The tumor suppressor microRNA let-7 inhibits human LINE-1 retrotransposition.

Nearly half of the human genome is made of transposable elements (TEs) whose activity continues to impact its structure and function. Among them, Long INterspersed Element class 1 (LINE-1 or L1) elements are the only autonomously active TEs in humans. L1s are expressed and mobilized in different cancers, generating mutagenic insertions that could affect tumor malignancy. Tumor suppressor microRNAs are ∼22nt RNAs that post-transcriptionally regulate oncogene expression and are frequently downregulated in cancer. Here we explore whether they also influence L1 mobilization. We show that downregulation of let-7 correlates with accumulation of L1 insertions in human lung cancer. Furthermore, we demonstrate that let-7 binds to the L1 mRNA and impairs the translation of the second L1-encoded protein, ORF2p, reducing its mobilization. Overall, our data reveals that let-7, one of the most relevant microRNAs, maintains somatic genome integrity by restricting L1 retrotransposition.

Bayesian approach to color-difference models based on threshold and constant-stimuli methods.

An alternative approach based on statistical Bayesian inference is presented to deal with the development of color-difference models and the precision of parameter estimation. The approach was applied to simulated data and real data, the latter published by selected authors involved with the development of color-difference formulae using traditional methods. Our results show very good agreement between the Bayesian and classical approaches. Among other benefits, our proposed methodology allows one to determine the marginal posterior distribution of each random individual parameter of the color-difference model. In this manner, it is possible to analyze the effect of individual parameters on the statistical significance calculation of a color-difference equation.

Statistical significance of normalized global alignment.

The comparison of homologous proteins from different species is a first step toward a function assignment and a reconstruction of the species evolution. Though local alignment is mostly used for this purpose, global alignment is important for constructing multiple alignments or phylogenetic trees. However, statistical significance of global alignments is not completely clear, lacking a specific statistical model to describe alignments or depending on computationally expensive methods like Z-score. Recently we presented a normalized global alignment, defined as the best compromise between global alignment cost and length, and showed that this new technique led to better classification results than Z-score at a much lower computational cost. However, it is necessary to analyze the statistical significance of the normalized global alignment in order to be considered a completely functional algorithm for protein alignment. Experiments with unrelated proteins extracted from the SCOP ASTRAL database showed that normalized global alignment scores can be fitted to a log-normal distribution. This fact, obtained without any theoretical support, can be used to derive statistical significance of normalized global alignments. Results are summarized in a table with fitted parameters for different scoring schemes.

Normalized global alignment for protein sequences.

Global alignment is used to compare proteins in different fields, for example in phylogenetic research. In order to reduce the length and composition dependence of global alignment scores, Z-score is computed with a Monte-Carlo algorithm. This technique requires a great number of sequence alignments on shuffled sequences, leading to a high computational cost. In this work, a normalized global alignment score is introduced in order to correct the length dependence of global alignments. This score is defined as the best ratio between the score of an alignment and its length, and an algorithm to compute it based on fractional programming is implemented. The properties and effectiveness of normalized global alignment applied to protein comparison are analyzed. Experiments with proteins selected from the SCOP ASTRAL database were run to study relationship of normalized global alignment with Z-score and performance in homologous detection. Results show that normalized global alignment has a computational cost equivalent to 2.5 Needleman-Wunsch runs and a linear relationship with Z-score. This linearity allows us to use normalized global alignment as a cheap substitute to a computationally expensive Z-score. Experiments show that normalized global alignment improves the ability to identify homologous proteins. Software used to compute normalized global alignments is available from http://www3.uji.es/∼peris/nga.