Detection of low-frequency resistance-mediating SNPs in next-generation sequencing data of Mycobacterium tuberculosis complex strains with binoSNP
Accurate drug resistance detection is key for guiding effective tuberculosis treatment. While genotypic resistance can be rapidly detected by molecular methods, their application is challenged by mixed mycobacterial populations comprising both susceptible and resistant cells (heteroresistance). For this, next-generation sequencing (NGS) based approaches promise the determination of variants even at low frequencies. However, accurate methods for a valid detection of low-frequency variants in NGS data are currently lacking. To tackle this problem, we developed the variant detection tool binoSNP which allows the determination of low-frequency single nucleotide polymorphisms (SNPs) in NGS datasets from Mycobacterium tuberculosis complex (MTBC) strains. By taking a reference-mapped file as input, binoSNP evaluates each genomic position of interest using a binomial test procedure. binoSNP was validated using in-silico, in-vitro, and serial patient isolates datasets comprising varying genomic coverage depths (100-500×) and SNP allele frequencies (1-30%). Overall, the detection limit for low-frequency SNPs depends on the combination of coverage depth and allele frequency of the resistance-associated mutation. binoSNP allows for valid detection of resistance associated SNPs at a 1% frequency with a coverage ≥400×. In conclusion, binoSNP provides a valid approach to detect low-frequency resistance-mediating SNPs in NGS data from clinical MTBC strains. It can be implemented in automated, end-user friendly analysis tools for NGS data and is a step forward towards individualized TB therapy.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
Scientific reports - 10(2020), 1 vom: 12. Mai, Seite 7874 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Dreyer, Viola [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Antitubercular Agents |
|---|
| Anmerkungen: |
Date Completed 30.11.2020 Date Revised 12.05.2021 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1038/s41598-020-64708-8 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM309817676 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM309817676 | ||
| 003 | DE-627 | ||
| 005 | 20231226201426.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41598-020-64708-8 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1032.xml |
| 035 | |a (DE-627)NLM309817676 | ||
| 035 | |a (NLM)32398743 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Dreyer, Viola |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Detection of low-frequency resistance-mediating SNPs in next-generation sequencing data of Mycobacterium tuberculosis complex strains with binoSNP |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 30.11.2020 | ||
| 500 | |a Date Revised 12.05.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Accurate drug resistance detection is key for guiding effective tuberculosis treatment. While genotypic resistance can be rapidly detected by molecular methods, their application is challenged by mixed mycobacterial populations comprising both susceptible and resistant cells (heteroresistance). For this, next-generation sequencing (NGS) based approaches promise the determination of variants even at low frequencies. However, accurate methods for a valid detection of low-frequency variants in NGS data are currently lacking. To tackle this problem, we developed the variant detection tool binoSNP which allows the determination of low-frequency single nucleotide polymorphisms (SNPs) in NGS datasets from Mycobacterium tuberculosis complex (MTBC) strains. By taking a reference-mapped file as input, binoSNP evaluates each genomic position of interest using a binomial test procedure. binoSNP was validated using in-silico, in-vitro, and serial patient isolates datasets comprising varying genomic coverage depths (100-500×) and SNP allele frequencies (1-30%). Overall, the detection limit for low-frequency SNPs depends on the combination of coverage depth and allele frequency of the resistance-associated mutation. binoSNP allows for valid detection of resistance associated SNPs at a 1% frequency with a coverage ≥400×. In conclusion, binoSNP provides a valid approach to detect low-frequency resistance-mediating SNPs in NGS data from clinical MTBC strains. It can be implemented in automated, end-user friendly analysis tools for NGS data and is a step forward towards individualized TB therapy | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 7 | |a Antitubercular Agents |2 NLM | |
| 700 | 1 | |a Utpatel, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Kohl, Thomas A |e verfasserin |4 aut | |
| 700 | 1 | |a Barilar, Ivan |e verfasserin |4 aut | |
| 700 | 1 | |a Gröschel, Matthias I |e verfasserin |4 aut | |
| 700 | 1 | |a Feuerriegel, Silke |e verfasserin |4 aut | |
| 700 | 1 | |a Niemann, Stefan |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Scientific reports |d 2011 |g 10(2020), 1 vom: 12. Mai, Seite 7874 |w (DE-627)NLM215703936 |x 2045-2322 |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2020 |g number:1 |g day:12 |g month:05 |g pages:7874 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41598-020-64708-8 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2020 |e 1 |b 12 |c 05 |h 7874 | ||