We present a new version of the program MatInspector that identifies TFBS in nucleotide sequences using a large library of weight matrices. By introducing a matrix family concept, optimized thresholds, and comparative analysis, the enhanced program produces concise results avoiding redundant and false-positive matches. We describe a number of programs based on MatInspector allowing in-depth promoter analysis (DiAlignTF, FrameWorker) and targeted design of regulatory sequences (SequenceShaper).

Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.gene expression | gene transcription | RNA processing | Gaussian process inference | RNA splicing I nduction of transcription through extracellular signaling can yield rapid changes in gene expression for many genes. Establishing the timing of events during this process is important for understanding the rate-limiting mechanisms regulating the response and vital for inferring causality of regulatory events. Several processes influence the patterns of mRNA abundance observed in the cell, including the kinetics of transcriptional initiation, elongation, splicing, and mRNA degradation. It was recently demonstrated that significant delays attributable to the kinetics of splicing can be an important factor in a focused study of genes induced by tumor necrosis factor (TNF-α) (1). Delayed transcription can play an important functional role in the cell, for example, inducing oscillations within negative feedback loops (2) or facilitating "justin-time" transcriptional programs with optimal efficiency (3). It is therefore important to identify such delays and to better understand how they are regulated. In this study, we combine RNA polymerase (pol-II) ChIP-Seq data with RNA-Seq data to study transcription kinetics of estrogen receptor (ER) signaling in breast cancer cells. Using an unbiased genome-wide modeling approach, we find evidence for large delays in mRNA production in 11% of the genes with a quantifiable signal in our data. A statistical analysis of gene...

Evaluation of cancer genomes in global context is of great interest in light of changing ethnic distribution of the world population. We focused our study on men of African ancestry because of their disproportionately higher rate of prostate cancer (CaP) incidence and mortality. We present a systematic whole genome analyses, revealing alterations that differentiate African American (AA) and Caucasian American (CA) CaP genomes. We discovered a recurrent deletion on chromosome 3q13.31 centering on the LSAMP locus that was prevalent in tumors from AA men (cumulative analyses of 435 patients: whole genome sequence, 14; FISH evaluations, 101; and SNP array, 320 patients). Notably, carriers of this deletion experienced more rapid disease progression. In contrast, PTEN and ERG common driver alterations in CaP were significantly lower in AA prostate tumors compared to prostate tumors from CA. Moreover, the frequency of inter-chromosomal rearrangements was significantly higher in AA than CA tumors. These findings reveal differentially distributed somatic mutations in CaP across ancestral groups, which have implications for precision medicine strategies.

We carried out a genome-wide prediction of scaffold/matrix attachment regions (S/MARs) in Arabidopsis. Results indicate no uneven distribution on the chromosomal level but a clear underrepresentation of S/MARs inside genes. In cases where S/MARs were predicted within genes, these intragenic S/MARs were preferentially located within the 5#-half, most prominently within introns 1 and 2. Using Arabidopsis whole-genome expression data generated by the massively parallel signature sequencing methodology, we found a negative correlation between S/MAR-containing genes and transcriptional abundance. Expressed sequence tag data correlated the same way with S/MAR-containing genes. Thus, intragenic S/MARs show a negative correlation with transcription level. For various genes it has been shown experimentally that S/MARs can function as transcriptional regulators and that they have an implication in stabilizing expression levels within transgenic plants. On the basis of a genome-wide in silico S/MAR analysis, we found a significant correlation between the presence of intragenic S/MARs and transcriptional down-regulation.

The LitMiner software is a literature data-mining tool that facilitates the identification of major gene regulation key players related to a user-defined field of interest in PubMed abstracts. The prediction of gene-regulatory relationships is based on co-occurrence analysis of key terms within the abstracts. LitMiner predicts relationships between key terms from the biomedical domain in four categories (genes, chemical compounds, diseases and tissues). Owing to the limitations (no direction, unverified automatic prediction) of the co-occurrence approach, the primary data in the LitMiner database represent postulated basic gene–gene relationships. The usefulness of the LitMiner system has been demonstrated recently in a study that reconstructed disease-related regulatory networks by promoter modelling that was initiated by a LitMiner generated primary gene list. To overcome the limitations and to verify and improve the data, we developed WikiGene, a Wiki-based curation tool that allows revision of the data by expert users over the Internet. LitMiner () and WikiGene () can be used unrestricted with any Internet browser.