@InProceedings{ mckinney-proc-scipy-2010,
  author    = { Wes McKinney },
  title     = { Data Structures for Statistical Computing in Python },
  booktitle = { Proceedings of the 9th Python in Science Conference },
  pages     = { 51 -- 56 },
  year      = { 2010 },
  editor    = { St{\'e}fan van der Walt and Jarrod Millman }
}

@article{matsen2010,
	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 = {2022-04-27},
	journal = {BMC Bioinformatics},
	author = {Matsen, Frederick A. and Kodner, Robin B. and Armbrust, E Virginia},
	month = {oct},
	year = {2010},
	keywords = {Branch Length, Confidence Score, Posterior Probability, Query Sequence, Reference Tree},
	pages = {538},
}

@article{hyatt2012,
	title = {Gene and translation initiation site prediction in metagenomic sequences},
	volume = {28},
	issn = {1367-4803},
	url = {https://doi.org/10.1093/bioinformatics/bts429},
	doi = {10.1093/bioinformatics/bts429},
	abstract = {Motivation: Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample; consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data.Results: We present MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify genes in short, anonymous coding sequences with a high degree of accuracy. The novel value of the method consists of enhanced translation initiation site identification, ability to identify sequences that use alternate genetic codes and confidence values for each gene call. We compare the results of MetaProdigal with other methods and conclude with a discussion of future improvements.Availability: The Prodigal software is freely available under the General Public License from http://code.google.com/p/prodigal/.Contact:hyattpd@ornl.govSupplementary Information: Supplementary data are available at Bioinformatics online.},
	number = {17},
	journal = {Bioinformatics},
	author = {Hyatt, Doug and LoCascio, Philip F. and Hauser, Loren J. and Uberbacher, Edward C.},
	month = {sep},
	year = {2012},
	pages = {2223--2230},
}

@article{parks2015b,
	title = {{CheckM}: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes},
	volume = {25},
	issn = {1088-9051, 1549-5469},
	shorttitle = {{CheckM}},
	url = {https://genome.cshlp.org/content/25/7/1043},
	doi = {10.1101/gr.186072.114},
	abstract = {Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.},
	language = {en},
	number = {7},
	urldate = {2022-04-27},
	journal = {Genome Research},
	author = {Parks, Donovan H. and Imelfort, Michael and Skennerton, Connor T. and Hugenholtz, Philip and Tyson, Gene W.},
	month = {jul},
	year = {2015},
	pmid = {25977477},
	pages = {1043--1055},
}

@misc{hmmer2022,
	title = {{HMMER}},
	url = {http://hmmer.org/},
	urldate = {2022-04-27},
}
