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Results for “immunology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 19 recordsLinked to original sources

bioRxiv2022

Deep Learning vs Gradient Boosting in age prediction on immunology profile

BackgroundThe aging process affects all systems of the human body, and the observed increase in inflammatory components affecting the immune system in old age can lead to the development of age-associated diseases and systemic inflammation. ResultsWe propose a small clock model SImAge based on a limited number of immunological biomarkers. To regress the chronological age from cytokine data, we first use a baseline Elastic Net model, gradient-boosted decision trees models, and several deep neural network architectures. For the full dataset of 46 immunological parameters, DANet, SAINT, FT-Transformer and TabNet models showed the best results for the test dataset. Dimensionality reduction of these models with SHAP values revealed the 10 most age-associated immunological parameters, taken to construct the SImAge small immunological clock. The best result of the SImAge model shown by the FT-Transformer deep neural network model has mean absolute error of 6.94 years and Pearson{rho} = 0.939 on the independent test dataset. Explainable artificial intelligence methods allow for explaining the model solution for each individual participant. ConclusionsWe developed an approach to construct a model of immunological age based on just 10 immunological parameters, coined SImAge, for which the FT-Transformer deep neural network model had proved to be the best choice. The model shows competitive results compared to the published studies on immunological profiles, and takes a smaller number of features as an input. Neural network architectures outperformed gradient-boosted decision trees, and can be recommended in the further analysis of immunological profiles.

bioinformatics↗

bioRxiv2025

ImmuneFM: Pre-training Foundation Model from Cytometry Data for Immunology Research

Immunology is an essential field in the biomedicine domain, which plays an important role in oncology, vaccines, infection, etc. With the increasing amount of data available in immunology and artificial intelligence technique development, there is a need to develop data-driven AI methods in the field. However, the data from various immunology studies is very hard to integrate into an AI-ready dataset due to the lack of a standard. Moreover, independent immunology studies data lacks enough labels to train the supervised model. Motivated by these challenges, we curated a large-scale AI-ready cytometry dataset for immunology from the publicly available ImmPort portal. We design the framework to pre-train a foundation model, ImmuneFM, on the cytometry dataset. ImmuneFM can be applied to a wide range of downstream immunology diseases with fine-tuning on a limited number of labeled samples. The experiment results on eight downstream tasks demonstrate the superior performance of ImmuneFM compared to baseline deep learning and traditional methods.

immunology↗

bioRxiv2020

Modeling the immunological pre-adaptation of HIV-1

It is becoming increasingly evident that the evolution of HIV-1 is to a large extent determined by the immunological background of the host. On the population-level this results in associations between specific human leukocyte antigen (HLA) alleles and polymorphic loci of the virus. Furthermore, some HLA alleles that were previously associated with slow progression to AIDS have been shown to lose their protective effect, because HLA-specific immunological escape variants have spread through the population. This phenomenon is known as immunological pre-adaptation. Apart from adapting to human immune responses, the set-point virus load (SPVL) of HIV-1 is thought to have evolved to values that optimize the population-level fitness of the virus. This suggestion is supported by considerable heritability of the SPVL. Previous modeling studies show that whether or not SPVL optimization is expected to occur depends sensitively on the underlying assumptions with respect to the extent of within-versus between-host selection. Here we use a detailed and semi-realistic multi-level HIV-1 model in which immunological pre-adaptation and SPVL evolution can emerge from the underlying interactions of the virus with the immune system of the host. This enables us to study the effect of immunological escape on disease progression, and how disease progression may be molded by SPVL evolution. We find that the time to AIDS could decrease significantly (0.5-1.0 years) in a HLA-dependent manner by immunological pre-adaptation over the long-term course of the epidemic (> 100 years). We find that SPVL is not expected to evolve to optimize the population-level fitness of HIV-1, even though high heritability of the SPVL emerges from continual selection of immune-escape mutations.

evolutionary biology↗

bioRxiv2017

The 10,000 Immunomes Project: A resource for human immunology

New immunological assays now enable rich measurements of human immune function, but difficulty attaining enough measurements across sufficiently large and diverse cohorts has hindered describing normal human immune physiology on a large scale. Here we present the 10,000 Immunomes Project (10KIP), a diverse human immunology reference derived from over 44,000 individuals across 242 studies from ImmPort, a publicly available resource of raw immunology study data and protocols. We carefully curated datasets, aggregating subjects from healthy/control arms and harmonizing data across studies. We demonstrate 10KIPs utility by describing variations in serum cytokines and leukocytes by age, race, and sex; defining a baseline cell-cytokine network; and using 10KIP as a common control to describe immunologic changes in pregnancy. Subject-level data is available for interactive visualization and download at http://10kImmunomes.org/. We believe 10KIP can serve as a common control cohort and will accelerate hypothesis generation by clinical and basic immunologists across diverse populations.\n\nOne Sentence SummaryAn open online resource of human immunology data from more than 10,000 normal subjects including interactive data visualization and download enables a new look at immune system differences across age and sex, rapid hypothesis generation, and creation of custom control cohorts.

immunology↗

bioRxiv2020

Morphological profiling of human T and NK lymphocytes identifies actin-mediated control of the immunological synapse

The detection and neutralization of infected cells and tumors by cytotoxic lymphocytes is a vital immune defense mechanism. The immunological synapse orchestrates the target recognition process and the subsequent cytotoxic activity. Here, we present an integrated experimental and computational strategy to systematically characterize the morphological properties of the immunological synapse of human cytotoxic lymphocytes. Our approach combines high-content imaging with an unbiased, data-driven identification of high-resolution morphological profiles. Such profiling discriminates with high accuracy immunological synapse perturbations induced by an array of actin drugs in both model cell lines and primary lymphocytes. It reveals inter-individual heterogeneity in lymphocyte morphological traits. Furthermore, it uncovers immunological synapse alterations in functionally defective CD8+ T cells from immunodeficient patients carrying ARPC1B mutations. Our study thus provides a foundation for the application of morphological profiling as a powerful and scalable approach to monitor lymphocyte activation status in experimental and disease settings.

immunology↗

bioRxiv2019

Developing T cells form an immunological synapse for passage through the β-selection checkpoint

The {beta}-selection checkpoint of T cell development tests whether the cell has recombined its genomic DNA to produce a functional T Cell Receptor {beta} (TCR{beta}) receptor. Passage through the {beta}-selection checkpoint requires the nascent TCR{beta} protein to mediate signaling through a pre-TCR complex. In this study, we show that developing T cells at the {beta}-selection checkpoint establish an immunological synapse in in vitro & in situ, resembling that of the mature T cell. The immunological synapse is dependent on two key signaling pathways known to be critical for the transition beyond the {beta}-selection checkpoint, Notch and CXCR4 signaling. In vitro and in situ analyses indicate that the immunological synapse promotes passage through the {beta}-selection checkpoint. Collectively, these data indicate that developing T cells regulate pre-TCR signaling through the formation of an immunological synapse. This signaling platform integrates cues from Notch, CXCR4, and MHC on the thymic stromal cell, to allow transition beyond the {beta}-selection checkpoint.\n\nSummaryT cell development requires testing whether genomic rearrangement has produced a T cell receptor capable of transmitting signals. Most T cells fail this test. Here, we show that passage through the {beta}-selection checkpoint requires assembly of a platform to support TCR signaling.

immunology↗

bioRxiv2021

Immunological factors, but not clinical features, predict visceral leishmaniasis relapse in patients co-infected with HIV

Visceral leishmaniasis (VL) has emerged as a clinically important opportunistic infection in HIV patients, as VL/HIV co-infected patients suffer from frequent VL relapse. Here, we followed cohorts of VL patients with or without HIV co-infections in Ethiopia and collected detailed clinical and immunological data during 12 months of follow-up. By the end of the study 78.1% of VL/HIV patients, but none of the VL only patients, had relapsed. Despite clinically defined cure, VL/HIV patients maintained high parasite loads, low BMI, hepatosplenomegaly and pancytopenia throughout follow-up. During detailed immunological study throughout the follow-up period, we identified three markers associated with VL relapse: i) failure to restore antigen-specific production of IFN{gamma}, ii) persistently low CD4+ T cell counts, and iii) high expression of PD1 on CD4+ T cells. We show that these three markers combine well in predicting VL relapse, and that all three measurements are needed for optimal predictive power. These three immunological markers can be measured in primary hospital settings in Ethiopia and can predict VL relapse after anti-leishmanial therapy. The use of our prediction model has the potential to improve disease management and patient care.

immunology↗

bioRxiv2021

Functional characterization of SARS-CoV-2 vaccine elicited antibodies in immunologically naive and pre-immune humans

As the COVID-19 pandemic continues, the authorization of vaccines for emergency use has been crucial in slowing down the rate of infection and transmission of the SARS-CoV-2 virus that causes COVID-19. In order to investigate the longitudinal serological responses to SARS-CoV-2 natural infection and vaccination, a large-scale, multi-year serosurveillance program entitled SPARTA (SARS SeroPrevalence and Respiratory Tract Assessment) was initiated at 4 locations in the U.S. The serological assay presented here measuring IgG binding to the SARS-CoV-2 receptor binding domain (RBD) detected antibodies elicited by SARS-CoV-2 infection or vaccination with a 95.5% sensitivity and a 95.9% specificity. We used this assay to screen more than 3100 participants and selected 20 previously infected pre-immune and 32 immunologically naive participants to analyze their antibody binding to RBD and viral neutralization (VN) responses following vaccination with two doses of either the Pfizer-BioNTech BNT162b2 or the Moderna mRNA-1273 vaccine. Vaccination not only elicited a more robust immune reaction than natural infection, but the level of neutralizing and anti-RBD antibody binding after vaccination is also significantly higher in pre-immune participants compared to immunologically naive participants (p<0.0033). Furthermore, the administration of the second vaccination did not further increase the neutralizing or binding antibody levels in pre-immune participants (p=0.69). However, ~46% of the immunologically naive participants required both vaccinations to seroconvert.

immunology↗

bioRxiv2021

Gut regulatory T cells mediate immunological tolerance in Salmonella Typhimurium-infected superspreader hosts by suppressing cytotoxic activity of T cells.

Superspreader hosts carry out most pathogen transmission events and are often disease tolerant since they remain asymptomatic despite high pathogen burdens. Here we describe the superspreader immune state that allows for disease tolerance. In a model of Salmonella infection, superspreader mice develop colitis with robust CD4+ and CD8+ T-cell responses, however, they remain asymptomatic. We found that superspreaders have significantly more regulatory T cells (Tregs) in the distal gut compared to non-superspreader infected hosts. Surprisingly, the depletion of Tregs did not induce pathogen clearance but rather exacerbated weight loss, increased gut inflammation, and compromised epithelial intestinal barrier. This loss of tolerance correlated with dramatic increases in cytotoxic CD4+ and CD8+ T cells. Interestingly, CD4 neutralization in Tregs-depleted superspreaders was sufficient to rescue tolerance. Our results indicate that Tregs play a crucial role in maintaining immunologic tolerance in the guts of superspreader mice by suppressing cytotoxic CD4+ and CD8+ T-cell activities. AUTHOR SUMMARYSuperspreader hosts are the main cause of disease transmission and a very important public health concern. Here, we evaluated the immunological tolerance of the Salmonella infected superspreaders in a mouse model. By manipulating Tregs, we demonstrated the immunological mechanism from the host to maintain health status and high pathogen burden. Tregs depletion in the superspreaders led to severe disease, with damage of the intestinal epithelia, and high morbidity without having any effect on shedding and systemic Salmonella burden. Furthermore, we demonstrated that the damage of the intestinal epithelia was related to cytotoxic activity of T cells. When Tregs were depleted, CD8+ T cells produced high levels of granzyme B and perforin. CD8+ T cells neutralization in Tregs depleted mice led to increased cytotoxic CD4+ T cells. Interestingly, neutralization of CD4+ T cells in the Tregs depleted mice led to a reduction in the CD8+ T cells producing granzyme B and it was sufficient to rescue host tolerance in this model. We demonstrate for the first time that cytotoxic CD4+ T cells damage the epithelial intestinal barrier and contribute to loss of tolerance in the context of a superspreader host. These findings open new perspectives to understand mechanisms of tolerance in the intestine of a superspreader host.

immunology↗

bioRxiv2019

Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of chimeric antigen receptor T cells

We propose and experimentally validate a label-free, volumetric, and automated assessment method of immunological synapse dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed approach enables automatic and quantitative spatiotemporal analyses of immunological synapse kinetics regarding morphological and biochemical parameters related to the total protein densities of immune cells, thus providing a new perspective for studies in immunology.

immunology↗

bioRxiv2026

Extravillous trophoblast model shows generation of bioequivalent N-glycans can maintain immunological protection against natural killer cell cytotoxicity

Extravillous trophoblasts (EVTs) are a trophoblast subpopulation critical for feto-maternal tolerance during early pregnancy, primarily using HLA-G to exert immunomodulatory effect, and possessing N-glycomic profiles distinct from other trophoblast subpopulations. However, whether the N-glycosylation confers distinct immunological properties to EVTs remains poorly understood. To investigate this, we employed JEG-3, a human choriocarcinoma cell line having the capacity to produce pregnancy-related hormones and expressing both HLA-C and HLA-G resembling placental EVTs, as an in vitro EVT model, alongside cell line JAR which exhibits villous trophoblast phenotypes distinct from JEG-3. Both cell lines were treated with kifunensine or swainsonine, inhibitors of -mannosidases, to remodel their N-glycosylation patterns. This led to significant remodelling of their N-glycomic profiles, with JEG-3 cells showing an increased level of polylactosamine chains and decreased levels of -2,6-sialylation and core -1,6-fucosylation. Western blot analysis showed that inhibiting -mannosidases altered only the composition of N-glycans on cell-surface HLA-G, without affecting the overall abundance of cell-surface HLA-G. In kifunensine-treated JEG-3 cells that predominantly express oligomannose type N-glycans, an intracellular accumulation of unfolded HLA-G fragments, increased hCG secretion, and down-regulations of EVT markers GATA3 and KRT7 were observed compared to untreated control, while swainsonine treatment did not impact N-glycan expression. Cytotoxicity assays using NK-92 as effector cells showed that the de-sialylation of JEG-3 by neuraminidase treatment led to increased NK-92 mediated killing. JEG-3 cell sustained its EVT immunological properties through generating bioequivalent N-glycans, exemplified by NK-92 cells pre-conditioned with used culture media of kifunensine-treated JEG-3 cells displaying reduced cytotoxicity toward NK-sensitive lymphoblast cell line K562, an effect not observed with swainsonine-treated JEG-3 cells. This model suggests that EVTs immunological properties are dependent on specific N-glycomic profiles that are maintained by unique N-glycosylation homeostasis, and overall improves our understanding of how EVTs maintain their immunomodulatory effect at the maternal-fetal interface.

immunology↗

bioRxiv2020

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

bioinformatics↗

bioRxiv2020

Surveying the vampire bat (Desmodus rotundus) serum proteome: a resource for identifying immunological proteins and detecting pathogens

Bats are increasingly studied as model systems for longevity and as natural hosts for some virulent viruses. Yet our ability to characterize immune mechanisms of viral tolerance and to quantify infection dynamics in wild bats is often limited by small sample volumes and few species-specific reagents. Here, we demonstrate how proteomics can overcome these limitations by using data-independent acquisition-based shotgun proteomics to survey the serum proteome of 17 vampire bats (Desmodus rotundus) from Belize. Using just 2 L of sample and relatively short separations of undepleted serum digests, we identified 361 proteins across five orders of magnitude. Data are available via ProteomeXchange with identifier PXD022885. Levels of immunological proteins in vampire bat serum were then compared to human plasma via published databases. Of particular interest were anti-viral and anti-bacterial components, circulating 20S proteasome complex, and proteins involved in redox activity; whether any results are specific to vampire bats could be assessed by future pan-mammalian analyses. Lastly, we used known virus proteomes to identify Rh186 from Macacine herpesvirus 3 and ORF1a from Middle East respiratory syndrome-related coronavirus, indicating that mass spectrometry-based techniques show promise for pathogen detection. Overall, these results can be used to design targeted mass-spectrometry assays to quantify immunological markers and detect pathogens. More broadly, our findings also highlight the application of proteomics in advancing wildlife immunology and pathogen surveillance.

molecular biology↗

bioRxiv2021

CD56 at the human NK cell lytic immunological synapse

CD56 is the main identifying cell surface molecule of NK cells and has been recently identified as a regulator of cytotoxic function in NK cell lines. Despite its newly defined role in lytic granule polarization and exocytosis, biological questions remain involving the localization and function of CD56 at the immunological synapse. Here we use confocal and structured illumination microscopy to demonstrate recruitment of CD56 to the peripheral supramolecular activating cluster (pSMAC) of the immunological synapse of lytic effector cells. We provide additional data demonstrating that cell lines that are less dependent on CD56 for function are not utilizing alternative pathways of cytotoxicity, and that those that are dependent on CD56 have normal expression of activating and adhesion receptors. Finally, we use actin reporter (LifeAct) expressing NK92 cell lines and live cell confocal microscopy to visualize live cell killing events with WT and CD56-KO cells. This work further characterizes the novel role for CD56 in cytotoxic function of NK cells and provides deeper insight into the role of CD56 at the NK cell immunological synapse.

cell biology↗

bioRxiv2022

Adera: A drug repurposing workflow for neuro-immunological investigations using neural networks.

Drug repurposing in the context of neuro-immunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and rather focuses on discovering new usage for known medications. Neuro-immunological diseases such as Alzheimers, Parkinson, multiple sclerosis and depression include various pathologies that resulted from the interaction between the central nervous system and the immune system. However, repurposing of medications is hindered by the vast amount of information that needs mining. To challenge the need for repurposing known medications for neuro-immunological diseases, we built a deep neural network named Adera to perform drug repurposing. The model uses two deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network we explored the usage of two different loss function, binary cross entropy and means square error (MSE). Furthermore, we investigated the effect of ten different network architecture with each loss function. Our results show that for the binary cross entropy loss function, the best architecture consists of a two layers of convolution neural network and it achieves a loss of less than 0.001. In the case of MSE loss function a shallow network using aRelu activation achieved an accuracy of over 98 % and loss of 0.001. Additionally, Adera was able to predict various drug repurposing targets in agreement with DRUG Repurposing Hub. These results establish the ability of Adera to repurpose with high accuracy drug candidates that can shorten the development of the drug cycle. The software could be downloaded from https://github.com/michel-phylo/ADERA1.

bioinformatics↗

bioRxiv2023

Mapping immunological and host receptor binding determinants of SARS-CoV spike protein utilizing the Qubevirus platform

The motifs involved in tropism and immunological interactions of SARS-CoV spike (S) protein were investigated utilizing the Qubevirus platform. We showed that separately, 14 overlapping peptide fragments representing the S protein (F1-14 of 100 residues each) could be inserted into the C-terminus of A1 on recombinant Qubevirus without affecting its viability. Additionally, recombinant phage expression resulted in the surface exposure of different engineered fragments in an accessible manner. The F6 from S425-525, was found to contain the binding determinant of the recombinant human angiotensin converting enzyme 2 (rhACE2), with the shortest active binding motif situated between residues S437-492. Upstream, another fragment, F7, containing an overlapping portion of F6 would not bind to rhACE2, confirming not just only that residues were linear but equally also the appropriate structural orientation of F6 upon the Qubevirus. The F6 (S441-460) and other inserts, including F7/F8 (S601-620) and F10 (S781-800), were demonstrated to contain important immunological determinants through recognition and binding of S protein specific (anti-S) antibodies. An engineered chimeric insert bearing the fusion of all three anti-S reactive epitopes, improved substantially the recognition and binding to their cognate antibodies. These results provide insights into humoral immune relevant epitopes and tropism characteristics of the S protein with implications for the development of subunit vaccines or other biologics against SARS-CoV. SignificanceMapping epitopes within the receptor binding domains of viruses which are essential for viral tropism is critical for developing antiviral agents and subunit vaccines. In this study we have engineered the surface of Qubevirus to display a peptide library derived from the SARS-CoV S protein. In biopanning with S protein antibodies, we have identified three peptide fragments (EP1, EP2 and EP3) which reacted selectively with antibodies specific to the S protein. We demonstrated that all recombinant phage displayed peptide fragments both individually and as chimera exposed important immunological epitopes to their cognate antibodies. A peptide fragment F6 situated at S425-525, was found containing the binding determinant of the recombinant human angiotensin converting enzyme 2 (rhACE2), with the shortest active binding motif situated between residues S437-492. The platform is rapidly to identify epitopes and receptor binding sites within viral receptors found in target host cell. Thus, this platform holds great significance.

synthetic biology↗

bioRxiv2024

Hippocampal reactive neural stem cells are able to phagocytose and have an immunological molecular signature

Hippocampal neural stem cells (NSCs) are the drivers of neurogenesis in the dentate gyrus (DG) of most mammals including humans. During neuronal hyperactivity NSCs become reactive NSCs (react-NSCs), characterized by their activation, morphological changes, and symmetric division, abandoning their neurogenic programme and transforming into reactive astrocytes. Here, using different pathological models that induce react-NSCs in the DG, we looked for novel features of react-NSCs both histologically and by total RNA sequencing. We report that in two pathological models were react-NSCs emerge (mesial temporal lobe epilepsy (MTLE) and traumatic brain injury (TBI)) react-NSCs are capable of phagocytosis of dead cells, a typical immunological function carried out mainly by microglia in the brain. Importantly, MTLE-induced react-NSCs show phagocytic function in tissue and a predominantly immunological molecular signature, with a broad upregulation of phagocytosis-related gene expression. Our results describe a new function of react-NSCs as phagocytic and immunologically active cells in the hippocampal neurogenic niche.

neuroscience↗

bioRxiv2024

Pannotator integrated with Medpipe provides immunological and subcellular location features using a microservice

Genome sequencing and assembly are trivial tasks nowadays. After assembling contigs and scaffolds from a genome, the subsequent step is annotation. An annotation evidencing the expected features, like rRNA, tRNA, and CDS, is a signal of the quality of our sequencing and assembly. Different techniques to obtain and reproduce DNA samples, as well as sequencing and assembly of genomes, can impact the quality of a genomes expected features. The Pannotator tool was conceived as an aid annotation tool focusing on the differences between assembling and its reference genome. Some of the key features for bacterial genome annotations are the subcellular location and immunological potential of a CDS. Instead of reimplementing the prediction of these features in Pannotator, we leveraged the capabilities of our microservice to provide them. In the end, Medpipe software was not modified, and Pannotator underwent minor changes to incorporate the subcellular location and immunological potential of all exported proteins annotated by the tool. Moreover, our Medpipe microservice can also be incorporated into other software. The Medpipe microservice is open to anyone, not only to our Pannotator tool. The successful integration of Medpipe to Pannotator, powered by the Medpipe microservice, offers a powerful approach to advanced genomic analysis. The Medpipe microservice, built on Kotlin with the Spring Boot framework, is instrumental in the automation of Medpipe processing. It achieves this using REST endpoints, such as the execution of Medpipe in an asynchronous manner, status retrieval, and prediction generation, which enhance the modularity and scalability of the microservice. The availability of endpoint documentation, detailed request examples, and logs make our microservice user-friendly. The results of this integration demonstrate the value of the information provided by Medpipe, enriching genomic annotation with additional details, such as the density of mature epitopes (MED) and protein subcellular location classification. The Pannotator has evolved beyond basic function annotation and now provides data on immunological potential, structure, and subcellular location after being integrated with our microservice. The Medpipe microservice is available at https://github.com/santosardr/medpipe-ms.git.

bioinformatics↗