text:
@misc{garrels_bash_2008,
	title = {Bash {Guide} for {Beginners}},
	url = {http://www.tldp.org/LDP/Bash-Beginners-Guide/html/},
	urldate = {2018-12-05},
	author = {Garrels, Machtelt},
	month = dec,
	year = {2008},
	file = {Bash Guide for Beginners:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/Z83BKM24/html.html:text/html}
}

@book{wickham_r_2017,
	title = {R for {Data} {Science}},
	url = {https://r4ds.had.co.nz/},
	abstract = {This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.},
	urldate = {2018-12-05},
	publisher = {O'Reilly},
	author = {Wickham, Hadley and Grolemund, Garrett},
	year = {2017},
	file = {Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/CJDVVKFI/r4ds.had.co.nz.html:text/html}
}

@book{wickham_advanced_2019,
	edition = {2nd},
	title = {Advanced {R}},
	url = {https://adv-r.hadley.nz/},
	abstract = {The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R’s quirks and shows how some parts that seem horrible do have a positive side.},
	urldate = {2018-12-05},
	publisher = {Chapham \& Hall},
	author = {Wickham, Hadley},
	year = {2019},
	file = {Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/48Z8VFQB/index.html:text/html}
}

@article{hugerth_degeprime_2014,
	title = {{DegePrime}, a {Program} for {Degenerate} {Primer} {Design} for {Broad}-{Taxonomic}-{Range} {PCR} in {Microbial} {Ecology} {Studies}},
	volume = {80},
	issn = {0099-2240, 1098-5336},
	url = {http://aem.asm.org/content/80/16/5116},
	doi = {10.1128/AEM.01403-14},
	abstract = {The taxonomic composition of a microbial community can be deduced by analyzing its rRNA gene content by, e.g., high-throughput DNA sequencing or DNA chips. Such methods typically are based on PCR amplification of rRNA gene sequences using broad-taxonomic-range PCR primers. In these analyses, the use of optimal primers is crucial for achieving an unbiased representation of community composition. Here, we present the computer program DegePrime that, for each position of a multiple sequence alignment, finds a degenerate oligomer of as high coverage as possible and outputs its coverage among taxonomic divisions. We show that our novel heuristic, which we call weighted randomized combination, performs better than previously described algorithms for solving the maximum coverage degenerate primer design problem. We previously used DegePrime to design a broad-taxonomic-range primer pair that targets the bacterial V3-V4 region (341F-805R) (D. P. Herlemann, M. Labrenz, K. Jurgens, S. Bertilsson, J. J. Waniek, and A. F. Andersson, ISME J. 5:1571–1579, 2011, http://dx.doi.org/10.1038/ismej.2011.41), and here we use the program to significantly increase the coverage of a primer pair (515F-806R) widely used for Illumina-based surveys of bacterial and archaeal diversity. By comparison with shotgun metagenomics, we show that the primers give an accurate representation of microbial diversity in natural samples.},
	language = {en},
	number = {16},
	urldate = {2014-09-18},
	journal = {Applied and Environmental Microbiology},
	author = {Hugerth, Luisa W. and Wefer, Hugo A. and Lundin, Sverker and Jakobsson, Hedvig E. and Lindberg, Mathilda and Rodin, Sandra and Engstrand, Lars and Andersson, Anders F.},
	month = aug,
	year = {2014},
	note = {00000 PMID: 24928874},
	pages = {5116--5123},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/B8L5866N/Hugerth et al. - 2014 - DegePrime, a Program for Degenerate Primer Design .pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/XEFVSBZN/5116.html:text/html}
}

@article{lane_rapid_1985,
	title = {Rapid determination of 16S ribosomal {RNA} sequences for phylogenetic analyses},
	volume = {82},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/content/82/20/6955},
	doi = {10.1073/pnas.82.20.6955},
	abstract = {Although the applicability of small subunit ribosomal RNA (16S rRNA) sequences for bacterial classification is now well accepted, the general use of these molecules has been hindered by the technical difficulty of obtaining their sequences. A protocol is described for rapidly generating large blocks of 16S rRNA sequence data without isolation of the 16S rRNA or cloning of its gene. The 16S rRNA in bulk cellular RNA preparations is selectively targeted for dideoxynucleotide-terminated sequencing by using reverse transcriptase and synthetic oligodeoxynucleotide primers complementary to universally conserved 16S rRNA sequences. Three particularly useful priming sites, which provide access to the three major 16S rRNA structural domains, routinely yield 800-1000 nucleotides of 16S rRNA sequence. The method is evaluated with respect to accuracy, sensitivity to modified nucleotides in the template RNA, and phylogenetic usefulness, by examination of several 16S rRNAs whose gene sequences are known. The relative simplicity of this approach should facilitate a rapid expansion of the 16S rRNA sequence collection available for phylogenetic analyses.},
	language = {en},
	number = {20},
	urldate = {2018-12-24},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Lane, D. J. and Pace, B. and Olsen, G. J. and Stahl, D. A. and Sogin, M. L. and Pace, N. R.},
	month = oct,
	year = {1985},
	pmid = {2413450},
	pages = {6955--6959},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/SLUCZXMA/Lane et al. - 1985 - Rapid determination of 16S ribosomal RNA sequences.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/GSWWYAR8/6955.html:text/html}
}

@article{chakravorty_detailed_2007,
	title = {A detailed analysis of 16S ribosomal {RNA} gene segments for the diagnosis of pathogenic bacteria.},
	volume = {69},
	issn = {0167-7012},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2562909/},
	doi = {10.1016/j.mimet.2007.02.005},
	abstract = {Bacterial 16S ribosomal RNA (rRNA) genes contain nine “hypervariable regions” (V1 – V9) that demonstrate considerable sequence diversity among different bacteria. Species-specific sequences within a given hypervariable region constitute useful targets for diagnostic assays and other scientific investigations. No single region can differentiate among all bacteria; therefore, systematic studies that compare the relative advantage of each region for specific diagnostic goals are needed. We characterized V1 - V8 in 110 different bacterial species including common blood borne pathogens, CDC-defined select agents and environmental microflora. Sequence similarity dendrograms were created for hypervariable regions V1 – V8, and for selected combinations of regions or short segments within individual hypervariable regions that might be appropriate for DNA probing and real-time PCR. We determined that V1 best differentiated among Staphylococcus aureus and coagulase negative Staphylococcus sp. V2 and V3 were most suitable for distinguishing all bacterial species to the genus level except for closely related enterobacteriaceae. V2 best distinguished among Mycobacterial species and V3 among Haemophilus species. The 58 nucleotides-long V6 could distinguish among most bacterial species except enterobacteriaceae. V6 was also noteworthy for being able to differentiate among all CDC-defined select agents including Bacillus anthracis, which differed from B. cereus by a single polymorphism. V4, V5, V7 and V8 were less useful targets for genus or species-specific probes. The hypervariable sequence-specific dendrograms and the “MEGALIGN” files provided online will be highly useful tools for designing specific probes and primers for molecular assays to detect pathogenic bacteria, including select agents.},
	number = {2},
	urldate = {2018-12-24},
	journal = {Journal of microbiological methods},
	author = {Chakravorty, Soumitesh and Helb, Danica and Burday, Michele and Connell, Nancy and Alland, David},
	month = may,
	year = {2007},
	pmid = {17391789},
	pmcid = {PMC2562909},
	pages = {330--339},
	file = {PubMed Central Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/UQDR9EF5/Chakravorty et al. - 2007 - A detailed analysis of 16S ribosomal RNA gene segm.pdf:application/pdf}
}

@article{yeh_taxon_2017,
	title = {Taxon disappearance from microbiome analysis indicates need for mock communities as a standard in every sequencing run},
	url = {http://biorxiv.org/lookup/doi/10.1101/206219},
	doi = {10.1101/206219},
	abstract = {Mock communities have been used in microbiome method development to help estimate biases introduced in PCR amplification, sequencing, and to optimize pipeline outputs. Nevertheless, the necessity of routine mock community analysis beyond initial method development is rarely, if ever, considered. Here we report that our routine use of mock communities as internal standards allowed us to discover highly aberrant and strong biases in the relative proportions of multiple taxa in a single Illumina HiSeqPE250 run. In this run, an important archaeal taxon virtually disappeared from all samples, and other mock community taxa showed \>2-fold high or low abundance, whereas a rerun of those identical amplicons (from the same reaction tubes) on a different date yielded 'normal' results. Although obvious from the strange mock community results, due to natural variation of microbiomes at our site, we easily could have missed the problem had we not used the mock communities. The 'normal' results were validated over 4 MiSeqPE300 runs and 3 HiSeqPE250 runs, and run-to-run variation was usually low (Bray-Curtis distance was 0.12±0.04). While validating these 'normal' results, we also discovered some mock microbial taxa had relatively modest, but consistent, differences between sequencing platforms. We suggest that using mock communities in every sequencing run is essential to distinguish potentially serious aberrations from natural variations. Such mock communities should have more than just a few members and ideally at least partly represent the samples being analyzed, to detect problems that show up only in some taxa, as we observed.},
	language = {en},
	urldate = {2018-07-09},
	author = {Yeh, Yi-Chun and Needham, David and Sieradzki, Ella and Fuhrman, Jed},
	month = oct,
	year = {2017},
	file = {Yeh et al. - 2017 - Taxon disappearance from microbiome analysis indic.pdf:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/TCRHBKX6/Yeh et al. - 2017 - Taxon disappearance from microbiome analysis indic.pdf:application/pdf}
}

@article{walters_improved_2016,
	title = {Improved {Bacterial} 16S {rRNA} {Gene} ({V}4 and {V}4-5) and {Fungal} {Internal} {Transcribed} {Spacer} {Marker} {Gene} {Primers} for {Microbial} {Community} {Surveys}},
	volume = {1},
	issn = {2379-5077},
	url = {http://msystems.asm.org/lookup/doi/10.1128/mSystems.00009-15},
	doi = {10.1128/mSystems.00009-15},
	abstract = {Designing primers for PCR-based taxonomic surveys that amplify a broad range of phylotypes in varied community samples is a difﬁcult challenge, and the comparability of data sets ampliﬁed with varied primers requires attention. Here, we examined the performance of modiﬁed 16S rRNA gene and internal transcribed spacer (ITS) primers for archaea/bacteria and fungi, respectively, with nonaquatic samples. We moved primer bar codes to the 5= end, allowing for a range of different 3= primer pairings, such as the 515f/926r primer pair, which ampliﬁes variable regions 4 and 5 of the 16S rRNA gene. We additionally demonstrated that modiﬁcations to the 515f/806r (variable region 4) 16S primer pair, which improves detection of Thaumarchaeota and clade SAR11 in marine samples, do not degrade performance on taxa already ampliﬁed effectively by the original primer set. Alterations to the fungal ITS primers did result in differential but overall improved performance compared to the original primers. In both cases, the improved primers should be widely adopted for amplicon studies.},
	language = {en},
	number = {1},
	urldate = {2018-07-09},
	journal = {mSystems},
	author = {Walters, William and Hyde, Embriette R. and Berg-Lyons, Donna and Ackermann, Gail and Humphrey, Greg and Parada, Alma and Gilbert, Jack A. and Jansson, Janet K. and Caporaso, J. Gregory and Fuhrman, Jed A. and Apprill, Amy and Knight, Rob},
	editor = {Bik, Holly},
	month = feb,
	year = {2016},
	pages = {e00009--15},
	file = {Walters et al. - 2016 - Improved Bacterial 16S rRNA Gene (V4 and V4-5) and.pdf:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/8AJBK3GL/Walters et al. - 2016 - Improved Bacterial 16S rRNA Gene (V4 and V4-5) and.pdf:application/pdf}
}

@article{parada_marine_2017,
	title = {Marine archaeal dynamics and interactions with the microbial community over 5 years from surface to seafloor},
	copyright = {© 2017 Nature Publishing Group},
	issn = {1751-7362},
	url = {http://www.nature.com.ezp.sub.su.se/ismej/journal/vaop/ncurrent/full/ismej2017104a.html?foxtrotcallback=true},
	doi = {10.1038/ismej.2017.104},
	abstract = {Marine archaea are critical contributors to global carbon and nitrogen redox cycles, but their temporal variability and microbial associations across the water column are poorly known. We evaluated seasonal variability of free living (0.2–1 μm size fraction) Thaumarchaea Marine Group I (MGI) and Euryarchaea Marine Group II (MGII) communities and their associations with the microbial community from surface to seafloor (890 m) over 5 years by 16S rRNA V4-V5 gene sequencing. MGI and MGII communities demonstrated distinct compositions at different depths, and seasonality at all depths. Microbial association networks at 150 m, 500 m and 890 m, revealed diverse assemblages of MGI (presumed ammonia oxidizers) and Nitrospina taxa (presumed dominant nitrite oxidizers, completing the nitrification process), suggesting distinct MGI-Nitrospina OTUs are responsible for nitrification at different depths and seasons, and depth- related and seasonal variability in nitrification could be affected by alternating MGI-Nitrospina assemblages. MGII taxa also showed distinct correlations to possibly heterotrophic bacteria, most commonly to members of Marine Group A, Chloroflexi, Marine Group B, and SAR86. Thus, both MGI and MGII likely have dynamic associations with bacteria based on similarities in activity or other interactions that select for distinct microbial assemblages over time. The importance of MGII taxa as members of the heterotrophic community previously reported for photic zone appears to apply throughout the water column.},
	language = {en},
	urldate = {2017-08-01},
	journal = {The ISME Journal},
	author = {Parada, Alma E. and Fuhrman, Jed A.},
	month = jul,
	year = {2017},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/8LT2YPXC/Parada and Fuhrman - 2017 - Marine archaeal dynamics and interactions with the.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/898A5WA5/ismej2017104a.html:text/html}
}

@article{salter_reagent_2014,
	title = {Reagent and laboratory contamination can critically impact sequence-based microbiome analyses},
	volume = {12},
	issn = {1741-7007},
	url = {https://doi.org/10.1186/s12915-014-0087-z},
	doi = {10.1186/s12915-014-0087-z},
	abstract = {The study of microbial communities has been revolutionised in recent years by the widespread adoption of culture independent analytical techniques such as 16S rRNA gene sequencing and metagenomics. One potential confounder of these sequence-based approaches is the presence of contamination in DNA extraction kits and other laboratory reagents.},
	number = {1},
	urldate = {2018-12-24},
	journal = {BMC Biology},
	author = {Salter, Susannah J. and Cox, Michael J. and Turek, Elena M. and Calus, Szymon T. and Cookson, William O. and Moffatt, Miriam F. and Turner, Paul and Parkhill, Julian and Loman, Nicholas J. and Walker, Alan W.},
	month = nov,
	year = {2014},
	pages = {87},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/7BJLP2A3/Salter et al. - 2014 - Reagent and laboratory contamination can criticall.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/6W9FK59C/s12915-014-0087-z.html:text/html}
}

@article{straub_null_2017,
	title = {A null model for microbial diversification},
	volume = {114},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org.ezp.sub.su.se/content/114/27/E5414},
	doi = {10.1073/pnas.1619993114},
	abstract = {Whether prokaryotes (Bacteria and Archaea) are naturally organized into phenotypically and genetically cohesive units comparable to animal or plant species remains contested, frustrating attempts to estimate how many such units there might be, or to identify the ecological roles they play. Analyses of gene sequences in various closely related prokaryotic groups reveal that sequence diversity is typically organized into distinct clusters, and processes such as periodic selection and extensive recombination are understood to be drivers of cluster formation (“speciation”). However, observed patterns are rarely compared with those obtainable with simple null models of diversification under stochastic lineage birth and death and random genetic drift. Via a combination of simulations and analyses of core and phylogenetic marker genes, we show that patterns of diversity for the genera Escherichia, Neisseria, and Borrelia are generally indistinguishable from patterns arising under a null model. We suggest that caution should thus be taken in interpreting observed clustering as a result of selective evolutionary forces. Unknown forces do, however, appear to play a role in Helicobacter pylori, and some individual genes in all groups fail to conform to the null model. Taken together, we recommend the presented birth−death model as a null hypothesis in prokaryotic speciation studies. It is only when the real data are statistically different from the expectations under the null model that some speciation process should be invoked.},
	language = {en},
	number = {27},
	urldate = {2017-07-04},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Straub, Timothy J. and Zhaxybayeva, Olga},
	month = jul,
	year = {2017},
	pmid = {28630293},
	keywords = {genetic drift, prokaryotic diversity, bacterial species, pathogen typing, neutral evolution},
	pages = {E5414--E5423},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/8L43V6H6/Straub and Zhaxybayeva - 2017 - A null model for microbial diversification.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/FUZEKT6D/E5414.html:text/html}
}

@article{cohan_what_2002,
	title = {What are bacterial species?},
	volume = {56},
	journal = {Annu Rev Microbiol},
	author = {Cohan, F. M},
	year = {2002},
	note = {Bacterial systematics has not yet reached a consensus for defining the fundamental unit of biological diversity, the species. The past half-century of bacterial systematics has been characterized by improvements in methods for demarcating species as phenotypic and genetic clusters, but species demarcation has not been guided by a theory-based concept of species. Eukaryote systematists have developed a universal concept of species: A species is a group of organisms whose divergence is capped by a force of cohesion; divergence between different species is irreversible; and different species are ecologically distinct. In the case of bacteria, these universal properties are held not by the named species of systematics but by ecotypes. These are populations of organisms occupying the same ecological niche, whose divergence is purged recurrently by natural selection. These ecotypes can be discovered by several universal sequence-based approaches. These molecular methods suggest that a typical named species contains many ecotypes, each with the universal attributes of species. A named bacterial species is thus more like a genus than a species.},
	keywords = {MLST, Speciation, ecotype, genetic exchange, species concept, systematics},
	pages = {457--487},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/L8EJLLVQ/Cohan - 2002 - What are Bacterial Species.pdf:application/pdf}
}

@article{koeppel_identifying_2008,
	title = {Identifying the fundamental units of bacterial diversity: {A} paradigm shift to incorporate ecology into bacterial systematics},
	volume = {105},
	copyright = {© 2008 by The National Academy of Sciences of the USA.                    Freely available online through the PNAS open access option.},
	issn = {0027-8424, 1091-6490},
	shorttitle = {Identifying the fundamental units of bacterial diversity},
	url = {http://www.pnas.org/content/105/7/2504},
	doi = {10.1073/pnas.0712205105},
	abstract = {The central questions of bacterial ecology and evolution require a method to consistently demarcate, from the vast and diverse set of bacterial cells within a natural community, the groups playing ecologically distinct roles (ecotypes). Because of a lack of theory-based guidelines, current methods in bacterial systematics fail to divide the bacterial domain of life into meaningful units of ecology and evolution. We introduce a sequence-based approach (“ecotype simulation”) to model the evolutionary dynamics of bacterial populations and to identify ecotypes within a natural community, focusing here on two Bacillus clades surveyed from the “Evolution Canyons” of Israel. This approach has identified multiple ecotypes within traditional species, with each predicted to be an ecologically distinct lineage; many such ecotypes were confirmed to be ecologically distinct, with specialization to different canyon slopes with different solar exposures. Ecotype simulation provides a long-needed natural foundation for microbial ecology and systematics.},
	language = {en},
	number = {7},
	urldate = {2018-12-25},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Koeppel, Alexander and Perry, Elizabeth B. and Sikorski, Johannes and Krizanc, Danny and Warner, Andrew and Ward, David M. and Rooney, Alejandro P. and Brambilla, Evelyne and Connor, Nora and Ratcliff, Rodney M. and Nevo, Eviatar and Cohan, Frederick M.},
	month = feb,
	year = {2008},
	pmid = {18272490},
	keywords = {Bacillus, ecotype, species concept, Evolution Canyon, periodic selection},
	pages = {2504--2509},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/PJQ5FEXG/Koeppel et al. - 2008 - Identifying the fundamental units of bacterial div.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/UMY7U64R/2504.html:text/html}
}

@article{caro-quintero_bacterial_2012,
	title = {Bacterial species may exist, metagenomics reveal},
	volume = {14},
	copyright = {© 2011 Society for Applied Microbiology and Blackwell Publishing Ltd},
	issn = {1462-2920},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1462-2920.2011.02668.x/abstract},
	doi = {10.1111/j.1462-2920.2011.02668.x},
	abstract = {Whether or not bacterial species exist remains an unresolved issue of paramount theoretical as well as practical consequences. Here we review and synthesize the findings emerging from metagenomic surveys of natural microbial populations and argue that microbial communities are predominantly organized in genetically and ecologically discernible populations, which possess the attributes expected for species. These sequence-discrete populations represent a major foundation for beginning high-resolution investigations on how populations are organized, interact, and evolve within communities. We also attempt to reconcile these findings with those of previous studies that reported indiscrete species and a genetic continuum within bacterial taxa and discuss the implications for the current bacterial species definition.},
	language = {en},
	number = {2},
	urldate = {2014-09-18},
	journal = {Environmental Microbiology},
	author = {Caro-Quintero, Alejandro and Konstantinidis, Konstantinos T.},
	month = feb,
	year = {2012},
	note = {00033},
	pages = {347--355},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/Z4A3JWTV/Caro-Quintero and Konstantinidis - 2012 - Bacterial species may exist, metagenomics reveal.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/8EXCY4UV/abstract.html:text/html}
}

@article{konstantinidis_bacterial_2006,
	title = {The bacterial species definition in the genomic era},
	volume = {361},
	url = {https://royalsocietypublishing.org/doi/full/10.1098/rstb.2006.1920},
	doi = {10.1098/rstb.2006.1920},
	abstract = {The bacterial species definition, despite its eminent practical significance for identification, diagnosis, quarantine and diversity surveys, remains a very difficult issue to advance. Genomics now offers novel insights into intra-species diversity and the potential for emergence of a more soundly based system. Although we share the excitement, we argue that it is premature for a universal change to the definition because current knowledge is based on too few phylogenetic groups and too few samples of natural populations. Our analysis of five important bacterial groups suggests, however, that more stringent standards for species may be justifiable when a solid understanding of gene content and ecological distinctiveness becomes available. Our analysis also reveals what is actually encompassed in a species according to the current standards, in terms of whole-genome sequence and gene-content diversity, and shows that this does not correspond to coherent clusters for the environmental Burkholderia and Shewanella genera examined. In contrast, the obligatory pathogens, which have a very restricted ecological niche, do exhibit clusters. Therefore, the idea of biologically meaningful clusters of diversity that applies to most eukaryotes may not be universally applicable in the microbial world, or if such clusters exist, they may be found at different levels of distinction.},
	number = {1475},
	urldate = {2018-12-25},
	journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
	author = {Konstantinidis, Konstantinos T and Ramette, Alban and Tiedje, James M},
	month = nov,
	year = {2006},
	pages = {1929--1940},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/U2753EHH/Konstantinidis Konstantinos T et al. - 2006 - The bacterial species definition in the genomic er.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/9PCQBF7X/rstb.2006.html:text/html}
}

@article{tindall_notes_2010,
	title = {Notes on the characterization of prokaryote strains for taxonomic purposes},
	volume = {60},
	url = {https://ijs.microbiologyresearch.org/content/journal/ijsem/10.1099/ijs.0.016949-0},
	doi = {10.1099/ijs.0.016949-0},
	abstract = {Taxonomy relies on three key elements: characterization, classification and nomenclature. All three elements are dynamic fields, but each step depends on the one which precedes it. Thus, the nomenclature of a group of organisms depends on the way they are classified, and the classification (among other elements) depends on the information gathered as a result of characterization. While nomenclature is governed by the Bacteriological Code, the classification and characterization of prokaryotes is an area that is not formally regulated and one in which numerous changes have taken place in the last 50 years. The purpose of the present article is to outline the key elements in the way that prokaryotes are characterized, with a view to providing an overview of some of the pitfalls commonly encountered in taxonomic papers.},
	number = {1},
	urldate = {2018-12-25},
	journal = {International Journal of Systematic and Evolutionary Microbiology},
	author = {Tindall, B. J. and Rosselló-Móra, R. and Busse, H.-J. and Ludwig, W. and Kämpfer, P.},
	year = {2010},
	pages = {249--266},
	file = {Full Text:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/VLWUVKQI/Tindall et al. - 2010 - Notes on the characterization of prokaryote strain.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/5MGJMJVU/ijs.0.html:text/html}
}

@article{kim_towards_2014,
	title = {Towards a taxonomic coherence between average nucleotide identity and 16S {rRNA} gene sequence similarity for species demarcation of prokaryotes},
	volume = {64},
	url = {https://ijs.microbiologyresearch.org/content/journal/ijsem/10.1099/ijs.0.059774-0},
	doi = {10.1099/ijs.0.059774-0},
	abstract = {Among available genome relatedness indices, average nucleotide identity (ANI) is one of the most robust measurements of genomic relatedness between strains, and has great potential in the taxonomy of bacteria and archaea as a substitute for the labour-intensive DNA–DNA hybridization (DDH) technique. An ANI threshold range (95–96 \%) for species demarcation had previously been suggested based on comparative investigation between DDH and ANI values, albeit with rather limited datasets. Furthermore, its generality was not tested on all lineages of prokaryotes. Here, we investigated the overall distribution of ANI values generated by pairwise comparison of 6787 genomes of prokaryotes belonging to 22 phyla to see whether the suggested range can be applied to all species. There was an apparent distinction in the overall ANI distribution between intra- and interspecies relationships at around 95–96 \% ANI. We went on to determine which level of 16S rRNA gene sequence similarity corresponds to the currently accepted ANI threshold for species demarcation using over one million comparisons. A twofold cross-validation statistical test revealed that 98.65 \% 16S rRNA gene sequence similarity can be used as the threshold for differentiating two species, which is consistent with previous suggestions (98.2–99.0 \%) derived from comparative studies between DDH and 16S rRNA gene sequence similarity. Our findings should be useful in accelerating the use of genomic sequence data in the taxonomy of bacteria and archaea.},
	number = {2},
	urldate = {2018-12-25},
	journal = {International Journal of Systematic and Evolutionary Microbiology},
	author = {Kim, Mincheol and Oh, Hyun-Seok and Park, Sang-Cheol and Chun, Jongsik},
	year = {2014},
	pages = {346--351},
	file = {Full Text:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/US6BLFPT/Kim et al. - 2014 - Towards a taxonomic coherence between average nucl.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/ZNL82GGY/ijs.0.html:text/html}
}

@article{edgar_uparse:_2013,
	title = {{UPARSE}: highly accurate {OTU} sequences from microbial amplicon reads},
	volume = {10},
	copyright = {© 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1548-7091},
	shorttitle = {{UPARSE}},
	url = {http://www.nature.com/nmeth/journal/v10/n10/abs/nmeth.2604.html},
	doi = {10.1038/nmeth.2604},
	abstract = {Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1\% incorrect bases in artificial microbial community tests, compared with {\textgreater}3\% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.},
	language = {en},
	number = {10},
	urldate = {2015-04-20},
	journal = {Nature Methods},
	author = {Edgar, Robert C.},
	month = oct,
	year = {2013},
	note = {00147},
	pages = {996--998},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/PPH824ZS/Edgar - 2013 - UPARSE highly accurate OTU sequences from microbi.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/P4EL5W7L/nmeth.2604.html:text/html}
}

@article{edgar_search_2010,
	title = {Search and clustering orders of magnitude faster than {BLAST}},
	volume = {26},
	issn = {1367-4803, 1460-2059},
	url = {http://bioinformatics.oxfordjournals.org/content/26/19/2460},
	doi = {10.1093/bioinformatics/btq461},
	abstract = {Motivation: Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification.
Results: UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.
Availability: Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch
Contact: robert@drive5.com
Supplementary information: Supplementary data are available at Bioinformatics online.},
	language = {en},
	number = {19},
	urldate = {2012-10-24},
	journal = {Bioinformatics},
	author = {Edgar, Robert C.},
	month = oct,
	year = {2010},
	pages = {2460--2461},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/RWBY4YE7/Edgar - 2010 - Search and clustering orders of magnitude faster t.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/TUP33SWE/2460.html:text/html}
}

@article{rognes_vsearch:_2016,
	title = {{VSEARCH}: a versatile open source tool for metagenomics},
	volume = {4},
	issn = {2167-8359},
	shorttitle = {{VSEARCH}},
	url = {https://peerj.com/articles/2584},
	doi = {10.7717/peerj.2584},
	abstract = {Background VSEARCH is an open source and free of charge multithreaded 64-bit tool for processing and preparing metagenomics, genomics and population genomics nucleotide sequence data. It is designed as an alternative to the widely used USEARCH tool (Edgar, 2010) for which the source code is not publicly available, algorithm details are only rudimentarily described, and only a memory-confined 32-bit version is freely available for academic use. Methods When searching nucleotide sequences, VSEARCH uses a fast heuristic based on words shared by the query and target sequences in order to quickly identify similar sequences, a similar strategy is probably used in USEARCH. VSEARCH then performs optimal global sequence alignment of the query against potential target sequences, using full dynamic programming instead of the seed-and-extend heuristic used by USEARCH. Pairwise alignments are computed in parallel using vectorisation and multiple threads. Results VSEARCH includes most commands for analysing nucleotide sequences available in USEARCH version 7 and several of those available in USEARCH version 8, including searching (exact or based on global alignment), clustering by similarity (using length pre-sorting, abundance pre-sorting or a user-defined order), chimera detection (reference-based or de novo), dereplication (full length or prefix), pairwise alignment, reverse complementation, sorting, and subsampling. VSEARCH also includes commands for FASTQ file processing, i.e., format detection, filtering, read quality statistics, and merging of paired reads. Furthermore, VSEARCH extends functionality with several new commands and improvements, including shuffling, rereplication, masking of low-complexity sequences with the well-known DUST algorithm, a choice among different similarity definitions, and FASTQ file format conversion. VSEARCH is here shown to be more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling, while on a par with USEARCH for paired-ends read merging. VSEARCH is slower than USEARCH when performing clustering and chimera detection, but significantly faster when performing paired-end reads merging and dereplication. VSEARCH is available at https://github.com/torognes/vsearch under either the BSD 2-clause license or the GNU General Public License version 3.0. Discussion VSEARCH has been shown to be a fast, accurate and full-fledged alternative to USEARCH. A free and open-source versatile tool for sequence analysis is now available to the metagenomics community.},
	language = {en},
	urldate = {2017-02-24},
	journal = {PeerJ},
	author = {Rognes, Torbjørn and Flouri, Tomáš and Nichols, Ben and Quince, Christopher and Mahé, Frédéric},
	month = oct,
	year = {2016},
	pages = {e2584},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/HF8YC949/Rognes m. fl. - 2016 - VSEARCH a versatile open source tool for metageno.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/S8UPE6ZM/2584.html:text/html}
}

@article{callahan_dada2:_2016,
	title = {{DADA}2: {High}-resolution sample inference from {Illumina} amplicon data},
	volume = {13},
	copyright = {© 2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	issn = {1548-7091},
	shorttitle = {{DADA}2},
	url = {http://www.nature.com.ezp.sub.su.se/nmeth/journal/v13/n7/full/nmeth.3869.html},
	doi = {10.1038/nmeth.3869},
	abstract = {We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.},
	language = {en},
	number = {7},
	urldate = {2016-09-19},
	journal = {Nature Methods},
	author = {Callahan, Benjamin J. and McMurdie, Paul J. and Rosen, Michael J. and Han, Andrew W. and Johnson, Amy Jo A. and Holmes, Susan P.},
	month = jul,
	year = {2016},
	keywords = {Metagenomics, Software, Statistical methods},
	pages = {581--583},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/TDHG4BNI/Callahan et al. - 2016 - DADA2 High-resolution sample inference from Illum.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/EFBL7DXJ/nmeth.3869.html:text/html}
}

@article{nearing_denoising_2018,
	title = {Denoising the {Denoisers}: an independent evaluation of microbiome sequence error-correction approaches},
	volume = {6},
	issn = {2167-8359},
	shorttitle = {Denoising the {Denoisers}},
	url = {https://peerj.com/articles/5364},
	doi = {10.7717/peerj.5364},
	abstract = {High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel “denoising” pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97\% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray–Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.},
	language = {en},
	urldate = {2018-10-29},
	journal = {PeerJ},
	author = {Nearing, Jacob T. and Douglas, Gavin M. and Comeau, André M. and Langille, Morgan G. I.},
	month = aug,
	year = {2018},
	pages = {e5364},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/L4C2FFSH/Nearing et al. - 2018 - Denoising the Denoisers an independent evaluation.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/M96USDEL/5364.html:text/html}
}

@article{amir_deblur_2017,
	title = {Deblur {Rapidly} {Resolves} {Single}-{Nucleotide} {Community} {Sequence} {Patterns}},
	volume = {2},
	copyright = {Copyright © 2017 Amir et al.. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.},
	issn = {2379-5077},
	url = {http://msystems.asm.org/content/2/2/e00191-16},
	doi = {10.1128/mSystems.00191-16},
	abstract = {High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur’s ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur.
IMPORTANCE Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.},
	language = {en},
	number = {2},
	urldate = {2017-04-25},
	journal = {mSystems},
	author = {Amir, Amnon and McDonald, Daniel and Navas-Molina, Jose A. and Kopylova, Evguenia and Morton, James T. and Xu, Zhenjiang Zech and Kightley, Eric P. and Thompson, Luke R. and Hyde, Embriette R. and Gonzalez, Antonio and Knight, Rob},
	month = apr,
	year = {2017},
	pages = {e00191--16},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/9PI3S9BU/Amir et al. - 2017 - Deblur Rapidly Resolves Single-Nucleotide Communit.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/UGJ9URVJ/e00191-16.html:text/html}
}

@article{wang_naive_2007,
	title = {Naïve {Bayesian} {Classifier} for {Rapid} {Assignment} of {rRNA} {Sequences} into the {New} {Bacterial} {Taxonomy}},
	volume = {73},
	copyright = {American Society for Microbiology},
	issn = {0099-2240, 1098-5336},
	url = {https://aem.asm.org/content/73/16/5261},
	doi = {10.1128/AEM.00062-07},
	abstract = {The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98\%) were of high estimated confidence (≥95\%) and high accuracy (98\%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89\% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.},
	language = {en},
	number = {16},
	urldate = {2018-10-29},
	journal = {Appl. Environ. Microbiol.},
	author = {Wang, Qiong and Garrity, George M. and Tiedje, James M. and Cole, James R.},
	month = aug,
	year = {2007},
	pmid = {17586664},
	pages = {5261--5267},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/RZTK72RU/Wang et al. - 2007 - Naïve Bayesian Classifier for Rapid Assignment of .pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/BVV8KT7I/5261.html:text/html}
}

@article{illumina_understanding_2014,
	title = {Understanding {Illumina} {Quality} {Scores}},
	url = {https://www.illumina.com/content/dam/illumina-marketing/documents/products/technotes/technote_understanding_quality_scores.pdf},
	abstract = {Quality tables are created to provide quality scores for runs generated by specific instrument configurations and versions of chemistry. When significant characteristics of the sequencing platform change, the quality model may require recalibration. Therefore, to generate the most accurate quality scores, Illumina recommends using only the software version optimized specifically for the hardware and chemistry configuration currently in use.},
	language = {en},
	journal = {Illumina technical notes},
	author = {Illumina},
	month = apr,
	year = {2014},
	pages = {2},
	file = {Understanding Illumina Quality Scores.pdf:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/EUPGKR77/Understanding Illumina Quality Scores.pdf:application/pdf}
}

@article{altschul_gapped_1997,
	title = {Gapped {BLAST} and {PSI}-{BLAST}: a new generation of protein database search programs.},
	volume = {25},
	number = {17},
	journal = {Nucleic Acids Res},
	author = {Altschul, S. F and Madden, T. L and Schaffer, A. A and Zhang, J. and Zhang, Z. and Miller, W. and Lipman, D. J},
	month = sep,
	year = {1997},
	note = {The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.},
	pages = {3389--3402}
}

@article{henikoff_amino_1992,
	title = {Amino acid substitution matrices from protein blocks.},
	volume = {89},
	issn = {0027-8424, 1091-6490},
	url = {http://www.pnas.org/cgi/doi/10.1073/pnas.89.22.10915},
	doi = {10.1073/pnas.89.22.10915},
	abstract = {Methods for alignment of protein sequences typically measure similait by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.},
	language = {en},
	number = {22},
	urldate = {2019-03-26},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Henikoff, S. and Henikoff, J. G.},
	month = nov,
	year = {1992},
	pages = {10915--10919},
	file = {Henikoff and Henikoff - 1992 - Amino acid substitution matrices from protein bloc.pdf:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/FBEEW4JN/Henikoff and Henikoff - 1992 - Amino acid substitution matrices from protein bloc.pdf:application/pdf}
}
@article{saitou_neighbor-joining_1987,
	title = {The neighbor-joining method: a new method for reconstructing phylogenetic trees.},
	volume = {4},
	number = {4},
	journal = {Mol Biol Evol},
	author = {Saitou, N. and Nei, M.},
	month = jul,
	year = {1987},
	note = {A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.},
	pages = {406--425}
}

@article{criscuolo_bmge_2010,
	title = {{BMGE} ({Block} {Mapping} and {Gathering} with {Entropy}): a new software for selection of phylogenetic informative regions from multiple sequence alignments},
	volume = {10},
	issn = {1471-2148},
	shorttitle = {{BMGE} ({Block} {Mapping} and {Gathering} with {Entropy})},
	url = {http://dx.doi.org/10.1186/1471-2148-10-210},
	doi = {10.1186/1471-2148-10-210},
	abstract = {The quality of multiple sequence alignments plays an important role in the accuracy of phylogenetic inference. It has been shown that removing ambiguously aligned regions, but also other sources of bias such as highly variable (saturated) characters, can improve the overall performance of many phylogenetic reconstruction methods. A current scientific trend is to build phylogenetic trees from a large number of sequence datasets (semi-)automatically extracted from numerous complete genomes. Because these approaches do not allow a precise manual curation of each dataset, there exists a real need for efficient bioinformatic tools dedicated to this alignment character trimming step.},
	urldate = {2016-06-16},
	journal = {BMC Evolutionary Biology},
	author = {Criscuolo, Alexis and Gribaldo, Simonetta},
	year = {2010},
	pages = {210},
	annote = {Pages 1-21 in PDF},
	file = {Full Text PDF:/home/dl/Zotero/storage/E9CXKRW9/Criscuolo och Gribaldo - 2010 - BMGE (Block Mapping and Gathering with Entropy) a.pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/NI2W4ADW/1471-2148-10-210.html:text/html}
}

@article{price_fasttree_2010,
	title = {{FastTree} 2 – {Approximately} {Maximum}-{Likelihood} {Trees} for {Large} {Alignments}},
	volume = {5},
	issn = {1932-6203},
	url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0009490},
	doi = {10.1371/journal.pone.0009490},
	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},
	urldate = {2017-04-28},
	journal = {PLOS ONE},
	author = {Price, Morgan N. and Dehal, Paramvir S. and Arkin, Adam P.},
	month = mar,
	year = {2010},
	keywords = {Phylogenetic analysis, Sequence Alignment, Ribosomal RNA, Biochemical simulations, Biophysical simulations, Multiple alignment calculation, Protein structure comparison, Optimization},
	pages = {e9490},
	file = {Full Text PDF:/home/dl/Zotero/storage/GJU6DVGR/Price m. fl. - 2010 - FastTree 2 – Approximately Maximum-Likelihood Tree.pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/UT92BZIX/article.html:text/html}
}

@article{huson_dendroscope:_2007,
	title = {Dendroscope: {An} interactive viewer for large phylogenetic trees},
	volume = {8},
	issn = {1471-2105},
	shorttitle = {Dendroscope},
	url = {https://doi.org/10.1186/1471-2105-8-460},
	doi = {10.1186/1471-2105-8-460},
	abstract = {Research in evolution requires software for visualizing and editing phylogenetic trees, for increasingly very large datasets, such as arise in expression analysis or metagenomics, for example. It would be desirable to have a program that provides these services in an effcient and user-friendly way, and that can be easily installed and run on all major operating systems. Although a large number of tree visualization tools are freely available, some as a part of more comprehensive analysis packages, all have drawbacks in one or more domains. They either lack some of the standard tree visualization techniques or basic graphics and editing features, or they are restricted to small trees containing only tens of thousands of taxa. Moreover, many programs are diffcult to install or are not available for all common operating systems.},
	number = {1},
	urldate = {2018-09-21},
	journal = {BMC Bioinformatics},
	author = {Huson, Daniel H. and Richter, Daniel C. and Rausch, Christian and Dezulian, Tobias and Franz, Markus and Rupp, Regula},
	month = nov,
	year = {2007},
	pages = {460},
	file = {Full Text PDF:/home/dl/Zotero/storage/YHI3B6UA/Huson et al. - 2007 - Dendroscope An interactive viewer for large phylo.pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/T6XQIHEI/1471-2105-8-460.html:text/html}
}

@article{lefort_fastme_2015,
	title = {{FastME} 2.0: {A} {Comprehensive}, {Accurate}, and {Fast} {Distance}-{Based} {Phylogeny} {Inference} {Program}},
	volume = {32},
	issn = {0737-4038},
	shorttitle = {{FastME} 2.0},
	url = {https://academic.oup.com/mbe/article/32/10/2798/1212138},
	doi = {10.1093/molbev/msv150},
	abstract = {Abstract.  FastME provides distance algorithms to infer phylogenies. FastME is based on balanced minimum evolution, which is the very principle of Neighbor Join},
	language = {en},
	number = {10},
	urldate = {2019-04-12},
	journal = {Molecular Biology and Evolution},
	author = {Lefort, Vincent and Desper, Richard and Gascuel, Olivier},
	month = oct,
	year = {2015},
	keywords = {FastME},
	pages = {2798--2800},
	file = {Full Text PDF:/home/dl/Zotero/storage/B8CEXDHE/Lefort et al. - 2015 - FastME 2.0 A Comprehensive, Accurate, and Fast Di.pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/CJ4FFD2T/1212138.html:text/html}
}

@article{huson_splitstree_1998,
	title = {{SplitsTree}: analyzing and visualizing evolutionary data.},
	volume = {14},
	issn = {1367-4803, 1460-2059},
	shorttitle = {{SplitsTree}},
	url = {http://bioinformatics.oxfordjournals.org/content/14/1/68},
	doi = {10.1093/bioinformatics/14.1.68},
	abstract = {MOTIVATION: Real evolutionary data often contain a number of different and sometimes conflicting phylogenetic signals, and thus do not always clearly support a unique tree. To address this problem, Bandelt and Dress (Adv. Math., 92, 47-05, 1992) developed the method of split decomposition. For ideal data, this method gives rise to a tree, whereas less ideal data are represented by a tree-like network that may indicate evidence for different and conflicting phylogenies. RESULTS: SplitsTree is an interactive program, for analyzing and visualizing evolutionary data, that implements this approach. It also supports a number of distances transformations, the computation of parsimony splits, spectral analysis and bootstrapping.},
	language = {en},
	number = {1},
	urldate = {2014-01-23},
	journal = {Bioinformatics},
	author = {Huson, D. H.},
	month = jan,
	year = {1998},
	note = {00922 PMID: 9520503},
	keywords = {SplitsTree},
	pages = {68--73},
	file = {Full Text PDF:/home/dl/Zotero/storage/NEGKRMJI/Huson - 1998 - SplitsTree analyzing and visualizing evolutionary.pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/FX4MHH7C/68.html:text/html}
}

@article{huson_dendroscope_2007,
	title = {Dendroscope: {An} interactive viewer for large phylogenetic trees},
	volume = {8},
	issn = {1471-2105},
	shorttitle = {Dendroscope},
	url = {https://doi.org/10.1186/1471-2105-8-460},
	doi = {10.1186/1471-2105-8-460},
	abstract = {Research in evolution requires software for visualizing and editing phylogenetic trees, for increasingly very large datasets, such as arise in expression analysis or metagenomics, for example. It would be desirable to have a program that provides these services in an effcient and user-friendly way, and that can be easily installed and run on all major operating systems. Although a large number of tree visualization tools are freely available, some as a part of more comprehensive analysis packages, all have drawbacks in one or more domains. They either lack some of the standard tree visualization techniques or basic graphics and editing features, or they are restricted to small trees containing only tens of thousands of taxa. Moreover, many programs are diffcult to install or are not available for all common operating systems.},
	number = {1},
	urldate = {2018-09-21},
	journal = {BMC Bioinformatics},
	author = {Huson, Daniel H. and Richter, Daniel C. and Rausch, Christian and Dezulian, Tobias and Franz, Markus and Rupp, Regula},
	month = nov,
	year = {2007},
	pages = {460},
	file = {Full Text PDF:/home/dl/Zotero/storage/YHI3B6UA/Huson et al. - 2007 - Dendroscope An interactive viewer for large phylo.pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/T6XQIHEI/1471-2105-8-460.html:text/html}
}

@article{bryant_neighbor-net_2004,
	title = {Neighbor-net: an agglomerative method for the construction of phylogenetic networks.},
	volume = {21},
	number = {2},
	journal = {Mol Biol Evol},
	author = {Bryant, D. and Moulton, V.},
	month = feb,
	year = {2004},
	note = {We present Neighbor-Net, a distance based method for constructing phylogenetic networks that is based on the Neighbor-Joining (NJ) algorithm of Saitou and Nei. Neighbor-Net provides a snapshot of the data that can guide more detailed analysis. Unlike split decomposition, Neighbor-Net scales well and can quickly produce detailed and informative networks for several hundred taxa. We illustrate the method by reanalyzing three published data sets: a collection of 110 highly recombinant Salmonella multi-locus sequence typing sequences, the 135 "African Eve" human mitochondrial sequences published by Vigilant et al., and a collection of 12 Archeal chaperonin sequences demonstrating strong evidence for gene conversion. Neighbor-Net is available as part of the SplitsTree4 software package.},
	keywords = {NeighborNet},
	pages = {255--265},
	file = {bryant_moulton_2004.pdf:/home/dl/Zotero/storage/IEVV5FCP/bryant_moulton_2004.pdf:application/pdf}
}

@article{gouy_seaview_2010,
	title = {{SeaView} {Version} 4: {A} {Multiplatform} {Graphical} {User} {Interface} for {Sequence} {Alignment} and {Phylogenetic} {Tree} {Building}},
	volume = {27},
	issn = {0737-4038},
	shorttitle = {{SeaView} {Version} 4},
	url = {https://academic.oup.com/mbe/article/27/2/221/970247},
	doi = {10.1093/molbev/msp259},
	abstract = {Abstract.  We present SeaView version 4, a multiplatform program designed to facilitate multiple alignment and phylogenetic tree building from molecular sequenc},
	language = {en},
	number = {2},
	urldate = {2019-04-12},
	journal = {Molecular Biology and Evolution},
	author = {Gouy, Manolo and Guindon, Stéphane and Gascuel, Olivier},
	month = feb,
	year = {2010},
	pages = {221--224},
	file = {Full Text PDF:/home/dl/Zotero/storage/9E4F4DJV/Gouy et al. - 2010 - SeaView Version 4 A Multiplatform Graphical User .pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/YQJVGYXX/970247.html:text/html}
}

@article{thompson_multiple_2003,
	title = {Multiple {Sequence} {Alignment} {Using} {ClustalW} and {ClustalX}},
	volume = {00},
	copyright = {© 2003 by John Wiley \& Sons, Inc. All rights reserved.},
	issn = {1934-340X},
	url = {https://currentprotocols.onlinelibrary.wiley.com/doi/abs/10.1002/0471250953.bi0203s00},
	doi = {10.1002/0471250953.bi0203s00},
	abstract = {The Clustal programs are widely used for carrying out automatic multiple alignment of nucleotide or amino acid sequences. The most familiar version is ClustalW, which uses a simple text menu system that is portable to more or less all computer systems. ClustalX features a graphical user interface and some powerful graphical utilities for aiding the interpretation of alignments and is the preferred version for interactive usage. Users may run Clustal remotely from several sites using the Web or the programs may be downloaded and run locally on PCs, Macintosh, or Unix computers. The protocols in this unit discuss how to use ClustalX and ClustalW to construct an alignment, and create profile alignments by merging existing alignments.},
	language = {en},
	number = {1},
	urldate = {2019-04-12},
	journal = {Current Protocols in Bioinformatics},
	author = {Thompson, Julie D. and Gibson, Toby J. and Higgins, Des G.},
	year = {2003},
	pages = {2.3.1--2.3.22},
	file = {Full Text PDF:/home/dl/Zotero/storage/AALE4UMJ/Thompson et al. - 2003 - Multiple Sequence Alignment Using ClustalW and Clu.pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/7DCGLGG9/0471250953.html:text/html}
}

@article{clamp_jalview_2004,
	title = {The {Jalview} {Java} alignment editor.},
	volume = {20},
	number = {3},
	journal = {Bioinformatics},
	author = {Clamp, M. and Cuff, J. and Searle, S. M and Barton, G. J},
	month = feb,
	year = {2004},
	note = {Multiple sequence alignment remains a crucial method for understanding the function of groups of related nucleic acid and protein sequences. However, it is known that automatic multiple sequence alignments can often be improved by manual editing. Therefore, tools are needed to view and edit multiple sequence alignments. Due to growth in the sequence databases, multiple sequence alignments can often be large and difficult to view efficiently. The Jalview Java alignment editor is presented here, which enables fast viewing and editing of large multiple sequence alignments.},
	pages = {426--427}
}

@book{felsenstein_inferring_2004,
	address = {Sunderland, Massachusetts},
	title = {Inferring phylogenies},
	publisher = {Sinauer Associates},
	author = {Felsenstein, J.},
	year = {2004}
}

@article{stamatakis_raxml_2014,
	title = {{RAxML} {Version} 8: {A} tool for {Phylogenetic} {Analysis} and {Post}-{Analysis} of {Large} {Phylogenies}},
	issn = {1367-4803, 1460-2059},
	shorttitle = {{RAxML} {Version} 8},
	url = {http://bioinformatics.oxfordjournals.org/content/early/2014/01/21/bioinformatics.btu033},
	doi = {10.1093/bioinformatics/btu033},
	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: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML.
Contact: Alexandros.Stamatakis@h-its.org},
	language = {en},
	urldate = {2014-01-24},
	journal = {Bioinformatics},
	author = {Stamatakis, Alexandros},
	month = jan,
	year = {2014},
	note = {00000 PMID: 24451623},
	pages = {btu033},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/C6EQ3WWP/Stamatakis - 2014 - RAxML Version 8 A tool for Phylogenetic Analysis .pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/WK29CFWK/bioinformatics.btu033.html:text/html}
}

@article{bouckaert_beast_2019,
	title = {{BEAST} 2.5: {An} advanced software platform for {Bayesian} evolutionary analysis},
	volume = {15},
	issn = {1553-7358},
	shorttitle = {{BEAST} 2.5},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006650},
	doi = {10.1371/journal.pcbi.1006650},
	abstract = {Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.},
	language = {en},
	number = {4},
	urldate = {2019-04-08},
	journal = {PLOS Computational Biology},
	author = {Bouckaert, Remco and Vaughan, Timothy G. and Barido-Sottani, Joëlle and Duchêne, Sebastián and Fourment, Mathieu and Gavryushkina, Alexandra and Heled, Joseph and Jones, Graham and Kühnert, Denise and Maio, Nicola De and Matschiner, Michael and Mendes, Fábio K. and Müller, Nicola F. and Ogilvie, Huw A. and Plessis, Louis du and Popinga, Alex and Rambaut, Andrew and Rasmussen, David and Siveroni, Igor and Suchard, Marc A. and Wu, Chieh-Hsi and Xie, Dong and Zhang, Chi and Stadler, Tanja and Drummond, Alexei J.},
	month = apr,
	year = {2019},
	keywords = {Evolutionary genetics, Phylogenetic analysis, Phylogenetics, Network analysis, Simulation and modeling, Gene flow, Genetic loci, Genetic networks},
	pages = {e1006650},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/WY3PJNGF/Bouckaert et al. - 2019 - BEAST 2.5 An advanced software platform for Bayes.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/2NNNMMYJ/article.html:text/html}
}

@article{ronquist_mrbayes_2003,
	title = {{MrBayes} 3: {Bayesian} phylogenetic inference under mixed models},
	volume = {19},
	journal = {Bioinformatics},
	author = {Ronquist, F. and Huelsenbeck, J. P},
	year = {2003},
	pages = {1572--1574}
}

@article{lartillot_phylobayes_2009,
	title = {{PhyloBayes} 3: a {Bayesian} software package for phylogenetic reconstruction and molecular dating},
	volume = {25},
	issn = {1460-2059, 1367-4803},
	shorttitle = {{PhyloBayes} 3},
	url = {https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp368},
	doi = {10.1093/bioinformatics/btp368},
	abstract = {Motivation: A variety of probabilistic models describing the evolution of DNA or protein sequences have been proposed for phylogenetic reconstruction or for molecular dating. However, there still lacks a common implementation allowing one to freely combine these independent features, so as to test their ability to jointly improve phylogenetic and dating accuracy.},
	language = {en},
	number = {17},
	urldate = {2019-04-23},
	journal = {Bioinformatics},
	author = {Lartillot, Nicolas and Lepage, Thomas and Blanquart, Samuel},
	month = sep,
	year = {2009},
	pages = {2286--2288},
	file = {Lartillot et al. - 2009 - PhyloBayes 3 a Bayesian software package for phyl.pdf:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/MML4VUUK/Lartillot et al. - 2009 - PhyloBayes 3 a Bayesian software package for phyl.pdf:application/pdf}
}

@article{guindon_estimating_2009,
	title = {Estimating maximum likelihood phylogenies with {PhyML}},
	volume = {537},
	issn = {1064-3745},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/19378142},
	doi = {10.1007/978-1-59745-251-9_6},
	abstract = {Our understanding of the origins, the functions and/or the structures of biological sequences strongly depends on our ability to decipher the mechanisms of molecular evolution. These complex processes can be described through the comparison of homologous sequences in a phylogenetic framework. Moreover, phylogenetic inference provides sound statistical tools to exhibit the main features of molecular evolution from the analysis of actual sequences. This chapter focuses on phylogenetic tree estimation under the maximum likelihood (ML) principle. Phylogenies inferred under this probabilistic criterion are usually reliable and important biological hypotheses can be tested through the comparison of different models. Estimating ML phylogenies is computationally demanding, and careful examination of the results is warranted. This chapter focuses on PhyML, a software that implements recent ML phylogenetic methods and algorithms. We illustrate the strengths and pitfalls of this program through the analysis of a real data set. PhyML v3.0 is available from (http://atgc\_montpellier.fr/phyml/).},
	urldate = {2010-10-08},
	journal = {Methods in Molecular Biology (Clifton, N.J.)},
	author = {Guindon, Stéphane and Delsuc, Frédéric and Dufayard, Jean-François and Gascuel, Olivier},
	year = {2009},
	pmid = {19378142},
	keywords = {Algorithms, Base Sequence, Software, User-Computer Interface, phylogeny},
	pages = {113--137}
}

@article{finn_hmmer_2015,
	title = {{HMMER} web server: 2015 update},
	issn = {0305-1048, 1362-4962},
	shorttitle = {{HMMER} web server},
	url = {http://nar.oxfordjournals.org/content/early/2015/05/04/nar.gkv397},
	doi = {10.1093/nar/gkv397},
	abstract = {The HMMER website, available at http://www.ebi.ac.uk/Tools/hmmer/, provides access to the protein homology search algorithms found in the HMMER software suite. Since the first release of the website in 2011, the search repertoire has been expanded to include the iterative search algorithm, jackhmmer. The continued growth of the target sequence databases means that traditional tabular representations of significant sequence hits can be overwhelming to the user. Consequently, additional ways of presenting homology search results have been developed, allowing them to be summarised according to taxonomic distribution or domain architecture. The taxonomy and domain architecture representations can be used in combination to filter the results according to the needs of a user. Searches can also be restricted prior to submission using a new taxonomic filter, which not only ensures that the results are specific to the requested taxonomic group, but also improves search performance. The repertoire of profile hidden Markov model libraries, which are used for annotation of query sequences with protein families and domains, has been expanded to include the libraries from CATH-Gene3D, PIRSF, Superfamily and TIGRFAMs. Finally, we discuss the relocation of the HMMER webserver to the European Bioinformatics Institute and the potential impact that this will have.},
	language = {en},
	urldate = {2015-05-07},
	journal = {Nucleic Acids Research},
	author = {Finn, Robert D. and Clements, Jody and Arndt, William and Miller, Benjamin L. and Wheeler, Travis J. and Schreiber, Fabian and Bateman, Alex and Eddy, Sean R.},
	month = may,
	year = {2015},
	pmid = {25943547},
	note = {00000 },
	pages = {gkv397},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/CFVRMIF5/Finn et al. - 2015 - HMMER web server 2015 update.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/HCVDTHPN/nar.gkv397.html:text/html}
}

@article{eddy_accelerated_2011,
	title = {Accelerated {Profile} {HMM} {Searches}},
	volume = {7},
	url = {http://dx.doi.org/10.1371/journal.pcbi.1002195},
	doi = {10.1371/journal.pcbi.1002195},
	abstract = {Author Summary
Searching sequence databases is one of the most important applications in computational molecular biology. The main workhorse in the field is the BLAST suite of programs. Since the introduction of BLAST in the 1990's, important theoretical advances in homology search methodology have been made using probabilistic inference methods and hidden Markov models (HMMs). However, previous software implementations of these newer probabilistic methods were slower than BLAST by about 100-fold. This hindered their utility, because computation speed is so critical with the rapidly increasing size of modern sequence databases. Here I describe the acceleration methods I implemented in a new, freely available profile HMM software package, HMMER3. HMMER3 makes profile HMM searches about as fast as BLAST, while retaining the power of using probabilistic inference technology.},
	number = {10},
	urldate = {2014-01-20},
	journal = {PLoS Comput Biol},
	author = {Eddy, Sean R.},
	month = oct,
	year = {2011},
	note = {00218},
	pages = {e1002195},
	file = {PLoS Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/UZKXNKP3/Eddy - 2011 - Accelerated Profile HMM Searches.pdf:application/pdf;PLoS Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/7CIPPBM3/infodoi10.1371journal.pcbi.html:text/html}
}

@article{sievers_clustal_omega_2014,
	title = {Fast, scalable generation of high-quality protein multiple sequence alignments using {Clustal} {Omega}},
	volume = {7},
	issn = {1744-4292},
	url = {http://msb.embopress.org/cgi/doi/10.1038/msb.2011.75},
	doi = {10.1038/msb.2011.75},
	language = {en},
	number = {1},
	urldate = {2016-05-10},
	journal = {Molecular Systems Biology},
	author = {Sievers, F. and Wilm, A. and Dineen, D. and Gibson, T. J. and Karplus, K. and Li, W. and Lopez, R. and McWilliam, H. and Remmert, M. and Soding, J. and Thompson, J. D. and Higgins, D. G.},
	month = apr,
	year = {2014},
	keywords = {clustal, omega, clustal omega},
	pages = {539--539},
	file = {539.full.pdf:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/ZUK79QW7/539.full.pdf:application/pdf}
}

@book{morrison_introduction_2011,
	address = {Uppsala},
	title = {An introduction to phylogenetic networks},
	isbn = {978-91-980099-0-3},
	language = {en},
	publisher = {RJR Productions},
	author = {Morrison, David A},
	year = {2011},
	note = {OCLC: 939959509},
	file = {Morrison - 2011 - An introduction to phylogenetic networks.pdf:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/L74W7WDU/Morrison - 2011 - An introduction to phylogenetic networks.pdf:application/pdf}
}

@article{huson_application_2006,
	title = {Application of {Phylogenetic} {Networks} in {Evolutionary} {Studies}},
	volume = {23},
	issn = {0737-4038, 1537-1719},
	url = {http://mbe.oxfordjournals.org/content/23/2/254},
	doi = {10.1093/molbev/msj030},
	abstract = {The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.},
	language = {en},
	number = {2},
	urldate = {2014-01-23},
	journal = {Molecular Biology and Evolution},
	author = {Huson, Daniel H. and Bryant, David},
	month = feb,
	year = {2006},
	note = {02314 PMID: 16221896},
	keywords = {Software, confidence intervals, networks, phylogeny},
	pages = {254--267},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/NVPJC8U8/Huson and Bryant - 2006 - Application of Phylogenetic Networks in Evolutiona.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/ZIGH8BVF/254.html:text/html}
}

@article{do_probcons_2005,
	title = {{ProbCons}: {Probabilistic} consistency-based multiple sequence alignment},
	volume = {15},
	issn = {1088-9051},
	shorttitle = {{ProbCons}},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15687296},
	doi = {10.1101/gr.2821705},
	abstract = {To study gene evolution across a wide range of organisms, biologists need accurate tools for multiple sequence alignment of protein families. Obtaining accurate alignments, however, is a difficult computational problem because of not only the high computational cost but also the lack of proper objective functions for measuring alignment quality. In this paper, we introduce probabilistic consistency, a novel scoring function for multiple sequence comparisons. We present ProbCons, a practical tool for progressive protein multiple sequence alignment based on probabilistic consistency, and evaluate its performance on several standard alignment benchmark data sets. On the BAliBASE, SABmark, and PREFAB benchmark alignment databases, ProbCons achieves statistically significant improvement over other leading methods while maintaining practical speed. ProbCons is publicly available as a Web resource.},
	number = {2},
	urldate = {2010-06-24},
	journal = {Genome Research},
	author = {Do, Chuong B and Mahabhashyam, Mahathi S P and Brudno, Michael and Batzoglou, Serafim},
	month = feb,
	year = {2005},
	pmid = {15687296},
	keywords = {Algorithms, Amino Acid Sequence, Benchmarking, Computational Biology, Databases, Protein, Genetic Variation, Molecular Sequence Data, Sequence Alignment, Software, Software Validation},
	pages = {330--340}
}

@article{matsen_pplacer_2010,
	title = {pplacer: linear time maximum-likelihood and {Bayesian} phylogenetic placement of sequences onto a fixed reference tree},
	volume = {11},
	issn = {1471-2105},
	shorttitle = {pplacer},
	url = {https://doi.org/10.1186/1471-2105-11-538},
	doi = {10.1186/1471-2105-11-538},
	abstract = {Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets.},
	number = {1},
	urldate = {2019-04-25},
	journal = {BMC Bioinformatics},
	author = {Matsen, Frederick A. and Kodner, Robin B. and Armbrust, E Virginia},
	month = oct,
	year = {2010},
	pages = {538},
	file = {Full Text PDF:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/7VRX5XIZ/Matsen et al. - 2010 - pplacer linear time maximum-likelihood and Bayesi.pdf:application/pdf;Snapshot:/home/daluab/.mozilla/firefox/ceesjlsm.default/zotero/storage/E5GCA8L6/1471-2105-11-538.html:text/html}
}

@article{mcmurdie_waste_2014,
	title = {Waste {Not}, {Want} {Not}: {Why} {Rarefying} {Microbiome} {Data} {Is} {Inadmissible}},
	volume = {10},
	issn = {1553-7358},
	shorttitle = {Waste {Not}, {Want} {Not}},
	url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003531},
	doi = {10.1371/journal.pcbi.1003531},
	abstract = {Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq.},
	language = {en},
	number = {4},
	urldate = {2018-06-16},
	journal = {PLOS Computational Biology},
	author = {McMurdie, Paul J. and Holmes, Susan},
	month = apr,
	year = {2014},
	keywords = {Microbiome, Statistical data, DNA sequencing, Simulation and modeling, Binomials, DNA sequence analysis, RNA analysis, RNA sequencing},
	pages = {e1003531},
	file = {Full Text PDF:/home/dl/Zotero/storage/8S73GT9Z/McMurdie and Holmes - 2014 - Waste Not, Want Not Why Rarefying Microbiome Data.pdf:application/pdf;Snapshot:/home/dl/Zotero/storage/DTXVNYEJ/article.html:text/html}
}

@article{gloor_microbiome_2017,
	title = {Microbiome {Datasets} {Are} {Compositional}: {And} {This} {Is} {Not} {Optional}},
	volume = {8},
	issn = {1664-302X},
	shorttitle = {Microbiome {Datasets} {Are} {Compositional}},
	url = {http://journal.frontiersin.org/article/10.3389/fmicb.2017.02224/full},
	doi = {10.3389/fmicb.2017.02224},
	abstract = {Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume speciﬁc properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We brieﬂy introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and ﬁnally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.},
	language = {en},
	urldate = {2018-12-20},
	journal = {Frontiers in Microbiology},
	author = {Gloor, Gregory B. and Macklaim, Jean M. and Pawlowsky-Glahn, Vera and Egozcue, Juan J.},
	month = nov,
	year = {2017},
	file = {Gloor et al. - 2017 - supplement.pdf:/home/dl/Zotero/storage/UPT49JLH/Gloor et al. - 2017 - Microbiome Datasets Are Compositional And This Is.pdf:application/pdf;Gloor_et_al._-_2017_-_Microbiome_Datasets_Are_Compositional_And_This_Is_IAU798SN.pdf:/home/dl/Zotero/storage/V4V9R7I8/Gloor_et_al._-_2017_-_Microbiome_Datasets_Are_Compositional_And_This_Is_IAU798SN.pdf:application/pdf}
}

@article{willis_rarefaction_2017,
	title = {Rarefaction, alpha diversity, and statistics},
	copyright = {© 2017, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NoDerivs 4.0 International), CC BY-ND 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/231878v1},
	doi = {10.1101/231878},
	abstract = {{\textless}p{\textgreater}Understanding the drivers of microbial diversity is a fundamental question in microbial ecology. Extensive literature discusses different methods for describing microbial diversity and documenting its effects on ecosystem function. However, it is widely believed that diversity depends on the number of reads that are sequenced. I discuss a statistical perspective on diversity, framing the diversity of an environment as an unknown parameter, and discussing the bias and variance of plug-in and rarefied estimates. I argue that by failing to account for both bias and variance, we invalidate analysis of alpha diversity. I describe the state of the statistical literature for addressing these problems, and suggest that measurement error modeling can address issues with variance, but bias corrections need to be utilized as well. I encourage microbial ecologists to avoid motivating their investigations with alpha diversity analyses that do not use valid statistical methodology.{\textless}/p{\textgreater}},
	language = {en},
	urldate = {2019-01-25},
	journal = {bioRxiv},
	author = {Willis, Amy},
	month = dec,
	year = {2017},
	pages = {231878},
	file = {Willis - 2017 - Rarefaction, alpha diversity, and statistics.pdf:/home/dl/Zotero/storage/9FN4ADWX/Willis - 2017 - Rarefaction, alpha diversity, and statistics.pdf:application/pdf}
}

repository: LNUc-EEMiS/compbio-fundamentals

repo_name: LNUc-EEMiS/compbio-fundamentals


text:
DESCRIPTION = "This is a small package that facilitates the unit testing process \
by aggregating PyUnit tests and making them easier to call from the command \
line and from within other unit tests."
SECTION = "devel/python"
HOMEPAGE = "http://oss.wxnet.org/pytester/index.html"
PRIORITY = "optional"
LICENSE = "GPL"
SRCNAME = "pytester"
PR = "ml1"

SRC_URI = "${SOURCEFORGE_MIRROR}/meta-tools/${SRCNAME}-${PV}.tar.gz"
S = "${WORKDIR}/${SRCNAME}-${PV}"

inherit distutils

SRC_URI[md5sum] = "8a7763e31e4cc3fdf9154c793dda3bc0"
SRC_URI[sha256sum] = "7d17f07aa470cac6afc1b7e68745c1bf1767913ce52b401296e21cab245fe26b"

license: MIT

repository: overo/overo-oe-natty

license_type: permissive

repo_name: overo/overo-oe-natty

branch_name: HEAD

extension: bb


text:
package main

import (
	"bufio"
	"encoding/json"
	"flag"
	"fmt"
	"io"
	"log"
	"os"
	"time"
)

type Name struct {
	Date        string
	Description string
}

var (
	newFlag   = flag.Bool("n", false, "create new reminder")
	clearFlag = flag.Bool("c", false, "clear reminders")
	listFlag  = flag.Bool("l", false, "list all reminders")
	helpFlag  = flag.Bool("h", false, "help")
)

func checkFile() {
	if _, err := os.Stat("reminders.json"); os.IsNotExist(err) {
		os.Create("reminders.json")
	}
}

func newReminder() {
	checkFile()

	var text string
	date := time.Now()
	dateString := date.String()

	file, err := os.OpenFile("reminders.json", os.O_RDWR|os.O_APPEND, os.ModePerm)
	if err != nil {
		log.Fatal(err)
	}

	defer file.Close()

	fmt.Print("Description: ")

	scanner := bufio.NewScanner(os.Stdin)
	if ok := scanner.Scan(); ok {
		text = scanner.Text()
	}

	reminder := &Name{dateString[:19], text}
	newReminder, err := json.Marshal(&reminder)
	if err != nil {
		log.Fatal(err)
	}

	file.Write(newReminder)
}

func removeReminders() {
	err := os.Remove("reminders.json")
	if err != nil {
		log.Fatal(err)
	}
}

func listReminders() error {
	file, err := os.Open("reminders.json")
	if err != nil {
		return err
	}

	decoder := json.NewDecoder(file)

	for {
		var content Name
		switch decoder.Decode(&content) {
		case nil:
			fmt.Printf("%s: %s\n", content.Date, content.Description)
		case io.EOF:
			return nil
		default:
			return err
		}
	}
}

func main() {
	flag.Parse()

	if len(os.Args) < 2 {
		fmt.Println("Usage:")
		flag.PrintDefaults()
	}

	if *newFlag {
		newReminder()
	}

	if *clearFlag {
		removeReminders()
	}

	if *helpFlag {
		flag.PrintDefaults()
	}

	if *listFlag {
		err := listReminders()
		if err != nil {
			log.Fatal(err)
		}
	}

}

repository: avinassh/Rem

repo_name: avinassh/Rem

branch_name: refs/heads/master

extension: go


text:
package internal

const (
	ErrAlreadyPaused       = "Already paused"
	ErrAlreadyPlaying      = "Already playing"
	ErrAlreadyUpToDate     = "Already up to date"
	ErrLoginFailed         = "Login failed"
	ErrNoActiveDevice      = "Player command failed: No active device found"
	ErrNoPlaylists         = "No playlists"
	ErrNoPrevious          = "No track before this one"
	ErrNotLoggedIn         = `You are not logged in: Run "spotify login"`
	ErrRestrictionViolated = "Player command failed: Restriction violated"
	ErrSavePodcast         = "Saving podcasts is not allowed"
)

repository: ankibahuguna/spotify-cli

repo_name: ankibahuguna/spotify-cli


text:
package core

import (
	"bytes"
	"fmt"
	"io/ioutil"
	"log"
	"os"
	"os/exec"
	"runtime"
	"strconv"
	"strings"
	"sync"
)

// isLocalhost returns true if host looks explicitly like a localhost address.
func isLocalhost(host string) bool {
	return host == "localhost" || host == "::1" || strings.HasPrefix(host, "127.")
}

// checkFdlimit issues a warning if the OS max file descriptors is below a recommended minimum.
func checkFdlimit() {
	const min = 4096

	// Warn if ulimit is too low for production sites
	if runtime.GOOS == "linux" || runtime.GOOS == "darwin" {
		out, err := exec.Command("sh", "-c", "ulimit -n").Output() // use sh because ulimit isn't in Linux $PATH
		if err == nil {
			// Note that an error here need not be reported
			lim, err := strconv.Atoi(string(bytes.TrimSpace(out)))
			if err == nil && lim < min {
				fmt.Printf("Warning: File descriptor limit %d is too low for production sites. At least %d is recommended. Set with \"ulimit -n %d\".\n", lim, min, min)
			}
		}
	}
}

// signalSuccessToParent tells the parent our status using pipe at index 3.
// If this process is not a restart, this function does nothing.
// Calling this function once this process has successfully initialized
// is vital so that the parent process can unblock and kill itself.
// This function is idempotent; it executes at most once per process.
func signalSuccessToParent() {
	signalParentOnce.Do(func() {
		if IsRestart() {
			ppipe := os.NewFile(3, "")               // parent is reading from pipe at index 3
			_, err := ppipe.Write([]byte("success")) // we must send some bytes to the parent
			if err != nil {
				log.Printf("[ERROR] Communicating successful init to parent: %v", err)
			}
			ppipe.Close()
		}
	})
}

// signalParentOnce is used to make sure that the parent is only
// signaled once; doing so more than once breaks whatever socket is
// at fd 4 (the reason for this is still unclear - to reproduce,
// call Stop() and Start() in succession at least once after a
// restart, then try loading first host of Corefile in the browser).
// Do not use this directly - call signalSuccessToParent instead.
var signalParentOnce sync.Once

// corefileGob maps bind address to index of the file descriptor
// in the Files array passed to the child process. It also contains
// the corefile contents and other state needed by the new process.
// Used only during graceful restarts where a new process is spawned.
type corefileGob struct {
	ListenerFds            map[string]uintptr
	Corefile               Input
	OnDemandTLSCertsIssued int32
}

// IsRestart returns whether this process is, according
// to env variables, a fork as part of a graceful restart.
func IsRestart() bool {
	return os.Getenv("COREDNS_RESTART") == "true"
}

// writePidFile writes the process ID to the file at PidFile, if specified.
func writePidFile() error {
	pid := []byte(strconv.Itoa(os.Getpid()) + "\n")
	return ioutil.WriteFile(PidFile, pid, 0644)
}

// CorefileInput represents a Corefile as input
// and is simply a convenient way to implement
// the Input interface.
type CorefileInput struct {
	Filepath string
	Contents []byte
	RealFile bool
}

// Body returns c.Contents.
func (c CorefileInput) Body() []byte { return c.Contents }

// Path returns c.Filepath.
func (c CorefileInput) Path() string { return c.Filepath }

// IsFile returns true if the original input was a real file on the file system.
func (c CorefileInput) IsFile() bool { return c.RealFile }

repository: aledbf/kube-to-coredns

repo_name: aledbf/kube-to-coredns


text:
DESCRIPTION = "QCOM Firmware for Google Pixel 3 / 3 XL"

FW_QCOM_NAME = "blueline"
EXTRA_DEVICE_NAME = "crosshatch"
AOSP_BUILD = "sp1a.210812.016.c1"
CHECKSUM_vendor = "0b9f3bc0"

SRC_URI[vendor.sha256sum] = "5e48f4769d3cdba3c958f956d13df56b60e18e8fe03893d38f4125b421ab7ff9"

require recipes-bsp/firmware-nexus/firmware-qcom-pixel.inc

repository: vchong/meta-qcom

repo_name: vchong/meta-qcom

gha_license_id: MIT

gha_language: BitBake