Bioinformatics Challenges Research Articles

T3E: a tool for characterising the epigenetic profile of transposable elements using ChIP-seq data

BackgroundDespite the advent of Chromatin Immunoprecipitation Sequencing (ChIP-seq) having revolutionised our understanding of the mammalian genome’s regulatory landscape, many challenges remain. In particular, because of their repetitive nature, the sequencing reads derived from transposable elements (TEs) pose a real bioinformatics challenge, to the point that standard analysis pipelines typically ignore reads whose genomic origin cannot be unambiguously ascertained.ResultsWe show that discarding ambiguously mapping reads may lead to a systematic underestimation of the number of reads associated with young TE families/subfamilies. We also provide evidence suggesting that the strategy of randomly permuting the location of the read mappings (or the TEs) that is often used to compute the background for enrichment calculations at TE families/subfamilies can result in both false positive and negative enrichments. To address these problems, we present the Transposable Element Enrichment Estimator (T3E), a tool that makes use of ChIP-seq data to characterise the epigenetic profile of associated TE families/subfamilies. T3E weights the number of read mappings assigned to the individual TE copies of a family/subfamily by the overall number of genomic loci to which the corresponding reads map, and this is done at the single nucleotide level. In addition, T3E computes ChIP-seq enrichment relative to a background estimated based on the distribution of the read mappings in the input control DNA.We demonstrated the capabilities of T3E on 23 different ChIP-seq libraries. T3E identified enrichments that were consistent with previous studies. Furthermore, T3E detected context-specific enrichments that are likely to pinpoint unexplored TE families/subfamilies with individual TE copies that have been frequently exapted as cis-regulatory elements during the evolution of mammalian regulatory networks.ConclusionsT3E is a novel open-source computational tool (available for use at: https://github.com/michelleapaz/T3E) that overcomes some of the pitfalls associated with the analysis of ChIP-seq data arising from the repetitive mammalian genome and provides a framework to shed light on the epigenetics of entire TE families/subfamilies.

An Attention Based Bidirectional LSTM Method to Predict the Binding of TCR and Epitope.

The T-cell epitope prediction has always been a long-term challenge in immunoinformatics and bioinformatics. Studying the specific recognition between T-cell receptor (TCR) and peptide-major histocompatibility complex (p-MHC) complexes can help us better understand the immune mechanism, it's also make a signification contribution in developing vaccines and targeted drugs. Meanwhile, more advanced methods are needed for distinguishing TCRs binding from different epitopes. In this paper, we introduce a hybrid model composed of bidirectional long short-term memory networks (BiLSTM), attention and convolutional neural networks (CNN) that can identified the binding of TCRs to epitopes. The BiLSTM can more completely extract amino acid forward and backward information in the sequence, and attention mechanism can focus on amino acids at certain positions from complex sequences to capture the most important feature, then CNN was used to further extract salient features to predict the binding of TCR-epitope. In McPAS dataset, the AUC value (the area under ROC curve) of naive TCR-epitope binding is 0.974 and specific TCR-epitope binding is 0.887. The model has achieved better prediction results than other existing models (TCRGP, ERGO, NetTCR), and some experiments are used to analyze the advantages of our model. The algorithm is available at https://github.com/bijingshu/BiAttCNN.git.

Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).

Three-dimensional protein structure prediction is one of the major challenges in bioinformatics. According to recent research findings, real-valued distance prediction plays a vital role in determining the unique three-dimensional protein structure. This paper proposes a novel methodology involving a deep residual dense network (DRDN) for predicting protein real-valued distance. The features extracted from the given query protein sequence and its corresponding homologous sequences are used for training the model. Multi-aligned homologous sequences for each query protein sequence are retrieved from five different databases using DeepMSA, HHblits, and HITS_PR_HHblits methods. The proposed method yielded outcomes of 3.89, 0.23, 0.45, and 0.63, respectively, corresponding to the evaluation metrics such as Absolute Error, Relative Error, High-accuracy Pairwise Distance Test (PDA), and Pairwise Distance Test (PDT). Further, the contact map is computed based on CASP criteria by converting the predicted real-valued distance, and it is evaluated using the precision metric. It is observed that precision of long-range top L/5 contact prediction on the CASP13 dataset by the proposed method, RaptorX, Zhang, trRosetta, JinboXu & JinLu, and Deepdist are 0.834, 0.657, 0.70, 0.785, 0.786, and 0.812, respectively. Also, Top-L/5 contact prediction on the CASP14 dataset evaluated using average precision resulted in 0.847, 0.707, 0.752, 0.783, 0.792, 0.817, and 0.825 respectively, corresponding to the proposed method, Zhang, RaptorX, trRosetta, Deepdist, JinboXu & JinLu, and Alphafold2.

The future of food and nutrition in ELIXIR

Non-communicable diseases are on the rise and are often related to food choices; nutrition affects infectious diseases too. Therefore, there is growing interest in research on public and personal health, as related to food, nutrition behaviour and well-being of consumers throughout the life cycle. These concepts and their relations are complex and only partially understood – more data is needed to improve our understanding. The required data include deep geno- and phenotyping data from human nutritional studies, covering metabolic and health, but also including behavioural and socio-economic data. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR’s recently established Food & Nutrition (F&N) Community. This white paper is the direct result of a strategy meeting that took place in September 2019 in The Hague (NL) and involved representatives of 14 countries representing the ELIXIR Nodes. The meeting led to the definition of F&N related bioinformatics challenges, including the use of standards for data reuse and sharing, and for interoperability of data, tools and services, advocacy and training. Resolving these bioinformatics challenges makes it possible to address a wide range of F&N-related challenges, such as definition of an individual health status, individual dietary needs, and finding complex intake biomarkers (to replace questionnaires). Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms, other ELIXIR Communities/Focus Groups and the European Strategy Forum on Research Infrastructures.

Treewidth-based algorithms for the small parsimony problem on networks

BackgroundPhylogenetic reconstruction is one of the paramount challenges of contemporary bioinformatics. A subtask of existing tree reconstruction algorithms is modeled by the Small Parsimony problem: given a tree T and an assignment of character-states to its leaves, assign states to the internal nodes of T such as to minimize the parsimony score, that is, the number of edges of T connecting nodes with different states. While this problem is polynomial-time solvable on trees, the matter is more complicated if T contains reticulate events such as hybridizations or recombinations, i.e. when T is a network. Indeed, three different versions of the parsimony score on networks have been proposed and each of them is NP-hard to decide. Existing parameterized algorithms focus on combining the number c of possible character-states with the number of reticulate events (per biconnected component).ResultsWe consider the parameter treewidth t of the underlying undirected graph of the input network, presenting dynamic programming algorithms for (slight generalizations of) all three versions of the parsimony problem on size-n networks running in times c^t {n^{O(1)}}, (3c)^t {n^{O(1)}}, and 6^{tc}n^{O(1)}, respectively. Our algorithms use a formulation of the treewidth that may facilitate formalizing treewidth-based dynamic programming algorithms on phylogenetic networks for other problems.ConclusionsOur algorithms allow the computation of the three popular parsimony scores, modeling the evolutionary development of a (multistate) character on a given phylogenetic network of low treewidth. Our results subsume and improve previously known algorithm for all three variants. While our results rely on being given a “good” tree-decomposition of the input, encouraging theoretical results as well as practical implementations producing them are publicly available. We present a reformulation of tree decompositions in terms of “agreeing trees” on the same set of nodes. As this formulation may come more natural to researchers and engineers developing algorithms for phylogenetic networks, we hope to render exploiting the input network’s treewidth as parameter more accessible to this audience.

Utility of tissue-specific gene expression scores for gene prioritization in Mendelian diseases.

In genetic testing of Mendelian diseases, it is a bioinformatics challenge to effectively prioritize disease-causing candidate genes listed from massively parallel sequencing. Tissue specificity of the gene expression levels may give a clue because it may reflect tissue-specific disease manifestation. However, considering poor correlations between mRNA and protein expression in some genes, it is not clear whether transcriptomics- or proteomics-based tissue specificity should be used to prioritize candidate genes. Therefore, we compared the efficiency of tissue-specific scores (TS scores) obtained from transcriptome and proteome data in prioritizing candidate genes for whole exome sequencing (WES) analysis of Mendelian disease patients. We show that both Protein and RNA TS scores are useful in prioritizing candidate genes in WES analysis, although diseases like coagulopathies get more benefit from Protein TS score. This study may provide useful evidence in developing new methods to effectively identify novel disease-causing genes.

Feature-scML: An Open-source Python Package for the Feature Importance Visualization of Single-Cell Omics with Machine Learning

Background: Inferring feature importance is both a promise and challenge in bioinformatics and computational biology. While multiple biological computation methods exist to identify decisive factors of single cell subpopulation, there is a need for a comprehensive toolkit that presents an intuitive and custom view of the feature importance. Objective: We developed a Feature-scML, a scalable and friendly toolkit that allows the users to visualize and reveal decisive factors for single cell omics analysis. Method: Feature-scML incorporates the following main three functions: (i) There are seven feature selection algorithms to comprehensively score and rank every feature. (ii) Four machine learning approaches and increment feature selection (IFS) strategy jointly determine the number of selected features. (iii) The Feature-scML supports the visualized feature importance, model performance evaluation, and model interpretation. The source code is available at https://github.com/liameihao/Feature-scML. Results: We systematically compared the performance of seven feature selection algorithms from Feature-scML on two single cell transcriptome datasets. It demonstrates the effectiveness and power of the Feature-scML. Conclusion: Feature-scML is effective for analyzing single-cell RNA omics datasets to automate the machine learning process and customize the visual analysis from the results.

Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data.

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.

Microbial Forensics: A Present to Future Perspective on Genomic Targets, Bioinformatic Challenges, and Applications

Introduction: The human microbiome is a compendium of all organisms on and within the human body (i.e., bacteria, archaea, fungi, and eukaryotes), where the diversity of taxa as well as their relative abundance contribute to the highly individualizing nature of an individual's microbial signature. Discussion: The effect of individual characteristics such as lifestyle, genetics, health, and geographic provenance on the human microbiome contribute to both the individuality and forensic usefulness of microbiome samples for the purposes of human identification, body fluid attribution, estimation of the post-mortem interval, or geolocation. Further, the use of technologies such as massively parallel sequencing and long-read sequencing, in combination with bioinformatic tools and machine learning models, allow for quick and accurate characterization of individual human microbiome samples. Conclusion: However, before microbial profiling can be implemented into routine forensic casework, extensive standardization and validation of sample types, collection procedures and analysis pipelines must be established.

Bovine Milk Fat Intervention in Early Life and Its Impact on Microbiota, Metabolites and Clinical Phenotype: A Multi-Omics Stacked Regularization Approach

The integration and analysis of multi-omics modalities is an important challenge in bioinformatics and data science in general. A standard approach is to conduct a series of univariate tests to determine the significance for each parameter, but this underestimates the connected nature of biological data and thus increases the number of false-negative errors. To mitigate this issue and to understand how different omics’ data domains are jointly affected, we used the Stacked Regularization model with Bayesian optimization over its full parameter space. We applied this approach to a multi-omics data set consisting of microbiota, metabolites and clinical data from two recent clinical studies aimed at detecting the impact of replacing part of the vegetable fat in infant formula with bovine milk fat on healthy term infants. We demonstrate how our model achieves a high discriminative performance, show the advantages of univariate testing and discuss the detected outcome in its biological context.

Using SPAdes, AUGUSTUS, and BLAST in an Automated Pipeline for Clustering Homologous Exome Sequences.

Cross-species exome sequencing approaches provide unprecedented avenues for obtaining genetic diversity, evolutionary relationships, and functional information from a variety of organisms including non-model species. These approaches offer cost-effective opportunities to study multiple individuals or species in parallel, but also create bioinformatics challenges in the application of multiple but powerful bioinformatics tools for the identification of homologous gene families across individual or species boundaries. Popular tools of this kind include SPAdes for sequence assembly, AUGUSTUS for ab initio gene prediction, and BLAST for building homologous sequence families. These tools can also be sophisticated in terms of installation and usage. Here, we present detailed steps on how to run these tools for the recovery and clustering of exon sequences from cross-species raw exome-capture data into homologous sequence families. We also present a utility pipeline, CODSEQCP, that automates these steps to cluster exon sequences, facilitating population genomics and evolutionary studies. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Reads assembly using SPAdes Basic Protocol 2: Coding sequence extraction using AUGUSTUS Basic Protocol 3: Sequence clustering using BLAST Alternate Protocol: How to run CODSEQCP.

Machine Learning applied to an RNA Classification Problem

Long RNA sequences can be classified as protein-coding messenger RNA (mRNA) or as long non-coding RNA (lncRNA). RNA classification based on sequence alone is a bioinformatics challenge with potential to enlarge our understanding of the causes of many human diseases. We chose to compare two classification approaches: (1) applications of correlation statistics and visualization tools, and (2) training a convolutional neural network. Both approaches used human RNA sequences from the public GenCode database. We found that the machine learning approach was superior, achieving an accuracy of 87.37%, which is not as high as some published classifiers. These results indicate that machine learning techniques are a more effective solution to the problem of human RNA classification compared to the other techniques tested. For future work, we propose to build, train, and compare other convolutional neural network models for this classification task.

Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs.

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future.

Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering.

Proteins are Nature's molecular machinery and comprise diverse roles while consisting of chemically similar building blocks. In recent years, protein engineering and design have become important research areas, with many applications in the pharmaceutical, energy, and biocatalysis fields, among others-where the aim is to ultimately create a protein given desired structural and functional properties. It is often critical to model the relationship between a protein's sequence, folded structure, and biological function to assist in such protein engineering pursuits. However, significant challenges remain in concretely mapping an amino acid sequence to specific protein properties and biological activities. Mutations may enhance or diminish molecular protein function, and the epistatic interactions between mutations result in an inherently complex mapping between genetic modifications and protein function. Therefore, estimating the quantitative effects of mutations on protein function(s) remains a grand challenge of biology, bioinformatics, and many related fields and would rapidly accelerate protein engineering tasks when successful. Such estimation is often known as variant effect prediction (VEP). However, progress has been demonstrated in recent years with the development of machine learning (ML) methods in modeling the relationship between mutations and protein function. In this Review, recent advances in variant effect prediction (VEP) are discussed as tools for protein engineering, focusing on techniques incorporating gains from the broader ML community and challenges in estimating biomolecular functional differences. Primary developments highlighted include convolutional neural networks, graph neural networks, and natural language embeddings for protein sequences.

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics.

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS2) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(−) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.

ORTHOSKIM: In silico sequence capture from genomic and transcriptomic libraries for phylogenomic and barcoding applications.

Low-coverage whole genome shotgun sequencing (or genome skimming) has emerged as a cost-effective method for acquiring genomic data in nonmodel organisms. This method provides sequence information on chloroplast genome (cpDNA), mitochondrial genome (mtDNA) and nuclear ribosomal regions (rDNA), which are over-represented within cells. However, numerous bioinformatic challenges remain to accurately and rapidly obtain such data in organisms with complex genomic structures and rearrangements, in particular for mtDNA in plants or for cpDNA in some plant families. Here we introduce the pipeline ORTHOSKIM, which performs in silico capture of targeted sequences from genomic and transcriptomic libraries without assembling whole organelle genomes. ORTHOSKIM proceeds in three steps: (i) global sequence assembly, (ii) mapping against reference sequences and (iii) target sequence extraction; importantly it also includes a range of quality control tests. Different modes are implemented to capture both coding and noncoding regions of cpDNA, mtDNA and rDNA sequences, along with predefined nuclear sequences (e.g., ultraconserved elements) or collections of single-copy orthologue genes. Moreover, aligned DNA matrices are produced for phylogenetic reconstructions, by performing multiple alignments of the captured sequences. While ORTHOSKIM is suitable for any eukaryote, a case study is presented here, using 114 genome-skimming libraries and four RNA sequencing libraries obtained for two plant families, Primulaceae and Ericaceae, the latter being a well-known problematic family for cpDNA assemblies. ORTHOSKIM recovered with high success rates cpDNA, mtDNA and rDNA sequences, well suited to accurately infer evolutionary relationships within these families. ORTHOSKIM is released under a GPL-3 licence and is available at: https://github.com/cpouchon/ORTHOSKIM.

Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2.

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.

Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism

Background: Macromolecule structure prediction remains a fundamental challenge of bioinformatics. Over the past several decades, the Rosetta framework has provided solutions to diverse challenges in computational biology. However, it is challenging to model RNA tertiary structures effectively when the de novo modeling of RNA involves solving a well-defined small puzzle. Methods: In this study, we introduce a stepwise Monte Carlo parallelization (SMCP) algorithm for RNA tertiary structure prediction. Millions of conformations were randomly searched using the Monte Carlo algorithm and stepwise ansatz hypothesis, and SMCP uses a parallel mechanism for efficient sampling. Moreover, to achieve better prediction accuracy and completeness, we judged and processed the modeling results. Results: A benchmark of nine single-stranded RNA loops drawn from riboswitches establishes the general ability of the algorithm to model RNA with high accuracy and integrity, including six motifs that cannot be solved by knowledge mining–based modeling algorithms. Experimental results show that the modeling accuracy of the SMCP algorithm is up to 0.14 Å, and the modeling integrity on this benchmark is extremely high. Conclusion: SMCP is an ab initio modeling algorithm that substantially outperforms previous algorithms in the Rosetta framework, especially in improving the accuracy and completeness of the model. It is expected that the work will provide new research ideas for macromolecular structure prediction in the future. In addition, this work will provide theoretical basis for the development of the biomedical field.

Bidirectional Long Short-Term Memory Network for Taxonomic Classification

Identifying and classifying Deoxyribonucleic Acid (DNA) sequences and their functions have been considered as the main challenges in bioinformatics. Advances in machine learning and Deep Learning (DL) techniques are expected to improve DNA sequence classification. Since the DNA sequence classification depends on analyzing textual data, Bidirectional Long Short-Term Memory (BLSTM) algorithms are suitable for tackling this task. Generally, classifiers depend on the patterns to be processed and the pre-processing method. This paper is concerned with a new proposed classification framework based on Frequency Chaos Game Representation (FCGR) followed by Discrete Wavelet Transform (DWT) and BLSTM. Firstly, DNA strings are transformed into numerical matrices by FCGR. Then, the DWT is used instead of the pooling layer as a tool of data compression. The benefit of using the DWT is two-fold. It preserves the useful information only that enables the following BLSTM training, effectively. Besides, DWT adds more important details to the encoded sequences due to finding effective features in the DNA fragments. Finally, the BLSTM model is trained to classify the DNA sequences. Evaluation metrics such as F1 score and accuracy show that the proposed framework outperforms the state-of-the-art algorithms. Hence, it can be used in DNA classification applications.

Accuracy of an amplicon-sequencing nanopore approach to identify variants in tuberculosis drug-resistance-associated genes.

A rapid and accurate diagnostic assay represents an important means to detect Mycobacterium tuberculosis , identify drug-resistant strains and ensure treatment success. Currently employed techniques to diagnose drug-resistant tuberculosis include slow phenotypic tests or more rapid molecular assays that evaluate a limited range of drugs. Whole-genome-sequencing-based approaches can detect known drug-resistance-conferring mutations and novel variations; however, the dependence on growing samples in culture, and the associated delays in achieving results, represents a significant limitation. As an alternative, targeted sequencing strategies can be directly performed on clinical samples at high throughput. This study proposes a targeted sequencing assay to rapidly detect drug-resistant strains of M. tuberculosis using the Nanopore MinION sequencing platform. We designed a single-tube assay that targets nine genes associated with drug resistance to seven drugs and two phylogenetic-determining regions to determine strain lineage and tested it in nine clinical isolates and six sputa. The study’s main aim is to calibrate MinNION variant calling to detect drug-resistance-associated mutations with different frequencies to match the accuracy of Illumina (the current gold-standard sequencing technology) from both culture and sputum samples. After calibrating Nanopore MinION variant calling, we demonstrated 100% agreement between Illumina WGS and our MinION set up to detect known drug resistance and phylogenetic variants in our dataset. Importantly, other variants in the amplicons are also detected, decreasing the recall. We identify minority variants and insertions/deletions as crucial bioinformatics challenges to fully reproduce Illumina WGS results.