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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Prognostic value, signal interaction network, and immune infiltration characteristics of MET gene expression in gastric cancer analyzed by multi-database bioinformatics

Objective Based on the bioinformatics method of multi-database integration, this study systematically analyzes the expression characteristics, clinical pathological correlation, prognostic value, potential molecular mechanisms, and immune infiltration patterns of hepatocyte growth factor receptor (MET) in gastric cancer. Methods The UALCAN and GEPIA databases were employed to examine the differential expression of MET between gastric cancer and normal gastric mucosal tissues, as well as its associations with clinicopathological features. Kaplan-Meier Plotter was utilized to evaluate the impact of MET expression on overall survival (OS) and progression-free survival (PFS). Protein-protein interaction (PPI) network was constructed via LinkedOmics, followed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of co-expressed genes. Four algorithms, TIMER, CIBERSORT, EPIC, and MCPcounter, were used to cross evaluate the correlation between MET expression and immune cell infiltration; Further validate the cell type specific expression of MET using the gastric cancer single-cell sequencing queues (GSE134520, GSE167297) built into the TISCH database. Results MET expression was significantly elevated in gastric cancer tissues compared with normal gastric mucosa (P < .05), and its expression level was significantly correlated with tumor grade and TNM stage. Patients with high MET expression exhibited significantly poorer OS and PFS than those with low MET expression (P < .05). The PPI network revealed that MET could interact with 20 key proteins, including EGFR, ERBB2, HGF, STAT3, and GRB2 etc. GO enrichment analysis suggests that differentially expressed genes are significantly enriched in functions such as the ERBB signaling pathway, cadherin binding, and DNA repair complexes; KEGG enrichment analysis showed that MET related genes were significantly enriched in pathways such as homologous recombination, nuclear cytoplasmic transport, mismatch repair, and oxidative phosphorylation. Immune infiltration analysis showed that the negative association between MET and B cells infiltration has cross algorithm robustness, while the association with neutrophils, CD8+ T cells, CD4+ T cells, and macrophages exhibits algorithmic heterogeneity or insignificance; There is no significant correlation between MET and common immune checkpoint molecules such as PD-1, PD-L1, CTLA4, etc. Single cell validation further confirmed that MET is mainly enriched in malignant epithelial cells and endothelial cells, and is almost not expressed in immune cells. Conclusions Multidimensional bioinformatic analyses demonstrate that elevated MET expression serves as an independent risk factor for unfavorable prognosis in gastric cancer. MET may mediate dual drug resistance in gastric cancer via crosstalk with multiple signaling molecules (including EGFR, ERBB2, HGF, STAT3 and GRB2) and dysregulation of the homologous recombination repair pathway. Results from multiple-algorithm immune infiltration analysis, single-cell dataset analysis and immune checkpoint correlation analysis indicate that MET exerts only modest direct regulatory effects on the gastric cancer immune microenvironment. This exploratory study offers systematic bioinformatic evidence supporting MET as a candidate prognostic biomarker and potential therapeutic target for gastric cancer. Further functional experiments and prospective cohort studies are required to validate its molecular mechanisms and clinical utility.

cancer biology↗

bioRxiv2015

Architecting a distributed bioinformatics platform with iRODS and iPlant Agave API

Over the past few years, cloud-based platforms have been proposed to address storage, management, and computation of large-scale data, especially in the field of genomics. However, for collaboration efforts involving multiple institutes, data transfer and management, interoperability and standardization among different platforms have imposed new challenges. This paper proposes a distributed bioinformatics platform that can leverage local clusters with remote computational clusters for genomic analysis using the unified bioinformatics workflow. The platform is built with a data server configured with iRODS, a computation cluster authenticated with iPlant Agave system, and web server to interact with the platform. A Genome-Wide Association Study workflow is integrated to validate the feasibility of the proposed approach.

Bioinformatics↗

bioRxiv2016

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools. These preconfigured workflows provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks, and, within the context of the same environment, to visualize and further interrogate their results. This bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications.

Bioinformatics↗

bioRxiv2016

PubData: search engine for bioinformatics databases worldwide

We propose a search engine and file retrieval system for all bioinformatics databases worldwide. PubData searches biomedical data in a user-friendly fashion similar to how PubMed searches biomedical literature. PubData is built on novel network programming, natural language processing, and artificial intelligence algorithms that can patch into the file transfer protocol servers of any user-specified bioinformatics database, query its contents, retrieve files for download, and adapt to the users search preferences.\n\nPubData is hosted as a user-friendly, cross-platform graphical user interface program developed using PyQt: http://www.pubdata.bio. The methods are implemented in Python, and are available as part of the PubData project at: https://github.com/Bohdan-Khomtchouk/PubData.

Bioinformatics↗

bioRxiv2017

Biopipe: A Lightweight System Enabling Comparison of Bioinformatics Tools and Workflows

Analyzing next generation sequencing data always requires researchers to install many tools, prepare input data compliant to the required data format, and execute the tools in specific orders. Such tool installation and workflow execution process is tedious and error-prone, and becomes very challenging when researchers need to compare multiple alternative tool chains. To mitigate this problem, we developed a new lightweight and portable system, Biopipe, to simplify the creation and execution of bioinformatics tools and workflows, and to further enable the comparison between alternative tools or workflows. Biopipe allows users to create and edit workflows with user-friendly web interfaces, and automates tool installation as well as workflow synthesis by downloading and executing predefined Docker images. With Biopipe, biologists can easily experiment with and compare different bioinformatics tools and workflows without much computer science knowledge. There are mainly two parts in Biopipe: a web application and a standalone Java application. They are freely available at http://bench.cs.vt.edu:8282/Biopipe-Workflow-Editor-0.0.1/index.xhtml and https://code.vt.edu/saima5/Biopipe-Run-Workflow\n\nContactnm8247@cs.vt.edu\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

A repository of web-based bioinformatics resources developed in India

IndiaBioDb (https://webs.iiitd.edu.in/raghava/indiabiodb/) is a manually curated comprehensive repository of bioinformatics resources developed and maintained by Indian researchers. This repository maintains information about 543 freely accessible functional resources that include around 258 biological databases. Each entry provides a complete detail about a resource that includes the name of resources, web link, detail of publication, information about the corresponding author, name of institute, type of resource. A user-friendly searching module has been integrated, which allows users to search our repository on any field. In order to retrieve categorized information, we integrate the browsing facility in this repository. This database can be utilized for extracting the useful information regarding the present scenario of bioinformatics inclusive of all research labs funded by government and private bodies of India. In addition to web interface, we also developed mobile to facilitate the scientific community.

bioinformatics↗

bioRxiv2020

Machine Boss: Rapid Prototyping of Bioinformatic Automata

MotivationMany C++ libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting, and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone. ResultsWe present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line "recipes" for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage, and nanopore sequence analysis. Availability and ImplementationMachine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/. ContactIan Holmes, ihh@berkeley.edu

bioinformatics↗

bioRxiv2020

Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON

BackgroundThe ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Consistent with observations for SARS-CoV, a closely related coronavirus responsible for the 2003 SARS outbreak, early reports identify a protective role for both humoral and cell-mediated immunity for SARS CoV-2. MethodsIn this study, we leveraged HLA-I and HLA-II T cell epitope prediction tools from RECON(R) (Real-time Epitope Computation for ONcology), our bioinformatic pipeline that was developed using proteomic profiling of individual HLA-I and HLA-II alleles to predict rules for peptide binding to a diverse set of such alleles. We applied these binding predictors to viral genomes from the Coronaviridae family, and specifically to identify SARS-CoV-2 T cell epitopes. ResultsTo test the suitability of these tools to identify viral T cell epitopes, we first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then use our HLA-I and HLA-II predictors to identify 11,776 HLA-I and 7,991 HLA-II candidate binding peptides across all 12 open reading frames (ORFs) of SARS-CoV-2. This extensive list of identified candidate peptides is driven by the length of the ORFs and the significant number of HLA-I and HLA-II alleles that we are able to predict (74 and 83, respectively), providing over 99% coverage for the US, European and Asian populations, for both HLA-I and HLA-II. From our SARS-CoV-2 predicted peptide-HLA-I allele pairs, 368 pairs identically matched previously reported pairs in the ViPR database, originating from other forms of coronaviruses. 320 of these pairs (89.1%) had a positive MHC-binding assay result. This analysis reinforces the validity our predictions. ConclusionsUsing this bioinformatic platform, we identify multiple putative epitopes for CD4+ and CD8+ T cells whose HLA binding properties cover nearly the entire population and thus may be effective when included in prophylactic vaccines against SARS-CoV-2 to induce broad cellular immunity.

bioinformatics↗

bioRxiv2020

BioLaboro: A bioinformatics system for detecting molecular assay signature erosion and designing new assays in response to emerging and reemerging pathogens

BackgroundEmerging and reemerging infectious diseases such as the novel Coronavirus disease, COVID-19 and Ebola pose a significant threat to global society and test the public health communitys preparedness to rapidly respond to an outbreak with effective diagnostics and therapeutics. Recent advances in next generation sequencing technologies enable rapid generation of pathogen genome sequence data, within 24 hours of obtaining a sample in some instances. With these data, one can quickly evaluate the effectiveness of existing diagnostics and therapeutics using in silico approaches. The propensity of some viruses to rapidly accumulate mutations can lead to the failure of molecular detection assays creating the need for redesigned or newly designed assays. ResultsHere we describe a bioinformatics system named BioLaboro to identify signature regions in a given pathogen genome, design PCR assays targeting those regions, and then test the PCR assays in silico to determine their sensitivity and specificity. We demonstrate BioLaboro with two use cases: Bombali Ebolavirus (BOMV) and the novel Coronavirus 2019 (SARS-CoV-2). For the BOMV, we analyzed 30 currently available real-time reverse transcription-PCR assays against the three available complete genome sequences of BOMV. Only two met our in silico criteria for successful detection and neither had perfect matches to the primer/probe sequences. We designed five new primer sets against BOMV signatures and all had true positive hits to the three BOMV genomes and no false positive hits to any other sequence. Four assays are closely clustered in the nucleoprotein gene and one is located in the glycoprotein gene. Similarly, for the SARS-CoV-2, we designed five highly specific primer sets that hit all 145 whole genomes (available as of February 28, 2020) and none of the near neighbors. ConclusionsHere we applied BioLaboro in two real-world use cases to demonstrate its capability; 1) to identify signature regions, 2) to assess the efficacy of existing PCR assays to detect pathogens as they evolve over time, and 3) to design new assays with perfect in silico detection accuracy, all within hours, for further development and deployment. BioLaboro is designed with a user-friendly graphical user interface for biologists with limited bioinformatics experience.

bioinformatics↗

bioRxiv2020

Companion vaccine Bioinformatic design tool reveals limited functional genomic variability of SARS-Cov-2 Spike Receptor Binding Domain

BackgroundTracking the genetic variability of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is a crucial challenge. Mainly to identify target sequences in order to generate robust vaccines and neutralizing monoclonal antibodies, but also to track viral genetic temporal and geographic evolution and to mine for variants associated with reduced or increased disease severity. Several online tools and bioinformatic phylogenetic analyses have been released, but the main interest lies in the Spike protein, which is the pivotal element of current vaccine design, and in the Receptor Binding Domain, that accounts for most of the neutralizing the antibody activity. MethodsHere, we present an open-source bioinformatic protocol, and a web portal focused on SARS-CoV-2 single mutations and minimal consensus sequence building as a companion vaccine design tool. Furthermore, we provide immunogenomic analyses to understand the impact of the most frequent RBD variations. ResultsResults on the whole GISAID sequence dataset at the time of the writing (October 2020) reveals an emerging mutation, S477N, located on the central part of the Spike protein Receptor Binding Domain, the Receptor Binding Motif. Immunogenomic analyses revealed some variation in mutated epitope MHC compatibility, T-cell recognition, and B-cell epitope probability for most frequent human HLAs. ConclusionsThis work provides a framework able to track down SARS-CoV-2 genomic variability.

bioinformatics↗

bioRxiv2020

Bioinformatic Analysis Reveals That Some Mutations May Affect On Both Spike Structure Damage and Ligand Binding Site

There are some mutations are known related to SARS-CoV-2. Together with these mutations known, I tried to show other newly mutations regionally. According to my results which 4326 whole sequences are used, I found that some mutations occur only in a certain region, while some other mutations are found in each regions. Especially in Asia, more than one mutation(three different mutations are found in QLA46612 isolated from South Korea) was seen in the same sequence. Although I detected a huge number of mutations (more than seventy in Asia) by regions, some of them were predicted that damage spikes protein structure by using bioinformatic tools.The predicted results are G75V(isolated from North America), T95I(isolated from South Korea), G143V(isolated from North America), M177I(isolated Asia), L293M(isolated from Asia), P295H(isolated from Asia), T393P(isolated from Europe), P507S(isolated from Asia), D614G(isolated from all regions) respectively. Also, in this study, I tried to show how possible binding sites of ligands change if the spike protein structure is damaged and whether more than one mutation affects ligand binding was estimated using bioinformatics tools. Interestingly, mutations that predicted to damage the structure do not affect ligand binding sites, whereas ligands binding sites were affected in those with multiple mutations.Focusing on mutations may opens up the window to exploit new future therapeutic targets.

bioinformatics↗

bioRxiv2021

RBCeq: An Integrated Bioinformatics Algorithm Designed to Improve Blood Type Compatibility Testing

While blood transfusion is an essential cornerstone of hematological care, patients that require repetitive transfusion remain at persistent risk of alloimmunization due to the diversity of human blood group polymorphisms. Next-generation sequencing (NGS) is an effective means of identifying genotypic and phenotypic variations among the blood groups, while the accurate interpretation of such NGS data is currently hampered by a lack of accessibility to bioinformatics support. To address this unmet need, we have developed the RBCeq (https://www.rbceq.org/) platform, which consists of a novel bioinformatics algorithm coupled with a user-friendly web server capable of comprehensively delineating different blood group variants from genomics data with advanced visualization of results. The software profiles genomic data for 36 blood group systems, including two transcription factors and can identify small genetic alterations, including small indels and copy number variants. The RBCeq algorithm was validated on 403 samples which include 58 complex serology cases from Australian Red Cross LifeBlood, 100 samples from The MedSeq Project (phs000958) and a further 245 from Indigenous Australian participants. The final blood typing data from RBCeq was 99.83% concordant for 403 samples (85 different antigens in 21 blood group systems) with that listed from the International Society for Blood Transfusion database.

bioinformatics↗

bioRxiv2021

HaVoC, a bioinformatic pipeline for reference-based consensus assembly and lineage assignment for SARS-CoV-2 sequences

BackgroundSARS-CoV-2 related research has increased in importance worldwide since December 2019. Several new variants of SARS-CoV-2 have emerged globally, of which the most notable and concerning currently are the UK variant B.1.1.7, the South African variant B1.351 and the Brazilian variant P.1. Detecting and monitoring novel variants is essential in SARS-CoV-2 surveillance. While there are several tools for assembling virus genomes and performing lineage analyses to investigate SARS-CoV-2, each is limited to performing singular or a few functions separately. ResultsDue to the lack of publicly available pipelines, which could perform fast reference-based assemblies on raw SARS-CoV-2 sequences in addition to identifying lineages to detect variants of concern, we have developed an open source bioinformatic pipeline called HaVoC (Helsinki university Analyzer for Variants Of Concern). HaVoC can reference assemble raw sequence reads and assign the corresponding lineages to SARS-CoV-2 sequences. ConclusionsHaVoC is a pipeline utilizing several bioinformatic tools to perform multiple necessary analyses for investigating genetic variance among SARS-CoV-2 samples. The pipeline is particularly useful for those who need a more accessible and fast tool to detect and monitor the spread of SARS-CoV-2 variants of concern during local outbreaks. HaVoC is currently being used in Finland for monitoring the spread of SARS-CoV-2 variants. HaVoC user manual and source code are available at https://www.helsinki.fi/en/projects/havoc and https://bitbucket.org/auto_cov_pipeline/havoc, respectively.

bioinformatics↗

bioRxiv2021

Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is etiologically related to metabolic disorder. The aim of our study was to screen out candidate genes of T2DM and to elucidate the underlying molecular mechanisms by bioinformatics methods. Expression profiling by high throughput sequencing data of GSE154126 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between T2DM and normal control were identified. And then, functional enrichment analyses of gene ontology (GO) and REACTOME pathway analysis was performed. Protein-protein interaction (PPI) network and module analyses were performed based on the DEGs. Additionally, potential miRNAs of hub genes were predicted by miRNet database. Transcription factors (TFs) of hub genes were detected by NetworkAnalyst database. Further, validations were performed by receiver operating characteristic curve (ROC) analysis and real-time polymerase chain reaction (RT-PCR). In total, 925 DEGs were identified in T2DM, including 447 up regulated genes and 478 down-regulated genes. Functional enrichment analysis results showed that up regulated DEGs were significantly enriched in defense response, neutrophil degranulation, cell adhesion and extracellular matrix organization. The top 10 hub genes, JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 were identified from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, ROC analysis and RT-PCR revealed that JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 might serve as biomarkers in T2DM. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of T2DM. The identified hub genes may participate in the onset and advancement of T2DM and serve as therapeutic targets.

bioinformatics↗

bioRxiv2021

Tree Diet: Reducing the Treewidth to Unlock FPT Algorithms in RNA Bioinformatics

Hard graph problems are ubiquitous in Bioinformatics, inspiring the design of specialized Fixed-Parameter Tractable algorithms, many of which rely on a combination of tree-decomposition and dynamic programming. The time/space complexities of such approaches hinge critically on low values for the treewidth tw of the input graph. In order to extend their scope of applicability, we introduce the TO_SCPLOWREEC_SCPLOW-DO_SCPLOWIETC_SCPLOW problem, i.e. the removal of a minimal set of edges such that a given tree-decomposition can be slimmed down to a prescribed treewidth tw'. Our rationale is that the time gained thanks to a smaller treewidth in a parameterized algorithm compensates the extra post-processing needed to take deleted edges into account. Our core result is an FPT dynamic programming algorithm for TO_SCPLOWREEC_SCPLOW-DO_SCPLOWIETC_SCPLOW, using 2O(tw)n time and space. We complement this result with parameterized complexity lower-bounds for stronger variants (e.g., NP-hardness when tw' or tw - tw' is constant). We propose a prototype implementation for our approach which we apply on difficult instances of selected RNA-based problems: RNA design, sequence-structure alignment, and search of pseudoknotted RNAs in genomes, revealing very encouraging results. This work paves the way for a wider adoption of tree-decomposition-based algorithms in Bioinformatics.

bioinformatics↗

bioRxiv2021

Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

bioinformatics↗

bioRxiv2021

Characterization and Structural Prediction of ORF10, ORF7b, ORF7a, ORF6, Membrane Glycoprotein, and Envelope Protein in SARS-CoV-2 Bangladeshi Variant through Bioinformatics Approach

The acute respiratory disease induced by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has become a global epidemic in just less than a year by the first half of 2020. The subsequent efficient human-to-human transmission of this virus eventually affected millions of people worldwide. The most devastating thing is that the infection rate is continuously uprising and resulting in significant mortality especially among the older age population and those with health co-morbidities. This enveloped, positive-sense RNA virus is chiefly responsible for the infection of the upper respiratory system. The virulence of the SARS-CoV-2 is mostly regulated by its proteins like entry to the host cell through fusion mechanism, fusion of infected cells with neighboring uninfected cells to spread the virus, inhibition of host gene expression, cellular differentiation, apoptosis, mitochondrial biogenesis, etc. But very little is known about the protein structures and functionalities. Therefore, the main purpose of this study is to learn more about these proteins through bioinformatics approaches. In this study, ORF10, ORF7b, ORF7a, ORF6, membrane glycoprotein, and envelope protein have been selected from a Bangladeshi Corona-virus strain G039392 and a number of bioinformatics tools (MEGA-X-V10.1.7, PONDR(R), ProtScale, ProtParam, SCRIBER, NetSurfP v2.0, IntFOLD, UCSF Chimera, and PyMol) and strategies were implemented for multiple sequence alignment and phylogeny analysis with 9 different variants, predicting hydropathicity, amino acid compositions, protein-binding propensity, protein disorders, 2D and 3D protein modeling. Selected proteins were characterized as highly flexible, structurally and electrostatically extremely stable, ordered, biologically active, hydrophobic, and closely related to the proteins of different variants. This detailed information regarding the characterization and structure of proteins of SARS-CoV-2 Bangladeshi variant was performed for the first time ever to unveil the deep mechanism behind the virulence features and also, this robust appraisal paves the future way for molecular docking, vaccine development targeting these characterized proteins.

bioinformatics↗

bioRxiv2022

Generalized Hypergeometric Distributions Generated by Birth-Death Process in Bioinformatics

Modern high-throughput biological systems detection methods generate empirical frequency distributions (EFD) which exhibit complex forms and have long right-side tails. Such EFD are often observed in normal and pathological processes, of which the probabilistic properties are essential, but the underlying probability mechanisms are poorly understood. To better understand the probability mechanisms driving biological complexity and the pathological role of extreme values, we propose that the observed skewed discrete distributions are generated by non-linear transition rates of birth and death processes (BDPs). We introduce a (3d+1)-parameter Generalized Gaussian Hypergeometric Probability ((3d+1)-GHP) model with the probabilities defined by a stationary solution of generalized BDP (g-BDP) and represented by generalized hypergeometric series with regularly varying function properties. We study the Regularly Varying 3d-Parameter Generalized Gaussian Hypergeometric Probability (3d-RGHP) functions regular variation properties, asymptotically constant slow varying component, unimodality and upward/ downward convexity which allows us to specify a family of 3d-RGHP models and study their analytical and numerical characteristics. The frequency distribution of unique muta-tions occurring in the human genome of patients with melanoma have been analyzed as an example application of our theory in bioinformatics. The results show that the parameterized model not only fits the heavy tail well, but also the entire EFD taken on the complete experimental outcome space. Our model provides a rigorous and flexible mathematical framework for analysis and application of skewed distributions generated by BDPs which often occur in bioinformatics and big data science.

bioinformatics↗