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Bioinformatics

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bioRxiv2021

ClusTRace, a bioinformatic pipeline for analyzing clusters in virus phylogenies

SummarySARS-CoV-2 is the highly transmissible etiologic agent of coronavirus disease 2019 (COVID-19) and has become a global scientific and public health challenge since December 2019. Several new variants of SARS-CoV-2 have emerged globally raising concern about prevention and treatment of COVID-19. Early detection and in depth analysis of the emerging variants allowing pre-emptive alert and mitigation efforts are thus of paramount importance. Here we present ClusTRace, a novel bioinformatic pipeline for a fast and scalable analysis of sequence clusters or clades in large viral phylogenies. ClusTRace offers several high level functionalities including outlier filtering, aligning, phylogenetic tree reconstruction, cluster or clade extraction, variant calling, visualization and reporting. ClusTRace was developed as an aid for COVID-19 transmission chain tracing in Finland and the main emphasis has been on fast and unsupervised screening of phylogenies for markers of super-spreading events and other features of concern, such as high rates of cluster growth and/or accumulation of novel mutations. AvailabilityAll code is freely available from https://bitbucket.org/plyusnin/clustrace/

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bioRxiv2021

Bioinformatics analysis of differentially expressed genes in non alcoholic fatty liver disease using next generation sequencing data

Non alcoholic fatty liver disease (NAFLD) is the most common metabolic disease in humans, affecting the majority of individuals. In the current investigation, we aim to identify potential key genes linked with NAFLD through bioinformatics analyses of next generation sequencing (NGS) dataset. NGS dataset of GSE135251 from the Gene Expression Omnibus (GEO) database were retrieved. Differentially expressed genes (DEGs) were obtained by DESeq2 package. g:Profiler database was further used to identify the potential gene ontology (GO) and REACTOME pathways. Protein-protein interaction (PPI) network was constructed using the Hippie interactome database. miRNet and NetworkAnalyst databases were used to establish a miRNA-hub gene regulatory network and TF-hub gene regulatory network for the hub genes. Hub genes were verified based on receiver operating characteristic curve (ROC) analysis. Totally, 951 DEGs were identified including 476 up regulated genes and 475 down regulated genes screened in NAFLD and normal control. GO showed that DEGs were significantly enhanced for signaling and regulation of biological quality. REACTOME pathway analysis revealed that DEGs were enriched in signaling by interleukins and extracellular matrix organization. ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 were indicated as hub genes from PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, ROC analysis revealed that ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 might serve as diagnostic biomarkers in NAFLD. The current investigation provided insights into the molecular mechanism of NAFLD that might be useful in further investigations.

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bioRxiv2021

A miRNAs Based Exploration of promising Biomarkers in Cervical Cancer using Bioinformatic Methods

Cervical Cancer (CC) is a gynecologic cancer. In this cancer early detection is incredibly tough because most of the patients are not have any specific symptoms that results in suspending the proper identification. In this work, we selected TCGA CESC datasets and miRNA Seq analysis was done. The expression profiles of miRNAs in cervical cancer datasets were investigated using bioinformatics tools. The expression profiles of miRNA in Normal tissue, primary tumor and metastatic samples were analyzed. Based on p-value, principal component analysis and comparative literature survey, we reported 6 over-expressed (5X) miRNA at metastatic stage namely, hsa-mir-363, hsa-mir-429, hsa-mir-141, hsa-mir-93, hsa-mir-203b and hsa-mir-18a. Expression profiles were compared in heatmap. The target genes for the selected miRNAs were investigated for interaction and pathway details. The identification of two hub proteins (PTEN and MYC) in Protein-Protein Interaction Network was followed by pathway analysis. Our results indicate that hsa-mir-363, hsa-mir-429, hsa-mir-141, hsa-mir-93, hsa-mir-203b and hsa-mir-18a could be a potential diagnostic biomarkers for early-stage CESC and serve as prognostic predictors for patients with CESC.

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bioRxiv2022

Cell delivery peptides for small interfering RNAs targeting SARS-CoV-2 new variants through a bioinformatics and deep learning design

Nucleic acid technologies with designed delivery systems have surged as one the most promising therapies of the future, due to their contribution in combating SARS-CoV-2 severe disease. Nevertheless, the emergence of new variants of concern still represents a real threat in the years to come. It is here that the use of small interfering RNA sequences to inhibit gene expression and, thus, protein synthesis, may complement the already developed vaccines, with faster design and production. Here, we have designed new sequences targeting COVID-19 variants and other related viral diseases through bioinformatics, while also addressing the limited number of delivery peptides by a deep learning approach. Two sequences databases were produced, from which 62 were able to target the virus mRNA, and ten displayed properties present in delivery peptides, which we compared to the broad use TAT delivery peptide.

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bioRxiv2022

A systematic comparison of current bioinformatic tools for glycoproteomics data

Glycosylation is one of the most common post-translational modifications and generates an enormous amount of proteomic diversity; changes in glycosylation are associated with nearly all disease states. Intact glycoproteomics seeks to determine the site-localization and composition of glycans along a protein backbone via mass spectrometry. Following data acquisition, raw files are analyzed using search algorithms to define peptide sequence, glycan composition, and site localization. Glycoproteomics is rapidly expanding, creating the pressing need to establish bioinformatic community standards. Recently, several new search algorithms were released, many of which vary in terms of search strategy, localization system, score cutoffs, and glycan databases, thus warranting a comprehensive comparison of these new programs along with existing programs. Here, we analyzed three common samples: an enriched cell lysate, a mixture of 6 glycoproteins, and a mucin-domain glycoprotein. All raw files were searched with comparable parameters among software and the results were extensively manually validated to compare accuracy and completion of the output. Our results highlight the continued need for manual validation of glycopeptide spectral matches, especially for O-glycopeptides. Despite this, O-Pair outperformed all other programs in correct identification of O-glycopeptides and its localization system proved to be useful. On the other hand, Byonic and pGlyco performed best for N-glycoproteomics; the former was best for proteome-wide searches, but the latter identified more N-glycosites in less complex samples. Overall, we summarize the strengths, weaknesses, and potential improvements for these search algorithms. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=52 SRC="FIGDIR/small/484528v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@17c371forg.highwire.dtl.DTLVardef@70532org.highwire.dtl.DTLVardef@e640b7org.highwire.dtl.DTLVardef@a2bb26_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2022

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) comprise the most common forms of autoimmune disease. The aim of this investigation was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of T1DM. The next generation sequencing (NGS) dataset GSE182870 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. Protein-protein interaction (PPI) network, modules, miRNA- hub gene regulatory network, and TF-hub gene regulatory network were conducted via comprehensive target prediction and network analyses. Finally, hub genes were validated by using receiver operating characteristic curve analysis. A total of 860 DEGs were screened out from NGS dataset, among which 477 genes were up regulated and 383 genes were down regulated. GO enrichment analysis indicated that up regulated genes were mainly involved in cellular metabolic process, intracellular anatomical structure and catalytic activity, and the down regulated genes were significantly enriched in cellular nitrogen compound biosynthetic process, protein containing complex and heterocyclic compound binding. REACTOME pathway enrichment analysis showed that the up regulated genes were mainly enriched in metabolism of carbohydrates and the down regulated genes were significantly enriched in metabolism of RNA. A PPI network, modules, miRNA-hub gene regulatory network and TF - hub gene regulatory network were constructed, and hub genes (MAPK14, RHOC, MAD2L1, TAF1, TRAF2, HSP90AA1, TP53, HSP90AB1, UBA52 and RACK1) were identified. This study provides further insights into the underlying pathogenesis of T1DM.

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bioRxiv2022

QuasiFlow: a bioinformatic tool for genetic variability analysis from next generation sequencing data

Populations of RNA and ssDNA viruses within their hosts contain a heterogeneous collection of variant genomes known as quasispecies. Large variability in mitochondrial DNA has also been found within the same organism, drawing an interesting parallel between the two situations. The advent of next-generation sequencing technologies facilitated studying genetic variation, but many open-source bioinformatic tools have to be combined in a non-trivial approach. Here it is presented QuasiFlow, a workflow based on well-stablished software that extracts reliable mutations and recombinations, even at low frequencies (~10-4), provided that at least 250 million nucleotides are analysed. Accurate prediction of mutations and recombinations has been demonstrated with synthetic reads and with in vitro rolling-circle amplification of a plant geminivirus. An in-depth analysis of viral quasispecies was performed and QuasiFlow revealed the coexistence in the plant of three virus genomes and distinct recombinations between some of them. Human mitochondrial variants were also investigated and high level of heteroplasmy (75%) was confirmed, and the relation between low-frequency heteroplasmy (0.1- 0.2%) and some human diseases, regardless of sex, was established. Hence, we propose that QuasiFlow may find use with known and emerging viruses to reveal evolutionary jumps and co-infections, with mitochondrial DNA to detect relevant heteroplasmy would otherwise be elusive, or even in other population studies such as those considering single cell sequencing.

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bioRxiv2022

USAT: a Bioinformatic Toolkit to Facilitate Interpretation and Comparative Visualization of Tandem Repeat Sequences

Tandem repeats (TR), which are highly variable genomic variants, are widely used in individual identification, disease diagnostics and evolutionary studies. The recent advances of sequencing technologies and bioinformatic tools facilitate calling TR haplotypes. Both length-based and sequence-based TR alleles are used in different applications. However, sequence-based TR alleles could provide the highest precision to characterize TR haplotypes. Analysis of the differences between or among TR haplotypes, especially at the single nucleotide level, is the focus of TR haplotype characterization. In this study, we developed a Universal STR Allele Toolkit (USAT) for TR haplotype analysis, which includes allele size conversion, sequence comparison of haplotypes, figure plotting and comparison for allele distribution, and interactive visualization. An example application of USAT for analysis of the CODIS core STR loci with benchmarking human individuals demonstrated the capabilities of USAT. USAT has a user-friendly graphic interface and runs in all major computing operating systems at a fast speed with parallel computing enabled. In summary, USAT is able to facilitate the interpretation, visualization, and comparisons of TRs.

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bioRxiv2022

PACeR: a bioinformatic pipeline for the analysis of chimeric RNA-seq data

MicroRNAs (miRNAs) are small non-coding RNAs that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets which results in inhibition of translation and often destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA-target interactions by cross-linking and ligation of miRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high throughput sequencing of the chimeric RNAs. Despite the strength of these direct chimeric miRNA:target profiling approaches, standardized pipelines for analyzing the resulting chimeric RNA sequencing data are not readily available. Here we present PACeR, a robust bioinformatic pipeline for the analysis of chimeric RNA sequencing data. PACeR consists of two parts, each of which are optimized for the distinctive characteristics of chimeric RNA sequencing reads: first, read processing and alignment and second, peak calling and motif analysis. We apply PACeR to chimeric RNA sequencing data generated in our lab as well as a published benchmark dataset. PACeR has minimal computational power requirements and contains extensive annotation to broaden accessibility for processing chimeric RNA sequencing data and enable insights to be gained about the targets of small non-coding RNAs in regulating diverse biological systems.

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bioRxiv2022

GeneToList: A web application to assist with gene identifiers for the non-bioinformatics-savvy scientist

The increasing incorporation of omics technology into clinical and translational medicine presents challenges to end users of the large and complex datasets that are generated by these methods. A particular challenge in genomics is that the nomenclature for genes is not uniform between large genomic databases or between commonly used genetic analysis tools. Furthermore, outdated genomic nomenclature can still be found amongst scientific communications including peer-reviewed manuscripts. Therefore, a web application (GeneToList) was developed to assist is gene ID conversion and alias matching, with a specific focus on achieving a user-friendly interface for the non-bioinformatics-savvy scientist. Availability and implementationGeneToList is available at https://www.genetolist.com/. The tool is a web application that is compatible with many standard browsers. ContactJoshua.Breidenbach@UToledo.edu or support@genetolist.com

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bioRxiv2022

A bioinformatics pipeline for the assessment of the evolutionary relationship of some Drosophila species based on class II transposons mapping

Transposons are mobile DNA sequences, known for their ability to insert into other locations in the genome. Genome sequencing allowed the identification of the high content of transposons in various model organisms. Here, we present a bioinformatics pipeline developed to estimate the evolutionary relationship between Drosophila melanogaster and other Drosophilidae based on the comparative analysis of the presence and distribution of class II transposons. Our study reveals that the presence and distribution of transposons hobo, HB, Tc1, Tc1-2, hopper and Bari1 points to close evolutionary relationship among D. melanogaster, D. simulans, D. sechellia and D. yakuba, which is in accordance with other data available in literature.

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bioRxiv2022

DRDs and Brain Derived Neurotrophic Factor share a common therapeutic ground: A novel bioinformatic approach sheds new light towards pharmacological treatment of Cognitive and Behavioral Disorders.

Cognitive and behavioral disorders are subgroups of mental health disorders. Both cognitive and behavioral disorders can occur in people of different ages, genders, and social backgrounds and they can cause serious physical, mental or social problems. The risk factors for these diseases are numerous, with a range from genetic and epigenetic factors to physical factors. In most cases, the appearance of such a disorder in an individual is a combination of his genetic profile and environmental stimuli. To date, researchers have not been able to identify the specific causes of these disorders and as such, there is urgent need for innovative study approaches. The aim of the present study was to identify the genetic factors which seem to be more directly responsible for the occurrence of a cognitive and/or behavioral disorder. More specifically, through bioinformatics tools and software as well as analytical methods such as systemic data and text mining, semantic analysis, and scoring functions, we extracted the most relevant single nucleotide polymorphisms (SNPs) and genes connected to these disorders. All the extracted SNPs were filtered, annotated, classified, and evaluated in order to create the "genomic grammar" of these diseases. The identified SNPs guided the search for top suspected genetic factors, dopamine receptors D and Neurotrophic Factor BDNF, for which regulatory networks were built. The identification of the "genomic grammar" and underlying factors connected to cognitive and behavioral disorders can aid the successful disease profiling, the establishment of novel pharmacological targets and provide the basis for personalized medicine, which takes into account the patients genetic background as well as epigenetic factors.

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bioRxiv2022

Structural, functional and evolutionary analysis of wheat WRKY45 protein: A combined bioinformatics and MD simulation approach

Bread wheat (Triticum aestivum L.) is the worlds second-most important cereal crop, as well as Indias. It is an allohexaploid composed of three homeologous sub-genomes (AA, BB, and DD), which is a constraint in determining the complete genome sequence. Several transcription factors have been implicated in both abiotic and biotic stress. WRKY transcription factors are among the best characterised in the context of pathogen defence mechanisms. Different members of the WRKY transcription factors have been shown to confer resistance to stress. But very little is known about the wheat WRKY transcription factors. In silico analysis of the TaWRKY45 protein was performed in the present study using several bioinformatics tools like motif scan, CD search, Netphos, NGlycos, GRAVY, and the SWISS MODEL. The study revealed that TaWRKY45 belongs to the group III family and contains hydrophilic proteins with 19 potential phosphorylation sites. TaWRKY45 protein was found to be orthologous to rice OsWRKY45 by phylogenetic analysis. The catalytic domain was analysed by motif scan which showed that TaWRKY45 has one WRKY domain and a C2-HC zinc finger motif. TaWRKY45s structure was determined to be more stable, more constrained, more compact, and have greater potential to interact with other molecules than OsWRKY45, according to MD simulation analysis. Thus, in silico analysis of transcription factors helps study protein function, interaction, and regulatory pathways.

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bioRxiv2022

Dissecting Complexity: The Hidden Impact of Application Parameters on Bioinformatics Research

Biology is a quest; an ongoing inquiry about the nature of life. How do the different forms of life interact? What makes up an ecosystem? How does a tiny bacterium work? To answer these questions biologists turn increasingly to sophisticated computational tools. Many of these tools are highly configurable, allowing customization in support of a wide range of uses. For example, algorithms can be tuned for precision, efficiency, type of inquiry, or for specific categories of organisms or their component subsystems. Ideally, configurability provides useful flexibility. However, the complex landscape of configurability may be fraught with pitfalls. This paper examines that landscape in bioinformatics tools. We propose a methodology, SOMATA, to facilitate systematic exploration of the vast choice of application parameters, and apply it to three different tools on a range of scientific inquires. We further argue that the tools themselves are complex ecosystems. If biologists explore these, ask questions, and experiment just as they do with their biological counterparts, they will benefit by both finding improved solutions to their problems as well as increasing repeatability and transparency. We end with a call to the community for an increase in shared responsibility and communication between tool developers and the biologists that use them in the context of complex system decomposition.

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bioRxiv2023

Integrated Bioinformatics Analysis Reveals APEX1 as a Potential Biomarker for Septic Cardiomyopathy

BackgroundA severe threat to human health is septic cardiomyopathy (SCM), a condition with high morbidity and fatality rates throughout the world. However, effective treatment targets are still lacking. Therefore, it is necessary and urgent to find new therapeutic targets of SCM. MethodsWe obtained gene chip datasets GSE79962, GSE53007 and GSE13205 from the GEO database. After data normalization, GSE79962 was used as the training set and screened for differentially expressed genes (DEGs). Then, the module genes most related to SCM were identified via weighted gene co-expression network analysis (WGCNA). The potential target genes of SCM were obtained by intersection of DEGs and WGCNA module genes. We further performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) function and pathway enrichment analyses on these genes. In addition, potential biomarkers were screened using machine learning algorithms and receiver operating characteristic (ROC) curve analysis. Gene Set Enrichment Analysis (GSEA) was then used to explore the mechanisms underlying the involvement of potential biomarkers. Finally, we validated the obtained potential biomarkers in test sets (GSE53007 and GSE13205). ResultsA total of 879 DEGs were obtained by differential expression analysis. WGCNA generated 2939 module genes significantly associated with SCM. The intersection of the two results produced 479 potential target genes. Enrichment analysis showed that these genes were involved in the positive regulation of protein kinase A signaling, histone deacetylase activity and T cell receptor signaling pathway, etc. Then, the results of machine learning algorithm and ROC analysis revealed that NEIL3, APEX1, KCNJ14 and TKTL1 had good diagnostic efficacy. GSEA results showed that these genes involved in signaling pathways mainly enriched in base excision repair and glycosaminoglycan biosynthesis pathways, etc. Notably, APEX1 was significantly up-regulated in the SCM groups of the two test sets and the AUC (area under curve) > 0.85. ConclusionsOur study identified NEIL3, APEX1, KCNJ14 and TKTL1 may play important roles in the pathogenesis of SCM through integrated bioinformatics analysis, and APEX1 may be a novel biomarker with great potential in the clinical diagnosis and treatment of SCM in the future.

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bioRxiv2023

Bioinformatics Approaches to Determine the Effect of SARS-CoV-2 Infection on Patients with Intrahepatic Cholangiocarcinoma

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals throughout the world, which poses a serious threat to human health. COVID-19 is a systemic disease that affects tissues and organs, including the lung and liver. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common liver cancer, and cancer patients are particularly at high risk of SARS-CoV-2 infection. The relationship between HCC and COVID-19 has been reported in previous studies, but ICC has been rare. With the methods of systems biology and bioinformatics, this study explored the link between COVID-19 and ICC. Transcriptional profiling of COVID-19 and ICC were obtained from the GEO database. A total of 70 common differentially expressed gene (DEGs) of both diseases were identified to investigate shared pathways. Then top-ranked 10 key DEGs (SCD, ACSL5, ACAT2, HSD17B4, ALDOA, ACSS1, ACADSB, CYP51A1, PSAT1, and HKDC1) were identified as hub genes by protein-protein interaction (PPI) network analysis. In addition, transcriptional regulatory networks regulating hub genes were revealed by hub Gene-transcription factor (TF) interaction analysis and hub gene-microRNA (miRNAs) interaction analysis. This study is expected to provide new references for future research and treatment of COVID-19 and ICC.

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bioRxiv2023

PolyAMiner-Bulk: A Machine Learning Based Bioinformatics Algorithm to Infer and Decode Alternative Polyadenylation Dynamics from bulk RNA-seq data

More than half of human genes exercise alternative polyadenylation (APA) and generate mRNA transcripts with varying 3 untranslated regions (UTR). However, current computational approaches for identifying cleavage and polyadenylation sites (C/PASs) and quantifying 3UTR length changes from bulk RNA-seq data fail to unravel tissue- and disease-specific APA dynamics. Here, we developed a next-generation bioinformatics algorithm and application, PolyAMiner-Bulk, that utilizes an attention-based machine learning architecture and an improved vector projection-based engine to infer differential APA dynamics accurately. When applied to earlier studies, PolyAMiner-Bulk accurately identified more than twice the number of APA changes in an RBM17 knockdown bulk RNA-seq dataset compared to current generation tools. Moreover, on a separate dataset, PolyAMiner-Bulk revealed novel APA dynamics and pathways in scleroderma pathology and identified differential APA in a gene that was identified as being involved in scleroderma pathogenesis in an independent study. Lastly, we used PolyAMiner-Bulk to analyze the RNA-seq data of post-mortem prefrontal cortexes from the ROSMAP data consortium and unraveled novel APA dynamics in Alzheimers Disease. Our method, PolyAMiner-Bulk, creates a paradigm for future alternative polyadenylation analysis from bulk RNA-seq data.

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bioRxiv2023

Network Pharmacology and Bioinformatics Methods Reveal the Mechanism of Sishenwan in the Treatment of diabetic nephropathy

Objective. In order to decipher the bioactive components and potential mechanisms of the traditional Chinese medicine (TCM) formula Sishenwan (SSW) for diabetic nephropathy (DN), we integrated network pharmacology and bioinformatics. Methods. The candidate compounds of SSW and relative targets were obtained from the TCMSP, BATMAN-TCM, SiwssTartgetPrediction, STITCH, and ChEMBL web servers. The UniProt database was used to translate the target names into gene names, and then constructed the herbal-compound-target network. DN-related targets were ascertained based on OMIM, CTD, GeneCards, DisGeNET, and GEO. Furthermore, there was a protein-protein interaction (PPI) network to explore the overlapping targets between SSW and DN, which focused on screening the pivotal targets by topology. GO and KEGG enrichment analyses were carried out to further understand the potential functions associated with the effect of SSW against DN. Eventually, molecular docking simulations were performed to validate the binding affinity between major bioactive components and hub genes. Results. A total of 120 candidate active compounds and 542 corresponding drug targets were derived, in which 195 targets intersected with DN. Then, KEGG pathway analysis showed that several signaling pathways were closely related to the process of SSW against DN, including the AGE-RAGE signaling pathway in diabetic complications, the TNF signaling pathway, and IL-17 signaling pathway, ect. The PPI network analysis identified PTGS2, CREB1, ESR1, TNF, IL1B, INS, AKT1, PPARG, and JUN were the top 9 hub targets. The molecular docking confirmed that the bioactive compounds of SSW had a firm binding affinity with hub targets. Conclusions. As a whole, the present study revealed that SSW exerted therapeutic effects on DN via modulating multi-targets with multi-compounds through multi-pathways.

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