Papers by Ivan Kulakovskiy
BMC Genomics, 2010
Background Recently, it has been discovered that the human genome contains many transcription sta... more Background Recently, it has been discovered that the human genome contains many transcription start sites for non-coding RNA. Regulatory regions related to transcription of this non-coding RNAs are poorly studied. Some of these regulatory regions may be associated with CpG islands located far from transcription start-sites of any protein coding gene. The human genome contains many such CpG islands; however, until now their properties were not systematically studied. Results We studied CpG islands located in different regions of the human genome using methods of bioinformatics and comparative genomics. We have observed that CpG islands have a preference to overlap with exons, including exons located far from transcription start site, but usually extend well into introns. Synonymous substitution rate of CpG-containing codons becomes substantially reduced in regions where CpG islands overlap with protein-coding exons, even if they are located far downstream from transcription start site. CAGE tag analysis displayed frequent transcription start sites in all CpG islands, including those found far from transcription start sites of protein coding genes. Computational prediction and analysis of published ChIP-chip data revealed that CpG islands contain an increased number of sites recognized by Sp1 protein. CpG islands containing more CAGE tags usually also contain more Sp1 binding sites. This is especially relevant for CpG islands located in 3' gene regions. Various examples of transcription, confirmed by mRNAs or ESTs, but with no evidence of protein coding genes, were found in CAGE-enriched CpG islands located far from transcription start site of any known protein coding gene. Conclusions CpG islands located far from transcription start sites of protein coding genes have transcription initiation activity and display Sp1 binding properties. In exons, overlapping with these islands, the synonymous substitution rate of CpG containing codons is decreased. This suggests that these CpG islands are involved in transcription initiation, possibly of some non-coding RNAs.
Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies ... more Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.
Background: Positional weight matrix (PWM) remains the most popular for quantification of transcr... more Background: Positional weight matrix (PWM) remains the most popular for quantification of transcription factor (TF) binding. PWM supplied with a score threshold defines a set of putative transcription factor binding sites (TFBS), thus providing a TFBS model. TF binding DNA fragments obtained by different experimental methods usually give similar but not identical PWMs. This is also common for different TFs from the same structural family. Thus it is often necessary to measure the similarity between PWMs. The popular tools compare PWMs directly using matrix elements. Yet, for log-odds PWMs, negative elements do not contribute to the scores of highly scoring TFBS and thus may be different without affecting the sets of the best recognized binding sites. Moreover, the two TFBS sets recognized by a given pair of PWMs can be more or less different depending on the score thresholds.
Stem cell reports, Jan 11, 2015
Analyses of gene expression in single mouse embryonic stem cells (mESCs) cultured in serum and LI... more Analyses of gene expression in single mouse embryonic stem cells (mESCs) cultured in serum and LIF revealed the presence of two distinct cell subpopulations with individual gene expression signatures. Comparisons with published data revealed that cells in the first subpopulation are phenotypically similar to cells isolated from the inner cell mass (ICM). In contrast, cells in the second subpopulation appear to be more mature. Pluripotency Gene Regulatory Network (PGRN) reconstruction based on single-cell data and published data suggested antagonistic roles for Oct4 and Nanog in the maintenance of pluripotency states. Integrated analyses of published genomic binding (ChIP) data strongly supported this observation. Certain target genes alternatively regulated by OCT4 and NANOG, such as Sall4 and Zscan10, feed back into the top hierarchical regulator Oct4. Analyses of such incoherent feedforward loops with feedback (iFFL-FB) suggest a dynamic model for the maintenance of mESC pluripote...
Database, 2015
Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by ... more Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by changes in the DNA sequence per se. Rather, covalent modifications of DNA and histones affect gene expression and genome stability via proteins that recognize and act upon such modifications. Many enzymes that catalyse epigenetic modifications or are critical for enzymatic complexes have been discovered, and this is encouraging investigators to study the role of these proteins in diverse normal and pathological processes. Rapidly growing knowledge in the area has resulted in the need for a resource that compiles, organizes and presents curated information to the researchers in an easily accessible and user-friendly form. Here we present EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets and products. EpiFactors contains information on 815 proteins, including 95 histones and protamines. For 789 of these genes, we include expressions values across several samples, in particular a collection of 458 human primary cell samples (for approximately 200 cell types, in many cases from three individual donors), covering most mammalian cell steady states, 255 different cancer cell lines (representing approximately 150 cancer subtypes) and 134 human postmortem tissues. Expression values were obtained by the FANTOM5 consortium using Cap Analysis of Gene Expression technique. EpiFactors also contains information on 69 protein complexes that are involved in epigenetic regulation. The resource is practical for a wide range of users, including biologists, pharmacologists and clinicians.
DNA sequence motif: a jack of all trades for ChIP-Seq data
Advances in protein chemistry and structural biology, 2013
Nowadays, chromatin immunoprecipitation followed by next-generation sequencing, often referred to... more Nowadays, chromatin immunoprecipitation followed by next-generation sequencing, often referred to as ChIP-Seq, has become an industry standard to study a landscape of DNA-protein interactions in vivo. ChIP-Seq captures highly specific protein-DNA interactions, such as transcription factors (TFs) bound to appropriate binding sites, and sparse patterns formed by different histone marks. In this review, we focus on DNA sequence analysis methods adequate for TF ChIP-Seq data. We discuss numerous tasks starting from basic DNA motif finding and motif discovery as is, further applied to explore various features of experimental data. We show how sequence analysis of ChIP-Seq data derives novel biological knowledge on multiple levels, from individual transcription factor binding sites to genome segments operating as regulatory modules. Finally, we provide an overview of existing software in the field.
Journal of bioinformatics and computational biology, 2013
Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) became a method of choice to... more Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) became a method of choice to locate DNA segments bound by different regulatory proteins. ChIP-Seq produces extremely valuable information to study transcriptional regulation. The wet-lab workflow is often supported by downstream computational analysis including construction of models of nucleotide sequences of transcription factor binding sites in DNA, which can be used to detect binding sites in ChIP-Seq data at a single base pair resolution. The most popular TFBS model is represented by positional weight matrix (PWM) with statistically independent positional weights of nucleotides in different columns; such PWMs are constructed from a gapless multiple local alignment of sequences containing experimentally identified TFBSs. Modern high-throughput techniques, including ChIP-Seq, provide enough data for careful training of advanced models containing more parameters than PWM. Yet, many suggested multiparametric model...
Background: The detailed analysis of transcriptional regulation is crucially important for unders... more Background: The detailed analysis of transcriptional regulation is crucially important for understanding biological processes. The gap gene network in Drosophila attracts large interest among researches studying mechanisms of transcriptional regulation. It implements the most upstream regulatory layer of the segmentation gene network. The knowledge of molecular mechanisms involved in gap gene regulation is far less complete than that of genetics of the system. Mathematical modeling goes beyond insights gained by genetics and molecular approaches. It allows us to reconstruct wild-type gene expression patterns in silico, infer underlying regulatory mechanism and prove its sufficiency. Results: We developed a new model that provides a dynamical description of gap gene regulatory systems, using detailed DNA-based information, as well as spatial transcription factor concentration data at varying time points. We showed that this model correctly reproduces gap gene expression patterns in wild type embryos and is able to predict gap expression patterns in Kr mutants and four reporter constructs. We used four-fold cross validation test and fitting to random dataset to validate the model and proof its sufficiency in data description. The identifiability analysis showed that most model parameters are well identifiable. We reconstructed the gap gene network topology and studied the impact of individual transcription factor binding sites on the model output. We measured this impact by calculating the site regulatory weight as a normalized difference between the residual sum of squares error for the set of all annotated sites and for the set with the site of interest excluded.
Uploads
Papers by Ivan Kulakovskiy