01797nas a2200217 4500008004100000245010400041210007000145260001300215300001100228490000700239520104100246100001701287700002001304700002101324700002201345700001501367700002401382700001601406700002501422856013201447 2011 eng d00aThe three-dimensional folding of the α-globin gene domain reveals formation of chromatin globules.0 athreedimensional folding of the αglobin gene domain reveals form c2011 Jan a107-140 v183 a

We developed a general approach that combines chromosome conformation capture carbon copy (5C) with the Integrated Modeling Platform (IMP) to generate high-resolution three-dimensional models of chromatin at the megabase scale. We applied this approach to the ENm008 domain on human chromosome 16, containing the α-globin locus, which is expressed in K562 cells and silenced in lymphoblastoid cells (GM12878). The models accurately reproduce the known looping interactions between the α-globin genes and their distal regulatory elements. Further, we find using our approach that the domain folds into a single globular conformation in GM12878 cells, whereas two globules are formed in K562 cells. The central cores of these globules are enriched for transcribed genes, whereas nontranscribed chromatin is more peripheral. We propose that globule formation represents a higher-order folding state related to clustering of transcribed genes around shared transcription machineries, as previously observed by microscopy.

1 aBaù, Davide1 aSanyal, Amartya1 aLajoie, Bryan, R1 aCapriotti, Emidio1 aByron, Meg1 aLawrence, Jeanne, B1 aDekker, Job1 aMarti-Renom, Marc, A uhttps://www.clinbioinfosspa.es/content/three-dimensional-folding-%CE%B1-globin-gene-domain-reveals-formation-chromatin-globules01599nas a2200145 4500008004100000245006100041210006000102300001100162520114400173653000801317653001801325100002201343700002401365856006401389 2009 eng d00aSARA: a server for function annotation of RNA structures0 aSARA a server for function annotation of RNA structures agkp4333 a

Recent interest in non-coding RNA transcripts has resulted in a rapid increase of deposited RNA structures in the Protein Data Bank. However, a characterization and functional classification of the RNA structure and function space have only been partially addressed. Here, we introduce the SARA program for pair-wise alignment of RNA structures as a web server for structure-based RNA function assignment. The SARA server relies on the SARA program, which aligns two RNA structures based on a unit-vector root-mean-square approach. The likely accuracy of the SARA alignments is assessed by three different P-values estimating the statistical significance of the sequence, secondary structure and tertiary structure identity scores, respectively. Our benchmarks, which relied on a set of 419 RNA structures with known SCOR structural class, indicate that at a negative logarithm of mean P-value higher or equal than 2.5, SARA can assign the correct or a similar SCOR class to 81.4% and 95.3% of the benchmark set, respectively. The SARA server is freely accessible via the World Wide Web at http://sgu.bioinfo.cipf.es/services/SARA/.

10aRNA10aRNA structure1 aCapriotti, Emidio1 aMarti-Renom, M., A. uhttp://nar.oxfordjournals.org/cgi/content/abstract/gkp433v103094nas a2200385 4500008004100000022001400041245013400055210006900189260001300258300001200271490000700283520182800290653001502118653001002133653002602143653002302169653002802192653002502220653003802245653002202283653001802305653001102323653001802334653001902352653003602371653001302407653003302420100002202453700002102475700001802496700002002514700002002534700002502554856012902579 2008 eng d a1098-100400aUse of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.0 aUse of estimated evolutionary strength at the codon level improv c2008 Jan a198-2040 v293 a

Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.

10aAlgorithms10aCodon10aComputational Biology10aDatabases, Protein10aDNA Mutational Analysis10aEvolution, Molecular10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aHumans10aIduronic Acid10aPoint Mutation10aPolymorphism, Single Nucleotide10aProteins10aTumor Suppressor Protein p531 aCapriotti, Emidio1 aArbiza, Leonardo1 aCasadio, Rita1 aDopazo, Joaquin1 aDopazo, Hernán1 aMarti-Renom, Marc, A uhttps://www.clinbioinfosspa.es/content/use-estimated-evolutionary-strength-codon-level-improves-prediction-disease-related-0