Is assigned to the genome for which the maximum probability is

Is assigned to the genome for which the maximum probability is reached, i.e., read xj is assigned to genome imax where imax arg max fPji ,i 1, ???,Kg: An assignment matrix A ji n|K can be constructed based on the read assignment, where aji 1 if read xj is assigned to genome i, and aji 0 otherwise. Then the total n P number of reads assigned to genome i is aji .j?(t) ?Tji log (Ri ){Mji log (p=(1{p))zLj log pNM-step. As the parameters can be maximized independently, we get:The proposed method, TAMER, applies to the candidate genomes to which the sequence reads have hits. Note the majority of the candidate genomes identified after performing BLAST are at the low ranks of the taxonomy tree, i.e., most of the genomes are species or substrings of species. Once a read is assigned to a specific genome, we also consider that it is assigned to taxa with higher taxonomic ranks. For example, suppose a read is assigned to Escherichia coli str. K-12 substr. MG1655. When we summarize reads assigned at different taxonomic ranks, this read is treated as that it is assigned to Escherichia. coli at rank Species, to Escherichia at rank Genus, to Enterobacteriaceae at rank of Family, and so on.Taxonomic Assignment of Metagenomic ReadsEstimates of Relative Genome AbundanceThe number of sequence reads generated by a genome is proportional not only to the number of copies of that genome in the metagenomics sample but also to the length of the genome [6]. Similar to [18], the relative genome abundance can be computed for known genomes which are present in the sample. Let Gi POR 8 denote the actual length of the genomeiin base pairs. Suppose there are Ci copies of genomeiin the sample. Assuming uniform distribution of reads across the multiple genomes, we have. Ri Ci GiK P h:(Ch 18055761 Gh )Simulation study 2. To compare TAMER with CARMA3 [10], we use the same evaluation dataset as in [10]. This CARMA3 evaluation dataset consists of 25,000 15755315 metagenomic reads which are randomly simulated from 25 bacterial genomes with an average read length of 265 bp. The online version of CARMA3, WebCARMA (http://webcarma.cebitec.uni-bielefeld. de/), with default parameters is used for taxonomic classification. We also perform the taxonomic analysis using TAMER and MEGAN, and compare their performance with CARMA3. When BLASTx and NR database are used, CARMA3 gives better taxonomic assignment than MEGAN [10]. Therefore we only present the results by MEGAN using MegaBLAST and NT database in this study.Real DatasetsThen the relative abundance of genome i (i.e., relative copy number) in the sample can be calculated by. Ci Ri =Gi : K K P P Ch (Rh =Gh )h 1 hAlgorithm ImplementationAll algorithms developed in this research are implemented in R, a free software environment for statistical computing and MedChemExpress CASIN graphics [19]. The R source codes are available at http://faculty.wcas. northwestern.edu/ hji403/MetaR.htm. For practical implemen tation, the scoring matrix M in equation (1) could require a huge storage space when the total number of reads is large. Recognizing that M is a sparse matrix, substantial memory requirement reductions can be achieved by storing only the non-zero matching scores. For the zero entries of Mji ,their influence on estimating the parameters is nominal because we have pLj {Mji (1{p)Mji pLj 0when Mji 0, for a small value of p(e.g., 0.02^35 = 3.4e-60). With the use of sparse matrix technique, detecting multiple genomes via the mixture model becomes very efficient. For e.Is assigned to the genome for which the maximum probability is reached, i.e., read xj is assigned to genome imax where imax arg max fPji ,i 1, ???,Kg: An assignment matrix A ji n|K can be constructed based on the read assignment, where aji 1 if read xj is assigned to genome i, and aji 0 otherwise. Then the total n P number of reads assigned to genome i is aji .j?(t) ?Tji log (Ri ){Mji log (p=(1{p))zLj log pNM-step. As the parameters can be maximized independently, we get:The proposed method, TAMER, applies to the candidate genomes to which the sequence reads have hits. Note the majority of the candidate genomes identified after performing BLAST are at the low ranks of the taxonomy tree, i.e., most of the genomes are species or substrings of species. Once a read is assigned to a specific genome, we also consider that it is assigned to taxa with higher taxonomic ranks. For example, suppose a read is assigned to Escherichia coli str. K-12 substr. MG1655. When we summarize reads assigned at different taxonomic ranks, this read is treated as that it is assigned to Escherichia. coli at rank Species, to Escherichia at rank Genus, to Enterobacteriaceae at rank of Family, and so on.Taxonomic Assignment of Metagenomic ReadsEstimates of Relative Genome AbundanceThe number of sequence reads generated by a genome is proportional not only to the number of copies of that genome in the metagenomics sample but also to the length of the genome [6]. Similar to [18], the relative genome abundance can be computed for known genomes which are present in the sample. Let Gi denote the actual length of the genomeiin base pairs. Suppose there are Ci copies of genomeiin the sample. Assuming uniform distribution of reads across the multiple genomes, we have. Ri Ci GiK P h:(Ch 18055761 Gh )Simulation study 2. To compare TAMER with CARMA3 [10], we use the same evaluation dataset as in [10]. This CARMA3 evaluation dataset consists of 25,000 15755315 metagenomic reads which are randomly simulated from 25 bacterial genomes with an average read length of 265 bp. The online version of CARMA3, WebCARMA (http://webcarma.cebitec.uni-bielefeld. de/), with default parameters is used for taxonomic classification. We also perform the taxonomic analysis using TAMER and MEGAN, and compare their performance with CARMA3. When BLASTx and NR database are used, CARMA3 gives better taxonomic assignment than MEGAN [10]. Therefore we only present the results by MEGAN using MegaBLAST and NT database in this study.Real DatasetsThen the relative abundance of genome i (i.e., relative copy number) in the sample can be calculated by. Ci Ri =Gi : K K P P Ch (Rh =Gh )h 1 hAlgorithm ImplementationAll algorithms developed in this research are implemented in R, a free software environment for statistical computing and graphics [19]. The R source codes are available at http://faculty.wcas. northwestern.edu/ hji403/MetaR.htm. For practical implemen tation, the scoring matrix M in equation (1) could require a huge storage space when the total number of reads is large. Recognizing that M is a sparse matrix, substantial memory requirement reductions can be achieved by storing only the non-zero matching scores. For the zero entries of Mji ,their influence on estimating the parameters is nominal because we have pLj {Mji (1{p)Mji pLj 0when Mji 0, for a small value of p(e.g., 0.02^35 = 3.4e-60). With the use of sparse matrix technique, detecting multiple genomes via the mixture model becomes very efficient. For e.