

@article{griffiths_pha4ge_2020,
	title = {The {PHA4GE} {SARS}-{CoV}-2 {Contextual} {Data} {Specification} for {Open} {Genomic} {Epidemiology}},
	url = {https://www.preprints.org/manuscript/202008.0220/v1},
	doi = {10.20944/preprints202008.0220.v1},
	abstract = {The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatic tools and resources, and advocate for greater openness, interoperability, accessibility and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a clear and present need for a fit-for-purpose, open source SARS-CoV-2 contextual data standard. As such, we have developed an extension to the INSDC pathogen package, providing a SARS-CoV-2 contextual data specification based on harmonisable, publicly available, community standards. The specification is implementable via a collection template, as well as an array of protocols and tools to support the harmonisation and submission of sequence data and contextual information to public repositories. Well-structured, rich contextual data adds value, promotes reuse, and enables aggregation and integration of disparate data sets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19.},
	language = {en},
	urldate = {2020-12-07},
	author = {Griffiths, Emma J. and Timme, Ruth E. and Page, Andrew J. and Alikhan, Nabil-Fareed and Fornika, Dan and Maguire, Finlay and Mendes, Catarina Inês and Tausch, Simon H. and Black, Allison and Connor, Thomas R. and Tyson, Gregory H. and Aanensen, David M. and Alcock, Brian and Campos, Josefina and Christoffels, Alan and Silva, Anders Gonçalves da and Hodcroft, Emma and Hsiao, William W. L. and Katz, Lee S. and Nicholls, Samuel M. and Oluniyi, Paul E. and Olawoye, Idowu B. and Raphenya, Amogelang R. and Vasconcelos, Ana Tereza R. and Witney, Adam A. and MacCannell, Duncan R.},
	month = aug,
	year = {2020},
	note = {Publisher: Preprints},
	file = {Full Text PDF:C\:\\Users\\Emma\\Zotero\\storage\\DKB3PGD2\\Griffiths et al. - 2020 - The PHA4GE SARS-CoV-2 Contextual Data Specificatio.pdf:application/pdf}
}


@misc{NextstrainNcov2020,
    title = {nextstrain/ncov},
    copyright = {MIT License},
    url = {https://github.com/nextstrain/ncov},
    abstract = {Nextstrain build for novel coronavirus SARS-CoV-2},
    urldate = {2020-11-09},
    author = {{The Nextstrain Team}},
    month = nov,
    year = {2020},
    note = {original-date: 2020-01-19T17:23:17Z},
    keywords = {ncov, nextstrain, sars-cov-2}
}


@article{Alm2020,
    title = {Geographical and temporal distribution of {SARS}-{CoV}-2 clades in the {WHO} {European} {Region}, {January} to {June} 2020},
    volume = {25},
    issn = {1560-7917},
    url = {https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.32.2001410},
    doi = {10.2807/1560-7917.ES.2020.25.32.2001410},
    abstract = {We show the distribution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three genomic nomenclature systems to all sequence data from the World Health Organization European Region available until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation, compare the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2.},
    language = {en},
    number = {32},
    urldate = {2020-08-26},
    journal = {Eurosurveillance},
    author = {Alm, Erik and Broberg, Eeva K. and Connor, Thomas and Hodcroft, Emma B. and Komissarov, Andrey B. and Maurer-Stroh, Sebastian and Melidou, Angeliki and Neher, Richard A. and O’Toole, Áine and Pereyaslov, Dmitriy and The WHO European Region Sequencing Laboratories {and} GISAID EpiCoV Group,},
    month = aug,
    year = {2020},
    note = {Publisher: European Centre for Disease Prevention and Control},
    pages = {2001410}
}


@article{Gardy2015,
    title = {Real-time digital pathogen surveillance — the time is now},
    volume = {16},
    issn = {1474-760X},
    url = {https://doi.org/10.1186/s13059-015-0726-x},
    doi = {10.1186/s13059-015-0726-x},
    abstract = {It is time to shake up public health surveillance. New technologies for sequencing, aided by friction-free approaches to data sharing, could have an impact on public health efforts.},
    urldate = {2018-01-08},
    journal = {Genome Biology},
    author = {Gardy, Jennifer and Loman, Nicholas J. and Rambaut, Andrew},
    month = jul,
    year = {2015},
    keywords = {Ebola, Real-time whole-genome sequencing, Surveillance, Salmonella},
    pages = {155},
    annote = {Pages 155 in PDF},
}


@article{Price2010,
    author = {Price, Morgan N. AND Dehal, Paramvir S. AND Arkin, Adam P.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments},
    year = {2010},
    month = {03},
    volume = {5},
    url = {https://doi.org/10.1371/journal.pone.0009490},
    pages = {1-10},
    abstract = {Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability.  Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory.  Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.},
    number = {3},
    doi = {10.1371/journal.pone.0009490}
}

@article{Stamatakis2014,
    author = {Stamatakis, Alexandros},
    title = "{RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies}",
    journal = {Bioinformatics},
    volume = {30},
    number = {9},
    pages = {1312-1313},
    year = {2014},
    month = {01},
    abstract = "{Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community.Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available.Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML.Contact:alexandros.stamatakis@h-its.orgSupplementary information:Supplementary data are available at Bioinformatics online.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btu033},
    url = {https://doi.org/10.1093/bioinformatics/btu033},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/30/9/1312/17345185/btu033.pdf},
}

@Article{Black2020,
author={Black, Allison
and MacCannell, Duncan R.
and Sibley, Thomas R.
and Bedford, Trevor},
title={Ten recommendations for supporting open pathogen genomic analysis in public health},
journal={Nature Medicine},
year={2020},
month={Jun},
day={01},
volume={26},
number={6},
pages={832-841},
abstract={Increasingly, public-health agencies are using pathogen genomic sequence data to support surveillance and epidemiological investigations. As access to whole-genome sequencing has grown, greater amounts of molecular data have helped improve the ability to detect and track outbreaks of diseases such as COVID-19, investigate transmission chains and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of whole-genome sequencing also poses new challenges for public-health agencies that must adapt to support a new set of expertise, which means that the capacity to perform genomic data assembly and analysis has not expanded as widely as the adoption of sequencing itself. In this Perspective, we make recommendations for developing an accessible, unified informatic ecosystem to support pathogen genomic analysis in public-health agencies across income settings. We hope that the creation of this ecosystem will allow agencies to effectively and efficiently share data, workflows and analyses and thereby increase the reproducibility, accessibility and auditability of pathogen genomic analysis while also supporting agency autonomy.},
issn={1546-170X},
doi={10.1038/s41591-020-0935-z},
url={https://doi.org/10.1038/s41591-020-0935-z}
}

@article {Bedford2020,
	author = {Bedford, T. and Greninger, A. L. and Roychoudhury, P. and Starita, L. M. and Famulare, M. and Huang, M. and Nalla, A. and Pepper, G. and Reinhardt, A. and Xie, H. and Shrestha, L. and Nguyen, T. N. and Adler, A. and Brandstetter, E. and Cho, S. and Giroux, D. and Han, P. D. and Fay, K. and Frazar, C. D. and Ilcisin, M. and Lacombe, K. and Lee, J. and Kiavand, A. and Richardson, M. and Sibley, T. R. and Truong, M. and Wolf, C. R. and Nickerson, D. A. and Rieder, M. J. and Englund, J. A. and The Seattle Flu Study Investigators and Hadfield, J. and Hodcroft, E. B. and Huddleston, J. and Moncla, L. H. and M{\"u}ller, N. F. and Neher, R. A. and Deng, X. and Gu, W. and Federman, S. and Chiu, C. and Duchin, J. S. and Gautom, R. and Melly, G. and Hiatt, B. and Dykema, P. and Lindquist, S. and Queen, K. and Tao, Y. and Uehara, A. and Tong, S. and MacCannell, D. and Armstrong, G. L. and Baird, G. S. and Chu, H. Y. and Shendure, J. and Jerome, K. R.},
	title = {Cryptic transmission of {SARS-CoV-2} in {W}ashington state},
	elocation-id = {eabc0523},
	year = {2020},
	month = sep,
	doi = {10.1126/science.abc0523},
	publisher = {American Association for the Advancement of Science},
	abstract = {Following its emergence in Wuhan, China, in late November or early December 2019, the SARS-CoV-2 virus has rapidly spread globally. Genome sequencing of SARS-CoV-2 allows reconstruction of its transmission history, although this is contingent on sampling. We have analyzed 453 SARS-CoV-2 genomes collected between 20 February and 15 March 2020 from infected patients in Washington State, USA. We find that most SARS-CoV-2 infections sampled during this time derive from a single introduction in late January or early February 2020 which subsequently spread locally before active community surveillance was implemented.},
	issn = {0036-8075},
	URL = {https://science.sciencemag.org/content/early/2020/09/09/science.abc0523},
	eprint = {https://science.sciencemag.org/content/early/2020/09/09/science.abc0523.full.pdf},
	journal = {Science}
}

@article{Neher:2015jr,
	Author = {Neher, Richard A and Bedford, Trevor},
	Journal = {Bioinformatics},
	Month = nov,
	Number = {21},
	Pages = {3546--3548},
	Title = {{nextflu: real-time tracking of seasonal influenza virus evolution in humans.}},
	Volume = {31},
	Year = {2015},
    doi = {10.1093/bioinformatics/btv381}
}

@article{Hadfield2018,
author = {Hadfield, J. and Megill, C. and Bell, S. M. and Huddleston, J. and Potter, B. and Callender, C. and Sagulenko, P. and Bedford, T. and Neher, R. A.},
title = {Nextstrain: real-time tracking of pathogen evolution},
journal = {Bioinformatics},
volume = {},
number = {},
pages = {bty407},
year = {2018},
month = {May},
doi = {10.1093/bioinformatics/bty407},
URL = {http://dx.doi.org/10.1093/bioinformatics/bty407},
eprint = {/oup/backfile/content_public/journal/bioinformatics/pap/10.1093_bioinformatics_bty407/2/bty407.pdf}
}

@article{Li2009,
    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},
    title = "{The Sequence Alignment/Map format and SAMtools}",
    journal = {Bioinformatics},
    volume = {25},
    number = {16},
    pages = {2078-2079},
    year = {2009},
    month = {06},
    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.netContact:rd@sanger.ac.uk}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btp352},
    url = {https://doi.org/10.1093/bioinformatics/btp352},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/25/16/2078/531810/btp352.pdf},
}

@article{Li2011,
    author = {Li, Heng},
    title = "{A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data}",
    journal = {Bioinformatics},
    volume = {27},
    number = {21},
    pages = {2987-2993},
    year = {2011},
    month = {09},
    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.netContact:hengli@broadinstitute.org}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btr509},
    url = {https://doi.org/10.1093/bioinformatics/btr509},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/27/21/2987/577342/btr509.pdf},
}

@article{Katoh2002,
    author = {Katoh, Kazutaka and Misawa, Kazuharu and Kuma, Kei‐ichi and Miyata, Takashi},
    title = "{MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform}",
    journal = {Nucleic Acids Research},
    volume = {30},
    number = {14},
    pages = {3059-3066},
    year = {2002},
    month = {07},
    abstract = "{A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT‐NS‐2) and the iterative refinement method (FFT‐NS‐i), are implemented in MAFFT. The performances of FFT‐NS‐2 and FFT‐NS‐i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT‐NS‐2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT‐NS‐i is over 100 times faster than T‐COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.}",
    issn = {0305-1048},
    doi = {10.1093/nar/gkf436},
    url = {https://doi.org/10.1093/nar/gkf436},
    eprint = {http://oup.prod.sis.lan/nar/article-pdf/30/14/3059/9488148/gkf436.pdf},
}

@article{Nguyen2014,
    author = {Nguyen, Lam-Tung and Schmidt, Heiko A. and von Haeseler, Arndt and Minh, Bui Quang},
    title = "{IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies}",
    journal = {Molecular Biology and Evolution},
    volume = {32},
    number = {1},
    pages = {268-274},
    year = {2014},
    month = {11},
    abstract = "{Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2\\% and 87.1\\% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7\\% and 47.1\\% of the DNA alignments and 42.2\\% and 100\\% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3–97.1\\%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.}",
    issn = {0737-4038},
    doi = {10.1093/molbev/msu300},
    url = {https://doi.org/10.1093/molbev/msu300},
    eprint = {http://oup.prod.sis.lan/mbe/article-pdf/32/1/268/13171186/msu300.pdf},
}

@article{Snakemake,
author = {K{\"o}ster, Johannes and Rahmann, Sven},
title = {{Snakemake—a scalable bioinformatics workflow engine}},
journal = {Bioinformatics},
year = {2012},
volume = {28},
number = {19},
pages = {2520--2522},
month = aug,
doi = {10.1093/bioinformatics/bts480}
}

@article{Sagulenko2018,
    author = {Sagulenko, Pavel and Puller, Vadim and Neher, Richard A},
    title = "{TreeTime: Maximum-likelihood phylodynamic analysis}",
    journal = {Virus Evolution},
    volume = {4},
    number = {1},
    year = {2018},
    month = {01},
    abstract = "{Mutations that accumulate in the genome of cells or viruses can be used to infer their evolutionary history. In the case of rapidly evolving organisms, genomes can reveal their detailed spatiotemporal spread. Such phylodynamic analyses are particularly useful to understand the epidemiology of rapidly evolving viral pathogens. As the number of genome sequences available for different pathogens has increased dramatically over the last years, phylodynamic analysis with traditional methods becomes challenging as these methods scale poorly with growing datasets. Here, we present TreeTime, a Python-based framework for phylodynamic analysis using an approximate Maximum Likelihood approach. TreeTime can estimate ancestral states, infer evolution models, reroot trees to maximize temporal signals, estimate molecular clock phylogenies and population size histories. The runtime of TreeTime scales linearly with dataset size.}",
    issn = {2057-1577},
    doi = {10.1093/ve/vex042},
    url = {https://doi.org/10.1093/ve/vex042},
    eprint = {http://oup.prod.sis.lan/ve/article-pdf/4/1/vex042/23361340/vex042.pdf},
}
