Key result
Ribomap assigns reads to potential origins proportional to estimated transcript abundance, yielding a more accurate estimate of ribosome profiles compared with a naïve mapping.
Ribomap improves the accuracy of ribosome profiling by addressing the multi-mapping problem using transcript abundance estimates.
May refine ribosome profiling in research; leaves open clinical cardiovascular adoption pending validation.
UNLABELLED: : Ribosome profiling is a recently developed high-throughput sequencing technique that captures approximately 30 bp long ribosome-protected mRNA fragments during translation. Because of alternative splicing and repetitive sequences, a ribosome-protected read may map to many places in the transcriptome, leading to discarded or arbitrary mappings when standard approaches are used. We present a technique and software that addresses this problem by assigning reads to potential origins proportional to estimated transcript abundance. This yields a more accurate estimate of ribosome profiles compared with a naïve mapping. AVAILABILITY AND IMPLEMENTATION: Ribomap is available as open source at http://www.cs.cmu.edu/∼ckingsf/software/ribomap CONTACT: carlk@cs.cmu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Wang et al. (2016) studied this question. Ribomap vs. naïve mapping was evaluated on estimate of ribosome profiles. Ribomap assigns reads to potential origins proportional to estimated transcript abundance, yielding a more accurate estimate of ribosome profiles compared with a naïve mapping.
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