A feature extraction free approach for protein interactome inference from co-elution data.

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Short human eccDNAs are predictable from sequences.

BACKGROUND: Ubiquitous presence of short extrachromosomal circular DNAs (eccDNAs) in eukaryotic cells has perplexed generations of biologists. Their widespread origins in the genome lacking apparent specificity led some studies to conclude their formation as random or near-random. Despite this, the search for specific formation of short eccDNA continues with a recent surge of interest in biomarker development. RESULTS: To shed new light on the conflicting views on short eccDNAs' randomness, here we present DeepCircle, a bioinformatics framework incorporating convolution- and attention-based neural networks to assess their predictability. Short human eccDNAs from different datasets indeed have low similarity in genomic locations, but DeepCircle successfully learned shared DNA sequence features to make accurate cross-datasets predictions (accuracy: convolution-based models: 79.65 ± 4.7%, attention-based models: 83.31 ± 4.18%). CONCLUSIONS: The excellent performance of our models shows that the intrinsic predictability of eccDNAs is encoded in the sequences across tissue origins. Our work demonstrates how the perceived lack of specificity in genomics data can be re-assessed by deep learning models to uncover unexpected similarity.

A Tail's Tale: Biomechanical Roles of Dorsal Thoracic Spine of Barnacle Nauplii.

Many marine invertebrates have complex life histories that begin with a planktonic larval stage. Similar to other plankton, these larval invertebrates often possess protruding body extensions, but their function beyond predator deterrence is not well-documented. For example, the planktonic nauplii of crustaceans have spines. Using the epibiotic pedunculate barnacle Octolasmis spp., we investigated how the dorsal thoracic spine affects swimming and fluid disturbance by comparing nauplii with their spines partially removed against those with intact spines. Our motion analysis showed that amputated Octolasmis spp. swam slower, in jerkier trajectories, and were less efficient per stroke cycle than those with intact spines. Amputees showed alterations in limb beat pattern: larger beat amplitude, increased phase lag, and reduced contralateral symmetry. These changes might partially help increase propulsive force generation and streamline the flow, but were insufficient to restore full function. Particle image velocimetry further showed that amputees had a larger relative area of influence, implying elevated risk by rheotactic predator. Body extensions and their interactions with limb motion play important biomechanical roles in shaping larval performance, which likely influences the evolution of form.