Aleksander Jankowski

Boruta - A System for Feature Selection

Machine learning methods are often used to classify objects described by hundreds of attributes; in many applications of this kind a great fraction of attributes may be totally irrelevant to the classification problem. Even more, usually one cannot decide a priori which attributes are relevant. In this paper we present an improved version of the algorithm for identification of the full set of truly important variables in an information system. It is an extension of the random forest method which utilises the importance measure generated by the original algorithm. It compares, in the iterative fashion, the importances of original attributes with importances of their randomised copies. We analyse performance of the algorithm on several examples of synthetic data, as well as on a biologically important problem, namely on identification of the sequence motifs that are important for aptameric activity of short RNA sequences.

SOXE transcription factors form selective dimers on non-compact DNA motifs through multifaceted interactions between dimerization and high-mobility group domains

The SOXE transcription factors SOX8, SOX9 and SOX10 are master regulators of mammalian development directing sex determination, gliogenesis, pancreas specification and neural crest development. We identified a set of palindromic SOX binding sites specifically enriched in regulatory regions of melanoma cells. SOXE proteins homodimerize on these sequences with high cooperativity. In contrast to other transcription factor dimers, which are typically rigidly spaced, SOXE group proteins can bind cooperatively at a wide range of dimer spacings. Using truncated forms of SOXE proteins, we show that a single dimerization (DIM) domain, that precedes the DNA binding high mobility group (HMG) domain, is sufficient for dimer formation, suggesting that DIM : HMG rather than DIM:DIM interactions mediate the dimerization. All SOXE members can also heterodimerize in this fashion, whereas SOXE heterodimers with SOX2, SOX4, SOX6 and SOX18 are not supported. We propose a structural model where SOXE-specific intramolecular DIM:HMG interactions are allosterically communicated to the HMG of juxtaposed molecules. Collectively, SOXE factors evolved a unique mode to combinatorially regulate their target genes that relies on a multifaceted interplay between the HMG and DIM domains. This property potentially extends further the diversity of target genes and cell-specific functions that are regulated by SOXE proteins.

Comprehensive prediction in 78 human cell lines reveals rigidity and compactness of transcription factor dimers

The binding of transcription factors (TFs) to their specific motifs in genomic regulatory regions is commonly studied in isolation. However, in order to elucidate the mechanisms of transcriptional regulation, it is essential to determine which TFs bind DNA cooperatively as dimers and to infer the precise nature of these interactions. So far, only a small number of such dimeric complexes are known. Here, we present an algorithm for predicting cell-type-specific TF-TF dimerization on DNA on a large scale, using DNase I hypersensitivity data from 78 human cell lines. We represented the universe of possible TF complexes by their corresponding motif complexes, and analyzed their occurrence at cell-type-specific DNase I hypersensitive sites. Based on ∼1.4 billion tests for motif complex enrichment, we predicted 603 highly significant cell-type-specific TF dimers, the vast majority of which are novel. Our predictions included 76% (19/25) of the known dimeric complexes and showed significant overlap with an experimental database of protein-protein interactions. They were also independently supported by evolutionary conservation, as well as quantitative variation in DNase I digestion patterns. Notably, the known and predicted TF dimers were almost always highly compact and rigidly spaced, suggesting that TFs dimerize in close proximity to their partners, which results in strict constraints on the structure of the DNA-bound complex. Overall, our results indicate that chromatin openness profiles are highly predictive of cell-type-specific TF-TF interactions. Moreover, cooperative TF dimerization seems to be a widespread phenomenon, with multiple TF complexes predicted in most cell types.

The new SIMD Implementation of the Smith-Waterman Algorithm on Cell Microprocessor

Algorithms for estimating similarity between two macromolecular sequences are of profound importance for molecular biology. The standard methods utilize so-called primary structure, that is a string of characters denoting the sequence of monomers in hetero-polymer. These methods find the substrings of maximal similarity, as defined by the so-called similarity matrix, for a pair of two molecules. The problem is solved either by the exact dynamic programming method, or by approximate heuristic methods. The approximate algorithms are almost two orders of magnitude faster in comparison with the standard version of the exact Smith-Waterman algorithm, when executed on the same hardware, hence the exact algorithm is relatively rarely used. Recently a very efficient implementation of Smith- Waterman algorithm utilizing SIMD extensions to the standard instruction set reduced the speed advantage of heuristic algorithms to factor of three. Here we present an improved implementation of the Smith-Waterman algorithm on the Cell processor. Implementation presented here achieves execution speed of approximately 9 GCUPS. The performance is independent on the scoring system. It is 4 to 10 times faster than best Smith-Waterman implementation running on a PC and 1.5 to 3 times faster than the same implementation running on Sony PlayStation 3. It is also 5 times faster than the recent implementation of the Smith- Waterman utilizing Nvidia GPU. Our implementation running on Sony PlayStation 3 has performance which is directly comparable with that of BLAST running on PC, being up to 4 times faster in the best case and no more than two times slower in the worst case. This performance level opens possibility for using the exact Smith-Waterman algorithm in applications, where currently approximate algorithms are used.

TACO: a general-purpose tool for predicting cell-type–specific transcription factor dimers

BackgroundCooperative binding of transcription factor (TF) dimers to DNA is increasingly recognized as a major contributor to binding specificity. However, it is likely that the set of known TF dimers is highly incomplete, given that they were discovered using ad hoc approaches, or through computational analyses of limited datasets.ResultsHere, we present TACO (Transcription factor Association from Complex Overrepresentation), a general-purpose standalone software tool that takes as input any genome-wide set of regulatory elements and predicts cell-type–specific TF dimers based on enrichment of motif complexes. TACO is the first tool that can accommodate motif complexes composed of overlapping motifs, a characteristic feature of many known TF dimers. Our method comprehensively outperforms existing tools when benchmarked on a reference set of 29 known dimers. We demonstrate the utility and consistency of TACO by applying it to 152 DNase-seq datasets and 94 ChIP-seq datasets.ConclusionsBased on these results, we uncover a general principle governing the structure of TF-TF-DNA ternary complexes, namely that the flexibility of the complex is correlated with, and most likely a consequence of, inter-motif spacing.

Romulus: robust multi-state identification of transcription factor binding sites from DNase-seq data

Motivation: Computational prediction of transcription factor (TF) binding sites in the genome remains a challenging task. Here, we present Romulus, a novel computational method for identifying individual TF binding sites from genome sequence information and cell-type–specific experimental data, such as DNase-seq. It combines the strengths of previous approaches, and improves robustness by reducing the number of free parameters in the model by an order of magnitude. Results: We show that Romulus significantly outperforms existing methods across three sources of DNase-seq data, by assessing the performance of these tools against ChIP-seq profiles. The difference was particularly significant when applied to binding site prediction for low-information-content motifs. Our method is capable of inferring multiple binding modes for a single TF, which differ in their DNase I cut profile. Finally, using the model learned by Romulus and ChIP-seq data, we introduce Binding in Closed Chromatin (BCC) as a quantitative measure of TF pioneer factor activity. Uniquely, our measure quantifies a defining feature of pioneer factors, namely their ability to bind closed chromatin. Availability and Implementation: Romulus is freely available as an R package at http://github.com/ajank/Romulus. Contact: ajank@mimuw.edu.pl Supplementary information: Supplementary data are available at Bioinformatics online.

Enhanceosome transcription factors preferentially dimerize with high mobility group proteins

BackgroundThe enhanceosome is an enhancer located upstream of the human interferon β gene, bound by transcription factor (TF) complex of extremely rigid structure. Within these rigid constraints, even a slight change of distances between transcription factor binding sites (TFBS) results in loss of functionality of the enhanceosome. We hypothesized that smaller subunits of the enhanceosome may entail TF complex formation in other regulatory regions.ResultsIn order to verify this hypothesis we systematically searched for dimerization preferences of the TFs that have TFBS in the enhanceosome. For this we utilized our recently developed tool, TACO. We performed this computational experiment in a cell-type–specific manner by utilizing cell-type–specific DNase-seq data for 105 human cell types. We also used 20 TRANSFAC motifs comprising not only the usual TFs constituting the enhanceosome but also the architectural proteins of High Mobility Group I(Y) (HMG I). A similar experiment used 42 DNase-seq data sets for mouse cell types. We found 137 statistically significant dimer predictions in the human genome, and 37 predictions in the mouse genome, that matched the positioning on the enhanceosome with ±2 bp tolerance. To characterize these predicted TF dimers, we performed functional analysis (Gene Ontology enrichment) for sets of genes which were in the neighbourhood of predicted dimer instances. A notable feature of these instances is that (1) most of them are located in introns of genes, (2) they are enriched in regulatory states, and (3) those instances that are located near transcription start sites are enriched for inclusion in computationally predicted enhancers. We also investigated similarity of dimer predictions between human and mouse.ConclusionsIt follows from our experiments that, except for homodimer formed by IRF proteins, the rest of the dimers were formed exclusively between one of the transcriptional activators (ATF-2/c-Jun and IRF) and a HMG I protein. NF- κB did not participate in forming dimers with other proteins. Dimers predicted in mouse were fully contained in those predicted in human, with exactly the same spacing and orientation. Intriguingly, in most of the cases the enhanceosome motifs have 1 bp wider spacing than the corresponding dimers predicted genome-wide, which is likely caused by the overall 3D structure constraints of the enhanceosome-bound complex.

DNA-mediated dimerization on a compact sequence signature controls enhancer engagement and regulation by FOXA1

Abstract FOXA1 is a transcription factor capable to bind silenced chromatin to direct context-dependent cell fate conversion. Here, we demonstrate that a compact palindromic DNA element (termed ‘DIV’ for its diverging half-sites) induces the homodimerization of FOXA1 with strongly positive cooperativity. Alternative structural models are consistent with either an indirect DNA-mediated cooperativity or a direct protein-protein interaction. The cooperative homodimer formation is strictly constrained by precise half-site spacing. Re-analysis of chromatin immunoprecipitation sequencing data indicates that the DIV is effectively targeted by FOXA1 in the context of chromatin. Reporter assays show that FOXA1-dependent transcriptional activity declines when homodimeric binding is disrupted. In response to phosphatidylinositol-3 kinase inhibition DIV sites pre-bound by FOXA1 such as at the PVT1/MYC locus exhibit a strong increase in accessibility suggesting a role of the DIV configuration in the chromatin closed-open dynamics. Moreover, several disease-associated single nucleotide polymorphisms map to DIV elements and show allelic differences in FOXA1 homodimerization, reporter gene expression and are annotated as quantitative trait loci. This includes the rs541455835 variant at the MAPT locus encoding the Tau protein associated with Parkinsons disease. Collectively, the DIV guides chromatin engagement and regulation by FOXA1 and its perturbation could be linked to disease etiologies.