
@article{moran15_hologenomeconcept,
	title = {The {Hologenome} {Concept}: {Helpful} or {Hollow}?},
	volume = {13},
	shorttitle = {The {Hologenome} {Concept}},
	url = {http://dx.doi.org/10.1371/journal.pbio.1002311},
	doi = {10.1371/journal.pbio.1002311},
	abstract = {It is accepted that microbial symbionts are important to the biology of their hosts, but this essay asks what evidence is needed to determine whether symbionts and hosts have coevolved and whether they form higher units of evolutionary selection.},
	number = {12},
	urldate = {2015-12-09},
	journal = {PLoS Biol},
	author = {Moran, Nancy A. and Sloan, Daniel B.},
	month = dec,
	year = {2015},
	pages = {e1002311},
	file = {moran15_hologenome__plos_biol__the_hologenome_concept.pdf:/home/kevin/work/bibliography/pdfs/moran15_hologenome__plos_biol__the_hologenome_concept.pdf:application/pdf},
}

@article{koster12_snakemakescalable,
	title = {Snakemake --- a scalable bioinformatics workflow engine},
	volume = {28},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/28/19/2520},
	doi = {10.1093/bioinformatics/bts480},
	abstract = {Summary: Snakemake is a workflow engine that provides a readable Python-based workflow definition language and a powerful execution environment that scales from single-core workstations to compute clusters without modifying the workflow. It is the first system to support the use of automatically inferred multiple named wildcards (or variables) in input and output filenames.
Availability: http://snakemake.googlecode.com.
Contact: johannes.koester@uni-due.de},
	language = {en},
	number = {19},
	urldate = {2016-03-30},
	journal = {Bioinformatics},
	author = {Köster, Johannes and Rahmann, Sven},
	month = jan,
	year = {2012},
	pmid = {22908215},
	pages = {2520--2522},
	file = {koster12_snakemake__bioinformatics__snakemake_---_a_scalable_bioinformatics_workflow_engine.pdf:/home/kevin/work/bibliography/pdfs/koster12_snakemake__bioinformatics__snakemake_---_a_scalable_bioinformatics_workflow_engine.pdf:application/pdf},
}

@article{schubert16_adapterremoval,
	title = {{AdapterRemoval} v2: rapid adapter trimming, identification, and read merging},
	volume = {9},
	issn = {1756-0500},
	shorttitle = {{AdapterRemoval} v2},
	url = {http://dx.doi.org/10.1186/s13104-016-1900-2},
	doi = {10.1186/s13104-016-1900-2},
	abstract = {As high-throughput sequencing platforms produce longer and longer reads, sequences generated from short inserts, such as those obtained from fossil and degraded material, are increasingly expected to contain adapter sequences. Efficient adapter trimming algorithms are also needed to process the growing amount of data generated per sequencing run.},
	urldate = {2016-06-07},
	journal = {BMC Research Notes},
	author = {Schubert, Mikkel and Lindgreen, Stinus and Orlando, Ludovic},
	year = {2016},
	keywords = {High-throughput sequencing, Data pre-processing, Adapter trimming, Adapter identification, Sequence Alignment},
	pages = {88},
	file = {schubert16_adapterremoval__bmc_research_notes__adapterremoval_v2.pdf:/home/kevin/work/bibliography/pdfs/schubert16_adapterremoval__bmc_research_notes__adapterremoval_v2.pdf:application/pdf},
}

@article{buchfink15_fastsensitive,
	title = {Fast and sensitive protein alignment using {DIAMOND}},
	volume = {12},
	copyright = {© 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1548-7091},
	url = {http://www.nature.com/nmeth/journal/v12/n1/full/nmeth.3176.html},
	doi = {10.1038/nmeth.3176},
	abstract = {The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.},
	language = {en},
	number = {1},
	urldate = {2016-07-13},
	journal = {Nature Methods},
	author = {Buchfink, Benjamin and Xie, Chao and Huson, Daniel H.},
	month = jan,
	year = {2015},
	keywords = {Software},
	pages = {59--60},
	file = {buchfink15_fast__nature_methods__fast_and_sensitive_protein_alignment_using_diamond.pdf:/home/kevin/work/bibliography/pdfs/buchfink15_fast__nature_methods__fast_and_sensitive_protein_alignment_using_diamond.pdf:application/pdf},
}

@article{ondov_mash:_2016,
	title = {Mash: fast genome and metagenome distance estimation using {MinHash}},
	volume = {17},
	issn = {1474-760X},
	shorttitle = {Mash},
	url = {http://dx.doi.org/10.1186/s13059-016-0997-x},
	doi = {10.1186/s13059-016-0997-x},
	abstract = {Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (                  https://github.com/marbl/mash                                  ).},
	urldate = {2016-08-30},
	journal = {Genome Biology},
	author = {Ondov, Brian D. and Treangen, Todd J. and Melsted, Páll and Mallonee, Adam B. and Bergman, Nicholas H. and Koren, Sergey and Phillippy, Adam M.},
	year = {2016},
	keywords = {metagenomics, Sequencing, comparative genomics, Alignment, Genomic distance, Nanopore},
	pages = {132},
	file = {ondov16_mash__genome_biology__mash.pdf:/home/kevin/work/bibliography/pdfs/ondov16_mash__genome_biology__mash.pdf:application/pdf},
}

@article{menzel16_fastsensitive,
	title = {Fast and sensitive taxonomic classification for metagenomics with {Kaiju}},
	volume = {7},
	issn = {2041-1723},
	url = {http://www.nature.com/doifinder/10.1038/ncomms11257},
	doi = {10.1038/ncomms11257},
	urldate = {2016-09-27},
	journal = {Nature Communications},
	author = {Menzel, Peter and Ng, Kim Lee and Krogh, Anders},
	month = apr,
	year = {2016},
	keywords = {Bioinformatics, Biological sciences, Genetics},
	pages = {11257},
	file = {menzel16_fast__nature_communications__fast_and_sensitive_taxonomic_classification_for_metagenomics_with_kaiju.pdf:/home/kevin/work/bibliography/pdfs/menzel16_fast__nature_communications__fast_and_sensitive_taxonomic_classification_for_metagenomics_with_kaiju.pdf:application/pdf},
}

@article{lindgreen12_adapterremoval,
	title = {{AdapterRemoval}: easy cleaning of next-generation sequencing reads},
	volume = {5},
	issn = {1756-0500},
	shorttitle = {{AdapterRemoval}},
	url = {http://dx.doi.org/10.1186/1756-0500-5-337},
	doi = {10.1186/1756-0500-5-337},
	abstract = {With the advent of next-generation sequencing there is an increased demand for tools to pre-process and handle the vast amounts of data generated. One recurring problem is adapter contamination in the reads, i.e. the partial or complete sequencing of adapter sequences. These adapter sequences have to be removed as they can hinder correct mapping of the reads and influence SNP calling and other downstream analyses.},
	urldate = {2017-02-12},
	journal = {BMC Research Notes},
	author = {Lindgreen, Stinus},
	year = {2012},
	pages = {337},
	file = {lindgreen12_adapterremoval__bmc_research_notes__adapterremoval.pdf:/home/kevin/work/bibliography/pdfs/lindgreen12_adapterremoval__bmc_research_notes__adapterremoval.pdf:application/pdf},
}

@article{li11_statisticalframework,
	title = {A statistical framework for {SNP} calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data},
	volume = {27},
	issn = {1367-4803},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3198575/},
	doi = {10.1093/bioinformatics/btr509},
	abstract = {Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty., Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors., Availability: http://samtools.sourceforge.net, Contact: hengli@broadinstitute.org},
	number = {21},
	urldate = {2017-05-30},
	journal = {Bioinformatics},
	author = {Li, Heng},
	month = nov,
	year = {2011},
	pmid = {21903627},
	pmcid = {PMC3198575},
	pages = {2987--2993},
	file = {li11_statistical__bioinformatics__a_statistical_framework_for_snp_calling,_mutation_discovery,_association.pdf:/home/kevin/work/bibliography/pdfs/li11_statistical__bioinformatics__a_statistical_framework_for_snp_calling,_mutation_discovery,_association.pdf:application/pdf},
}

@article{lu17_brackenestimating,
	title = {Bracken: estimating species abundance in metagenomics data},
	volume = {3},
	issn = {2376-5992},
	shorttitle = {Bracken},
	url = {https://peerj.com/articles/cs-104},
	doi = {10.7717/peerj-cs.104},
	abstract = {Metagenomic experiments attempt to characterize microbial communities using high-throughput DNA sequencing. Identification of the microorganisms in a sample provides information about the genetic profile, population structure, and role of microorganisms within an environment. Until recently, most metagenomics studies focused on high-level characterization at the level of phyla, or alternatively sequenced the 16S ribosomal RNA gene that is present in bacterial species. As the cost of sequencing has fallen, though, metagenomics experiments have increasingly used unbiased shotgun sequencing to capture all the organisms in a sample. This approach requires a method for estimating abundance directly from the raw read data. Here we describe a fast, accurate new method that computes the abundance at the species level using the reads collected in a metagenomics experiment. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. We demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.},
	language = {en},
	urldate = {2017-08-08},
	journal = {PeerJ Computer Science},
	author = {Lu, Jennifer and Breitwieser, Florian P. and Thielen, Peter and Salzberg, Steven L.},
	month = jan,
	year = {2017},
	pages = {e104},
	file = {lu17_bracken__peerj_computer_science__bracken.pdf:/home/kevin/work/bibliography/pdfs/lu17_bracken__peerj_computer_science__bracken.pdf:application/pdf},
}

@article{murray_kwip:_2017,
	title = {{kWIP}: {The} k-mer weighted inner product, a de novo estimator of genetic similarity},
	volume = {13},
	issn = {1553-7358},
	shorttitle = {{kWIP}},
	url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005727},
	doi = {10.1371/journal.pcbi.1005727},
	abstract = {Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or “samples”) in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.},
	number = {9},
	urldate = {2017-09-10},
	journal = {PLOS Computational Biology},
	author = {Murray, Kevin D. and Webers, Christfried and Ong, Cheng Soon and Borevitz, Justin and Warthmann, Norman},
	month = sep,
	year = {2017},
	keywords = {Rice, Population genetics, Genome analysis, Genome sequencing, Comparative genomics, Gene sequencing, Genomics, Sequence alignment},
	pages = {e1005727},
	file = {murray17_kwip__plos_computational_biology__kwip.pdf:/home/kevin/work/bibliography/pdfs/murray17_kwip__plos_computational_biology__kwip.pdf:application/pdf},
}

@article{li13_aligningsequence,
	title = {Aligning sequence reads, clone sequences and assembly contigs with {BWA}-{MEM}},
	url = {https://arxiv.org/abs/1303.3997v2},
	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},
	language = {en},
	urldate = {2019-01-02},
	author = {Li, Heng},
	month = mar,
	year = {2013},
	file = {li13_aligning____aligning_sequence_reads,_clone_sequences_and_assembly_contigs_with_bwa-mem.pdf:/home/kevin/work/bibliography/pdfs/li13_aligning____aligning_sequence_reads,_clone_sequences_and_assembly_contigs_with_bwa-mem.pdf:application/pdf},
}

@article{murray_landscape_2019,
	title = {Landscape drivers of genomic diversity and divergence in woodland {Eucalyptus}},
	volume = {28},
	issn = {0962-1083},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7065176/},
	doi = {10.1111/mec.15287},
	abstract = {Spatial genetic patterns are influenced by numerous factors, and they can vary even among coexisting, closely related species due to differences in dispersal and selection. Eucalyptus (L'Héritier 1789; the “eucalypts”) are foundation tree species that provide essential habitat and modulate ecosystem services throughout Australia. Here we present a study of landscape genomic variation in two woodland eucalypt species, using whole‐genome sequencing of 388 individuals of Eucalyptus albens and Eucalyptus sideroxylon. We found exceptionally high genetic diversity (π ≈ 0.05) and low genome‐wide, interspecific differentiation (F
ST = 0.15) and intraspecific differentiation between localities (F
ST ≈ 0.01–0.02). We found no support for strong, discrete population structure, but found substantial support for isolation by geographic distance (IBD) in both species. Using generalized dissimilarity modelling, we identified additional isolation by environment (IBE). Eucalyptus albens showed moderate IBD, and environmental variables have a small but significant amount of additional predictive power (i.e. IBE). Eucalyptus sideroxylon showed much stronger IBD and moderate IBE. These results highlight the vast adaptive potential of these species and set the stage for testing evolutionary hypotheses of interspecific adaptive differentiation across environments.},
	number = {24},
	urldate = {2020-08-26},
	journal = {Molecular Ecology},
	author = {Murray, Kevin D. and Janes, Jasmine K and Jones, Ashley and Bothwell, Helen M and Andrew, Rose L and Borevitz, Justin O},
	month = dec,
	year = {2019},
	pmid = {31647597},
	pmcid = {PMC7065176},
	pages = {5232--5247},
	file = {murray19_landscape__molecular_ecology__landscape_drivers_of_genomic_diversity_and_divergence_in_woodland_eucalyptus.pdf:/home/kevin/work/bibliography/pdfs/murray19_landscape__molecular_ecology__landscape_drivers_of_genomic_diversity_and_divergence_in_woodland_eucalyptus.pdf:application/pdf},
}

@misc{karasov_drought_2022,
	title = {Drought selection on {Arabidopsis} populations and their microbiomes},
	copyright = {© 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/2022.04.08.487684v1},
	doi = {10.1101/2022.04.08.487684},
	abstract = {Microbes affect plant health, stress tolerance1 and life history2. In different regions of the globe, plants are colonized by distinct pathogenic and commensal microbiomes, but the factors driving their geographic variation are largely unknown3. We identified and measured the core leaf microbiome of Arabidopsis thaliana in its native range, from almost 300 populations across Europe. Comparing the distribution of the approximately 500 major bacterial phylotypes, we discovered marked, geography-dependent differences in microbiome composition within A. thaliana and between A. thaliana and other Brassicaceae, with two distinct microbiome types segregating along a latitudinal gradient. The differences in microbiome composition mirror the spatial genetics of A. thaliana, with 52-68\% of variance in the first two principal coordinates of microbiome type explained by host genotype. Microbiome composition is best predicted by drought-associated metrics that are well known to be a major selective agent on A. thaliana populations. The reproducible and predictable associations between specific microbes and water availability raise the possibility that drought not only directly shapes genetic variation in A. thaliana, but does so also indirectly through its effects on the leaf microbiome.},
	language = {en},
	urldate = {2023-05-29},
	publisher = {bioRxiv},
	author = {Karasov, Talia L. and Neumann, Manuela and Shirsekar, Gautam and Monroe, Grey and Team, Pathodopsis and Weigel, Detlef and Schwab, Rebecca},
	month = apr,
	year = {2022},
	note = {Pages: 2022.04.08.487684
Section: New Results},
	file = {karasov22_droughtselection____drought_selection_on_arabidopsis_populations_and_their_microbiomes.pdf:/home/kevin/work/bibliography/pdfs/karasov22_droughtselection____drought_selection_on_arabidopsis_populations_and_their_microbiomes.pdf:application/pdf},
}

@article{regalado_combining_2020,
	title = {Combining whole-genome shotgun sequencing and {rRNA} gene amplicon analyses to improve detection of microbe–microbe interaction networks in plant leaves},
	volume = {14},
	copyright = {2020 The Author(s)},
	issn = {1751-7370},
	url = {https://www.nature.com/articles/s41396-020-0665-8},
	doi = {10.1038/s41396-020-0665-8},
	abstract = {Microorganisms from all domains of life establish associations with plants. Although some harm the plant, others antagonize pathogens or prime the plant immune system, support the acquisition of nutrients, tune plant hormone levels, or perform additional services. Most culture-independent plant microbiome research has focused on amplicon sequencing of the 16S rRNA gene and/or the internal transcribed spacer (ITS) of rRNA genomic loci, which show the relative abundance of the microbes to each other. Here, we describe shotgun sequencing of 275 wild Arabidopsis thaliana leaf microbiomes from southwest Germany, with additional bacterial 16S and eukaryotic ITS1 rRNA amplicon data from 176 of these samples. Shotgun data, which unlike the amplicon data capture the ratio of microbe to plant DNA, enable scaling of microbial read abundances to reflect the microbial load on the host. In a more cost-effective hybrid strategy, we show they also allow a similar scaling of amplicon data to overcome compositionality problems. Our wild plants were dominated by bacterial sequences, with eukaryotes contributing only a minority of reads. Microbial membership showed weak associations with both site of origin and plant genotype, both of which were highly confounded in this dataset. There was large variation among microbiomes, with one extreme comprising samples of low complexity and a high load of microorganisms typical of infected plants, and the other extreme being samples of high complexity and a low microbial load. Critically, considering absolute microbial load led to fundamentally different conclusions about microbiome assembly and the interaction networks among major taxa.},
	language = {en},
	number = {8},
	urldate = {2023-05-29},
	journal = {The ISME Journal},
	author = {Regalado, Julian and Lundberg, Derek S. and Deusch, Oliver and Kersten, Sonja and Karasov, Talia and Poersch, Karin and Shirsekar, Gautam and Weigel, Detlef},
	month = aug,
	year = {2020},
	note = {number: 8
publisher: Nature Publishing Group},
	keywords = {Microbiome, Metagenomics},
	pages = {2116--2130},
	annote = {ContentsAbstractIntroductionMethodsSampling, processing of plants, and metagenomic library preparationBatch 0—single plantBatch 1—nine plant test of shearing methodsCovaris basedShearase basedBatch 2—set of 90 plantsBatch 3—set of 176 plants16S rDNA V4 library construction and sequencing for batch 3 plantsITS1 library construction and sequencing for batch 3plantsAmplicon quality processing, clustering, and classificationMetagenome read QC and host data removalMetagenomic profilingTaxonomic correlations and network computationPlant genotypingAdjusting 16S rDNA amplicon data by bacterial loadComparison of 16S rDNA reads from the metagenome to 16S rDNA V4 ampliconsResultsSequencing and analysis approachOverview of microbial taxa in the metagenomesInfluence of site, season, and host geneticsIntermicrobial correlation networksConcordance between metagenome and amplicon dataDiscussionSupplementary informationACKNOWLEDGMENTSCompliance with ethical standardsACKNOWLEDGMENTSReferences},
	annote = {ContentsAbstractIntroductionMethodsSampling, processing of plants, and metagenomic library preparationBatch 0—single plantBatch 1—nine plant test of shearing methodsCovaris basedShearase basedBatch 2—set of 90 plantsBatch 3—set of 176 plants16S rDNA V4 library construction and sequencing for batch 3 plantsITS1 library construction and sequencing for batch 3plantsAmplicon quality processing, clustering, and classificationMetagenome read QC and host data removalMetagenomic profilingTaxonomic correlations and network computationPlant genotypingAdjusting 16S rDNA amplicon data by bacterial loadComparison of 16S rDNA reads from the metagenome to 16S rDNA V4 ampliconsResultsSequencing and analysis approachOverview of microbial taxa in the metagenomesInfluence of site, season, and host geneticsIntermicrobial correlation networksConcordance between metagenome and amplicon dataDiscussionSupplementary informationACKNOWLEDGMENTSCompliance with ethical standardsACKNOWLEDGMENTSReferences},
	file = {regalado_20_combiningwholegenome__the_isme_journal__combining_whole-genome_shotgun_sequencing_and_rrna_gene_amplicon_analyses_to.pdf:/home/kevin/work/bibliography/pdfs/regalado_20_combiningwholegenome__the_isme_journal__combining_whole-genome_shotgun_sequencing_and_rrna_gene_amplicon_analyses_to.pdf:application/pdf;regalado20_combiningwholegenome__the_isme_journal__combining_whole-genome_shotgun_sequencing_and_rrna_gene_amplicon_analyses_to.pdf:/home/kevin/work/bibliography/pdfs/regalado20_combiningwholegenome__the_isme_journal__combining_whole-genome_shotgun_sequencing_and_rrna_gene_amplicon_analyses_to.pdf:application/pdf;regalado20_combiningwholegenome__the_isme_journal__combining_whole-genome_shotgun_sequencing_and_rrna_gene_amplicon_analyses_to.pdf:/home/kevin/work/bibliography/pdfs/regalado20_combiningwholegenome__the_isme_journal__combining_whole-genome_shotgun_sequencing_and_rrna_gene_amplicon_analyses_to2.pdf:application/pdf},
}

@article{sedlazeck13_nextgenmapfast,
	title = {{NextGenMap}: fast and accurate read mapping in highly polymorphic genomes},
	volume = {29},
	issn = {1367-4803, 1367-4811},
	shorttitle = {{NextGenMap}},
	url = {https://academic.oup.com/bioinformatics/article/29/21/2790/195626},
	doi = {10.1093/bioinformatics/btt468},
	abstract = {Abstract
            Summary: When choosing a read mapper, one faces the trade off between speed and the ability to map reads in highly polymorphic regions. Here, we report NextGenMap, a fast and accurate read mapper, which reduces this dilemma. NextGenMap aligns reads reliably to a reference genome even when the sequence difference between target and reference genome is large, i.e. highly polymorphic genome. At the same time, NextGenMap outperforms current mapping methods with respect to runtime and to the number of correctly mapped reads. NextGenMap efficiently uses the available hardware by exploiting multi-core CPUs as well as graphic cards (GPUs), if available. In addition, NextGenMap handles automatically any read data independent of read length and sequencing technology.
            Availability: NextGenMap source code and documentation are available at: http://cibiv.github.io/NextGenMap/
            Contact: fritz.sedlazeck@univie.ac.at
            Supplementary information:  Supplementary data are available at Bioinformatics online.},
	language = {en},
	number = {21},
	urldate = {2023-10-04},
	journal = {Bioinformatics},
	author = {Sedlazeck, Fritz J. and Rescheneder, Philipp and Von Haeseler, Arndt},
	month = nov,
	year = {2013},
	pages = {2790--2791},
	file = {sedlazeck13_nextgenmapfast__bioinformatics__nextgenmap.pdf:/home/kevin/work/bibliography/pdfs/sedlazeck13_nextgenmapfast__bioinformatics__nextgenmap.pdf:application/pdf},
}

@article{kim16_centrifugerapid,
	title = {Centrifuge: rapid and sensitive classification of metagenomic sequences},
	issn = {1088-9051, 1549-5469},
	shorttitle = {Centrifuge},
	url = {https://genome.cshlp.org/content/early/2016/11/16/gr.210641.116},
	doi = {10.1101/gr.210641.116},
	abstract = {Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space.},
	language = {en},
	urldate = {2023-10-04},
	journal = {Genome Research},
	author = {Kim, Daehwan and Song, Li and Breitwieser, Florian P. and Salzberg, Steven L.},
	month = oct,
	year = {2016},
	pmid = {27852649},
	note = {Company: Cold Spring Harbor Laboratory Press
Distributor: Cold Spring Harbor Laboratory Press
Institution: Cold Spring Harbor Laboratory Press
Label: Cold Spring Harbor Laboratory Press
Publisher: Cold Spring Harbor Lab},
	file = {kim16_centrifugerapid__genome_research__centrifuge.pdf:/home/kevin/work/bibliography/pdfs/kim16_centrifugerapid__genome_research__centrifuge.pdf:application/pdf;kim16_centrifugerapid__genome_research__centrifuge.pdf:/home/kevin/work/bibliography/pdfs/kim16_centrifugerapid__genome_research__centrifuge2.pdf:application/pdf},
}

@article{beber23_taxpastataxonomic,
	title = {{TAXPASTA}: {TAXonomic} {Profile} {Aggregation} and {STAndardisation}},
	volume = {8},
	issn = {2475-9066},
	shorttitle = {{TAXPASTA}},
	url = {https://joss.theoj.org/papers/10.21105/joss.05627},
	doi = {10.21105/joss.05627},
	abstract = {Beber et al., (2023). TAXPASTA: TAXonomic Profile Aggregation and STAndardisation. Journal of Open Source Software, 8(87), 5627, https://doi.org/10.21105/joss.05627},
	language = {en},
	number = {87},
	urldate = {2023-10-04},
	journal = {Journal of Open Source Software},
	author = {Beber, Moritz E. and Borry, Maxime and Stamouli, Sofia and Yates, James A. Fellows},
	month = jul,
	year = {2023},
	pages = {5627},
	file = {beber23_taxpastataxonomic__journal_of_open_source_software__taxpasta.pdf:/home/kevin/work/bibliography/pdfs/beber23_taxpastataxonomic__journal_of_open_source_software__taxpasta.pdf:application/pdf},
}

@article{ewels16_multiqcsummarize,
	title = {{MultiQC}: summarize analysis results for multiple tools and samples in a single report},
	volume = {32},
	issn = {1367-4811},
	shorttitle = {{MultiQC}},
	doi = {10.1093/bioinformatics/btw354},
	abstract = {MOTIVATION: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis.
RESULTS: We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization.
AVAILABILITY AND IMPLEMENTATION: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info
CONTACT: phil.ewels@scilifelab.se.},
	language = {eng},
	number = {19},
	journal = {Bioinformatics (Oxford, England)},
	author = {Ewels, Philip and Magnusson, Måns and Lundin, Sverker and Käller, Max},
	month = oct,
	year = {2016},
	pmid = {27312411},
	pmcid = {PMC5039924},
	keywords = {Computational Biology, Software, Quality Control, High-Throughput Nucleotide Sequencing},
	pages = {3047--3048},
	file = {ewels16_multiqcsummarize__bioinformatics_(oxford,_england)__multiqc.pdf:/home/kevin/work/bibliography/pdfs/ewels16_multiqcsummarize__bioinformatics_(oxford,_england)__multiqc.pdf:application/pdf;ewels16_multiqcsummarize__bioinformatics_(oxford,_england)__multiqc.pdf:/home/kevin/work/bibliography/pdfs/ewels16_multiqcsummarize__bioinformatics_(oxford,_england)__multiqc2.pdf:application/pdf},
}

@article{li18_minimap2pairwise,
	title = {Minimap2: pairwise alignment for nucleotide sequences},
	volume = {34},
	issn = {1367-4803, 1367-4811},
	shorttitle = {Minimap2},
	url = {https://academic.oup.com/bioinformatics/article/34/18/3094/4994778},
	doi = {10.1093/bioinformatics/bty191},
	abstract = {Abstract
            
              Motivation
              Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.
            
            
              Results
              Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database. It works with accurate short reads of ≥100 bp in length, ≥1 kb genomic reads at error rate ∼15\%, full-length noisy Direct RNA or cDNA reads and assembly contigs or closely related full chromosomes of hundreds of megabases in length. Minimap2 does split-read alignment, employs concave gap cost for long insertions and deletions and introduces new heuristics to reduce spurious alignments. It is 3–4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mappers at higher accuracy, surpassing most aligners specialized in one type of alignment.
            
            
              Availability and implementation
              https://github.com/lh3/minimap2
            
            
              Supplementary information
              Supplementary data are available at Bioinformatics online.},
	language = {en},
	number = {18},
	urldate = {2023-10-04},
	journal = {Bioinformatics},
	author = {Li, Heng},
	editor = {Birol, Inanc},
	month = sep,
	year = {2018},
	pages = {3094--3100},
	file = {li18_minimap2pairwise__bioinformatics__minimap2.pdf:/home/kevin/work/bibliography/pdfs/li18_minimap2pairwise__bioinformatics__minimap2.pdf:application/pdf},
}

@article{li21_newstrategies,
	title = {New strategies to improve minimap2 alignment accuracy},
	volume = {37},
	issn = {1367-4803, 1367-4811},
	url = {https://academic.oup.com/bioinformatics/article/37/23/4572/6384570},
	doi = {10.1093/bioinformatics/btab705},
	abstract = {Abstract
            
              Summary
              We present several recent improvements to minimap2, a versatile pairwise aligner for nucleotide sequences. Now minimap2 v2.22 can more accurately map long reads to highly repetitive regions and align through insertions or deletions up to 100 kb by default, addressing major weakness in minimap2 v2.18 or earlier.
            
            
              Availability and implementation
              https://github.com/lh3/minimap2.},
	language = {en},
	number = {23},
	urldate = {2023-10-04},
	journal = {Bioinformatics},
	author = {Li, Heng},
	editor = {Alkan, Can},
	month = dec,
	year = {2021},
	pages = {4572--4574},
	file = {li21_newstrategies__bioinformatics__new_strategies_to_improve_minimap2_alignment_accuracy.pdf:/home/kevin/work/bibliography/pdfs/li21_newstrategies__bioinformatics__new_strategies_to_improve_minimap2_alignment_accuracy.pdf:application/pdf},
}

@article{danecek21_twelveyears,
	title = {Twelve years of {SAMtools} and {BCFtools}},
	volume = {10},
	issn = {2047-217X},
	doi = {10.1093/gigascience/giab008},
	abstract = {BACKGROUND: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods.
FINDINGS: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines.
CONCLUSION: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed {\textgreater}1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.},
	language = {eng},
	number = {2},
	journal = {GigaScience},
	author = {Danecek, Petr and Bonfield, James K. and Liddle, Jennifer and Marshall, John and Ohan, Valeriu and Pollard, Martin O. and Whitwham, Andrew and Keane, Thomas and McCarthy, Shane A. and Davies, Robert M. and Li, Heng},
	month = feb,
	year = {2021},
	pmid = {33590861},
	pmcid = {PMC7931819},
	keywords = {next generation sequencing, Genome, Software, variant calling, Genomics, High-Throughput Nucleotide Sequencing, bcftools, data analysis, high-throughput sequencing, samtools},
	pages = {giab008},
	file = {danecek21_twelveyears__gigascience__twelve_years_of_samtools_and_bcftools.pdf:/home/kevin/work/bibliography/pdfs/danecek21_twelveyears__gigascience__twelve_years_of_samtools_and_bcftools.pdf:application/pdf},
}

@article{li09_sequencealignment,
	title = {The {Sequence} {Alignment}/{Map} format and {SAMtools}},
	volume = {25},
	issn = {1367-4811},
	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.},
	language = {eng},
	number = {16},
	journal = {Bioinformatics (Oxford, England)},
	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 and {1000 Genome Project Data Processing Subgroup}},
	month = aug,
	year = {2009},
	pmid = {19505943},
	pmcid = {PMC2723002},
	keywords = {Molecular Sequence Data, Genome, Sequence Analysis, DNA, Computational Biology, Base Sequence, Software, Sequence Alignment, Algorithms, Genomics},
	pages = {2078--2079},
	file = {li09_sequencealignment__bioinformatics_(oxford,_england)__the_sequence_alignment-map_format_and_samtools.pdf:/home/kevin/work/bibliography/pdfs/li09_sequencealignment__bioinformatics_(oxford,_england)__the_sequence_alignment-map_format_and_samtools.pdf:application/pdf;li09_sequencealignment__bioinformatics_(oxford,_england)__the_sequence_alignment-map_format_and_samtools.pdf:/home/kevin/work/bibliography/pdfs/li09_sequencealignment__bioinformatics_(oxford,_england)__the_sequence_alignment-map_format_and_samtools2.pdf:application/pdf},
}

@article{yates23_nfcore,
	title = {nf-core/taxprofiler},
	copyright = {Open Access},
	shorttitle = {nf-core/taxprofiler},
	url = {https://zenodo.org/record/7728364},
	abstract = {{\textless}code{\textgreater}Added{\textless}/code{\textgreater} \#298 {\textless}strong{\textgreater}New classifier{\textless}/strong{\textgreater} ganon (added by @jfy133) \#312 {\textless}strong{\textgreater}New classifier{\textless}/strong{\textgreater} KMCP (added by @sofstam) \#318 {\textless}strong{\textgreater}New classifier{\textless}/strong{\textgreater} MetaPhlAn4 (MetaPhlAn3 support remains) (added by @LilyAnderssonLee) \#276 Implemented batching in the KrakenUniq samples processing (added by @Midnighter) \#272 Add saving of final 'analysis-ready-reads' to dedicated directory (❤️ to @alexhbnr for request, added by @jfy133) \#303 Add support for taxpasta profile standardisation in single sample pipeline runs (❤️ to @artur-matysik for request, added by @jfy133) \#308 Add citations and bibliographic information to the MultiQC methods text of tools used in a given pipeline run (added by @jfy133) \#315 Updated to nf-core pipeline template v2.9 (added by @sofstam \&amp; @jfy133) \#319 Added support for virus hit expansion in Kaiju (❤️ to @dnlrxn for requesting, added by @jfy133) \#323 Add ability to skip sequencing quality control tools (❤️ to @vinisalazar for requesting, added by @jfy133) \#345 Add simple tutorial to explain how to get up and running with an nf-core/taxprofiler run (added by @jfy133) \#355 Add support for TAXPASTA's {\textless}code{\textgreater}--add-rank-lineage{\textless}/code{\textgreater} to output (❤️ to @MajoroMask for request, added by @Midnighter, @sofstam, @jfy133) \#368 Add the ability to ignore profile errors caused by empty profiles and other validation errors when merging multiple profiles using TAXPASTA (added by @Midnighter and @LilyAnderssonLee) {\textless}code{\textgreater}Fixed{\textless}/code{\textgreater} \#271 Improved standardised table generation documentation for mOTUs manual database download tutorial (♥ to @prototaxites for reporting, fix by @jfy133) \#269 Reduced output files in AWS full test output due to very large files (fix by @jfy133) \#270 Fixed warning for host removal index parameter, and improved index checks (♥ to @prototaxites for reporting, fix by @jfy133) \#274 Substituted the samtools/bam2fq module with samtools/fastq module (fix by @sofstam) \#275 Replaced function used for error reporting to more Nextflow friendly method (fix by @jfy133) \#285 Fixed overly large log files in Kraken2 output (♥ to @prototaxites for reporting, fix by @Midnighter \&amp; @jfy133) \#286 Runtime optimisation of MultiQC step via improved log file processing (fix by @Midnighter \&amp; @jfy133) \#289 Pipeline updated to nf-core template 2.8 (fix by @Midnighter \&amp; @jfy133) \#290 Minor database input documentation improvements (♥ to @alneberg for reporting, fix by @jfy133) \#305 Fix docker/podman registry definition for tower compatibility (fix by @adamrtalbot, @jfy133) \#304 Correct mistake in kaiju2table documentation, only single rank can be supplied (♥ to @artur-matysik for reporting, fix by @jfy133) \#307 Fix databases being sometimes associated with the wrong tool (e.g. Kaiju) (fix by @jfy133, @Midnighter and @LilyAnderssonLee) \#313 Fix pipeline not providing error when database sheet does not have a header (♥ to @noah472 for reporting, fix by @jfy133) \#330 Added better tagging to allow disambiguation of Kraken2 steps of Kraken2 vs Bracken (♥ to @MajoroMask for requesting, added by @jfy133) \#334 Increase the memory of the FALCO process to 4GB (fix by @LilyAnderssonLee) \#332 Improved meta map stability for more robust pipeline resuming (fix by @jfy133) \#338 Fixed wrong file 'out' file going to {\textless}code{\textgreater}centrifuge kreport{\textless}/code{\textgreater} module (♥ to @LilyAnderssonLee for reporting, fix by @jfy133) \#342 Fixed docs/usage to correctly list the required database files for Bracken and tips to obtain Kraken2 databases (fix by @husensofteng) \#350 Reorganize the CI tests into separate profiles in preparation for implementation of nf-test (fix by @LilyAnderssonLee) \#364 Add autoMounts to apptainer profile in nextflow.config (♥ to @hkaspersento for reporting, fix by @LilyAnderssonLee) \#372 Update modules to use quay.io nf-core mirrored containers (♥ to @maxulysse for pointing out, fix by @LilyAnderssonLee and @jfy133) {\textless}code{\textgreater}Dependencies{\textless}/code{\textgreater} Tool Previous version New version MultiQC 1.13 1.15 TAXPASTA 0.2.3 0.6.0 MetaPhlAn 3.0.12 4.0.6 fastp 0.23.2 0.23.4 samtools 1.16.1 1.17 {\textless}code{\textgreater}Deprecated{\textless}/code{\textgreater} \#338 Updated Centrifuge module to not generate (undocumented) SAM alignments by default if --save\_centrifuge\_reads supplied, due to a Centrifuge bug modifying profile header. SAM alignments can still be generated if {\textless}code{\textgreater}--out-fmt{\textless}/code{\textgreater} supplied in {\textless}code{\textgreater}database.csv{\textless}/code{\textgreater} (♥ to @LilyAnderssonLee for reporting, fix by @jfy133)},
	urldate = {2023-10-09},
	author = {Yates, James A. Fellows and Stamouli, Sofia and Andersson-Li, Lili and Beber, Moritz E. and Mesilaakso, Lauri and {Nf-Core Bot} and Christensen, Thomas A. and {Mahwash Jamy} and {JIANHONG OU} and Stepien, Rafal and Borry, Maxime and {Husen M. Umer} and Syme, Robert and Hübner, Alex and {Zandra Fagernäs}},
	month = sep,
	year = {2023},
	doi = {10.5281/ZENODO.7728364},
}

@article{koster21_snakemakeworkflows,
	title = {snakemake-workflows/dna-seq-gatk-variant-calling},
	copyright = {Open Access},
	shorttitle = {snakemake-workflows/dna-seq-gatk-variant-calling},
	url = {https://zenodo.org/record/4677629},
	abstract = {Formatting according to best practices (snakefmt).},
	urldate = {2023-10-09},
	author = {Köster, Johannes and {Micwessolly} and Kuthe, Elias and De Coster, Wouter},
	month = may,
	year = {2021},
	doi = {10.5281/ZENODO.4677629},
}

@misc{ahrens_genomic_2021,
	title = {Genomic constraints to drought adaptation},
	copyright = {© 2021, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/2021.08.07.455511v1},
	doi = {10.1101/2021.08.07.455511},
	abstract = {Global shifts in climate and precipitation patterns are altering the diversity and structure of forests. The ability for species to adapt is especially difficult for long lived foundation species with unknown genetic and trait diversity. We harnessed genomic, physiological, and climate data to determine adaptation constraints. We used denovo assembly and 6.5 million genomic variants with drought related traits from 432 individuals sourced from across the complete range of the foundation tree species Corymbia calophylla. We found genomic variants determining traits predominantly in gene regulatory regions. The ability for populations to adapt was limited by within population genetic diversity associated with traits, and epistatic interactions within traits and pleiotropic interactions among traits. Nevertheless, we could accurately predict adaptive traits using genomic and climate data to guide reforestation. Our study indicated that some populations may contain variation sufficient for the species to adapt to the effects of drought, while other populations will need increased variability from those sources.},
	language = {en},
	urldate = {2023-10-09},
	publisher = {bioRxiv},
	author = {Ahrens, Collin W. and Murray, Kevin D. and Mazanec, Richard A. and Ferguson, Scott and Bragg, Jason and Jones, Ashley and Tissue, David T. and Byrne, Margaret and Borevitz, Justin O. and Rymer, Paul D.},
	month = aug,
	year = {2021},
	note = {Pages: 2021.08.07.455511
Section: New Results},
	file = {Full Text PDF:/home/kevin/work/bibliography/zotdir/storage/UM62FW75/Ahrens et al. - 2021 - Genomic constraints to drought adaptation.pdf:application/pdf},
}

@article{wood_improved_2019,
	title = {Improved metagenomic analysis with {Kraken} 2},
	volume = {20},
	copyright = {2019 The Author(s).},
	issn = {1474-760X},
	url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1891-0},
	doi = {10.1186/s13059-019-1891-0},
	abstract = {Although Kraken’s k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85\%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.},
	language = {en},
	number = {1},
	urldate = {2023-10-09},
	journal = {Genome Biology},
	author = {Wood, Derrick E. and Lu, Jennifer and Langmead, Ben},
	month = dec,
	year = {2019},
	note = {Number: 1
Publisher: BioMed Central},
	pages = {1--13},
	file = {Full Text PDF:/home/kevin/work/bibliography/zotdir/storage/HJWSL9WG/Wood et al. - 2019 - Improved metagenomic analysis with Kraken 2.pdf:application/pdf},
}

@article{pedersen_mosdepth_2018,
	title = {Mosdepth: quick coverage calculation for genomes and exomes},
	volume = {34},
	issn = {1367-4811},
	shorttitle = {Mosdepth},
	doi = {10.1093/bioinformatics/btx699},
	abstract = {SUMMARY: Mosdepth is a new command-line tool for rapidly calculating genome-wide sequencing coverage. It measures depth from BAM or CRAM files at either each nucleotide position in a genome or for sets of genomic regions. Genomic regions may be specified as either a BED file to evaluate coverage across capture regions, or as a fixed-size window as required for copy-number calling. Mosdepth uses a simple algorithm that is computationally efficient and enables it to quickly produce coverage summaries. We demonstrate that mosdepth is faster than existing tools and provides flexibility in the types of coverage profiles produced.
AVAILABILITY AND IMPLEMENTATION: mosdepth is available from https://github.com/brentp/mosdepth under the MIT license.
CONTACT: bpederse@gmail.com.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
	language = {eng},
	number = {5},
	journal = {Bioinformatics (Oxford, England)},
	author = {Pedersen, Brent S. and Quinlan, Aaron R.},
	month = mar,
	year = {2018},
	pmid = {29096012},
	pmcid = {PMC6030888},
	keywords = {Humans, Sequence Analysis, DNA, Software, Genome, Human, Algorithms, Genomics, High-Throughput Nucleotide Sequencing, Exome Sequencing},
	pages = {867--868},
	file = {pedersen18_mosdepthquick__bioinformatics_(oxford,_england)__mosdepth.pdf:/home/kevin/work/bibliography/pdfs/pedersen18_mosdepthquick__bioinformatics_(oxford,_england)__mosdepth.pdf:application/pdf},
}

@article{ziemann_five_2023,
	title = {The five pillars of computational reproducibility: bioinformatics and beyond},
	volume = {24},
	issn = {1477-4054},
	shorttitle = {The five pillars of computational reproducibility},
	url = {https://doi.org/10.1093/bib/bbad375},
	doi = {10.1093/bib/bbad375},
	abstract = {Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.},
	number = {6},
	urldate = {2023-10-23},
	journal = {Briefings in Bioinformatics},
	author = {Ziemann, Mark and Poulain, Pierre and Bora, Anusuiya},
	month = nov,
	year = {2023},
	pages = {bbad375},
	file = {Full Text PDF:/home/kevin/work/bibliography/zotdir/storage/K2SM9MVD/Ziemann et al. - 2023 - The five pillars of computational reproducibility.pdf:application/pdf;Snapshot:/home/kevin/work/bibliography/zotdir/storage/A84GJ5UZ/7326135.html:text/html},
}

@article{buchfink_sensitive_2021,
	title = {Sensitive protein alignments at tree-of-life scale using {DIAMOND}},
	volume = {18},
	copyright = {2021 The Author(s)},
	issn = {1548-7105},
	url = {https://www.nature.com/articles/s41592-021-01101-x},
	doi = {10.1038/s41592-021-01101-x},
	abstract = {We are at the beginning of a genomic revolution in which all known species are planned to be sequenced. Accessing such data for comparative analyses is crucial in this new age of data-driven biology. Here, we introduce an improved version of DIAMOND that greatly exceeds previous search performances and harnesses supercomputing to perform tree-of-life scale protein alignments in hours, while matching the sensitivity of the gold standard BLASTP.},
	language = {en},
	number = {4},
	urldate = {2024-02-01},
	journal = {Nature Methods},
	author = {Buchfink, Benjamin and Reuter, Klaus and Drost, Hajk-Georg},
	month = apr,
	year = {2021},
	note = {Number: 4
Publisher: Nature Publishing Group},
	keywords = {Computational biology and bioinformatics, Genome informatics, Genomic analysis, Sequencing, Software},
	pages = {366--368},
	file = {Full Text PDF:/home/kevin/work/bibliography/zotdir/storage/E84YGSTR/Buchfink et al. - 2021 - Sensitive protein alignments at tree-of-life scale.pdf:application/pdf},
}
