Abstract
Coverage analysis is essential when analysing massive parallel sequencing (MPS) data. The analysis indicates existence of false negatives or positives in a region of interest or poorly covered genomic regions. There are several tools that have excellent performance when doing coverage analysis on a few samples with predefined regions. However, there is no current tool for collecting samples over a longer period of time for aggregated coverage analysis of multiple samples or sequencing methods. Furthermore, current coverage analysis tools do not generate customized coverage reports or enable exploratory coverage analysis without extensive bioinformatic skill and access to the original alignment files. We present Chanjo, a user friendly coverage analysis tool for persistent storage of coverage data, that, accompanied with Chanjo Report, produces coverage reports that summarize coverage data for predefined regions in an elegant manner. Chanjo Report can produce both structured coverage reports and dynamic reports tailored to a subset of genomic regions, coverage cut-offs or samples. Chanjo stores data in an SQL database where thousands of samples can be added over time, which allows for aggregate queries to discover problematic regions. Chanjo is well tested, supports whole exome and genome sequencing, and follows common UNIX standards, allowing for easy integration into existing pipelines. Chanjo is easy to install and operate, and provides a solution for persistent coverage analysis and clinical-grade reporting. It makes it easy to set up a local database and automate the addition of multiple samples and report generation. To our knowledge there is no other tool with matching capabilities. Chanjo handles the common file formats in genetics, such as BED and BAM, and makes it easy to produce PDF coverage reports that are highly valuable for individuals with limited bioinformatic expertise. We believe Chanjo to be a vital tool for clinicians and researchers performing MPS analysis.
Highlights
Compared to extensive serial Sanger sequencing, exome sequencing can be done at a small fraction of the cost per sample and whole exome sequencing (WES) has been more or less established in the clinic for a few years[1]
For the first time it is possible to analyze complete human genomes within reasonable time and cost. This will further increase the pace of implementation of massively parallel sequencing (MPS) in new areas, such as diagnostics of inherited genetic disease
We have developed Chanjo, a fast and flexible toolkit for seamless coverage analysis of genomic and biological features across multiple samples
Summary
Compared to extensive serial Sanger sequencing, exome sequencing can be done at a small fraction of the cost per sample (the same order of magnitude as one average-sized gene) and whole exome sequencing (WES) has been more or less established in the clinic for a few years[1]. For the first time it is possible to analyze complete human genomes within reasonable time and cost This will further increase the pace of implementation of massively parallel sequencing (MPS) in new areas, such as diagnostics of inherited genetic disease. When analyzing the enormous data volume from WES and WGS, it is important to identify underrepresented genomic regions by calculating and tracking coverage quality control metrics. There is no present solution for persistent storage of coverage data that makes comparisons of hundreds or thousands of samples possible This is essential to locate genomic regions that are hard to sequence and where the local sequencing pipeline gives insufficient information. To our knowledge there are no tools that support dynamic report generation To address these needs, we have developed Chanjo, a fast and flexible toolkit for seamless coverage analysis of genomic and biological features across multiple samples. We believe Chanjo to be a vital tool for clinicians and researchers performing MPS analysis
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