Dataset Information

Benchmark study comparing liftover tools for genome conversion of epigenome sequencing data.

ABSTRACT: As reference genome assemblies are updated there is a need to convert epigenome sequence data from older genome assemblies to newer versions, to facilitate data integration and visualization on the same coordinate system. Conversion can be done by re-alignment of the original sequence data to the new assembly or by converting the coordinates of the data between assemblies using a mapping file, an approach referred to as 'liftover'. Compared to re-alignment approaches, liftover is a more rapid and cost-effective solution. Here, we benchmark six liftover tools commonly used for conversion between genome assemblies by coordinates, including UCSC liftOver, rtracklayer::liftOver, CrossMap, NCBI Remap, flo and segment_liftover to determine how they performed for whole genome bisulphite sequencing (WGBS) and ChIP-seq data. Our results show high correlation between the six tools for conversion of 43 WGBS paired samples. For the chromatin sequencing data we found from interval conversion of 366 ChIP-Seq datasets, segment_liftover generates more reliable results than USCS liftOver. However, we found some regions do not always remain the same after liftover. To further increase the accuracy of liftover and avoid misleading results, we developed a three-step guideline that removes aberrant regions to ensure more robust genome conversion between reference assemblies.

SUBMITTER: Luu PL

PROVIDER: S-EPMC7671393 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Benchmark study comparing liftover tools for genome conversion of epigenome sequencing data.

Luu Phuc-Loi PL Ong Phuc-Thinh PT Dinh Thanh-Phuoc TP Clark Susan J SJ

NAR genomics and bioinformatics 20200806 3

As reference genome assemblies are updated there is a need to convert epigenome sequence data from older genome assemblies to newer versions, to facilitate data integration and visualization on the same coordinate system. Conversion can be done by re-alignment of the original sequence data to the new assembly or by converting the coordinates of the data between assemblies using a mapping file, an approach referred to as 'liftover'. Compared to re-alignment approaches, liftover is a more rapid an ...[more]

PMID: 33575605

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Project description:For the robust practice of genomic medicine, sequencing results must be compatible, regardless of the sequencing technologies and algorithms used. Presently, genome sequencing is still an imprecise science and is complicated by differences in the chemistry, coverage, alignment, and variant-calling algorithms. We identified ~3.33 million single nucleotide variants (SNVs) and ~3.62 million SNVs in the SJK genome using SOLiD and Illumina data, respectively. Approximately 3 million SNVs were concordant between the two platforms while 68,532 SNVs were discordant; 219,616 SNVs were SOLiD-specific and 516,080 SNVs were Illumina-specific (i.e., platform-specific). Concordant, discordant, and platform-specific SNVs were further analyzed and characterized. Overall, a large portion of heterozygous SNVs that were discordant with genotyping calls of single nucleotide polymorphism chips were highly confident. Approximately 70% of the platform-specific SNVs were located in regions containing repetitive sequences. Such platform-specificity may arise from differences between platforms, with regard to read length (36 bp and 72 bp vs. 50 bp), insert size (~100-300 bp vs. ~1-2 kb), sequencing chemistry (sequencing-by-synthesis using single nucleotides vs. ligation-based sequencing using oligomers), and sequencing quality. When data from the two platforms were merged for variant calling, the proportion of callable regions of the reference genome increased to 99.66%, which was 1.43% higher than the average callability of the two platforms, representing ~40 million bases. In this study, we compared the differences in sequencing results between two sequencing platforms. Approximately 90% of the SNVs were concordant between the two platforms, yet ~10% of the SNVs were either discordant or platform-specific, indicating that each platform had its own strengths and weaknesses. When data from the two platforms were merged, both the overall callability of the reference genome and the overall accuracy of the SNVs improved, demonstrating the likelihood that a re-sequenced genome can be revised using complementary data.

Dataset Information

Benchmark study comparing liftover tools for genome conversion of epigenome sequencing data.

Publications

Benchmark study comparing liftover tools for genome conversion of epigenome sequencing data.

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