Identification of conserved regulatory elements by comparative genome analysis

BACKGROUND: For genes that have been successfully delineated within the human genome sequence, most regulatory sequences remain to be elucidated. The annotation and interpretation process requires additional data resources and significant improvements in computational methods for the detection of regulatory regions. One approach of growing popularity is based on the preferential conservation of functional sequences over the course of evolution by selective pressure, termed 'phylogenetic footprinting'. Mutations are more likely to be disruptive if they appear in functional sites, resulting in a measurable difference in evolution rates between functional and non-functional genomic segments. RESULTS: We have devised a flexible suite of methods for the identification and visualization of conserved transcription-factor-binding sites. The system reports those putative transcription-factor-binding sites that are both situated in conserved regions and located as pairs of sites in equivalent positions in alignments between two orthologous sequences. An underlying collection of metazoan transcription-factor-binding profiles was assembled to facilitate the study. This approach results in a significant improvement in the detection of transcription-factor-binding sites because of an increased signal-to-noise ratio, as demonstrated with two sets of promoter sequences. The method is implemented as a graphical web application, ConSite, which is at the disposal of the scientific community at . CONCLUSIONS: Phylogenetic footprinting dramatically improves the predictive selectivity of bioinformatic approaches to the analysis of promoter sequences. ConSite delivers unparalleled performance using a novel database of high-quality binding models for metazoan transcription factors. With a dynamic interface, this bioinformatics tool provides broad access to promoter analysis with phylogenetic footprinting

Incidental vocabulary acquisition from stories: Second and fourth graders learn more from listening than reading

Both reading and language experiences contribute to vocabulary development, but questions remain as to what effect each has and when. This article investigates the effects that reading, telling and sharing a story have on vocabulary acquisition. Children (N = 37) were told nine stories in a randomized, single-blind and counterbalanced 2 × 3 mixed design. The between-subjects variable was grade (2 vs 4) and the within-subjects factor was the story condition, being either read (adult read aloud) or told (free story telling) to the children, or read silently by the children (independent reading). Each story contained two rare target words that were unlikely to have been previously known to the children. Measures of receptive vocabulary, decoding, reading comprehension and target vocabulary acquisition from the story were also administered. Children in grade 4 performed better on the vocabulary acquisition test and there was a main effect for story condition; children learnt the least number of words when reading the stories independently and the most from the free story telling condition. Implications for vocabulary learning and the importance of oral language exposure – even for established readers in primary school – are discussed

A new generation of JASPAR, the open-access repository for transcription factor binding site profiles

JASPAR is the most complete open-access collection of transcription factor binding site (TFBS) matrices. In this new release, JASPAR grows into a meta-database of collections of TFBS models derived by diverse approaches. We present JASPAR CORE—an expanded version of the original, non-redundant collection of annotated, high-quality matrix-based transcription factor binding profiles, JASPAR FAM—a collection of familial TFBS models and JASPAR phyloFACTS—a set of matrices computationally derived from statistically overrepresented, evolutionarily conserved regulatory region motifs from mammalian genomes. JASPAR phyloFACTS serves as a non-redundant extension to JASPAR CORE, enhancing the overall breadth of JASPAR for promoter sequence analysis. The new release of JASPAR is available at

In Silico Detection of Sequence Variations Modifying Transcriptional Regulation

Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation

Target specificity among canonical nuclear poly(A) polymerases in plants modulates organ growth and pathogen response

Polyadenylation of pre-mRNAs is critical for efficient nuclear export, stability, and translation of the mature mRNAs, and thus for gene expression. The bulk of pre-mRNAs are processed by canonical nuclear poly(A) polymerase (PAPS). Both vertebrate and higher-plant genomes encode more than one isoform of this enzyme, and these are coexpressed in different tissues. However, in neither case is it known whether the isoforms fulfill different functions or polyadenylate distinct subsets of pre-mRNAs. Here we show that the three canonical nuclear PAPS isoforms in Arabidopsis are functionally specialized owing to their evolutionarily divergent C-terminal domains. A strong loss-of-function mutation in PAPS1 causes a male gametophytic defect, whereas a weak allele leads to reduced leaf growth that results in part from a constitutive pathogen response. By contrast, plants lacking both PAPS2 and PAPS4 function are viable with wild-type leaf growth. Polyadenylation of SMALL AUXIN UP RNA (SAUR) mRNAs depends specifically on PAPS1 function. The resulting reduction in SAUR activity in paps1 mutants contributes to their reduced leaf growth, providing a causal link between polyadenylation of specific pre-mRNAs by a particular PAPS isoform and plant growth. This suggests the existence of an additional layer of regulation in plant and possibly vertebrate gene expression, whereby the relative activities of canonical nuclear PAPS isoforms control de novo synthesized poly(A) tail length and hence expression of specific subsets of mRNAs

JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles.

International audienceJASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release

JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles.

International audienceJASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release

Dual-initiation promoters with intertwined canonical and TCT/TOP transcription start sites diversify transcript processing

Variations in transcription start site (TSS) selection reflect diversity of preinitiation complexes and can impact on post-transcriptional RNA fates. Most metazoan polymerase II-transcribed genes carry canonical initiation with pyrimidine/purine (YR) dinucleotide, while translation machinery-associated genes carry polypyrimidine initiator (5’-TOP or TCT). By addressing the developmental regulation of TSS selection in zebrafish we uncovered a class of dual-initiation promoters in thousands of genes, including snoRNA host genes. 5’-TOP/TCT initiation is intertwined with canonical initiation and used divergently in hundreds of dual-initiation promoters during maternal to zygotic transition. Dual-initiation in snoRNA host genes selectively generates host and snoRNA with often different spatio-temporal expression. Dual-initiation promoters are pervasive in human and fruit fly, reflecting evolutionary conservation. We propose that dual-initiation on shared promoters represents a composite promoter architecture, which can function both coordinately and divergently to diversify RNAs

JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release