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

Hrant Hovhannisyan; Ahmed Hafez; Carlos Llorens; Toni Gabaldón

doi:10.1093/bioinformatics/btz626

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.

Authors

Hrant Hovhannisyan^{1

2}, Ahmed Hafez^{2

3

4}, Carlos Llorens³, Toni Gabaldón^{1

2

5}

Affiliations

¹ Centre for Genomic Regulation, Department of Bioinformatics and Genomics, The Barcelona Institute of Science and Technology, Barcelona, Spain.
² Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
³ Biotechvana S.L., Parc Científic Universitat de València, Valencia, Spain.
⁴ Faculty of Computers and Information, Minia University, Minia, Egypt.
⁵ Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genomics
High-Throughput Nucleotide Sequencing*
Research Design*
Sequence Analysis, DNA
Software