SEQSIM – A novel bioinformatics tool for comparisons of promoter regions – a case study of calcium binding protein spermatid associated 1 (CABS1)

SEQSIM – A novel bioinformatics tool for comparisons of promoter regions – a case study of calcium binding protein spermatid associated 1 (CABS1) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SEQSIM – A novel bioinformatics tool for comparisons of promoter regions – a case study of calcium binding protein spermatid associated 1 (CABS1) Joy Ramielle L. Santos, Weijie Sun, A. Dean Befus, Marcelo Marcet-Palacios This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5441650/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jun, 2025 Read the published version in BMC Bioinformatics → Version 1 posted 10 You are reading this latest preprint version Abstract Background Understanding transcriptional regulation requires an in-depth analysis of promoter regions, which house vital cis-regulatory elements such as core promoters, enhancers, and silencers. Despite the significance of these regions, genome-wide characterization remains a challenge due to data complexity and computational constraints. Traditional bioinformatics tools like Clustal Omega face limitations in handling extensive datasets, impeding comprehensive analysis. To bridge this gap, we developed SEQSIM, a sequence comparison tool leveraging an optimized Needleman-Wunsch algorithm for high-speed comparisons. SEQSIM can analyze complete human promoter datasets in under an hour, overcoming prior computational barriers. Results Applying SEQSIM, we conducted a case study on CABS1 , a gene associated with spermatogenesis and stress response but lacking well-defined functions. Our genome-wide promoter analysis revealed 41 distinct homology clusters, with CABS1 residing within a cluster that includes promoters of genes such as VWCE, SPOCK1 , and TMX2 . These associations suggest potential co-regulatory networks. Additionally, our findings unveiled conserved promoter motifs and long-range regulatory sequences, including LINE-1 transposable element fragments shared by CABS1 and nearby genes, implying evolutionary conservation and regulatory significance. Conclusions These results provide insight into potential gene regulation mechanisms, enhancing our understanding of transcriptional control and suggesting new pathways for functional exploration. Future studies incorporating SEQSIM could elucidate co-regulatory networks and chromatin interactions that impact gene expression. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background The regulation of the initiation of transcription is an essential and complex process involving various regulatory nucleotide sequences adjacent to genes. These cis-regulatory elements (CREs) include promoters, enhancers, silencers and other elements that determine when a gene is expressed. Other regulatory elements that can be more distal to the gene start site, such as transposable elements, insulators, boundary elements, and DNA that code for non-coding RNAs, add further complexity to the regulation of gene expression ( 1 , 2 ). For example, sites where CTCF binding factor, a protein that binds specifically to the CCCTC motif, act as insulators and facilitate the reorganization of 3D chromatin structure. This is important in bringing promoters, enhancers and transcriptional machinery together to facilitate initiation ( 3 ). Although all human genes are differentially regulated at the transcriptional level, there are common regulatory elements used according to gene type. For example, elements such as NF-kb binding sites are present in inducible genes but absent in house-keeping genes ( 4 , 5 ). By performing a genome-wide analysis of promoter sequence homology, we will more clearly elucidate the diversity of human promoters and assess the level of similarity within these sequence clusters. We hypothesize that promoters that belong to the same homology cluster can then be aligned based on sequence to highlight conserved regulatory elements. This novel analysis will also help predict groups of genes that are likely to be co-regulated and potentially highlight co-regulation networks. In an effort to understand human promoter sequence diversity, Gagniuc et al. performed sequence homology analysis of 8512 promoter sequences available in a promoter sequence database ( 6 ). The authors categorized promoters into 10 groups according to a 2-dimensional (2D) analysis consisting of plots of Kappa Index of Coincidence vs C + G % within the − 499 to + 100 positions relative to the transcription initiation site. This paper suggested categorizing the promoters according to the presence of promoter elements (TATA box, GC-box and CCAAT-box), DNA curvature, or nucleosome positioning( 7 – 12 ). Unfortunately, this approach was limited by the simplistic nature of 2D comparisons, leading to strictly tabular analyses of promoter types, and was thus unable to group promoters based on predicted functional families. Other limitations of this study were the small number of promoters available in databases at the time and a relatively short selected DNA sequence e.g. (-1950 to -522) ( 13 ). To overcome these limitations, we performed a comprehensive promoter homology analysis of 2000 nucleotides upstream of every human gene. Commonly used sequence similarity tools, like Clustal Omega, have file size restrictions that limit full-genome dataset analysis. Thus, import of a genome-wide dataset using Clustal Omega is not feasible due to file size limitations and prohibitively lengthy computational times, estimated to be > 1 year to complete using modern equipment at Compute Canada servers. Thus we developed an in-house software Sequence Similarity (SEQSIM) that employs a sequence similarity algorithm based on the Needleman-Wunsch algorithm ( 14 ) with extensive modification to speed up performance. SEQSIM enabled fast, pairwise comparison of promoters of every human gene in the Genome Reference Consortium Human Build 38 (GRCh38.p14). SEQSIM could be especially useful in the study of under studied genes. We selected calcium binding protein spermatid-associated 1 ( CABS1) , a poorly characterized gene, as a case study to evaluate SEQSIM. The CABS1 protein is most abundant in the testis, and found in salivary glands ( 15 ). Recent analysis of GeoData has detected CABS1 mRNA widely expressed in human tissues( 16 ). To date, CABS1 has been associated with spermatogenesis in several species and with acute psychological stress in humans( 17 – 20 ). A 7 amino acid peptide sequence in human CABS1 near its carboxyl terminus has anti-inflammatory activity. Using CABS1 as a case study presents a unique challenge and opportunity as it does not have another homologous human gene or known protein isoforms. In silico studies suggested that CABS1 likely interacts directly with other proteins( 21 ). Thus, a SEQSIM analysis of CABS1 promoter and identification of other genes with similar promoter sequences may help reveal CABS1-interacting partners or proteins involved in a CABS1-associated network. 2. Methods The pipeline involves: data mining of genome data using a Python script; utilization of a specialized scoring function to draw comparisons between promoter sequences (SEQSIM software); homology matrix visualization and data clustering using various programs outside of SEQSIM; and validation using 3rd party software. This pipeline is summarized in Fig. 1 . 2.1 – SEQSIM Software Development The software, SEQSIM (Sequence Similarity), was created to perform DNA sequence similarity analyses at a rate of 27 x 10 6 comparisons per minute, completing a full human genome analysis in under 1 hour. SEQSIM imports a list of named sequences and compares all sequences against each other to generate a sequence homology matrix with a percent similarity based on our comparison algorithm. 2.1.1 - Data Mining and Preparation of Comma Separated Values (CSV) Import File To prepare the input files required for SEQSIM, a data mining python script was used to extract information from the GRCh38. Chromosome information was downloaded in text format from the NCBI ( https://www.ncbi.nlm.nih.gov/gene ) with the corresponding chromosome search criteria, e.g. 1[CH] AND human[ORGN] . The script then extracted information such as the nominal gene symbol and name used to reference the promoter, gene type, gene chromosomal location and coordinates, directionality, gene description and gene exon count. Next the chromosome sequence was downloaded from NCBI’s GenBank repository ( 22 ). NC_000001, refers to chromosome 1. Each chromosome FASTA sequence was downloaded separately. The first nucleotide of each gene and gene orientation was then used to extract the 2000 nucleotide UGRs in the 5’ to 3’ direction. The program compiled all this information into a comma separated values (CSV) file. For SEQSIM, an additional CSV file was created with the identification of the sequence in the first column and the sequences to be analyzed in the next column. All sequences were required to be equal in size (2000 nucleotides) and were imported into the program as a string, a sequence of non-numerical characters. 2.1.2 - Comparison Algorithm The program currently defines a pairwise similarity score, normalized in the range of 0 and 1 between two nucleotide sequences (strings) of the same size (N = 2000 nucleotides). An anchoring sequence is identified, X, and aligned to a second sequence, Y (comparator sequence). When both sequences are perfectly aligned so that both 2000 nucleotide (i.e.: N = 2000) sequences are squared to each other, we call this position 1 ( \(\:i\) ). At position 1 ( \(\:i\) = 1), the program finds the longest string of nucleotides within the anchoring sequence that corresponds exactly to a segment in the comparator sequence. A match of 1 nucleotide corresponds to a value of 1 and each subsequent nucleotide match adds 1 point. When a mismatch is discovered, implying that the two sequences are not identical, the comparator sequence Y is shifted left by one nucleotide (ie: comparing position 1 of the anchoring sequence with position 2 of the comparator sequence) and another comparison check is conducted at this second position. It is important to note that despite detecting mismatches, SEQSIM still compares sequences downstream of mismatched nucleotides. This process repeats until 2000 comparisons have been made between the anchor sequence X and the entire comparator sequence Y, creating a row of data that identifies the length of the segment in the comparator sequence that exactly matches a segment in the anchor sequence. This process is repeated for positions 2 to 2000 of the anchoring sequence to generate 1999 more rows of data and 4,000,000 data points for further processing (see Fig. 2 and Supplemental Fig. 1 for a more comprehensive example). In the resulting array, the maximum value generated from each positional comparison on the anchor sequence is then carried into our scoring algorithm (Supplementary Fig. 1 for detailed math functions). The scoring function assigns a value between 0 and 1, which can then be converted into a percentage. Higher scores are assigned to sequences that share long segments of matching nucleotides. 2.1.3 - User Interface Currently, no graphical user interface is available for SEQSIM. More information and a sample of the code can be found in the Supplementary Materials. 2.2 - Cluster Analysis Cluster analysis of promoter similarity was performed using the open-source, network visualization software, Gephi (version 0.9.2). Data used in the analyses consisted of a set of nodes, the promoter gene ID, and edges, their pairwise similarity score. To visualize the network, Gephi’s continuous graph layout algorithm, ForceAtlas2, was implemented due to its ability to graph clusters in large datasets with high precision and speed. The algorithm used a force-directed approach to layout the nodes in 2D space, with nodes with higher similarity drawn closer together. ForceAtlas2 also utilizes a gravity function that allows nodes to stay clustered and prevent them from drifting apart ( 23 ). Once the data was imported to Gephi, default settings for ForceAtlas2 were used, including a gravity value of 1.0. To generate images, the “Prevent Overlap” option under Behavior Alternatives was enabled. ForceAtlas2 was run until convergence was achieved. Nodes that did not appear to cluster were filtered using modularity filters, such as degree distribution. The filter range parameters displayed nodes that had greater than 10 connections (edges) to other nodes. 2. 3 - Heatmap Generation Heatmaps were generated using conditional formatting in Microsoft Excel. Inputs for each heatmap consisted of a matrix of numerical values (the similarity scores, S) between promoters within a specific chromosome in sequential order. The color scale chosen represented the range of values within the data set, red being a high similarity score and white a low score. Each chromosome matrix was imported into Excel. The conditional formatting tool was then applied. Under the “highlight cell rules” option, a custom rule was created that utilized a 3-color scale. Depending on the chromosome, the color scale could be adjusted to match the specific data being analyzed. Red indicated high similarity scores and white low scores. 2.4 - Validation with Clustal Omega and Multiple Sequence Alignment The promoter sequences used in SEQSIM were exported into FASTA format. The resulting file was imported into the online server for Clustal Omega, a widely used tool for multiple sequence alignment (MSA). Due to the upload file size limitations of Clustal Omega, validations were conducted piecewise, 100 by 100 UGRs per analysis. The resulting Percent Identity Matrices generated using Clustal Omega were then visualized using the methods described in Section 2.3 . For some datasets, Clustal Omega automatically generated an identity matrix with a different order of UGRs. Rearranging the data to match pre-existing matrices was conducted in Microsoft Excel. Clustal Omega heatmaps and SEQSIM heatmaps were then compared to each other and superimposed to identify similarities and obvious differences. Alignments from Clustal Omega were downloaded in FASTA file format. Similar UGR matches to that of CABS1 , or sequences of interest were visualized using NCBI’s Multiple Sequence Alignment Viewer 1.23.0 by uploading the resulting FASTA file to the server. The promoter of CABS1 , our case study gene, was then set as the “anchor” so all other sequences were compared relative to CABS1’s promoter. The other sequences were also arranged from highest to lowest homology to the anchor. For clarity, the resulting alignment was colored green for matching nucleotide sequences and red for differences. Any large regions of interest were searched in NCBI’s nucleotide BLAST (Basic Local Alignment Search Tool) server. To visualize and analyze the extended 10000 nucleotide alignments, we also used JalView Desktop, a tool used for in-depth sequence analysis ( 24 ). 2.5 – Determination of Homology Island Boundaries To determine the boundaries of highly homologous regions, referred to as “islands” between promoters and an anchor (reference) sequence, a systematic approach based on sequence alignment and homology scoring was utilized. The starting point of an “island” was defined as the position where 10 consecutive nucleotides in the promoter sequence exactly matched the corresponding nucleotides in the anchor sequence. A sliding window approach was used to compute a homology score across the sequences. Using a 5-nucleotide window, a score of 1 was assigned for an exact match and 0 for each mismatch, calculating the homology score as the number of matches divided by the window size (e.g., 4 matches in a 5-nucleotide window equals a score of 0.8). This window was shifted by 1 nucleotide along the entire comparison length. When the homology score first reaches 0, indicating no matches within that window, the nucleotide coordinate marking the end of the island was determined by subtracting the window size (5 nucleotides) from the coordinate of the first 0 score. 3. Results 3.1 – SEQSIM Generated Score Matrix and Cluster Map SEQSIM generated a 57064 x 57064 matrix of similarity scores between the promoters of every human gene. Due to computational restraints, we only performed a Gephi ForceAtlas2 cluster analysis of approximately the top 10% most similar promoters of the complete matrix. The Louvain method to determine modularity gave a score of 0.840 discovering 41 total communities or clusters (Fig. 3 A-C). The largest cluster (cluster 1) had 402 promoter members and the smallest (cluster 41) just 1 member. The 5 largest clusters were significantly larger in size than the remaining cluster population. These 5 main clusters contained more than 100 promoters (nodes) each, adding up to 67.4% of all clustered promoters (Fig. 3 B). The clustering algorithm, ForceAtlas2, also showed relationships among the clusters. The closer the clusters are together (i.e.: the edges are shorter), the more similar their promoters. For example, genes within Cluster 3 and Cluster 4 have more similar promoters to each other than with Cluster 2. We found no correlation between SEQSIM clustering and chromosomal location, i.e., promoters in a single cluster could be from many chromosomes. However, it was interesting that some of the smaller, satellite clusters featured promoters of genes entirely from a single chromosome. Such clusters ranged in size from 1 to 32 promoter nodes. For example, all 32 members of the 12th largest cluster were promoters from chromosome 11, and all 17 members of the 23rd largest cluster were from chromosome 1. Conversely, the main clusters contained promoters from nearly every chromosome. Proportionally, the number of promoters from each chromosome in a cluster varied greatly. The CABS1 promoter is located on Cluster 3, the third largest cluster comprised of 385 members, with 259 members directly connected to CABS1. The promoter for CABS1 is highly connected to many others within the cluster, which corresponds to its increased node size. These included many promoters for non-coding RNA and pseudo genes. According to our algorithm, the promoter of CABS1 (red node in Fig. 3 B) is highly similar to the promoters of the protein-coding genes VWCE, SPOCK1, THSD4, RNF39 , and TMX2 . Not surprisingly, these highly homologous promoters were validated in Clustal Omega with homology scores of 78.70%, 81.99%, 82.89%, 96.54%, and 77.09% to CABS1 promoter. Interestingly, these 5 genes were inter-chromosomal, not found on chromosome 4 like CABS1 (see Table 1 ). Of the 385 promoters in the CABS1 cluster, only 20 were in chromosome 4 with CABS1 , and of the 20, 8 were unannotated genes, 9 were pseudogenes, 2 were long non-protein coding RNA, leaving only one as a promoter for a protein coding gene: integrin binding sialoprotein ( IBSP ). We attempted to characterize each cluster more thoroughly by identifying potential functional similarities through bioinformatics databases such as the Database for Annotation, Visualization and Integrated Discovery (DAVID) ( 25 , 26 ). Through DAVID, we searched for biological processes, cellular components, molecular functions, pathways and more. However, when queried, no single, defining similarity was found in functional annotations within clusters. Interestingly, when clustered using DAVID, our SEQSIM clusters often fragmented into smaller sub-families. For example, the genes of 319/385 members within SEQSIM cluster 3 separated into 10 sub-families with enrichment scores as high as 2.33 based on a variety of annotations like molecular function, gene sequence features and biological processes. Table 1 – Summary Table of Promoters of interest related to CABS1. Promoters of known genes in the Inter-Chromosomal and Intra-Chromosomal sections with high similarity to CABS1 promoter. Inter-chromosomal promoters were found to be clustered with CABS1. Also, in the table are SMR3A and 3B, genes of interest due to evolutionary history and potential functional parallels with SMR1 in rat and CABS1 in human (See Discussion). The promoters of these genes did not cluster with CABS1 and did not show high similarity in promoters. 3.2 – Heatmap of Adjacent Promoters around Case Study promoter of CABS1 in Chromosome 4 The heatmap was generated for 200 promoters for genes flanking the CABS1 gene loci. Promoters are shown in the same order as they appear in chromosome 4 (Fig. 4 A). Within Chromosome 4, and within 50 genes on either side of CABS1 , the promoters for LINC02562 , MUC7 and UGT2B11 closely resemble the promoter of CABS1 . Other matches included genes that have not yet been defined (i.e: LOC genes). Comparative validation using Clustal Omega (Fig. 4 B) provided similar results. With Clustal Omega, two additional promoters for genes RNU4ATAC9P and OPRPN showed a homology score greater than 40% within the 50 closest genes to CABS1 . Interestingly, the latter is associated with male reproductive function among other roles like CABS1 ( 27 ), see Table 1 . The MSA of the CABS1 promoter to their homologous counterparts ( LOC105377261 , UGT2B11 , MUC7 , LINC02562 , LOC107986230 , RNU4ATAC9P and OPRPN ) revealed large “islands” of 100% similar regions. In a BLAST analysis, these regions were determined to be portions of a long-interspersed nuclear element-1 (LINE-1) with a length of 10,031 nt. To fully investigate how much of the LINE-1 element was present in the promoter of CABS1 and the other associated promoters, we conducted an extended sequence analysis of 10k nt. Among these 7 promoters, CABS1 retained the largest segment of the LINE-1 element, specifically a segment of 5452 nt spanning open reading frames 1 and 2 of LINE-1 (Fig. 5 ). 3. 3 – Interesting Patterns in Other Chromosomes While our primary focus was on the case study gene CABS1 , we extended the comprehensive analysis to generate similarity heatmaps for every chromosome (beyond the scope of this manuscript). The heatmaps for each chromosome revealed intriguing patterns of similarity between adjacent promoters, which appear to correlate with loops observed in topologically associated domains (TAD). Figure 6 A, shows a small section of the first 100 promoters on chromosome 1, with similarity scores shown as color intensity. Striking patterns of similarity in the first 100 promoters resembled a series of diagonal slashes (arrows in Fig. 6 B). Additionally, we observed more intriguing patterns such as the checkerboard-like background (open arrow heads point at both the light and dark segments of the checkerboard) emphasized in Fig. 6 B. These patterns, though distinct from the diagonal slashes, may inform encoded genomic interactions. In Fig. 6 C (without digital enhancement seen in Fig. 6 B), distinct bright red (i.e.: very similar promoters) clusters of similarity among adjacent promoters were also observed. One example is shown with the closed arrowhead in 6B. It’s interesting to note that when highly similar adjacent promoters form clusters, other promoters around the islands are highly dissimilar. This is reminiscent of how TADs form physical loops in chromatin and interact at a higher frequency within the TAD versus adjacent TADs. These results highlight that genes that are looped into close proximities within their TADs also share similar promoters. 4. Discussion It is widely accepted that promoters are important in conditions like cancer, diabetes and neurodegenerative disorders ( 28 – 31 ). These promoters, when changed, can alter the start of gene transcription and consequently gene expression significantly. Our study provides new insight into the number of promoter homology clusters in the human genome. Importantly, within the first 100 genes of chromosome 1, we discovered a pattern of promoter homology not previously seen, in which genes share promoter homology with a limited number of adjacent genes. This homology results in a checkerboard pattern of similarity observed in heatmaps (See Fig. 6 B and C) which appears to repeat itself throughout the genome. Statistical analysis of modularity revealed 41 clusters of promoters based on sequence similarity, with 67.4% of linked promoters appearing in the main 5 clusters (Fig. 3 B). This could indicate 5 main “classes” of promoters, with several smaller sub-types representing the other 36 clusters (Fig. 3 C). Gagniuc et al. previously categorized promoters into 10 generic classes based on sequence features and associated regulatory proteins. Our results agree with these observations in that there are indeed different categories or classes of promoters ( 6 ). Our findings also help complete the overall picture by including all human promoters in the analysis and expanding the number of promoter nucleotides in the study. Interestingly, when using the DAVID database to explore potential functional similarities within clusters, no overarching functional pattern emerged. Instead, our SEQSIM clusters fragmented into smaller sub-families, such as cluster 3, which split into 10 sub-groups with varying degrees of functional enrichment. This fragmentation suggests that promoters grouped by sequence similarity may not always share unified biological roles. Future studies could benefit from further exploration of these sub-families to better understand their regulatory significance. Integrating additional bioinformatics tools and refining clustering methodologies might help identify more subtle relationships and functional nuances within promoter groups. The examination of promoter sequence homology is essential in elucidating regulatory mechanisms and expression patterns for genes. Rhoads and McIntosh showed that genes like the salicylic acid-inducible alternative oxidase gene aox1 and those involved in disease resistance share similar sequences in their promoters( 32 ). Thus, these genes might be controlled in similar ways or have evolved together, which is important for determining how genes respond to environmental changes or stress. Likewise, another study focused on a gene in Brassica napus pollen and identified several genes in different species with homologous sequences in their promoter regions that potentially correspond to similar regulatory roles ( 33 ). CABS1 is a potential biomarker of stress that we have studied over the years. Although we have found CABS1 expression in several tissues ( 16 , 34 ) its role in biology is not fully understood. We used the SEQSIM dataset to gain further insight into the biology of CABS1. The CABS1 promoter was in the third largest cluster, connected to 259 different promoters. This discovery implies that CABS1 transcriptional initiation is regulated similarly to many other human genes. Interestingly, many of these highly associated promoters belong to non-coding RNA genes, pseudogenes, and protein-coding genes. SPOCK1 transcripts, for example, are also widely expressed in the human body with particularly high expression levels in testis, much like CABS1 ( 35 ). The biological functions of the 385 members in cluster 3 catalogued using public databases such as GeneCards, showed their heterogeneity, leading to little concrete insight. For example, VWCE and SPOCK1 are both protein-coding genes whose promoters are clustered with CABS1 . However, their functions vary: SPOCK1 primarily regulates extracellular matrix remodeling and cell migration whereas VWCE supports extracellular matrix structural integrity and vascular development ( 36 ). In the future, studies could be conducted to determine the likelihood that genes within the same promoter cluster are co-expressed. Genes that are close and clustered together tend to be co-expressed ( 37 ). However, our findings imply that factors beyond physical proximity, such as sequence similarity and shared control signals that extend across the genome, could influence gene coregulation and co-expression. For example, these long-range interactions could be a complex network of transcription factors that interact with similar sequences in each of the genes mentioned. We also observed similarities between promoters on the same chromosome as CABS1 . This proximity-associated similarity could imply a localized regulatory mechanism or co-evolution of functionally related genes. CABS1 is primarily expressed in testicular tissues and is thought to be involved in the late stages of spermatogenesis, and it also has been found in human saliva ( 16 ). Notably, MUC7 (mucin-7, secreted) is a member of the mucin family of proteins which is specifically known for its role in saliva, contributing to innate defense in the oral cavity. While other neighboring genes lack distinct tissue or fluid expression patterns similar to CABS1 , their chromosomal proximity and high promoter similarity to CABS1 could indicate critical regulatory interactions. LINC02562 (long intergenic non-protein coding RNA 2562) is part of a class of RNAs that, while non-coding, can influence gene regulation. Long non-coding RNAs are involved in diverse biological processes, including chromatin remodeling, transcriptional control and post-transcriptional modifications ( 38 , 39 ). Similarly, the small nuclear RNA U4atac Pseudogene 9 ( RNU4ATAC9P ) may be involved in minor spliceosome function. Traditionally thought to be non-functional, recent studies have suggested that pseudogenes, and types of pseudogenes like retrotransposons, may play a regulatory role in gene expression ( 39 , 40 ). CABS1 research is intertwined with studies on the rat gene Vcsa1 (absent in humans) and its protein, submandibular rat 1 (SMR1), which have significant roles in inflammation, shock response, and neuroprotection ( 15 ). Specifically, human CABS1 shares a similar amino acid sequence near the carboxyl terminal (TDIFELL) with rat SMR1 (TDIFEGG), and both sequences have anti-inflammatory activities ( 15 ). On chromosome 4, CABS1 is surrounded by other genes with activities similar to rat Vcsa1 , so it was interesting that the promoter of CABS1 did not have high similarity to that of related adjacent genes, SMR3A or SMR3B that are similarly associated with male reproductive function ( 41 – 43 ). Dissimilar promoters in genes with potentially similar biological activities could be a result of evolutionary divergence and the need for tissue-specific expression. This diversity may help the same or similar functions to be carried out in different tissues, developmental stages, or environmental conditions. We also discovered in the CABS1 case study that the high scores generated by SEQSIM highlighted truncated LINE-1 ( 44 , 45 ) fragments in the promoters of associated genes. Transposable elements (TEs) are DNA sequences capable of moving or “jumping” within the genome. In all cases, the LINE-1 element sequence was highly truncated. We were surprised to discover that the surviving LINE-1 regions were of similar size and from the same general fragment from the original sequence. These truncated sequences may play a similar role for several genes. The CABS1 promoter has extensive homology with LINE-1 retaining a large segment of this element, especially within the LINE-1 open reading frame 2 (ORF2). ORF2 typically codes for a protein with endonuclease and reverse transcriptase activity, which is essential for LINE-1’s autonomous retro-transposition. However, RNA data from GenBank revealed no transcripts in these regions. We suspect that transcripts cannot be found in this region due to the extensive redundancy of these ORFs throughout the genome, making it challenging to trace any specific ORF as the transcript source. Although there is no confirmation of transcripts, functional RNA or protein could still originate from the CABS1 promoter region. These retained sequences also seem to be biased towards the gene transcription start site. The presence of such a significant LINE-1 segment within the promoter of CABS1 and its neighboring genes could have multiple implications. LINE-1 elements are known to influence genomic architecture and function, potentially affecting gene expression through regulatory sequences or by altering chromatin states ( 46 , 47 ). TEs can lead to the formation of new chromatin loops and domains, that affect the proximity of regulatory elements to target genes, impacting transcription and genomic structure (48). Distinct TE subfamilies that function as tissue-specific enhancers in colon and liver cancers have been previously identified ( 49 ). These TEs are characterized by genomic features associated with active enhancers, such as epigenetic marks and transcription factor binding, and are associated with differentially expressed genes in these types of cancer. The presence of the large islands of identical sequences in the multiple sequence alignments suggests that these features are evolutionarily conserved in the promoter and may hold functional importance. Variations or mutations in these conserved regions may cause gene dysregulation and abnormal phenotypes. A potential future application using data from SEQSIM could be the comparative analysis of promoters among individuals or populations to identify genetic variants associated with increased susceptibility to dysregulation. It would also be interesting to study whether the TEs responsible for these large islands of homology are conserved across species, and if they retained their historical regulatory functions, or acquired new roles over evolution, such as altering 3D chromatin shape ( 50 , 51 ). Our pilot analysis which extended beyond Chromosome 4 revealed intriguing patterns of promoter similarity across other chromosomes. SEQSIM revealed patterns of promoter similarity with adjacent genes that may infer 3D chromatin conformation. For example, adjacent genes with highly homologous promoters could imply that these genes share resources in their microenvironments to regulate gene expression. Another explanation is the presence of jumping elements in these highly similar regions. TEs are likely to copy themselves into other parts of the genome that are nearby or were once nearby. The diagonal slashes and checkerboard-like patterns observed in Fig. 6 indicate that such repetition is not coincidental but is a previously overlooked structural characteristic of the human genome. SEQSIM also identified previously undetected patterns within the chromosomes, surpassing the data in existing 3D genome databases ( 52 ). The role of promoters in chromosomal architecture, such as TADs or chromosomal territories that facilitate specific gene interactions, remains poorly understood. The observed clusters of highly similar adjacent promoters, surrounded by regions of lower similarity, resemble the physical looping and interaction patterns characteristic of TADs, suggesting that these promoter clusters may participate in or reflect higher-order chromatin organization. SEQSIM advances the field by providing some initial examples of the types of interactions that may occur. While the functional significance of these patterns remains to be elucidated, the similarity patterns could indicate distinct chromosomal neighborhoods where genes share similar chromatin environments, regulatory accessibility or proximity to nuclear compartments influencing gene expression. For example, 3D chromatin architecture in T-cells supports specific factor binding and interaction with regulators like CTCF (CCCTC-binding factor_ and SATB1 (special AT-rich Sequence Binding Protein 1) to control gene expression ( 53 ). The identified promoter patterns could indicate discrete functional regions within a chromosome, where neighboring genes work together to fulfill specific cellular functions, similar to TADs. Co-regulated gene clusters exist in yeast, such as those involved in ribosome biogenesis, suggesting spatial arrangement supports coordinated expression ( 54 , 55 ). Adjacent genes within the same TAD may share similar chromatin states, including histone modifications, DNA methylation patterns, or nucleosome occupancy ( 56 ). SEQSIM’s unique heatmap patterns, when compared to conventional Hi-C data, reveal novel interactions that Hi-C might miss due to limits in resolution and the nature of Hi-C data filtering techniques, especially for transient or condition-specific interactions. For example, interleukin genes, IL-4 , IL-5 , and IL-13 , are known to exist on the same TAD and changes in chromatin state in this TAD elicits a TH2 immune response( 57 ). Relationships between these genes can be confirmed and visualized using SEQSIM; however, we are also able to see promoter relationships with other adjacent genes such as RAD50 , TH2-LCR and TH2LCRR (See Supplementary Fig. 2) that were not previously apparent in Hi-C data. The unique relationships shown in SEQSIM may help fill these gaps in Hi-C data, and exploring these relationships may uncover novel gene networks or pathways involved in specific biological processes or diseases. Currently, our SEQSIM analysis has only investigated about 10% of the sequence homology data, as we do not have a method to conduct a full cluster analysis on the entire genome. We plan to explore artificial intelligence techniques and other iterative computational approaches to enable more complete processing of our data. Additionally, further research utilizing complementary techniques, such as functional genomics, chromatin conformation capture assays, and gene perturbation studies, can shed light on the functional implications of observed patterns and provide a deeper understanding of the intricate organization and regulation of genes within chromosomes. 5. Conclusion Our research demonstrates the utility of SEQSIM for comprehensive analysis and comparison of promoters across the human genome. Our novel approach revealed distinct promoter similarity clusters, indicating potential co-regulation, unique gene sets associated with different biological pathways, and potential presence of evolutionary conserved regions. Notably, identical sequence segments may be critical regulatory elements or TE, implying an intricate interplay of genomic elements in gene regulation. While computational constraints limited the depth of our analysis, the observed similarity patterns among adjacent gene promoters have sparked new hypotheses about co-regulation, chromosomal organization, and evolutionary significance. Future experimental validation and the use of additional techniques will shed light on these observations, elucidating the complexities of genome organization and regulation thereby enriching our understanding of human health and disease. Declarations Ethics Approval and Consent to Participate Not Applicable. Consent for Publication Not Applicable. Competing Interests The authors have declared that no competing interests exist. Funding This work was supported by the Natural Sciences and Engineering Research Council of Canada [RGPIN-2020-04553]. Author Contribution JRLS: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Figure creation, Writing – Original Draft, Writing – review & editing.WS: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing - review & editing.DB: Conceptualization, Writing – review & editingMM: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing Acknowledgement We thank Gilbert Lee for providing technical support for this project. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.SEQSIM is freely available, implemented in C++ for Linux and Windows platforms. All files are included in the supplementary documentation or downloadable at this link https://sites.ualberta.ca/~joyramie/SEQSIM.zip. Additional resources, including a user guide and supplementary data, are downloadable with this publication or at https://sites.ualberta.ca/~joyramie/SEQSIM.html. References Weake VM, Workman JL. Inducible gene expression: diverse regulatory mechanisms. Nat Rev Genet. 2010;11(6):426–37. Maston GA, Evans SK, Green MR. Transcriptional regulatory elements in the human genome. Annu Rev Genomics Hum Genet. 2006;7:29–59. Holwerda SJB, de Laat W. CTCF: the protein, the binding partners, the binding sites and their chromatin loops. Philos Trans R Soc B Biol Sci. 2013;368(1620):20120369. De Jesús TJ, Ramakrishnan P. 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GREAM: a web server to short-list potentially important genomic repeat elements based on over-/under-representation in specific chromosomal locations, such as the gene neighborhoods, within or across 17 mammalian species. PLoS ONE. 2015;10(7):e0133647. Lötscher E, Siwka W, Zimmer FJ, Grummt F, Zachau HG. Ttransposed human immunoglobulin C kappa gene regions carry clusters of conserved sequence elements. Gene. 1988;69(2):225–36. Wang Y, Song F, Zhang B, Zhang L, Xu J, Kuang D, et al. The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol. 2018;19(1):151. Papadogkonas G, Papamatheakis DA, Spilianakis C. 3D genome organization as an epigenetic determinant of transcription regulation in T cells. Front Immunol [Internet]. 2022 [cited 2023 Sep 18];13. https://www.frontiersin.org/articles/ 10.3389/fimmu.2022.921375 Hardan A, Botero J, Arnone J. 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Supplementary Files S1StepbyStepMethod.tif S2HeatmapofSEQSIMPromoterSimilarity.jpg Cite Share Download PDF Status: Published Journal Publication published 09 Jun, 2025 Read the published version in BMC Bioinformatics → Version 1 posted Editorial decision: Revision requested 14 Jan, 2025 Reviews received at journal 09 Jan, 2025 Reviews received at journal 23 Dec, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers invited by journal 20 Nov, 2024 Editor invited by journal 19 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 12 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5441650","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":384963331,"identity":"c6c63384-7ed4-4a40-b4a6-766a9aa78551","order_by":0,"name":"Joy Ramielle L. Santos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACxgYwJcHAD2QfJk2LJJBxGMwiGhgcIFYLc3uP2YefORZyxjeSHxwuqLhXx9/A/PADXof1nDGe2btNwtjsRprB4RlniiUkDrAZ47WKcUaOMQPvNonEbTcSDA7ztiVIMBzgwe86kBbGv9sk6jfPSP9wmPdfgoT8AR7mH4S0MANtSTCQyAHa0pAgYXCAhw2/LT3Hipllt0kYzjjzpuDwjGMJkhsPs5lZ4NNi2N68mfHttjp5/vb0jY8LahL45Y43P76BV0sDhwGaEDM+9UAgz8D+gICSUTAKRsEoGPEAALGbSU4x8+oMAAAAAElFTkSuQmCC","orcid":"","institution":"University of Alberta","correspondingAuthor":true,"prefix":"","firstName":"Joy","middleName":"Ramielle L.","lastName":"Santos","suffix":""},{"id":384963333,"identity":"9c2cf785-ae1c-40b6-841b-31f19cf2f143","order_by":1,"name":"Weijie Sun","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Sun","suffix":""},{"id":384963336,"identity":"ae51e2e0-e15c-43bd-9db0-8ff5c454ad60","order_by":2,"name":"A. Dean Befus","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"Dean","lastName":"Befus","suffix":""},{"id":384963337,"identity":"96d1e390-68a8-46ae-b41d-35842c0ce434","order_by":3,"name":"Marcelo Marcet-Palacios","email":"","orcid":"","institution":"University of Alberta","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"","lastName":"Marcet-Palacios","suffix":""}],"badges":[],"createdAt":"2024-11-12 18:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5441650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5441650/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12859-025-06160-x","type":"published","date":"2025-06-09T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71612026,"identity":"496e80d4-2acb-41fc-9a24-38f6eacae4fc","added_by":"auto","created_at":"2024-12-17 06:56:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethodology Pipeline using the novel comparison software generated for this study.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e The methodology occurred in three distinct phases, data mining, data processing, and data visualization approaches. Ongoing and future approaches are also denoted by the dashed arrows.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/d113d8ebaf1711fc7add6f8c.jpg"},{"id":71612024,"identity":"f2467a4f-6440-48da-91bd-5b6044342b60","added_by":"auto","created_at":"2024-12-17 06:56:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eExample used to develop the scoring algorithm used in our software.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e (A) Defines the variables and sequence notation used in our algorithm in a sample case study using the sequences X = ACTACT and Y = ATACTA. Variable L indicates the length of the longest matching substring of the comparator sequence minus one. The variable ‘i’ indicates the position on the first nucleotide of the first sequence (the reference sequence) in the notation, L(X,Y). Position 1 on Sequence X and Y is indicated by the black arrow. (B) The largest substring match, highlighted in red, in Sequence Y when compared to each position, i, in Sequence X minus 1, or L(X,i,Y). A weighing function is applied, squaring the maximum L(X,i,Y) and summing the values. (C) The largest L(Y,i,X) with the same weighing function applied. (D) The values calculated in Panels (B) and (C) substituted into the overall scoring function (S) used in our algorithm with a value between 0 to 1.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/adf1daf5af29c8badaa9c808.jpg"},{"id":71612777,"identity":"4dda5151-38f0-4120-b0f2-2cf40a9a8260","added_by":"auto","created_at":"2024-12-17 07:04:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":452667,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCluster analysis of the top 10% most similar promoters of the human genome. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Methodology Pipeline for generating the cluster diagrams in Gephi 0.9.2. 10% of our 57064 x 57064 large matrix was visualized using Gephi 0.9.2, an open-source data visualization software. Each promoter is depicted in the software as a circle, or a ‘node.’ When there is similarity between sequences, a line, also known as an ‘edge,’ is drawn between each sequence. The more similar the sequence, the shorter the edge thus bringing two nodes closer together. For clarity, the edges were removed from the figure but the relationships between nodes and clusters remain intact. (B) Depicts the network diagram of the topmost similar promoters mined from the GRCh38. Due to computational restraints, only 10% of the analyzed promoters can be displayed. From the analysis, 5 main clusters were discovered colored green, pink, blue, black and orange. Several other smaller clusters (grey) were also observed. A zoomed view of cluster 3 shows the CABS1 promoter in red. Nodes are sized according to the number of other promoters to which they are similar. The higher the number of hits, the larger the node. CABS1, with its multiple connections to many other promoters, appears to be a large contributor to its cluster. The CABS1 cluster is the 3\u003c/em\u003e\u003csup\u003e\u003cem\u003erd\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e largest cluster in terms of node quantity, however its nodes appear larger visually due to their multiple connections. (C) Characterization curve of clusters in the human genome. Over 41 clusters were discovered with varying populations of 1 to 402 nodes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/18850092b939937e7750ff97.jpg"},{"id":71614383,"identity":"b7171457-d282-44ca-a1c5-a5b9d5b7e8c0","added_by":"auto","created_at":"2024-12-17 07:12:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnalysis of the CABS1 neighborhood in Chromosome 4. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Partial heatmap with section breaks generated from our software (red). For the purposes of this figure, we have selectively omitted portions of the linear genome leaving a buffer of 1 UGR before and after a similar UGR to CABS1. The grey bars depict the omissions in the linear genome. The first break between IFITM3P1 and LOC105377267 is from nucleotide 66094526 to 68300322. The other two breaks are [69216529 to 70334981] and [70532743 to 75037055]. (B) The same region was validated with Clustal Omega. The omissions for Panel B are as follows: [66094526 to 66003201], [68350090 to 68300322], [70532743 to 72953850], and [72979588 to 75037055] and differ from Panel A due to new UGR similarities discovered during Clustal Omega validation. The CABS1 UGR showed high similarity to a few adjacent UGRs for MUC7, RNU4ATAC9P, and LINC02562, as depicted by the more intense red or blue colour. CABS1 UGR also showed similarity to more distal UGRs of undefined genes (LOC genes). New gene UGRs with relatively high similarity were discovered upon validation with the less penalizing Clustal Omega algorithm such as OPRPN and RNU4ATAC9P and conversely, some unique similarities were highlighted using SEQSIM, such as UGT2B11, the names of these are highlighted green.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/2235fe2b53d11e6a3f48199b.jpg"},{"id":71612030,"identity":"2e1309af-c14c-4b48-992c-4f1e76e89c62","added_by":"auto","created_at":"2024-12-17 06:56:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSequence Alignment of Multiple Genes with LINE-1 element.\u003c/strong\u003e\u003c/em\u003e \u003cem\u003eA scale bar of 12,000 nucleotides (12k nt) is shown above the aligned sequences. The LINE-1 element is at the top, and its open-reading frame locations are labelled. The grey boxes below the LINE-1 element represent islands of high homology discovered in a multiple sequence alignment between LINE-1 and the promoters of CABS1, LOC107986230, LINC02562, UGT2B11, LOC105377261, MUC7, and RNU4ATAC9P. For simplification purposes, only islands greater in length than 100 nt are shown. The OPRPN promoter did not reveal any large islands of homology with the same LINE-1 element and thus this promoter was not represented in the figure. Notably, the CABS1 promoter has the largest region of homology, with a 5452 nt region showing 99% homology to LINE-1.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/db1ed3e2512758c29d2f5664.jpg"},{"id":71612779,"identity":"4dcaabe8-3110-4e93-b8e7-04a1e4e18fc8","added_by":"auto","created_at":"2024-12-17 07:04:19","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":80466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eHeatmap data visualization for the first 100 gene promoters of Chromosome 1. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Gene view of the first ~600 kb of Chromosome 1 and a segment included in our heatmaps. (B) Overemphasized graphical representation of the patterns that can be extracted from the heatmap depicted in 6C. The black arrowhead indicates one homology cluster of interest due to the overall pattern created in the map. The white arrowheads indicate a darker and lighter red area observed in the first 100 genes. The black arrows show the diagonal slashes observed in the heatmap. The darker red indicates higher similarity in the promoters while the light red area depicts less similarity to adjacent promoters. White areas indicate little to no similarity. (C) The actual heatmap without digital enhancement generated using our novel comparison software and algorithm.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/113013aa79060b0806526ffa.jpg"},{"id":84726653,"identity":"5e08caed-8ca8-470f-bfc7-8565288428a8","added_by":"auto","created_at":"2025-06-16 16:07:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2032857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/0349583d-7765-4a97-9593-82e8b3bc67d8.pdf"},{"id":71612053,"identity":"716013d3-1a6b-4721-8b61-66f1c29449e6","added_by":"auto","created_at":"2024-12-17 06:56:20","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28157004,"visible":true,"origin":"","legend":"","description":"","filename":"S1StepbyStepMethod.tif","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/152039d2e1fafd046663cf2a.tif"},{"id":71612041,"identity":"5dfa3414-b0e4-43ca-9e79-ad656050c10f","added_by":"auto","created_at":"2024-12-17 06:56:20","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":174535,"visible":true,"origin":"","legend":"","description":"","filename":"S2HeatmapofSEQSIMPromoterSimilarity.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5441650/v1/4d86a43bf72667f7d1b74fea.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"SEQSIM – A novel bioinformatics tool for comparisons of promoter regions – a case study of calcium binding protein spermatid associated 1 (CABS1)","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThe regulation of the initiation of transcription is an essential and complex process involving various regulatory nucleotide sequences adjacent to genes. These cis-regulatory elements (CREs) include promoters, enhancers, silencers and other elements that determine when a gene is expressed. Other regulatory elements that can be more distal to the gene start site, such as transposable elements, insulators, boundary elements, and DNA that code for non-coding RNAs, add further complexity to the regulation of gene expression (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For example, sites where CTCF binding factor, a protein that binds specifically to the CCCTC motif, act as insulators and facilitate the reorganization of 3D chromatin structure. This is important in bringing promoters, enhancers and transcriptional machinery together to facilitate initiation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Although all human genes are differentially regulated at the transcriptional level, there are common regulatory elements used according to gene type. For example, elements such as NF-kb binding sites are present in inducible genes but absent in house-keeping genes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). By performing a genome-wide analysis of promoter sequence homology, we will more clearly elucidate the diversity of human promoters and assess the level of similarity within these sequence clusters. We hypothesize that promoters that belong to the same homology cluster can then be aligned based on sequence to highlight conserved regulatory elements. This novel analysis will also help predict groups of genes that are likely to be co-regulated and potentially highlight co-regulation networks.\u003c/p\u003e \u003cp\u003eIn an effort to understand human promoter sequence diversity, Gagniuc et al. performed sequence homology analysis of 8512 promoter sequences available in a promoter sequence database (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The authors categorized promoters into 10 groups according to a 2-dimensional (2D) analysis consisting of plots of Kappa Index of Coincidence vs C\u0026thinsp;+\u0026thinsp;G % within the \u0026minus;\u0026thinsp;499 to +\u0026thinsp;100 positions relative to the transcription initiation site. This paper suggested categorizing the promoters according to the presence of promoter elements (TATA box, GC-box and CCAAT-box), DNA curvature, or nucleosome positioning(\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Unfortunately, this approach was limited by the simplistic nature of 2D comparisons, leading to strictly tabular analyses of promoter types, and was thus unable to group promoters based on predicted functional families. Other limitations of this study were the small number of promoters available in databases at the time and a relatively short selected DNA sequence e.g. (-1950 to -522) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). To overcome these limitations, we performed a comprehensive promoter homology analysis of 2000 nucleotides upstream of every human gene.\u003c/p\u003e \u003cp\u003eCommonly used sequence similarity tools, like Clustal Omega, have file size restrictions that limit full-genome dataset analysis. Thus, import of a genome-wide dataset using Clustal Omega is not feasible due to file size limitations and prohibitively lengthy computational times, estimated to be \u0026gt;\u0026thinsp;1 year to complete using modern equipment at Compute Canada servers. Thus we developed an in-house software Sequence Similarity (SEQSIM) that employs a sequence similarity algorithm based on the Needleman-Wunsch algorithm (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) with extensive modification to speed up performance. SEQSIM enabled fast, pairwise comparison of promoters of every human gene in the Genome Reference Consortium Human Build 38 (GRCh38.p14).\u003c/p\u003e \u003cp\u003eSEQSIM could be especially useful in the study of under studied genes. We selected calcium binding protein spermatid-associated 1 (\u003cem\u003eCABS1)\u003c/em\u003e, a poorly characterized gene, as a case study to evaluate SEQSIM. The CABS1 protein is most abundant in the testis, and found in salivary glands (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Recent analysis of GeoData has detected \u003cem\u003eCABS1\u003c/em\u003e mRNA widely expressed in human tissues(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). To date, CABS1 has been associated with spermatogenesis in several species and with acute psychological stress in humans(\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A 7 amino acid peptide sequence in human CABS1 near its carboxyl terminus has anti-inflammatory activity. Using \u003cem\u003eCABS1\u003c/em\u003e as a case study presents a unique challenge and opportunity as it does not have another homologous human gene or known protein isoforms. \u003cem\u003eIn silico\u003c/em\u003e studies suggested that CABS1 likely interacts directly with other proteins(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Thus, a SEQSIM analysis of \u003cem\u003eCABS1\u003c/em\u003e promoter and identification of other genes with similar promoter sequences may help reveal CABS1-interacting partners or proteins involved in a CABS1-associated network.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe pipeline involves: data mining of genome data using a Python script; utilization of a specialized scoring function to draw comparisons between promoter sequences (SEQSIM software); homology matrix visualization and data clustering using various programs outside of SEQSIM; and validation using 3rd party software. This pipeline is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u0026ndash; SEQSIM Software Development\u003c/h2\u003e \u003cp\u003eThe software, \u003cem\u003eSEQSIM\u003c/em\u003e (Sequence Similarity), was created to perform DNA sequence similarity analyses at a rate of 27 x 10\u003csup\u003e6\u003c/sup\u003e comparisons per minute, completing a full human genome analysis in under 1 hour. SEQSIM imports a list of named sequences and compares all sequences against each other to generate a sequence homology matrix with a percent similarity based on our comparison algorithm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 - Data Mining and Preparation of Comma Separated Values (CSV) Import File\u003c/h2\u003e \u003cp\u003eTo prepare the input files required for SEQSIM, a data mining python script was used to extract information from the GRCh38. Chromosome information was downloaded in text format from the NCBI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the corresponding chromosome search criteria, e.g. \u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e1[CH] AND human[ORGN]\u003c/span\u003e. The script then extracted information such as the nominal gene symbol and name used to reference the promoter, gene type, gene chromosomal location and coordinates, directionality, gene description and gene exon count. Next the chromosome sequence was downloaded from NCBI\u0026rsquo;s GenBank repository (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). NC_000001, refers to chromosome 1. Each chromosome FASTA sequence was downloaded separately. The first nucleotide of each gene and gene orientation was then used to extract the 2000 nucleotide UGRs in the 5\u0026rsquo; to 3\u0026rsquo; direction. The program compiled all this information into a comma separated values (CSV) file.\u003c/p\u003e \u003cp\u003eFor SEQSIM, an additional CSV file was created with the identification of the sequence in the first column and the sequences to be analyzed in the next column. All sequences were required to be equal in size (2000 nucleotides) and were imported into the program as a string, a sequence of non-numerical characters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 - Comparison Algorithm\u003c/h2\u003e \u003cp\u003eThe program currently defines a pairwise similarity score, normalized in the range of 0 and 1 between two nucleotide sequences (strings) of the same size (N\u0026thinsp;=\u0026thinsp;2000 nucleotides). An anchoring sequence is identified, X, and aligned to a second sequence, Y (comparator sequence). When both sequences are perfectly aligned so that both 2000 nucleotide (i.e.: N\u0026thinsp;=\u0026thinsp;2000) sequences are squared to each other, we call this position 1 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e). At position 1 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e = 1), the program finds the longest string of nucleotides within the anchoring sequence that corresponds exactly to a segment in the comparator sequence. A match of 1 nucleotide corresponds to a value of 1 and each subsequent nucleotide match adds 1 point. When a mismatch is discovered, implying that the two sequences are not identical, the comparator sequence Y is shifted left by one nucleotide (ie: comparing position 1 of the anchoring sequence with position 2 of the comparator sequence) and another comparison check is conducted at this second position. It is important to note that despite detecting mismatches, SEQSIM still compares sequences downstream of mismatched nucleotides. This process repeats until 2000 comparisons have been made between the anchor sequence X and the entire comparator sequence Y, creating a row of data that identifies the length of the segment in the comparator sequence that exactly matches a segment in the anchor sequence. This process is repeated for positions 2 to 2000 of the anchoring sequence to generate 1999 more rows of data and 4,000,000 data points for further processing (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplemental Fig.\u0026nbsp;1 for a more comprehensive example).\u003c/p\u003e \u003cp\u003eIn the resulting array, the maximum value generated from each positional comparison on the anchor sequence is then carried into our scoring algorithm (Supplementary Fig.\u0026nbsp;1 for detailed math functions). The scoring function assigns a value between 0 and 1, which can then be converted into a percentage. Higher scores are assigned to sequences that share long segments of matching nucleotides.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 - User Interface\u003c/h2\u003e \u003cp\u003eCurrently, no graphical user interface is available for SEQSIM. More information and a sample of the code can be found in the Supplementary Materials.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 - Cluster Analysis\u003c/h2\u003e \u003cp\u003eCluster analysis of promoter similarity was performed using the open-source, network visualization software, Gephi (version 0.9.2). Data used in the analyses consisted of a set of nodes, the promoter gene ID, and edges, their pairwise similarity score. To visualize the network, Gephi\u0026rsquo;s continuous graph layout algorithm, ForceAtlas2, was implemented due to its ability to graph clusters in large datasets with high precision and speed. The algorithm used a force-directed approach to layout the nodes in 2D space, with nodes with higher similarity drawn closer together. ForceAtlas2 also utilizes a gravity function that allows nodes to stay clustered and prevent them from drifting apart (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnce the data was imported to Gephi, default settings for ForceAtlas2 were used, including a gravity value of 1.0. To generate images, the \u0026ldquo;Prevent Overlap\u0026rdquo; option under Behavior Alternatives was enabled. ForceAtlas2 was run until convergence was achieved. Nodes that did not appear to cluster were filtered using modularity filters, such as degree distribution. The filter range parameters displayed nodes that had greater than 10 connections (edges) to other nodes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.\u003cem\u003e3 - Heatmap Generation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eHeatmaps were generated using conditional formatting in Microsoft Excel. Inputs for each heatmap consisted of a matrix of numerical values (the similarity scores, S) between promoters within a specific chromosome in sequential order. The color scale chosen represented the range of values within the data set, red being a high similarity score and white a low score.\u003c/p\u003e \u003cp\u003eEach chromosome matrix was imported into Excel. The conditional formatting tool was then applied. Under the \u0026ldquo;highlight cell rules\u0026rdquo; option, a custom rule was created that utilized a 3-color scale. Depending on the chromosome, the color scale could be adjusted to match the specific data being analyzed. Red indicated high similarity scores and white low scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 - Validation with Clustal Omega and Multiple Sequence Alignment\u003c/h2\u003e \u003cp\u003eThe promoter sequences used in SEQSIM were exported into FASTA format. The resulting file was imported into the online server for Clustal Omega, a widely used tool for multiple sequence alignment (MSA). Due to the upload file size limitations of Clustal Omega, validations were conducted piecewise, 100 by 100 UGRs per analysis. The resulting Percent Identity Matrices generated using Clustal Omega were then visualized using the methods described in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e. For some datasets, Clustal Omega automatically generated an identity matrix with a different order of UGRs. Rearranging the data to match pre-existing matrices was conducted in Microsoft Excel. Clustal Omega heatmaps and SEQSIM heatmaps were then compared to each other and superimposed to identify similarities and obvious differences.\u003c/p\u003e \u003cp\u003eAlignments from Clustal Omega were downloaded in FASTA file format. Similar UGR matches to that of \u003cem\u003eCABS1\u003c/em\u003e, or sequences of interest were visualized using NCBI\u0026rsquo;s Multiple Sequence Alignment Viewer 1.23.0 by uploading the resulting FASTA file to the server. The promoter of \u003cem\u003eCABS1\u003c/em\u003e, our case study gene, was then set as the \u0026ldquo;anchor\u0026rdquo; so all other sequences were compared relative to \u003cem\u003eCABS1\u0026rsquo;s\u003c/em\u003e promoter. The other sequences were also arranged from highest to lowest homology to the anchor. For clarity, the resulting alignment was colored green for matching nucleotide sequences and red for differences. Any large regions of interest were searched in NCBI\u0026rsquo;s nucleotide BLAST (Basic Local Alignment Search Tool) server. To visualize and analyze the extended 10000 nucleotide alignments, we also used JalView Desktop, a tool used for in-depth sequence analysis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 \u0026ndash; Determination of Homology Island Boundaries\u003c/h2\u003e \u003cp\u003eTo determine the boundaries of highly homologous regions, referred to as \u0026ldquo;islands\u0026rdquo; between promoters and an anchor (reference) sequence, a systematic approach based on sequence alignment and homology scoring was utilized. The starting point of an \u0026ldquo;island\u0026rdquo; was defined as the position where 10 consecutive nucleotides in the promoter sequence exactly matched the corresponding nucleotides in the anchor sequence.\u003c/p\u003e \u003cp\u003eA sliding window approach was used to compute a homology score across the sequences. Using a 5-nucleotide window, a score of 1 was assigned for an exact match and 0 for each mismatch, calculating the homology score as the number of matches divided by the window size (e.g., 4 matches in a 5-nucleotide window equals a score of 0.8). This window was shifted by 1 nucleotide along the entire comparison length.\u003c/p\u003e \u003cp\u003eWhen the homology score first reaches 0, indicating no matches within that window, the nucleotide coordinate marking the end of the island was determined by subtracting the window size (5 nucleotides) from the coordinate of the first 0 score.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 \u0026ndash; SEQSIM Generated Score Matrix and Cluster Map\u003c/h2\u003e\n \u003cp\u003eSEQSIM generated a 57064 x 57064 matrix of similarity scores between the promoters of every human gene. Due to computational restraints, we only performed a Gephi ForceAtlas2 cluster analysis of approximately the top 10% most similar promoters of the complete matrix. The Louvain method to determine modularity gave a score of 0.840 discovering 41 total communities or clusters (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). The largest cluster (cluster 1) had 402 promoter members and the smallest (cluster 41) just 1 member. The 5 largest clusters were significantly larger in size than the remaining cluster population. These 5 main clusters contained more than 100 promoters (nodes) each, adding up to 67.4% of all clustered promoters (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eThe clustering algorithm, ForceAtlas2, also showed relationships among the clusters. The closer the clusters are together (i.e.: the edges are shorter), the more similar their promoters. For example, genes within Cluster 3 and Cluster 4 have more similar promoters to each other than with Cluster 2. We found no correlation between SEQSIM clustering and chromosomal location, i.e., promoters in a single cluster could be from many chromosomes. However, it was interesting that some of the smaller, satellite clusters featured promoters of genes entirely from a single chromosome. Such clusters ranged in size from 1 to 32 promoter nodes. For example, all 32 members of the 12th largest cluster were promoters from chromosome 11, and all 17 members of the 23rd largest cluster were from chromosome 1. Conversely, the main clusters contained promoters from nearly every chromosome. Proportionally, the number of promoters from each chromosome in a cluster varied greatly.\u003c/p\u003e\n \u003cp\u003eThe \u003cem\u003eCABS1\u003c/em\u003e promoter is located on Cluster 3, the third largest cluster comprised of 385 members, with 259 members directly connected to CABS1. The promoter for \u003cem\u003eCABS1\u003c/em\u003e is highly connected to many others within the cluster, which corresponds to its increased node size. These included many promoters for non-coding RNA and pseudo genes. According to our algorithm, the promoter of \u003cem\u003eCABS1\u003c/em\u003e (red node in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) is highly similar to the promoters of the protein-coding genes \u003cem\u003eVWCE, SPOCK1, THSD4, RNF39\u003c/em\u003e, and \u003cem\u003eTMX2\u003c/em\u003e. Not surprisingly, these highly homologous promoters were validated in Clustal Omega with homology scores of 78.70%, 81.99%, 82.89%, 96.54%, and 77.09% to \u003cem\u003eCABS1\u003c/em\u003e promoter. Interestingly, these 5 genes were inter-chromosomal, not found on chromosome 4 like \u003cem\u003eCABS1\u003c/em\u003e (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the 385 promoters in the \u003cem\u003eCABS1\u003c/em\u003e cluster, only 20 were in chromosome 4 with \u003cem\u003eCABS1\u003c/em\u003e, and of the 20, 8 were unannotated genes, 9 were pseudogenes, 2 were long non-protein coding RNA, leaving only one as a promoter for a protein coding gene: integrin binding sialoprotein (\u003cem\u003eIBSP\u003c/em\u003e).\u003c/p\u003e\n \u003cp\u003eWe attempted to characterize each cluster more thoroughly by identifying potential functional similarities through bioinformatics databases such as the Database for Annotation, Visualization and Integrated Discovery (DAVID) (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e). Through DAVID, we searched for biological processes, cellular components, molecular functions, pathways and more. However, when queried, no single, defining similarity was found in functional annotations within clusters. Interestingly, when clustered using DAVID, our SEQSIM clusters often fragmented into smaller sub-families. For example, the genes of 319/385 members within SEQSIM cluster 3 separated into 10 sub-families with enrichment scores as high as 2.33 based on a variety of annotations like molecular function, gene sequence features and biological processes.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1 \u0026ndash; Summary Table of Promoters of interest related to CABS1.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePromoters of known genes in the Inter-Chromosomal and Intra-Chromosomal sections with high similarity to CABS1 promoter. Inter-chromosomal promoters were found to be clustered with CABS1. Also, in the table are SMR3A and 3B, genes of interest due to evolutionary history and potential functional parallels with SMR1 in rat and CABS1 in human (See Discussion). The promoters of these genes did not cluster with CABS1 and did not show high similarity in promoters.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 \u0026ndash; Heatmap of Adjacent Promoters around Case Study promoter of CABS1 in Chromosome 4\u003c/h2\u003e\n \u003cp\u003eThe heatmap was generated for 200 promoters for genes flanking the \u003cem\u003eCABS1\u003c/em\u003e gene loci. Promoters are shown in the same order as they appear in chromosome 4 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Within Chromosome 4, and within 50 genes on either side of \u003cem\u003eCABS1\u003c/em\u003e, the promoters for \u003cem\u003eLINC02562\u003c/em\u003e, \u003cem\u003eMUC7\u003c/em\u003e and \u003cem\u003eUGT2B11\u003c/em\u003e closely resemble the promoter of \u003cem\u003eCABS1\u003c/em\u003e. Other matches included genes that have not yet been defined (i.e: \u003cem\u003eLOC\u003c/em\u003e genes). Comparative validation using Clustal Omega (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB) provided similar results. With Clustal Omega, two additional promoters for genes \u003cem\u003eRNU4ATAC9P\u003c/em\u003e and \u003cem\u003eOPRPN\u003c/em\u003e showed a homology score greater than 40% within the 50 closest genes to \u003cem\u003eCABS1\u003c/em\u003e. Interestingly, the latter is associated with male reproductive function among other roles like \u003cem\u003eCABS1\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e), see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe MSA of the \u003cem\u003eCABS1\u003c/em\u003e promoter to their homologous counterparts (\u003cem\u003eLOC105377261\u003c/em\u003e, \u003cem\u003eUGT2B11\u003c/em\u003e, \u003cem\u003eMUC7\u003c/em\u003e, \u003cem\u003eLINC02562\u003c/em\u003e, \u003cem\u003eLOC107986230\u003c/em\u003e, \u003cem\u003eRNU4ATAC9P\u003c/em\u003e and \u003cem\u003eOPRPN\u003c/em\u003e) revealed large \u0026ldquo;islands\u0026rdquo; of 100% similar regions. In a BLAST analysis, these regions were determined to be portions of a long-interspersed nuclear element-1 (LINE-1) with a length of 10,031 nt. To fully investigate how much of the LINE-1 element was present in the promoter of \u003cem\u003eCABS1\u003c/em\u003e and the other associated promoters, we conducted an extended sequence analysis of 10k nt. Among these 7 promoters, \u003cem\u003eCABS1\u003c/em\u003e retained the largest segment of the LINE-1 element, specifically a segment of 5452 nt spanning open reading frames 1 and 2 of LINE-1 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.\u003cem\u003e3 \u0026ndash; Interesting Patterns in Other Chromosomes\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eWhile our primary focus was on the case study gene \u003cem\u003eCABS1\u003c/em\u003e, we extended the comprehensive analysis to generate similarity heatmaps for every chromosome (beyond the scope of this manuscript). The heatmaps for each chromosome revealed intriguing patterns of similarity between adjacent promoters, which appear to correlate with loops observed in topologically associated domains (TAD).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, shows a small section of the first 100 promoters on chromosome 1, with similarity scores shown as color intensity. Striking patterns of similarity in the first 100 promoters resembled a series of diagonal slashes (arrows in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). Additionally, we observed more intriguing patterns such as the checkerboard-like background (open arrow heads point at both the light and dark segments of the checkerboard) emphasized in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB. These patterns, though distinct from the diagonal slashes, may inform encoded genomic interactions. In Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC (without digital enhancement seen in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB), distinct bright red (i.e.: very similar promoters) clusters of similarity among adjacent promoters were also observed. One example is shown with the closed arrowhead in 6B. It\u0026rsquo;s interesting to note that when highly similar adjacent promoters form clusters, other promoters around the islands are highly dissimilar. This is reminiscent of how TADs form physical loops in chromatin and interact at a higher frequency within the TAD versus adjacent TADs. These results highlight that genes that are looped into close proximities within their TADs also share similar promoters.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIt is widely accepted that promoters are important in conditions like cancer, diabetes and neurodegenerative disorders (\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These promoters, when changed, can alter the start of gene transcription and consequently gene expression significantly. Our study provides new insight into the number of promoter homology clusters in the human genome. Importantly, within the first 100 genes of chromosome 1, we discovered a pattern of promoter homology not previously seen, in which genes share promoter homology with a limited number of adjacent genes. This homology results in a checkerboard pattern of similarity observed in heatmaps (See Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and C) which appears to repeat itself throughout the genome.\u003c/p\u003e \u003cp\u003eStatistical analysis of modularity revealed 41 clusters of promoters based on sequence similarity, with 67.4% of linked promoters appearing in the main 5 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This could indicate 5 main \u0026ldquo;classes\u0026rdquo; of promoters, with several smaller sub-types representing the other 36 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Gagniuc et al. previously categorized promoters into 10 generic classes based on sequence features and associated regulatory proteins. Our results agree with these observations in that there are indeed different categories or classes of promoters (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Our findings also help complete the overall picture by including all human promoters in the analysis and expanding the number of promoter nucleotides in the study.\u003c/p\u003e \u003cp\u003eInterestingly, when using the DAVID database to explore potential functional similarities within clusters, no overarching functional pattern emerged. Instead, our SEQSIM clusters fragmented into smaller sub-families, such as cluster 3, which split into 10 sub-groups with varying degrees of functional enrichment. This fragmentation suggests that promoters grouped by sequence similarity may not always share unified biological roles. Future studies could benefit from further exploration of these sub-families to better understand their regulatory significance. Integrating additional bioinformatics tools and refining clustering methodologies might help identify more subtle relationships and functional nuances within promoter groups.\u003c/p\u003e \u003cp\u003eThe examination of promoter sequence homology is essential in elucidating regulatory mechanisms and expression patterns for genes. Rhoads and McIntosh showed that genes like the salicylic acid-inducible alternative oxidase gene \u003cem\u003eaox1\u003c/em\u003e and those involved in disease resistance share similar sequences in their promoters(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Thus, these genes might be controlled in similar ways or have evolved together, which is important for determining how genes respond to environmental changes or stress. Likewise, another study focused on a gene in \u003cem\u003eBrassica napus\u003c/em\u003e pollen and identified several genes in different species with homologous sequences in their promoter regions that potentially correspond to similar regulatory roles (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCABS1 is a potential biomarker of stress that we have studied over the years. Although we have found \u003cem\u003eCABS1\u003c/em\u003e expression in several tissues (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) its role in biology is not fully understood. We used the SEQSIM dataset to gain further insight into the biology of CABS1. The \u003cem\u003eCABS1\u003c/em\u003e promoter was in the third largest cluster, connected to 259 different promoters. This discovery implies that \u003cem\u003eCABS1\u003c/em\u003e transcriptional initiation is regulated similarly to many other human genes. Interestingly, many of these highly associated promoters belong to non-coding RNA genes, pseudogenes, and protein-coding genes. \u003cem\u003eSPOCK1\u003c/em\u003e transcripts, for example, are also widely expressed in the human body with particularly high expression levels in testis, much like \u003cem\u003eCABS1\u003c/em\u003e(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The biological functions of the 385 members in cluster 3 catalogued using public databases such as GeneCards, showed their heterogeneity, leading to little concrete insight. For example, \u003cem\u003eVWCE\u003c/em\u003e and \u003cem\u003eSPOCK1\u003c/em\u003e are both protein-coding genes whose promoters are clustered with \u003cem\u003eCABS1\u003c/em\u003e. However, their functions vary: SPOCK1 primarily regulates extracellular matrix remodeling and cell migration whereas VWCE supports extracellular matrix structural integrity and vascular development (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In the future, studies could be conducted to determine the likelihood that genes within the same promoter cluster are co-expressed. Genes that are close and clustered together tend to be co-expressed (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). However, our findings imply that factors beyond physical proximity, such as sequence similarity and shared control signals that extend across the genome, could influence gene coregulation and co-expression. For example, these long-range interactions could be a complex network of transcription factors that interact with similar sequences in each of the genes mentioned.\u003c/p\u003e \u003cp\u003eWe also observed similarities between promoters on the same chromosome as \u003cem\u003eCABS1\u003c/em\u003e. This proximity-associated similarity could imply a localized regulatory mechanism or co-evolution of functionally related genes. CABS1 is primarily expressed in testicular tissues and is thought to be involved in the late stages of spermatogenesis, and it also has been found in human saliva (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Notably, MUC7 (mucin-7, secreted) is a member of the mucin family of proteins which is specifically known for its role in saliva, contributing to innate defense in the oral cavity. While other neighboring genes lack distinct tissue or fluid expression patterns similar to \u003cem\u003eCABS1\u003c/em\u003e, their chromosomal proximity and high promoter similarity to \u003cem\u003eCABS1\u003c/em\u003e could indicate critical regulatory interactions. \u003cem\u003eLINC02562\u003c/em\u003e (long intergenic non-protein coding RNA 2562) is part of a class of RNAs that, while non-coding, can influence gene regulation. Long non-coding RNAs are involved in diverse biological processes, including chromatin remodeling, transcriptional control and post-transcriptional modifications (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Similarly, the small nuclear RNA U4atac Pseudogene 9 (\u003cem\u003eRNU4ATAC9P\u003c/em\u003e) may be involved in minor spliceosome function. Traditionally thought to be non-functional, recent studies have suggested that pseudogenes, and types of pseudogenes like retrotransposons, may play a regulatory role in gene expression (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCABS1 research is intertwined with studies on the rat gene \u003cem\u003eVcsa1\u003c/em\u003e (absent in humans) and its protein, submandibular rat 1 (SMR1), which have significant roles in inflammation, shock response, and neuroprotection (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Specifically, human CABS1 shares a similar amino acid sequence near the carboxyl terminal (TDIFELL) with rat SMR1 (TDIFEGG), and both sequences have anti-inflammatory activities (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). On chromosome 4, \u003cem\u003eCABS1\u003c/em\u003e is surrounded by other genes with activities similar to rat \u003cem\u003eVcsa1\u003c/em\u003e, so it was interesting that the promoter of \u003cem\u003eCABS1\u003c/em\u003e did not have high similarity to that of related adjacent genes, \u003cem\u003eSMR3A\u003c/em\u003e or \u003cem\u003eSMR3B\u003c/em\u003e that are similarly associated with male reproductive function (\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Dissimilar promoters in genes with potentially similar biological activities could be a result of evolutionary divergence and the need for tissue-specific expression. This diversity may help the same or similar functions to be carried out in different tissues, developmental stages, or environmental conditions.\u003c/p\u003e \u003cp\u003eWe also discovered in the \u003cem\u003eCABS1\u003c/em\u003e case study that the high scores generated by SEQSIM highlighted truncated LINE-1 (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) fragments in the promoters of associated genes. Transposable elements (TEs) are DNA sequences capable of moving or \u0026ldquo;jumping\u0026rdquo; within the genome. In all cases, the LINE-1 element sequence was highly truncated. We were surprised to discover that the surviving LINE-1 regions were of similar size and from the same general fragment from the original sequence. These truncated sequences may play a similar role for several genes. The \u003cem\u003eCABS1\u003c/em\u003e promoter has extensive homology with LINE-1 retaining a large segment of this element, especially within the LINE-1 open reading frame 2 (ORF2). ORF2 typically codes for a protein with endonuclease and reverse transcriptase activity, which is essential for LINE-1\u0026rsquo;s autonomous retro-transposition. However, RNA data from GenBank revealed no transcripts in these regions. We suspect that transcripts cannot be found in this region due to the extensive redundancy of these ORFs throughout the genome, making it challenging to trace any specific ORF as the transcript source. Although there is no confirmation of transcripts, functional RNA or protein could still originate from the \u003cem\u003eCABS1\u003c/em\u003e promoter region. These retained sequences also seem to be biased towards the gene transcription start site. The presence of such a significant LINE-1 segment within the promoter of \u003cem\u003eCABS1\u003c/em\u003e and its neighboring genes could have multiple implications. LINE-1 elements are known to influence genomic architecture and function, potentially affecting gene expression through regulatory sequences or by altering chromatin states (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). TEs can lead to the formation of new chromatin loops and domains, that affect the proximity of regulatory elements to target genes, impacting transcription and genomic structure (48). Distinct TE subfamilies that function as tissue-specific enhancers in colon and liver cancers have been previously identified (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). These TEs are characterized by genomic features associated with active enhancers, such as epigenetic marks and transcription factor binding, and are associated with differentially expressed genes in these types of cancer. The presence of the large islands of identical sequences in the multiple sequence alignments suggests that these features are evolutionarily conserved in the promoter and may hold functional importance. Variations or mutations in these conserved regions may cause gene dysregulation and abnormal phenotypes. A potential future application using data from SEQSIM could be the comparative analysis of promoters among individuals or populations to identify genetic variants associated with increased susceptibility to dysregulation. It would also be interesting to study whether the TEs responsible for these large islands of homology are conserved across species, and if they retained their historical regulatory functions, or acquired new roles over evolution, such as altering 3D chromatin shape (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur pilot analysis which extended beyond Chromosome 4 revealed intriguing patterns of promoter similarity across other chromosomes. SEQSIM revealed patterns of promoter similarity with adjacent genes that may infer 3D chromatin conformation. For example, adjacent genes with highly homologous promoters could imply that these genes share resources in their microenvironments to regulate gene expression. Another explanation is the presence of jumping elements in these highly similar regions. TEs are likely to copy themselves into other parts of the genome that are nearby or were once nearby. The diagonal slashes and checkerboard-like patterns observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicate that such repetition is not coincidental but is a previously overlooked structural characteristic of the human genome.\u003c/p\u003e \u003cp\u003eSEQSIM also identified previously undetected patterns within the chromosomes, surpassing the data in existing 3D genome databases (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). The role of promoters in chromosomal architecture, such as TADs or chromosomal territories that facilitate specific gene interactions, remains poorly understood. The observed clusters of highly similar adjacent promoters, surrounded by regions of lower similarity, resemble the physical looping and interaction patterns characteristic of TADs, suggesting that these promoter clusters may participate in or reflect higher-order chromatin organization. SEQSIM advances the field by providing some initial examples of the types of interactions that may occur. While the functional significance of these patterns remains to be elucidated, the similarity patterns could indicate distinct chromosomal neighborhoods where genes share similar chromatin environments, regulatory accessibility or proximity to nuclear compartments influencing gene expression. For example, 3D chromatin architecture in T-cells supports specific factor binding and interaction with regulators like CTCF (CCCTC-binding factor_ and SATB1 (special AT-rich Sequence Binding Protein 1) to control gene expression (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The identified promoter patterns could indicate discrete functional regions within a chromosome, where neighboring genes work together to fulfill specific cellular functions, similar to TADs. Co-regulated gene clusters exist in yeast, such as those involved in ribosome biogenesis, suggesting spatial arrangement supports coordinated expression (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Adjacent genes within the same TAD may share similar chromatin states, including histone modifications, DNA methylation patterns, or nucleosome occupancy (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). SEQSIM\u0026rsquo;s unique heatmap patterns, when compared to conventional Hi-C data, reveal novel interactions that Hi-C might miss due to limits in resolution and the nature of Hi-C data filtering techniques, especially for transient or condition-specific interactions. For example, interleukin genes, \u003cem\u003eIL-4\u003c/em\u003e, \u003cem\u003eIL-5\u003c/em\u003e, and \u003cem\u003eIL-13\u003c/em\u003e, are known to exist on the same TAD and changes in chromatin state in this TAD elicits a TH2 immune response(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Relationships between these genes can be confirmed and visualized using SEQSIM; however, we are also able to see promoter relationships with other adjacent genes such as \u003cem\u003eRAD50\u003c/em\u003e, \u003cem\u003eTH2-LCR\u003c/em\u003e and \u003cem\u003eTH2LCRR\u003c/em\u003e (See Supplementary Fig.\u0026nbsp;2) that were not previously apparent in Hi-C data. The unique relationships shown in SEQSIM may help fill these gaps in Hi-C data, and exploring these relationships may uncover novel gene networks or pathways involved in specific biological processes or diseases.\u003c/p\u003e \u003cp\u003eCurrently, our SEQSIM analysis has only investigated about 10% of the sequence homology data, as we do not have a method to conduct a full cluster analysis on the entire genome. We plan to explore artificial intelligence techniques and other iterative computational approaches to enable more complete processing of our data. Additionally, further research utilizing complementary techniques, such as functional genomics, chromatin conformation capture assays, and gene perturbation studies, can shed light on the functional implications of observed patterns and provide a deeper understanding of the intricate organization and regulation of genes within chromosomes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur research demonstrates the utility of SEQSIM for comprehensive analysis and comparison of promoters across the human genome. Our novel approach revealed distinct promoter similarity clusters, indicating potential co-regulation, unique gene sets associated with different biological pathways, and potential presence of evolutionary conserved regions. Notably, identical sequence segments may be critical regulatory elements or TE, implying an intricate interplay of genomic elements in gene regulation.\u003c/p\u003e \u003cp\u003eWhile computational constraints limited the depth of our analysis, the observed similarity patterns among adjacent gene promoters have sparked new hypotheses about co-regulation, chromosomal organization, and evolutionary significance. Future experimental validation and the use of additional techniques will shed light on these observations, elucidating the complexities of genome organization and regulation thereby enriching our understanding of human health and disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have declared that no competing interests exist.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Natural Sciences and Engineering Research Council of Canada [RGPIN-2020-04553].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJRLS: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Figure creation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; review \u0026amp; editing.WS: Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing - review \u0026amp; editing.DB: Conceptualization, Writing \u0026ndash; review \u0026amp; editingMM: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Gilbert Lee for providing technical support for this project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.SEQSIM is freely available, implemented in C++ for Linux and Windows platforms. All files are included in the supplementary documentation or downloadable at this link https://sites.ualberta.ca/~joyramie/SEQSIM.zip. Additional resources, including a user guide and supplementary data, are downloadable with this publication or at https://sites.ualberta.ca/~joyramie/SEQSIM.html.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeake VM, Workman JL. Inducible gene expression: diverse regulatory mechanisms. Nat Rev Genet. 2010;11(6):426\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaston GA, Evans SK, Green MR. Transcriptional regulatory elements in the human genome. Annu Rev Genomics Hum Genet. 2006;7:29\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolwerda SJB, de Laat W. CTCF: the protein, the binding partners, the binding sites and their chromatin loops. 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Sci Immunol. 2023;8(85):eadg3917.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5441650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5441650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUnderstanding transcriptional regulation requires an in-depth analysis of promoter regions, which house vital cis-regulatory elements such as core promoters, enhancers, and silencers. Despite the significance of these regions, genome-wide characterization remains a challenge due to data complexity and computational constraints. Traditional bioinformatics tools like Clustal Omega face limitations in handling extensive datasets, impeding comprehensive analysis. To bridge this gap, we developed SEQSIM, a sequence comparison tool leveraging an optimized Needleman-Wunsch algorithm for high-speed comparisons. SEQSIM can analyze complete human promoter datasets in under an hour, overcoming prior computational barriers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eApplying SEQSIM, we conducted a case study on \u003cem\u003eCABS1\u003c/em\u003e, a gene associated with spermatogenesis and stress response but lacking well-defined functions. Our genome-wide promoter analysis revealed 41 distinct homology clusters, with \u003cem\u003eCABS1\u003c/em\u003e residing within a cluster that includes promoters of genes such as \u003cem\u003eVWCE, SPOCK1\u003c/em\u003e, and \u003cem\u003eTMX2\u003c/em\u003e. These associations suggest potential co-regulatory networks. Additionally, our findings unveiled conserved promoter motifs and long-range regulatory sequences, including LINE-1 transposable element fragments shared by \u003cem\u003eCABS1\u003c/em\u003e and nearby genes, implying evolutionary conservation and regulatory significance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results provide insight into potential gene regulation mechanisms, enhancing our understanding of transcriptional control and suggesting new pathways for functional exploration. Future studies incorporating SEQSIM could elucidate co-regulatory networks and chromatin interactions that impact gene expression.\u003c/p\u003e","manuscriptTitle":"SEQSIM – A novel bioinformatics tool for comparisons of promoter regions – a case study of calcium binding protein spermatid associated 1 (CABS1)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 06:56:14","doi":"10.21203/rs.3.rs-5441650/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-14T11:55:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-09T06:40:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-24T01:32:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287710736274462737911693986091716177332","date":"2024-11-29T00:03:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170113215792306436826524844024463531854","date":"2024-11-25T08:03:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-21T01:55:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-19T11:48:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T02:04:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T02:03:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2024-11-12T17:58:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5182993-d8b3-49ce-ab36-a49803a14f40","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T16:05:00+00:00","versionOfRecord":{"articleIdentity":"rs-5441650","link":"https://doi.org/10.1186/s12859-025-06160-x","journal":{"identity":"bmc-bioinformatics","isVorOnly":false,"title":"BMC Bioinformatics"},"publishedOn":"2025-06-09 15:58:01","publishedOnDateReadable":"June 9th, 2025"},"versionCreatedAt":"2024-12-17 06:56:14","video":"","vorDoi":"10.1186/s12859-025-06160-x","vorDoiUrl":"https://doi.org/10.1186/s12859-025-06160-x","workflowStages":[]},"version":"v1","identity":"rs-5441650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5441650","identity":"rs-5441650","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}