Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.

The intrinsic DNA sequence preferences and cell type-specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence models of cell type-specific genomic occupancy of a TF in one species should generalize to closely matched cell types in a related species. To assess the viability of cross-species TF binding prediction, we train neural networks to discriminate ChIP-seq peak locations from genomic background and evaluate their performance within and across species. Cross-species predictive performance is consistently worse than within-species performance, which we show is caused in part by species-specific repeats. To account for this domain shift, we use an augmented network architecture to automatically discourage learning of training species-specific sequence features. This domain adaptation approach corrects for prediction errors on species-specific repeats and improves overall cross-species model performance. Our results show that cross-species TF binding prediction is feasible when models account for domain shifts driven by species-specific repeats.

GenoPipe: identifying the genotype of origin within (epi)genomic datasets.

Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of many laboratories. Technical mistakes can and do occur at nearly every stage of a genomics assay (i.e. cell line contamination, reagent swapping, tube mislabelling, etc.) and are often difficult to identify post-execution. However, the DNA sequenced in genomic experiments contains certain markers (e.g. indels) encoded within and can often be ascertained forensically from experimental datasets. We developed the Genotype validation Pipeline (GenoPipe), a suite of heuristic tools that operate together directly on raw and aligned sequencing data from individual high-throughput sequencing experiments to characterize the underlying genome of the source material. We demonstrate how GenoPipe validates and rescues erroneously annotated experiments by identifying unique markers inherent to an organism's genome (i.e. epitope insertions, gene deletions and SNPs).

Differential abilities to engage inaccessible chromatin diversify vertebrate Hox binding patterns.

Although Hox genes encode for conserved transcription factors (TFs), they are further divided into anterior, central and posterior groups based on their DNA-binding domain similarity. The posterior Hox group expanded in the deuterostome clade and patterns caudal and distal structures. We aimed to address how similar Hox TFs diverge to induce different positional identities. We studied Hox TF DNA-binding and regulatory activity during an in vitro motor neuron differentiation system that recapitulates embryonic development. We found diversity in the genomic binding profiles of different Hox TFs, even among the posterior group paralogs that share similar DNA-binding domains. These differences in genomic binding were explained by differing abilities to bind to previously inaccessible sites. For example, the posterior group HOXC9 had a greater ability to bind occluded sites than the posterior HOXC10, producing different binding patterns and driving differential gene expression programs. From these results, we propose that the differential abilities of posterior Hox TFs to bind to previously inaccessible chromatin drive patterning diversification.This article has an associated 'The people behind the papers' interview.

Fabrication of polyaniline/graphene oxide composites for implementation in humidity sensing.

This work reports the measurement of impedance variations under various humidity conditions at frequency ranges between 100 Hz and 5 MHz. An electrochemical polymerization process has been used in the synthesis including varying the mass ratios of graphene oxide (GO) in polyaniline. An electrochemical deposition method has been used to produce a sample film on an indium tin oxide glass slide. The percentage relative humidity (RH%) of the samples has been estimated to be 20-90%. Impedance and humidity had an inverse relationship, i.e. the impedance value decreased with an increase in humidity. In contrast with platinum capacitive humidity sensors (HS), the GO-based HS had a sensitivity of 75-99%, which was ~10-fold more than that of traditional sensors. With three different parameter weight % of GO, the frequency range have been 100 Hz to 5 MHz and RH% has been found to 20-90%. The HS showed a fast response and recovery time. Therefore, GO appears to be a useful material for building HS with high sensitivity for a comprehensive approach.

GenoPipe: identifying the genotype of origin within (epi)genomic datasets.

Confidence in experimental results is critical for discovery. As the scale of data generation in genomics has grown exponentially, experimental error has likely kept pace despite the best efforts of many laboratories. Technical mistakes can and do occur at nearly every stage of a genomics assay (i.e., cell line contamination, reagent swapping, tube mislabelling, etc.) and are often difficult to identify post-execution. However, the DNA sequenced in genomic experiments contains certain markers (e.g., indels) encoded within and can often be ascertained forensically from experimental datasets. We developed the Genotype validation Pipeline (GenoPipe), a suite of heuristic tools that operate together directly on raw and aligned sequencing data from individual high-throughput sequencing experiments to characterize the underlying genome of the source material. We demonstrate how GenoPipe validates and rescues erroneously annotated experiments by identifying unique markers inherent to an organism’s genome (i.e., epitope insertions, gene deletions, and SNPs).

An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding.

BACKGROUND: Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor's DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transcription factor binding in a given cell type have been well characterized. For instance, the binding sites for a majority of transcription factors display concurrent chromatin accessibility. However, concurrent chromatin features reflect the binding activities of the transcription factor itself and thus provide limited insight into how genome-wide TF-DNA binding patterns became established in the first place. To understand the determinants of transcription factor binding specificity, we therefore need to examine how newly activated transcription factors interact with sequence and preexisting chromatin landscapes. RESULTS: Here, we investigate the sequence and preexisting chromatin predictors of TF-DNA binding by examining the genome-wide occupancy of transcription factors that have been induced in well-characterized chromatin environments. We develop Bichrom, a bimodal neural network that jointly models sequence and preexisting chromatin data to interpret the genome-wide binding patterns of induced transcription factors. We find that the preexisting chromatin landscape is a differential global predictor of TF-DNA binding; incorporating preexisting chromatin features improves our ability to explain the binding specificity of some transcription factors substantially, but not others. Furthermore, by analyzing site-level predictors, we show that transcription factor binding in previously inaccessible chromatin tends to correspond to the presence of more favorable cognate DNA sequences. CONCLUSIONS: Bichrom thus provides a framework for modeling, interpreting, and visualizing the joint sequence and chromatin landscapes that determine TF-DNA binding dynamics.

Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.

Transcription factors (TFs) selectively bind distinct sets of sites in different cell types. Such cell type-specific binding specificity is expected to result from interplay between the TF's intrinsic sequence preferences, cooperative interactions with other regulatory proteins, and cell type-specific chromatin landscapes. Cell type-specific TF binding events are highly correlated with patterns of chromatin accessibility and active histone modifications in the same cell type. However, since concurrent chromatin may itself be a consequence of TF binding, chromatin landscapes measured prior to TF activation provide more useful insights into how cell type-specific TF binding events became established in the first place. Here, we review the various sequence and chromatin determinants of cell type-specific TF binding specificity. We identify the current challenges and opportunities associated with computational approaches to characterizing, imputing, and predicting cell type-specific TF binding patterns. We further focus on studies that characterize TF binding in dynamic regulatory settings, and we discuss how these studies are leading to a more complex and nuanced understanding of dynamic protein-DNA binding activities. We propose that TF binding activities at individual sites can be viewed along a two-dimensional continuum of local sequence and chromatin context. Under this view, cell type-specific TF binding activities may result from either strongly favorable sequence features or strongly favorable chromatin context.