COMIT: identification of noncoding motifs under selection in coding sequences

DistAMo: A Web-Based Tool to Characterize DNA-Motif Distribution on Bacterial Chromosomes

Short DNA motifs are involved in a multitude of functions such as for example chromosome segregation, DNA replication or mismatch repair. Distribution of such motifs is often not random and the specific chromosomal pattern relates to the respective motif function. Computational approaches which quantitatively assess such chromosomal motif patterns are necessary. Here we present a new computer tool DistAMo (Distribution Analysis of DNA Motifs). The algorithm uses codon redundancy to calculate the relative abundance of short DNA motifs from single genes to entire chromosomes. Comparative genomics analyses of the GATC-motif distribution in γ-proteobacterial genomes using DistAMo revealed that (i) genes beside the replication origin are enriched in GATCs, (ii) genome-wide GATC distribution follows a distinct pattern, and (iii) genes involved in DNA replication and repair are enriched in GATCs. These features are specific for bacterial chromosomes encoding a Dam methyltransferase. The new software is available as a stand-alone or as an easy-to-use web-based server version at http://www.computational.bio.uni-giessen.de/distamo.

Computational approaches, databases and tools for in silico motif discovery

Motifs are the biologically significant fragments of nucleotide or peptide sequences in a specific pattern. Motifs are categorized as structural motifs and sequence motifs. These are discovered by phylogenetic studies of similar genes across species. Structural motifs are formed by three dimensional arrangements of amino acids consisting of two or more α helices or β strands whereas sequence motifs are formed by the nucleotide fragments appearing in the exons of a gene. The arrangement of residues in structural motifs may not be continuous while it is continuous in sequence motifs. Sequence motifs may encode to the structural motifs. The algorithms used for motif discovery are important part of the bio-computational studies. The purpose of motif discovery is to identify patterns in biopolymer (nucleotide or protein) sequences to understand the structure and function of the molecules and their evolutionary aspects. The main aim of this paper is to provide systematic compilation of a review on different approaches, databases and tools used in motif discovery.

Transcriptional enhancers in protein-coding exons of vertebrate developmental genes

Many conserved noncoding sequences function as transcriptional enhancers that regulate gene expression. Here, we report that protein-coding DNA also frequently contains enhancers functioning at the transcriptional level. We tested the enhancer activity of 31 protein-coding exons, which we chose based on strong sequence conservation between zebrafish and human, and occurrence in developmental genes, using a Tol2 transposable GFP reporter assay in zebrafish. For each exon we measured GFP expression in hundreds of embryos in 10 anatomies via a novel system that implements the voice-recognition capabilities of a cellular phone. We find that 24/31 (77%) exons drive GFP expression compared to a minimal promoter control, and 14/24 are anatomy-specific (expression in four anatomies or less). GFP expression driven by these coding enhancers frequently overlaps the anatomies where the host gene is expressed (60%), suggesting self-regulation. Highly conserved coding sequences and highly conserved noncoding sequences do not significantly differ in enhancer activity (coding: 24/31 vs. noncoding: 105/147) or tissue-specificity (coding: 14/24 vs. noncoding: 50/105). Furthermore, coding and noncoding enhancers display similar levels of the enhancer-related histone modification H3K4me1 (coding: 9/24 vs noncoding: 34/81). Meanwhile, coding enhancers are over three times as likely to contain an H3K4me1 mark as other exons of the host gene. Our work suggests that developmental transcriptional enhancers do not discriminate between coding and noncoding DNA and reveals widespread dual functions in protein-coding DNA.

CodingMotif: exact determination of overrepresented nucleotide motifs in coding sequences

BACKGROUND

It has been increasingly appreciated that coding sequences harbor regulatory sequence motifs in addition to encoding for protein. These sequence motifs are expected to be overrepresented in nucleotide sequences bound by a common protein or small RNA. However, detecting overrepresented motifs has been difficult because of interference by constraints at the protein level. Sampling-based approaches to solve this problem based on codon-shuffling have been limited to exploring only an infinitesimal fraction of the sequence space and by their use of parametric approximations.

RESULTS

We present a novel O(N(log N)2)-time algorithm, CodingMotif, to identify nucleotide-level motifs of unusual copy number in protein-coding regions. Using a new dynamic programming algorithm we are able to exhaustively calculate the distribution of the number of occurrences of a motif over all possible coding sequences that encode the same amino acid sequence, given a background model for codon usage and dinucleotide biases. Our method takes advantage of the sparseness of loci where a given motif can occur, greatly speeding up the required convolution calculations. Knowledge of the distribution allows one to assess the exact non-parametric p-value of whether a given motif is over- or under- represented. We demonstrate that our method identifies known functional motifs more accurately than sampling and parametric-based approaches in a variety of coding datasets of various size, including ChIP-seq data for the transcription factors NRSF and GABP.

CONCLUSIONS

CodingMotif provides a theoretically and empirically-demonstrated advance for the detection of motifs overrepresented in coding sequences. We expect CodingMotif to be useful for identifying motifs in functional genomic datasets such as DNA-protein binding, RNA-protein binding, or microRNA-RNA binding within coding regions. A software implementation is available at http://bioinformatics.bc.edu/chuanglab/codingmotif.tar.

Locating protein-coding sequences under selection for additional, overlapping functions in 29 mammalian genomes

The degeneracy of the genetic code allows protein-coding DNA and RNA sequences to simultaneously encode additional, overlapping functional elements. A sequence in which both protein-coding and additional overlapping functions have evolved under purifying selection should show increased evolutionary conservation compared to typical protein-coding genes--especially at synonymous sites. In this study, we use genome alignments of 29 placental mammals to systematically locate short regions within human ORFs that show conspicuously low estimated rates of synonymous substitution across these species. The 29-species alignment provides statistical power to locate more than 10,000 such regions with resolution down to nine-codon windows, which are found within more than a quarter of all human protein-coding genes and contain ∼2% of their synonymous sites. We collect numerous lines of evidence that the observed synonymous constraint in these regions reflects selection on overlapping functional elements including splicing regulatory elements, dual-coding genes, RNA secondary structures, microRNA target sites, and developmental enhancers. Our results show that overlapping functional elements are common in mammalian genes, despite the vast genomic landscape.

When needles look like hay: how to find tissue-specific enhancers in model organism genomes

A major prerequisite for the investigation of tissue-specific processes is the identification of cis-regulatory elements. No generally applicable technique is available to distinguish them from any other type of genomic non-coding sequence. Therefore, researchers often have to identify these elements by elaborate in vivo screens, testing individual regions until the right one is found. Here, based on many examples from the literature, we summarize how functional enhancers have been isolated from other elements in the genome and how they have been characterized in transgenic animals. Covering computational and experimental studies, we provide an overview of the global properties of cis-regulatory elements, like their specific interactions with promoters and target gene distances. We describe conserved non-coding elements (CNEs) and their internal structure, nucleotide composition, binding site clustering and overlap, with a special focus on developmental enhancers. Conflicting data and unresolved questions on the nature of these elements are highlighted. Our comprehensive overview of the experimental shortcuts that have been found in the different model organism communities and the new field of high-throughput assays should help during the preparation phase of a screen for enhancers. The review is accompanied by a list of general guidelines for such a project.

Conserved microRNA targeting in Drosophila is as widespread in coding regions as in 3'UTRs

MicroRNAs (miRNAs) are a class of short noncoding RNAs that regulate protein-coding genes posttranscriptionally. In animals, most known miRNA targeting occurs within the 3'UTR of mRNAs, but the extent of biologically relevant targeting in the ORF or 5'UTR of mRNAs remains unknown. Here, we develop an algorithm (MinoTar-miRNA ORF Targets) to identify conserved regulatory motifs within protein-coding regions and use it to estimate the number of preferentially conserved miRNA-target sites in ORFs. We show that, in Drosophila, preferentially conserved miRNA targeting in ORFs is as widespread as it is in 3'UTRs and that, while far less abundant, conserved targets in Drosophila 5'UTRs number in the hundreds. Using our algorithm, we predicted a set of high-confidence ORF targets and selected seven miRNA-target pairs from among these for experimental validation. We observed down-regulation by the miRNA in five out of seven cases, indicating our approach can recover functional sites with high confidence. Additionally, we observed additive targeting by multiple sites within a single ORF. Altogether, our results demonstrate that the scale of biologically important miRNA targeting in ORFs is extensive and that computational tools such as ours can aid in the identification of such targets. Further evidence suggests that our results extend to mammals, but that the extent of ORF and 5'UTR targeting relative to 3'UTR targeting may be greater in Drosophila.