Whole genome alignment of eukaryote species remains an important method for the determination of sequence and structural variations and can also be used to ascertain the representative non-redundant core-genome sequence of a population. Many whole genome alignment tools were first developed for the more mature analysis of prokaryote species with few current tools containing the functionality to process larger genomes of eukaryotes as well as genomes of more divergent species. In addition, the functionality of these tools becomes computationally prohibitive due to the significant compute resources needed to handle larger genomes.In this research, we present CoreDetector, an easy-to-use general-purpose program that can align the core-genome sequences for a range of genome sizes and divergence levels. To illustrate the flexibility of CoreDetector, we conducted alignments of a large set of closely related fungal pathogen and hexaploid wheat cultivar genomes as well as more divergent fly and rodent species genomes. In all cases, compared to existing multiple genome alignment tools, CoreDetector exhibited improved flexibility, efficiency, and competitive accuracy in tested cases.https://github.com/mfruzan/CoreDetector.CoreDetector was developed in the cross platform, and easily deployable, Java language. A packaged pipeline is readily executable in a bash terminal without any external need for Perl or Python environments. Installation, example data, and usage instructions for CoreDetector are freely available from  During evolution of the species, genomes undergo small sequence changes, such as nucleotide substitutions, insertions and deletions, and large-scale duplications, translocations and inversions. More recently, within species, extensive structural variations (SV), such as presence–absence variation, copy number variation, and chromosomal rearrangements, have been identified due to the sequencing and analysis of near complete genomes  sequences between individuals, multiple sequence alignment (MSA) algorithms are used for smaller genomic regions, represented by protein-coding genes, RNA transcripts, and gene promoter sequences  is inevitably more complicated, and with the rapid development of pangenomes, ranging from small microbes to large plants and vertebrates, fast and efficient MGA algorithms are required to better understand structural changes underlying functional diversity and evolutionary events within and between species. While MSA methods can detect basic evolutionary events, such as nucleotide substitution, insertion and deletion, MGA methods can further reveal larger recombinant events and SVs. The majority of current MGA methods work in two stages. In the first stage, local alignment blocks, called High-scoring Segment Pairs (HSP), are prepared using sequence pairwise alignments, with commonly used tools, such as MUMmer , GSAlignThe MGA of core and dispensable genomic regions for larger and more genetically diverse individuals in a population can be computationally challenging. A more efficient approach is to perform a core-genome alignment of the conserved sequence to better measure genetic changes between the individuals in the population and show not only the evolutionary relationships within a population but provide further insight into core gene functions and how these may shift over time or geography. Core-genome alignment algorithms ignore the relationships between smaller subsets of genomes, and are therefore inherently more scalable  is initially selected as the query from the pool of genomes for pairwise alignment using the fast and efficient pairwise alignment tool, Minimap2 . The queOnce all genome alignments are completed, for each HSP entry of the last alignment (MAF file), a backtracking algorithm extracts each subject sequence region from the original pairwise alignment to build a full MSA alignment containing all sequences. The backtracking algorithm then repeatedly updates the sequence alignment profile by adjusting the number and position of gaps in that profile to reconstruct a final core-genome alignment. A more detailed overview of the backtracking algorithm can be found in Drosophila species (fly), average genome sequence size 180 Mbp (Pyrenophora tritici-repentis isolates (Triticum aestivum) with average genome size of 14.5 Gbp were downloaded from Ensemble plants database https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-57/fasta/. The fungal pathogen, fly, rodent, and wheat genome accessions and identifiers used in this study are available in To evaluate the performance of CoreDetector, two real eukaryotic datasets supported by TimeTree , and v3.5 PHYLIP software for the fungal pathogens  gene annotations in gene transfer format, downloaded from FlyBase (Mus musculus (version GCF_000001635.27_GRCm39) gene annotations downloaded from NCBI, using v2.27.1 BedTools intersect   v. open-ed genes   slim analysis was conducted on the core gene set using R version 4.2.0 with v2.52.0 biomaRt and v1.58.0 GSEABase packages.Software runtime and memory consumption of sequenced-based aligners CoreDetector, v1.2.3 Mugsy , v1.2 PaThe representative “ground truth” phylogenetic trees sourced from the TimeTree project . Across In the case of the rodent data, with the exception of CoreDetector, all the alignment tools were unable to analyze the dataset on the specified compute resources used in this study. The assembly-free and alignment-free software Skmer . The numFor the fungal pathogen data, as a ground truth tree was not available a TreeCmp .A visual comparison of the fungal pathogen CoreDetector (left), Parsnsp (center), and Phylonium (right) trees showed three cluster groups based on the geographic source of the pathogenic isolates  for EuroThe core-genome alignments from the programs that ran successfully were then compared for each of the fungal pathogen, fly, and rodent datasets . The funFor the 12 fly species, core alignment lengths for CoreDetector and ProgressiveCactus were 2.38 Mbp and 88.2 kb, respectively . CompareThe annotated GOs for the core fungal genes identified by CoreDetector were further investigated to see if any specific groups for molecular functions, biological processes, or cellular components were more abundant in the 3392 uniquely identified genes compared to the core genes . The oveIn this study, we have developed CoreDetector, a computational program that is not limited to intraspecific analysis. CoreDetector successfully aligned the core genomes within closely related species, as shown for the fungal pathogen and wheat cultivar genomes used in this study, and also across diverse species, such as the fly (genus) and rodent (order) genome datasets. CoreDetector computationally scaled from the diploid smaller fungal pathogen to larger rodent and hexaploid plant genomes without the need for high-performance computing resources, and in the case of the larger and more diverse rodent dataset, CoreDetector was the only tool that was able to complete in a timely manner in this study.An important feature of CoreDetector is its independence from any guide tree, which can be extremely useful for multi-genome projects on new populations and non-model organisms, where a single reference genome is unknown. CoreDetector also provides an option to use more efficient pairwise alignment tools with two modes of sensitivity that improves the capture of homologous sequences from divergent populations and minimizes false positives.The additional ability of CoreDetector to use available chromosome information to aid the identification of true orthologues is a feature we believe is implemented for the first time in this study. For hexaploid wheat, individual cultivars have three near duplicated diploid genomes A, B, and D  obtained by CoreDetector, which represented 6.1% of the smallest protein-coding sequence, does reflect a significant level of divergence for a set of species, which diverged nearly 40 million years ago  was larger than a previous orthologous protein clustering analysis of predicted genes, which was 56% based on clustering of orthologous proteins (btad628_Supplementary_DataClick here for additional data file.


The developed method can be used to search for PPSs in various eukaryotic genomes.We have developed a new method for promoter sequence classification based on a genetic algorithm and the MAHDS sequence alignment method. We have created four classes of human promoters, combining 17,310 sequences out of the 29,598 present in the EPD database. We searched the human genome for potential promoter sequences (PPSs) using dynamic programming and position weight matrices representing each of the promoter sequence classes. A total of 3,065,317 potential promoter sequences were found. Only 1,241,206 of them were located in unannotated parts of the human genome. Every other PPS found intersected with either true promoters, transposable elements, or interspersed repeats. We found a strong intersection between PPSs and Alu elements as well as transcript start sites. The number of false positive PPSs is estimated to be 3 × 10 Promoter regions are important for RNA transcription . Such rePromoter sequence recognition can be performed using neural networks trained on the existing set of promoters , such as5 false positive predictions for the human genome. There are approximately 3 × 104 known promoters in the human genome [However, all of these methods have a common flaw. They produce a large number of false positives that can interfere with true promoter identification. Even the best of them will make a false positive prediction every few tens of thousands of nucleotides ,21,22,23n genome , so suchx < 2.4, where x is the average number of substitutions per nucleotide [x > 2.4, pairwise alignments become statistically insignificant and there is no way to generate statistically significant multiple alignments from them. However, statistically significant multiple alignments can exist for x > 2.4 and can be generated for a number of sequences greater than 2. This topic has been discussed in detail in [To solve the promoter diversity problem, we used the MAHDS method  for the cleotide  among thetail in . x < 4.4 [x ≈ 3.6 [Unlike other methods, MAHDS is able to generate multiple alignments for sequences with x < 4.4 ,30. The [x ≈ 3.6 ; so, staArabidopsis thaliana and Oryza sativa genomes [−8 per nucleotide. While promoters have on average x ≈ 3.6 substitutions per nucleotide, for some of them this number can be much higher. Therefore, even with the MAHDS method, there is a need for promoter classification. In studies [A. thaliana genome [O. sativa genome [x > 4.4, which would reduce the promoter class specificity of the PWM. Promoter regions from −499 to +100 were used for PWM generation. However, there is a strong triplet periodicity in the +1 to +100 region [The MAHDS method has previously been used for promoter prediction in the  genomes ,31. The  studies ,31, 500 a genome  and 5 cl0 region , which mIn this study, we describe a new method for promoter sequence classification based on a genetic algorithm and the MAHDS method . It alloWe shuffled each of the human promoter sequences . We then created classes from this set of sequences using an algorithm described in Then, we divided 29,598 human genome promoter sequences into classes. We created four promoter classes that met the minimum size requirement. In total, these four classes contained 17,310 promoter sequences, or 58% of the original set. The volume of each class is shown in R, a measure of class scalability. R is the ratio between the probabilities of finding a promoter sequence in the test sample and in the training sample. The lower R is, the more the class is tuned to the specific sequences from the training set. As we can see, the R value for the first two classes is close to 1, which means that there is no overfitting. As for the other classes, the higher the class number, the less scalable it becomes. This is probably due to the fact that most of the common features of the promoter sequences were already present in the previous classes. For each of the created classes, we calculated Fasta files and PWMs for the four created promoter classes are available as We then searched the human genome for the PPS. We searched each chromosome independently for + and − strands. We also searched inverted and shuffled chromosomes for the PPS. Such sequences have the same nucleotide content as the original chromosomes. The PPS search on randomly shuffled chromosomes was performed to estimate the number of false positives for our search method. The PPS search on inverted chromosomes was essential to estimate the number of symmetric PPSs.Z > 6 in the human genome. We also found 76,656 sequences with Z > 6 on inverted chromosomes. The number of false positives was 186 for the whole genome. The length of the human genome was approximately 3 × 109 nucleotides. The estimation of the number of false positives was performed for shuffled chromosomes for both + and – strands; thus, the number of false positives for the method used was estimated to be 186/6 × 109 ≈ 3 × 10−8 per nucleotide. The number of PPSs for human genome chromosomes, shuffled chromosomes, and inverted chromosomes are shown in In total, we found 3,065,317 non-intersecting PPSs with Pr set with first and second class matrices to select the minimum Z value for positive PPS prediction. Such distributions are shown in We also calculated Z value distributions for alignments of non-intersecting sequences from the 19th chromosome + strand, the shuffled 19th chromosome + strand, and the Pr set are not offset. Z value distributions for other chromosomes and other class matrices are similar to these, with minor changes.We chose the minimum level Z > 6 for positive PPS prediction as the closest integer to the upper limit of the Z value distribution for alignments of sequences from the shuffled 19th chromosome with the class matrices . It can j-type of annotated sequences, the Zr(j) coefficient represents the deviation from an expected number of intersections with randomly distributed PPSs in the human genome. Near-zero Zr(j) values are indicative of a random intersection that does not have statistical significance. Large positive values indicate a positive correlation between PPSs and j-type sequence positions. Large negative values indicate a negative correlation between the positions of PPSs and j-type sequences.We studied the intersection of human genome PPS coordinates with different types of annotated sequences. For PPS intersections with a Coordinates of true promoters and promoters for non-coding genes were obtained from the EPD database ,13,33. AThe results of the PPS intersection with various annotated sequences are shown in Zr = 57, indicating its high statistical significance. However, the predicted PPSs only intersect with 10,755 true promoters, while it is possible to predict 17,310 true promoters using four PWM matrices for promoter classes. We assume that the reason is that the sites with higher alignment weights were found in close proximity to some of the true promoters. The presence of such sites may indicate the existence of multiple promoters for a gene [Zr = −12, indicating a lack of positive correlation, as it should do.The intersection of PPSs with the promoter set has a rather large value of r a gene  or alterr a gene . As a coZr = 8, despite the fact that a large proportion of RNA genes use RNA polymerase II for transcription. We assume that this is due to the difference in use of regulatory elements.We also intersected PPSs with a set of promoters for non-coding RNA genes. Such genes can use different RNA polymerases for transcription. This intersection has a rather low positive value of Zr = 58, which is close to that of true promoters. At the same time, we found intersections with only 50,895 out of 223,949 TSSs from the refTSS database [The intersection of PPSs with TSSs has a value of database . The reaZr values were obtained for intersections with Alu elements. This may seem surprising since Alu elements contain the RNA polymerase III promoter [Zr values are not due to the promoter structure, but to the structure of the rest of the Alu elements. Alu elements are relatively rich in CpG residues [Among the PPS intersections with different types of repetitive sequences, the highest promoter . Howeverresidues , while aresidues . Alu eleresidues  and may residues .At the residues  and therresidues .Zr = 53 was also obtained for long terminal repeats (LTRs), which have also been found to be involved in gene expression regulation [Zr values are related to either Alu or LTR. A large value of gulation . All othSet1 contained randomly selected promoter sequences with coordinates −499:20 from the EPD database [Set2 consisted of random 520 nucleotide sequences with the same nucleotide distribution as the human genome. Set3 consisted of randomly selected PPSs obtained in this study. Volumes of these sets were denoted as sN. The results of applying the FPROM, TSSW, and NNPP methods to these sets are shown in We compared our method with several existing promoter prediction methods: FPROM , TSSW 4, and NNPdatabase . Set2 coSet1 represent true positives, positive predictions from Set2 represent false positives, and the number of positive predictions from Set3 represents the degree of correlation between the studied method and ours.Here, positive predictions from Set1 relatively well, as 327 out of 1000 promoter sequences were found. The number of found PPSs from Set3 was 24, which is significantly more than the 11 false positives found in Set2.FPROM is based on a linear discriminant function including functional motifs and oligonucleotide composition description. The method includes two independent functions for TATA and non-TATA promoters. The sensitivity threshold was set at sensitivity = 0.6 for both types of promoters. Here, Ns = 1000 sequences. This method predicts promoters from TSSW recognizes the PolII promoter region and transcription start site. It does not require any parameters. This method gave results comparable to FPROM .Drosophila melanogaster genomes. The minimum promoter score was set to 0.9. NNPP appeared to be incapable of promoter prediction, as the number of true positives and false positives was almost equal  was then computed using the MAHDS method. Pairwise alignments were generated for each sequence from the Ps set with the selected position weight matrix W, and the Z value was calculated for each of the pairwise alignments. Promoters whose alignments with the position weight matrix W had Z > 5.0 were grouped into a promoter class and W became its class matrix. Then, all of the promoters of the created class were removed from the Ps set and the procedure was repeated until the size of the created classes became less than 100 sequences. Such an algorithm was used to classify the promoters from the A. thaliana and O. sativa genomes [The previous version of the promoter classification algorithm  was star genomes ,31.x > 4.4. This would result in a suboptimal position weight matrix, as the MAHDS method is only able to generate statistically significant multiple alignments for sequences with x < 4.4 [Ko = 102 seeds for each class. Each seed Or(k) was considered as an organism. The purpose of using the genetic algorithm was to create a seed with the largest possible mF value, meaning that it would contain the most similar promoters. To do this, organisms were subjected to crosses, which created a new organism consisting of sequences from two other organisms, and mutations, which replaced several sequences in the organism with sequences from the Pr set. The best-performing organism became the seed for the promoter class. In this study, the classes contained promoters with Z > 6.0. In this way, the specificity of the W1 matrix, which is a promoter class PWM, was significantly improved.Due to the random selection of 500 promoters to be used as seed for each class, such sets could contain sequences with  x < 4.4 ,30, thusR, which shows the ratio between the probability of finding a promoter sequence in the test set and in the training set. The R values for the four classes were between 1.077 and 0.648, and for each class, the R value was lower than for the previous one. This meant that the position weight matrices for the four promoter classes combined most of the common features of human promoter sequences.We created four classes of promoter sequences, covering 17,310 of the 29,598 sequences in the EPD database. For each of the classes, we calculated the scalability measure We compared our method with several existing promoter prediction methods: FPROM , TSSW 4, and NNP−8 per nucleotide, which is several orders of magnitude lower than any other existing promoter search method [Zr value, which indicated the deviation from an expected number of intersections if the PPSs were randomly distributed in the genome. The intersections of PPSs with true promoters had a value of Zr = 5, indicating their high statistical significance. Such an intersection confirms the effectiveness of our promoter prediction method. The intersection with TSSs had a similar value of Zr = 58. Those PPSs that intersect with TSSs have a high probability of being true promoters. Thus, an experimental verification of some selected PPSs can be suggested as future work.We searched 24 chromosomes from the human genome and found 3,065,317 PPSs, with an estimated number of false positives at 3 × 10h method . We analZr = 60, and that with exon sequences was Zr = 39. This distribution of PPSs across genes suggests the presence of unknown regulatory pathways related to alternative splicing [Zr values for intersections with Alu elements are also evidence for the presence of alternative splicing, referred to as Alu exonization [The intersection of PPSs with intron sequences had a value of splicing  or altersplicing . High Zrnization . This phOut of a total of 3,065,317 potential promoter sequences that we found, most of them intersected with known promoters, genes, transposable elements, or dispersed repeats, but 1,241,206 of them were located in non-annotated regions of the human genome. Such PPSs located in non-annotated regions may be associated with unknown copies of known repetitive sequences or transposable elements or unknown genes. They may also indicate the location of microRNAs ,54. In tPr and its elements were denoted as Pr(i), i = 1, 2, …, 29,598. The sequences we used were in the range of −499 to +20, which was chosen to reduce the influence of triplet periodicity [We used 29,598 human genome promoter sequences from the EPD database . We calliodicity  that occiodicity .Pr set as possible in a single class. At the same time, there needed to be a minimum number of promoters from other classes in the created class. To solve these tasks and to classify promoter sequences, we developed a mathematical method based on a genetic algorithm.The task was to include as many promoter sequences from the Pr1 containing 104 randomly selected promoter sequences from the Pr set. We also created a second set of promoter sequences, Pr2, containing sequences from the Pr set that were not selected in the Pr1 set. This set would be used as a test set to analyze the reproducibility of the promoter search method. To speed up the computation, we created a smaller training set Po = 102 randomly selected promoter sequences from Pr1 set. In this way, we created Ko = 102 organisms and named them Or(k), k = 1, 2, …, Ko. For each organism Or(k), multiple alignment http://victoria.biengi.ac.ru/mahds/auth. Here, i is an index of a promoter in the organism Or(k), i = 1, 2, …, Po and j = 1, 2, …, L, where L is the length of the multiple alignment. Columns with a number of bases less than Po/2 were deleted from the multiple alignment tL. tL ≤ L. Then, a frequency matrix kM with j = 1, …, tL and l = 1, 2, …, 16 was calculated for the multiple alignment After that, we created an “organism” for the genetic algorithm. It had kM matrix, i varies from 1 to Po and j varies from 1 to tL. f(a) = 1, f(t) = 2, f(c) = 3, and f(g) = 4, where a, t, c and g are DNA bases. If kAt) or kAt is equal to *, then the kM matrix remains unchanged. Next, we computed the PWM matrix—kV—using the kM matrix:When filling the i varies from 1 to tL, j varies from 1 to 16, kV matrix so that the R2 and dK parameters were the same for all of the matrices with different k values. dK = 0 and R2 = 45,000. Here, p1(i) is 1/tL for each i;p(l) and p(m) are the probabilities of l-type or m-type nucleotides in the nucleotide pair : p(l) = q(l)A/L, where q(l) is the number of l-type nucleotides in kAl, and AL is the length of all of the sequences in the multiple alignment kAl. The matrix transformation procedure is described in detail in [R2 = 45,000 and dK = 0. After the transformation, we obtained the kW matrix. Such a matrix transformation is necessary so that the similarity function F  [Here, etail in . The tranction F  has a siWk  are aligPr1 set with the kW matrix. The alignment algorithm is described in Z(k) value. This procedure is described in N(k) from the Pr1 set with Z > Z0 and an alignment length greater than 490 nucleotides. The number N(k) is an objective function for an organism Or(k). The calculation of N(k) was performed for each k from 1 to Ko.Next, we created local alignments for each of the promoter sequences from the N(k) in descending order and reordered Or(k) accordingly. k varied from 1 to Ko. This meant that N(1) would be the largest of all N(k) and would correspond to an Or(1) organism. For the Or(1) organism, we also created local alignments of each of the promoter sequences from the Pr set with the W1 matrix. For these alignments, the broader promoter region −655:+176 was used to find sequences shifted by up to 30% of their length, relative to the W1 matrix. The number of alignments with Z > Z0 and a length greater than 490 nucleotides was called Nf. The same method was used to determine the number of alignments with a broader promoter region of sequences from the Pr1 and Pr2 sets. The obtained numbers of alignments with Z > Z0 and a length greater than 490 nucleotides were designated as N1 and N2, respectively.Then, we ranked Or(k), where k was in the interval from Ko-9 to Ko. These were the 10 organisms with the lowest N(k). Then, we generated 10 new Or(k). To generate a pair of new organisms, we selected 2 parent organisms with k = k1 and k = k2, k1 ≠ k2. The selection of k1 and k2 was performed randomly and the probability of selecting k1 and k2 was proportional to the values of N(k1) and N(k2).Next, we removed 10 organisms Or were created due to the two-point crossover of the parent organisms. Two coordinates i and j were randomly chosen in the interval from 1 to Po. The first child contained promoter sequences from 1 to i − 1 and from j + 1 to Po of the organism Or(k1) and promoter sequences from i to j of organism Or(k2). The second child contained promoter sequences from 1 to i − 1 and from j + 1 to Po of organism Or(k2) and promoter sequences from i to j of organism Or(k1). Each of the children was subjected to 5 mutations. Each of the mutations could, with a probability of 50%, either swap the order of two promoter sequences in the child or replace one of the promoters with a new one chosen randomly from the Pr1 set. However, each promoter sequence from the Pr1 set cannot be present more than once in any organism Or, including children. Thus, if a promoter is present more than once in a child, all of its repeats will be replaced by randomly selected sequences from the Pr1 set.Two new organisms (children) kW was created and the N(k) value was calculated. Then, all organisms were ranked in descending order of N(k), and for the Or(1) organism, the Nf, N1, and N2 values were calculated. Then, 10 organisms Or(k) with k in the interval from Ko-9 to Ko were removed and 10 child organisms were created. This process was repeated until Nf did not increase for 60 cycles.In total, 5 pairs of parent organisms were selected and 10 children were created. For each of the 10 children, a position weight matrix W1 matrix became the promoter sequence class matrix and the Nf promoter sequences were grouped into one class. The dimension of the matrix was 16 × tL. The N1 and N2 values were used to calculate the scalability of the promoter search with the W1 matrix. The size of the Pr1 set S1 was 104 sequences and the size of the Pr2 set S2 was equal to the size of the Pr set minus 104. The scalability coefficient Pr2 set to the probability of finding an alignment with a promoter sequence from the Pr1 set.After the genetic algorithm finished, the Pr set and the algorithm was repeated for the next class. New classes were created until the last class size Nf was smaller than N0. The value of N0 was determined by classifying random sequences. The exact value is calculated in All sequences contained in the created class were removed from the S1 is a promoter sequence of length L1. We created the sequence S2, which contained the numbers 1, 2, …, tL. L1 = 520 if the promoter sequence is selected from the Pr or Pr1 sets. If the alignment is made for a wider promoter region from −655 to +176 nucleotides, then L1 = 832. For the local alignment construction, we calculated the alignment matrix F [S1 sequence is located along the X axis and the S2 sequence is located along the Y axis. To calculate the local alignment, we filled the matrix zero column and zero row with zeros. F = F = 0, Fx = xF = 0, Fy = yF = 0 for each i from 1 to L1 and k from 0 to tL. Then, we filled the F matrix first column and first row:x F . Here, ti varies from 1 to L1, and k varies from 0 to tL. Such filling of the first column and the first row is caused by the fact that we need to consider correlations between neighboring nucleotides when creating the alignment. It was impossible to do this for the first row and column, and so they were filled without the W matrix and nucleotide correlation. Therefore, we used the B matrix instead of the W matrix:Here, F matrix were filled, as shown below:The other rows of the i varies from 1 to L1 and k varies from 0 to tL; d = 25; e = 6. At the same time, for each , we calculated n = S1(j) + 4(S1(i) − 1)) and t. We considered the pairwise correlation of bases when searching for an alignment in the W matrix, and so we introduced the parameter n. To calculate n, we needed to determine the last base added to the alignment before . If the last base from the S1 sequence  was s(k − t), then j = k − t and n = S1(k − t) + (S1(i) − 1) * 4. If t = 1, it corresponded to a diagonal shift in the F matrix and there was no deletion in the S1 sequence. If t > 1, it corresponded to a t − 1 base deletion in the S1 sequence. Here, t was a parameter that characterized the presence or absence of deletions and their length.Here, S1 sequence, there may be deletions in the S2 sequence. In this case, t = 0 and the alignment will skip several columns of the W matrix. Since the W matrix was created for pairs of nucleotides from adjacent columns, its use is incorrect in the case of deletions in the S2 sequence. Therefore, for such situations, instead of the W matrix, the B matrix is used, which lacks correlations between pairs of nucleotides.Instead of deletions in the F matrix, we found the maximum value of its cells and denoted it as mF and its coordinates as mi and mk. Simultaneously with the F matrix, we filled the reverse transition matrix [S1 and S2 sequences. The start and end coordinates of the local alignment were denoted as i0, k0, and mi, mk, respectively.After filling the n matrix . This alW matrix, the S1 and S2 sequences, the alignment start coordinates i0 and k0, the alignment end coordinates mi and mk, and the similarity function value mF. We also created a Q set with 13,000 shuffled S1 sequences. We then created a global alignment of each sequence from the Q set with the S2 sequence. To create the global alignment, we removed 0 from the fourth row in Equations (3), (4) and (8). We also changed the initial conditions for F, Fx, and Fy. F = −kd; F = −id; Fx = −kd; xF = −id; Fy = −kd; and yF = −id for each i from 1 to L1 and k from 0 to tL. Then, we created a vector Fr(i) = QFi) − QFi). Here, QFi)( is a similarity function between S1 and Q(i) sequences, where i varies from 1 to 13,000. Next, the mean Fr(i) and its variance D(Fr) were determined. Then, the statistical significance was estimated as Z allowed us to minimize the influence of the alignment length. It also allowed us to compare alignments of sequences with different position weight matrices kW. We estimated the statistical significance of an alignment using the Monte Carlo method. For the Monte Carlo method, we used the W1 created in S1 sequence with length L1 = 700. After creating the alignment, we calculated its statistical significance. But, this time, the number of random sequences in the set was 103 and the length of each sequence was 700 nucleotides. Then, the alignment window was moved by 73 nucleotides and all calculations were repeated. We only considered alignments whose length was not less than 490 nucleotides. If the alignment length was less than this, its Z = 0.0. Only alignments with Z > Z0 were considered.Matrices for classes kZp(i) and Zkm(i) for each chromosome, where i was the local alignment start coordinate in the chromosome and k was the promoter class number. kZp(i) is a vector for the + chromosome strand, Zkm(i) is a vector for the—chromosome strand. For each vector kZp(i) and Zkm(i), we found the local maximum coordinate i. Here, the local maximum coordinate was mi, whereby kZp(mi) > kZp(i) for each i from mi − tL to mi + tL. We denoted the local maximum mi vectors as kLp and kLm for the + and − DNA strands, respectively. Priority was given to alignments with the largest Z value, regardless of the matrix index.A PPS search was performed for each of the created classes. As a result, we obtained vectors Cpi and Cni (the sequences were written from the end to the beginning) and for shuffled chromosome sequences Cpr and Cnr. The PPS search within the Cpr and Cnr sequences allowed us to estimate the number of false positives for the PPS search within the human genome. The calculation results showed that the number of false positives was about 3 × 10−8 per nucleotide.A PPS search was also performed for inverted chromosome sequences Z, the coordinates on the chromosome, the DNA strand (+ or −), the position weight matrix W1, and the promoter class represented by this matrix.As a result, we created lists of PPSs for each chromosome. For each PPS found, we show its level of statistical significance Z0 threshold on promoter sequences from the Pr set and on the shuffled 19th chromosome + strand. We then calculated the number of predictions with different Z values and plotted them on a bar graph , i = 1, 2, …, 100) with a distance between them greater than 520 nucleotides. Each set Kor(i) was intersected with each of the annotated sequence types. Pr is the number of intersections between the j-type annotated sequences and the sequence set Kor(i). We then calculated the mean D(Pr(j)). The estimate of statistical significance was calculated as Zr(j) > 6.0, then there was a high probability of a positive correlation between the PPSs and the j-type annotated sequence locations. If Zr(j) < −6.0, then there was a high probability that the PPSs would avoid the type j annotated sequences. In other cases, there was only a random intersection between the PPSs and certain annotated sequences.For each annotated sequence type, we also estimated the statistical significance of its intersection with PPSs using the Monte Carlo method. We created 100 sets of random PPS coordinates . It took about 2 weeks to create the promoter classes and about 4 weeks to scan all 24 chromosomes. −8 per nucleotide. This method was compared with three other methods for predicting promoter sequences within the human genome: FPROM [We have developed a new method for the classification of promoter sequences based on a genetic algorithm. This method was applied to the classification of promoters of protein-coding genes from the human genome. For each of the four classes created, we calculated PWMs using the MAHDS method . We thene: FPROM , TSSW [4e: FPROM , and NNPe: FPROM . Our mete: FPROM .


The KEGG metabolic maps are transformed into linear Enzymatic Step Sequences (ESS) using the Breadth First Search (BFS) algorithm. To do this, the KGML files are retrieved, and directed graph representations are created; where the nodes represent enzymes or enzymatic complexes, and the edges represent a compound, that is the 'product' from one reaction and a 'substrate' for the next. Then, a set of initialization nodes are selected, and used as the root for the construction of the BFS tree. This tree is used as a guide to the construction of the ESS. From each leaf , the path is traced backwards until it reaches the root metabolic map and with two or fewer neighbors in the graph. In a second step, the ESS are compared with a Dynamic Programing algorithm, considering an “ad hoc” substitution matrix, and minimizing the global score. The dissimilarity values between two EC numbers ranged from 0 to 1, where 0 indicates similar EC numbers, and 1 indicates different EC numbers. Finally, the alignment is evaluated by using the normalized entropy-based function, considering a threshold of ≤ 0.27 as significant.  Specifications tableHere, we present a method to functionally compare metabolic pathways obtained from the KEGG database. At first, the Kyoto Encyclopedia of Genes and Genomes *(KEGG) metabolic maps are converted into graphs where the nodes are enzymes and edges are product-substrate relationships. Then, the breadth-first search algorithm is used to extract linear Enzymatic Step Sequences (ESS) from the metabolic map graphs, and store them in a database. These ESS represent consecutive proteins or complexes of proteins with enzymatic roles, functionally linked to each other, where their catalytic activities are represented by the Enzyme Commission numbers. In a second step, pairwise comparisons of these ESS are achieved by using the Needleman and Wunsch (dynamic programming) algorithm, to identify the most significant pairs of  sequences. This article presents an update of the method published by Poot-Hernandez et al. i.e., those maps that describe consecutive enzymatic steps with a product/substrate relationship. These maps are easily recognized because the KEGG map code has numbers below “01,000″. For example, the map “00,010″ represents the Glycolysis/Gluconeogenesis pathway, whereas the map “00,230″ represents the Purine metabolism pathway. In these maps, an enzymatic reaction may represent a single enzyme or an enzymatic complex, and a single reaction may be represented by more than one gene product, such as isoenzymes.The metabolic maps stored in the KEGG database are used to create the database of ESSs. The first step is to download the KGML files that describe the metabolic maps  algorithm is used. To do this, each metabolic map is converted into a graph in which the nodes represent the enzymatic reactions and the edges represent the product/substrate relationships between them b. This mi.e., it only follows the nodes in forward direction. For this reason, the directed graph option tends to create less ESS. The ESS are produced from each tree tracking the path from the leaves to the root node  is constructed by filtering identical ESS and leaving only one representative.To use the BFS algorithm it is necessary to select a node that will be used as BFS tree root. We called these nodes “initialization nodes” b. The inoot node d. The seThe nrESSs are organized into a relational database, where each EC number contained in a sequence is related to its corresponding gene(s), organism species , and metabolic map (KEGG). In addition, each nrESS is linked to the original ESS. Finally, all the ESSs alignments can be conducted using the nrESS dataset and referring to the original data when necessary. See https://github.com/acpooth/essdb) where the structure of the database is also described. The construction of the graphs and the BFS trees is conducted with Python scripts using the NetworkX module  algorithm has been implemented. This algorithm works in a similar way as the classic tools to align nucleotide or amino acid sequences with the difference that it evaluates the similarities of enzymatic steps using an entropy-based scoring function. This function was previously created by our group as part of the objective function of a genetic algorithm to build multiple ESS alignments vice versa. Thus, this function acts as a dissimilarity measure and it is calculated as follows.The function measures the homogeneity of a column in an alignment using the Shannon entropy equation. In this regard, each EC number is composed of 3 “columns”, each of which corresponds to the first 3 levels of the EC classification. As the function measures entropy, high entropy values mean different symbols in a column, whereas low entropy values mean similar symbols. In other words, as the function values increase, the similarity of EC numbers decreases, and 1,EC2), considering the first three classification levels (1.l2.l3l) is evaluated according the following equation:For the case of the multiple ESS alignments 1, w2, w3w correspond to the weight factors for each classification level, and 1, H2, H3H to the entropy measured for each aligned level. The weight factors used were 15, 10 and 1 for the first, second and third classification levels respectively. These factors were selected based on the qualitative observation of the alignments generated. As mentioned above, the entropy for each classification level, l, was calculated according the following equation based in information theory Where s is the number of symbols in the aligned level l and ip is the probability to find said symbol. The probability pi is calculated by column (EC level), dividing the number of occurrences of a symbol by the number of sequences. As mentioned before, this function was created for the evaluation of multiple ESS alignments, where there can be many symbols (EC numbers) in a column of an alignment. However, in the case of a pairwise alignment, there can be a maximum of 2 symbols and a minimum of 1 symbol per column (EC level). For this reason, we can simplify the calculation of the EC dissimilarity as follows: If the classifications of both EC numbers in l are equal, then lH = 0, if they are different then l = 1H. Therefore, when the entropy value for two EC numbers is high, the dissimilarity is high. In contrast, when the entropy value is low, the dissimilarity is small.Where S. The matrix represents the pairwise comparison of 319 different 3 levels of the EC numbers stored in the KEGG database. The number 9.9.9 was added to describe enzymes with no EC assigned and it is similar only to itself.For the special case of pairwise ESS alignment, the dynamic programming NW algorithm can be much faster than the genetic algorithm previously used S are: S1.2.7, 1.2.7 = 0; S1.2.7, 3.2.2 = 1; S2.3.1, 2.4.1 = 0.423; S4.3.1, 4.3.2 = 0.0384; i.e., low entropy values (close to cero) suggest ESS similarity, whereas values close to 1, suggest different ESS.Another advantage of the scoring matrix is that it can take into account the hierarchy of the EC numbers; for instance, giving a value of 1 to all the EC pairs that are different in the first level of classification regardless of whether the second or third numbers are identical. This also applies to the second classification level. For this reason, the score of the matrix S is different from the value obtained with the1 and ESS2 given a scoring matrix S, a DP matrix, M, with shape n x m is constructed. Where n is the length of ESS1 and m is the length of 2ESS.The matrix M is filled according to the following rule. For each cell i,jM:To perform the NW algorithm of the ESSECiECjS is the value of dissimilarity assigned in the matrix S between the EC numbers iEC and jEC. The gap penalization value is set to 1, equal to the maximum dissimilarity between two EC numbers. In this way, the best values add up in the diagonal of the matrix M that represents the optimum alignment. Therefore, the best score of the matrix is traced back to build the alignment, adding gaps in the corresponding and optimal positions. The gaps are represented with the string “-.-.-” in the ESS alignment.Where After the alignment is performed, it is evaluated using a normalized scoring function that allows to take into account the gaps and to restrict the score value in the range of 0 to 1. The score is calculated as follow:H is the mean homogeneity (entropy) of the aligned ESS and GP is the penalization for the gaps.Where The homogeneity is measured by the equation:n is the length of the alignment, and ECiECjS is the score of dissimilarity in the matrix S of the two EC numbers aligned in the position i. When an EC number is aligned with a gap (-.-.-), it adds up to the homogeneity as maximum dissimilarity (1). The gap penalization is measured by the equation:Where iGB is the number of gap blocks in the ESS ii, TG is the total number of gap characters in the ESS i (the gap character is “-.-.-“) and ns is the number of ESS . This equation evaluates the mean concentration of the gaps. The numerator becomes smaller as the number of gap blocks increases, thus decreasing the gap penalization. The gaps at the beginning and the end of the ESSs are not included in the count of gaps for the gap penalization, but they are included in the homogeneity measure.Where i.e., that point with higher dispersion of the real data relative to the mean random databases scores and where the loss of nrESS due to extreme dissimilarity was less than 1%. This score can be used as a “rule of thumb” and a good starting point to search for similarities in ESS.The statistical significance of the nrESS alignments, is determined by comparing the alignment scores of the real database against the scores from 10 different random databases. These random sequences were constructed by shuffling the EC number content of the entire database, maintaining the nrESS length and EC composition of the original ESS. Each random database was submitted to the same all-versus-all. alignment approach used for the real data, and the distribution of alignment scores considered the mean ± SD. The threshold considered statistically significant corresponded to a score of ≤ 0.27, In the following, two examples of the command line tool are shown, where the ESS of the low part of the Glycolysis and the Inosine Monophosphate (IMP) pathways were compared and their similarities displayed.usage: alignESS.py pair [-h] [-l] ess1 ess2 positional arguments:ess1 ESS (3 levels EC numbers. Colon separated.(2.7.1:5.3.1:5.3.1: 2.7.1:4.1.2:1.2.1)ess2 ESS (3 levels EC numbers). Colon separated.(5.3.1:5.3.1: sss4.2.1) optional arguments:-h, –help show this help message and exit-l, –localize the alignment is trimmed to the coverage of the shortest ESS and the score is then calculated to the trimmed alignment. This method allows to find 'local-like' alignments between ESS of different sizes.$inputalignESS.py pair 2.7.1:5.3.1:5.3.1:2.7.1:4.1.2:1.2.1 5.3.1:5.3.1:4.2.1$outputess1: 2.7.1:5.3.1:5.3.1:2.7.1:4.1.2:1.2.1ess2: -.-.-:5.3.1:5.3.1:-.-.-:4.2.1:-.-.- score = 0.56698vs all fashion in the complete ESS database, by using the following command:The second example shows a comparison in an all usage: alignESS.py dbalign [-h] [-db2 ESSDB2] [-o OUTFILE] [-t THRESHOLD] [-nproc NPROC] [-l] [-align] essdb1 positional arguments:essdb1ESSs database 1. Sqlite3 file with nrseqs table or text file with oneESS in each line. If the essdb2 argument is not specified, the program performs the all-vs-all alignment in essdb1. This argument can also be a single ESS, in this case the -db2 argument is necessary. optional arguments:-h, –help show this help message and exit-db2 ESSDB2, –essdb2 ESSDB2ESSs database 2. Sqlite3 file with nrseqs table or text file with one ESS in each line-o OUTFILE, –outfile OUTFILEOutfile name to report scores. By default, the file only contains the id of the ESSs and the score of the alignment.If argument '-align' is set, then the file contains the aligned ESSs-t THRESHOLD, –threshold THRESHOLDThreshold score to filter results in the range 0–1 [0.27]. If the threshold is high (>0.6) and the databases are large,results may saturate theRAM memory, beware!-nproc NPROC Number of processes to execute analysis. It can be created more processes than cores in the processor, so the speedupof the analysis depends on the number of cores available-l, –localize the alignment is trimmed to the coverage of the shortest ESS and the score is then calculated to the trimmedalignment. This method allows to find 'local-like' alignments between ESS of different size-align If set, the outfile contains the alignment of each ESS pair below the threshold. Beware, if the databases are largeand the threshold high, the file may be huge or the RAM memory will collapse.$inputalignESS.py dbalign seqs.db$outputi.e., considered as significant:Random sample of the output stored in file output.txt. By default, the database alignment option returns only the scores with values less than 0.27 (see below); The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Bioinformatics education can be defined as the teaching and learning of how to use software tools, along with mathematical and statistical analysis, to solve biological problems. Although many resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these topics benefits both lecturers and students. Unfortunately, educational software for visualizing step-by-step processes in the user experience of sequence alignment algorithms is rare. In this article, an educational visualization tool for biological sequence alignment is presented, and the source code is released in order to encourage the collaborative power of open-source software, with the expectation of further contributions from the community in the future. Two different modules are integrated to enable a student to investigate the characteristics of alignment algorithms. What are the most crucial bioinformatics algorithms every bioinformatics student should know about? It could be useful to understand the principles of Bayesian and maximum likelihood tree construction. Alternatively, learning about homology modeling could be important. Perhaps, students must understand basic global, local, and multiple sequence alignments. In the end, this choice depends on the field of study, since bioinformatics is a broad term encompassing many different areas and methods of research.Nonetheless, many people would agree that the most important algorithms to understand are basic sequence alignment algorithms. Sequence similarity is what we use as an approximation for biological and functional similarity, and alignment is the way to measure it. If one learns the underlying dynamic programming algorithm, then all the different classes of alignment become simple modifications of the parameters and edge cases of that algorithm.Although abundant learning resources are available, most students still struggle to understand even the simplest sequence alignment algorithms. Applying visualizations to these algorithmic processes benefits both lecturers and students. Unfortunately, educational software for visualizing step-by-step processes in the user experience is rare. Visualizations of various types can help researchers and learners to achieve their goals. We observed that implementing visual learning materials significantly improved students’ performance.There are stand-alone educational platforms that host numerous tools and databases for bioinformatics research and allow training to take place in a controlled environment, such as Geneious Basic, The BioKit, and OCRA -6. HowevWe implemented a visualization tool for two alignment algorithms with the use of TKinter, an official Python library used with the Tk GUI toolkit. This Python implementation is potentially beneficial for further development since Python is becoming the most well-suited programming language for bioinformatics and deep learning applications .There are four phases in the implementation: matrix initialization, similarity score calculation, traceback and result generation . As showm+1)×(n+1) zero matrix appears next to entry fields, with m and n indicating the length of sequence 1 and sequence 2, respectively. After clicking the “execution” tab, the first row and the first column of the matrix score are filled with initial values that play the role of a boundary condition. The value in this boundary condition is the first difference between the NW and SW algorithms; the former uses a sequence number depending on the penalty gap value, while the latter simply makes all values zero.The scoring process begins by clicking the “execution” and “next” button tabs consecutively. An  will be scored row by row recursively, subject to a boundary condition.Repeatedly clicking the next button, “>”, alters the score of each matrix’s element. Here, both algorithms use a similar scoring scheme for matches, mismatches, and gaps such that each entry in the matrix s is the substitution score for residues i and j, and g is the gap penalty with i = 1, 2, 3, …, m+1 and j = 1, 2, 3, …, n+1. The similarity score calculation matrix up to a certain number of steps can be seen in In The user continuously clicks the next button until the last element starts the traceback process, visualized along the elements in a green background color. Traceback is a process to find the highest set of sequential scores, which defines an optimal alignment. The initial value for this process is the second difference between the NW and SW algorithms. As shown in Overall, users can follow the alignment process step by step, giving them a real experience of applying the NW and SW algorithms, as well as writing it in their own pen-and-paper notes. We have conducted several tests to demonstrate this educational tool’s performance.


Accurate definition of the structural basis of TCRs and their engagement of peptide–MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR–peptide–MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR–peptide–MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR–peptide–MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at   T cell immunity is a key component of immune protection from viruses and pathogens , such asSeveral algorithms have been developed to perform modeling of unbound TCRs , and TCRHere, we describe the development of TCRmodel2, which is a major update of our previously released TCR modeling web server, TCRmodel . While tThe TCRmodel2 modeling pipeline was generated through several modifications of the AlphaFold pipeline and database, as noted below. These changes were separately implemented in the AlphaFold v2.2 and v2.3 codes (both downloaded from the AlphaFold GitHub repository), to enable comparative performance of the two AlphaFold models in the context of TCRmodel2.Given that the AlphaFold feature selection stage includes sequence searches against large databases containing a large variety of proteins for each input chain, we reduced the databases to contain prospective TCR and MHC hits to speed up the multiple sequence alignment (MSA) building step. The AlphaFold pipeline was run with four representative human and murine TCR and MHC sequences, using the reduced database option. All hits from the TCR and MHC sequence searches against the Small BFD [Big Fantastic Database ], UniRefThe AlphaFold template search, which utilizes an MSA built from the input sequence to search against PDB sequences to identify templates for each input chain , was fouet al. , predicted TM  score, ipTM  score and model confidence, which is a linear combination of pTM and ipTM (0.2 × pTM + 0.8 × ipTM) . Additiohttps://flask.palletsprojects.com/). Users can choose to model a TCR–pMHC complex (Class I or Class II) or an unbound TCR. In both run modes, the users can either provide the target sequences or build the sequences on the fly by selecting the target TCR or MHC genes. The latter option makes possible the modeling of sequences by the information collected from databases such as VDJdb  criteria , which aP-values were calculated with ggpubr package in R (r-project.org), and receiver operating characteristic (ROC) area under the curve (AUC) calculations were performed using the pROC package . In contrast, generation of five TCR–pMHC models using the standard AlphaFold pipeline on the same computer cluster takes ∼5–7 h, of which over 90% of the time is spent in the feature generation and MSA building stage.To benchmark the TCR–pMHC modeling accuracy of TCRmodel2, we assembled a set of nonredundant TCR–pMHC structures from TCR3d that were released after 30 April 2018, with the date cutoff selected to avoid overlap with the AlphaFold (v2.2) model training set. Nonredundancy and other criteria for benchmark case selection are detailed in the ‘Materials and Methods’ section. In total, we identified 48 test cases, including 32 Class I complexes and 16 Class II complexes . Comparihttps://github.com/deepmind/alphafold/blob/main/docs/technical_note_v2.3.0.md), we implemented TCRmodel2 with the AlphaFold 2.3 model and pipeline to test whether it would lead to accuracy improvement over TCRmodel2 with the AlphaFold 2.2 model. This was benchmarked using 20 TCR–pMHC test cases with release dates after September 2021 (to ensure no overlap with the AlphaFold 2.3 training set), which is a subset of the original benchmark set , which includes an updated training set of structures (up to 30 September 2021) and additional recycling iterations during modeling , in comparison with TCRmodel  and AlphaFold . We founr = 0.75; P < 0.001). Model confidence scores also showed significant correlations with individual accuracy metrics Fnat, L-RMSD and I-RMSD  and Class II (N = 47) complexes are shown in To further assess expected model confidence for structurally uncharacterized TCR–pMHC complexes, we used TCRmodel2 to model additional Class I and Class II complexes obtained from the VDJdb database . The disAs an example of TCR–pMHC complex modeling in TCRmodel2, the server was used to predict the structure of a human TCR in complex with an immunodominant SARS-CoV-2 nucleocapsid epitope presented by the Class I HLA-B*07:02 MHC. The complex has not been structurally characterized, nor have any complexes with TCRs targeting that epitope, and its sequence is from a set of TCRs from COVID-19 recovered and unexposed donors reported in a recent study to bind that peptide (sequence: SPRWYFYYL) and MHC . Of note+ T cell recognition of the p53 R175H hotspot mutation, we input the TCR Vα and Vβ sequences into the TCRmodel2 submission page, along with a p53 peptide sequence containing the mutant residue , and selected the HLA-DRA*01:01 and HLA-DRB1*13:01 MHC genes. The Results page from TCRmodel2 included the top-ranked model of that TCR–pMHC complex  that was found to target the common Class II MHC allele HLA-DRB1*13:01 and a p53 neoantigen with the R175H mutation . While s85-TCR1 t complex , which h complex .The TCRmodel2 web server provides the community with a deep learning method to accurately model structures of TCRs and TCR–pMHC complexes. Its TCR–pMHC accuracy is higher than AlphaFold, it runs faster and without the need for dedicated computing resources, and it provides a submission and output interface designed for TCR and TCR–pMHC modeling. TCRmodel2 is distinguished from another recently reported AlphaFold-based TCR–pMHC modeling method , as it dFuture possible developments of TCRmodel2 can address improving the accuracy of confidence estimates, and increasing the overall success rate, including the generation of near-native (CAPRI High criteria) accuracy models, through additional optimizations of the modeling pipeline. Additional testing and developments may focus on application of TCRmodel2 for related complexes of interest, such as TCR-mimic antibodies , which ehttps://github.com/piercelab/tcrmodel2) and Zenodo (https://doi.org/10.5281/zenodo.7853278). Structural models from the benchmarking reported in this manuscript are available from the authors upon request.The TCRmodel2 code is available on GitHub (gkad356_Supplemental_FileClick here for additional data file.


The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, the structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold, and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling CDR3 still represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies. Nanobodies (Nbs) are the single binding domains of camelid heavy chain antibodies. Structurally, they share similarities with the variable heavy chain domain (VH) of traditional antibodies, consisting of a highly conserved region called framework and the antigen recognition region formed by three hypervariable loops, also called complementarity determining regions (CDRs) . Nbs areThe number of applications for Nbs is expanding steadily ,5,6,7,8.Several AI methods have been developed in recent years to tackle the problem of protein modeling ,29,30. IIn this study, we have compared the performance in Nb modeling of six state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2 , OmegaFoFor this study, we built a curated, non-redundant dataset of Nbs, none of which had been included in any of the training sets of the benchmarked programs. Following the procedure described above, we obtained a dataset of 75 unique Nbs with a median resolution of 2.59 Å .The median sequence identity between the Nbs and the rest of the structures not contained in our dataset was between 56 and 71% . On the We compared the performance of six AI models for 3D structure prediction of Nbs: OmegaFold (OF), AlphaFold2 (AF2), IgFold (IF), NanoNet (NN), ESMFold (ESM), and trRosetta (referred to as Yang-Server in the latest CASP15 and herein) (YS). Modeling accuracy was initially evaluated using global superposition structural similarity metrics—TM-score, GDT_TS, and GDT_HA—traditionally used in CASP competitions. In general, all tested programs performed well according to these global metrics. The Yang-Server showed the most discrete performance with medians of 0.87, 0.84, and 0.65 for TM-score, GDT_TS, and GDT_HA, respectively. On the other hand, OmegaFold, AlphaFold2, ESMFold, IgFold, and NanoNet, in decreasing order, showed medians above 0.91 for TM-score and GDT_TS, respectively, and above 0.78 for GDT_HA .TM-score and GDT_TS estimate the percent structural similarity between the model and the experimental structure. Values above 0.5 indicate that both structures have the same folding, while values above 0.9 indicate that they are structurally identical . HoweverBecause of the high conservation of the immunoglobulin domain framework, it is expected that all programs should correctly predict the structure of this region. Interestingly, while OmegaFold, AlphaFold2, IgFold, Nanonet, and ESMFold predicted the Fw structure with high accuracy (0.6 ≤ RMSD median ≤ 0.7), the Yang-Server yielded more discrete results (RMSD median = 1.2). In fact, only the Yang-Server shows significant statistical differences with respect to the other programs . ModelinCDR3 predictions, on the other hand, are the most inaccurate (2.5 ≤ median RMSD ≤ 4.7), with about or more than 50% of the structures showing RMSD > 2.5 Å . SignifiAs shown above, global superposition metrics are not suitable for estimating the accuracy of Nb modeling due to their structural characteristics. At the sequence region level, we observed a considerable variation in the accuracy of CDR modeling, especially for CDR3. We then analyzed the structures generated by the tested programs at the sequence position level to identify the regions that mark the differences in modeling. For each sequence position, we compared the RMSD values for Cα atoms and the whole amino acids between the predicted and experimental structures .All programs, except the Yang-Server, are consistent regarding framework modeling, with slight variations in the N-terminal region and non-CDR loops. The Yang-Server shows slight structural variations in the whole framework as compared to the rest of the programs, while a greater variation is observed in the N-terminal segment. NanoNet uses Modeller, while IgFold and the Yang-Server use Rosetta for side-chain modeling. NanoNet shows considerable RMSD variations when all heavy atoms are considered, followed by IgFold and Yang-Server with fewer variations . The sidFinally, the main differences were found for CDR3 modeling. The lowest RMSD distributions by position were achieved by OmegaFold, followed by AlphaFold2, IgFold, ESMFold, Nanonet, and Yang-Server. Except for Yang-Server, the differences are relatively small for short CDR3s, becoming more accentuated for Nbs with the longest loops. The C-terminal segment of CDR3 shows the lowest variations, probably associated with the frequent formation of secondary structure elements in this region, while the N-terminal part shows more discrete results. Nonetheless, in both cases the structural variations are considerable. The observed differences in modeling performance can be due to the intrinsic characteristics of each AI model and the representation and structural variability of the Nbs with different CDR3 lengths in their training sets. OmegaFold does not require a multiple sequence alignment (MSA), instead using a new combination of a large pre-trained language model for sequence modeling and a geometry-inspired transformer model for structure prediction. According to its authors, this allows the modeling of orphan proteins and antibodies from their amino acid sequences . SimilarAlphaFold2, on the other hand, predicts the structure from neural networks and training procedures based on evolutionary, physical, and geometrical constraints of protein structures. To do so, it requires the protein primary sequence and a multiple sequence alignment, therefore, sequence identity and coverage of the different regions are crucial to obtain an accurate model . Yang-SeThe accuracy of CDR3 modeling depends mainly on its length . SeveralDepending on the AI model, the median RMSD of the predictions varies along the CDR3 length range. In most cases, OmegaFold achieved the best predictions, followed by AlphaFold2, ESMFold, IgFold, Nanonet, and lastly, Yang-Server. Although no direct correlation between CDR3 length and RMSD values is observed among the experimental structures, the structural variability might influence the predictions. For example, for length 15, where the structural variation in CDR3 is considerable, the predictions are relatively consistent, especially for OmegaFold, which yields RMSD values all below 2 Å . This isFor lengths 19 and 24, a few models with high RMSD are generated. For these particular cases, the experimental structure has marked differences from the rest of the Nbs with the same CDR3 length. In 7tpr_D  length . For lenThe confidence value is an important metric in protein structure modeling that allows to estimate how reliable a model can be considered. NanoNet does not produce any metric to estimate its modeling confidence. OmegaFold, AlphaFold2, ESMFold, and Yang-Server do offer a measure of confidence called pLDDT  on a 0–100 scale, which corresponds to the predicted model score of the lDDT-Cα metric ,33. IgFoAlphaFold2 and OmegaFold report values of pLDDT below 70 for predicted CDR3s, which correlates with the RMSD values obtained for these loops between the models and their crystallographic structures . Yang-SeAlthough the obtained correlation coefficients are significant, it is not possible to establish a priori whether a model is reliable or not. Since CDR3 is the region that interacts more frequently with the antigen, further studies are required to estimate whether the generated model can be used for bioinformatics approaches that demand high structural accuracy, such as protein–protein docking.Among the tested programs, only AlphaFold2 and OmegaFold allow parameter modification, specifically the number of recycles, which controls the degree of structural model refinement. In several cases, AlphaFold2 has been shown to improve the prediction of disordered structures or de novo proteins by increasing the number of recycles . OmegaFop-value > 0.05). On the other hand, using 20 recycles with OmegaFold does not translate into any considerable variation for any Nb region. Interestingly, using four recycles slightly improves CDR1 and CDR2 predictions, while losing accuracy in CDR3 modeling. However, statistically, there are no significant differences in any case . ESMFold is extremely fast, obtaining results in approximately one second. However, this may depend on the demand on the server, so it might have limitations in the number of requests. Yang-Server modeling can take approximately one hour due to the algorithm and server capacity (only 30 active jobs at a time). In both cases, however, it is possible to install a standalone version for local use.Nb libraries may contain billions of sequences, with many possible different structures. In recent approaches, library design seeks to favor structures with certain CDR3 geometries  that will presumably bind to specific antigens ,43. WithFor OmegaFold, the computation time improves when decreasing the number of recycles from ten (default) to four, without affecting its accuracy. On the other hand, we found that increasing the number of recycles beyond the default value drastically increases the computational time without any noticeable benefit in modeling accuracy for both OmegaFold and AlphaFold2. It is worth noting that NanoNet may include sidechain modeling with Modeller, which would increase the computational time by a factor of 170–900 approximately, depending on the number of sequences being simultaneously processed. Finally, energy minimization not only does not improve modeling results, but it also adds computation time. The extra time required varied between 10 and 50 s per structure using our hardware configuration.We started from the SAbDaB database , containhttps://predictioncenter.org/index.cgi, accessed on 1 May 2023). The number of these AI protein modeling programs is rapidly increasing, making it difficult to perform comprehensive benchmarking. For this study, we selected six AI modeling programs that have stood out for their performance in general protein modeling and/or antibody modeling.Currently, AI methods have reached a high level of precision in protein modeling, as evidenced in the latest CASP competitions, where the first positions have been occupied by robust AI-based models  that incorporates an application programming interface (API) to perform protein modeling easily and quickly. The fourth program chosen for our study is the Yang-Server [https://predictioncenter.org/casp15/zscores_final.cgi, accessed on 1 May 2023). Finally, we included two programs—IgFold [The first choice was AlphaFold2 , which hg-Server ,50, whics—IgFold  and Nanos—IgFold —that wers—IgFold ,26,33. BAlphaFold2 is an AI model developed by DeepMind that incorporates a neural network architecture and training procedures based on evolutionary, physical, and geometrical constraints of protein structures . The AlpAlphaFold2 is composed mainly of two blocks: (1) the sequence information module, and (2) the structure module, both based on transformers. The first module, called Evoformer, processes the input and generates a multiple sequence alignment (MSA) and a residue pair matrix. The main innovation in the Evoformer block is the mechanisms to exchange information within the MSA and pair representations, that enable direct reasoning on the spatial and evolutionary relationships. The second module generates the 3D structure using the pair representation and the single representation of the MSA, with a mechanism that allows simultaneous local refinement of all parts of the structure, reasoning about unrepresented side chain atoms, and weighing the correct residue orientations. After an initial structure is generated, an interactive recycling process is carried out that reuses the entire network to obtain a refined final structure . AlphaFohttps://github.com/YoshitakaMo/localcolabfold, accessed on 1 May 2023). For Nbs in complex with an antigen, we employed AlphaFold-multimer [ColabFold, on the other hand, offers a user-friendly and fast implementation of AlphaFold2 . In thismultimer  as impleOmegaFold was the first computational method to successfully predict high-resolution protein structure from its single primary sequence alone . It useshttps://esmatlas.com/, last accessed on 23 April 2023).ESMFold  is an AIThe Yang-Server is a recent implementation, with several improvements, of trRosetta (transform-restrained Rosetta) . InitialIgFold uses a principle similar to ESMFold but is specifically applied to antibodies. The input is processed by AntiBERTy , a transNanonet is a deep learning model based on a convolutional neural network . This moWe used TM-score (template modeling score) , GDT_TS post hoc test. Dunn’s test is the appropriate nonparametric pairwise multiple comparison procedure when a Kruskal–Wallis test is rejected. Calculations were performed using the bioinfokit tool (v 1.0.5) [To estimate the differences between the metrics used in this study, we performed a Kruskal–Wallis one-way analysis of variance. In significant cases, we used Dunn’s test with the Benjamini-Hochberg correction as a v 1.0.5) .Calculations were performed using low-end and mid-range hardware (AMD Ryzen 7 3700 and a GPU Nvidia 1660 Super 6GB VRAM and 16 GB RAM). All programs were installed in a standalone Miniconda environment with Python 3.8.13, following the instructions given by their developers.For energy minimization, we used OpenMM v7.7.0  with theNowadays, computer-assisted methods have become essential components in protein engineering, especially for antibodies and nanobodies. Whether for structure characterization, antigen interaction, or affinity enhancement through an in silico affinity maturation process, having reliable structural models is extremely important. Using a poor-quality model can lead to erroneous conclusions and low efficiency in further experimental development.Multiple studies have shown the superiority of AI programs over conventional homology modeling approaches for modeling protein structures. In this study, we have evaluated the performance of six state-of-the-art AI programs in modeling Nb structures. To this aim, we generated a test dataset containing 75 unique Nbs not included in the training sets of the evaluated programs. The performance of different models was assessed using global metrics, as well as metrics for different regions within the structure. The results show that global metrics such as TM-score, GDT-TS, and GDT-HA are unsuitable for Nb structural model evaluation since the modeling errors of highly variable, but functionally important regions such as CDR3 get diluted when using these metrics. We then evaluated the modeling accuracy separately for the framework and CDR regions. OmegaFold achieved the best results, followed by AlphaFold2, ESMFold, IgFold, Nanonet, and Yang-Server.Although the evaluated AI models represent a leap forward in Nb modeling, they are still far from providing completely reliable structural models. This study confirms that, while modeling of the framework region is consistently good in all cases, CDR modeling remains a challenge, especially for CDR3. For this loop, the RMSDs of the generated models are in most cases considerably high compared to the crystallographic structures. Although the median RMSD is relatively low for all AI models, only 52, 44, 35, 29, 25, and 15% of the CDR3 structures generated with OmegaFold, AlphaFold2, ESMFold, IgFold, Nanonet, and Yang-Server, respectively, were modeled with less than 2.5 Å difference compared to the crystallographic structures. Energy minimization did not improve the results. Since CDR3 is extremely important for antigen interaction, the obtained models may not be suitable for applications that require high accuracy, such as protein–protein docking.Modeling with these AI programs can be performed using hardware in the low- to mid-range, which facilitates their use in common bioinformatics laboratories. In these conditions, the calculation times vary from a few to hundreds of seconds. Nanonet is the fastest model, followed by IgFold, OmegaFold, and lastly, AlphaFold2, while ESMFold and Yang-Server can be used on their dedicated servers. According to our results, OmegaFold is the most efficient AI program for Nb modeling, being relatively fast and achieving the best results. Similarly, both ESMFold and AlphaFold2 may be used as an alternative, yielding quite similar results compared to OmegaFold.The Inherent limitations of this type of study must also be considered. The rapid development of artificial intelligence methods for protein modeling makes it almost impossible to keep the evaluation of their performance up to date in real time. In addition, the time lag between the selection of training sets and the release of the programs can lead to biases in a benchmarking study. In this study, we selected a set of nanobodies not included in the training sets of the evaluated programs. This restriction, also associated with the limited number of crystallographic structures available, reduces the representativeness of the evaluation set. Currently, several dozens of AI programs have been developed for protein modeling, as evidenced by the recent CASP15 competition (2022) that included more than twenty AI methods. For this study, we chose to evaluate six of these programs, based on their performance in CASP15, their reported applications (popularity), and accessibility. Therefore, the Nb modeling capabilities of the other AI methods have not yet been evaluated.So far, although there have been substantial advances, the accuracy of the generated models is still limited and Nb modeling remains a challenge. However, the fast development and improvement of AI models, along with the increase of available crystallographic structures, augur significant advancements in Nb modeling in the near future.


Assuming that a high-quality reference tree is available, the idea is to seek the correct placement of the new sequences in that tree. Recently, alignment-free approaches to phylogenetic placement have emerged, both to circumvent the need to align the new sequences and to avoid the calculations that typically follow the alignment step. A promising approach is based on the inference of k-mers that can be potentially related to the reference sequences, also called phylo-k-mers. However, its usage is limited by the time and memory-consuming stage of reference data preprocessing and the large numbers of k-mers to consider.Phylogenetic placement enables phylogenetic analysis of massive collections of newly sequenced DNA, when k-mers based on mutual information, which can significantly improve the efficiency of placement, at the cost of a small loss in placement accuracy. This method is implemented in IPK, a new tool for computing phylo-k-mers that significantly outperforms the software previously available. We also present EPIK, a new software for phylogenetic placement, supporting filtered phylo-k-mer databases. Our experiments on real-world data show that EPIK is the fastest phylogenetic placement tool available, when placing hundreds of thousands and millions of queries while still providing accurate placements.We suggest a filtering method for selecting informative phylo-IPK and EPIK are freely available at https://github.com/phylo42/IPK and https://github.com/phylo42/EPIK. Both are implemented in C++ and Python and supported on Linux and MacOS. Phylogenetic placement is an increasingly popular task in phylogenetic analysis . The limitation of alignment-based methods is that, even though queries are typically short, their alignment to long reference sequences is poorly scalable with the ever-increasing amounts of sequencing data delivered by current technologies.Early phylogenetic placement tools such as APPLES, followed by APPLES-2 , and makes it highly scalable in the number of queries to place. Moreover, RAPPAS proved to be highly accurate, especially for short queries , a tool for efficient computation of phylo-k-mers. IPK improves the running times of the phylo-k-mer construction step by up to two orders of magnitude. We also introduce phylo-k-mer filtering—a method to reduce large phylo-k-mer collections with little or no loss in placement accuracy. These developments improve the scalability of computing phylo-k-mers to larger phylogenies. Second, we present EPIK (Evolutionary Placement with Informative K-mers), an optimized parallel implementation of placement with filtered phylo-k-mers. EPIK substantially outperforms its predecessor. We provide experiments on placement accuracy and speed, showing that EPIK can place millions of short queries on a single thread in a matter of minutes or hours. When placing large collections of queries, EPIK outperforms the state-of-the-art in placement speed, while remaining highly accurate.Here, we take further steps to overcome the challenges of phylogenetic placement and introduce two tools that together form the successor of A denote a reference sequence alignment, and T denote a reference phylogenetic tree whose leaves are in one-to-one correspondence with the sequences of A. Sequences can be either composed of nucleotides or amino acids. Besides A and T, a collection of query sequences is also given. Then, for any query q, phylogenetic placement aims to identify the branch from which q likely diverged from the rest of the tree. Some placement methods also estimate the precise position of the divergence along the identified branch, and the length of the pendant branch terminating in q. Note that the reference tree remains fixed, and the queries are not actually added to the tree; this leaves the evolutionary relationships between queries unresolved. Also, we assume that the query sequence is homologous to parts of the reference sequences. This is an essential prerequisite to most phylogenetic placement methods  at any site (position) of the alignment for a given node of the phylogeny. k-mers whose probability is greater than a user-defined threshold k consecutive sites in the alignment. For this step, we apply a new divide-and-conquer algorithm instead of the branch-and-bound algorithm used in RAPPAS, because the new algorithm is shown to be faster in practice [see The next step is to find, for any ghost node, all k-mer w generated in the above step, we consider a phylo-k-mer y is the branch corresponding to the ghost node where w was generated, and k consecutive columns in A, and over the two ghost nodes corresponding to y. Such phylo-k-mers can later be retrieved from what we call a phylo-k-mer database: a set of k-mers mapped to lists of tuples k-mer w for branches y such that For each k-mers do not contribute equally to placement inference, we designed phylo-k-mer filtering, an approach for selecting informative k-mers. For every k-mer, we compute a filter value indicating how informative for placement the k-mer is, and sort k-mers by filter value. We propose two algorithms to form the phylo-k-mer database: in memory for speed and on disk for low memory usage. We describe both algorithms in Since such databases can reach huge sizes, and because IPK and EPIK is the possibility of filtering phylo-k-mers. This means selecting the k-mers that are the most informative for phylogenetic placement according to a measure derived from information theory. We view phylo-k-mer-based phylogenetic placement as a classification problem, where (i) the observations to classify are the queries, (ii) the classes that they must be assigned to are the branches of the reference tree, and, finally, (iii) the features used for the classification of a query are all the possible k-mers.One of the main novelties of k-mers) that maximize the mutual information (MI) between the class (branch) variable Y and the variable k-mer w is present in the query .Similarly to feature selection in text classification , we wishY (i.e. reduction in Y’s entropy) resulting from observing In information theory, the mutual information between these random variables, denoted rving Xw :IEPIK only uses the presence of k-mers (and not their absence) to determine the placement of a query (see Section 2.3), the filtering is based on the following modification of the above formula, which only keeps the term with Since k-mer w [i.e. c is independent of w and thus does not play any role in the filtering of k-mers. In k-mers maximizing Here, k-mers as follows. First, we compute k-mers. Second, we sort k-mers in descending order of k-mers are closer to the beginning of the database file. For placement, we deserialize k-mers in their order until we reach a certain proportion of the entire database’s size or the memory limit set by the user.We filter phylo-k-mer database, EPIK places query sequences in the following fashion  likelihood weight ratios, computed as follows (where b is the base of the logarithm):On the basis of a precomputed phylo- fashion : First, PEWO , a framework recently developed by EPIK’s placement with and without filtering, as well as the accuracy of the other state-of-the-art phylogenetic placement methods. We used the pruning-based accuracy procedure (PAC) of PEWO that computes the accuracy of phylogenetic placement as follows. First, given a reference alignment and a reference tree, it randomly selects a subtree to remove (“prune”) from the original tree. Second, it extracts query reads from the sequences associated with the leaves removed. Then, query reads are placed on the remaining tree. Finally, for every placed query read, it computes the node distance between the observed placement and the expected placement. Node distance is defined as the number of tree nodes on the path between the branch reported as the query’s top-scoring placement (the observed placement) and the branch that was created as a result of pruning the subtree (the expected placement). We used node distance as it is the most commonly used way of evaluating phylogenetic placement accuracy . For each pruning, we report the mean node distance across several 150-bp-long queries generated for that pruning. The number of generated queries per pruning is variable, as it depends on the number of sequences in the pruned subtree, and on the length of those sequences. We ran all tools with their default parameters, except RAPPAS, for which we used the defaults of EPIK: RAPPAS’ default k, we can expect increased placement accuracy; we used these values to facilitate comparison with EPIK. We ran EPA-ng, pplacer, and APPLES2 using query-reference alignments constructed with HMMER in the way it is implemented by default in PEWO.We compared the accuracy of EPIK, we used seven real-world datasets of amino acid and nucleotide sequences, where the number of reference sequences ranged from 140 to 3748. All of these datasets have been previously used as benchmarks for the accuracy of phylogenetic placement methods. Three datasets consist of sequences for widely used metabarcoding markers: D218, D500 .To test the placement accuracy of k-mers from the database, we expect placement accuracy to decrease. We aimed to answer two questions: how quickly does the accuracy decay with increased filtering, and how much can we filter out without hurting placement accuracy significantly? To investigate this, we performed filtering using a parameter k-mers until it was impossible to add the next k-mer and its associated tuples, without exceeding the database size of When we remove phylo-k-mer databases applying the corresponding filtering ratios k-mers in random order until a fraction We used pruning-based accuracy evaluation in the following manner. First, for a given set of input data and a range of values of IPK and EPIK against other tools using the RES workflow of PEWO  Xeon(R) W-2133 CPU @ 3.60 GHz/8.25 MB Cache/62 GB RAM and an HDD with SATA 3.0 and 64 MB cache. We ran all tools in a single-thread mode . For the running time, we consider the total wall clock time; for the RAM consumption, we consider the peak maximum resident set size (RSS). Measurements were averaged over three repeats for every stage of computation of every program.IPK, a new tool for computing phylo-k-mers. A few essential differences set it apart from RAPPAS’s phylo-k-mer computation stage. First, we made IPK a standalone tool to ease the development of new applications of phylo-k-mers. IPK computes a database of phylo-k-mers for any given reference A and T and provides transparent access to this database to query k-mers. Applications such as EPIK and SHERPAS . Third, IPK computes mutual information values for phylo-k-mers that are later used by EPIK for phylo-k-mer filtering. By default, IPK creates the database in memory and then stores it on disk. Alternatively, it can create the database directly on disk to reduce RAM consumption at the cost of longer execution . This allows for placement even if the database is larger than the amount of RAM available. Also, EPIK can be run in parallel in shared memory for higher speed . For an example of how to run IPK and EPIK, see Even when running in a single thread, EPIK (with unfiltered databases) compared to the state-of-the-art on real-world datasets. D500, D652, see EPIK shows better accuracy compared to other state-of-the-art tools. These results are to some extent consistent with the ones of RAPPAS using Here, we assess the placement accuracy of EPIK was comparable to the best performing methods (EPA-ng and pplacer) for HCV and D140, yet was inferior to App-SpaM for HIV. App-SpaM cannot be run for D140 since it does not support protein sequences. Also, RAPPAS could not build phylo-k-mer databases for HIV, since it exceeded the 32 GB RAM limit.For the viral datasets , EPIK waEPA-ng and pplacer tend to have very similar node distance distributions; also, the distributions for RAPPAS and EPIK are matching almost perfectly. However, there exist slight differences between RAPPAS and EPIK  decrease in placement accuracy.Note that EPIK in the number of query sequences to place. EPA-ng and APPLES2 by “preprocessing” we mean the alignment of queries to the reference alignment, while for RAPPAS and EPIK we mean the construction of the phylo-k-mer database. App-SpaM does not require any preprocessing. We present some results on preprocessing times in the next section. We stopped any jobs that required more than 32 GB of RAM.Here, we evaluate the scalability of the state-of-the-art tools and EPIK showed better placement time than RAPPAS in all experiments. The running time improvement over RAPPAS varied among datasets and the number of queries placed. For example, while placing ten million queries to D652, EPIK was around 14 times faster (29 min versus 6 h 49 min); for neotrop and ten million queries, it was 18 times faster (1 h 34 min versus 28 h). The lower relative performance of EPIK for fewer queries is due to the overhead required by loading the phylo-k-mer database in memory that is constant and does not depend on the number of queries placed. We also note that the loaded database size greatly affects the memory consumption of EPIK  was more than one hundred times faster than RAPPAS: the speed-up was 147×, 124×, 239×, and 120× for D652, neotrop, HCV, and tara, respectively. Also, IPK clearly outperforms RAPPAS in phylo-k-mer computation for other k-mer lengths  can be safely excluded. For example, we could reduce the database sizes by a factor of 16 with a minor loss in placement accuracy for datasets of metabarcoding markers, and at least by a factor of 4 for datasets of complete viral genomes.Even if computed on disk, a phylo-IPK+EPIK. First, they are currently not suited for metagenomic applications that require large reference sequences, like large eukaryotic genomes. For these data, our approach may require using larger values of k and limit the possibility of filtering. Hence, extending the applicability of phylo-k-mer-based methods to longer genomes seems challenging. Second, the mutual information of a given k-mer can only be computed once all its branch-specific scores are available. A perspective for future work is to develop an algorithm to assess k-mer informativeness directly during phylo-k-mer computation.Let us point out two limitations of EPIK requires the precomputation of the database with IPK, a step that is avoided by alignment-based methods. So how large shall the set of input reads be to counterbalance the precomputation time? In all our experiments (see Section 4.4), aligning a million short reads within the reference sequences was always significantly slower than computing phylo-k-mers for the references with IPK. Thus, already with circa a million short queries, it is faster to use alignment-free placement with phylo-k-mers than alignment-based tools.Using IPK as a standalone tool provides flexibility and eases the application of phylo-k-mers to other bioinformatic problems, thanks to a carefully designed API. For example, SHERPAS, another software based on phylo-k-mers, can readily use databases built by IPK to infer recombination patterns in viral sequences (k-mers for protein family homology detection is under development. We hope that further phylo-k-mer based solutions will be developed in the future.Implementing EPIK already offers high computational efficiency with a single thread, as well as multi-threading. Currently, for millions of queries or more, EPIK is the fastest phylogenetic placement software. Since the combination IPK+EPIK achieves high accuracy—comparable to state-of-the-art tools—when placing short metabarcoding reads, we hope it will become the tool of choice for large-scale metabarcoding applications.The C++ implementation of btad692_Supplementary_DataClick here for additional data file.