CROSSMAPPER: estimating cross-mapping rates and optimizing experimental design in multi-species sequencing studies

Hovhannisyan, Hrant, 1992-; Hafez Khafaga, Ahmed Ibrahem, 1987-; Llorens, Carlos, 1968-; Gabaldón Estevan, Juan Antonio, 1973-

doi:http://dx.doi.org/10.1093/bioinformatics/btz626

CROSSMAPPER: estimating cross-mapping rates and optimizing experimental design in multi-species sequencing studies

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Hovhannisyan H, Hafez A, Llorens C, Gabaldón T. CROSSMAPPER: estimating cross-mapping rates and optimizing experimental design in multi-species sequencing studies. Bioinformatics. 2020 Feb 1; 36(3): 925-927. DOI: 10.1093/bioinformatics/btz626

Abstract

MOTIVATION: Numerous sequencing studies, including transcriptomics of host-pathogen systems, sequencing of hybrid genomes, xenografts, mixed species systems, metagenomics and meta-transcriptomics, involve samples containing genetic material from divergent organisms. A crucial step in these studies is identifying from which organism each sequencing read originated, and the experimental design should be directed to minimize biases caused by cross-mapping of reads to incorrect source genomes. Additionally, pooling of sufficiently different genetic material into a single sequencing library could significantly reduce experimental costs but requires careful planning and assessment of the impact of cross-mapping. Having these applications in mind we designed Crossmapper, the first to our knowledge tool able to assess cross-mapping prior to sequencing, therefore allowing optimization of experimental design. RESULTS: Using any combination of reference genomes, Crossmapper performs read simulation and back-mapping of those reads to the pool of references, quantifies and reports the cross-mapping rates for each organism. Crossmapper performs these analyses with numerous user-specified parameters, including, among others, read length, read layout, coverage, mapping parameters, genomic or transcriptomic data. Additionally, it outputs the results in highly interactive and publication-ready reports. This allows the user to perform multiple comparisons at once and choose the experimental setup minimizing cross-mapping rates. Moreover, Crossmapper can be used for resource optimization in sequencing facilities by pooling different samples into one sequencing library. AVAILABILITY AND IMPLEMENTATION: Crossmapper is a command line tool implemented in Python 3.6 and available as a conda package, allowing effortless installation. The source code, detailed information and a step-by-step tutorial is available at our GitHub page https://github.com/Gabaldonlab/crossmapper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.