scholarly journals HLAncPred: A method for predicting promiscuous non-classical HLA binding sites

2021 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Gajendra P. S. Raghava
Keyword(s):  
Information Technology ◽  
Computational Biology ◽  
Validation Dataset ◽  
New Delhi ◽  
List Type ◽  
T Cell Therapy ◽  
Hla Alleles ◽  
Community Interest ◽  
Hla Binding ◽  
In Silico Models

AbstractIn the last two decades, ample of methods have been developed to predict the classical HLA binders in an antigen. In contrast, limited attempts have been made to develop methods for predicting binders for non-classical HLA; due to the scarcity of sufficient experimental data and lack of community interest. Of Note, non-classical HLA plays a crucial immunomodulatory role and regulates various immune responses. Recent studies revealed that non-classical HLA (HLA-E & HLA-G) based immunotherapies have many advantages over classical HLA based-immunotherapy, particularly against COVID-19. In order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of non-classical HLA alleles (HLA-G and HLA-E). All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning based-models achieved more than 0.98 AUC for HLA-G alleles on validation or independent dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.88 on the validation dataset for HLA-E*01:01, HLA-E*01:03, respectively. We have summarized the models developed in the past for non-classical HLA binders and compared with the models developed in this study. Moreover, we have also predicted the non-classical HLA binders in the spike protein of different variants of virus causing COVID-19 including omicron (B.1.1.529) to facilitate the community. One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. In order to predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred), and a standalone package.Key PointsNon-classical HLAs play immunomodulatory roles in the immune system.HLA-E restricted T-cell therapy may reduce COVID-19 associated cytokine storm.In silico models developed for predicting binders for HLA-G and HLA-E.Identification of non-classical HLA binders in strains of coronavirusA webserver for predicting promiscuous binders for non-classical HLA allelesAuthor’s BiographyAnjali Dhall is currently working as Ph.D. in Bioinformatics from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Sumeet Patiyal is currently working as Ph.D. in Bioinformatics from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Gajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.

scholarly journals IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides for managing COVID-19 severity

2021 ◽  
Author(s):  
Shipra Jain ◽  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Gajendra P. S. Raghava
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Computational Biology ◽  
Web Server ◽  
New Delhi ◽  
List Type ◽  
Independent Dataset ◽  
Interleukin 13 ◽  
Immunoregulatory Cytokine ◽  
Key Features

AbstractInterleukin 13 (IL-13) is an immunoregulatory cytokine that is primarily released by activated T-helper 2 cells. It induces the pathogenesis of many allergic diseases, such as airway hyperresponsiveness, glycoprotein hypersecretion and goblet cell hyperplasia. IL-13 also inhibits tumor immunosurveillance, which leads to carcinogenesis. In recent studies, elevated IL-13 serum levels have been shown in severe COVID-19 patients. Thus it is important to predict IL-13 inducing peptides or regions in a protein for designing safe protein therapeutics particularly immunotherapeutic. This paper describes a method developed for predicting, designing and scanning IL-13 inducing peptides. The dataset used in this study contain experimentally validated 313 IL-13 inducing peptides and 2908 non-inducing homo-sapiens peptides extracted from the immune epitope database (IEDB). We have extracted 95 key features using SVC-L1 technique from the originally generated 9165 features using Pfeature. Further, these key features were ranked based on their prediction ability, and top 10 features were used for building machine learning prediction models. In this study, we have deployed various machine learning techniques to develop models for predicting IL-13 inducing peptides. These models were trained, test and evaluated using five-fold cross-validation techniques; best model were evaluated on independent dataset. Our best model based on XGBoost achieves a maximum AUC of 0.83 and 0.80 on the training and independent dataset, respectively. Our analysis indicate that certain SARS-COV2 variants are more prone to induce IL-13 in COVID-19 patients. A standalone package as well as a web server named ‘IL-13Pred’ has been developed for predicting IL-13 inducing peptides (https://webs.iiitd.edu.in/raghava/il13pred/).Key PointsInterleukin-13, an immunoregulatory cytokine plays an important role in increasing severity of COVID-19 and other diseases.IL-13Pred is a highly accurate in-silico method developed for predicting the IL-13 inducing peptides/ epitopes.IL-13 inducing peptides are reported in various SARS-CoV2 strains/variants proteins.This method can be used to detect IL-13 inducing peptides in vaccine candidates.User friendly web server and standalone software is freely available for IL-13PredAuthor’s BiographyShipra Jain is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Anjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Sumeet Patiyal is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Gajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.

scholarly journals 'Off-the-shelf’ allogeneic antigen-specific adoptive T-cell therapy for the treatment of multiple EBV-associated malignancies

2021 ◽  
Vol 9 (2) ◽  
pp. e001608
Author(s):  
Debottam Sinha ◽  
Sriganesh Srihari ◽  
Kirrliee Beckett ◽  
Laetitia Le Texier ◽  
Matthew Solomon ◽  
...  
Keyword(s):  
T Cell ◽  
Cell Therapy ◽  
Adoptive T Cell Therapy ◽  
Nuclear Antigen ◽  
T Cell Therapy ◽  
In Vivo Models ◽  
Hla Alleles ◽  
Cell Therapies ◽  
Wide Range

BackgroundEpstein-Barr virus (EBV), an oncogenic human gammaherpesvirus, is associated with a wide range of human malignancies of epithelial and B-cell origin. Recent studies have demonstrated promising safety and clinical efficacy of allogeneic ‘off-the-shelf’ virus-specific T-cell therapies for post-transplant viral complications.MethodsTaking a clue from these studies, we developed a highly efficient EBV-specific T-cell expansion process using a replication-deficient AdE1-LMPpoly vector that specifically targets EBV-encoded nuclear antigen 1 (EBNA1) and latent membrane proteins 1 and 2 (LMP1 and LMP2), expressed in latency II malignancies.ResultsThese allogeneic EBV-specific T cells efficiently recognized human leukocyte antigen (HLA)-matched EBNA1-expressing and/or LMP1 and LMP2-expressing malignant cells and demonstrated therapeutic potential in a number of in vivo models, including EBV lymphomas that emerged spontaneously in humanized mice following EBV infection. Interestingly, we were able to override resistance to T-cell therapy in vivo using a ‘restriction-switching’ approach, through sequential infusion of two different allogeneic T-cell therapies restricted through different HLA alleles. Furthermore, we have shown that inhibition of the programmed cell death protein-1/programmed death-ligand 1 axis in combination with EBV-specific T-cell therapy significantly improved overall survival of tumor-bearing mice when compared with monotherapy.ConclusionThese findings suggest that restriction switching by sequential infusion of allogeneic T-cell therapies that target EBV through distinct HLA alleles may improve clinical response.

scholarly journals Development and validation of a clinical and genetic model for predicting risk of severe COVID-19

2021 ◽  
Author(s):  
Gillian S. Dite ◽  
Nicholas M. Murphy ◽  
Richard Allman
Keyword(s):  
Logistic Regression ◽  
Genetic Model ◽  
Characteristic Curve ◽  
Accurate Prediction ◽  
Training Dataset ◽  
Validation Dataset ◽  
Individual Risk ◽  
Prototype Model ◽  
List Type ◽  
New Model

SummaryClinical and genetic risk factors for severe COVID-19 are often considered independently and without knowledge of the magnitudes of their effects on risk. Using SARS-CoV-2 positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk=1.77, 95% confidence interval [CI]=1.64, 1.90) and had excellent discrimination (area under the receiver operating characteristic curve=0.732, 95% CI=0.708, 0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α=−0.08; 95% CI=−0.21, 0.05) and no evidence or over- or under-dispersion of risk (β=0.90, 95% CI=0.80, 1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.Key resultsAccurate prediction of the risk of severe COVID-19 can inform public heath interventions and empower individuals to make informed choices about their day-to-day activities.Age and sex alone do not accurately predict risk of severe COVID-19.Our clinical and genetic model to predict risk of severe COVID-19 performs extremely well in terms of discrimination and calibration.

scholarly journals MENUMBUHKAN MINAT BACA MASYARAKAT MELALUI TAMAN BACAAN MASYARAKAT BERBASIS TEKNOLOGI INFORMASI

2016 ◽  
Vol 4 (1) ◽  
pp. 43
Author(s):  
Asep Saepudin ◽  
Bunga Nisa Mentari
Keyword(s):  
Information Technology ◽  
Daily Living ◽  
External Factor ◽  
Cognitive Domain ◽  
Internal Factors ◽  
Community Interest ◽  
Library Program ◽  
The Impact ◽  
Descriptive Method ◽  
Community Library

AbstractThe purpose of this study is to analyze the community reading inter- est at Community Library, the implementation of the Community Library program based on information technology, and the third, the impact of Community Library program based on information technology to increase the community interest in the Community Library. The method used is descriptive method with qualitative ap- proach. subjects numbered  ve persons consisting of one manager, three participants, and one facilitator of Community Library. The results of this study is: (1) the community interest in reading at Community Library arising from internal factors and external fac- tors. however, that has more in uence that external factor because people tend to be always invited, persuaded, and given encourage- ment from others; second implementation of the Community Library based information technology is done through of the following stag- es: organizing, mobilizing, and coaching; thrid impact of Commu- nity Library based technology infornmation views of the cognitive domain, it is known that the citizens involved in the Community Library program has subsequently internalized the new knowledge, and applied in everyday. in the a ective domain, citizens have a pos- itive a itude towards the movement of at least ten minutes reading through the “games” in Community Library. In conclusion, Com- munity Library has increased the ability to read community that are useful in performing activities of daily living. AbstrakTujuan penelitian ini yaitu untuk me-nganalisis minat baca masyarakat di wilayah Taman Bacaan Masyarakat Sukamulya Cerdas, pelaksanaan kegiatan Taman Bacaan Masyarakat ber- basis Information Technology, dan dampak pe-ngelolaan Taman Bacaan Masyarakat (TBM) berbasis IT terhadap peningkatan minat baca masyarakat di wilayah TBM Sukmulya Cerdas. Metode penelitian yang digunakan yaitu metode deskriptif dengan pendekatan kualitatif. Subyek penelitian berjumlah lima orang terdiri dari satu orang pengelola, tiga orang peserta kegiatan, dan satu orang fasilitator TBM Sukamulya Cerdas. Hasil penelitian adalah bahwa minat baca masyarakat sekitar TBM timbul dari faktor internal dan faktor eksternal (faktor eksternal lebih berpengaruh karena masyarakat cenderung harus selalu diajak, dibujuk, serta diberikan dorongan dari orang lain), pelaksanaan kegiatan TBM berbasis teknologi in- formasi dilakukan melalui tahapan pengorganisasian, peng- gerakkan, dan pembinaan, dampak kegiatan TBM berbasis teknologi informasi dari ranah kognitif (masyarakat memi- liki pengetahuan baru yang selanjutnya diinternalisasi dan diterapkan dalam kehidupan sehari-hari), dan ranah afektif (warga masyarakat memiliki sikap positif terhadap gerakan membaca buku minimal sepuluh menit melalui “games” di TBM). Simpulan penelitian adalah bahwa TBM telah menin- gkatkan kemampuan membaca masyarakat yang bermanfaat dalam menjalankan aktivitas hidup sehari-hari.

Massive Parallel Sequencing of Full-Length B-Cell Receptor Sequences Reveals HLA-Dependent Shaping of the B-Cell Immune Repertoire

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 4143-4143
Author(s):  
Marvyn T. Koning ◽  
Sander A.J. van der Zeeuw ◽  
Marcelo Navarrete ◽  
Cornelis A.M. van Bergen ◽  
Valeri Nteleah ◽  
...  
Keyword(s):  
T Cell ◽  
Follicular Lymphoma ◽  
B Cell ◽  
B Cell Lymphoma ◽  
Cell Receptor ◽  
B Cell Receptor ◽  
Full Length ◽  
Lymphoma Cells ◽  
Hla Alleles ◽  
Hla Binding

Abstract Peptides of the B-cell receptor (BCR) may be presented in HLA molecules and therefore be recognized as epitopes by T cells. Bioinformatic evidence indicates that follicular lymphoma cells are selected against expression of a clonal BCR with a high cumulative predicted binding of BCR-derived peptides to the respective patient's HLA complex (Strothmeyer, Blood 2010). This observation suggests T-cell-mediated immunosurveillance against outgrowth of follicular lymphoma cells according to BCR HLA binding strength. Here, we investigate whether this phenomenon pertains to peripheral B cells in 6 healthy donors: 2 donors homozygous for HLA A01*01 / B08*01, 2 homozygous for HLA A02*01 / B7*02, and 2 donors heterozygous for these alleles. Unbiased representation of full-length V(D)J sequences was considered essential for correct data interpretation. PCR primers annealing to conserved motifs of BCR variable regions (e.g. BIOMED-2 protocol) fail to amplify a fraction of BCR, particularly those modified by somatic hypermutation. Therefore, we developed an improved anchored PCR strategy: cDNA was synthesized from poly(A)-RNA from peripheral blood with primers that anneal to specific Ig constant regions. In the same reaction, the 3' cDNA end is extended by switching to an oligonucleotide template containing an anchor sequence (SMART technology; Clontech). Anchor-tagged cDNA was amplified with a primer annealing to the anchor in combination with a nested constant region-specific reverse primer. Dumbbell adapters were added to the termini of 250 ng of purified PCR products. Circular consensus sequencing of single molecules was performed on the PacBio platform (Pacific Biosciences). Using one SMRT PacBio cell per amplicon, separate sequence libraries were created for μ, γ, κ, and λ BCR transcripts. Sequences covered by at least five reads were selected with SMRT Portal software to obtain >95% of sequences without sequence errors as demonstrated on multiple B-cell lines. Selected sequences were analysed by HighV-QUEST software (Alamyar, Immunome Research 2012). After exclusion of non-BCR sequences and duplicate BCR transcripts, a median of 5318 (range: 670-8752) individual BCR sequences was obtained per library. Binding affinity of nonamers in in-silico-translated BCR were calculated for the 4 HLA alleles by the NetMHC 3.4 algorithm. The fractions of BCR lacking any weak HLA binding peptide (NetMHC IC50 <500nM) within a library were compared between donors positive or negative for any HLA molecule. μ VDJ transcripts without HLA binding peptides were significantly more frequent for all HLA alleles in donors that actually express that particular allele (Table). With the exception of HLA A01*01, similar results were observed for γ transcripts. While the fraction of κ VJ transcripts without an HLA binder was overall higher in HLA A01*01 and B08*01, HLA-positive individuals had higher proportions of non-HLA binding sequences. λ transcripts were less likely to contain HLA binders with respect to HLA B07*02 and B08*01 but not to the HLA A alleles. Analogous analyses were performed for CDR3 regions as annotated by HighV-QUEST plus six amino acids on either flank. In 10 of 16 analyses, CDR3 sequences were less likely to contain an HLA binder in HLA-positive individuals; in three analyses an opposite effect was seen (Table). These results indicate that the peripheral BCR repertoire is shaped by HLA alleles in healthy individuals, most likely by T-cell mediated recognition of BCR peptides. Ongoing studies expand this fundamental finding with respect to the IC50 threshold, the number of nonamers, and additional HLA alleles. Our results warrant investigation of the potential role of HLA-dependent shaping of the BCR repertoire for the immune defense and the development of autoimmune disease and B-cell lymphoma. Table 1V(D)J without HLA binding peptideCDR3 without HLA binding peptideHLADonorμγκλμγΚλ A01*01Positive21%41%61%37%87%90%98%70%Negative16%42%59%38%92%92%96%65%P<0.001n.s.<0.01n.s.<0.001n.s.<0.01<0.001 A02*01Positive6%4%3%32%77%77%77%70%Negative4%1%2%32%75%69%78%78%P<0.001<0.001<0.01n.s.<0.01<0.001n.s.<0.001 B07*02Positive31%13%3%13%79%73%91%96%Negative27%8%2%6%79%69%90%98%P<0.001<0.01<0.01<0.001n.s.<0.05<0.05<0.001 B08*01Positive30%35%64%64%89%87%92%96%Negative14%28%62%61%88%82%90%93%P<0.001<0.001<0.01<0.001<0.01<0.001<0.01<0.001 Disclosures No relevant conflicts of interest to declare.

International Conference on Sustainable Development in Design & Manufacturing

2016 ◽  
Vol 8 (1) ◽  
pp. 75
Author(s):  
Editorial Board
Keyword(s):  
Information Technology ◽  
Sustainable Development ◽  
21St Century ◽  
Technical Education ◽  
College Campus ◽  
New Delhi ◽  
Computer Engineering ◽  
College Of Engineering ◽  
Engineering Institute ◽  
State Of Art

Saraswati College of Engineering is a leading Engineering Institute, established in the year 2004 by Hon. Prithviraj Deshmukh and Smt. Vrushali Deshmukh. The college is approved by AICTE, New Delhi and affiliated to University of Mumbai, India. The college campus is beautifully landscaped in a lush green stretch of land spread across Kharghar Hills, SCOE offers UG Engineering Courses in Civil, Mechanical, Electronics &amp; Telecommunication, Computer, Automobile and Information Technology. SCOE also offers PG courses in Civil, Mechanical, Electronics &amp; Telecommunication and Computer Engineering. SCOE is established with a purpose of imparting state of art technical education to aspiring engineers of 21st Century. Efforts are taken by enhancing the employability &amp; skills of students to bridge gap between Industry &amp; Institute.

A scientometric analysis of DESIDOC Journal of Library & Information Technology (2010-2014)

2016 ◽  
Vol 33 (7) ◽  
pp. 8-12 ◽  
Author(s):  
Imran Khan
Keyword(s):  
Information Technology ◽  
Information Science ◽  
Scientometric Analysis ◽  
Original Research ◽  
New Delhi ◽  
Research Papers ◽  
Content Type ◽  
Depth Analysis ◽  
Important Quality ◽  
Number Of Citations

Purpose This paper aims to perform a scientometric analysis of DESIDOC Journal of Library and Information Technology (DJLIT) to find out the quality, popularity and impact of the international journal published by DESIDOC. Design/methodology/approach Scientometric analysis of five volumes (from Volume No. 30 to 34) from the year 2010 to 2014 of DJLIT covering 30 issues containing 307 contributions was performed. All the bibliographic details were noted and recorded in tabular form for the purpose of in-depth analysis. Based on the analysis of the recorded data, findings have been presented. Findings The study shows a trend of gradual growth in contributions published during the period of study, with an average number of 61 contributions per volume of the journal. Maximum number of contributions/research papers (70) were found to be published in the year 2012, whereas the minimum (50) in the year 2010. The study reveals that DJLIT gives maximum importance to the original research papers for the purpose of publishing, which attained top position of publications with a total of 277 (90.23 per cent). A maximum number of contributions during the period of study are from joint authors, with a total of 188 (61.24 per cent). Maximum number of contributions are from India, with a total of 273 (88.93 per cent). New Delhi, Maharashtra and Karnataka were found to be the biggest domestic contributors during the period of study, with 68 (24.91 per cent), 39 (14.29 per cent) and 30 (10.99 per cent) contributions, respectively. It appears that the coverage of DJLIT, even being an international journal in the field of library and information science (LIS), is not very broad and its scope is broadly confined to India only. Majority of the authors preferred journals as their major source of information, providing the highest number of citations totaling 2,447 (51.89 per cent), while websites attained the second position with 1,015 (21.52 per cent) citations, followed by books with 613 (13 per cent) citations. The study further reveals that maximum number of citations totaling 1,109 (23.52 per cent) out of 4,716 were received in the year 2013, while least citations totaling 700 (14.84 per cent) were recorded in the year 2010. One of the most important quality of DJLIT is that it has great concern for emerging and new tools, techniques and technologies in the LIS profession and their impact and application in the field. The journal regularly publishes special issues in every volume on such themes that have great impact on the LIS profession, and it has published 16 special issues on various important themes during the period of study. DJLIT, having free online access through the internet, is the highly preferred journal for communication, knowledge acquisition and reference by the LIS professionals. The journal has great potential of attaining new heights of popularity and impact all over the world in the LIS profession. It is suggested that the journal should try to get high-quality papers from foreign authors too, which may be useful in enhancing its global impact and reputation. Research limitations/implications The present study is confined to the data collected from 30 issues of the five volumes of the DJLIT from the year 2010 to 2014, while the journal is continuously being published since the year 1981. Hence, the results may vary if the studies of different blocks of the years of publication of the journal are performed. The present study may not be fully representative in all the results, but it gives a trend regarding publication of the DJLIT. Originality/value Scientometric analysis of journals has been attempted in very few numbers. Hence, the present study will be a source of idea to other researchers.

scholarly journals SeroBA: rapid high-throughput serotyping of Streptococcus pneumoniae from whole genome sequence data

2017 ◽  
Author(s):  
Lennard Epping ◽  
Andries J. van Tonder ◽  
Rebecca A. Gladstone ◽  
Stephen D. Bentley ◽  
Andrew J. Page ◽  
...  
Keyword(s):  
Streptococcus Pneumoniae ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome Sequence ◽  
Validation Dataset ◽  
List Type ◽  
Whole Genome ◽  
Link Type ◽  
Computational Performance ◽  
The Impact

ABSTRACTStreptococcus pneumoniae is responsible for 240,000 - 460,000 deaths in children under 5 years of age each year. Accurate identification of pneumococcal serotypes is important for tracking the distribution and evolution of serotypes following the introduction of effective vaccines. Recent efforts have been made to infer serotypes directly from genomic data but current software approaches are limited and do not scale well. Here, we introduce a novel method, SeroBA, which uses a hybrid assembly and mapping approach. We compared SeroBA against real and simulated data and present results on the concordance and computational performance against a validation dataset, the robustness and scalability when analysing a large dataset, and the impact of varying the depth of coverage in the cps locus region on sequence-based serotyping. SeroBA can predict serotypes, by identifying the cps locus, directly from raw whole genome sequencing read data with 98% concordance using a k-mer based method, can process 10,000 samples in just over 1 day using a standard server and can call serotypes at a coverage as low as 10x. SeroBA is implemented in Python3 and is freely available under an open source GPLv3 license from: https://github.com/sanger-pathogens/seroba.DATA SUMMARYThe reference genome Streptococcus pneumoniae ATCC 700669 is available from National Center for Biotechnology Information (NCBI) with the accession number: FM211187Simulated paired end reads for experiment 2 have been deposited in FigShare: https://doi.org/10.6084/m9.figshare.5086054.v1Accession numbers for all other experiments are listed in Supplementary Table S1 and Supplementary Table S2.I/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. ⊠IMPACT STATEMENTThis article describes SeroBA, a A-mer based method for predicting the serotypes of Streptococcus pneumoniae from Whole Genome Sequencing (WGS) data. SeroBA can identify 92 serotypes and 2 subtypes with constant memory usage and low computational costs. We showed that SeroBA is able to reliably predict serotypes at a depth of coverage as low as 10x and is scalable to large datasets.

scholarly journals Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Linjun Zhou ◽  
Deling Fan ◽  
Wei Yin ◽  
Wen Gu ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Acute Toxicity ◽  
In Silico ◽  
Water Solubility ◽  
Expert Knowledge ◽  
Aquatic Toxicity ◽  
Predictive Ability ◽  
Validation Dataset ◽  
In Silico Models ◽  
In Silico Tools

Abstract Background A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset. Results In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools. Conclusion QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.

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