The recent advances in sequencing technologies enable the assembly of individual genomes to the quality of the reference genome. How to integrate multiple genomes from the same species and make the integrated representation accessible to biologists remains an open challenge. Here, we propose a graph-based data model and associated formats to represent multiple genomes while preserving the coordinate of the linear reference genome. We implement our ideas in the minigraph toolkit and demonstrate that we can efficiently construct a pangenome graph and compactly encode tens of thousands of structural variants missing from the current reference genome. The human reference genome is a fundamental resource for human genetics and biomedical research. The primary sequences of the reference genome GRCh38  are a mopangenome, which refers to a collection of genomes  on a segment in the reference graph matching an interval  on the query. Given a list of anchors sorted by x, let f(i) be the maximal chaining score up to the ith anchor in the list. f(i) can be computed by:α= min{min{yi−yj,xi−xj},wi} is the number of matching bases between anchor i and j. β is the gap penalty. Let gji=|(yi−yj)−(xi−xj)| be the gap length and dji= min{yi−yj,xi−xj} be the smaller distance between the two anchors. Minigraph uses the following gap cost:G=100000 in the graph construction mode, c1=edw− and c2=0.05·edw−. By default, d=0.01 is the expected per-base sequence divergence and w=19 is the minimizer length. In comparison, minimap2 applies G=5000,c1=0.19 and c2=0. Minigraph allows much larger gaps between minimizers and more heavily penalizes gaps.where O(n2) algorithm where n is the number of anchors. This algorithm is slow for large n. Minimap2 introduces heuristics to speed up the computation by approximating this equation. It works well for minimap2 that only allows small gaps and has base-level alignment as a fix to chaining errors. However, as minigraph intends to chain much longer gaps, the minimap2 algorithm occasionally misses the optimal alignment in long segmental duplications and produces false variations. Minigraph introduces a new heuristic to speed up chaining.Solving Eq. Dynamic 1-dimension Range-Min-Query Before we move onto the minigraph solution, we will first introduce Range-Min-Query (RMQ). Given a set of 2-tuples {},RMQ returns the minimum sj among {sj:a≤yj≤b}. We implemented 1-dimension RMQ with a modified AVL tree, a type of balanced binary search tree , we first find the smallest and the largest nodes within interval  using the standard algorithm. In this example, the two nodes are  and , respectively. We then traverse the path between the two nodes to find the minimum. With a balanced tree structure, we do not need to descend into subtrees. The time complexity is O(m logm), where m is the number of nodes in the tree. We can insert nodes to or delete nodes from the tree while maintaining the property of the tree. This achieves dynamic RMQ.ree Fig. . When peChaining with a linear gap cost function A linear gap cost takes the form of β′=c1[(yi−yj)+(xi−xj)]. Given a list of anchors  sorted by position xi, letf′(i) in O(n logn) time with RMQ  on segment si in the graph matches  on the query sequence. A graph chain satisfies the following conditions: if i<j,yi<yj; if i<j and si=sj, we have xi<xj; if si≠si+1, the two segments are adjacent on the graph. It is an extension to linear chains.The graph chaining algorithm results in one or multiple graph chains. A primary chains that do not greatly overlap with each other on the query sequence. A region on the query is considered to be orthogonal to the reference if the region is contained in a primary chain longer than 100 kb and it is not intersecting other primary chains longer than 20 kb.Using the minimap2 algorithm , minigrahi for each adjacent pair of anchors  and . Let hi is computed asMinigraph scans primary chains in orthogonal regions and identifies subregions where the query subsequences significantly differs from the corresponding reference subsequences. To achieve that, minigraph computes a score η is the density of anchors averaged across all primary graph chains. Define H. Minigraph uses the Ruzzo-Tompa algorithm [hi), which correspond to divergent regions. In each identified divergent region, minigraph performs base alignment [where lgorithm  to identlignment , 59 betwentclass1pt{minimahttps://github.com/lh3/etrf). An LCR is classified as VNTR if 70% of the LCR is VNTR; similarly, an LCR is classified as STR if 70% is STR; the rest are classified as “Other-LCR” in Fig. We applied RepeatMasker  v1.332 tI0. We align sequences inserted to GRCh38 against GRCh38 with “minimap2 -cxasm20 -r2k” and filter out alignments with mapping quality below 5. Let I be the set of GRCh38 intervals that are contained in alignments with identity between a and b. The blacklist regions are computed by I0∪I∖I, where “ ∪” denotes the interval union operation and “ ∖” denotes interval subtraction.For each variation in the graph, we extend its genomic interval on GRCh38 by 50bp from each end. We name this set of intervals as Additional file 1 Review history.


Single Molecule Sequencing (SMS) technology can produce longer reads with higher sequencing error rate. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. Most existing mapping tools generally adopt the traditional seed-and-extend strategy, and the candidate aligned regions for each query read are selected either by counting the number of matched seeds or chaining a group of seeds. However, for all the existing mapping tools, the coverage ratio of the alignment region to the query read is lower, and the read alignment quality and efficiency need to be improved. Here, we introduce smsMap, a novel mapping tool that is specifically designed to map the long reads of SMS to a reference genome.smsMap was evaluated with other existing seven SMS mapping tools  on both simulated and real-life SMS datasets. The experimental results show that smsMap can efficiently achieve higher aligned read coverage ratio and has higher sensitivity that can align more sequences and bases to the reference genome. Additionally, smsMap is more robust to sequencing errors.H. sapiens genome with over 3 billion base pairs). The source code of smsMap can be freely downloaded from https://github.com/NWPU-903PR/smsMap.smsMap is computationally efficient to align SMS reads, especially for the larger size of the reference genome (e.g.,  Technological breakthroughs in high-throughput sequencing (HTS) platforms have triggered a revolution in genomics –5, whichRecently, a number of available methods (or tools) for mapping SMS long reads to the reference genome, such as BLASR , BWA-MEMAll of the above mapping methods designed for SMS reads follow the canonical seed-and-extension paradigm , 30, thaThe candidate aligned regions for each query read are selected either by counting the number of matched seeds  or chaining a group of seeds that are co-linear or close to each other . However, the candidate aligned region of each query read is always a part of the read and cannot completely cover the whole read length. Although the non-seed fragments are subsequently aligned with dynamic programming, they are still within the candidate regions. For the two ends of the query read, which are not covered by the candidate aligned region, all existing methods do not align them and directly output as the soft clipping. As a result, these methods designed for SMS reads usually focus on producing local mapping results for the query read, other than obtaining the whole end-to-end alignment, leading to low aligned coverage . The aligned coverage is the percentage of one query sequence aligned to a reference genome, which reflects the effectively aligned size of the query sequence. Many researchers consider the alignments with higher aligned coverage as the valid alignment results –34. AddiHerein, we proposed a new SMS sequence mapping method  that aims to get the end-to-end accurate alignment against the reference genome for a query read. smsMap mainly contains three steps. It first constructs the BWT-FM index for the reference genome, then finds the starting positions in the query read and reference genome, and lastly a column reduction banded alignment method is developed to obtain the detailed dynamic alignment results from the located starting positions to the two ends of the query read and the genome, which can cover the whole read length. The experiments on simulated and real-life PacBio datasets show that smsMap can achieve more aligned coverage than other mapping tools. Also, smsMap is more sensitive that can map more reads and bases onto the reference genome.E. coli, A. thaliana, C. elegans and H. sapiens (CHM1), were generated by PacBio sequencing platform. All methods were executed on an Ubuntu 16.04.5 server with 16 3.2-GHz Intel Xeon (E5-2667V4) processors and 128 GB of RAM. The parameters used for each mapping tools are given in Table SsmsMap is implemented in C++ language with multithreading, and it can be run in both Linux and Windows systems. To evaluate the performance and efficiency of smsMap, we compared our smsMap with other seven state-of-art long read mapping tools, such as BLASR , BWA-MEMpiens CHM, were gep bases (here p = 0.9L(rτ)), we consider this read to be correctly mapped on the genome  is the match score or mismatch penalty for the u-th base in segment rd and the v ' '-th base in gd, sci(u) is the starting column index and floor is the floor function. Here we define the bandwidth b = αlr, where lr is the read length, α is a width coefficient. The default value of α is 0.1, which can cover almost all aligned paths  × 2b and l(ru) × 2b, we applied the banded alignment algorithm on these two matrices to align rd with gd, and ru with gu, respectively. In the end, we combine the aligning results of downstream segments  and upstream segments  to get the mapping result of the query read and the reference genome.After obtaining the two low column matrices of Additional file 1: Figure S1. An example to explain how to calculate the metrics of cFAR, cFAB and cARC. The numbers above each read are the start and end aligned positions. Figure S2. The base-to-base alignments for different methods with one sequence (length: 296 bp). lordFAST and minimap2 fail to align this sequence. The statistics of the alignments are listed in Table S15. Figure S2. Diagram of using three bandwidths for aligning. (A) Too large bandwidth. (B) Too small bandwidth. (C) Appropriate bandwidth, it not only covers the backtracking, but also reduces the memory usage. Figure S3. The distribution of width coefficient for simulated datasets with different error rates ranging from 5 to 30%. Table S1. Running command lines of different mapping programs. Table S2. Parameter settings of NPBSS for generating simulated datasets. Table S3. Read number and total bases of simulated datasets. Table S4. The cFAR, cFAB and cACR for different methods on the simulated datasets. Table S5. Alignment scores of different methods for E. coli simulated datasets. The values in the brackets are the min. and max. Alignment scores. Table S6. The cFAR, cFAB and cACR for different methods on the simulated datasets of H. sapiens (CHM1). Table S7. Different SVs types and its breakpoints in the genome. Table S8. PacBio datasets website links. Table S9. Reference genome website links. Table S10. Statistics of E. coli, A. thaliana, C. elegans and H. sapiens datasets. Table S11. FAR(%), FAB(%) and ACR(%) of eight methods on E. coli UTI89 dataset. Table S12. Agreement of different alignment methods for A. thaliana dataset. Table S13. Agreement of different alignment methods for C. elegans dataset. Table S14. Agreement of different alignment methods for H. sapiens dataset. Table S15. Aligned results of different alignment methods for one sequence. . Table S16. The average and maximum of width coefficient for simulated datasets.


The accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy.We investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment.We observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly, and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification. Since its introduction in 2008 –3, transIn various assessments on simulated data –10, thesWe focus on the effect of read mapping on the resulting transcript quantification estimates. Specifically, our goal is not to compare other aspects of transcript quantification, but rather to isolate, as much as possible, the effect of read mapping methodology. Hence, we have attempted to keep the quantification method consistent in our analysis pipelines, while changing the alignment and mapping methodologies. To compare the effect of different alignment and mapping methods on RNA-seq transcript quantification and related downstream analysis, we have picked tools from three different categories of mapping strategies: (1) unspliced alignment of RNA-seq reads directly to the transcriptome, (2) spliced alignment of RNA-seq reads to the annotated genome (with subsequent projection to the transcriptome), and (3) (unspliced) lightweight mapping (quasi-mapping) of RNA-seq reads directly to the transcriptome. While numerous different lightweight mapping approaches exist , 13, 17,Due to the absence of a ground truth in experimental data, it is difficult to categorically specify which approaches produce more accurate estimates. However, by investigating the divergence we observe among the quantifications produced by different methods, and the differences in read mapping that lead to this divergence in quantifications, we uncover some primary failure cases of different alignment and mapping strategies. This leads us to compare and combine the results of different alignment strategies, and allows us to curate a set of oracle alignments for experimental samples. Comparing various approaches to the oracle provides further evidence for a hypothesis, raised in  and 14]14], thatdecoy regions from the genome. The normal Salmon index is then augmented with these decoy sequences, which are handled in a special manner during mapping and alignment scoring, leading to a reduction in such cases of false mappings. We benchmark two variants of this approach: one in which we extract a small collection of decoy sequences via the procedure mentioned above (designated as SA), and one in which we align against the transcriptome and whole genome simultaneously, allowing us to detect fragments that better map to a non-transcriptomic target. The latter approach, which essentially treats the whole genome as decoy sequence, is denoted as SAF. We benchmark these approaches on both simulated data and a broad collection of experimental RNA-seq samples and demonstrate that they lead to improved concordance with the abundance estimates obtained via quantification following traditional alignment.Finally, we introduce an improved mapping algorithm, selective alignment (SA), that is designed to remain fast, while simultaneously eliminating many of the mapping errors made by lightweight approaches. SA is integrated into the Salmon  transcriFor benchmarking, we used quasi-mapping and SA (with either the sequence similar decoy regions or the whole genome), both available in the Salmon  program,We used Salmon as the main quantification engine in our analyses since it supports quantification from quasi-mapping, SA, and via the output of traditional aligners and we wanted to use a single, consistent quantification method in our pipelines in order to focus on and accurately compare the alignment strategies, minimizing the effect of other confounding factors. To the best of our knowledge, Salmon is the only quantification tool that has support for both lightweight mapping approaches and quantification using traditional alignments. We used Salmon in alignment mode to process the output from Bowtie2 and STAR. In tests on the initial simulated data, we also included RSEM. To remove variability in the quantification methods that is ancillary to our focus on mapping and alignment, we used the –useEM flag in Salmon for comparison against the EM-based algorithm of RSEM. Likewise, to eliminate variability due to the target set of transcripts being quantified, we passed the –keepDuplicates option to Salmon when indexing for subsequent mapping using quasi-mapping or SABowtie2—Alignment with Bowtie2 to the target transcriptome and allowing alignments with indels, followed by quantification using Salmon in alignment mode.Bowtie2_strict—Alignment with Bowtie2 to the target transcriptome and disallowing alignments with indels , followed by quantification using Salmon in alignment mode.Bowtie2_RSEM—Alignment with Bowtie2 to the target transcriptome and disallowing alignments with indels, followed by quantification using RSEM.STAR—Alignment with STAR to the target genome (aided with the GTF annotation of the transcriptome) and projected to the transcriptome allowing alignments with indels and soft clipping, followed by quantification using Salmon in alignment mode.STAR_strict—Alignment with STAR to the target genome (aided with the GTF annotation of the transcriptome) and projected to the transcriptome and disallowing alignments with indels or soft clipping, followed by quantification using Salmon in alignment mode.STAR_RSEM—Alignment with STAR to the target genome (aided with the GTF annotation of the transcriptome) and projected to the transcriptome and disallowing alignments with indels or soft clipping, followed by quantification using RSEM.quasi—quasi-mapping directly to the target transcriptome, coupled with quantification using Salmon in non-alignment mode.SA—Selective alignment directly to the target transcriptome and a set of decoy sequences (high similarity with the transcriptome), coupled with quantification using Salmon in non-alignment mode  directly to the target transcriptome and the genome, treated as decoy sequences, coupled with quantification using Salmon in non-alignment mode . We also simulated data using the RSEM simulator, seeded from the same input sample, and observed broadly similar trends Additional File We observed that, though there are differences in correlation, all pipelines had somewhat similar overall performance on this simulated dataset, with the exception of STAR, which exhibited the lowest correlation. On this data, quasi-mapping performed better than aligning to the genome (and then projecting to the transcriptome) and very similar to traditional alignment against the transcriptome (both with and without the strict parameters for Bowtie2). We observed that SA and SAF performed Overall, the analysis on this synthetic dataset gives an impression that quantifications resulting from the different mapping approaches exhibit similar accuracy and that all approaches quantify transcript abundances relatively well. While this is true for these simulated data, we show below that this observation does not generalize to experimental data. We posit that this is because, though great advancements have been made in improving the realism of simulated RNA-seq data, these simulations still fail to capture some of the complexities of experimental data. We describe below one particular way in which the realism of the simulated data can be increased by accounting for variations between the sequenced reads and the transcriptome used for quantification.An observation we made from the previous simulation was that disallowing indels using the RSEM parameters (used for strict and RSEM versions) for Bowtie2 and STAR did not adversely affect accuracy compared to using the default parameters of each method. We hypothesized that this is because the reads are simulated exactly from the reference transcriptome that is being used for alignment and quantification, and only sequencing errors (which are taken to consist entirely of substitution errors) are introduced by the simulator. Yet, in experimental data, the sample being quantified likely exhibits variation with respect to the reference against which the reads are aligned. Some of these variants will be single nucleotide variants (SNVs), while others will be indels and yet others may be larger structural variants. Thus, restricting alignments to disallow indels seems undesirable, unless one is quantifying against a personalized reference that is known to contain the variants present in the sample, which can potentially improve the accuracy of transcript quantification , 24.Similarly, another shortcoming of simulation methods is the inability to simulate sequencing reads from intergenic and intronic regions of the genome, or from transcripts that are not part of the provided annotation but which might be present in experimental samples. Experimental datasets are generally more complex and include reads that originate from segments that are not part of the annotated transcripts. These can adversely affect methods that align reads against the transcriptome, instead of the genome, leading to inaccurate read mapping and, ultimately, a decrease in transcript quantification accuracy.To test the hypothesis that disallowing indels in the alignments will adversely affect quantification accuracy when simulating from a reference transcriptome containing realistic variants compared to the reference, we performed the following experiment. We obtained VCF files from the Sanger Mouse Genomes websiteTo study the effect of sequencing reads from unannotated regions of the genome and bring simulated data another step closer to experimental data, we added one more layer of complexity to the simulation pipeline. Instead of simulating reads directly from the PWK strain’s reference, we aligned reads from the experimental dataset SRR327047) against the PWK strain’s reference using HISAT2 , then as047 againTo further analyze the influence of indels on quantification, we aligned the transcript sequences from the PWK strain and the original reference using edlib  and counDue to both the theoretical concerns and the practical evidence shown here, we proceeded in representing the alignment-based methods by using Bowtie2 and STAR in our comparisons, in configurations that allow indels to occur in the alignments, and excluded from our main analyses the “strict” versions of the pipelines. However, where mentioned  on experimental datasets, we randomly selected 200 human RNA-seq experiments from the NCBI database for further investigation. We then filtered the selected samples to include only paired-end libraries having a minimum read length of 75 bp. After applying these filters, we were left with a set of 109 samples. This consists of 69 bulk RNA-seq samples and 40 full-length single-cell RNA-seq samples. Though we anticipate that alignment and mapping methodology may also influence tagged-end single-cell RNA-seq protocols, we exclude such samples from our analyses here since the processing methodologies for such protocols are considerably different from those used in bulk RNA-seq quantification . However, we do include full-length single-cell datasets in our analysis because the alignment and mapping methods we compare here are frequently used in conjunction with existing transcript-level quantification tools to process full-length single-cell data, as suggested by several existing studies , 30. BefSince no ground truth transcript abundances were available for these 109 experimental datasets, it became more difficult to analyze the accuracy of the different pipelines. However, we explored, manually, some of the cases where differences in mappings and alignments led to divergence of quantification estimates between methods. Between Bowtie2, quasi-mapping, and STAR, the mappings seemed to fall into one of two major categories. In one case, Bowtie2 seemed to be appropriately reporting a more comprehensive set of best-scoring mappings than STAR and quasi-mapping. In the other case, the resulting sequencing fragment seemed to clearly arise from some unannotated region of the genome—either from intronic or from intergenic sequence—and it was spuriously assigned by Bowtie2 and quasi-mapping to some set of annotated transcripts . This led to the following observation: when the fragment truly originates from the annotated transcriptome, Bowtie2 appears the most sensitive and accurate method in aligning the read to the appropriate subset of transcripts, as is also supported by the variant transcriptome simulations from the “oracle method that avoids the obvious shortcomings, as listed above, of either of the constituent methods. To derive the oracle alignments in each sample, we used the following approach. First, we aligned the reads for the sample using both Bowtie2 and STAR, and for STAR, we retained both the genomic and transcriptomic BAM files . Subsequently, we examined the reads that were aligned to the transcriptome using Bowtie2 and were aligned to the genome using STAR, but which STAR did not project to the transcriptome. For each such read, we examined the best-scoring transcriptomic alignment records produced by Bowtie2 as well as the best-scoring genomic alignment records produced by STAR. We compared the quality of these alignments between the tools by first parsing the extended CIGAR string (the MD tag) and assigning a score to each reported alignment. In our scoring scheme, we assigned 1 to every matched base while penalizing soft clips, SNPs, and indels by assigning a score of 0. We reported the score of an alignment as the sum of the number of properly matched bases along the ends of the read. If the transcriptomic alignment of Bowtie2 was of equal or higher quality to the genomic alignment, then we retained the transcriptomic alignment. Otherwise, we marked the fragment’s alignment records for removal. We then processed the original Bowtie2 BAM file for the sample, removing alignments for all fragments that have been marked for removal. The result was a filtered version of the Bowtie2 BAM file in which spurious transcriptomic alignments have been removed. We quantified the sample by providing Salmon with this filtered BAM file, and refer to the resulting quantification estimates as the oracle estimates for this sample.These observations led us to combine information from both Bowtie2 and STAR to derive an While other complex alignment scenarios may occur, these oracle estimates represent quantification based on the set of alignments that avoid the obvious shortcomings of the different approaches being considered. Specifically, being based on alignment rather than lightweight mapping, all alignments benefit from the improved sensitivity of Bowtie2’s search procedure and are guaranteed to support a matching of the read to the reference of at least the required quality. Further, since these alignments are derived from Bowtie2, they likely correspond to a correct and comprehensive set of transcripts when the fragment does, in fact, originate from the annotated transcriptome. Finally, in the case where the fragment does not originate from an annotated transcript, and is instead the product of transcription from an unannotated locus, novel splicing, or intron retention, the corresponding alignment records have been removed using information from STAR’s alignment to the genome, so that the fragment is not spuriously allocated to annotated transcripts. This allows the oracle to overcome at least some of the shortcomings of the approaches that comprise it. For example, on the complex simulated data where reads are generated using the StringTie2-assembled transcripts, presented in Table In terms of Spearman correlation between all methods, we observed the highest average pairwise concordance between the oracle and SAF, in both the bulk and single-cell samples Fig. . SAF hadWith respect to the concordance of the different approaches with each other, Bowtie2 tended to correlate highly with SA, while STAR tended to correlate highly with SAF, though SA and SAF were, themselves, highly correlated. Also, Bowtie2 and STAR both had a higher correlation with the oracle than they did with each other. This was somewhat expected since the oracle was created by considering the alignments of both Bowtie2 and STAR. However, this also suggested that the manners in which these approaches diverged from the oracle were largely distinct . The quantifications from lightweight mapping exhibited the lowest overall correlation with the oracle. These results were also indicative of the types of divergence between simulated and experimental datasets that we expected to observe. The trend was similar when comparing TPM values after discarding transcripts shorter than 300 bp , SNP variants, and small indel variants, which are sample-dependent rather than reference-dependent, are missing. Furthermore, transcription and sequencing in experimental RNA-seq samples are not limited to the fully spliced, annotated transcript sequences present in a reference database, even for organisms as well-characterized as human and mouse. Reads derived from unannotated genomic regions that bear resemblance to annotated transcripts, or which only partially overlap the annotated features, can then be spuriously aligned to the annotated features, leading to inaccurate quantification of their abundances. Since such effects can vary from sample to sample, they do not affect the estimated expression of the annotated features in a uniform way and can therefore affect subsequent analyses, such as differential expression testing.We realize that sample-dependent features are difficult to simulate and that all of the major features or processes affecting a sample may not even be known, but not including such effects diminishes the realism of the simulated data, and this lack of complexity can be observed in the divergence of the performance of different quantification pipelines compared to how they performed in experimental samples. Identifying other factors present in experimental datasets but lacking in simulations, and determining how to faithfully simulate these factors, seems an important area for future research.One of the most common downstream uses of transcript and gene abundance estimates is differential gene expression analysis. Errors in the transcript quantification phase can lead to incorrect detection of differentially expressed genes across conditions. Therefore, quantification is a crucial step for accurate differential gene expression (DGE) estimation and other downstream analyses. To show the influence of quantification on DGE, we performed a case study on three recently published datasets, where sequencing was done for RNA profiling of differences between the healthy and diseased samples. The first dataset is comprised of human ALS motor neurons and studies the effect of the SOD1A4V mutation (2 patient-derived samples) versus (3) isogenic controls PRJNA236453 ). The se453 . Thep value ≤0.01  of 0.01 was used). The overlaps of the resulting gene sets were computed. While we had focused on transcript-level analysis in the paper until now, here we looked at differences in gene-level differential expression. This demonstrated that the quantification issues caused by lightweight mapping or misalignment of reads could be of relevance even when one is performing gene-level analyses.We aligned and quantified reads from all samples using the SA, SAF, quasi-mapping, STAR, and Bowtie2 pipelines. The transcript-level counts were summed to the gene level using tximport , and difpvalue of these genes under quasi-mapping was 0.034, 0.069, and 0.05 in the three datasets, showing that lightweight mapping tended to deviate by a large margin from the other methods. Further, lightweight mapping also tended to discover a considerable number of distinct genes that were called as differentially expressed by this approach but not by any of the other approaches , and which may have represented false positives. In each sample, the number of genes with at least one transcript shorter than 300 bp constituted less than 10% of the total number of genes called differentially expressed only under the lightweight mapping-based quantification, so this effect was unlikely to be driving these differences. In all cases, despite having a large overlap in DE calls with the alignment-based methods, SA produced quantifications that yielded the fewest isolated DE calls. A similar trend was observed when using an FDR of 0.05, as shown in Additional File The results, visualized using UpSetR  and presErrors in quantification can affect differential expression analysis not just at the gene level, but at the transcript level as well. In order to study this effect at the transcript level, we used the third dataset from the previous section, with the zika-infected neural progenitor cells. The other two datasets were only analyzed at the gene level, as previous analyses of these datasets were carried out at the gene level, and they also have relatively low numbers of replicates within each condition, limiting the power for transcript-level analysis. As with the gene-level analysis, the alignment and quantification for all samples was done using the SA, SAF, quasi-mapping, STAR, and Bowtie2 pipelines. Transcript differential expression analysis was done using sleuth , and trap value was 0.027. At an FDR of 0.05, this increased to 0.09, highlighting the difference between the quantification estimates from lightweight mapping and alignment-based methods , a more comprehensive and systematic mechanism for scoring potential mapping loci , and anThe results of benchmarking the different approaches on multiple simulated and experimental datasets lead to a number of conclusions. First, despite the fact that major strides have been made in improving the realism of simulated RNA-seq data, there still remain numerous ways in which simulated data fail to recapitulate the intricacies and challenges of experimental data. One of these is the fact that simulations are almost always carried out on precisely the same transcriptome that is used for quantification, while, in experimental samples, individual variation exists between the sample being assayed and the transcriptome being used for quantification. Another effect not commonly captured in simulation, but prevalent in real data, is the sequencing of reads from unannotated, alternatively spliced transcripts, from transcripts with retained introns, from otherwise unannotated genomic loci sharing sequence similarity with annotated transcripts, and from contamination with the sample that may share sequence similarity, to some extent, with the target transcriptome. These effects, along with others that we have not fully characterized in this manuscript, make alignment and quantification in experimental samples much more challenging than in simulated data. Hence, we observed that when quantifying across a broad sample of experimental datasets, the quantification results obtained using different mapping and alignment approaches can demonstrate considerable variation. Together, these results suggest that quantification based purely on lightweight mapping approaches can fail to achieve the accuracy that is obtainable by the same inference algorithms when using traditional alignments and that these errors in quantification can also affect downstream analyses, even at the gene level  seemed to display the best concordance, on average, with the quantifications resulting from oracle alignments. SA yielded similar but slightly better accuracy than alignment to the transcriptome using Bowtie2. This is likely, in part, because it is accounting for the sequence similar decoys that can lead alignment to only the target transcriptome astray. The main benefit of SAF is that it aligns to a reference index that contains both the fully spliced transcript sequences as well as the entire underlying genome . This allows SAF to obtain the type of sensitivity that is exhibited by approaches like Bowtie2 and SA when the read truly arises from the annotated transcriptome, but also allows it, like STAR, to avoid spuriously aligning a read to an annotated transcript when it is better explained by some other genomic locus. In the experimental data, both alignment-based approaches and selective alignment methodologies performed better than quasi-mapping, though the manner in which these methods differ from quasi-mapping, and from each other, was not identical.When trying to choose an approach, a choice can be made by the user performing the analysis based on any time-accuracy tradeoff they wish to make. In terms of speed, we observed that quasi-mapping is the fastest approach, followed by SA and SAF and then STAR. Bowtie2 was considerably slower than all three of these approaches. However, in terms of accuracy, we found that SAF yielded the best results, followed by alignment to the genome (with subsequent transcriptomic projection) using STAR and SA (using carefully selected decoy sequences). Bowtie2 generally performed similarly to SA, but without the benefit of decoy sequences, seemed to admit more spurious mappings. Finally, lightweight mapping of sequencing reads to the transcriptome showed the lowest overall consistency with quantifications derived from the oracle alignments. The analyses carried out in this manuscript suggest that, with respect to accurate quantification of annotated transcripts, alignment scoring is an important component, but the various pre-existing alignment approaches excelled in different cases. SA takes steps toward addressing the shortcomings of existing alignment-based approaches without making large compromises on speed. This is done by indexing parts of the genome that are sequence similar to the transcriptome or, as in the case of SAF, the entire genome in addition to the annotated transcriptome, hence exhibiting the sensitivity of Bowtie2 in transcriptomic alignment, while avoiding the spurious alignment of reads that do not truly originate from some annotated transcript, like STAR. This approach seemed to provide the highest overall accuracy, at least for the purposes of quantifying an annotated set of transcripts.https://github.com/COMBINE-lab/SalmonTools/blob/master/scripts/generateDecoyTranscriptome.sh.Alignment against the genome and transcriptome both have their advantages and disadvantages, as discussed earlier. To avoid aligning genomic reads against the transcriptome, without the need to index the complete genome, requires finding regions with high sequence similarity between them. To obtain similar sequences within a reference, we mapped the spliced transcript sequences against a version of the genome where all exon segments were hard-masked . We performed this mapping using MashMap , with a Selective alignment is based on the pufferfish indexing data structure first described in . MoreoveThe mapping approach works in 5 distinct phases (for paired-end reads). First, exact matches between the read and transcriptome are collected. Second, the set of transcripts to be considered for further processing are extracted. Third, exact match chaining and chain scoring, using the algorithm of , is usedIn the first phase of the mapping algorithm, uni-MEMs  are collM denote the number of matches for the transcript, orientation pair with the maximum number of matches for the current read. An optional (user-determined) filtering policy is applied, whereby any transcript and orientation pair that does not have at least τM matches is discarded from further consideration. The value of τ is a user-specified fraction, set as 0.65 by default. This optional filtering policy, termed as “filtering before chaining,” is disabled by default, but can be enabled via the command-line option –hitFilterPolicy BEFORE. Note that it was not enabled in our experiments and the default was used.In the second phase, uni-MEMs are projected to their corresponded reference loci, and the exact matches are collated by reference and orientation. Let S be the best chaining score obtained for any mapping of the current read. An optional filter is applied where any mapping with a chaining score less than τ′S is discarded from further consideration. By default, this filter, termed as “fitering after chaining,” is enabled and τ′ is set as 0.65.Next, matches along each transcript, orientation pair are sorted and compacted. This compaction is necessary since it is possible to have matches that are directly adjacent on both the read and reference, but which were not extracted as a single exact match during the first phase because the underlying uni-MEM terminated. This compaction phase eliminates such fragmentation due to uni-MEM termination and reduces the number of exact matches that must be considered by the chaining algorithm. Candidate mapping locations are determined by applying the chaining algorithm of minimap2  to the eFor paired-end reads, the pairs are merged by determining, for each transcript, the locations of the read ends that respect the expected mapping constraints . If passed the appropriate flag –allowDovetails, then dovetailed  mappingsAll putative mappings are scored using the ksw2 , 47 librFinally, all of the relevant alignments are grouped by their associated alignments scores. Any alignments that fall below the (user-provided) threshold  for a minimum valid alignment score are discarded. During alignment scoring, the score of the best alignment for a given fragment to any decoy sequence as well as to any non-decoy sequence is computed and stored. If the best alignment score to a decoy sequence is strictly greater than the best alignment score to a non-decoy sequence, then all of the fragment’s mappings are considered invalid and the fragment is not considered for quantification. Otherwise, any alignments to decoy sequences are filtered out, and the remaining alignments to valid transcripts are further processed by Salmon using range-factorized equivalence classes , which aOnce processed by the above procedure, each fragment can be classified as having one of three distinct statuses; it can be (i) aligned to the transcriptome, (ii) best-aligned to the decoy sequence, or (iii) unaligned to any indexed sequence under the current parameters. For the purposes of quantification, statuses (ii) and (iii) are treated equivalently. If a read is best-aligned to the decoy sequence, or completely unaligned within the index, it is discarded for the purposes of quantification and does not contribute to the abundance of the annotated transcripts. For the purposes of providing metadata for a quantification run, Salmon does retain a count of the number of fragments that were best-aligned to decoy sequence, and this information is available in the meta_info.json file it produces.contiguous alignments to decoy sequences will be reported as such in the SAM file.While the fragments that are best-aligned to decoy sequences are discarded for the purposes of quantification, they may differ in character from sequences that were entirely unaligned, and they may be useful for subsequent analysis, perhaps with an expanded annotation. To aid users in more easily analyzing decoy-aligned sequences, decoy alignments are included in the SAM file produced by Salmon when the –writeMappings flag is provided. Alignments to decoy sequences are tagged in the SAM output with the XT:A:D tag, to allow them to be more easily identified and filtered from standard alignments to annotated transcripts. Finally, while there may be many cases in which the selective alignments to decoy sequences are useful in subsequent analysis, it is important to note that selective alignment is not splice-aware. Therefore, only fragments with reasonable-quality This manuscript introduces the idea of selective alignment over a reference sequence indexed using the pufferfish  data strWe attempted to normalize for some mapping-related differences between methods that have little to do with the ability of the aligner to appropriately find the correct loci for a read, and instead have to do with constraints placed on what constitutes a valid mapping. Specifically, when projecting to the transcriptome, STAR disallows orphan mappings . Likewise, it is recommended practice in existing alignment-based quantification tools , 15, 16,We explored differences that arise between quantification based on alignment of the sequencing reads to the genome and the transcriptome. We considered genomic alignment here to be the process of alignment to the genome—with the benefit of a known annotation—with subsequent projection to the transcriptome. That is, genomic alignment is characterized based on running STAR (with appropriate parameters) to align the reads to the genome, and then making use of the transcriptomically projected alignments output by STAR via the –quantMode TranscriptomeSAM flag (as would be used in, e.g., a STAR/RSEM11]11]-basedThe human GENCODE v29 reference includes transcripts as short as 8 bp, which is much shorter than a single sequencing read or the typical fragment length in most RNA-seq experiments. While RNA-seq might not be the appropriate method to quantify these transcripts, depending on the alignment method, they may have mapped reads and obtain non-zero expression values. In our analyses, we observed that lightweight mapping methods that do not perform end-to-end alignment tend to assign reads to shorter transcripts when there is an exact match. This effect has been explored in some detail by . In suchWe used Salmon v0.15.0 for quasi-mapping and Salmon v1.0 for SA and SAF, Bowtie2 version 2.3.4.3, STAR version 2.6.1b, tximport version 1.12.3, DESeq2 version 1.24.0, kallisto version 0.45.1, edgeR version 3.24.3, limma version 3.38.3, RSEM version 1.2.28, Trim Galore version 0.5.0, bedtools v2.28.0, HISAT2 version 2.1.0, StringTie2 version 2.1.1, sleuth version 0.30.0, and MashMap v2.0. All simulated datasets were generated using Polyester version 1.18.0.trim_galore-q 20 –phred33 –length 20 –paired < fastq file>For quality trimming the reads, we used the following command:STAR–genomeFastaFiles < fasta file> –sjdbGTFfile < gtf file> –sjdbOverhang 100Bowtie2defaultsalmon-k 23 –keepDuplicateskallisto-k 23For indexing, we use the following extra command line arguments, along with the regular indexing and threads parameters:SA and SAF–mimicBT2 –useEMquasi–rangeFactorizationBins 4 –discardOrphansQuasi –useEM –noSABowtie2–sensitive -k 200 -X 1000 –no-discordant –no-mixed –gbar 1Bowtie2_strict–sensitive –dpad 0 –gbar 99999999 –mp 1,1 –np 1 –score-min L,0,-0.1 –no-mixed –no-discordant -k 200 -I 1 -X 1000Bowtie2_RSEM–sensitive –dpad 0 –gbar 99999999 –mp 1,1 –np 1 –score-min L,0,-0.1 –no-mixed –no-discordant -k 200 -I 1 -X 1000STAR–outFilterType BySJout –alignSJoverhangMin 8 –outFilterMultimapNmax 20 –alignSJDBoverhangMin 1 –outFilterMismatchNmax 999 –outFilterMismatchNoverReadLmax 0.04 –alignIntronMin 20 –alignIntronMax 1000000 –alignMatesGapMax 1000000 –readFilesCommand zcat –outSAMtype BAM Unsorted –quantMode TranscriptomeSAM –outSAMattributes NH HI AS NM MD –quantTranscriptomeBan SingleendSTAR_strict–outFilterType BySJout –alignSJoverhangMin 8 –outFilterMultimapNmax 20 –alignSJDBoverhangMin 1 –outFilterMismatchNmax 999 –outFilterMismatchNoverReadLmax 0.04 –alignIntronMin 20 –alignIntronMax 1000000 –alignMatesGapMax 1000000 –readFilesCommand zcat –outSAMtype BAM Unsorted –quantMode TranscriptomeSAM –outSAMattributes NH HI AS NM MD –quantTranscriptomeBan IndelSoftclipSingleendSTAR_RSEM–outFilterType BySJout –alignSJoverhangMin 8 –outFilterMultimapNmax 20 –alignSJDBoverhangMin 1 –outFilterMismatchNmax 999 –outFilterMismatchNoverReadLmax 0.04 –alignIntronMin 20 –alignIntronMax 1000000 –alignMatesGapMax 1000000 –readFilesCommand zcat –outSAMtype BAM Unsorted –quantMode TranscriptomeSAM –outSAMattributes NH HI AS NM MD –quantTranscriptomeBan IndelSoftclipSingleendRSEMdefaultkallistodefault or –rf-stranded as appropriateFor quantification, we use the following extra command line, along with regular index and threads, with each tool we compare against:Additional file 1 Supplementary Material for “Alignment and mapping methodology influence transcript abundance estimation”. The file contains supplementary tables and figures for the manuscript.Additional file 2 Review history.


However, the alignment performance is largely affected by repetitive sequences in the reference genome, which extensively exist in species from bacteria to mammals. Aligning repeating sequences might lead to tremendous candidate locations, bringing about a challenging computational burden. Thus, most alignment tools prefer to simply discard highly repetitive seeds, but this may cause the true alignment to be missed. Using maximal approximate matches (MAMs) as seeds is an option, but MEMs seeds may fail due to sequencing errors or genomic variations in MEMs seeds. Here, we propose a novel sequence alignment algorithm, named MAM, which can efficiently align short DNA sequences. MAM first builds a modified Burrows-Wheeler transform (BWT) structure of a reference genome to accelerate approximate seed matching. Then, MAM uses maximal approximate matches (MAMs) seeds to reduce the candidate locations. Finally, MAM applies an affine-gap-penalty dynamic programming to extend MAMs seeds. Experimental results on simulated and real sequencing datasets show that MAM achieves better performance in speed than other state-of-the-art alignment tools. The source code is available at  The development of next-generation sequencing (NGS) technologies has led to a rapid decline in the sequencing cost and had a tremendous impact on genomic research  structure. MAM-index combines the conventional FM-index and an auxiliary data structure, which accelerates approximate string matching. We propose a MAMs based seeding algorithm to calculate the candidate locations. We present a novel sequence alignment tool, named MAM, that can efficiently align highly repetitive DNA sequences by using maximal approximate matches (MAMs) as seeds. To evaluate the performance of MAM, we performed alignment experiments on both simulated and real datasets, and compared our results with those of Bowtie and BWA-MEM. Experiments on simulated and real sequencing datasets show that MAM achieves better performance in speed than the other alignment programs.linit via the global FM-index. To reduce ambiguous candidate locations caused by highly repetitive seeds, we introduce maximal approximate matches as seeds to re-seed all initial seeds with more than m occurrences. The overview of MAM seeding is shown in linit = 20 bp initial seed based on the global FM-index, and obtain global suffix array interval  stores the initial index of the predecessor sequence in Rel. Given an i-th iteration seed si and a next iteration seed si+1 = pred(si) + si + succ(si), if si+1 occurs in the reference genome sequence, then pred(si) and succ(si) can be searched via sBWT. Supposing that pred(si) is rankpred ranked in all predecessor sequences and succ(si) is ranksucc ranked in all successor sequences, ranksucc should be a value of LtoRel[j] for j in the range  stores the pointer to si+1 in the MAM-index. With Rel[i], the seed jumps to the next iteration. The RA of seed CGACTA is shown in The RA consists of x nt exact match seeds in reads. If seeds occur more than k times, MAM iteratively extends seeds by finding approximate matches in both directions until the occurrences of seeds are less than k or seeds are not extensible. After seeding, MAM generates locations of seeds. All locations are sorted in ascending order, and all locations whose distances are less than d are placed within the same chain. Then, all chains are sorted by seeds number in descending order. After chains are sorted, MAM performs a striped Smith-Waterman algorithm for the top k chains. We choose the best score location as the best alignment.MAM follows the canonical seed-and-extend paradigm. MAM generates every Algorithm 3. In Algorithm 3, the function backwardSearchGlobal is a conventional backward search algorithm, and the function backwardSearchLocal is shown in Algorithm 4. C  is a function that contains the number of occurrences of lexically smaller characters in the i-th rotation sBWT. The function Occ  is the number of occurrences of character c in the sbwti[1 : k].The algorithm of alignment with MAM is shown in https://github.com/weiquan/mam.We have implemented MAM to align short reads to a reference genome. The default output format is SAM format. MAM is distributed under the GNU General Public License (GPL). The source code is available at The performance of MAM has been compared with those of the he most widely used alignment alignment tools BWA-MEM (version 0.7.17) and BOWTIE2 (version 2.3.5). All aligners were tested on two simulated datasets and two high-throughput sequencing (HTS) datasets to assess their speed, sensitivity, and accuracy. All benchmarks were conducted on a desktop computer with 32 GB of RAM and a 3.30 GHz Intel i9-7900X processor with a total of 10 CPU cores running Linux Ubuntu 18.04.We simulated 4 million 100 and 150 bp Illumina-like reads from the human genome GRCh38 using Mason2  and 4 million 148 bp reads sequenced with the Illumina HiSeq 2000 (SRA ID: SRR1766443) to the human reference genome (GRCh38). All aligners were run with the default settings. As the true alignment locations of reads are not known in real datasets, we define that an alignment is a good alignment if the alignment score between the read and the reference is higher than 85% of the highest score .The results are shown in Enormous amounts of short read aligners have been developed for fast and accurate alignment of reads to a reference genome. However, aligning repetitive DNA sequences to the reference genome is still a concern. Some of the aligners employ MEMs seeds to reduce candidate locations in repetitive regions. However, MEMs seeds may fail due to genomic variations and sequencing errors. To this purpose, we use MAMs seeds to filter candidate locations in the seeding stage. Although MAMs seeds could be searched via the conventional FM-index, it is ineffective.Herein, we propose a variation FM-index (MAM-index) to search MAMs seeds quickly and present a short read alignment tool (MAM). We have demonstrated the performance of MAM on aligning sequences to the human genome, and compared it with the most widely used alignment tools, BWA-MEM and BOWTIE2. The result shows MAM is more efficient than BWA-MEM and BOWTIE2 with similar accuracy and sensitivity. In addition, accuracy and sensitivity of MAM could be improved by using shorter initial seed length, which means MAM has the potential to align sequences to complex genomic regions. Although MAM requires more memory than BWA-MEM and BOWTIE2, memory is not a practical concern on modern computer servers.All datasets generated for this study are included in the article/supplementary material.WQ and GQ designed the algorithm of indexing and seeding. WQ implemented the algorithm and performed in silico experiments. DG co-wrote the manuscript and co-performed the experiments. BL and YW revised the manuscript and provided funding support.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Here we provide bioinformatic evidence that the Organo-Arsenical Exporter (ArsP), Endoplasmic Reticulum Retention Receptor (KDELR), Mitochondrial Pyruvate Carrier (MPC), L-Alanine Exporter (AlaE), and the Lipid-linked Sugar Translocase (LST) protein families are members of the Transporter-Opsin-G Protein-coupled Receptor (TOG) Superfamily. These families share domains homologous to well-established TOG superfamily members, and their topologies of transmembranal segments (TMSs) are compatible with the basic 4-TMS repeat unit characteristic of this Superfamily. These repeat units tend to occur twice in proteins as a result of intragenic duplication events, often with subsequent gain/loss of TMSs in many superfamily members. Transporters within the ArsP family allow microbial pathogens to expel toxic arsenic compounds from the cell. Members of the KDELR family are involved in the selective retrieval of proteins that reside in the endoplasmic reticulum. Proteins of the MPC family are involved in the transport of pyruvate into mitochondria, providing the organelle with a major oxidative fuel. Members of family AlaE excrete L-alanine from the cell. Members of the LST family are involved in the translocation of lipid-linked glucose across the membrane. These five families substantially expand the range of substrates of transport carriers in the superfamily, although KDEL receptors have no known transport function. Clustering of protein sequences reveals the relationships among families, and the resulting tree correlates well with the degrees of sequence similarity documented between families. The analyses and programs developed to detect distant relatedness, provide insights into the structural, functional, and evolutionary relationships that exist between families of the TOG superfamily, and should be of value to many other investigators. Ondb.org/) .Transporters play roles in numerous processes essential for life. The Transporter-Opsin-G protein-coupled receptor (TOG) Superfamily is exceptionally diverse, including channels, secondary carriers, and primary active transporters . CurrentWe have improved our methods by incorporating additional requirements to detect distant homology between pairs of transporter families. We rely on the transitivity property of homology  to infer homology between families, where the following requirements should be satisfied by pairs of candidate homologs: 1) significant sequence similarity; 2) topological agreement consistent with the TMS repeat units of the families involved; 3) overlap of the characteristic domains of both families in the alignment, 4) conservation of sequence motifs, and 5) structural similarity consistent with the repeat units of both families if 3D structures are available (See section: Detection of homology between pairs of families). Given the significant growth of publicly available sequence data since our first publication , we alsoSince the first member of the ArsP family TC: 2.A.119) was functionally characterized 9 was fun.Campylobacter jejuni, a pathogen causing gastroenteritis in humans, is prevalent in poultry and is resistant to the organic arsenic compound, roxarsone (4-hydroxy-3-nitrobenzenearsonic acid), which has been used as a food additive in the poultry industry to promote growth. Shen et al. [Campylobacter. Analysis of multiple C. jejuni isolates from various animal species revealed that the presence of an intact arsP gene is associated with elevated resistance to roxarsone. In addition, inactivation of arsP in C. jejuni resulted in a 4-fold reduction in the minimum inhibitory concentration (MIC) of roxarsone and nitarsone compared to the wild-type strain. Furthermore, cloning of arsP into a C. jejuni strain lacking a functional arsP gene led to 8- and 64-fold increases in the MICs of roxarsone and nitarsone, respectively. Neither mutation nor overexpression of arsP affected the MICs of inorganic arsenic including arsenite and arsenate. Moreover, acquisition of the arsP gene in C. jejuni accumulated less roxarsone than the wild type strain lacking the arsP gene. These results indicated that ArsP functions as an efflux transporter for extrusion of organic arsenic and contributes to resistance to these compounds in C. jejuni [n et al.  showed t. jejuni .Members of family KDELR (TC: 9.B.191) are involved in the selective retrieval of proteins that reside in the endoplasmic reticulum (ER). The ER-Golgi system has been studied using biochemical, genetic, and electron and light microscopic techniques, leading to an understanding of many aspects of trafficking from the ER to the Golgi apparatus . This inLactococcus lactis promoted transport of pyruvate across the membrane [Drosophila, and humans [Drosophila mutants lacking MPC1 display impaired pyruvate metabolism, with an accumulation of upstream metabolites and depletion of tricarboxylic acid cycle intermediates. Loss of yeast MPC1 results in defective mitochondrial pyruvate uptake, and silencing of MPC1 or MPC2 in mammalian cells impairs pyruvate oxidation. A point mutation in MPC1 provides resistance to a known inhibitor of the mitochondrial pyruvate carrier. Human genetic studies of three families with children suffering from lactic acidosis and hyperpyruvatemia revealed a causal locus that mapped to MPC1, changing single amino acids that are conserved throughout eukaryotes. Thus, MPC1 and MPC2 form an essential part of the mitochondrial pyruvate carrier [Members of family MPC (TC: 2.A.105) are involved in the transport of pyruvate, the end product of glycolysis, into mitochondria. This is an essential process that provides the organelle with a major fuel source. Herzig et al.  reportedmembrane . MPC1 and humans . MPC1 an carrier . MPCs ha carrier .E. coli strain lacks an inducible L-alanine export system and accumulates intracellular L-alanine with a reduction in the L-alanine export rate. When the mutant was used to clone genes that complement the dipeptide-hypersensitive phenotype, two uncharacterized genes, ygaW and ytfF, and two characterized genes, yddG and yeaS, were identified [ygaW had the most striking impact on both the intra- and extracellular L-alanine levels among the four genes identified, Hori et al. [ygaW gene in the non-L-alanine-metabolizing strain. The resulting isogenic mutant showed the same intra- and extracellular L-alanine levels as observed in the dipeptide-hypersensitive mutant obtained by chemical mutagenesis. When each gene was overexpressed in the wild-type strain, which does not intrinsically excrete alanine, only the ygaW gene conferred on the cells the ability to excrete alanine. In addition, expression of the ygaW gene was induced in the presence of the dipeptide. Thus, YgaW is likely to be the physiologically most relevant exporter for L-alanine in E. coli. More recently, two charged residues (R45 and D84) were found to be essential for AlaE efflux activity [Members of family AlaE (TC: 2.A.104) are involved in the excretion of L-alanine from the cell. A mutant that is hypersensitive to L-alanyl-L-alanine from a non-L-alanine-metabolizing entified . Overexpi et al.  disrupteactivity .Shigella flexneri bacteriophage SfX, SfV and SfII each has a 3-gene operon encoding a glucosyltransferase (GtrX), which is involved in full O antigen modification (serotype Y to serotype X conversion). Besides the gtrX gene, the other two genes in the gtr locus of SfX are also involved in the O antigen modification process. The first gene in the cluster (gtrA) encodes a small hydrophobic protein involved in the translocation of lipid-linked glucose across the cytoplasmic membrane. The second gene in the cluster (gtrB) encodes an enzyme catalysing the transfer of the glucose residue from UDP-glucose to a lipid carrier. The third gene (gtrX) encodes a bacteriophage-specific glucosyltransferase which is largely responsible for the final step, i.e., attaching the glucosyl molecules onto the correct sugar residue of the O antigen repeat unit. A three-step model for the glucosylation of bacterial O antigen has been proposed [Salmonella phage P22 also has genes involved in serotype conversion, and they are homologous to the Shigella phage operons cited above [E. coli also has these genes, probably because they were incorporated into the bacterial chromosome [Shigella SfV and SfX phage GtrX proteins have 4 TMSs [S. enterica subspecies I-specific LPS modification that enhances long-term intestinal colonization [Members of family LST (TC: 2.A.129) are involved in the translocation of lipid-linked glucose across the membrane and have a 4 TMS topology. proposed . Salmoneed above . E. colie 4 TMSs . The 12–nization .Transmembranal segments may contain compositional biases and low complexity regions that inflate alignment scores (of otherwise unrelated sequences) beyond thresholds of statistical significance , 26. ThuIn the following discussion, the position of a protein or family within the homology transitivity path is specified by appending the corresponding letter, within parenthesis, to the end of the accession , XP_018986354(C), etc.). A and D will always refer to the original query proteins within the two TCDB families being compared, but the name of the families can also be used  or 2.A.43.1.1(D)). Alignments will be abbreviated by concatenating letters with dashes . E-values are calculated with the Smith-Waterman algorithm as implemented in SSEARCH  unless oMembership to TOG was inferred by comparing all homologous proteins within a candidate family against a positive control comprised of the set of homologs for each of the established families in the TOG superfamily , and to −8). In this family, even more significant alignments can be found when comparing the repeats of different ArsP homologs. For example, −15) between two different halves of ArsP homologs PIU02666 and WP_094226599 that provides evidence for the 4-TMS repeat unit. Members of this family may also have 7 TMSs. −44) between 8-TMS ArsP member WP_069955515 (TC: 2.A.119.1.5) and its 7-TMS homolog KIL52798 that illustrates how the loss of the N-terminal TMS from a precursor protein with 8 TMSs explains well the origin of the 3+1+3 topology observed in ArsP and other TOG families, which can be more properly written as a 3+4 topology.The characteristic repeat unit of ArsP is 4+4 where the two 4-TMS units can be separated by regions containing 2 or 4 TMSs, but other compatible topologies are also observed. −9) with TMSs 1–3 of LCT homolog OAD01438(C), in agreement with the observation that 7-TMS proteins in family LCT lost the N-terminal TMS [−6; See We identified significant alignments between members of the ArsP family and the established TOG families LCT, TSUP, and NiCoT. inal TMS , decreas−11) between families ArsP(A) and TSUP(D) aligns TMSs 5–7 of ArsP member WP_082464241(B) with TMSs 5–7 of TSUP member AHF91483(C), which corresponds to the first 3 TMSs of the second repeat units in both proteins. Both proteins B and C have 8 TMS with a 4+4 topology. Only one of the aligned TMSs in the TSUP homolog is classified as simple by TMSOC [−6), and it covers all 4 TMSs of the second repeat unit.by TMSOC . Given t−8; −5; see −4; We identified the topology for KDELR(A) as 4+4 with borderline significance  between families KDELR(A) and Sweet(D), where all 7 TMSs in both proteins KXJ91449(B) and XP_010536596(C) are covered in the alignment. All TMSs in the alignment are complex according to the TMSOC classification [Family KDELR was found to be related to families Sweet and LCT within TOG. fication . Hydrophfication . All tog−9) and show good overlap of hydrophobic peaks (−5) without the need of projection, thus providing further support to the relationship between both families. The alignment shown in ic peaks . All TMS−34) suggests that members with 3 TMSs lost the N-terminal TMS as observed in several TOG families.Members of family MPC(A) (TC: 2.A.105) typically have 3 TMSs that probably originated from a 4-TMS precursor with loss of the N-terminal TMS. −10) aligns all 3 TMSs of MPC member XP_008085704(B) with TMSs 5–7 of the Sweet member BAJ94651(C). All TMSs involved in the alignment are complex according to the TMSOC classification [−3). The characteristic Pfam domains of families MPC (PF03650) and Sweet  belong to the same clan (CL0141) further supporting their relationship.Our analyses identified a relationship between families MPC(A) and Sweet(D). fication . This alThere are three recognized 3-TMS MPC isoforms known as MPC1, MPC2 and MPC3 types, where MPC3 (found in yeast) shows ~75% sequence identity to MPC2 , while M−8) with the 4 TMSs of the first repeat unit in ArsP(D) homolog WP_087291645(C). All TMSs involved in the alignment are complex according to the TMSOC classification [−7) the Pfam domain (PF11449) of family ArsP(D) onto the full length of AlaE(A) homolog WP_039030005(B), which provides additional support to the relationship between these families.Members of family AlaE have 4 characteristic TMSs. Our analyses identify a relationship between families AlaE(A) and ArsP(D). fication . We were−8) covers almost all 4 TMSs of protein B (−6), fully covering the aligned region (This family has 4 characteristic TMSs. We identified a relationship between families LST(A) and Sweet (D) through the proteins WP_020576852(B) and WP_068470014(C). rotein B  and the rotein B , althougd region .Furthermore, we identified additional significant but lower scoring alignments between these two families that completely cover the second repeat unit of Sweet homologs. Given this evidence we are confident that families LST and Sweet are related.The relationships among all families within the TOG superfamily  were tes−8) between PnuC(A) homolog OFX33391(B) and Sweet(D) homolog OWY93661(C). Only one TMSs in the alignment is classified as simple by TMSOC [It has been reported that families PnuC and Sweet have 3+1+3 topologies , 35. Eviby TMSOC . Both prby TMSOC , 35. Thiby TMSOC . Thus, f−8) was identified with family MR(D), see −4). Altogether, this evidence is not sufficient to justify the membership of family PNaS in the TOG superfamily. This exemplifies a well-known problem, namely that sequence similarity alone may not be sufficient to detect homology when comparing membrane proteins [Proteins in family PNaS show a 4+4+2 characteristic topology . When coproteins , 26.B. subtilis CaTA homologue YetJ (TC: 1.A.14.2.3) [Originally, we identified the CaTA family, formerly the TEGT family, as a potential TOG member due to significant sequence alignments with other TOG families  suggesting that members of CaTA lost the C-terminal TMS. The crystal structure of the .14.2.3)  revealed.14.2.3) . Alterna.14.2.3) –46. On t−13) were detected between members of NicO and the TOG-member NiCoT familiy. The significance of the alignment cannot be explained by the presence of TMSs with high compositional bias because all 6 TMSs in NicO members are complex or in the twilight zone [Members of the NicO family, a member of the LysE superfamily, typically have 6 predicted TMSs organized in a 3+3 topology that contrasts with the known 4+4 topology in the NiCoT family . Notwithght zone . Based o2+ and Co2+. Two possibilities thus exist to account for their appreciable levels of sequence similarity: divergence from a common origin, or convergence to form similar heavy metal ion binding sites within unrelated transmembrane domains. While we favor the former possibility, further studies will be required to prove or disprove this conjecture.It is interesting that the NicO and NiCoT families, each within different superfamilies and having internal repeats of 3 and 4 TMSs, respectively, exhibited significantly similar sequences, leading us to suggest that their dissimilar repeat units derived from a common origin. These two families, however, are specific for the same substrate, the divalent metal cations NiIn our approach, we scanned for conserved sequence regions across the expanded TOG superfamily using the MEME suite . Based o−3 and p-values < 10−6, a strong preference for TMSs 1–2 and 5–6 is apparent in most TOG families with 3 or 7 TMSs. These two pairs of TMSs correspond to TMSs 2–3 of the ancestral 4-TMSs repeat unit that gave rise to the TOG superfamily. That is, TMSs 1–2 correspond to TMSs 2–3 of the first 4-TMS repeat unit after loss of the first TMS, and TMSs 5–6 correspond to TMSs 2–3 of the second 4-TMS repeat unit. This is true for the vast majority of MAST hits in families Sweet , LCT , KDELR , MPC , MR  and GPCR . MAST matches are also observed with the equivalent TMSs 2–3 in 4-TMS members of families AlaE  and LST , as well as in the 8-TMS family NiCoT .Besides the discriminating power, the 50 aas MEME model reveals a clear pattern. If we only consider MAST matches showing E-values < 10−6) and highlighted all functional residues that fall within these regions. In the structure of xanthorhodopsin of the eubacterium Salinibacter ruber (PDB: 3DDL), a 7-TMS MR member and light-driven proton pump with a dual chromophore [Arabidopsis thaliana [Gallus gallus 7-TMS KDELR receptor (PDB: 6I6H) locked in the apo ER state, KDEL retrieval signal-bound Golgi state, and in complex with an antagonistic synthetic nanobody [To further investigate the implications of these particular segments of the proteins, we selected proteins with available 3D structures and extracted from the respective articles the annotated functional residues . Then, we mapped onto the structures the segments matched by MAST , the fact that key residues reside in these regions boosts our confidence. We should note that two families consistently have MAST hits mapping to other TMSs. The 8-TMS family ArsP maps it to TMSs 7–8  and the 7-TMS family HelioR finds it in TMSs 6–7 . These TMSs correspond to TMSs 3–4 of the basic 4-TMS TOG repeat unit. This suggests that the sequence profile encoded in the identified 50aa MEME model is composed of at least two similar regions as a result of the MEME model being built using the One Occurrence Per Sequence mode (OOPS), but the sequences of TMSs 1–2 and 5–6 are clearly dominant.To investigate the overall relationships among the new and preexisting families within the TOG superfamily, we selected a representative set of 306 proteins  and clusGiven that all of our homology inferences show Pfam domain overlap and TMS topologies compatible with the repeat units of the families involved, the relative confidence in our homology inferences can be expressed with a score that increases proportionally to the number of TMSs in the alignments and is inversely related to the magnitude of the E-value. We used this score to define 3 arbitrary levels of confidence: high, medium and low . Fig 12 Our methodology  has alloThe five new families , now ideMost surprising was the finding that the KDELR family belongs to the TOG superfamily, given that a molecular transport function for the KDEL receptors has not been demonstrated, but the significant structural similarity with the Sweet family confirmed its membership to TOG . These rAs mentioned in the introduction, the TOG superfamily was previously known to include several families of secondary carriers, light-driven primary active transporters as well as photoreceptors and photon-activated ion channels (TCs: 3.E.1 and 3.E.3), and G-protein coupled receptors . Several GPCRs, in addition to their receptor functions, can sometimes catalyze ion transport, probably by a channel mechanism, as well as transmembrane lipid flipping –56. The Oryza sativa [Arabidopsis thaliana [Expansion of the TOG superfamily, as suggested here, and the methodologies we have developed to detect distant homologous relationships will be useful to many investigators. In addition to the types of analyses described, these methodologies can be used to identify potential templates from remote homologs with available structural information to aid in predicting and solving 3D protein structures. For example, the recent definition and characterization of the Anoctamin superfamily TC: 1.A.17) established the relationships and topological similarities among the ANO (TC: 1.A.17.1), CSC (TC: 1.A.17.5), and 5 other families .A.17 est, 61. Thea sativa  and Arabthaliana , which ithaliana . Furtherthaliana , confirmhttps://github.com/SaierLaboratory).All programs developed for this project can be downloaded directly from our GitHub repository  set we selected 10 families for which no evidence of homology with any family in TOG has been reported in the literature. The application of multiple methods failed to identify relationships between TOG and the NC. Currently, families in the NC do not belong to any existing superfamily in TCDB, and their members contain at least 3 TMSs , but we Initial searches for strong repeat signals within single proteins were carried out with our program tmsRepeat. This program cuts a query transporter sequence into TMS bundles of predefined size (based on HMMTOP predictions), and non-overlapping bundles are aligned using SSEARCH . For eac−10 and coverage ≥ 60% of the smaller protein) with the same TMS topology as the query protein. Multiple alignments were generated with MAFFT [When clear repeat signals could not be detected with tmsRepeat, we retrieved all available candidate homologs from NCBI , then the domain was considered to be present in the query protein. This process is implemented in our program getDomainTopology.Query sequences were compared against Pfam  with the−15 and coverage > 70% of the smaller protein. To form the training set for motif identification with MEME [From the set of homologs extracted for each family as described in section “Homology detection between families”, we selected those showing E-value < 10ith MEME , we seleith MEME . This pr−50, convergence distance ≤ 10−7, and using the numerically correct (nc) object function to select the best motif in each of the, at most, 1000 iterations of the Expectation Maximization algorithm. We used MAST to scan for motif matches with an E-value < 10−3 and p-value < 10−3, but we focused our discussion on matches with p-value < 10−6. MEME was run in two modes: 1) every sequence in the training set contributes one site (OOPS), and 2) each sequence could contribute zero or one site (ZOOPS). We searched for the top 10 motifs with a width of 15–50 residues that showed an E-value < 10https://www.R-project.org/) to perform a hierarchical clustering; based on a distance matrix calculated from bit scores generated by local Smith-Waterman alignments as implemented in SSEARCH [https://www.gimp.org/). We extracted 361sequences available in TCDB for all families included in this report and selected a representative set of 306 proteins that maximizes the among-family divergences while preserving the relative sizes of the families. Sequences were clustered with our program mkProteinClusters [ SSEARCH . In a prN is the number of TMSs in the alignment. The underlying assumption is that the greater the number of TMSs in the alignment and the smaller the E-value, the more reliable the inferences are. The score was normalized to 1.0 based on the highest scoring inference, and three arbitrary levels of confidence were defined: high confidence (Score ≥ 0.6), medium confidence (0.6 > Score ≥ 0.4), and low (Score < 0.4). https://gephi.org/).Due to the lack of 3D structures in the new families being incorporated into TOG, our inferences relied primarily on four of the five criteria specified in When full protein 3D structures do not align properly, evidence of homology can still be detected by cutting structures of transporters into bundles of 3 or more transmembrane α-helices (α-TMSs) based upon to size of the repeat units in TOG families. We extracted α-TMSs from OPM  as well S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Fig−42). Notice how the 2 central TMSs of protein WP_094226599 are mostly aligned with gaps (interruptions in the red curve). D. Hydropathy of the 4-TMS alignment  that provides evidence for the repeat. Interruptions in the hydropathy curves of panels C and D indicate gaps in the corresponding sequence alignments.A representative alignment between proteins PIU02666 and WP_094226599, illustrating the 4-TMS repeat unit in ArsP as identified by AncientRep  see Met. Thin bl(TIF)Click here for additional data file.S2 Fig−44) between WP_069955515 (red) and KIL52798 (blue). Interruptions in the hydropathy curves of panel C indicate gaps in the sequence alignment. Thin black vertical lines with wedges in panels A-B delimit the region of these proteins involved in the alignment presented in panel C. The loss of the N-terminal TMS in homolog KIL52798, rather than the addition of a TMS in WP_069955515, is evident because 1) it is not part of the alignment; and 2) the Pfam domain PF03773 includes the first TMS.A. Hydropathy of 7-TMS ArsP homolog KIL52798. B. Hydropathy of 8-TMS ArsP member WP_069955515 (TC: 2.A.119.1.5). C. Hydropathy of the alignment Click here for additional data file.S3 Fig−49) between OAD01438 (red) and XP_011392522 (blue). Interruptions in the hydropathy curves of panel C indicate gaps in the sequence alignment. Thin black vertical lines with wedges in panels A-B delimit the regions of these proteins involved in the alignment presented in panel C. There are two pieces of evidence supporting the loss of the N-terminal TMS in homolog KIL52798, and thus a 3+4 topology in LCT members with 7 TMSs: 1) the first TMS is not part of the alignment; and 2) The similarity of the hydropathy curve between the first and second 4-TMS halves is evident (Panel B).A. Hydropathy plot of 7-TMS LCT homolog OAD01438. B. Hydropathy plot of 8-TMS LCT homolog XP_011392522. C. Hydropathy plot of the alignment Click here for additional data file.S4 Fig−47) between ArsP member Q8EJL9 and its homologue WP_082464241. C. Hydropathy plot of ArsP homolog WP_082464241. Note that both proteins Q8EJL9 and WP_082464241 share two properties: 1) the third hydrophobic peak is composed of two TMSs; and 2) there is an extra (not colored) C-terminal hydrophobic peak. Both properties can be easily observed from alignments with other ArsP members, for example WP_069955515 (TC: 2.A.119.1.5), and by the regions covered by the Pfam domain PF03773. D. Hydropathy of TSUP member Q9UYH7 (TC: 2.A.102.4.1). E. Hydropathy of the alignment  between TSUP member Q9UYH7 and its homologue AHF91483. F. Hydropathy of TSUP homolog AHF91483. G. Hydropathy of the 3-TMS alignment  between ArsP homologue WP_082464241and TSUP homologue AHF91483. Only the regions where hydrophobic peaks overlap are highlighted in the alignments. The full alignment in panel G is covered by the Pfam domains of both proteins, and the domain (PF03773) in WP_082464241 can be projected to AHF91483 , further supporting the relationship between the two families.Hydropathy plots are presented across the homology transitivity path between families ArsP and TSUP. Refer to the legend of (TIF)Click here for additional data file.S5 Fig−13) between ArsP member WP_099137450 and its homologue WP_066228546. C. Hydropathy plot of ArsP homolog WP_066228546. Note the extra N-terminal TMS in protein WP_066228546, which can be easily observed both from the region that aligns with member WP_099137450 and the region covered by the Pfam domain PF03773. D. Hydropathy plot of NiCoT member Q7S3L8 (TC: 2.A.52.1.8). E. Hydropathy plot of the alignment  between NiCoT member Q7S3L8 and its homologue PHH64764. F. Hydropathy of NiCoT homolog PHH64764. G. Hydropathy plot of the 4-TMS alignment  between ArsP homologue WP_066228546 and NiCoT homologue PHH64764. Only the regions where hydrophobic peaks overlap are highlighted in the alignments. The alignment in panel G is covered by the Pfam domains of both proteins, and the domain (PF03773) in WP_066228546 can be projected to PHH64764  further supporting the relationship between these two families.Hydropathy plots are presented across the homology transitivity path between families ArsP and NiCoT. Refer to the legend of (TIF)Click here for additional data file.S6 Fig−22) between XP_003307390 (red) and XP_020093034 (blue). Interruptions in the hydropathy curves of panel C indicate gaps in the sequence alignment. Thin black vertical lines with wedges in panels A-B delimit the regions of these proteins involved in the alignment presented in panel C. Notice that the alignment contains only hydrophobic peaks 2–8 in XP_020093034. The exclusion of the first hydrophobic peak in XP_020093034 from the alignment provides evidence supporting the loss of the N-terminal TMS in XP_003307390 and other 7-TMS family members.A. Hydropathy plot of 7-TMS KDELR homolog XP_003307390. B. Hydropathy plot of 8-TMS KDELR homolog XP_020093034. C. Hydropathy plot of the alignment Click here for additional data file.S7 Fig−20) between KDELR member P24390 and its homologue PIA50795. C. Hydropathy plot of KDELR homolog PIA50795. Note that the alignment starts in the second hydrophobic peak of homolog PIA50795, which further supports the loss of the N-terminal TMS from KDELR homologs with 7 TMSs. D. Hydropathy plot of LCT member Q60441 (TC: 2.A.43.3.1). E. Hydropathy plot of the alignment  between LCT member Q60441 and its homologue KXN87232. F. Hydropathy of LCT homolog KXN87232. G. Hydropathy plot of the 5-TMS alignment  between KDELR homolog PIA50795 and LCT homolog KXN87232. Only the regions where hydrophobic peaks overlap are highlighted in the alignments. The alignment in panel G includes most of the Pfam domains of both proteins. In addition, KDELR domain (PF00810) is directly found in LCT homologue KXN87232  without the need of projection, further supporting the relationship between both families.Hydropathy plots are presented across the homology transitivity path between families KDELR and LCT. Refer to the legend of (TIF)Click here for additional data file.S8 Fig−14) between the full proteins. D. Hydropathy plot of the 4-TMS alignment  that provides evidence for the repeat. The good overlap of the hydropathy curves increases the significance of the alignment. Interruptions in the hydropathy curves of panels C and D indicate gaps in the corresponding sequence alignments.A representative alignment between 8-TMS proteins WP_027672312 and OWU65212 illustrates the 4-TMS repeat unit in PnuC as identified by AncientRep . Thin ve(TIF)Click here for additional data file.S9 Fig−23) between OFX33391 (red) and WP_092670402 (blue). Interruptions in the hydropathy curves of panel C indicate gaps in the sequence alignment. Thin black vertical lines with wedges in panels A and B delimit the regions of these proteins involved in the alignment presented in panel C. The loss of the N-terminal TMS in homolog OFX33391 is supported by the fact that the first TMS in WP_092670402 (panel B) is not part of the alignment shown in Panel C.Representative alignment supporting the loss of the N-terminal TMS in PnuC members with 7 TMSs. A. Hydropathy of 7-TMS PnuC homolog OFX33391. B. Hydropathy of 8-TMS PnuC homolog WP_092670402. C. Hydropathy of the alignment Click here for additional data file.S10 Fig−5) between TMSs 1–4 (blue) and TMSs 5–8 (green). Interruptions in the hydropathy curves indicate gaps in the sequence alignment.The repeat unit of Sweet members with 7 TMS is 3+1+3 where TMSs 1–3 are highly similary to TMSs 5–7. Here we present a representative example of a Sweet homolog with 8 TMSs showing a 4-TMS repeat as identified by our program tmsRepeat see . A. Hydr(TIF)Click here for additional data file.S11 Fig−19) between ANC68268 and PFH37642. D. Hydropathy plot of 3-TMS Sweet member C3WG44 (TC: 2.A.123.2.3). E. Hydropathy plot of 4-TMS Sweet homolog WP_082029922. F. Hydropathy plot of the alignment  between ANC68268 and PFH37642. The loss of the N-terminal TMS is supported by the fact that the first TMS in proteins PFH37642 and WP_082029922 is not part of their respective alignments, and that all TMSs in proteins ANC68268 and C3WG44 are aligned.Representative alignments of Sweet homologs supporting the loss of the N-terminal TMS from original proteins with 8 (panels A-C) and 4 TMSs (panels D-F). The thin black bars with wedges in panels A, B, D, and E, delimit the regions of the proteins that participate in the alignments shown in panels C and F. Interruptions in the hydropathy curves of panels C and F indicate gaps in the corresponding sequence alignments. A. Hydropathy plot of 7-TMS Sweet member ANC68268 (TC: 2.A.123.1.27). B. Hydropathy plot of 8-TMS Sweet homolog PFH37642. This is the same protein used in (TIF)Click here for additional data file.S12 Fig−22) between Q8EDN0 and its homologue OFX33391. C. Hydropathy plot of 7-TMS PnuC homolog OFX33391. D. Hydropathy plot of Sweet member H3GD93 (TC: 2.A.123.1.22). This protein consists of the quadruplication of a 7-TMS precursor protein. E. Hydropathy plot of the 7-TMS alignment  between H3GD93 and its homologue OWY93661. Note that OWY93661 has significant alignments  with all 4 repeats in H3GD93; however, we present only the top scoring alignment with the fourth repeat as indicated in panel D. F. Hydropathy of the Sweet homolog OWY93661. G. Hydropathy plot of the 4-TMS alignment  between PnuC homolog OFX33391 and Sweet homolog OWY93661. Only the regions where hydrophobic peaks overlap are highlighted in the alignments. The alignment of hydrophobic peaks between OFX33391 (peaks 2–5) and OWY93661 (peaks 3–7) is not consistent with their common topologies (3+1+3 or 3+4), suggesting that the evolution of the TMS architecture in family PnuC followed a different path as compared to families in the TOG superfamily, possibly involving an internal rearrangement of TMSs [Hydropathy plots are presented across the homology transitivity path between families PnuC and Sweet. Refer to the legend of  of TMSs .(TIF)Click here for additional data file.S13 Fig−40) between the full proteins. D. Hydropathy plot of the 4-TMS alignment  that provides evidence for the repeat. Interruptions in the hydropathy curves of panels C and D indicate gaps in the corresponding sequence alignments.A representative alignment between proteins WP_015713122 and OPL07780 with 10 hydrophobic peaks each, suggests the 4-TMS repeat unit in PNaS as identified by AncientRep . Note th(TIF)Click here for additional data file.S14 Fig−46) between M7AKZ4 and its homolog WP_026901115. C. Hydropathy plot of PNaS homolog WP_026901115. D. Hydropathy plot of MR member Q12117 (TC: 3.E.1.4.6). E. Hydropathy plot of the 7-TMS alignment  between Q12117 and its homologue AJP85873. F. Hydropathy of the MR homolog AJP85873. G. Hydropathy plot of the 5-TMS alignment  between PNaS homolog WP_026901115 and MR homolog AJP85873. Only the regions where hydrophobic peaks overlap are highlighted in the alignments. Note that 1) relative to the full protein WP_026901115 (panel C), the alignment in panel G starts in TMS 6, or the second TMS of the second 4-TMS repeat unit, and relative to AJP85873 (panel F) the alignment starts on the third TMS, or TMS 4 of the first 4-TMS repeat unit considering the loss of the N-terminal TMS in family MR [Hydropathy plots are presented across the homology transitivity path between families PNaS and MR. Refer to the legend of (TIF)Click here for additional data file.S15 Fig−4) between the full proteins. D. Hydropathy plot of the 3-TMS alignment  that provides evidence for the repeat. Interruptions in the hydropathy curves of panels C and D indicate gaps in the corresponding sequence alignments.A representative alignment between proteins EFQ46215 and WP_005742819 supporting the TMS topology 3+3+1 in agreement with available 3D structural data. Thin black bars with wedges delimit the regions involved in the alignment of the two full-length proteins. Orange and cyan bars highlight hydrophobicity peaks  for each full protein, respectively. A. Hydropathy plot of protein EFQ46215. TMSs 1–3 (shaded in dark gray) participate in the alignment shown in panel D. B. Hydropathy plot of protein WP_005742819. TMSs 4–6 (shaded in dark gray) participate in the alignment shown in panel D. C. Hydropathy plot of the alignment Click here for additional data file.S16 FigSalinibacter ruber (PDB: 3DDL). The segment of TMSs 1–2 matched by MAST is shown in red, the retinal cofactor is shown in cyan, and residues making contact with retinal (< 6 Å) within the MAST region are shown in yellow. All other residues are grayed.Cartoon representation of the 7-TMS xanthorhodopsin structure in (TIF)Click here for additional data file.S17 FigArabidopsis thaliana (PDB: 5XPD). The segment of TMSs 1–2 matched by MAST is shown in red, the substrate analog, 2’ deoxycytidine 5’ monophosphate, is shown in cyan, and residues making contact with the substrate within the MAST region are shown in yellow. All other residues are grayed.Cartoon representation of the 7-TMS SWEET13 structure in (TIF)Click here for additional data file.S18 FigGallus gallus (PDB: 6I6H). The segment of TMSs 1–2 matched by MAST is shown in red, the TAEKDEL signal peptide bound to the pocket is shown in cyan, and residues making contact with the peptide within the MAST region are shown in yellow. All other residues are grayed.Cartoon representation of the 7-TMS KDEL receptor structure in (TIF)Click here for additional data file.S1 FileFolders and ouput files are compressed in a zip file. Folder MEME contains the motif models; folder MAST contains the motifs matching the training sets, and folder DATA contains both the training and test sets. Check the included README file for a detailed description.(ZIP)Click here for additional data file.S2 FileSequences were renamed to facilitate visualization of the tree. The family name is followed by the 2 last digits of the TC numbers, separated by underscores, to identify the sytems: Family names followed the following convention: SW (Sweet), TS (TSUP), LC (LCT), NI (NiCoT), OS (OST), MR (MR), HE (HelioR), GP (GPCR), AR (ArsP), KD (KDELR), MP (MPC), and LS (LST).(FAA)Click here for additional data file.S3 FileIndividual sequences are named following the same format as (TREE)Click here for additional data file.