@article {Edgar2004,
	title = {{MUSCLE: multiple sequence alignment with high accuracy and high throughput.}},
	journal = {Nucleic acids research},
	volume = {32},
	number = {5},
	year = {2004},
	pages = {1792{\textendash}7},
	abstract = {We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.},
	keywords = {Algorithms, Amino Acid Motifs, Amino Acid Sequence, Internet, Molecular Sequence Data, Protein, Protein: methods, Reproducibility of Results, Sequence Alignment, Sequence Alignment: methods, Sequence Analysis, Software, Time Factors},
	issn = {1362-4962},
	doi = {10.1093/nar/gkh340},
	url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=390337\&tool=pmcentrez\&rendertype=abstract},
	author = {Edgar, Robert C}
}
@article {Stephens2001,
	title = {{A new statistical method for haplotype reconstruction from population data.}},
	journal = {American journal of human genetics},
	volume = {68},
	number = {4},
	year = {2001},
	pages = {978{\textendash}89},
	abstract = {Current routine genotyping methods typically do not provide haplotype information, which is essential for many analyses of fine-scale molecular-genetics data. Haplotypes can be obtained, at considerable cost, experimentally or (partially) through genotyping of additional family members. Alternatively, a statistical method can be used to infer phase and to reconstruct haplotypes. We present a new statistical method, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms; often, error rates are reduced by > 50\%, relative to its nearest competitor. Furthermore, our algorithm performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.},
	keywords = {Algorithms, Calibration, Computer Simulation, Gene Frequency, Gene Frequency: genetics, Haplotypes, Haplotypes: genetics, Humans, Research Design, Sensitivity and Specificity, Statistics as Topic},
	issn = {0002-9297},
	doi = {10.1086/319501},
	url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1275651\&tool=pmcentrez\&rendertype=abstract},
	author = {Stephens, M and Smith, N J and Donnelly, P}
}
@article {Stephens2005,
	title = {{Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation.}},
	journal = {American journal of human genetics},
	volume = {76},
	number = {3},
	year = {2005},
	pages = {449{\textendash}62},
	abstract = {Although many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both "blocklike" and "nonblocklike" patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes; for reconstruction of haplotypes obtained from trios in an autosomal region; and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site.},
	keywords = {Algorithms, Alleles, Biometry, Chromosomes, Data Interpretation, Genetic, Genomics, Genomics: statistics \& numerical data, Genotype, Haplotypes, Haplotypes: genetics, Human, Humans, Linkage Disequilibrium, Male, Models, Software, Statistical, X, X: genetics},
	issn = {0002-9297},
	doi = {10.1086/428594},
	url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1196397\&tool=pmcentrez\&rendertype=abstract},
	author = {Stephens, Matthew and Scheet, Paul}
}
@article {Tamura2011,
	title = {{MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.}},
	journal = {Molecular biology and evolution},
	volume = {28},
	number = {10},
	year = {2011},
	month = {oct},
	pages = {2731{\textendash}9},
	abstract = {Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.},
	keywords = {Algorithms, Amino Acid Substitution, Bayes Theorem, Computational Biology, Computational Biology: methods, Computer Simulation, Data Mining, Data Mining: methods, Databases, DNA, Evolution, Genetic, Internet, Likelihood Functions, Models, Molecular, Molecular Biology, Molecular Biology: methods, Mutation, Phylogeny, Sequence Alignment, Sequence Analysis, User-Computer Interface},
	issn = {1537-1719},
	doi = {10.1093/molbev/msr121},
	url = {http://mbe.oxfordjournals.org/content/28/10/2731.abstract},
	author = {Tamura, Koichiro and Peterson, Daniel and Peterson, Nicholas and Stecher, Glen and Nei, Masatoshi and Kumar, Sudhir}
}
