Comparison of Compression-Based Measures with Application to the Evolution of Primate Genomes

BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach

Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity. However, experimental methods highly rely on the limited structural-related information from drug-target pairs, domain knowledge, and time-consuming assays. On the other hand, learning-based methods have shown an acceptable prediction performance. However, most of them utilize several simple and complex types of proteins and drug compounds data, ranging from the protein sequences to the topology of a graph representation of drug compounds, employing multiple deep neural networks for encoding and feature extraction, and so, leads to the computational overheads. In this study, we propose a unified measure for protein sequence encoding, named BiComp, which provides compression-based and evolutionary-related features from the protein sequences. Specifically, we employ Normalized Compression Distance and Smith-Waterman measures for capturing complementary information from the algorithmic information theory and biological domains, respectively. We utilize the proposed measure to encode the input proteins feeding a new deep neural network-based method for drug-target binding affinity prediction, named BiComp-DTA. BiComp-DTA is evaluated utilizing four benchmark datasets for drug-target binding affinity prediction. Compared to the state-of-the-art methods, which employ complex models for protein encoding and feature extraction, BiComp-DTA provides superior efficiency in terms of accuracy, runtime, and the number of trainable parameters. The latter achievement facilitates execution of BiComp-DTA on a normal desktop computer in a fast fashion. As a comparative study, we evaluate BiComp’s efficiency against its components for drug-target binding affinity prediction. The results have shown superior accuracy of BiComp due to the orthogonality and complementary nature of Smith-Waterman and Normalized Compression Distance measures for protein sequences. Such a protein sequence encoding provides efficient representation with no need for multiple sources of information, deep domain knowledge, and complex neural networks.

Visual Analysis of Research Paper Collections Using Normalized Relative Compression

The analysis of research paper collections is an interesting topic that can give insights on whether a research area is stalled in the same problems, or there is a great amount of novelty every year. Previous research has addressed similar tasks by the analysis of keywords or reference lists, with different degrees of human intervention. In this paper, we demonstrate how, with the use of Normalized Relative Compression, together with a set of automated data-processing tasks, we can successfully visually compare research articles and document collections. We also achieve very similar results with Normalized Conditional Compression that can be applied with a regular compressor. With our approach, we can group papers of different disciplines, analyze how a conference evolves throughout the different editions, or how the profile of a researcher changes through the time. We provide a set of tests that validate our technique, and show that it behaves better for these tasks than other techniques previously proposed.

Metagenomic Composition Analysis of an Ancient Sequenced Polar Bear Jawbone from Svalbard

The sequencing of ancient DNA samples provides a novel way to find, characterize, and distinguish exogenous genomes of endogenous targets. After sequencing, computational composition analysis enables filtering of undesired sources in the focal organism, with the purpose of improving the quality of assemblies and subsequent data analysis. More importantly, such analysis allows extinct and extant species to be identified without requiring a specific or new sequencing run. However, the identification of exogenous organisms is a complex task, given the nature and degradation of the samples, and the evident necessity of using efficient computational tools, which rely on algorithms that are both fast and highly sensitive. In this work, we relied on a fast and highly sensitive tool, FALCON-meta, which measures similarity against whole-genome reference databases, to analyse the metagenomic composition of an ancient polar bear (Ursus maritimus) jawbone fossil. The fossil was collected in Svalbard, Norway, and has an estimated age of 110,000 to 130,000 years. The FASTQ samples contained 349 GB of nonamplified shotgun sequencing data. We identified and localized, relative to the FASTQ samples, the genomes with significant similarities to reference microbial genomes, including those of viruses, bacteria, and archaea, and to fungal, mitochondrial, and plastidial sequences. Among other striking features, we found significant similarities between modern-human, some bacterial and viral sequences (contamination) and the organelle sequences of wild carrot and tomato relative to the whole samples. For each exogenous candidate, we ran a damage pattern analysis, which in addition to revealing shallow levels of damage in the plant candidates, identified the source as contamination.