Mutual Enrichment in Ranked Lists and the Statistical Assessment of Position Weight Matrix Motifs

Statistics in ranked lists is important in analyzing molecular biology measurement data, such as ChIP-seq, which yields ranked lists of genomic sequences. State of the art methods study fixed motifs in ranked lists. More flexible models such as position weight matrix (PWM) motifs are not addressed in this context. To assess the enrichment of a PWM motif in a ranked list we use a PWM induced second ranking on the same set of elements. Possible orders of one ranked list relative to the other are modeled by permutations. Due to sample space complexity, it is difficult to characterize tail distributions in the group of permutations. In this paper we develop tight upper bounds on tail distributions of the size of the intersection of the top of two uniformly and independently drawn permutations and demonstrate advantages of this approach using our software implementation, mmHG-Finder, to study PWMs in several datasets.Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013

Discovering Motifs in Ranked Lists of DNA Sequences

Computational methods for discovery of sequence elements that are enriched in a target set compared with a background set are fundamental in molecular biology research. One example is the discovery of transcription factor binding motifs that are inferred from ChIP–chip (chromatin immuno-precipitation on a microarray) measurements. Several major challenges in sequence motif discovery still require consideration: (i) the need for a principled approach to partitioning the data into target and background sets; (ii) the lack of rigorous models and of an exact p-value for measuring motif enrichment; (iii) the need for an appropriate framework for accounting for motif multiplicity; (iv) the tendency, in many of the existing methods, to report presumably significant motifs even when applied to randomly generated data. In this paper we present a statistical framework for discovering enriched sequence elements in ranked lists that resolves these four issues. We demonstrate the implementation of this framework in a software application, termed DRIM (discovery of rank imbalanced motifs), which identifies sequence motifs in lists of ranked DNA sequences. We applied DRIM to ChIP–chip and CpG methylation data and obtained the following results. (i) Identification of 50 novel putative transcription factor (TF) binding sites in yeast ChIP–chip data. The biological function of some of them was further investigated to gain new insights on transcription regulation networks in yeast. For example, our discoveries enable the elucidation of the network of the TF ARO80. Another finding concerns a systematic TF binding enhancement to sequences containing CA repeats. (ii) Discovery of novel motifs in human cancer CpG methylation data. Remarkably, most of these motifs are similar to DNA sequence elements bound by the Polycomb complex that promotes histone methylation. Our findings thus support a model in which histone methylation and CpG methylation are mechanistically linked. Overall, we demonstrate that the statistical framework embodied in the DRIM software tool is highly effective for identifying regulatory sequence elements in a variety of applications ranging from expression and ChIP–chip to CpG methylation data. DRIM is publicly available at http://bioinfo.cs.technion.ac.il/drim

Clinically driven semi-supervised class discovery in gene expression data

Abstract Motivation: Unsupervised class discovery in gene expression data relies on the statistical signals in the data to exclusively drive the results. It is often the case, however, that one is interested in constraining the search space to respect certain biological prior knowledge while still allowing a flexible search within these boundaries. Results: We develop an approach to semi-supervised class discovery. One component of our approach uses clinical sample information to constrain the search space and guide the class discovery process to yield biologically relevant partitions. A second component consists of using known biological annotation of genes to drive the search, seeking partitions that manifest strong differential expression in specific sets of genes. We develop efficient algorithmics for these tasks, implementing both approaches and combinations thereof. We show that our method is robust enough to detect known clinical parameters in accordance with expected clinical values. We also use our method to elucidate cardiovascular disease (CVD) putative risk factors. Availability: MonoClaD (Monotone Class Discovery). See http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/ Supplementary information: Supplementary data is available at http://bioinfo.cs.technion.ac.il/people/zohar/MonoClad/software.html Contact: [email protected]

Linear-Regression on Packed Encrypted Data in the Two-Server Model

Developing machine learning models from federated training data, containing many independent samples, is an important task that can significantly enhance the potential applicability and prediction power of learned models. Since single users, like hospitals or individual labs, typically collect data-sets that do not support accurate learning with high confidence, it is desirable to combine data from several users without compromising data privacy. In this paper, we develop a privacy-preserving solution for learning a linear regression model from data collectively contributed by several parties (``data owners\u27\u27). Our protocol is based on the protocol of Giacomelli et al. (ACNS 2018) that utilized two non colluding servers and Linearly Homomorphic Encryption (LHE) to learn regularized linear regression models. Our methods use a different LHE scheme that allows us to significantly reduce both the number and runtime of homomorphic operations, as well as the total runtime complexity. Another advantage of our protocol is that the underlying LHE scheme is based on a different (and post-quantum secure) security assumption than Giacomelli et al. Our approach leverages the Chinese Remainder Theorem, and Single Instruction Multiple Data representations, to obtain our improved performance. For a 1000 x 40 linear regression task we can learn a model in a total of 3 seconds for the homomorphic operations, compared to more than 100 seconds reported in the literature. Our approach also scales up to larger feature spaces: we implemented a system that can handle a 1000 x 100 linear regression task, investing minutes of server computing time after a more significant offline pre-processing by the data owners. We intend to incorporate our protocol and implementations into a comprehensive system that can handle secure federated learning at larger scales