Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of Plasmodium falciparum Genes.
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ABSTRACT:
SUBMITTER: Suratanee A
PROVIDER: S-EPMC8468833 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Motivation5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays a vital role in gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications in DNA like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, and ignoring the imbalanced distribution of methylation sites in real-world scenarios.ResultsHere, we develop NanoCon, a deep hybrid network coupled with contrastive learning strategy to detect 5mC methylation sites from Nanopore reads. In particular, we adopted a contrastive learning module to alleviate the issues caused by imbalanced data distribution in nanopore sequencing, offering a more accurate and robust detection of 5mC sites. Evaluation results demonstrate that NanoCon outperforms existing methods, highlighting its potential as a valuable tool in genomic sequencing and methylation prediction. In addition, we also verified the effectiveness of our representation learning ability on two datasets by visualizing the dimension reduction of the features of methylation and nonmethylation sites from our NanoCon. Furthermore, cross-species and cross-5mC methylation motifs experiments indicated the robustness and the ability to perform transfer learning of our model. We hope this work can contribute to the community by providing a powerful and reliable solution for 5mC site detection in genomic studies.Availability and implementationThe project code is available at https://github.com/Challis-yin/NanoCon.
| S-EPMC10873575 | biostudies-literature
Project description:Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we propose predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originate from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 33 million protein sequences. Replicating the conditions of CAFA3, our method reaches an Fmax of 37 ± 2%, 50 ± 3%, and 57 ± 2% for BPO, MFO, and CCO, respectively. Numerically, this appears close to the top ten CAFA3 methods. When restricting the annotation transfer to proteins with < 20% pairwise sequence identity to the query, performance drops (Fmax BPO 33 ± 2%, MFO 43 ± 3%, CCO 53 ± 2%); this still outperforms naïve sequence-based transfer. Preliminary results from CAFA4 appear to confirm these findings. Overall, this new concept is likely to change the annotation of proteins, in particular for proteins from smaller families or proteins with intrinsically disordered regions.
| S-EPMC7806674 | biostudies-literature
Project description:BACKGROUND:The emergence and transnational spread of artemisinin resistance in Plasmodium falciparum in the Greater Mekong Sub-region (GMS) is a serious threat to malaria elimination in the region and could present a threat to malaria control in Africa. Recently, the Lao Government adopted the goal of malaria elimination by 2030, for which monitoring of artemisinin-resistant malaria within the country is indispensable. This study's objectives were to assess the distribution of k13 mutations in Laos. METHODS:Plasmodium falciparum isolates (n?=?1151) were collected from five southern provinces in Laos between 2015 and 2016, and three isolates from the northernmost province bordering China in 2017. Polymorphisms of the k13 gene and two flanking regions were analysed to estimate relationship among the isolates. RESULTS:In the five southern provinces, overall 55.5% of the isolates possessed artemisinin-resistant mutations of the k13 gene (C580Y, P574L, R539T, Y493H). The C580Y was the predominant mutation (87.2%). The frequencies of the k13 mutations were heterogeneous in the five southern provinces, but with a clear tendency showing the highest frequency in the south (72.5%) and to a lower degree when moving northward (28.0%). The three isolates from the Lao-Chinese border also possessed the C580Y mutation. Analysis of the flanking loci demonstrated that these three isolates were genetically very close to resistant strains originating from western Cambodia. CONCLUSIONS:Artemisinin resistance was observed to be rapidly increasing and spreading northwards through Laos and has now reached the Chinese border. The Lao and Chinese governments, as well as the international community, should make dedicated efforts to contain the spread of k13 mutations within Laos and in the GMS.
| S-EPMC6307170 | biostudies-literature
Project description:BackgroundUnderstanding the dynamics of gametocyte production in polyclonal Plasmodium falciparum infections requires a genotyping method that detects distinct gametocyte clones and estimates their relative frequencies. Here, a marker was identified and evaluated to genotype P. falciparum mature gametocytes using amplicon deep sequencing.MethodsA data set of polymorphic regions of the P. falciparum genome was mined to identify a gametocyte genotyping marker. To assess marker resolution, the number of unique haplotypes in the marker region was estimated from 95 Malawian P. falciparum whole genome sequences. Specificity of the marker for detection of mature gametocytes was evaluated using reverse transcription-polymerase chain reaction of RNA extracted from NF54 mature gametocytes and rings from a non-gametocyte-producing strain of P. falciparum. Amplicon deep sequencing was performed on experimental mixtures of mature gametocytes from two distinct parasite clones, as well as gametocyte-positive P. falciparum field isolates to evaluate the quantitative ability and determine the limit of detection of the genotyping approach.ResultsA 400 bp region of the pfs230 gene was identified as a gametocyte genotyping marker. A larger number of unique haplotypes was observed at the pfs230 marker (34) compared to the sera-2 (18) and ama-1 (14) markers in field isolates from Malawi. RNA and DNA genotyping accurately estimated gametocyte and total parasite clone frequencies when evaluating agreement between expected and observed haplotype frequencies in gametocyte mixtures, with concordance correlation coefficients of 0.97 [95% CI: 0.92-0.99] and 0.92 [95% CI: 0.83-0.97], respectively. The detection limit of the genotyping method for male gametocytes was 0.41 pfmget transcripts/µl [95% CI: 0.28-0.72] and for female gametocytes was 1.98 ccp4 transcripts/µl [95% CI: 1.35-3.68].ConclusionsA region of the pfs230 gene was identified as a marker to genotype P. falciparum gametocytes. Amplicon deep sequencing of this marker can be used to estimate the number and relative frequency of parasite clones among mature gametocytes within P. falciparum infections. This gametocyte genotyping marker will be an important tool for studies aimed at understanding dynamics of gametocyte production in polyclonal P. falciparum infections.
| S-EPMC10999092 | biostudies-literature
Project description:Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.
| S-EPMC8912097 | biostudies-literature
Project description:BackgroundGenomic surveillance is crucial for monitoring malaria transmission and understanding parasite adaptation to interventions. Zambia lacks prior nationwide efforts in malaria genomic surveillance among African countries.MethodsWe conducted genomic surveillance of Plasmodium falciparum parasites from the 2018 Malaria Indicator Survey in Zambia, a nationally representative household survey of children under five years of age. We whole-genome sequenced and analyzed 241 P. falciparum genomes from regions with varying levels of malaria transmission across Zambia and estimated genetic metrics that are informative about transmission intensity, genetic relatedness between parasites, and selection.ResultsWe provide genomic evidence of widespread within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in Zambia. Our analysis reveals country-level clustering of parasites from Zambia and neighboring regions, with distinct separation in West Africa. Within Zambia, identity by descent (IBD) relatedness analysis uncovers local spatial clustering and rare cases of long-distance sharing of closely related parasite pairs. Genomic regions with large shared IBD segments and strong positive selection signatures implicate genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Furthermore, differences in selection signatures, including drug resistance loci, are observed between eastern and western Zambian parasite populations, suggesting variable transmission intensity and ongoing drug pressure.ConclusionsOur findings enhance our understanding of nationwide P. falciparum transmission in Zambia, establishing a baseline for analyzing parasite genetic metrics as they vary over time and space. These insights highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.
| S-EPMC10998850 | biostudies-literature
Project description:PurposeDeveloping large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically generated annotations in development of high-performance segmentation models for small research-quality challenge datasets.Materials and methodsWe used a large retrospective dataset from our institution comprised of 1,620 clinically generated segmentations, and two challenge datasets (PROMISE12: 50 patients, ProstateX-2: 99 patients). We trained a 3D U-Net convolutional neural network (CNN) segmentation model using our entire dataset, and used that model as a template to train models on the challenge datasets. We also trained versions of the template model using ablated proportions of our dataset, and evaluated the relative benefit of those templates for the final models. Finally, we trained a version of the template model using an out-of-domain brain cancer dataset, and evaluated the relevant benefit of that template for the final models. We used five-fold cross-validation (CV) for all training and evaluation across our entire dataset.ResultsOur model achieves state-of-the-art performance on our large dataset (mean overall Dice 0.916, average Hausdorff distance 0.135 across CV folds). Using this model as a pre-trained template for refining on two external datasets significantly enhanced performance (30% and 49% enhancement in Dice scores respectively). Mean overall Dice and mean average Hausdorff distance were 0.912 and 0.15 for the ProstateX-2 dataset, and 0.852 and 0.581 for the PROMISE12 dataset. Using even small quantities of data to train the template enhanced performance, with significant improvements using 5% or more of the data.ConclusionWe trained a state-of-the-art model using unrefined clinical prostate annotations and found that its use as a template model significantly improved performance in other prostate segmentation tasks, even when trained with only 5% of the original dataset.
| S-EPMC8232529 | biostudies-literature
Project description:Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learning models, DeepSEA and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K), conditioning on a broad set of coding, conserved and regulatory annotations. We aggregated annotations across all (respectively blood or brain) tissues/cell-types in meta-analyses across all (respectively 11 blood or 8 brain) traits. The annotations were highly enriched for disease heritability, but produced only limited conditionally significant results: non-tissue-specific and brain-specific Basenji-H3K4me3 for all traits and brain traits respectively. We conclude that deep learning models have yet to achieve their full potential to provide considerable unique information for complex disease, and that their conditional informativeness for disease cannot be inferred from their accuracy in predicting regulatory annotations.
| S-EPMC7499261 | biostudies-literature
Project description:MotivationExperimental testing and manual curation are the most precise ways for assigning Gene Ontology (GO) terms describing protein functions. However, they are expensive, time-consuming and cannot cope with the exponential growth of data generated by high-throughput sequencing methods. Hence, researchers need reliable computational systems to help fill the gap with automatic function prediction. The results of the last Critical Assessment of Function Annotation challenge revealed that GO-terms prediction remains a very challenging task. Recent developments on deep learning are significantly breaking out the frontiers leading to new knowledge in protein research thanks to the integration of data from multiple sources. However, deep models hitherto developed for functional prediction are mainly focused on sequence data and have not achieved breakthrough performances yet.ResultsWe propose DeeProtGO, a novel deep-learning model for predicting GO annotations by integrating protein knowledge. DeeProtGO was trained for solving 18 different prediction problems, defined by the three GO sub-ontologies, the type of proteins, and the taxonomic kingdom. Our experiments reported higher prediction quality when more protein knowledge is integrated. We also benchmarked DeeProtGO against state-of-the-art methods on public datasets, and showed it can effectively improve the prediction of GO annotations.Availability and implementationDeeProtGO and a case of use are available at https://github.com/gamerino/DeeProtGO.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9524999 | biostudies-literature
Project description:BackgroundMalaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites.MethodsThin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4',6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears.ResultsDetection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples.ConclusionQuantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.
| S-EPMC7671488 | biostudies-literature