AI-driven discovery can greatly reduce design time and enhance new therapeutics' effectiveness. Models using simulators explore broad design spaces but risk violating implicit constraints due to a lack of experimental priors. For example, in a new analysis across diverse models on the GuacaMol benchmark using supervised classifiers, over 60\% of molecules proposed had a high probability of being mutagenic. In this work, we introduce Medex, a dataset of priors for design problems extracted from literature describing compounds used in lab settings. It is constructed with LLM pipelines for discovering therapeutic entities in relevant paragraphs and summarizing information in concise fair-use facts. Medex consists of 32.3 million pairs of natural language facts, and appropriate entity representations (i.e. SMILES or RefSeq IDs). To demonstrate the potential of the data, we train LLM, CLIP, and LLaVA architectures to reason jointly about text and design targets and evaluate on tasks from the Therapeutic Data Commons (TDC). Medex is highly effective for creating models with strong priors: in supervised prediction problems that use our data for pretraining, our best models with 15M learnable parameters outperform larger 2B TxGemma on both regression and classification TDC tasks, and perform comparably to 9B models on average. Models built with Medex can be used as constraints while optimizing for novel molecules in GuacaMol, resulting in proposals that are safer and nearly as effective. We release our dataset on HuggingFace at https://huggingface.co/datasets/DocAndDesign/Medex, and will provide expanded versions as the available literature grows.

SUMMARY: Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests to users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis. AVAILABILITY AND IMPLEMENTATION: Program and code will be available at http://majiq.biociphers.org/majiq-spel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: A key component in many RNA-Seq-based studies is contrasting multiple replicates from different experimental conditions. In this setup, replicates play a key role as they allow to capture underlying biological variability inherent to the compared conditions, as well as experimental variability. However, what constitutes a 'bad' replicate is not necessarily well defined. Consequently, researchers might discard valuable data or downstream analysis may be hampered by failed experiments. Results: Here we develop a probability model to weigh a given RNA-Seq sample as a representative of an experimental condition when performing alternative splicing analysis. We demonstrate that this model detects outlier samples which are consistently and significantly different compared with other samples from the same condition. Moreover, we show that instead of discarding such samples the proposed weighting scheme can be used to downweight samples and specific splicing variations suspected as outliers, gaining statistical power. These weights can then be used for differential splicing (DS) analysis, where the resulting algorithm offers a generalization of the MAJIQ algorithm. Using both synthetic and real-life data, we perform an extensive evaluation of the improved MAJIQ algorithm in different scenarios involving perturbed samples, mislabeled samples, same condition groups, and different levels of coverage, showing it compares favorably to other tools. Overall, this work offers an outlier detection algorithm that can be combined with any splicing pipeline, a generalized and improved version of MAJIQ for DS detection, and evaluation metrics with matching code and data for DS algorithms. Availability and implementation: Software and data are accessible via majiq.biociphers.org/norton_et_al_2017/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Advancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The first involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work, we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors. RESULTS: We perform a detailed comparison of two previous modeling approaches, Bayesian and Deep Neural networks, dissecting the confounding effects of datasets and target functions. We then develop a new target function for AS prediction in exon skipping events and show it significantly improves model accuracy. Next, we develop a modeling framework that leverages transfer learning to incorporate CLIP-Seq, knockdown and over expression experiments, which are inherently noisy and suffer from missing values. Using several datasets involving key splice factors in mouse brain, muscle and heart we demonstrate both the prediction improvements and biological insights offered by our new models. Overall, the framework we propose offers a scalable integrative solution to improve splicing code modeling as vast amounts of relevant genomic data become available. AVAILABILITY AND IMPLEMENTATION: Code and data available at: majiq.biociphers.org/jha_et_al_2017/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Alternative splicing is a major contributor to cellular diversity in mammalian tissues and relates to many human diseases. An important goal in understanding this phenomenon is to infer a 'splicing code' that predicts how splicing is regulated in different cell types by features derived from RNA, DNA and epigenetic modifiers. METHODS: We formulate the assembly of a splicing code as a problem of statistical inference and introduce a Bayesian method that uses an adaptively selected number of hidden variables to combine subgroups of features into a network, allows different tissues to share feature subgroups and uses a Gibbs sampler to hedge predictions and ascertain the statistical significance of identified features. RESULTS: Using data for 3665 cassette exons, 1014 RNA features and 4 tissue types derived from 27 mouse tissues (http://genes.toronto.edu/wasp), we benchmarked several methods. Our method outperforms all others, and achieves relative improvements of 52% in splicing code quality and up to 22% in classification error, compared with the state of the art. Novel combinations of regulatory features and novel combinations of tissues that share feature subgroups were identified using our method. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Transcripts from approximately 95% of human multi-exon genes are subject to alternative splicing (AS). The growing interest in AS is propelled by its prominent contribution to transcriptome and proteome complexity and the role of aberrant AS in numerous diseases. Recent technological advances enable thousands of exons to be simultaneously profiled across diverse cell types and cellular conditions, but require accurate identification of condition-specific splicing changes. It is necessary to accurately identify such splicing changes to elucidate the underlying regulatory programs or link the splicing changes to specific diseases. RESULTS: We present a probabilistic model tailored for high-throughput AS data, where observed isoform levels are explained as combinations of condition-specific AS signals. According to our formulation, given an AS dataset our tasks are to detect common signals in the data and identify the exons relevant to each signal. Our model can incorporate prior knowledge about underlying AS signals, measurement quality and gene expression level effects. Using a large-scale multi-tissue AS dataset, we demonstrate the advantage of our method over standard alternative approaches. In addition, we describe newly found tissue-specific AS signals which were verified experimentally, and discuss associated regulatory features. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A key aspect of transcriptional regulation is the binding of transcription factors to sequence-specific binding sites that allow them to modulate the expression of nearby genes. Given models of such binding sites, one can scan regulatory regions for putative binding sites and construct a genome-wide regulatory network. In such genome-wide scans, it is crucial to control the amount of false positive predictions. Recently, several works demonstrated the benefits of modeling dependencies between positions within the binding site. Yet, computing the statistical significance of putative binding sites in this scenario remains a challenge. RESULTS: We present a general, accurate and efficient method for computing p-values of putative binding sites that is applicable to a large class of probabilistic binding site and background models. We demonstrate the accuracy of the method on synthetic and real-life data. AVAILABILITY: The procedure for scanning DNA sequences and computing the statistical significance of putative binding site scores is available upon request at http://compbio.cs.huji.ac.il/CIS/ CONTACT: [email protected].

MOTIVATION: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. RESULTS: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.

The availability of whole genome sequences and high-throughput genomic assays opens the door for in silico analysis of transcription regulation. This includes methods for discovering and characterizing the binding sites of DNA-binding proteins, such as transcription factors. A common representation of transcription factor binding sites is a position specific score matrix (PSSM). This representation makes the strong assumption that binding site positions are independent of each other. In this work, we explore Bayesian network representations of binding sites that provide different tradeoffs between complexity (number of parameters) and the richness of dependencies between positions. We develop the formal machinery for learning such models from data and for estimating the statistical significance of putative binding sites. We then evaluate the ramifications of these richer representations in characterizing binding site motifs and predicting their genomic locations. We show that these richer representations improve over the PSSM model in both tasks.

We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key components of this process: the prediction of gene regulation events from sequence motifs in the gene's promoter region, and the prediction of mRNA expression from combinations of gene regulation events in different settings. Our approach has several advantages. By learning promoter sequence motifs that are directly predictive of expression data, it can improve the identification of binding site patterns. It is also able to identify combinatorial regulation via interactions of different transcription factors. Finally, the general framework allows us to integrate additional data sources, including data from the recent binding localization assays. We demonstrate our approach on the cell cycle data of Spellman et al., combined with the binding localization information of Simon et al. We show that the learned model predicts expression from sequence, and that it identifies coherent co-regulated groups with significant transcription factor motifs. It also provides valuable biological insight into the domain via these co-regulated "modules" and the combinatorial regulation effects that govern their behavior.

The recent growth in genomic data and measurement of genome-wide expression patterns allows to examine gene regulation by transcription factors using computational tools. In this work, we present a class of mathematical models that help in understanding the connections between transcription factors and functional classes of genes based on genetic and genomic data. These models represent the joint distribution of transcription factor binding sites and of expression levels of a gene in a single model. Learning a combined probability model of binding sites and expression patterns enables us to improve the clustering of the genes based on the discovery of putative binding sites and to detect which binding sites and experiments best characterize a cluster. To learn such models from data, we introduce a new search method that rapidly learns a model according to a Bayesian score. We evaluate our method on synthetic data as well as on real data and analyze the biological insights it provides.