@Article{Ukkonen1985Finding,
 Title                    = {Finding Approximate Patterns in Strings},
 Author                   = {Ukkonen, Esko},
 Journal                  = {Journal of Algorithms},
 Year                     = {1985},
 Number                   = {1},
 Pages                    = {132--137},
 Volume                   = {6},
 Bibsource                = {DBLP, http://dblp.uni-trier.de},
 Ee                       = {http://dx.doi.org/10.1016/0196-6774(85)90023-9},
}

@PhdThesis{Martin2013Algorithms,
 Title                    = {Algorithms and Tools for the Analysis of High-Throughput DNA Sequencing Data},
 Author                   = {Martin, Marcel},
 School                   = {TU Dortmund},
 Year                     = {2013},
 Timestamp                = {2014.02.09},
 Url                      = {http://hdl.handle.net/2003/31824}
}

@Book{Gusfield1997Algorithms,
 Title                    = {Algorithms on Strings, Trees and Sequences},
 Author                   = {Gusfield, Dan},
 Publisher                = {Cambridge University Press},
 Year                     = {1997},
 Month                    = may,
 ISBN                     = {0521585198},
 Shorttitle               = {Algorithms on Strings, Trees and Sequences}
}

@Article{Boettiger2015Docker,
 Title                    = {An introduction to Docker for reproducible research, with examples from the R environment},
 Author                   = {Boettiger, Carl},
 Journal                  = {ACM SIGOPS Operating Systems Review},
 Year                     = {2015},
 Number                   = {1},
 Volume                   = {49},
 Url                      = {http://dx.doi.org/10.1145/2723872.2723882}
}

@Misc{Kurtzer2016Singularity,
 Author   = {Kurtzer, Gregory M},
 Title    = {Singularity Linux application and environment containers for science},
 Url      = {http://dx.doi.org/10.5281/zenodo.60736},
 Version  = {2.1.2},
 Year     = {2016}
}

@Misc{Andrews2010FastQC,
 Author   = {Andrews, Simon},
 Title    = {{FastQC: a quality control tool for high throughput sequence data}},
 Url      = {http://www.bioinformatics.babraham.ac.uk/projects/fastqc},
 Version  = {0.11.5},
 Year     = {2010}
}

@Article{Ewels2016MultiQC,
 Title                    = {{MultiQC: Summarize analysis results for multiple tools and samples in a single report}},
 Author                   = {Ewels, Philip and Magnusson, Måns and Lundin, Sverker and Käller, Max},
 Journal                  = {Bioinformatics},
 Year                     = {2016},
 Number                   = {19},
 Volume                   = {32},
 Pages                    = {3047--3048},
 Url                      = {http://dx.doi.org/10.1093/bioinformatics/btw354}
}

@Article{DiTommaso2017Nextflow,
 Title                    = {Nextflow enables reproducible computational workflows},
 Author                   = {Di Tommaso, Paolo and Chatzou, Maria and Floden, Evan W and Barja, Pablo Prieto and Palumbo, Emilio and Notredame, Cedric},
 Journal			      = {Nat Biotech},
 Year					  = {2017},
 Number                   = {4},
 Volume                   = {35},
 Pages                    = {316--319},
 Url		              = {http://dx.doi.org/10.1038/nbt.3820}
}

@article{wang_estimation_2012,
	title = {Estimation of sequencing error rates in short reads},
	volume = {13},
	issn = {1471-2105},
	url = {http://dx.doi.org/10.1186/1471-2105-13-185},
	doi = {10.1186/1471-2105-13-185},
	abstract = {Short-read data from next-generation sequencing technologies are now being generated across a range of research projects. The fidelity of this data can be affected by several factors and it is important to have simple and reliable approaches for monitoring it at the level of individual experiments.},
	journal = {BMC Bioinformatics},
	author = {Victoria Wang, Xin and Blades, Natalie and Ding, Jie and Sultana, Razvan and Parmigiani, Giovanni},
	month = jul,
	year = {2012},
	pages = {185}
}

@article{schubert_adapterremoval_2016,
	title = {{AdapterRemoval} v2: rapid adapter trimming, identification, and read merging},
	volume = {9},
	issn = {1756-0500},
	shorttitle = {{AdapterRemoval} v2},
	doi = {10.1186/s13104-016-1900-2},
	language = {eng},
	journal = {BMC research notes},
	author = {Schubert, Mikkel and Lindgreen, Stinus and Orlando, Ludovic},
	month = feb,
	year = {2016},
	pmid = {26868221},
	pmcid = {PMC4751634},
	keywords = {Algorithms, Base Sequence, High-Throughput Nucleotide Sequencing},
	pages = {88}
}

@article{del_fabbro_extensive_2013,
	title = {An extensive evaluation of read trimming effects on {Illumina} {NGS} data analysis.},
	volume = {8},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871669/},
	doi = {10.1371/journal.pone.0085024},
	abstract = {Next Generation Sequencing is having an extremely strong impact in biological and medical research and diagnostics, with applications ranging from gene expression quantification to genotyping and genome reconstruction. Sequencing data is often provided as raw reads which are processed prior to analysis 1 of the most used preprocessing procedures is read trimming, which aims at removing low quality portions while preserving the longest high quality part of a NGS read. In the current work, we evaluate nine different trimming algorithms in four datasets and three common NGS-based applications (RNA-Seq, SNP calling and genome assembly). Trimming is shown to increase the quality and reliability of the analysis, with concurrent gains in terms of execution time and computational resources needed.},
	language = {English},
	number = {12},
	journal = {PLoS One},
	author = {Del Fabbro, Cristian and Scalabrin, Simone and Morgante, Michele and Giorgi, Federico M},
	month = jan,
	year = {2013},
	pmid = {24376861},
	keywords = {QualityControl, Sequencing},
	pages = {e85024--None}
}

@article{sturm_seqpurge:_2016,
	title = {{SeqPurge}: highly-sensitive adapter trimming for paired-end {NGS} data},
	volume = {17},
	issn = {1471-2105},
	shorttitle = {{SeqPurge}},
	url = {http://dx.doi.org/10.1186/s12859-016-1069-7},
	doi = {10.1186/s12859-016-1069-7},
	abstract = {Trimming of adapter sequences from short read data is a common preprocessing step during NGS data analysis. When performing paired-end sequencing, the overlap between forward and reverse read can be used to identify excess adapter sequences. This is exploited by several previously published adapter trimming tools. However, our evaluation on amplicon-based data shows that most of the current tools are not able to remove all adapter sequences and that adapter contamination may even lead to spurious variant calls.},
	urldate = {2016-08-19TZ},
	journal = {BMC Bioinformatics},
	author = {Sturm, Marc and Schroeder, Christopher and Bauer, Peter},
	year = {2016},
	pages = {208}
}

@article{bock_analysing_2012,
	title = {Analysing and interpreting {DNA} methylation data},
	volume = {13},
	copyright = {© 2012 Nature Publishing Group},
	issn = {1471-0056},
	url = {http://www.nature.com/nrg/journal/v13/n10/full/nrg3273.html},
	doi = {10.1038/nrg3273},
	abstract = {DNA methylation is an epigenetic mark that has suspected regulatory roles in a broad range of biological processes and diseases. The technology is now available for studying DNA methylation genome-wide, at a high resolution and in a large number of samples. This Review discusses relevant concepts, computational methods and software tools for analysing and interpreting DNA methylation data. It focuses not only on the bioinformatic challenges of large epigenome-mapping projects and epigenome-wide association studies but also highlights software tools that make genome-wide DNA methylation mapping more accessible for laboratories with limited bioinformatics experience.},
	language = {en},
	number = {10},
	urldate = {2016-08-19TZ},
	journal = {Nature Reviews Genetics},
	author = {Bock, Christoph},
	month = oct,
	year = {2012},
	pages = {705--719}
}

@article{martin_cutadapt_2011,
	title = {Cutadapt removes adapter sequences from high-throughput sequencing reads},
	volume = {17},
	copyright = {Authors who publish with this journal agree to the following terms:  Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a  Creative Commons Attribution License  that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.  Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.  Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See  The Effect of Open Access ).},
	issn = {2226-6089},
	url = {http://journal.embnet.org/index.php/embnetjournal/article/view/200},
	language = {en},
	number = {1},
	urldate = {2016-04-11TZ},
	journal = {EMBnet.journal},
	author = {Martin, Marcel},
	month = may,
	year = {2011},
	keywords = {Next generation sequencing, adapter removal, microRNA, small RNA},
	pages = {pp. 10--12}
}

@article{jiang_skewer:_2014,
	title = {Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads.},
	volume = {15},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4074385/},
	doi = {10.1186/1471-2105-15-182},
	language = {English},
	journal = {BMC Bioinformatics},
	author = {Jiang, Hongshan and Lei, Rong and Ding, Shou-Wei and Zhu, Shuifang},
	month = jan,
	year = {2014},
	pmid = {24925680},
	keywords = {QualityControl, Sequencing},
	pages = {182--None}
}

@article{dohm_substantial_2008,
	title = {Substantial biases in ultra-short read data sets from high-throughput {DNA} sequencing},
	volume = {36},
	issn = {0305-1048, 1362-4962},
	url = {http://nar.oxfordjournals.org/content/36/16/e105},
	doi = {10.1093/nar/gkn425},
	abstract = {Novel sequencing technologies permit the rapid production of large sequence data sets. These technologies are likely to revolutionize genetics and biomedical research, but a thorough characterization of the ultra-short read output is necessary. We generated and analyzed two Illumina 1G ultra-short read data sets, i.e. 2.8 million 27mer reads from a Beta vulgaris genomic clone and 12.3 million 36mers from the Helicobacter acinonychis genome. We found that error rates range from 0.3\% at the beginning of reads to 3.8\% at the end of reads. Wrong base calls are frequently preceded by base G. Base substitution error frequencies vary by 10- to 11-fold, with A {\textgreater} C transversion being among the most frequent and C {\textgreater} G transversions among the least frequent substitution errors. Insertions and deletions of single bases occur at very low rates. When simulating re-sequencing we found a 20-fold sequencing coverage to be sufficient to compensate errors by correct reads. The read coverage of the sequenced regions is biased; the highest read density was found in intervals with elevated GC content. High Solexa quality scores are over-optimistic and low scores underestimate the data quality. Our results show different types of biases and ways to detect them. Such biases have implications on the use and interpretation of Solexa data, for de novo sequencing, re-sequencing, the identification of single nucleotide polymorphisms and DNA methylation sites, as well as for transcriptome analysis.},
	language = {en},
	number = {16},
	urldate = {2016-11-30TZ},
	journal = {Nucleic Acids Research},
	author = {Dohm, Juliane C. and Lottaz, Claudio and Borodina, Tatiana and Himmelbauer, Heinz},
	month = sep,
	year = {2008},
	pmid = {18660515},
	pages = {e105--e105}
}

@article{depristo_framework_2011,
	title = {A framework for variation discovery and genotyping using next-generation {DNA} sequencing data},
	volume = {43},
	copyright = {© 2011 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1061-4036},
	url = {http://www.nature.com/ng/journal/v43/n5/full/ng.806.html},
	doi = {10.1038/ng.806},
	abstract = {Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass ({\textasciitilde}4×) 1000 Genomes Project datasets.},
	language = {en},
	number = {5},
	urldate = {2015-09-25TZ},
	journal = {Nature Genetics},
	author = {DePristo, Mark A. and Banks, Eric and Poplin, Ryan and Garimella, Kiran V. and Maguire, Jared R. and Hartl, Christopher and Philippakis, Anthony A. and del Angel, Guillermo and Rivas, Manuel A. and Hanna, Matt and McKenna, Aaron and Fennell, Tim J. and Kernytsky, Andrew M. and Sivachenko, Andrey Y. and Cibulskis, Kristian and Gabriel, Stacey B. and Altshuler, David and Daly, Mark J.},
	month = may,
	year = {2011},
	pages = {491--498}
}

@article{huang_art:_2012,
	title = {{ART}: a next-generation sequencing read simulator},
	volume = {28},
	issn = {1367-4803, 1460-2059},
	shorttitle = {{ART}},
	url = {http://bioinformatics.oxfordjournals.org/content/28/4/593},
	doi = {10.1093/bioinformatics/btr708},
	language = {en},
	number = {4},
	urldate = {2016-08-19TZ},
	journal = {Bioinformatics},
	author = {Huang, Weichun and Li, Leping and Myers, Jason R. and Marth, Gabor T.},
	month = feb,
	year = {2012},
	pmid = {22199392},
	pages = {593--594}
}

@article{encode_project_consortium_integrated_2012,
	title = {An integrated encyclopedia of {DNA} elements in the human genome},
	volume = {489},
	issn = {1476-4687},
	doi = {10.1038/nature11247},
	abstract = {The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80\% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.},
	language = {eng},
	number = {7414},
	journal = {Nature},
	author = {{ENCODE Project Consortium}},
	month = sep,
	year = {2012},
	pmid = {22955616},
	pmcid = {PMC3439153},
	keywords = {Alleles, Animals, Binding Sites, Chromatin, Chromatin Immunoprecipitation, Chromosomes, Human, DNA, DNA Footprinting, DNA methylation, DNA-Binding Proteins, Deoxyribonuclease I, Encyclopedias as Topic, Exons, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Genome-Wide Association Study, Histones, Humans, Mammals, Molecular Sequence Annotation, Neoplasms, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Proteins, Regulatory Sequences, Nucleic Acid, Sequence Analysis, RNA, Transcription Factors, Transcription, Genetic, genomics},
	pages = {57--74}
}

@article{dobin_star:_2013,
	title = {{STAR}: ultrafast universal {RNA}-seq aligner},
	volume = {29},
	issn = {1367-4803, 1460-2059},
	shorttitle = {{STAR}},
	url = {http://bioinformatics.oxfordjournals.org/content/29/1/15},
	doi = {10.1093/bioinformatics/bts635},
	language = {en},
	number = {1},
	urldate = {2016-11-30TZ},
	journal = {Bioinformatics},
	author = {Dobin, Alexander and Davis, Carrie A. and Schlesinger, Felix and Drenkow, Jorg and Zaleski, Chris and Jha, Sonali and Batut, Philippe and Chaisson, Mark and Gingeras, Thomas R.},
	month = jan,
	year = {2013},
	pmid = {23104886},
	pages = {15--21}
}

@article{pedersen_fast_2014,
	title = {Fast and accurate alignment of long bisulfite-seq reads},
	url = {http://arxiv.org/abs/1401.1129},
	abstract = {Summary: Longer sequencing reads, with at least 200 bases per template are now common. While traditional aligners have adopted new strategies to improve the mapping of longer reads, aligners specific to bisulfite-sequencing were optimized when much shorter reads were the norm. We sought to perform the first comparison using longer reads to determine which aligners were most accurate and efficient and to evaluate a novel software tool, bwa-meth, built on a traditional mapper that supports insertions, deletions and clipped alignments. We gauge accuracy by comparing the number of on and off-target reads from a targeted sequencing project and by simulations. Availability and Implementation: The benchmarking scripts and the bwa-meth software are available at https://github/com/brentp/bwa-meth/ under the MIT License.},
	urldate = {2016-08-26TZ},
	journal = {arXiv:1401.1129 [q-bio]},
	author = {Pedersen, Brent S. and Eyring, Kenneth and De, Subhajyoti and Yang, Ivana V. and Schwartz, David A.},
	month = jan,
	year = {2014},
	note = {arXiv: 1401.1129},
	keywords = {Quantitative Biology - Genomics}
}

@article{harrow_gencode:_2012,
	title = {{GENCODE}: the reference human genome annotation for {The} {ENCODE} {Project}.},
	volume = {22},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3431492/},
	doi = {10.1101/gr.135350.111},
	abstract = {The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35\% of transcriptional start sites are supported by CAGE clusters and 62\% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.},
	language = {English},
	number = {9},
	journal = {Genome research},
	author = {Harrow, Jennifer and Frankish, Adam and Gonzalez, Jose M and Tapanari, Electra and Diekhans, Mark and Kokocinski, Felix and Aken, Bronwen L and Barrell, Daniel and Zadissa, Amonida and Searle, Stephen and Barnes, If and Bignell, Alexandra and Boychenko, Veronika and Hunt, Toby and Kay, Mike and Mukherjee, Gaurab and Rajan, Jeena and Despacio-Reyes, Gloria and Saunders, Gary and Steward, Charles and Harte, Rachel and Lin, Michael and Howald, Cédric and Tanzer, Andrea and Derrien, Thomas and Chrast, Jacqueline and Walters, Nathalie and Balasubramanian, Suganthi and Pei, Baikang and Tress, Michael and Rodriguez, Jose Manuel and Ezkurdia, Iakes and van Baren, Jeltje and Brent, Michael and Haussler, David and Kellis, Manolis and Valencia, Alfonso and Reymond, Alexandre and Gerstein, Mark and Guigó, Roderic and Hubbard, Tim J},
	month = sep,
	year = {2012},
	pmid = {22955987},
	keywords = {Annotation},
	pages = {1760--1774}
}


@article{li_sequence_2009,
	title = {The {Sequence} {Alignment}/{Map} format and {SAMtools}},
	volume = {25},
	issn = {1367-4803},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2723002/},
	doi = {10.1093/bioinformatics/btp352},
	abstract = {Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments., Availability: http://samtools.sourceforge.net, Contact: rd@sanger.ac.uk},
	number = {16},
	journal = {Bioinformatics},
	author = {Li, Heng and Handsaker, Bob and Wysoker, Alec and Fennell, Tim and Ruan, Jue and Homer, Nils and Marth, Gabor and Abecasis, Goncalo and Durbin, Richard},
	month = aug,
	year = {2009},
	pmid = {19505943},
	pmcid = {PMC2723002},
	pages = {2078--2079}
}

@article{li_aligning_2013,
	title = {Aligning sequence reads, clone sequences and assembly contigs with {BWA}-{MEM}},
	url = {http://arxiv.org/abs/1303.3997},
	abstract = {Summary: BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For mapping 100bp sequences, BWA-MEM shows better performance than several state-of-art read aligners to date. Availability and implementation: BWA-MEM is implemented as a component of BWA, which is available at http://github.com/lh3/bwa. Contact: hengli@broadinstitute.org},
	urldate = {2016-08-26TZ},
	journal = {arXiv:1303.3997 [q-bio]},
	author = {Li, Heng},
	month = mar,
	year = {2013},
	note = {arXiv: 1303.3997},
	keywords = {Quantitative Biology - Genomics}
}

@article{neph_bedops:_2012,
	title = {{BEDOPS}: high-performance genomic feature operations},
	volume = {28},
	issn = {1367-4803},
	shorttitle = {{BEDOPS}},
	url = {https://academic.oup.com/bioinformatics/article/28/14/1919/218826/BEDOPS-high-performance-genomic-feature-operations},
	doi = {10.1093/bioinformatics/bts277},
	number = {14},
	urldate = {2017-07-10TZ},
	journal = {Bioinformatics},
	author = {Neph, Shane and Kuehn, M. Scott and Reynolds, Alex P. and Haugen, Eric and Thurman, Robert E. and Johnson, Audra K. and Rynes, Eric and Maurano, Matthew T. and Vierstra, Jeff and Thomas, Sean and Sandstrom, Richard and Humbert, Richard and Stamatoyannopoulos, John A.},
	month = jul,
	year = {2012},
	pages = {1919--1920}
}
