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Article ; Online: miWords: transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes.

Gupta, Sagar / Shankar, Ravi

2023 Volume 24, Issue 2

Abstract: Discovering pre-microRNAs (miRNAs) is the core of miRNA discovery. Using traditional sequence/structural features, many tools have been published to discover miRNAs. However, in practical applications like genomic annotations, their actual performance ... ...

Abstract	Discovering pre-microRNAs (miRNAs) is the core of miRNA discovery. Using traditional sequence/structural features, many tools have been published to discover miRNAs. However, in practical applications like genomic annotations, their actual performance has been very low. This becomes more grave in plants where unlike animals pre-miRNAs are much more complex and difficult to identify. A huge gap exists between animals and plants for the available software for miRNA discovery and species-specific miRNA information. Here, we present miWords, a composite deep learning system of transformers and convolutional neural networks which sees genome as a pool of sentences made of words with specific occurrence preferences and contexts, to accurately identify pre-miRNA regions across plant genomes. A comprehensive benchmarking was done involving >10 software representing different genre and many experimentally validated datasets. miWords emerged as the best one while breaching accuracy of 98% and performance lead of ~10%. miWords was also evaluated across Arabidopsis genome where also it outperformed the compared tools. As a demonstration, miWords was run across the tea genome, reporting 803 pre-miRNA regions, all validated by small RNA-seq reads from multiple samples, and most of them were functionally supported by the degradome sequencing data. miWords is freely available as stand-alone source codes at https://scbb.ihbt.res.in/miWords/index.php.
MeSH term(s)	Animals ; MicroRNAs/genetics ; MicroRNAs/chemistry ; Deep Learning ; Software ; Genomics ; Genome, Plant ; Arabidopsis/genetics
Chemical Substances	MicroRNAs
Language	English
Publishing date	2023-03-04
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbad088
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Article ; Online: DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery.

Ritu / Gupta, Sagar / Sharma, Nitesh Kumar / Shankar, Ravi

Genomics

2022 Volume 114, Issue 5, Page(s) 110443

Abstract: We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambiguity ... ...

Abstract	We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambiguity of boundaries and incomplete sequences. It scored consistently high for performance metrics while breaching accuracy of >98% when tested across a large number of validated instances. During multiple benchmarkings DeepPlnc consistently outperformed all the compared tools and maintained a highly significant lead in the range of 2.5%- 4.6% from the second best performing tool (p-value << 0.01). DeepPlnc was used to annotate a de novo assembled transcriptome of a himalayan species where again it suggested its much better suitability for genome and transcriptome annotation purposes than the existing tools. DeepPlnc has been made freely available as a web-server and stand-alone program at https://scbb.ihbt.res.in/DeepPlnc/.
MeSH term(s)	Deep Learning ; Molecular Sequence Annotation ; Plants/genetics ; RNA, Long Noncoding/genetics ; Software ; Transcriptome
Chemical Substances	RNA, Long Noncoding
Language	English
Publishing date	2022-08-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	356334-0
ISSN	1089-8646 ; 0888-7543
ISSN (online)	1089-8646
ISSN	0888-7543
DOI	10.1016/j.ygeno.2022.110443
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Article: DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery

Ritu / Gupta, Sagar / Sharma, Nitesh Kumar / Shankar, Ravi

Genomics. 2022 Sept., v. 114, no. 5

2022

Abstract	We present here a bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties. Unlike most of the existing software, it works accurately even in conditions with ambiguity of boundaries and incomplete sequences. It scored consistently high for performance metrics while breaching accuracy of >98% when tested across a large number of validated instances. During multiple benchmarkings DeepPlnc consistently outperformed all the compared tools and maintained a highly significant lead in the range of 2.5%- 4.6% from the second best performing tool (p-value << 0.01). DeepPlnc was used to annotate a de novo assembled transcriptome of a himalayan species where again it suggested its much better suitability for genome and transcriptome annotation purposes than the existing tools. DeepPlnc has been made freely available as a web-server and stand-alone program at https://scbb.ihbt.res.in/DeepPlnc/.
Keywords	computer software ; genome ; genomics ; lead ; transcriptome
Language	English
Dates of publication	2022-09
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	356334-0
ISSN	1089-8646 ; 0888-7543
ISSN (online)	1089-8646
ISSN	0888-7543
DOI	10.1016/j.ygeno.2022.110443
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Article: Anti-Thymocyte Globulin Induced Acute Lung Injury in a Case of Renal Allograft Recipient.

Gupta, Nimish / Gupta, Sagar / Mongha, Ritesh / Aggarwal, Sanjay

Indian journal of nephrology

2021 Volume 31, Issue 6, Page(s) 592–594

Language	English
Publishing date	2021-09-21
Publishing country	India
Document type	Journal Article
ZDB-ID	2134388-3
ISSN	1998-3662 ; 0971-4065
ISSN (online)	1998-3662
ISSN	0971-4065
DOI	10.4103/ijn.IJN_500_20
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Article: HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.

Gupta, Sagar / Nerli, Santrupti / Kandy, Sreeja Kutti / Mersky, Glenn L / Sgourakis, Nikolaos G

bioRxiv : the preprint server for biology

2023

Abstract: The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptide/HLA (pHLA, the human MHC) structures has been mired ... ...

Abstract	The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptide/HLA (pHLA, the human MHC) structures has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within a curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these representative backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer peptide/HLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in terms of structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work provide a framework for linking conformational diversity with antigen immunogenicity and receptor cross-reactivity.
Language	English
Publishing date	2023-03-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.20.533510
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Article ; Online: HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes.

Gupta, Sagar / Nerli, Santrupti / Kutti Kandy, Sreeja / Mersky, Glenn L / Sgourakis, Nikolaos G

Nature communications

2023 Volume 14, Issue 1, Page(s) 6349

Abstract: The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by ... ...

Abstract	The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.
MeSH term(s)	Humans ; Epitopes, T-Lymphocyte ; Peptides/chemistry ; Receptors, Antigen, T-Cell ; Histocompatibility Antigens ; Histocompatibility Antigens Class I/metabolism
Chemical Substances	Epitopes, T-Lymphocyte ; Peptides ; Receptors, Antigen, T-Cell ; Histocompatibility Antigens ; Histocompatibility Antigens Class I
Language	English
Publishing date	2023-10-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-42163-z
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Article ; Online: RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features.

Pradhan, Upendra K / Meher, Prabina K / Naha, Sanchita / Pal, Soumen / Gupta, Sagar / Gupta, Ajit / Parsad, Rajender

Briefings in functional genomics

2023 Volume 22, Issue 5, Page(s) 401–410

Abstract: RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of ... ...

Abstract	RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of cell state. In order to detect RBPs, a number of computational models have been developed. These methods made use of datasets from several eukaryotic species, specifically from mice and humans. Although some models have been tested on Arabidopsis, these techniques fall short of correctly identifying RBPs for other plant species. Therefore, the development of a powerful computational model for identifying plant-specific RBPs is needed. In this study, we presented a novel computational model for locating RBPs in plants. Five deep learning models and ten shallow learning algorithms were utilized for prediction with 20 sequence-derived and 20 evolutionary feature sets. The highest repeated five-fold cross-validation accuracy, 91.24% AU-ROC and 91.91% AU-PRC, was achieved by light gradient boosting machine. While evaluated using an independent dataset, the developed approach achieved 94.00% AU-ROC and 94.50% AU-PRC. The proposed model achieved significantly higher accuracy for predicting plant-specific RBPs as compared to the currently available state-of-art RBP prediction models. Despite the fact that certain models have already been trained and assessed on the model organism Arabidopsis, this is the first comprehensive computer model for the discovery of plant-specific RBPs. The web server RBPLight was also developed, which is publicly accessible at https://iasri-sg.icar.gov.in/rbplight/, for the convenience of researchers to identify RBPs in plants.
MeSH term(s)	Humans ; Animals ; Mice ; Arabidopsis/genetics ; Arabidopsis/metabolism ; Algorithms ; Biological Evolution ; RNA-Binding Proteins/genetics ; RNA-Binding Proteins/chemistry ; RNA-Binding Proteins/metabolism ; Computational Biology/methods ; Binding Sites
Chemical Substances	RNA-Binding Proteins
Language	English
Publishing date	2023-05-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2540916-5
ISSN	2041-2657 ; 2041-2649 ; 2041-2647
ISSN (online)	2041-2657
ISSN	2041-2649 ; 2041-2647
DOI	10.1093/bfgp/elad016
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Article ; Online: Role of Alternative Elicitor Transporters in the Onset of Plant Host Colonization by Streptomyces scabiei 87-22

Francis, Isolde M. / Bergin, Danica / Deflandre, Benoit / Gupta, Sagar / Salazar, Joren J. C. / Villagrana, Richard / Stulanovic, Nudzejma / Ribeiro Monteiro, Silvia / Kerff, Frédéric / Loria, Rosemary / Rigali, Sébastien

Biology (Basel). 2023 Feb. 01, v. 12, no. 2

2023

Abstract: Plant colonization by Streptomyces scabiei, the main cause of common scab disease on root and tuber crops, is triggered by cello-oligosaccharides, cellotriose being the most efficient elicitor. The import of cello-oligosaccharides via the ATP-binding ... ...

Abstract	Plant colonization by Streptomyces scabiei, the main cause of common scab disease on root and tuber crops, is triggered by cello-oligosaccharides, cellotriose being the most efficient elicitor. The import of cello-oligosaccharides via the ATP-binding cassette (ABC) transporter CebEFG-MsiK induces the production of thaxtomin phytotoxins, the central virulence determinants of this species, as well as many other metabolites that compose the ‘virulome’ of S. scabiei. Homology searches revealed paralogues of the CebEFG proteins, encoded by the cebEFG2 cluster, while another ABC-type transporter, PitEFG, is encoded on the pathogenicity island (PAI). We investigated the gene expression of these candidate alternative elicitor importers in S. scabiei 87-22 upon cello-oligosaccharide supply by transcriptomic analysis, which revealed that cebEFG2 expression is highly activated by both cellobiose and cellotriose, while pitEFG expression was barely induced. Accordingly, deletion of pitE had no impact on virulence and thaxtomin production under the conditions tested, while the deletion of cebEFG2 reduced virulence and thaxtomin production, though not as strong as the mutants of the main cello-oligosaccharide transporter cebEFG1. Our results thus suggest that both ceb clusters participate, at different levels, in importing the virulence elicitors, while PitEFG plays no role in this process under the conditions tested. Interestingly, under more complex culture conditions, the addition of cellobiose restored thaxtomin production when both ceb clusters were disabled, suggesting the existence of an additional mechanism that is involved in sensing or importing the elicitor of the onset of the pathogenic lifestyle of S. scabiei.
Keywords	ABC transporters ; Streptomyces scabiei ; cellobiose ; elicitors ; gene expression ; host plants ; metabolites ; pathogenicity islands ; phytotoxins ; scab diseases ; transcriptomics ; virulence
Language	English
Dates of publication	2023-0201
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2661517-4
ISSN	2079-7737
ISSN	2079-7737
DOI	10.3390/biology12020234
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery.

Sharma, Nitesh Kumar / Gupta, Sagar / Kumar, Ashwani / Kumar, Prakash / Pradhan, Upendra Kumar / Shankar, Ravi

iScience

2021 Volume 24, Issue 12, Page(s) 103381

Abstract: Identifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra- ...

Abstract	Identifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. The seeds work as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network. The developed models also received support from MD-simulation studies. The implemented software, RBPSpot, scored consistently high for all the performance metrics including average accuracy of ∼90% across a large number of validated datasets. It outperformed the compared tools, including some with much complex deep-learning models, during a comprehensive benchmarking process. RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies.
Language	English
Publishing date	2021-10-30
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2021.103381
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Article ; Online: miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

Pradhan, Upendra Kumar / Sharma, Nitesh Kumar / Kumar, Prakash / Kumar, Ashwani / Gupta, Sagar / Shankar, Ravi

PloS one

2021 Volume 16, Issue 10, Page(s) e0258550

Abstract: Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the ... ...

Abstract	Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.
MeSH term(s)	Bayes Theorem ; Databases, Genetic ; Humans ; Machine Learning ; MicroRNAs/metabolism ; Protein Binding ; RNA-Binding Proteins/metabolism ; User-Computer Interface
Chemical Substances	MicroRNAs ; RNA-Binding Proteins
Language	English
Publishing date	2021-10-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0258550
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