewer provided . AdditioJobs are linked to the users’ accounts, which comprise an instant sign-in. Users can navigate to previous jobs run, as well as to different stages in their current jobs, alter parameters and rerun jobs. Email notifications can also be turned on to notify users when a job is complete or requires attention.PRIMO makes use of a unique system to submit jobs to the underlying cluster . JMS 2323 has beWhile the PRIMO website was designed to promote user involvement during each step in the homology modeling process, the backend scripts are capable of performing fully automated modeling. Here we present the accuracy of PRIMO, when no user intervention occurs during the modeling process.To assess the tools and algorithms incorporated into PRIMO, an evaluation study was performed by modeling proteins with known structure from the PDB, using templates ranging from 20% to 90% sequence identity, as well as using five different alignment approaches. After modeling and filtering as explained in the Methods section, the final set included 5 869 modeled targets, comprising 293 450 models, to be evaluated.Due to the scale of the models produced, evaluations were performed using MODELLER’s DOPE Z-score, the results of which are shown in The PDB structures of both the template and target PDBs were included in the DOPE Z-score evaluations, in order to get an idea of the quality of these structures. Similarly, each target was remodeled using itself as a template to represent modeling under ideal conditions (100% target-template sequence identity). These remodeled targets never matched the quality scores of either the templates or target PDB files. The models in the 80–90% sequence identity bin were the only set that on average matched the quality of the remodeled targets.The reason for modeling protein targets from the PDB was to be able to evaluate the models produced, by comparing them to known structures. This was done by assessing the RMSD of these structures . One of In all instances, a similar trend was observed to that shown in the DOPE Z-score assessments. This was not entirely surprising since low DOPE Z-scores (below -0.5) have been previously shown to correspond to lower RMSD values . At loweAn alternative RMSD measure was also considered by calculating the RMSD value between the template PDB and target PDB, and then subtracting this from the values shown in To account for the limitations of calculating RMSD scores, three additional scores were calculated to compare the top models to their respective protein targets. These included two global scores, TM score  and GDT-http://cameo3d.org/. Four different modeling options were registered to demonstrate results of using different template identification and alignment approaches, without adding too much additional strain to the PRIMO server. The scores for models produced by PRIMO are comparable to other published servers, such as Phyre2 [PRIMO has also been registered to participate in the CAMEO project , which as Phyre2 , and areAn additional set of tests were run to quantify the effect of using MODELLER’s different refinement options. The very slow refinement option was selected for the benchmark tests. These results were then supplemented with results using refinement level during modeling set to none and fast . When coOverall, the benchmark results observed are promising, especially since the PRIMO site was designed with user intervention in mind. By altering parameters, such as using more than one template, manually editing the alignment and increasing the number of models produced, users could easily improve on the results reported here by interacting with the PRIMO pipeline.To demonstrate the potential ways to use PRIMO, we designed two simple case studies which involved modeling PfHsp70-x and HsTXK proteins.PfHsp70-x is by no means a challenging target to model and can be considered as a typical protein users might model when using PRIMO. There are templates available with good sequence coverage and sequence identity, making this protein ideal for homology modeling. One of the interesting properties of PfHsp70-x is that, as an Hsp70, it takes on different structural conformations in its different functional states. The PDB contains several structures capturing the different conformations of Hsp70. Thus, homologs of this protein from other organisms can be modeled in these different conformations. This showcases one of the important features of PRIMO; namely the template viewer, which allows users to select and view the conformations of different templates in a similar manner seen when using SWISS-MODEL. This is important because the top models in this case study produced using PRIMO involved multiple template modeling, which should not be done with template structures in different conformations.The full set of evaluations is summarized in As part of this case study, we used other online servers to model PfHsp70-x. Our comparison was against the automated features of these servers, but it should be noted that only SWISS-MODEL and HHpred provided a template selection option. Of these, only the SWISS-MODEL interface gave a clear indication of template conformation, which is as important consideration when modeling Hsp70s. As an alignment editing option, HHpred provided a text editor displaying the PIR file to be used by MODELLER. This was nice feature as it gives an indication of PIR file format in addition to allowing users to edit the alignment. It does however, require the user to trim the sequences manually before submitting the job for modeling, which only becomes apparent after the model is returned. The other servers assessed were fully automated, providing to no options beyond the initial input screen. When considering the model evaluation results in Modeling HsTXK. This was a more challenging target to model, as reflected in the evaluation scores These two case studies were not meant to be a comprehensive assessment of PRIMO compared to other modeling servers, but it was encouraging to see that with this target at least, PRIMO performed well against the other servers assessed for most of the evaluation tools used .As a modeling tool, PRIMO aims to provide a middle ground between the lack of control caused by full automation and the difficulty and tedious nature of writing scripts and using modeling programs though the command line. The site can identify templates using both HHsearch and BLAST, perform sequence alignments with one of five different alignment options, and perform homology modeling using MODELLER. PRIMO incorporates a job history system which allows quick and easy navigation among the different steps of a specific modeling job, as well as navigation between different jobs. With this, users can perform several modeling jobs in parallel, while also being able to go back and alter modeling parameters to achieve the best results.The PRIMO pipeline allows users of varying levels of experience to perform homology modeling interactively and reliably. While this ‘hands on’ approach to modeling is largely encouraged, we still aim to ensure that the automated modeling features of this pipeline are as accurate as possible. The accuracy tests reported here demonstrate that the automation of these algorithms can be done with promising accuracy down to 40% sequence identity, which is where comparative modeling is known to reach its limits . The accAs a web interface, PRIMO is platform independent and requires no personal computing power. The site currently provides a means for modeling protein monomers using one or more templates and provides functionality to allow protein-ligand complex modeling. Unlike other servers, PRIMO allows users to select specific ligands and ions to be included in the modeling process.Since PRIMO works via communication with the JMS, adding to the features and functionality of this pipeline can be achieved by simply adding new tools to the JMS. In future we will add more options for template identification, sequence alignment and model evaluation where possible. For now, PRIMO provides a quick and easy way to perform homology modeling, while allowing users to make alterations and improvements to their modeling jobs.In summary, PRIMO prides itself on providing flexibility during the modeling process, giving users the ability to exercise a certain degree of control over their modeling jobs. It allows users to edit parameters and rerun jobs, while the navigational bar and job history features allow users to attempt multiple modeling approaches in tandem to optimize their modeling results. The site incorporates a user friendly design, which is simple to use, yet robust. The site is intuitive to use, with all options being easy to find and test out; which adds to the educational value of the site, as users gain hand-on experience with homology modeling. Users can adjust parameters and see the effect on the models produced. Apart from the model evaluation options provided by the site, PRIMO links to various other evaluation servers, which inexperienced users should find helpful. In addition to this, tips and tricks are provided in the loading screen to give novice users suggestions as to how they may improve their modeling runs.Future development will focus on providing more features, such as protein complexes, including modeling of biological assemblies specified within template PDB files. PRIMO encourages user involvement in the homology modeling process and as such we shall also aim to provide additional options for each of the stages.https://primo.rubi.ru.ac.zaThe PRIMO webserver may be accessed freely for academic use at S1 FigThe box plots show measured target-template sequence identity for all modeling sets, divided into their sequence identity bins and alignment program used, as measured based on the PIR file used for modeling. These are shown for all models produced before the filtering step .(TIF)Click here for additional data file.S2 FigDiff value was calculated by subtracting the RMSD value between the template PDB target PDB from the RMSD value measured between the top model and the target PDB.Results are shown for the data set used in (TIF)Click here for additional data file.S1 TableProteins PfHsp70-x (A) and HsTXK (B) were modeled and evaluated. Models for each server are shown along with quality scores measured by ProSA, Verify3D, the QMEAN server, PROCHECK and DOPE Z-score. The PROCHECK results are sub-divided as follows: Fav–Residues in most favored regions; Add—Residues in additional allowed regions; Gen—Residues in generously allowed regions; Dis. Residues in disallowed regions.(DOCX)Click here for additional data file.


L/5 long‐range contact precision of 27%. MetaPSICOV method bases on a combination of classical contact prediction features, enhanced with three distinct covariation methods embedded in a two‐stage neural network predictor. Some unique features of our approach are (1) the tuning between the classical and covariation features depending on the depth of the input alignment and (2) a hybrid approach to generate deepest possible multiple‐sequence alignments by combining jackHMMer and HHblits. We discuss the CONSIP2 pipeline, our results and show that where the method underperformed, the major factor was relying on a fixed set of parameters for the initial sequence alignments and not attempting to perform domain splitting as a preprocessing step. Proteins 2016; 84(Suppl 1):145–151. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.Here we present the results of residue–residue contact predictions achieved in CASP11 by the CONSIP2 server, which is based around our MetaPSICOV contact prediction method. On a set of 40 target domains with a median family size of around 40 effective sequences, our server achieved an average top‐ The highest precision for those methods and for methods evaluated in previous CASP experiments hovered around 20%  included 36,473,742 entries, that is, it had grown by 67% in the intervening period. In light of this, we attempted to supplement the sequence information for each query sequence. If the HHblits output alignment (A3M file) had <2000 sequences, we constructed an “on the fly” custom database for HHblits using jackHMMer to identify possible homologous sequences in an up‐to‐date release of the UniRef100 database . Then, the prebuilt custom database, along with the query sequence was used as an input for HHblits. Finally, we compared outputs of both runs—HHblits‐UniRef20 and HHblits‐jackHMMer, and selected the alignment with the most sequences as the input for MetaPSICOV. In this way, HHblits was always used to generate the alignments, but any additional sequences detected in new releases of UniRef100 could be incorporated.Crucial elements for contact predictions based on amino acid covariation are accurate large and diverse multiple sequence alignments. We developed a workflow to take advantage of as much public sequence data as possible Fig. . By defahttp://bioinf.cs.ucl.ac.uk/MetaPSICOV), mutual information measuresThe complete MetaPSICOV approach has already been described elsewhere.The covariation methods were selected to represent different approaches to solving the statistical decoupling problem inherent in residue covariation‐based contact predictions  and, therefore, produce only partially overlapping results. Aside from covariation methods, we used classical neural network‐based contact prediction features (for a full list of features used please refer to MetaPSICOV paperNeff) rather than raw sequence numbers to account for this redundancy. In our case, we cluster the protein sequences at a 62% identity threshold (which is the same clustering threshold used to compile the standard BLOSUM62 matrix used in BLAST) and then take the number of clusters as the Neff value.Large MSAs can contain many redundant sequences that do not contribute significant contact information. Therefore, like other authors,de novo structure prediction using MetaPSICOV contacts, we used our previously described approach.Z‐coordinate values within the lipid membrane.To perform SR‐RR, sequence separation < 23) and the other for the long‐range contacts (ELR‐RR). The potentials have the form of a square well with exponential decay above the contact distance threshold (8 Å Cβ‐Cβ distance) and are scaled according to the predicted contact probability for each pair of residues with PPV ≥0.5. In the original PSICOV, the contacts were predicted only for the residues where a covariational signal was observed. Therefore, it was suitable to use a single contact‐related function. In case of MetaPSICOV predictions, where contact probabilities for every pair of residues are reported, we found that separating short‐range and long‐range contact potentials yielded better results. Otherwise, the contributions from short‐range contacts (more abundant and easier to predict) outweigh the impact of long‐range contacts what results in lower long‐range contact satisfaction.We used two residue–residue contact potentials—one representing short‐range contacts  was adjacent to a TBM domain (not assessed here) and thus accumulated more sequences than would otherwise be included in the MSA for the FM domain alone. This would influence MetaPSICOV to overweight the contribution for covariation compared to other features. The other cause of worse than expected performance was anchoring of the alignments toward the more populated easier domain (TBM), resulting in a poor alignment quality for the FM region, but a good alignment for the remainder of the chain.Neff compared to the initial result for the whole protein sequence , and T0789 (PDB id: 4W4L). T0827‐D2 (FM domain) lost almost 250 sequences upon realignment, gaining 10% precision. T0789‐D2 lost 200 A different situation was observed for T0826. This target has 2 domains—D2 is a globular TBM domain (not considered here) and D1 is a transmembrane FM domain. When realigned, the number of sequences in D1 did not change substantially (422 vs. 424 effective sequences), but the contact prediction precision significantly increased. In this case, the improvement came about solely because the sequence alignment was no longer anchored to the C‐terminal (TBM) domain.Although we are focussing here on our contact prediction results, the utility of contact predictions can be put in better context by looking at our (group Jones‐UCL) free modeling predictions made using our predicted contact lists. In every FM case, CONSIP2 server predictions and alignments were manually inspected for domain boundaries, alignment errors, or transmembrane targets and rerun, if needed.L/5 precision = 44%) we were able to produce a model with TM‐score = 0.60  FM targets (including two transmembrane proteins) we were able to achieve GDT‐TS scores > 40 (TM‐score > 0.5).The analysis of outliers in our predictions clearly suggests that multi‐domain proteins should be processed per‐domain. The current CONSIP2 (and MetaPSICOV) server accepts the whole sequence and does not attempt to split the sequence into domains. This resulted in some subpar predictions. We hope to address this shortcoming before the next CASP experiment.Another aspect is setting the sequence coverage criteria appropriately. On one hand, a too stringent sequence coverage requirement might result in losing some sequence information for targets where the sequence data is already scarce. On the other hand, a too permissive sequence coverage requirement could result in finding partial matches for some targets and allowing for insufficient coverage in multi‐domain proteins. It is generally easy to spot and correct these issues when looking at the alignments by eye, but difficult to come up with a completely automated system that can deal with the wide variety of scenarios across the whole range of CASP targets.Our method was also designed to tackle the contact predictions for globular domains as the most general case. In the case of transmembrane domains, building too deep alignments could result in unrelated sequences or drifted domains being included and thus generating false positives in the predicted list of contacts. The notable case here is T0826‐D1, where we included over 400 sequences in the alignment but the contact prediction accuracy was very poor (precision below 10%). Thanks to manual tweaking in the FM category and rerunning the contact predictions on a new alignment, we were able to obtain a satisfactory model and a high contact prediction precision (above 60%).CONSIP2 was created to achieve the deepest achievable sequence alignments by constructing HHblits databases on the fly. It would, however, be more desirable to have a complete up to date HHblits database. We realize that clustering over 35 million sequences is a serious computational endeavour, particularly for a single research group, but perhaps this is a challenge best handled by a community‐wide distributed computing approach, as sequence alignments are required universally and public sequence data is growing at an ever increasing rate.In CASP11, we were able to improve the state‐of‐the‐art in residue–residue contact prediction by combining classical contact prediction features with three covariation‐based methods, within a neural network framework. In our approach, we put an emphasis on constructing the largest possible multiple‐sequence alignments. An important aspect of MetaPSICOV is its ability to modulate the relative impact of covariation‐based and classical features depending on the quality and depth of the MSA. If the alignment is shallow, then covariation features are downweighted, whereas for deep and diverse alignments the covariation features are dominant. It is possible that further improvements of the way this is currently handled could result in further increases in prediction accuracy for smaller numbers of sequences, and this is clearly a focus point for future research.de novo prediction problem for different sizes of MSAs. MetaPSICOV works very well at approximately 200 Neff and above, so although we were able to significantly decrease the required number of sequences in an alignment, there is still a gap between what is feasible for sequence family sizes as they currently are, in the majority case at least, and what is needed to exploit these modeling techniques for really challenging cases. It is clear, that with the current methods it is impossible to solve the protein structure prediction problem with the use of contact predictions alone unless we accumulate many more sequences or are better able to deal with alignments with fewer effective sequences.Overall, we were able to achieve a precision of 27% on the CASP11 contact prediction set which had a median of 44 effective sequences . Our CASP11 results probably show reasonably well what are the realistic expectations for what contact predictions can contribute to the Improvements in this area could come from two directions. One is the evolution of contact prediction algorithms. On the side of covariation, the major issues of indirect coupling and phylogenetic bias were ameliorated in recent years, but are still unresolved.The other issue is the availability of sequence data. We observe an increase in the median Pfam family size,


This ACS approach has been further generalized by some recent work, either greedily or exactly, by allowing a bounded number of mismatches in the common substrings.Alignment-free sequence comparison approaches have been garnering increasing interest in various data- and compute-intensive applications such as phylogenetic inference for large-scale sequences. While We present ALFRED-G, a greedy alignment-free distance estimator for phylogenetic tree reconstruction based on the concept of the generalized ACS approach. In this algorithm, we have investigated a new heuristic to efficiently compute the lengths of common strings with mismatches allowed, and have further applied this heuristic to phylogeny reconstruction. Performance evaluation using real sequence datasets shows that our heuristic is able to reconstruct comparable, or even more accurate, phylogenetic tree topologies than the kmacs heuristic algorithm at highly competitive speed.http://alurulab.cc.gatech.edu/phylo).ALFRED-G is an alignment-free heuristic for evolutionary distance estimation between two biological sequences. This algorithm is implemented in C++ and has been incorporated into our open-source ALFRED software package ( Accurate estimation of the evolutionary distance between two sequences is fundamental and critical to phylogenetic analysis aiming to reconstruct the correct evolutionary history and estimate the time of divergence between species. One popular approach to evolutionary distance estimation relies on sequence alignment. Typically, the pipeline for alignment-based phylogenetic inference generally works by three steps. Firstly, we perform all-to-all pairwise sequence alignment to gain a pairwise distance matrix for the input sequences. The evolutionary distance between two sequences in the matrix is typically inferred from an optimal alignment, e.g. equal to one minus percent identity in the optimal alignment. Secondly, we construct a guide tree from the pairwise distance matrix and then conduct progressive alignment of multiple sequences following the order determined by the guide tree. Finally, we infer a phylogenetic tree from the resulting multiple alignments using a tree inference program which can be distance-, parsimony-, bayesian, or likelihood-based. Nevertheless, it needs to be stressed that we could also choose to construct a phylogenetic tree directly from the pairwise distance matrix computed in the first step, using some distance-based tree construction algorithm such as unweighted pair group method with arithmetic mean (UPGMA)  or neighd sequences of average length n, the time complexity for pairwise distance matrix computation can be as high as O(d2n2) when using pairwise alignment. In contrast, by using alignment-free exact k-mer (a k-mer is a string of k characters) counting, the whole computation can be done in O(d2n) time, significantly reducing the run-time by a factor of n. Moreover, alignment-free approaches are capable of overcoming some difficulties, which challenge alignment-based approaches, such as genetic recombination and shuffling during the evolution process.Although they may have high accuracy, alignment-based approaches involve high computational cost, thus resulting in slow speed. This is because pairwise alignment using dynamic programming has a quadratic complexity with respect to sequence length. This is even more challenging when constructing the phylogenetic tree for a large number of sequences, especially long sequences (e.g. eukaryotic genomes). In this case, some research efforts have been devoted to accelerating the tree construction using high performance computing architectures –6. In adk-mer counting , and the substring that starts at position i and ends at position j by X[i…j]. For brevity, we use Xi to denote the suffix of X starting at i. The total length of X and Y is denoted by n. A key data structure in our algorithm is the generalized suffix tree (GST). The GST of X and Y is a compact trie of all suffixes of X and Y. It consists of n leaves and at most n−1 internal nodes. Corresponding to each leaf, there is a unique suffix of X or Y. The edges are labeled with a sequence of characters. The string-depth of a node u is the length of the string obtained by concatenating the edge labels on the path from the root of GST to u. The space and the construction time of GST are O(n) . Then for each internal node u in GST, process the set u. Let h be the string-depth of u. Then (h+1) is the first position, in which the prefixes of two suffixes in h+1)th character all suffixes to be identical, or equivalently first (h+1) characters to be the same. To do so, we follow the steps below: i,key), where key is the lexicographic rank of the suffix Xi+h+1 among all suffixes of X and Y. In other words, key is the lexicographic rank of the suffix obtained by deleting the first (h+1) characters of Xi. Using GST, we can compute key in constant time.Map each j,key), where key is the lexicographic rank of Yj+h+1 among all suffixes of X and Y.Likewise, map each key.Sort all pairs in the ascending order of i,·), find the closest pairs, say  and , towards the left and right side (if they exist) that are created from a suffix of Y, and update A[i]←arg maxj∈{a,b,A[i]}|LCP1|.For each pair  After processing all internal nodes as described above, compute the following and report it as our approximation to A[i] will be correctly updated to βi while processing the lowest common ancestor node of the leaves corresponding to Xi and H is the height of GST and its expected value is O(logn) [It can be easily verified that  O(logn) .http://alurulab.cc.gatech.edu/phylo). This algorithm takes a collection of sequences as input and computes an approximation to ACSk for all pairs of sequences. For this, we have used the open-source libdivsufsort library [ALFRED-G is implemented in C++ and is incorporated in our open-source ALFRED software package ( library  to const library  to build library ) and the library ).  using Escherichia coli K12 substrain MG1655 as the outgroup. The multiple sequence alignment file is available at http://alurulab.cc.gatech.edu/phylo, from which the raw sequences corresponding to the 32 Roseobacter genomes are extracted and then used in our study. In our study, we have used the phylogenetic tree presented in Newton et al. [The eukaryotic sequence dataset is constructed by Newton et al.  from 32 n et al.  as the rproml program available in PHYLIP [The protein sequence dataset is taken from BAliBASE (v3.0) , which in PHYLIP , which id sequences, we first compute the distance between any sequence pair and then construct a pairwise distance matrix of size d×d. Subsequently, the neighbor-joining (NJ) algorithm [Given a set of lgorithm  is appliAll experiments are preformed in an Apple Macbook Pro (Mid-2012 model) running Mac OS 10.10.4 (OS X Yosemite). The machine features a 2.9 GHz dual-core Intel Core i7-3667U processor with 4MB L3 cache and 8GB RAM.k, ranging from 0 to 9. Note that for k=0, both kmacs and ALFRED-G are the same as the ACS method.As our method is closely related to kmacs, we compared the performance of ALFRED-G with kmacs in terms of speed and accuracy (based on RF-distance) for different values of k, ALFRED-G provides either the same or better accuracy (in terms of RF distance). Interestingly, for k=4 and 5, the phylogenetic tree created based on ALFRED-G coincides exactly with the reference tree , however the difference in run-time is independent of k.Figure In the earlier work by Leimeister and Morgenstern , it has In this paper, we have introduced a greedy alignment-free approach to estimating the evolutionary distance between two sequences. The core of the heuristic is to identify a 1-mismatch longest substring in sequence Y that appears as a prefix of any given suffix in sequence X, and vice versa. This heuristic has been further applied to reconstruct the phylogenetic tree, given a collection of sequences that are believed to be close enough and have some evolutionary relationship between them. The performance of our heuristic has been evaluated using three real datasets: one prokaryotic dataset, one eukaryotic dataset and one protein dataset, in terms of tree-topology RF score and speed. Our experimental results show that our heuristic can exactly reconstruct the same phylogenetic tree topology with the reference tree for the prokaryotic dataset, whereas kmacs cannot. On the remaining two datasets, our heuristic also demonstrates comparable or even better performance than kmacs. As for speed, our heuristic is slightly slower than kmacs.ACSk, between sequences, thus overestimating their distances, i.e. Distk.Although our heuristic has been shown effective for phylogenetic inference, there are still some limitations that could be improved in the future. Firstly, our heuristic assumes an evolution model having only mismatches, not involving insertions or deletions, for simplicity. This model may not exactly fit the real evolutionary process given a collection of sequences. Nevertheless, our performance evaluation has shown that even though there are some insertions or deletions between sequences , their evolutionary distances can still be estimated with reasonable accuracy using our heuristic. However, it should be noted that the existence of insertions or deletions may cause our heuristic to underestimate the similarity values, i.e. Secondly, our heuristic assumes that the homologous regions between two sequences are on the same strand. Actually, this is not always the case. Given a homologous region, the substring in sequence X may have an opposite strand to the corresponding homology in sequence Y. In this case, directly applying our heuristic to such sequences may overestimate the distance, since these homologies with opposite strands are not counted in the computation of similarity values. In some sense, we would expect that the estimation accuracy of alignment-free approaches could be further improved by incorporating support for strand differences in homologies.D (0≤D≤1) between two sequences as a similarity measure, but proposed to use two different distance computations depending on the value of D. In this case, Edgar computed the distance as − ln(1−D−D2/5) if D>0.25, and retrieved the distance value from a pre-computed lookup table, otherwise. Hence, it may be beneficial to design some new distance computation equations that better match our approach. Finally, considering the generality and fast speed of our heuristic, we would expect that related research in bioinformatics and computational biology could benefit from our algorithm.Thirdly, our heuristic has only used Eq.  to estim