• B. Kehr, D. Weese, and K. Reinert, “STELLAR: fast and exact local alignments,” Bmc bioinformatics, vol. 12, iss. Suppl 9, p. S15, 2011.
    [Bibtex]
    @article{Kehr:2011vo,
    author = {Kehr, B. and Weese, David and Reinert, Knut},
    title = {{STELLAR: fast and exact local alignments}},
    journal = {BMC Bioinformatics},
    year = {2011},
    volume = {12},
    number = {Suppl 9},
    pages = {S15},
    publisher = {BioMed Central Ltd},
    pmid = {22151882},
    pmcid = {PMC3283304},
    read = {Yes},
    rating = {0},
    date-added = {2011-12-01T08:31:26GMT},
    date-modified = {2016-01-14T20:14:14GMT},
    url = {http://www.biomedcentral.com/qc/1471-2105/12/S9/S15/},
    local-url = {file://localhost/Users/reinert/Dropbox/Library.papers3/Articles/2011/Kehr/BMC%20Bioinformatics%202011%20Kehr.pdf},
    file = {{BMC Bioinformatics 2011 Kehr.pdf:/Users/reinert/Dropbox/Library.papers3/Articles/2011/Kehr/BMC Bioinformatics 2011 Kehr.pdf:application/pdf}},
    uri = {url{papers3://publication/uuid/630CE1B9-6448-4291-920F-CE4C53F31751}}
    }
  • [DOI] M. Holtgrewe, A. Emde, D. Weese, and K. Reinert, “A Novel And Well-Defined Benchmarking Method For Second Generation Read Mapping,” Bmc bioinformatics, vol. 12, iss. 1, p. 210, 2011.
    [Bibtex]
    @article{Holtgrewe:2011fj,
    author = {Holtgrewe, Manuel and Emde, A and Weese, David and Reinert, Knut},
    title = {{A Novel And Well-Defined Benchmarking Method For Second Generation Read Mapping}},
    journal = {BMC Bioinformatics},
    year = {2011},
    volume = {12},
    number = {1},
    pages = {210},
    publisher = {BioMed Central Ltd},
    affiliation = {Department of Computer Science, Free University of Berlin, Takustr, Germany. holtgrewe@inf.fu-berlin.de},
    doi = {10.1186/1471-2105-12-210},
    pmid = {21615913},
    pmcid = {PMC3128034},
    language = {English},
    read = {Yes},
    rating = {0},
    date-added = {2011-06-08T20:42:08GMT},
    date-modified = {2016-01-14T20:14:11GMT},
    abstract = {Second generation sequencing technologies yield DNA sequence data at ultra high-throughput. Common to most biological applications is a mapping of the reads to an almost identical or highly similar reference genome. The assessment of the quality of read mapping results is not straightforward and has not been formalized so far. Hence, it has not been easy to compare different read mapping approaches in a unified way and to determine which program is the best for what task.We present a new benchmark method, called Rabema (Read Alignment BEnchMArk), for read mappers. It consists of a strict definition of the read mapping problem and of tools to evaluate the result of arbitrary read mappers supporting the SAM output format.We show the usefulness of the benchmark program by performing a comparison of popular read mappers. The tools supporting the benchmark are licensed under the GPL and available from http://www.seqan.de/projects/rabema.html.},
    url = {http://www.biomedcentral.com/1471-2105/12/210/abstract},
    local-url = {file://localhost/Users/reinert/Dropbox/Library.papers3/Articles/2011/Holtgrewe/BMC%20Bioinformatics%202011%20Holtgrewe.pdf},
    file = {{BMC Bioinformatics 2011 Holtgrewe.pdf:/Users/reinert/Dropbox/Library.papers3/Articles/2011/Holtgrewe/BMC Bioinformatics 2011 Holtgrewe.pdf:application/pdf;BMC Bioinformatics 2011 Holtgrewe.pdf:/Users/reinert/Dropbox/Library.papers3/Articles/2011/Holtgrewe/BMC Bioinformatics 2011 Holtgrewe.pdf:application/pdf}},
    uri = {url{papers3://publication/doi/10.1186/1471-2105-12-210}}
    }