RCK: accurate and efficient inference of sequence- and structure-based protein–RNA binding models from RNAcompete data

Motivation: Protein-RNA interactions, which play vital roles in many processes, are mediated through both RNA sequence and structure. CLIP-based methods, which measure protein-RNA binding in vivo, suffer from experimental noise and systematic biases, whereas in vitro experiments capture a clearer signal of protein RNA-binding. Among them, RNAcompete provides binding affinities of a specific protein to more than 240 000 unstructured RNA probes in one experiment. The computational challenge is to infer RNA structure- and sequence-based binding models from these data. The state-of-the-art in sequence models, Deepbind, does not model structural preferences. RNAcontext models both sequence and structure preferences, but is outperformed by GraphProt. Unfortunately, GraphProt cannot detect structural preferences from RNAcompete data due to the unstructured nature of the data, as noted by its developers, nor can it be tractably run on the full RNACompete dataset. Results: We develop RCK, an efficient, scalable algorithm that infers both sequence and structure preferences based on a new k-mer based model. Remarkably, even though RNAcompete data is designed to be unstructured, RCK can still learn structural preferences from it. RCK significantly outperforms both RNAcontext and Deepbind in in vitro binding prediction for 244 RNAcompete experiments. Moreover, RCK is also faster and uses less memory, which enables scalability. While currently on par with existing methods in in vivo binding prediction on a small scale test, we demonstrate that RCK will increasingly benefit from experimentally measured RNA structure profiles as compared to computationally predicted ones. By running RCK on the entire RNAcompete dataset, we generate and provide as a resource a set of protein-RNA structure-based models on an unprecedented scale.National Institutes of Health (U.S.) (Grant R01GM081871

Optimized Sequence Library Design for Efficient In Vitro Interaction Mapping

Sequence libraries that cover all k-mers enable universal, unbiased measurements of binding to both oligonucleotides and peptides. While the number of k-mers grows exponentially in k, space on all experimental platforms is limited. Here, we shrink k-mer library sizes by using joker characters, which represent all characters in the alphabet simultaneously. We present the JokerCAKE (joker covering all k-mers) algorithm for generating a short sequence such that each k-mer appears at least p times with at most one joker character per k-mer. By running our algorithm on a range of parameters and alphabets, we show that JokerCAKE produces near-optimal sequences. Moreover, through comparison with data from hundreds of DNA-protein binding experiments and with new experimental results for both standard and JokerCAKE libraries, we establish that accurate binding scores can be inferred for high-affinity k-mers using JokerCAKE libraries. JokerCAKE libraries allow researchers to search a significantly larger sequence space using the same number of experimental measurements and at the same cost. We present a new compact sequence design that covers all k-mers utilizing joker characters and develop an efficient algorithm to generate such designs. We show through simulations and experimental validation that these sequence designs are useful for identifying high-affinity binding sites at significantly reduced cost and space. Keywords: sequence libraries; microarray design; de Bruijn graphNational Institutes of Health (U.S.) (Grant R01GM081871

Transcription factor family‐specific DNA shape readout revealed by quantitative specificity models

Transcription factors (TFs) achieve DNA-binding specificity through contacts with functional groups of bases (base readout) and readout of structural properties of the double helix (shape readout). Currently, it remains unclear whether DNA shape readout is utilized by only a few selected TF families, or whether this mechanism is used extensively by most TF families. We resequenced data from previously published HT-SELEX experiments, the most extensive mammalian TF–DNA binding data available to date. Using these data, we demonstrated the contributions of DNA shape readout across diverse TF families and its importance in core motif-flanking regions. Statistical machine-learning models combined with feature-selection techniques helped to reveal the nucleotide position-dependent DNA shape readout in TF-binding sites and the TF family-specific position dependence. Based on these results, we proposed novel DNA shape logos to visualize the DNA shape preferences of TFs. Overall, this work suggests a way of obtaining mechanistic insights into TF–DNA binding without relying on experimentally solved all-atom structures

Excited states of linear polyenes

We present density matrix renormalisation group calculations of the Pariser- Parr-Pople-Peierls model of linear polyenes within the adiabatic approximation. We calculate the vertical and relaxed transition energies, and relaxed geometries for various excitations on long chains. The triplet (3Bu+) and even- parity singlet (2Ag+) states have a 2-soliton and 4-soliton form, respectively, both with large relaxation energies. The dipole-allowed (1Bu-) state forms an exciton-polaron and has a very small relaxation energy. The relaxed energy of the 2Ag+ state lies below that of the 1Bu- state. We observe an attraction between the soliton-antisoliton pairs in the 2Ag+ state. The calculated excitation energies agree well with the observed values for polyene oligomers; the agreement with polyacetylene thin films is less good, and we comment on the possible sources of the discrepencies. The photoinduced absorption is interpreted. The spin-spin correlation function shows that the unpaired spins coincide with the geometrical soliton positions. We study the roles of electron-electron interactions and electron-lattice coupling in determining the excitation energies and soliton structures. The electronic interactions play the key role in determining the ground state dimerisation and the excited state transition energies.Comment: LaTeX, 15 pages, 9 figure

Deep learning for protein-RNA interactions

Non UBCUnreviewedAuthor affiliation: Ben-Gurion UniversityResearche

Efficient Design of Compact Unstructured RNA Libraries Covering All k-mers

Current microarray technologies to determine RNA structure or measure protein-RNA interactions rely on single-stranded, unstructured RNA probes on a chip covering together all k-mers. Since space on the array is limited, the problem is to efficiently design a compact library of unstructured ℓ-long RNA probes, where each k-mer is covered at least p times. Ray et al. designed such a library for specific values of k, ℓ, and p using ad-hoc rules. To our knowledge, there is no general method to date to solve this problem. Here, we address the problem of finding a minimum-size covering of all k-mers by ℓ-long sequences with the desired properties for any value of k, ℓ, and p. As we prove that the problem is NP-hard, we give two solutions: the first is a greedy algorithm with a logarithmic approximation ratio; the second, a heuristic greedy approach based on random walks in de Bruijn graphs. The heuristic algorithm works well in practice and produces a library of unstructured RNA probes that is only ∼1.1-times greater in size compared to the theoretical lower bound. We present results for typical values of k and probe lengths ℓ and show that our algorithm generates a library that is significantly smaller than the library of Ray et al.; moreover, we show that our algorithm outperforms naive methods. Our approach can be generalized and extended to generate RNA or DNA oligo libraries with other desired properties. The software is freely available online. Keywords: de Bruijn graph; microarray library design; RNA secondary structureUnited States. National Institutes of Health (Grant R01GM081871

A Randomized Parallel Algorithm for Efficiently Finding Near-Optimal Universal Hitting Sets

© Springer Nature Switzerland AG 2020. As the volume of next generation sequencing data increases, an urgent need for algorithms to efficiently process the data arises. Universal hitting sets (UHS) were recently introduced as an alternative to the central idea of minimizers in sequence analysis with the hopes that they could more efficiently address common tasks such as computing hash functions for read overlap, sparse suffix arrays, and Bloom filters. A UHS is a set of k-mers that hit every sequence of length L, and can thus serve as indices to L-long sequences. Unfortunately, methods for computing small UHSs are not yet practical for real-world sequencing instances due to their serial and deterministic nature, which leads to long runtimes and high memory demands when handling typical values of k (e.g. k > 13). To address this bottleneck, we present two algorithmic innovations to significantly decrease runtime while keeping memory usage low: (i) we leverage advanced theoretical and architectural techniques to parallelize and decrease memory usage in calculating k-mer hitting numbers; and (ii) we build upon techniques from randomized Set Cover to select universal k-mers much faster. We implemented these innovations in PASHA, the first randomized parallel algorithm for generating near-optimal UHSs, which newly handles k > 13. We demonstrate empirically that PASHA produces sets only slightly larger than those of serial deterministic algorithms; moreover, the set size is provably guaranteed to be within a small constant factor of the optimal size. PASHA’s runtime and memory-usage improvements are orders of magnitude faster than the current best algorithms. We expect our newly-practical construction of UHSs to be adopted in many high-throughput sequence analysis pipelines

Assessment of algorithms for inferring positional weight matrix motifs of transcription factor binding sites using protein binding microarray data.

The new technology of protein binding microarrays (PBMs) allows simultaneous measurement of the binding intensities of a transcription factor to tens of thousands of synthetic double-stranded DNA probes, covering all possible 10-mers. A key computational challenge is inferring the binding motif from these data. We present a systematic comparison of four methods developed specifically for reconstructing a binding site motif represented as a positional weight matrix from PBM data. The reconstructed motifs were evaluated in terms of three criteria: concordance with reference motifs from the literature and ability to predict in vivo and in vitro bindings. The evaluation encompassed over 200 transcription factors and some 300 assays. The results show a tradeoff between how the methods perform according to the different criteria, and a dichotomy of method types. Algorithms that construct motifs with low information content predict PBM probe ranking more faithfully, while methods that produce highly informative motifs match reference motifs better. Interestingly, in predicting high-affinity binding, all methods give far poorer results for in vivo assays compared to in vitro assays

G4mismatch: Deep neural networks to predict G-quadruplex propensity based on G4-seq data.

G-quadruplexes are non-B-DNA structures that form in the genome facilitated by Hoogsteen bonds between guanines in single or multiple strands of DNA. The functions of G-quadruplexes are linked to various molecular and disease phenotypes, and thus researchers are interested in measuring G-quadruplex formation genome-wide. Experimentally measuring G-quadruplexes is a long and laborious process. Computational prediction of G-quadruplex propensity from a given DNA sequence is thus a long-standing challenge. Unfortunately, despite the availability of high-throughput datasets measuring G-quadruplex propensity in the form of mismatch scores, extant methods to predict G-quadruplex formation either rely on small datasets or are based on domain-knowledge rules. We developed G4mismatch, a novel algorithm to accurately and efficiently predict G-quadruplex propensity for any genomic sequence. G4mismatch is based on a convolutional neural network trained on almost 400 millions human genomic loci measured in a single G4-seq experiment. When tested on sequences from a held-out chromosome, G4mismatch, the first method to predict mismatch scores genome-wide, achieved a Pearson correlation of over 0.8. When benchmarked on independent datasets derived from various animal species, G4mismatch trained on human data predicted G-quadruplex propensity genome-wide with high accuracy (Pearson correlations greater than 0.7). Moreover, when tested in detecting G-quadruplexes genome-wide using the predicted mismatch scores, G4mismatch achieved superior performance compared to extant methods. Last, we demonstrate the ability to deduce the mechanism behind G-quadruplex formation by unique visualization of the principles learned by the model