PySNV for complex intra-host variation detection.

Liandong Li, Haoyi Fu, Wentai Ma, Mingkun Li
Author Information
  1. Liandong Li: Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China.
  2. Haoyi Fu: Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China. ORCID
  3. Wentai Ma: Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China.
  4. Mingkun Li: Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China. ORCID

Abstract

MOTIVATION: Intra-host variants refer to genetic variations or mutations that occur within an individual host organism. These variants are typically studied in the context of viruses, bacteria, or other pathogens to understand the evolution of pathogens. Moreover, intra-host variants are also explored in the field of tumor biology and mitochondrial biology to characterize somatic mutations and inherited heteroplasmic mutations. Intra-host variants can involve long insertions, deletions, and combinations of different mutation types, which poses challenges in their identification. The performance of current methods in detecting of complex intra-host variants is unknown.
RESULTS: First, we simulated a dataset comprising 10 samples with 1869 intra-host variants involving various mutation patterns and benchmarked current variant detection software. The results indicated that though current software can detect most variants with F1-scores between 0.76 and 0.97, their performance in detecting long indels and low frequency variants was limited. Thus, we developed a new software, PySNV, for the detection of complex intra-host variations. On the simulated dataset, PySNV successfully detected 1863 variant cases (F1-score: 0.99) and exhibited the highest Pearson correlation coefficient (PCC: 0.99) to the ground truth in predicting variant frequencies. The results demonstrated that PySNV delivered promising performance even for long indels and low frequency variants, while maintaining computational speed comparable to other methods. Finally, we tested its performance on SARS-CoV-2 replicate sequencing data and found that it reported 21% more variants compared to LoFreq, the best-performing benchmarked software, while showing higher consistency (62% over 54%) within replicates. The discrepancies mostly exist in low-depth regions and low frequency variants.
AVAILABILITY AND IMPLEMENTATION: https://github.com/bnuLyndon/PySNV/.

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Grants

  1. 82161148009/National Natural Science Foundation of China

MeSH Term

High-Throughput Nucleotide Sequencing
Software
Mutation
INDEL Mutation
Genetic Variation

Word Cloud

Created with Highcharts 10.0.0variantsintra-hostperformancesoftware0PySNVmutationslongcurrentcomplexvariantdetectionlowfrequencyIntra-hostvariationswithinpathogensbiologycanmutationmethodsdetectingsimulateddatasetbenchmarkedresultsindels99MOTIVATION:refergeneticoccurindividualhostorganismtypicallystudiedcontextvirusesbacteriaunderstandevolutionMoreoveralsoexploredfieldtumormitochondrialcharacterizesomaticinheritedheteroplasmicinvolveinsertionsdeletionscombinationsdifferenttypesposeschallengesidentificationunknownRESULTS:Firstcomprising10samples1869involvingvariouspatternsindicatedthoughdetectF1-scores7697limitedThusdevelopednewsuccessfullydetected1863casesF1-score:exhibitedhighestPearsoncorrelationcoefficientPCC:groundtruthpredictingfrequenciesdemonstrateddeliveredpromisingevenmaintainingcomputationalspeedcomparableFinallytestedSARS-CoV-2replicatesequencingdatafoundreported21%comparedLoFreqbest-performingshowinghigherconsistency62%54%replicatesdiscrepanciesmostlyexistlow-depthregionsAVAILABILITYANDIMPLEMENTATION:https://githubcom/bnuLyndon/PySNV/variation

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