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Bioinformatics

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bioRxiv2022

GenErode: a bioinformatics pipeline to investigate genome erosion in endangered and extinct species

BackgroundMany wild species have suffered drastic population size declines over the past centuries, which have led to genomic erosion processes characterized by reduced genetic diversity, increased inbreeding, and accumulation of harmful mutations. Yet, genomic erosion estimates of modern-day populations often lack concordance with dwindling population sizes and conservation status of threatened species. One way to directly quantify the genomic consequences of population declines is to compare genome-wide data from pre-decline museum samples and modern samples. However, doing so requires computational data processing and analysis tools specifically adapted to comparative analyses of degraded, ancient or historical, DNA data with modern DNA data as well as personnel trained to perform such analyses. ResultsHere, we present a highly flexible, scalable, and modular pipeline to compare patterns of genomic erosion using samples from disparate time periods. The GenErode pipeline uses state-of-the-art bioinformatics tools to simultaneously process whole-genome re-sequencing data from ancient/historical and modern samples, and to produce comparable estimates of several genomic erosion indices. No programming knowledge is required to run the pipeline and all bioinformatic steps are well-documented, making the pipeline accessible to users with different backgrounds. GenErode is written in Snakemake and Python3 and uses Conda and Singularity containers to achieve reproducibility on high-performance compute clusters. The source code is freely available on GitHub (https://github.com/NBISweden/GenErode). ConclusionsGenErode is a user-friendly and reproducible pipeline that enables the standardization of genomic erosion indices from temporally sampled whole genome re-sequencing data.

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bioRxiv2022

A Deep Learning Bioinformatics Approach to Modeling Protein-Ligand Interaction with cryo-EM Data in 2021 Ligand Model Challenge

Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps.This results demonstrate that the deep learning bioinformatics approach is a promising direction to model protein-ligand interaction on cryo-EM data using prior structural information. The source code, data, and instruction to reproduce the results are available on GitHub repository : https://github.com/jianlin-cheng/DeepProLigand

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bioRxiv2022

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

Focal segmental glomerulosclerosis (FSGS) is a renal disease leading threat to human health around the world. Here we aimed to explore novel molecular and potential therapeutic targets in FSGS through adopting integrated bioinformatics tools. Next generation sequencing (NGS) data of GSE197307 was available from Gene Expression Omnibus (GEO) database. Furthermore, differentially expressed genes (DEGs) were screened using the DESeq2 package in R software. Gene ontology (GO) and REACTOME pathway enrichment analyses of DEGs were performed via g:Profiler. Then, the protein-protein interaction (PPI) network, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed using the HIPPIE, miRNet and NetworkAnalyst databases. Hub genes were validated using the receiver operating characteristic curve (ROC) analysis. By performing DEGs analysis, 488 up regulated genes and 488 down regulated genes were successfully identified from GSE197307, respectively. And they were mainly enriched in the terms of multicellular organismal process, cell periphery, protein binding, metabolism, immune system process, signaling receptor binding and immune system. Based on the data of protein-protein interaction (PPI), miRNA-hub gene regulatory network and TF-hub gene regulatory network, the top hub genes were ranked, including ILK, DDX5, MATR3, ALB, FOS, MKI67, PLK1, TNF, LCK and GTSE1. Bioinformatics analysis of NGS data identified signaling pathways and hub genes, potentially representing molecular mechanisms for the occurrence, progression, and risk prediction in FSGS.

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bioRxiv2022

A comprehensive proteomic and bioinformatics analysis of human spinal cord injury plasma identifies proteins associated with the complement cascade and liver function as potential prognostic indicators of neurological outcome

1.1.1. IntroductionSpinal Cord Injury (SCI) is a major cause of disability, with complications post-injury often leading to life-long health issues with need of extensive treatment. Neurological outcome post-SCI can be variable and difficult to predict, particularly in incomplete injured patients. The identification of specific SCI biomarkers in blood, may be able to improve prognostics in the field. This study has utilised proteomic and bioinformatics methodologies to investigate differentially expressed proteins in plasma samples across human SCI cohorts with the aim of identifying prognostic biomarkers and biological pathway alterations that relate to neurological outcome. 1.2. Methods and MaterialsBlood samples were taken, following informed consent, from ASIA impairment scale (AIS) grade C "Improvers" (those who experienced an AIS grade improvement) and "Non-Improvers" (No AIS change), and AIS grade A and D at <2 weeks ("Acute") and approx. 3 months ("Sub-acute") post-injury. The total protein concentration from each sample was extracted, with pooled samples being labelled and non-pooled samples treated with ProteoMiner beads. Samples were then analysed using two 4-plex isobaric tag for relative and absolute quantification (iTRAQ) analyses and a label-free experiment for comparison, before quantifying with mass spectrometry. Data are available via ProteomeXchange with identifiers PXD035025 and PXD035072 for the iTRAQ and label-free experiments respectively. Proteomic datasets were analysed using OpenMS (version 2.6.0). R (version 4.1.4) and in particular, the R packages MSstats (version 4.0.1) and pathview (version 1.32.0) were used for downstream analysis. Proteins of interest identified from this analysis were further validated by enzyme-linked immunosorbent assay (ELISA). 1.3. ResultsThe data demonstrated proteomic differences between the cohorts, with the results from the iTRAQ approach supporting those of the label-free analysis. A total of 79 and 87 differentially abundant proteins across AIS and longitudinal groups were identified from the iTRAQ and label-free analyses, respectively. Alpha-2-macroglobulin (A2M), retinol binding protein 4 (RBP4), serum amyloid A1 (SAA1), Peroxiredoxin 2, Apolipoprotein A1 (ApoA1) and several immunoglobulins were identified as bio-logically relevant and differentially abundant, with potential as individual prognostic biomarkers of neurological outcome. Bioinformatics analyses revealed that the majority of differentially abundant proteins were components of the complement cascade and most interacted directly with the liver. 1.4. ConclusionsMany of the proteins of interest identified using proteomics were detected only in a single group and therefore have potential as a binary (present or absent) biomarkers, RBP4 and PRX-2 in particular. Additional investigations into the chronology of these proteins, and their levels in other tissues (cerebrospinal fluid in particular) are needed to better understand the underlying pathophysiology, including any potentially modifiable targets. Pathway analysis highlighted the complement cascade as being significant across groups of differential functional recovery.

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bioRxiv2023

The Identification of Key Genes and Biological Pathways in Polycystic Ovary Syndrome and its Complications by Next Generation Squancing (NGS) and Bioinformatics Analysis

Polycystic ovary syndrome (PCOS) is is associated with infertility, obesity, insulin resistance, hyperinsulinemia, type 2 diabetes mellitus, hypertension, cardiovascular problems, neurological and psychological problems and cancer. The specific mechanism of PCOS and its complications remains unclear. The aim of this study was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of PCOS and its complications. The next generation squancing (NGS) datset GSE199225 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. A protein-protein interaction (PPI) network was constructed and module analysis was performed using HiPPIE and cytoscape. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed for research. The expression of the hub genes was validated using receiver operating characteristic (ROC) curve analysis. We have identified 957 DEGs in total, including 478 up regulated genes and 479 down regulated gene. GO and REACTOME illustrated that DEGs in PCOS were significantly enriched in regulation of molecular function, developmental process, interferon signaling and platelet activation, signaling and aggregation. Finally, through analyzing the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes HSPA5, PLK1, RIN3, DBN1, CCDC85B, DISC1, AR, MTUS2, LYN and TCF4 by the Cytoscape software. This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to PCOS and its complications. These analysis results provide us with novel ideas for finding biomarkers and treatment methods for PCOS and its complications.

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bioRxiv2023

Virotest: a bioinformatics pipeline for virus identification in plants.

High-throughput DNA sequencing is being introduced as an emerging technology for viral diagnosis. Despite several tools being available, lack of standardized bioinformatic pipelines and price are major limitations for its implementation. Many extant tools focus on the assembly of virus-derived small RNAs into contigs and their subsequent annotation. Better sensitivity has been reported by several studies when this approach is complemented with read mapping on known viral genome sequences as an ancillary step, requiring less number of reads and allowing price optimization during DNA sequencing. This publication introduces virotest, a bioinformatics pipeline for the identification of viruses in plants from small RNA data. The tool prioritizes read mapping over sequence assembly for the purpose of obtaining better sensitivities and requiring lower sequencing depths.

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bioRxiv2023

Genomics to Notebook (g2nb): extending the electronic notebook to address the challenges of bioinformatics analysis

We present Genomics to Notebook (g2nb), an environment that combines the JupyterLab notebook system with widely-used bioinformatics platforms. Galaxy, GenePattern, and the JavaScript versions of IGV and Cytoscape are currently available within g2nb. The analyses and visualizations within those platforms are presented as cells in a notebook, making thousands of genomics methods available within the notebook metaphor and allowing notebooks to contain workflows utilizing multiple software packages on remote servers, all without the need for programming. The g2nb environment is, to our knowledge, the only notebook-based system that incorporates multiple bioinformatics analysis platforms into a notebook interface.

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bioRxiv2023

Deep Learning for Protein Structure Prediction: Advancements in Structural Bioinformatics

MotivationAccurate prediction of protein structures is crucial for understanding protein function, stability, and interactions, with far-reaching implications in drug discovery and protein engineering. As the fields of structural bioinformatics and artificial intelligence continue to converge, a standardized model for protein structure prediction is still yet to be seen as even large models like AlphaFold continue to change architectures. To this end, we provide a comprehensive literature review highlighting the latest advancements and challenges in deep learning-based structure prediction, as well as a benchmark system for structure prediction and visualization of amino acid protein sequences. ResultsWe present ProteiNN, a Transformer-based model for end-to-end single-sequence protein structure prediction, motivated by the need for accurate and efficient methods to decipher protein structures and their roles in biological processes and a system to perform prediction on user-input protein sequences. The model leverages the transformer architectures powerful representation learning capabilities to predict protein secondary and tertiary structures directly from integer-encoded amino acid sequences. Our results demonstrate that ProteiNN is effective in predicting secondary structures, though further improvements are necessary to enhance the models performance in predicting higher-level structures. This work thus showcases the potential of transformer-based architectures in structure prediction and lays the foundation for future research in structural bioinformatics and related fields.

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bioRxiv2023

Benchmarking Bioinformatic Virus Identification Tools Using Real-World Metagenomic Data across Biomes

As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training/reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compared the performance of ten state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools had highly variable true positive rates (0 - 68%) and false positive rates (0 - 15%). PPR-Meta best distinguished viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identified different subsets of the benchmarking data and all tools, except for Sourmash, found unique viral contigs. Tools performance could be improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking provides guidance on choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments for viromics researchers.

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bioRxiv2023

Identification of immune-related genes for the diagnosis of ischaemic heart failure based on bioinformatics and three machine learning models

BackgroundThe role of immune cells in the pathogenesis of ischaemic heart failure (IHF) is well-established. However, identifying key diagnostic candidate genes in patients with IHF remains a challenge. Therefore, this study aimed to use bioinformatics and machine learning algorithms to identify potential diagnostic genes for IHF. MethodsTwo IHF datasets were obtained from the GEO database, and key genes for IHF were identified using Limma and WGCNA. Functional enrichment analysis was performed to explore the potential mechanisms of IHF. Next, we used three machine learning algorithms, namely LASSO, RF, and SVM-REF, to identify immune-related diagnostic genes for IHF. ssGSEA enrichment analysis was also completed. To assess the diagnostic value of the identified genes, we developed nomogram and validated them on additional GEO datasets. Finally, an immune infiltration analysis was conducted using the CIBERSORT algorithm to explore immune cell dysregulation in IHF. ResultsOur analysis yielded a total of 92 key genes associated with IHF. Enrichment analysis revealed that the mechanisms underlying IHF are mostly associated with immunity and inflammation. Using the machine learning algorithms, we identified four IHF diagnostic genes, namely RNASE2, MFAP4, CHRDL1, and KCNN3. We constructed nomogram and validated the diagnostic value of these genes on additional GEO datasets. The results showed that these four genes had high diagnostic value (AUC value of 0.961). Furthermore, our immune infiltration analysis revealed the presence of three dysregulated immune cells in IHF, namely Macrophages M2, Monocytes, and T cells gamma delta. ConclusionIn summary, our study identified four potential diagnostic candidate genes for IHF by using bioinformatics and machine learning algorithms. We also explored the potential molecular mechanisms of IHF and the immune cell infiltration environment of the failing myocardium. These findings provide new insights into the pathogenesis, diagnosis, and treatment of IHF.

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bioRxiv2023

Six novel nsSNPs affect RUNX1 gene may leading to Acute Myeloid Leukemia (AML) using Bioinformatics approach

BackgroundRUNX1 is one of the most frequently mutated genes in human AMLs, most of RUNX1 mutations in acute myeloid leukemia (AML) are missense or deletion-truncation and behave as loss-of-function mutations. The molecular consequences of cancer associated mutations in Acute myeloid leukemia (AML) linked factors are not very well understood. Here, we recognize possible pathogenic SNPs in the RUNX1 gene as Functional differences caused by SNPs might have harmful effects on protein structure and function using various computational tools. MethodologyData gained from NCBI database and various tools used to study nsSNPs which they are: SIFT, Polyphen-2, Provean, SNAP2, I-Mutant, Project Hope, Raptor X, PolymiRTS and Gene MANIA. ResultOur study reveals six novel SNPs observed to be the most damaging SNPs that affect structure and function of RUNX1 gene using various bioinformatics tools. ConclusionThis study revealed 7 damaging SNPs, 6 novel nsSNP out of them in the RUNX1 gene that leads to AML, by using different bioinformatics tools. Also, 23 functional classes were predicted in 8 SNPs in the 3UTR, among them, 6 alleles disrupted a conserved miRNA site and 16 derived alleles created a new site of miRNA. This might result in the de regulation of the gene function. Hopefully, these results will help in genetic studying and diagnosis of AML improvement.

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bioRxiv2023

Bioinformatics Illustrations Decoded by ChatGPT: The Good, The Bad, and The Ugly

Emerging studies underscore the promising capabilities of large language model-based chatbots in conducting fundamental bioinformatics data analyses. The recent feature of accepting image-inputs by ChatGPT motivated us to explore its efficacy in deciphering bioinformatics illustrations. Our evaluation with examples in cancer research, including sequencing data analysis, multimodal network-based drug repositioning, and tumor clonal evolution, revealed that ChatGPT can proficiently explain different plot types and apply biological knowledge to enrich interpretations. However, it struggled to provide accurate interpretations when quantitative analysis of visual elements was involved. Furthermore, while the chatbot can draft figure legends and summarize findings from the figures, stringent proofreading is imperative to ensure the accuracy and reliability of the content.

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bioRxiv2023

Bioinfo-Bench: A Simple Benchmark Framework for LLM Bioinformatics Skills Evaluation

AO_SCPLOWBSTRACTC_SCPLOWLarge Language Models (LLMs) have garnered significant recognition in the life sciences for their capacity to comprehend and utilize knowledge. The contemporary expectation in diverse industries extends beyond employing LLMs merely as chatbots; instead, there is a growing emphasis on harnessing their potential as adept analysts proficient in dissecting intricate issues within these sectors. The realm of bioinformatics is no exception to this trend. In this paper, we introduce BO_SCPLOWIOINFOC_SCPLOW-BO_SCPLOWENCHC_SCPLOW, a novel yet straightforward benchmark framework suite crafted to assess the academic knowledge and data mining capabilities of foundational models in bioinformatics. BO_SCPLOWIOINFOC_SCPLOW-BO_SCPLOWENCHC_SCPLOW systematically gathered data from three distinct perspectives: knowledge acquisition, knowledge analysis, and knowledge application, facilitating a comprehensive examination of LLMs. Our evaluation encompassed prominent models ChatGPT, Llama, and Galactica. The findings revealed that these LLMs excel in knowledge acquisition, drawing heavily upon their training data for retention. However, their proficiency in addressing practical professional queries and conducting nuanced knowledge inference remains constrained. Given these insights, we are poised to delve deeper into this domain, engaging in further extensive research and discourse. It is pertinent to note that project BO_SCPLOWIOINFOC_SCPLOW-BO_SCPLOWENCHC_SCPLOW is currently in progress, and all associated materials will be made publicly accessible.1

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bioRxiv2023

Pepid: a Highly Modifiable, Bioinformatics-Oriented Peptide Search Engine

MotivationCurrent peptide search engines are optimized for wet-lab workflows, i.e. they operate in an "end-to-end" manner to achieve good identification results, not to be modified or provide algorithmic insight. This makes developing new software methods to solve problems in peptide identification methods difficult, often requiring a full engine rewrite. Recently, many deep learning methods were proposed as solutions to various parts of the peptide identification task, but virtually none of those methods have been implemented in any actual peptide search process. We believe that the lack of a reliable bioinformatics research platform for peptide identification that enables such integrations is slowing down proteomics research as a whole. ResultsWe present pepid, a bioinformatics research-oriented peptide search engine. Unlike other search engines, pepid is specifically designed with ease of computational research in mind. Our design is highly flexible and allows easy modifications with little required software development expertise, allowing researchers to focus on analysing and improving peptide identification methods.It also takes recent computational trends into account, such as the recent slew of deep learning publications in proteomics, and features a multi-phased batched operations design that is more appropriate than the spectrum batch "end-to-end" designs of existing search engines for those approaches. We show that pepid is competitive with common engines in terms of both identification rates and runtime, forming a minimum required baseline to enable further identification research. Availability and ImplementationPepid is available as open source software under the MIT license at https://github.com/lemieux-lab/pepid. Other data referenced in the text is 3rd party. The selected yeast proteome can be found on SwissProt with accession ID UP000002311 while the human proteomes accession ID is UP0000005640. The ProteomeTools spectra can be found in the PRIDE archive under accession D PXD004732 and the One Hour Yeast Proteome can be found at the ChorusProject at https://chorusproject.org/anonymous/download/experiment/-8823069691100997209 and https://chorusproject.org/anonymous/download/experiment/449795368199176159.

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bioRxiv2023

Decoding Prognostic Markers Bioinformatics Insights into PIH1D1 and p53 in Children's Brain Cancer

Genetic alterations in normal brain cells lead to the development of brain tumors (BT). The incidence of newly diagnosed cases is on the rise over time. Understanding the molecular biology of pediatric brain tumors is crucial for advancing novel therapeutic approaches to prevent or effectively manage this disease. The R2TP complex, a conserved co-chaperone from yeast to mammals, including RUVBL1, RUVBL2, PIH1D1, and RPAP3 in humans, plays a crucial role in the assembly and maturation of various multi-subunit complexes. This study evaluates the expression of PIH1D1 and p53 in pediatric brain cancers using The Cancer Genome Atlas (TCGA) data through the UALCAN platform--a novel, user-friendly resource for cancer OMICS data analysis. Our analysis revealed elevated expression levels of PIH1D1 in pediatric brain tumors across all age groups compared to normal tissues, suggesting its potential as an early detection marker and a prognostic indicator. Additionally, P53 emerged as a promising target for brain tumor treatment, warranting exploration for age-specific applications. The Venn Diagram analysis further delineated key genes within the PIH1D1 and P53 networks, providing insights into the genetic landscape of childhood brain cancer. Despite these findings, the study has limitations, primarily relying on bioinformatics databases. Future research will incorporate clinical tissues and cell lines in in vivo and in vitro experiments to unravel the mechanistic roles of PIH1D1 and p53 in brain tumors. This comprehensive approach bridges bioinformatics and clinical exploration, contributing to a holistic understanding of pediatric brain cancer molecular biology and paving the way for targeted therapeutic interventions.

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bioRxiv2024

Comparative Evaluation of Bioinformatic Pipelines for Full-Length Viral Genome Assembly

The increasingly widespread application of next-generation sequencing (NGS) in clinical diagnostics and epidemiological research has generated a demand for robust, fast, automated, and user-friendly bioinformatic workflows. To guide the choice of tools for the assembly of full-length viral genomes from NGS datasets, we assessed the performance and applicability of four widely adopted bioinformatic pipelines (shiver - for which we created a user-friendly Dockerized version, referred to as dshiver; SmaltAlign, viral-ngs, and V-pipe) using both simulated datasets and real-world HIV-1 paired-end short- read sequences and default settings. All four pipelines produced high-quality consensus genome assemblies and minority variant calls when the reference sequence used for assembly had high similarity to the analyzed sample. However, while shiver and SmaltAlign showed robust performance also with more divergent samples (non-matching subtypes), viral-ngs and V-Pipe proved to be sensitive to genetic distance from the reference sequence. With empirical datasets, SmaltAlign and viral-ngs exhibited substantially shorter runtime compared to V-Pipe and shiver. In terms of applicability, V-Pipe provides the broadest functionalities; SmaltAlign and dshiver combine user-friendliness with robustness; while the use of viral-ngs requires a less computational resources compared to other tools. To conclude, all four pipelines can perform well in terms of quality metrics; however, the reference sequence needs to be adjusted to closely match the sample data for viral-ngs and V-Pipe. Differences in user-friendliness and runtime may guide the choice of the pipeline in a particular setting. The new Dockerized version of shiver offers ease of use in addition to the accuracy and robustness of the original pipeline.

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bioRxiv2024

Bioinformatic evaluation of the potential oral-gut translocation of periodontal pathogens in patients with colorectal polyps

ObjectiveThis study aimed to characterize the profiles of the oral and gut microbiota of patients with colorectal polyps using 16S rRNA gene sequencing and bioinformatic approaches. BackgroundPrevious studies have shown microbial translocation from the oral cavity to the gut, implying pathogenic impacts on gastroesophageal disease, including colorectal cancer (CRC). However, its details remain unclear. MethodsTwenty patients scheduled for endoscopic colorectal polypectomy were enrolled in this study. Oral samples (saliva and subgingival dental plaque) and intestinal samples (feces and swab of intestinal mucosa) were collected during preoperative and 6-month-postoperative reassessment periods. After sequencing the V3-V4 region of the bacterial 16S rRNA gene, several bioinformatic analyses (bacterial composition, diversity, core microbiome, and shared ASV) were performed on pre- and postoperative samples for each subject. ResultsThe bacterial composition was dominated by Bacteroides, Streptococcus, Fusobacterium, Veillonella, and Prevotella_7 in all four samples. Beta diversity analysis using weighted UniFrac distance distinctly segregated the samples between oral and intestinal environments in the principal coordinate analysis plot. Core microbiome analysis revealed that Streptococcus and Porphyromonas were dominantly shared in intra-oral environments. Additionally, alongside Streptococcus, periodontitis-related bacteria, such as Veillonella, Fusobacterium, Porphyromonas, Prevotella_7, Haemophilus, and Prevotella, were identified as shared genera between oral and intestinal environments. Finally, shared ASV analysis demonstrated that Streptococcus was shared in the oral and intestinal environments of most patients, while periodontal pathogens were shared in some patients. ConclusionsThe core microbiome and shared ASV analyses revealed that several genes are shared between oral and intestinal environments in patients with colorectal polyps, indicating the oral-gut translocation of periodontitis-related bacteria. Further large-scale studies are needed to elucidate their involvement in CRC.

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bioRxiv2024

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

The regulation of gene expression is carefully overseen by upstream gene regions (UGRs) which include promoters, enhancers, and other regulatory elements. Understanding these regions is difficult using standard bioinformatic approaches due to the scale of the human genome. Here we present SEQSIM, a novel bioinformatics tool based on a modified Needleman-Wunsch algorithm that allows for fast, comprehensive, and accurate comparison of UGRs across the human genome. In this study, we detailed the applicability and validity of SEQSIM through an extensive case study of the calcium binding protein spermatid-associated 1 (CABS1). By analyzing 2000 base pairs upstream of every human gene, SEQSIM identified distinct clusters of UGRs, revealing conserved motifs and suggesting potential regulatory interactions. Our analysis identified 41 clusters, the second largest of which contains the CABS1 UGR. Studying the other members of the CABS1 cluster could offer new insights into its regulatory mechanisms and suggest broader implications for genes involved in similar pathways or functions. The development and implementation of SEQSIM represents a significant step forward for the genomics field, providing a powerful new tool to dissect the complexity of the human genome and gain a better understanding of how gene expression is regulated. The study not only shows that SEQSIM is an effective means to identify potential regulatory elements and gene clusters, but also opens up new lines of inquiry to understand overall genomic architecture.

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