scholarly journals Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Thomas Badet ◽  
Simone Fouché ◽  
Fanny E. Hartmann ◽  
Marcello Zala ◽  
Daniel Croll
Keyword(s):  
Machine Learning ◽  
Structural Variation ◽  
Chromosomal Rearrangements ◽  
Training Sequence ◽  
Specific Sequence ◽  
Sequence Features ◽  
Dna Instability ◽  
Recent Emergence ◽  
Mutational Hotspots ◽  
Duplication Events

AbstractSpecies harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We use machine learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Retrotransposons explain most inversion, indel and duplication events. We apply our model to Arabidopsis thaliana and show that our approach extends to more complex genomes. Finally, we analyze complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations are enriched for new rearrangements consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation accurately predicts the position of >74% of newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.

scholarly journals Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen

2020 ◽  
Author(s):  
Thomas Badet ◽  
Simone Fouché ◽  
Fanny E. Hartmann ◽  
Daniel Croll
Keyword(s):  
Machine Learning ◽  
Structural Variation ◽  
Chromosomal Rearrangements ◽  
Training Sequence ◽  
Specific Sequence ◽  
Sequence Features ◽  
Dna Instability ◽  
Recent Emergence ◽  
Mutational Hotspots ◽  
Element Activity

Species harbour extensive structural variation underpinning recent adaptive evolution and major disease phenotypes. Most sequence rearrangements are generated non-randomly along the genome through non-allelic recombination and transposable element activity. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyse a global set of telomere-to-telomere genome assemblies of a major fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We used machine-learning to train a model of indel and translocation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Low-copy LINE retrotransposons and specific DNA transposons explain most indel and translocation dynamics, respectively. We retrain our model on Arabidopsis thaliana and show that our modelling approach can be extended to more complex genomes. Finally, we analysed complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations were enriched for newly generated structural variation consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation predicted 79% and 99% of the translocations and indels in the pedigree, respectively. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

scholarly journals Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tanglong Yuan ◽  
Nana Yan ◽  
Tianyi Fei ◽  
Jitan Zheng ◽  
Juan Meng ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Efficiency ◽  
Codon Optimization ◽  
Sequence Context ◽  
Learning Methods ◽  
Specific Sequence ◽  
Uracil Dna Glycosylase ◽  
Base Editing ◽  
Machine Learning Methods ◽  
Deep Learning Model

AbstractEfficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the sequence context predictable via machine-learning methods. By changing the species origin and relative position of uracil-DNA glycosylase and deaminase, together with codon optimization, we obtain optimized C-to-G BEs (OPTI-CGBEs) for efficient C-to-G transversion. The motif preference of OPTI-CGBEs for editing 100 endogenous sites is determined in HEK293T cells. Using a sgRNA library comprising 41,388 sequences, we develop a deep-learning model that accurately predicts the OPTI-CGBE editing outcome for targeted sites with specific sequence context. These OPTI-CGBEs are further shown to be capable of efficient base editing in mouse embryos for generating Tyr-edited offspring. Thus, these engineered CGBEs are useful for efficient and precise base editing, with outcome predictable based on sequence context of targeted sites.

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

scholarly journals Comprehensive structural variation genome map of individuals carrying complex chromosomal rearrangements

PLoS Genetics ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. e1007858 ◽  
Author(s):  
Jesper Eisfeldt ◽  
Maria Pettersson ◽  
Francesco Vezzi ◽  
Josephine Wincent ◽  
Max Käller ◽  
...  
Keyword(s):  
Structural Variation ◽  
Chromosomal Rearrangements ◽  
Complex Chromosomal Rearrangements ◽  
Genome Map

scholarly journals Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhixun Zhao ◽  
Xiaocai Zhang ◽  
Fang Chen ◽  
Liang Fang ◽  
Jinyan Li
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Case Studies ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Feature Space ◽  
Sequence Features ◽  
Independent Test ◽  
Benchmark Datasets

Abstract Background DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. Results The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. Conclusions The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

CRiSP: accurate structure prediction of disulfide-rich peptides with cystine-specific sequence alignment and machine learning

2020 ◽  
Vol 36 (11) ◽  
pp. 3385-3392
Author(s):  
Zi-Lin Liu ◽  
Jing-Hao Hu ◽  
Fan Jiang ◽  
Yun-Dong Wu
Keyword(s):  
Machine Learning ◽  
Sequence Alignment ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Prediction Method ◽  
General Purpose ◽  
Supplementary Information ◽  
Model Quality ◽  
Specific Sequence ◽  
Structure Information

Abstract Motivation High-throughput sequencing discovers many naturally occurring disulfide-rich peptides or cystine-rich peptides (CRPs) with diversified bioactivities. However, their structure information, which is very important to peptide drug discovery, is still very limited. Results We have developed a CRP-specific structure prediction method called Cystine-Rich peptide Structure Prediction (CRiSP), based on a customized template database with cystine-specific sequence alignment and three machine-learning predictors. The modeling accuracy is significantly better than several popular general-purpose structure modeling methods, and our CRiSP can provide useful model quality estimations. Availability and implementation The CRiSP server is freely available on the website at http://wulab.com.cn/CRISP. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Deconvolving sequence features that discriminate between overlapping regulatory annotations

2017 ◽  
Author(s):  
Akshay Kakumanu ◽  
Silvia Velasco ◽  
Esteban Mazzoni ◽  
Shaun Mahony
Keyword(s):  
Embryonic Stem ◽  
Cell Types ◽  
The Novel ◽  
Cell Type ◽  
Dynamic Binding ◽  
Specific Sequence ◽  
Sequence Features ◽  
Binding Behavior ◽  
Distinct Sequence ◽  
Regulatory Sites

AbstractGenomic loci with regulatory potential can be identified and annotated with various properties. For example, genomic sites may be annotated as being bound by a given transcription factor (TF) in one or more cell types. The same sites may be further labeled as being proximal or distal to known promoters. Given such a collection of labeled sites, it is natural to ask what sequence features are associated with each annotation label. However, discovering such label-specific sequence features is often confounded by overlaps between annotation labels; e.g. if regulatory sites specific to a given cell type are also more likely to be promoter-proximal, it is difficult to assess whether motifs identified in that set of sites are associated with the cell type or associated with promoters. In order to meet this challenge, we developed SeqUnwinder, a principled approach to deconvolving interpretable discriminative sequence features associated with overlapping annotation labels. We demonstrate the novel analysis abilities of SeqUnwinder using three examples. Firstly, we show SeqUnwinder’s ability to unravel sequence features associated with the dynamic binding behavior of TFs during motor neuron programming from features associated with chromatin state in the initial embryonic stem cells. Secondly, we characterize distinct sequence properties of multi-condition and cell-specific TF binding sites after controlling for uneven associations with promoter proximity. Finally, we demonstrate the scalability of SeqUnwinder to discover cell-specific sequence features from over one hundred thousand genomic loci that display DNase I hypersensitivity in one or more ENCODE cell lines.Availabilityhttps://github.com/seqcode/sequnwinder

scholarly journals TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering

2018 ◽  
Author(s):  
Donald Freed ◽  
Renke Pan ◽  
Rafael Aldana
Keyword(s):  
Machine Learning ◽  
Normal Tissue ◽  
Structural Variation ◽  
Somatic Mutations ◽  
Variant Calling ◽  
Somatic Variant ◽  
Accurate Detection ◽  
Machine Learning Model ◽  
Early Engineering ◽  
Somatic Mutation Calling

AbstractDetection of somatic mutations in tumor samples is important in the clinic, where treatment decisions are increasingly based upon molecular diagnostics. However, accurate detection of these mutations is difficult, due in part to intra-tumor heterogeneity, contamination of the tumor sample with normal tissue and pervasive structural variation. Here, we describe Sentieon TNscope, a haplotype-based somatic variant caller with increased accuracy relative to existing methods. An early engineering version of TNscope was used in our submission to the most recent ICGC-DREAM Somatic Mutation calling challenge. In that challenge, TNscope is the leader in accuracy for SNVs, indels and SVs. To further improve variant calling accuracy, we combined the improvements in the variant caller with machine learning. We benchmarked TNscope using in-silico mixtures of well-characterized Genome in a Bottle (GIAB) samples. TNscope displays higher accuracy than the other benchmarked tools and the accuracy is substantially improved by the machine learning model.

scholarly journals Deep generative selection models of T and B cell receptor repertoires with soNNia

2021 ◽  
Vol 118 (14) ◽  
pp. e2023141118
Author(s):  
Giulio Isacchini ◽  
Aleksandra M. Walczak ◽  
Thierry Mora ◽  
Armita Nourmohammad
Keyword(s):  
Machine Learning ◽  
B Cell ◽  
Cell Receptor ◽  
T Cell Subsets ◽  
Sequence Information ◽  
Specific Sequence ◽  
Linear Classifiers ◽  
Functional Roles ◽  
Biophysical Models ◽  
Selection Processes

Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here, we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically, using only repertoire-level sequence information, we classify CD4+ and CD8+ T cells, find correlations between receptor chains arising during selection, and identify T cell subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.

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