@article{culebront,
     author = {Orjuela, Julie and Comte, Aurore and Ravel, S\'ebastien and Charriat, Florian and Vi, Tram and Sabot, Fran\c{c}ois  and Cunnac, S\'ebastien },
     title = {CulebrONT: a streamlined long reads multi-assembler pipeline for prokaryotic and eukaryotic genomes},
     journal = {Peer Community Journal},
     eid = {e46},
     publisher = {Peer Community In},
     volume = {2},
     year = {2022},
     doi = {10.24072/pcjournal.153},
     url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.153/}
}

@article{snakemake,
    author = {Köster, Johannes and Rahmann, Sven},
    title = "{Snakemake—a scalable bioinformatics workflow engine}",
    journal = {Bioinformatics},
    volume = {28},
    number = {19},
    pages = {2520-2522},
    year = {2012},
    month = {08},
    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}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/bts480},
    url = {https://doi.org/10.1093/bioinformatics/bts480},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/28/19/2520/819790/bts480.pdf},
}

@article{singularity,
    author = {Kurtzer, Gregory M. AND Sochat, Vanessa AND Bauer, Michael W.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Singularity: Scientific containers for mobility of compute},
    year = {2017},
    month = {05},
    volume = {12},
    url = {https://doi.org/10.1371/journal.pone.0177459},
    pages = {1-20},
    abstract = {Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.},
    number = {5},
    doi = {10.1371/journal.pone.0177459}
}

@article{bwa,
  title     = "Fast and accurate short read alignment with {Burrows-Wheeler}
               transform",
  author    = "Li, Heng and Durbin, Richard",
  abstract  = "MOTIVATION: The enormous amount of short reads generated by the
               new DNA sequencing technologies call for the development of fast
               and accurate read alignment programs. A first generation of hash
               table-based methods has been developed, including MAQ, which is
               accurate, feature rich and fast enough to align short reads from
               a single individual. However, MAQ does not support gapped
               alignment for single-end reads, which makes it unsuitable for
               alignment of longer reads where indels may occur frequently. The
               speed of MAQ is also a concern when the alignment is scaled up
               to the resequencing of hundreds of individuals. RESULTS: We
               implemented Burrows-Wheeler Alignment tool (BWA), a new read
               alignment package that is based on backward search with
               Burrows-Wheeler Transform (BWT), to efficiently align short
               sequencing reads against a large reference sequence such as the
               human genome, allowing mismatches and gaps. BWA supports both
               base space reads, e.g. from Illumina sequencing machines, and
               color space reads from AB SOLiD machines. Evaluations on both
               simulated and real data suggest that BWA is approximately 10-20x
               faster than MAQ, while achieving similar accuracy. In addition,
               BWA outputs alignment in the new standard SAM (Sequence
               Alignment/Map) format. Variant calling and other downstream
               analyses after the alignment can be achieved with the open
               source SAMtools software package. AVAILABILITY:
               http://maq.sourceforge.net.",
  journal   = "Bioinformatics",
  publisher = "Oxford University Press (OUP)",
  volume    =  25,
  number    =  14,
  pages     = "1754--1760",
  month     =  jul,
  year      =  2009,
  copyright = "http://creativecommons.org/licenses/by-nc/2.0/uk/",
  language  = "en"
}

@article{lees2010theoretical,
  title={Theoretical maximum capacity as benchmark for empty vehicle redistribution in personal rapid transit},
  author={Lees-Miller, John and Hammersley, John and Wilson, R},
  journal={Transportation Research Record: Journal of the Transportation Research Board},
  number={2146},
  pages={76--83},
  year={2010},
  publisher={Transportation Research Board of the National Academies}
}

@article{urmson2008autonomous,
  title={Autonomous driving in urban environments: Boss and the urban challenge},
  author={Urmson, Chris and Anhalt, Joshua and Bagnell, Drew and Baker, Christopher and Bittner, Robert and Clark, MN and Dolan, John and Duggins, Dave and Galatali, Tugrul and Geyer, Chris and others},
  journal={Journal of Field Robotics},
  volume={25},
  number={8},
  pages={425--466},
  year={2008},
  publisher={Wiley Online Library}
}

@inproceedings{geiger2012we,
  title={Are we ready for autonomous driving? the kitti vision benchmark suite},
  author={Geiger, Andreas and Lenz, Philip and Urtasun, Raquel},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012},
  pages={3354--3361},
  year={2012},
  organization={IEEE}
}

@article{snakemake:2012,
    author = {Köster, Johannes and Rahmann, Sven},
    title = "{Snakemake—a scalable bioinformatics workflow engine}",
    journal = {Bioinformatics},
    volume = {28},
    number = {19},
    pages = {2520-2522},
    year = {2012},
    month = {08},
    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}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/bts480},
    url = {https://doi.org/10.1093/bioinformatics/bts480},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/28/19/2520/819790/bts480.pdf},
}

@article{singularity:2017,
    author = {Kurtzer, Gregory M. AND Sochat, Vanessa AND Bauer, Michael W.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Singularity: Scientific containers for mobility of compute},
    year = {2017},
    month = {05},
    volume = {12},
    url = {https://doi.org/10.1371/journal.pone.0177459},
    pages = {1-20},
    abstract = {Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.},
    number = {5},
    doi = {10.1371/journal.pone.0177459}
}


@article{kokot_kmc_2017,
	title = {{KMC} 3: counting and manipulating k-mer statistics},
	volume = {33},
	issn = {1367-4803},
	shorttitle = {{KMC} 3},
	url = {https://doi.org/10.1093/bioinformatics/btx304},
	doi = {10.1093/bioinformatics/btx304},
	abstract = {Counting all k-mers in a given dataset is a standard procedure in many bioinformatics applications. We introduce KMC3, a significant improvement of the former KMC2 algorithm together with KMC tools for manipulating k-mer databases. Usefulness of the tools is shown on a few real problems.Program is freely available at http://sun.aei.polsl.pl/REFRESH/kmc.Supplementary data are available at Bioinformatics online.},
	number = {17},
	urldate = {2021-03-11},
	journal = {Bioinformatics},
	author = {Kokot, Marek and Długosz, Maciej and Deorowicz, Sebastian},
	month = sep,
	year = {2017},
	pages = {2759--2761},
	file = {Full Text PDF:/home/orjuela/snap/zotero-snap/common/Zotero/storage/RWTXFTLK/Kokot et al. - 2017 - KMC 3 counting and manipulating k-mer statistics.pdf:application/pdf;Snapshot:/home/orjuela/snap/zotero-snap/common/Zotero/storage/B3QJ2LY7/3796399.html:text/html},
}


@article{luu_pcadapt_2017,
	title = {pcadapt: an {R} package to perform genome scans for selection based on principal component analysis},
	volume = {17},
	issn = {1755-0998},
	shorttitle = {pcadapt},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12592},
	doi = {10.1111/1755-0998.12592},
	abstract = {The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population structure is ascertained with principal component analysis, the package is fast and works with large-scale data. It can handle missing data and pooled sequencing data. By contrast to population-based approaches, the package handle admixed individuals and does not require grouping individuals into populations. Since its first release, pcadapt has evolved in terms of both statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans available in the 2.0 and later versions of the package. When hierarchical population structure occurs, Mahalanobis distance is more powerful than the communality statistic that was implemented in the first version of the package. Using simulated data, we compare pcadapt to other computer programs for genome scans (BayeScan, hapflk, OutFLANK, sNMF). We find that the proportion of false discoveries is around a nominal false discovery rate set at 10\% with the exception of BayeScan that generates 40\% of false discoveries. We also find that the power of BayeScan is severely impacted by the presence of admixed individuals whereas pcadapt is not impacted. Last, we find that pcadapt and hapflk are the most powerful in scenarios of population divergence and range expansion. Because pcadapt handles next-generation sequencing data, it is a valuable tool for data analysis in molecular ecology.},
	language = {en},
	number = {1},
	urldate = {2023-03-15},
	journal = {Molecular Ecology Resources},
	author = {Luu, Keurcien and Bazin, Eric and Blum, Michael G. B.},
	year = {2017},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/1755-0998.12592},
	keywords = {Mahalanobis distance, outlier detection, population genetics, principal component analysis, R package},
	pages = {67--77},
	file = {Full Text PDF:/home/orjuela/snap/zotero-snap/common/Zotero/storage/YUZ5PC4A/Luu et al. - 2017 - pcadapt an R package to perform genome scans for .pdf:application/pdf;Snapshot:/home/orjuela/snap/zotero-snap/common/Zotero/storage/MC3NFBCF/1755-0998.html:text/html},
}

@article{prive_performing_2020,
	title = {Performing {Highly} {Efficient} {Genome} {Scans} for {Local} {Adaptation} with {R} {Package} pcadapt {Version} 4},
	volume = {37},
	issn = {0737-4038},
	url = {https://doi.org/10.1093/molbev/msaa053},
	doi = {10.1093/molbev/msaa053},
	abstract = {R package pcadapt is a user-friendly R package for performing genome scans for local adaptation. Here, we present version 4 of pcadapt which substantially improves computational efficiency while providing similar results. This improvement is made possible by using a different format for storing genotypes and a different algorithm for computing principal components of the genotype matrix, which is the most computationally demanding step in method pcadapt. These changes are seamlessly integrated into the existing pcadapt package, and users will experience a large reduction in computation time (by a factor of 20–60 in our analyses) as compared with previous versions.},
	number = {7},
	urldate = {2023-03-15},
	journal = {Molecular Biology and Evolution},
	author = {Privé, Florian and Luu, Keurcien and Vilhjálmsson, Bjarni J and Blum, Michael G B},
	month = jul,
	year = {2020},
	pages = {2153--2154},
	file = {Snapshot:/home/orjuela/snap/zotero-snap/common/Zotero/storage/P4UZGEY3/5826356.html:text/html;Texte intégral:/home/orjuela/snap/zotero-snap/common/Zotero/storage/QHBY45Y6/Privé et al. - 2020 - Performing Highly Efficient Genome Scans for Local.pdf:application/pdf},
}

@article{voichek_identifying_2020,
	title = {Identifying genetic variants underlying phenotypic variation in plants without complete genomes},
	volume = {52},
	issn = {1061-4036},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610390/},
	doi = {10.1038/s41588-020-0612-7},
	abstract = {Structural variants and presence/absence polymorphisms are common in plant genomes, yet they are routinely overlooked in genome-wide association studies ({GWAS}). Here, we expand the type of genetic variants detected in {GWAS} to include major deletions, insertions, and rearrangements. We first use raw sequencing data directly to derive short sequences, k-mers, that mark a broad range of polymorphisms independently of a reference genome. We then link k-mers associated with phenotypes to specific genomic regions. Using this approach, we re-analyzed 2,000 traits in Arabidopsis thaliana, tomato, and maize populations. Associations identified with k-mers recapitulate those found with single-nucleotide polymorphisms ({SNPs}), but with stronger statistical support. Importantly, we discovered new associations with structural variants and with regions missing from reference genomes. Our results demonstrate the power of performing {GWAS} before linking sequence reads to specific genomic regions, which allows detection of a wider range of genetic variants responsible for phenotypic variation.},
	pages = {534--540},
	number = {5},
	journaltitle = {Nature genetics},
	journal = {Nature genetics},
	shortjournal = {Nat Genet},
	author = {Voichek, Yoav and Weigel, Detlef},
	urldate = {2022-05-05},
	date = {2020-05-01},
	pmid = {32284578},
	pmcid = {PMC7610390},
	file = {Voichek_Weigel_2020_Identifying genetic variants underlying phenotypic variation in plants without.pdf:/home/orjuela/snap/zotero-snap/common/Zotero/storage/48UCUKBI/Voichek_Weigel_2020_Identifying genetic variants underlying phenotypic variation in plants without.pdf:application/pdf},
}

@article{mehrab_efficient_2021,
	title = {Efficient association mapping from k-mers—An application in finding sex-specific sequences},
	volume = {16},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245058},
	doi = {10.1371/journal.pone.0245058},
	abstract = {Genome wide association studies ({GWAS}) attempt to map genotypes to phenotypes in organisms. This is typically performed by genotyping individuals using microarray or by aligning whole genome sequencing reads to a reference genome. Both approaches require knowledge of a reference genome which hinders their application to organisms with no or incomplete reference genomes. This caveat can be removed by using alignment-free association mapping methods based on k-mers from sequencing reads. Here we present an improved implementation of an alignment free association mapping method. The new implementation is faster and includes additional features to make it more flexible than the original implementation. We have tested our implementation on an E. Coli ampicillin resistance dataset and observe improvement in execution time over the original implementation while maintaining accuracy in results. We also demonstrate that the method can be applied to find sex specific sequences.},
	pages = {e0245058},
	number = {1},
	journaltitle = {{PLOS} {ONE}},
	shortjournal = {{PLOS} {ONE}},
	author = {Mehrab, Zakaria and Mobin, Jaiaid and Tahmid, Ibrahim Asadullah and Rahman, Atif},
	urldate = {2022-04-24},
	date = {2021-01-07},
	langid = {english},
	note = {Publisher: Public Library of Science},
	keywords = {Genomics, Bacterial genomics, Chromosome mapping, Genome-wide association studies, Jellyfish, Principal component analysis, Sequence alignment, X chromosomes},
	file = {Mehrab et al_2021_Efficient association mapping from k-mers—An application in finding.pdf:/home/orjuela/snap/zotero-snap/common/Zotero/storage/XPK2CXLN/Mehrab et al_2021_Efficient association mapping from k-mers—An application in finding.pdf:application/pdf;Snapshot:/home/orjuela/snap/zotero-snap/common/Zotero/storage/235DAVJ6/article.html:text/html},
}

@article{rahman_association_2018,
	title = {Association mapping from sequencing reads using k-mers},
	volume = {7},
	issn = {2050-084X},
	url = {https://doi.org/10.7554/eLife.32920},
	doi = {10.7554/eLife.32920},
	abstract = {Genome wide association studies ({GWAS}) rely on microarrays, or more recently mapping of sequencing reads, to genotype individuals. The reliance on prior sequencing of a reference genome limits the scope of association studies, and also precludes mapping associations outside of the reference. We present an alignment free method for association studies of categorical phenotypes based on counting k-mers in whole-genome sequencing reads, testing for associations directly between k-mers and the trait of interest, and local assembly of the statistically significant k-mers to identify sequence differences. An analysis of the 1000 genomes data show that sequences identified by our method largely agree with results obtained using the standard approach. However, unlike standard {GWAS}, our method identifies associations with structural variations and sites not present in the reference genome. We also demonstrate that population stratification can be inferred from k-mers. Finally, application to an E.coli dataset on ampicillin resistance validates the approach.},
	pages = {e32920},
	journaltitle = {{eLife}},
    journal = {{eLife}},
	author = {Rahman, Atif and Hallgrímsdóttir, Ingileif and Eisen, Michael and Pachter, Lior},
	editor = {Flint, Jonathan},
	urldate = {2021-03-19},
	date = {2018-06-13},
	note = {Publisher: {eLife} Sciences Publications, Ltd},
	keywords = {k-mers, association mapping, cardiovascular diseases, reference free, sequencing},
	file = {Rahman et al_2018_Association mapping from sequencing reads using k-mers.pdf:/home/orjuela/snap/zotero-snap/common/Zotero/storage/5944PXFD/Rahman et al_2018_Association mapping from sequencing reads using k-mers.pdf:application/pdf},
}


@article{marks_representation_2021,
	title = {Representation and participation across 20 years of plant genome sequencing},
	volume = {7},
	rights = {2021 The Author(s)},
	issn = {2055-0278},
	url = {https://www.nature.com/articles/s41477-021-01031-8},
	doi = {10.1038/s41477-021-01031-8},
	abstract = {The field of plant genome sequencing has grown rapidly in the past 20 years, leading to increases in the quantity and quality of publicly available genomic resources. The growing wealth of genomic data from an increasingly diverse set of taxa provides unprecedented potential to better understand the genome biology and evolution of land plants. Here we provide a contemporary view of land plant genomics, including analyses on assembly quality, taxonomic distribution of sequenced species and national participation. We show that assembly quality has increased dramatically in recent years, that substantial taxonomic gaps exist and that the field has been dominated by affluent nations in the Global North and China, despite a wide geographic distribution of study species. We identify numerous disconnects between the native range of focal species and the national affiliation of the researchers studying them, which we argue are rooted in colonialism—both past and present. Luckily, falling sequencing costs, widening availability of analytical tools and an increasingly connected scientific community provide key opportunities to improve existing assemblies, fill sampling gaps and empower a more global plant genomics community.},
	pages = {1571--1578},
	number = {12},
	journaltitle = {Nature Plants},
    journal = {Nature Plants},
	shortjournal = {Nat. Plants},
	author = {Marks, Rose A. and Hotaling, Scott and Frandsen, Paul B. and {VanBuren}, Robert},
	urldate = {2023-05-04},
	date = {2021-12},
	langid = {english},
	note = {Number: 12
Publisher: Nature Publishing Group},
	keywords = {Taxonomy, {DNA} sequencing, Plant genetics},
	file = {Full Text PDF:/home/orjuela/snap/zotero-snap/common/Zotero/storage/B938IS3M/Marks et al. - 2021 - Representation and participation across 20 years o.pdf:application/pdf},
}

@article{samtools,
  doi = {10.1093/bioinformatics/btp352},
  url = {https://doi.org/10.1093/bioinformatics/btp352},
  year = {2009},
  month = jun,
  publisher = {Oxford University Press ({OUP})},
  volume = {25},
  number = {16},
  pages = {2078--2079},
  author = {H. Li and B. Handsaker and A. Wysoker and T. Fennell and J. Ruan and N. Homer and G. Marth and G. Abecasis and R. Durbin and},
  title = {The Sequence Alignment/Map format and {SAMtools}},
  journal = {Bioinformatics}
}


@article{seqtk,
}

@article{bedtools,
}

@article{mergetags,
}

@article{lfmm,
}

@article{snmf,
}

@article{mergetags,
}