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ABSTRACT: We propose a novel, efficient and intuitive approach of estimating mRNA abundances from the whole transcriptome shotgun sequencing (RNA-Seq) data. Our method, NEUMA (Normalization by Expected Uniquely Mappable Area), is based on effective length normalization using uniquely mappable areas of gene and mRNA isoform models. Using the known transcriptome sequence model such as RefSeq, NEUMA pre-computes the numbers of all possible gene-wise and isoform-wise informative reads: the former being sequences mapped to all mRNA isoforms of a single gene exclusively and the latter uniquely mapped to a single mRNA isoform. The results are used to estimate the effective length of genes and transcripts, taking experimental distributions of fragment size into consideration. Quantitative RT-PCR based on 27 randomly selected genes in two human cell lines and computer simulation experiments demonstrated superior accuracy of NEUMA over other recently developed methods. NEUMA covers a large proportion of genes and mRNA isoforms and offers a measure of consistency ('consistency coefficient') for each gene between an independently measured gene-wise level and the sum of the isoform levels. NEUMA is applicable to both paired-end and single-end RNA-Seq data. We propose that NEUMA could make a standard method in quantifying gene transcript levels from RNA-Seq data.
Nucleic Acids Research 11/2010; 39(2):e9. · 8.03 Impact Factor
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ABSTRACT: Efficient semitransparent organic photovoltaic (OPV) cells are presented in an inverted geometry employing ZnS/ Ag/ WO₃ (ZAW) as a top anode and ITO/ Cs₂CO₃ as a bottom cathode. Upon identification of the light absorption that differs depending on the illumination direction, the degree of the absorption asymmetry is tuned by varying the ZAW structure to maximize the efficiency for one direction or to balance it for both directions. Power conversion efficiency close to that of conventional opaque OPV cells is demonstrated in semitransparent cells for the ITO side illumination by taking advantage of the internal reflection occurring at the organic/ZAW interface. Cells with efficiencies that are reduced but balanced for both illumination directions are also demonstrated.
Optics Express 11/2010; 18 Suppl 4:A513-21. · 3.59 Impact Factor
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ABSTRACT: We propose a simple, sensitive measure of synonymous codon usage bias, the Relative Codon Adaptation Index (rCAI), as a way to discriminate better between highly biased and unbiased regions, compared with the widely used Codon Adaptation Index (CAI). CAI is a geometric mean of the relative usage of codons in a gene, and is calculated using the codon usage table trained with a set of highly expressed genes. In contrast, rCAI is computed by subtracting the background codon usage trained with two noncoding frames of highly expressed genes from the codon usage in the coding frame. rCAI has higher signal-to-noise ratio than CAI, considering that noncoding frames would not show codon bias. Translation efficiency and protein abundance correlates comparably or better with rCAI than CAI or other measures such as 'effective number of codons' and 'SCUMBLE offsets'. Within overlapping coding regions, one of the two coding frames dominates in codon usage bias according to rCAI. Presumably, rCAI could substitute CAI in diverse applications.
Evolutionary bioinformatics online 01/2010; 6:47-55. · 1.23 Impact Factor
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ABSTRACT: We developed algorithms that find a set of single nucleotide polymorphism (SNP) markers based on interval regularity, given either the number of SNPs to choose (m) or the desired interval (I), subject to minimum variance or minimum sum of squared deviations from I. In both cases, the number of all possible sets increases exponentially with respect to the number of input SNPs (n), but our algorithms find the minima only with O(n(2)) calculations and comparisons by elimination of redundancy.
Bioinformatics 04/2004; 20(4):581-2. · 5.47 Impact Factor
