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We have used the Apache Spark environment, where a single driver node runs the high-level driver program, which schedules tasks for multiple worker nodes. (D) Implementation details of SeQuiLa-cov. As a result, a set of non-overlapping coverage vectors are calculated, which is further integrated into the depth of coverage for the whole input data set. The coverage values from partial_coverage 1 for overlap 12 are removed from partial_coverage 1 and added to the head of partial_coverage 2. On the figure, 2 overlaps are shown, one of them situated between partial_coverage 1 and partial_coverage 2 (overlap 12 of length 4) encompassing positions chr3:101–104. When an overlap is identified, the corresponding coverage values from the preceding vector’s tail are cut and added to the head values of the subsequent vector. Because of the possibility of overlapping of ranges between 2 consecutive data slices, an additional correction step needs to be performed. The algorithm first calculates the partial events vector for available data slices and subsequently produces a corresponding partial partial_coverage vector. , slice n), each containing a subset of input aligned reads. Assuming that we run our calculations in a distributed environment, the computation nodes do not work on the whole input data set (table read_set) but on n smaller data partitions (slice 1, slice 2. (C) Concept of distributed version of events-based algorithm. For the purpose of this example, we assume that the BAM file for sample1 contains only reads from chr3. Therefore, it outputs a table as a result, allowing for customizing a query using Data Manipulation Language, e.g., in the SELECT or WHERE clause.
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bdg_coverage is implemented as a table-valued function. The presented call for coverage method takes sample identifier (sample1) and result type (blocks) as input parameters. The first statement creates a relational table read_set over compressed BAM files using the provided custom Data Source, whereas the second statement demonstrates the use of the bdg_coverage function to calculate depth of coverage for a specified sample. (B) Provided SQL API to interact with NGS data.
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The algorithm may produce 3 typically used coverage types: (i) per-base coverage, which includes the coverage value for each genomic position separately, (ii) blocks, which lists adjacent positions with equal coverage values merged into a single interval, and (iii) fixed-length windows coverage, which generates a set of equal-size, non-overlapping and tiling genomic ranges and outputs the arithmetic mean of base coverage values for each region. The depth of coverage for a genomic locus is calculated using the cumulative sum of all elements in the events vector preceding the specified position. Subsequently, it iterates the list of reads and increments/decrements by 1 the values of the events vector at the indexes corresponding to start/end positions of each read. Given a genomic chromosome and a set of aligned sequencing reads, the algorithm allocates “events” vector. (A) General concept of events-based algorithm for depth of coverage calculation. SeQuiLa-cov: functionality, algorithm, and implementation. Well-known, state-of-the-art solutions include samtools depth , bedtools genomecov , GATK DepthOfCoverage , sambamba, and mosdepth (see comparison presented in Table 1). Finally, depth of coverage is one of the most computationally intensive parts of differential expression analysis using RNA-sequencing data at single-base resolution .Ī number of tools supporting this operation have been developed, with 22 of them specified in the Omictools catalog . In other applications, the coverage is computed to assess the quality of the sequencing data (e.g., to calculate the percentage of genome with ≥30× read depth) or to identify genomic regions overlapped by an insufficient number of reads for reliable variant calling .
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In particular, copy number variant detection pipelines require obtaining sufficient read depth of the analyzed samples. The coverage calculation is a frequently performed but time-consuming step in the analysis of next-generation sequencing (NGS) data. Given a set of sequencing reads and a genomic contig, depth of coverage for a given position is defined as the total number of reads overlapping the locus.