ICGC Benchmarking Exercise
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ABSTRACT:
PROVIDER: EGAS00001000433 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The main objective of this benchmark is the comparison of the full sequencing pipeline of different ICGC partners, including procedures, methods and performance of library preparation and whole-genome deep-sequencing. A secondary objective will be a follow-up comparison of data analysis pipelines for identification of germline and somatic variants subsequent to the results of the ICGC Somatic Variant Calling Pipeline Benchmark.
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Project description:Spatial transcriptomics technologies that can quantify gene expression in space are transforming contemporary biology research. Some of such methods use spatially barcoded bead arrays that are optically sequenced by a microscopy setup to detect bead barcodes in space which can be consecutively matched to cell barcodes from the respective single cell sequencing experiment. To have good quality barcodes and a high number of barcode matches in space, robust and efficient computational pipelines are needed to process raw microscopy images and call the bases of bead barcodes accurately. Here, we present Optocoder, a computational pipeline that takes raw optical sequencing microscopy images as input and outputs bead barcodes in space. Optocoder efficiently aligns images, detects beads, and corrects for confounding factors of the fluorescence signal such as crosstalk and phasing before base calling. Furthermore, we implement a machine learning pipeline that is trained using the signal from the beads that match to illumina barcodes in order to predict non-matching bead barcodes which can boost up the number of barcode matches. We benchmark Optocoder using data from an in-house spatial transcriptomics platform as well as data from the Slide-seq method and we show that it can efficiently process both datasets with minimal modification.
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Project description:Strand-specific massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline for the comparison of library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in complexity, strand-specificity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in any organism. Examination of 11 different strand-specific RNA-Seq libraries from 7 distinct methods; also 2 control non-strand-specific RNA-Seq libraries. To assess the performance of each strand-specific library in digital expression profiling, we compared them to reference expression measurements estimated from expression profiles using competitive hybridization of a mid-log RNA sample vs. genomic DNA using Agilent arrays.
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Project description:Abstract The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina Human Methylation 450k BeadChip is widely used to quantify DNA methylation, nevertheless the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate datasets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished datasets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that a) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; b) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); c) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive dataset suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450k arrays. DNA samples from peripheral blood or CD14+ monocytes were included in the study. DNA methylation levels were profiled using Illumina 450K arrays. Specifically, 50 biological sample replicates from PB and 36 biological sample replicates from monocytes were randomly assigned to 8 BeadChips with technical replicates and processed in one run (a total of 96 DNA samples). Eight samples were technically replicated in pairs, while one sample was represented in a trio of replicates. Different analysis pipelines were compared, however, the file uploaded refers to the best scored. In our publication we used this one to make all analyses and conclusions.
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