From First Base: The Sequence of the Tip of the X Chromosome of Drosophila melanogaster, a Comparison of Two Sequencing Strategies

We present the sequence of a contiguous 2.63 Mb of DNA extending from the tip of the X chromosome ofDrosophila melanogaster. Within this sequence, we predict 277 protein coding genes, of which 94 had been sequenced already in the course of studying the biology of their gene products, and examples of 12 different transposable elements. We show that an interval between bands 3A2 and 3C2, believed in the 1970s to show a correlation between the number of bands on the polytene chromosomes and the 20 genes identified by conventional genetics, is predicted to contain 45 genes from its DNA sequence. We have determined the insertion sites ofP-elements from 111 mutant lines, about half of which are in a position likely to affect the expression of novel predicted genes, thus representing a resource for subsequent functional genomic analysis. We compare the European Drosophila Genome Project sequence with the corresponding part of the independently assembled and annotated Joint Sequence determined through “shotgun” sequencing. Discounting differences in the distribution of known transposable elements between the strains sequenced in the two projects, we detected three major sequence differences, two of which are probably explained by errors in assembly; the origin of the third major difference is unclear. In addition there are eight sequence gaps within the Joint Sequence. At least six of these eight gaps are likely to be sites of transposable elements; the other two are complex. Of the 275 genes in common to both projects, 60% are identical within 1% of their predicted amino-acid sequence and 31% show minor differences such as in choice of translation initiation or termination codons; the remaining 9% show major differences in interpretation

STAMP: a web tool for exploring DNA-binding motif similarities

STAMP is a newly developed web server that is designed to support the study of DNA-binding motifs. STAMP may be used to query motifs against databases of known motifs; the software aligns input motifs against the chosen database (or alternatively against a user-provided dataset), and lists of the highest-scoring matches are returned. Such similarity-search functionality is expected to facilitate the identification of transcription factors that potentially interact with newly discovered motifs. STAMP also automatically builds multiple alignments, familial binding profiles and similarity trees when more than one motif is inputted. These functions are expected to enable evolutionary studies on sets of related motifs and fixed-order regulatory modules, as well as illustrating similarities and redundancies within the input motif collection. STAMP is a highly flexible alignment platform, allowing users to ‘mix-and-match’ between various implemented comparison metrics, alignment methods (local or global, gapped or ungapped), multiple alignment strategies and tree-building methods. Motifs may be inputted as frequency matrices (in many of the commonly used formats), consensus sequences, or alignments of known binding sites. STAMP also directly accepts the output files from 12 supported motif-finders, enabling quick interpretation of motif-discovery analyses. STAMP is available at http://www.benoslab.pitt.edu/stam

FOOTER: a web tool for finding mammalian DNA regulatory regions using phylogenetic footprinting

FOOTER is a newly developed algorithm that analyzes homologous mammalian promoter sequences in order to identify transcriptional DNA regulatory ‘signals’. FOOTER uses prior knowledge about the binding site preferences of the transcription factors (TFs) in the form of position-specific scoring matrices (PSSMs). The PSSM models are generated from known mammalian binding sites from the TRANSFAC database. In a test set of 72 confirmed binding sites (most of them not present in TRANSFAC) of 19 TFs, it exhibited 83% sensitivity and 72% specificity. FOOTER is accessible over the web at

DNA Familial Binding Profiles Made Easy: Comparison of Various Motif Alignment and Clustering Strategies

Transcription factor (TF) proteins recognize a small number of DNA sequences with high specificity and control the expression of neighbouring genes. The evolution of TF binding preference has been the subject of a number of recent studies, in which generalized binding profiles have been introduced and used to improve the prediction of new target sites. Generalized profiles are generated by aligning and merging the individual profiles of related TFs. However, the distance metrics and alignment algorithms used to compare the binding profiles have not yet been fully explored or optimized. As a result, binding profiles depend on TF structural information and sometimes may ignore important distinctions between subfamilies. Prediction of the identity or the structural class of a protein that binds to a given DNA pattern will enhance the analysis of microarray and ChIP–chip data where frequently multiple putative targets of usually unknown TFs are predicted. Various comparison metrics and alignment algorithms are evaluated (a total of 105 combinations). We find that local alignments are generally better than global alignments at detecting eukaryotic DNA motif similarities, especially when combined with the sum of squared distances or Pearson's correlation coefficient comparison metrics. In addition, multiple-alignment strategies for binding profiles and tree-building methods are tested for their efficiency in constructing generalized binding models. A new method for automatic determination of the optimal number of clusters is developed and applied in the construction of a new set of familial binding profiles which improves upon TF classification accuracy. A software tool, STAMP, is developed to host all tested methods and make them publicly available. This work provides a high quality reference set of familial binding profiles and the first comprehensive platform for analysis of DNA profiles. Detecting similarities between DNA motifs is a key step in the comparative study of transcriptional regulation, and the work presented here will form the basis for tool and method development for future transcriptional modeling studies

Extracting biologically significant patterns from short time series gene expression data

<p>Abstract</p> <p>Background</p> <p>Time series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult.</p> <p>Results</p> <p>We developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, <it>ASTRO </it>and <it>MiMeSR</it>, are inspired by the <it>rank order preserving </it>framework and the <it>minimum mean squared residue </it>approach, respectively. However, <it>ASTRO </it>and <it>MiMeSR </it>differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO) annotations and chromatin immunoprecipitation (ChIP-chip) data.</p> <p>Conclusion</p> <p>Our approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at <url>http://www.benoslab.pitt.edu/astro/</url>.</p

COEM: Cross-Modal Embedding for MetaCell Identification

Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal \textbf{E}mbedding for \textbf{M}etaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient sequencing coverage. COEM outperforms the state-of-the-art method SEACells by efficiently identifying accurate and well-separated metacells across datasets with continuous and discrete cell types. Furthermore, COEM significantly improves peak-to-gene association analyses, and facilitates complex gene regulatory inference tasks.Comment: 5 pages, 2 figures, ICML workshop on computational biolog

Regulatory conservation of protein coding and microRNA genes in vertebrates: lessons from the opossum genome

BACKGROUND: Being the first noneutherian mammal sequenced, Monodelphis domestica (opossum) offers great potential for enhancing our understanding of the evolutionary processes that take place in mammals. This study focuses on the evolutionary relationships between conservation of noncoding sequences, cis-regulatory elements, and biologic functions of regulated genes in opossum and eight vertebrate species. RESULTS: Analysis of 145 intergenic microRNA and all protein coding genes revealed that the upstream sequences of the former are up to twice as conserved as the latter among mammals, except in the first 500 base pairs, where the conservation is similar. Comparison of promoter conservation in 513 protein coding genes and related transcription factor binding sites (TFBSs) showed that 41% of the known human TFBSs are located in the 6.7% of promoter regions that are conserved between human and opossum. Some core biologic processes exhibited significantly fewer conserved TFBSs in human-opossum comparisons, suggesting greater functional divergence. A new measure of efficiency in multigenome phylogenetic footprinting (base regulatory potential rate [BRPR]) shows that including human-opossum conservation increases specificity in finding human TFBSs. CONCLUSION: Opossum facilitates better estimation of promoter conservation and TFBS turnover among mammals. The fact that substantial TFBS numbers are located in a small proportion of the human-opossum conserved sequences emphasizes the importance of marsupial genomes for phylogenetic footprinting-based motif discovery strategies. The BRPR measure is expected to help select genome combinations for optimal performance of these algorithms. Finally, although the etiology of the microRNA upstream increased conservation remains unknown, it is expected to have strong implications for our understanding of regulation of their expression

enoLOGOS: a versatile web tool for energy normalized sequence logos

enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at

The role of RNA folding free energy in the evolution of the polymerase genes of the influenza A virus

RNA folding free energy is important for the evolution and host-adaptation of the influenza virus. Human virus polymerase genes are shown to have substantially higher folding free energy values than their avian counterparts