SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

Gabbassov, Einar; Moreno-Molina, Miguel; Comas, Iñaki; Libbrecht, Maxwell W.; Chindelevitch, Leonid

doi:10.1099/mgen.0.000607

Cited by 14 publications

(19 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We compared the accuracy of our new tool, MixInfect2, against previous methods ( MixInfect 6 , SplitStrains 16 and QuantTB 17 ) for detecting mixed infections and to estimate the major strain proportion from the dataset of 36 in vitro mixed samples and 12 non-mixed (‘pure’) strains. The average coverage in all these samples was relatively high, ranging from 356- to 482-fold.…”

Section: Resultsmentioning

confidence: 99%

“…Here, we present a new tool for identifying multi- strain Mtb samples from WGS data, MixInfect2, that builds upon a previously a published method 6 to improve the accuracy of mixed infection detection. We compare this new method to three previous tools for identifying mixed infection (MixInfect 6 , SplitStrains 16 , and QuantTB 17 ) and then assess different approaches for reconstructing the constituent strain sequences of the 36 in vitro mixed samples at varying major and minor strain proportions. Finally, we apply the optimal approach to a real-world dataset of over 2,000 Mtb isolates collected from the Republic of Moldova between 2018 - 2019 to predict the constituent strain sequences of mixed samples and place these strains into putative transmission clusters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new method for detecting mixedMycobacterium tuberculosisinfection and reconstructing constituent strains provides insights into transmission

Sobkowiak,

Cudahy,

Chitwood

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Mixed infection with multiple strains of the same pathogen in a single host can present clinical and analytical challenges. Whole genome sequence (WGS) data can identify signals of multiple strains in samples, though the precision of previous methods can be improved. Here, we present MixInfect2, a new tool to accurately detect mixed samples from Mycobacterium tuberculosis WGS data. We then evaluate three approaches for reconstructing the underlying mixed constituent strain sequences. This allows these samples to be included in downstream analysis to gain insights into the epidemiology and transmission of mixed infections. Methods: We employed a Gaussian mixture model to cluster allele frequencies at mixed sites (hSNPs) in each sample to identify signals of multiple strains. Building upon our previous tool, MixInfect, we increased the accuracy of classifying in vitro mixed samples through multiple improvements to the bioinformatic pipeline. Major and minor proportion constituent strains were reconstructed using three approaches and assessed by comparing the estimated sequence to the known constituent strain sequence. Lastly, mixed infections in a real-world Mycobacterium tuberculosis population from Moldova were detected with MixInfect2 and clusters of recent transmission that included major and minor constituent strains were built. Results: All 36/36 in vitro mixed and 12/12 non-mixed samples were correctly classified with MixInfect2, and major strain proportions estimated with high accuracy, outperforming previous tools. Reconstructed major strain sequences closely matched the true constituent sequence by taking the allele at the highest frequency at hSNPs, while the best performing approach to reconstruct the minor proportion strain sequence was identifying the closest non-mixed isolate in the same population, though no approach was effective when the minor strain proportion was at 5%. Finally, fewer mixed infections were identified in Moldova than previous estimates (6.6% vs 17.4%) and we found multiple instances where the constituent strains of mixed samples were present in transmission clusters. Conclusions: MixInfect2 accurately detects samples with evidence of mixed infection from WGS data and provides an excellent estimate of the mixture proportions. While there are limitations in reconstructing the constituent strain sequences of mixed samples, we present recommendations for the best approach to include these isolates in further analyses.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new method for detecting mixedMycobacterium tuberculosisinfection and reconstructing constituent strains provides insights into transmission

Sobkowiak,

Cudahy,

Chitwood

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To mimic the benchmark dataset used to evaluate QuantTB, we generated two standard datasets (ART-TBmix1 and ART-TBmix2) as per the simulation procedure described in [14][12] using ART simulator [33]. Both ART-TBmix1 and ART-TBmix2 consists of 800 samples at four distinct levels of coverage: 10x, 20x, 90x-10x, and 70x-30x, each with 200 samples.…”

Section: Methodsmentioning

confidence: 99%

“…Although this method is simple and does not require any overhead of keeping track of the new strains, it reveals only the strains’ proportions and not their identity (mutational profile), and also cannot robustly handle cases when strains are present in equal proportions. SplitStrains [14] is a new statistical-based method that aligns with MixInfect due to its non-reliance on the reference database. The procedure uses a likelihood ratio test to resolve the heterogeneity of the samples, followed by the assignment of reads to a strain using the Expectation-Maximization (EM) algorithm and Naive Bayes classifier, in order to finally report the strain proportions.…”

Section: Introductionmentioning

confidence: 99%

Demixer: A probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample

Brintha,

Narayanan

2024

Preprint

View full text Add to dashboard Cite

Multi-drug resistant or hetero-resistant Tuberculosis (TB) hinders the successful treatment of TB. Hetero-resistant TB occurs when multiple strains of the TB-causing bacterium with varying degrees of drug susceptibility are present in an individual. Existing studies predicting the proportion and identity of strains in a mixed infection sample rely on a reference database of known strains. A main challenge then is to identify de novo strains not present in the reference database, while quantifying the proportion of known strains. We present Demixer, a probabilistic generative model that uses a combination of reference-based and reference-free techniques to delineate mixed infection strains in whole genome sequencing (WGS) data. Demixer extends a topic model widely used in text mining to represent known mutations and discover novel ones. Parallelization and other heuristics enabled Demixer to process large datasets like CRyPTIC (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium). In both synthetic and experimental benchmark datasets, our proposed method precisely detected the identity (e.g., 91.67% accuracy on the experimental in vitro dataset) as well as the proportions of the mixed strains. In real-world applications, Demixer revealed novel high confidence mixed infections (101 out of 1,963 Malawi samples analyzed), and new insights into the global frequency of mixed infection (2% at the most stringent threshold in the CRyPTIC dataset) and its significant association to drug resistance. Our approach is generalizable and hence applicable to any bacterial and viral WGS data.

show abstract

“…This technique can be applied for genotypic and phenotypic characterization of organisms and profiling of drug susceptibility [82,83] including detecting mutations in new drugs such as bedaquiline and delamanid [84]. The performance of WGS technology is further enhanced by incorporating a novel method 'SplitStrains' which helps to analyze WGS data of patients having mixed infections [85]. Due to high cost, the need of robust technologies, and technical expertise, initially, there was limited WGS utility in low-income countries [86].…”

Section: Whole Genome Sequencing (Wgs)mentioning

confidence: 99%

Tuberculosis Diagnosis: Updates and Challenges

Shivakumar¹,

Shettigar²

2023

Bacterial Infectious Diseases Annual Volume 2023

View full text Add to dashboard Cite

Tuberculosis (TB) is caused by a single infectious agent, Mycobacterium tuberculosis, and a public health concern due to increased cases of drug-resistance and high mortality rates. Rapid identification of tuberculosis is necessary for its early treatment and to prevent the emergence of drug-resistant strains. For effective management of patients, rapid, cost-effective, and point-of-care (POC) diagnostic methods are required. The commonly used screening and identification methods are clinical examination, radiography, sputum smear microscopy, culture method, serological method, and tuberculin skin test. In addition, several molecular methods such as NAAT based GeneXpert, loop-mediated isothermal amplification (LAMP), line probe assay (LPA), whole genome sequencing (WGS) and other non-invasive methods of lateral flow urine lipoarabinomannan assay (LF-LAM) and eNose assays are developed. Sputum smear microscopy, Xpert MTB/RIF, and LED-Fluorescence microscopy (LED-FM) are the preferred methods to use in peripheral laboratories. The non-invasive methods of tuberculosis diagnosis are more beneficial in patients from whom collecting sputum sample is difficult particularly in children and HIV co-infected patients. Molecular methods can simultaneously identify the pathogen, M. tuberculosis, and mutations in drug-resistance genes. Even though, many advanced methods are currently available, accurate and affordable diagnostic method for tuberculosis is still challenging. Here, we review and highlight the uses and challenges of currently available conventional and advanced diagnostic methods of tuberculosis screening and diagnosis.

show abstract

SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

Cited by 14 publications

References 39 publications

A new method for detecting mixedMycobacterium tuberculosisinfection and reconstructing constituent strains provides insights into transmission

A new method for detecting mixedMycobacterium tuberculosisinfection and reconstructing constituent strains provides insights into transmission

Demixer: A probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample

Tuberculosis Diagnosis: Updates and Challenges

Contact Info

Product

Resources

About