Blood cell fate changes without cell cycle transition

Comment on: Di Tullio A, et al. Cell Cycle 2012; 11:2739-46.     

转录因子结合位点的计算预测方法研究进展

转录因子结合位点的计算预测是研究基因转录调控的重要环节,但现有算法的预测特异性偏低.在深入分析转录因子结合位点生物特征的基础上,对当前基于保守模体和基于比较基因组学的两类计算预测方法进行了综述,指出了方法各自的优点和不足,并探讨了可能的改进方向.     

Computational methods in protein structure prediction

This review presents the advances in protein structure prediction from the computational methods perspective. The approaches are classified into four major categories: comparative modeling, fold recognition, first principles methods that employ database information, and first principles methods without database information. Important advances along with current limitations and challenges are presented.     

Computational methods in noncoding RNA research

Non protein-coding RNAs (ncRNAs) are a research hotspot in bioinformatics. Recent discoveries have revealed new ncRNA families performing a variety of roles, from gene expression regulation to catalytic activities. It is also believed that other families are still to be unveiled. Computational methods developed for protein coding genes often fail when searching for ncRNAs. Noncoding RNAs functionality is often heavily dependent on their secondary structure, which makes gene discovery very different from protein coding RNA genes. This motivated the development of specific methods for ncRNA research. This article reviews the main approaches used to identify ncRNAs and predict secondary structure. During the execution of this work, AML was supported by CAPES fellowship.     

Generation of diverse neural cell types through direct conversion

A characteristic of neurological disorders is the loss of critical populations of cells that the body is unable to replace,thus there has been much interest in identifying methods of generating clinically relevant numbers of cells to replace those that have been damaged or lost.The process of neural direct conversion,in which cells of one lineage are converted into cells of a neural lineage without first inducing pluripotency,shows great potential,with evidence of the generation of a range of functional neural cell types both in vitro and in vivo,through viral and non-viral delivery of exogenous factors,as well as chemical induction methods.Induced neural cells have been proposed as an attractive alternative to neural cells derived from embryonic or induced pluripotent stem cells,with prospective roles in the investigation of neurological disorders,including neurodegenerative disease modelling,drug screening,and cellular replacement for regenerative medicine applications,however further investigations into improving the efficacy and safety of these methods need to be performed before neural direct conversion becomes a clinically viable option.In this review,we describe the generation of diverse neural cell types via direct conversion of somatic cells,with comparison against stem cell-based approaches,as well as discussion of their potential research and clinical applications.     

Computational methods for exon detection

Computer methods for the complete and accurate detection of genes in vertebrate genomic sequences are still a long way to perfection. The intermediate task of identifying the coding moiety of genes (coding exons) is now reasonably well achieved using a combination of methods. After reviewing the intrinsic difficulties in interpreting vertebrate genomic sequences, this article presents the state-of-the-art, with an emphasis on similarity search methods and the resources available through Internet.     

Computational methods for transcriptional regulation

How is the information from a thousand gene-expression arrays, the location of more than two hundred regulatory factors, and nine sequenced genomes to be integrated into a global view of the regulatory network in budding yeast? Computational methods that fit incomplete noisy data provide the outlines of regulatory pathways, but the errors are not quantified. In the fly, embryonic patterning has proved amenable to computational prediction, but only when the DNA-binding preferences of the relevant factors are taken into account. In both these model organisms, simply restricting attention to regulatory sequences that align with related species (i.e. "conserved") discards much information regarding what is functional.     

Computational methods for diffusion-influenced biochemical reactions

MOTIVATION: We compare stochastic computational methods accounting for space and discrete nature of reactants in biochemical systems. Implementations based on Brownian dynamics (BD) and the reaction-diffusion master equation are applied to a simplified gene expression model and to a signal transduction pathway in Escherichia coli. RESULTS: In the regime where the number of molecules is small and reactions are diffusion-limited predicted fluctuations in the product number vary between the methods, while the average is the same. Computational approaches at the level of the reaction-diffusion master equation compute the same fluctuations as the reference result obtained from the particle-based method if the size of the sub-volumes is comparable to the diameter of reactants. Using numerical simulations of reversible binding of a pair of molecules we argue that the disagreement in predicted fluctuations is due to different modeling of inter-arrival times between reaction events. Simulations for a more complex biological study show that the different approaches lead to different results due to modeling issues. Finally, we present the physical assumptions behind the mesoscopic models for the reaction-diffusion systems. AVAILABILITY: Input files for the simulations and the source code of GMP can be found under the following address: http://www.cwi.nl/projects/sic/bioinformatics2007/     

Computational methods for protein function analysis

Two recent advances have had the greatest impact on protein function analysis so far: the complete sequences of genomes and mRNA expression level profiles. The former has spurred the development of novel techniques to study protein function: phylogenetic profiles and gene clusters. The latter has introduced a method, not based on sequence homology, that enables one to group together functionally related genes.     

Computational methods for Gene Orthology inference

Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple 'tree-like' mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.     

ChIP‐based methods for the identification of long‐range chromatin interactions

Chromatin immunoprecipitation (ChIP) is an important technique for studying protein–DNA interactions. Whole genome ChIP methods have enjoyed much success, but are limited in that they cannot uncover important long‐range chromatin interactions. Chromosome conformation capture (3C) and related methods are capable of detecting remote chromatin interactions, but are tedious, have low signal‐to‐noise ratios, and are not genome‐wide. Although the addition of ChIP to 3C (ChIP–3C) would conceivably reduce noise and increase specificity for chromatin interaction detection, there are concerns that simple mixing of the ChIP and 3C protocols would lead to high levels of false positives. In this essay, we dissect current ChIP‐ and 3C‐based methodologies, discuss the models of specific as opposed to non‐specific chromatin interactions, and suggest approaches to separate specific chromatin complexes from non‐specific chromatin fragments. We conclude that the combination of sonication‐based chromatin fragmentation, ChIP‐based enrichment, chromatin proximity ligation and Paired‐End Tag ultra‐high‐throughput sequencing will be a winning implementation for genome‐wide, unbiased and de novo discovery of long‐range chromatin interactions, which will help to establish an emerging field for studying human chromatin interactomes and genome regulation networks in three‐dimensional spaces. J. Cell. Biochem. 107: 30–39, 2009. © 2009 Wiley‐Liss, Inc.