RESUMEN
In silico analyses of Leishmania spp. genome data are a powerful resource to improve the understanding of these pathogens' biology. Trypanosomatids such as Leishmania spp. have their protein-coding genes grouped in long polycistronic units of functionally unrelated genes. The control of gene expression happens by a variety of posttranscriptional mechanisms. The high degree of synteny among Leishmania species is accompanied by highly conserved coding sequences (CDS) and poorly conserved intercoding untranslated sequences. To identify the elements involved in the control of gene expression, we conducted an in silico investigation to find conserved intercoding sequences (CICS) in the genomes of L. major, L. infantum, and L. braziliensis. We used a combination of computational tools, such as Linux-Shell, PERL and R languages, BLAST, MSPcrunch, SSAKE, and Pred-A-Term algorithms to construct a pipeline which was able to: (i) search for conservation in target-regions, (ii) eliminate CICS redundancy and mask repeat elements, (iii) predict the mRNA's extremities, (iv) analyze the distribution of orthologous genes within the generated LeishCICS-clusters, (v) assign GO terms to the LeishCICS-clusters, and (vi) provide statistical support for the gene-enrichment annotation. We associated the LeishCICS-cluster data, generated at the end of the pipeline, with the expression profile of L. donovani genes during promastigote-amastigote differentiation, as previously evaluated by others (GEO accession: GSE21936). A Pearson's correlation coefficient greater than 0.5 was observed for 730 LeishCICS-clusters containing from 2 to 17 genes. The designed computational pipeline is a useful tool and its application identified potential regulatory cis elements and putative regulons in Leishmania.
Asunto(s)
Secuencia Conservada , ADN Protozoario/genética , Leishmania braziliensis/genética , Leishmania infantum/genética , Leishmania major/genética , Secuencias Reguladoras de Ácidos Nucleicos , Secuencia de Bases , Biología Computacional , Genoma de Protozoos , Análisis de Secuencia de ADNRESUMEN
HTself is a web-based bioinformatics tool designed to deal with the classification of differential gene expression in low replication microarray studies. It is based on a statistical test that uses self-self experiments to derive intensity-dependent cutoffs. We developed an extension of HTself, originally released in 2005, by calculating P values instead of using a fixed acceptance level α. As before, the statistic used to compute single-spot P values is obtained from the Gaussian kernel density estimator method applied to self-self data. Different spots corresponding to the same biological gene (replicas) give rise to a set of independent P values that can be combined by well-known statistical methods. The combined P value can be used to decide whether a gene can be considered differentially expressed or not. HTself2 is a new version of HTself that uses P values combination. It is implemented as a user-friendly desktop application to help laboratories without a bioinformatics infrastructure.
Asunto(s)
Biología Computacional/métodos , Perfilación de la Expresión Génica/clasificación , Modelos Estadísticos , Programas Informáticos , Algoritmos , Rhodophyta/genética , Factores de TiempoRESUMEN
One of the goals of gene expression experiments is the identification of differentially expressed genes among populations that could be used as markers. For this purpose, we implemented a model-free Bayesian approach in a user-friendly and freely available web-based tool called BayBoots. In spite of a common misunderstanding that Bayesian and model-free approaches are incompatible, we merged them in the BayBoots implementation using the Kernel density estimator and Rubin 's Bayesian Bootstrap. We used the Bayes error rate (BER) instead of the usual P values as an alternative statistical index to rank a class marker's discriminative potential, since it can be visualized by a simple graphical representation and has an intuitive interpretation. Subsequently, Bayesian Bootstrap was used to assess BER 's credibility. We tested BayBoots on microarray data to look for markers for Trypanosoma cruzi strains isolated from cardiac and asymptomatic patients. We found that the three most frequently used methods in microarray analysis: t-test, non-parametric Wilcoxon test and correlation methods, yielded several markers that were discarded by a time-consuming visual check. On the other hand, the BayBoots graphical output and ranking was able to automatically identify markers for which classification performance was consistent. BayBoots is available at: http://www.vision.ime.usp.br/~rvencio/BayBoots.
Asunto(s)
Humanos , Animales , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Genes Protozoarios/genética , Modelos Genéticos , Teorema de Bayes , Trypanosoma cruzi/genética , Cardiomiopatía Chagásica/parasitología , Marcadores GenéticosRESUMEN
SpotWhatR is a user-friendly microarray data analysis tool that runs under a widely and freely available R statistical language (http://www.r-project.org) for Windows and Linux operational systems. The aim of SpotWhatR is to help the researcher to analyze microarray data by providing basic tools for data visualization, normalization, determination of differentially expressed genes, summarization by Gene Ontology terms, and clustering analysis. SpotWhatR allows researchers who are not familiar with computational programming to choose the most suitable analysis for their microarray dataset. Along with well-known procedures used in microarray data analysis, we have introduced a stand-alone implementation of the HTself method, especially designed to find differentially expressed genes in low-replication contexts. This approach is more compatible with our local reality than the usual statistical methods. We provide several examples derived from the Blastocladiella emersonii and Xylella fastidiosa Microarray Projects. SpotWhatR is freely available at http://blasto.iq.usp.br/~tkoide/SpotWhatR, in English and Portuguese versions. In addition, the user can choose between [quot ]single experiment[quot ] and [quot ]batch processing[quot ] versions.