We have analyzed gene expression in various brain regions of humans and chimpanzees. Within both human chimpanzee individuals, the transcriptomes cerebral cortex are very similar to each other differ more between individuals than among within an individual. In contrast, cortex, caudate nucleus, cerebellum substantially from other. Between chimpanzees, 10% genes their at least one region brain. The majority these differences shared all regions. Whereas encoding proteins involved signal...

Abstract Background Surprisingly little is known about the organization and distribution of tRNA genes tRNA-related sequences on a genome-wide scale. While gene complements are usually reported in passing as part genome annotation efforts, peculiar features such tandem arrangements Entamoeba histolytica have been described some detail, systematic comparative studies rare mostly restricted to bacteria. We therefore set out survey genomic arrangement pseudogenes wide range eukaryotes identify...

ABSTRACT Overexpression of antisense chromosomal cis -encoded noncoding RNAss (ncRNAs) in glutamine synthetase I resulted a decrease growth, protein synthesis, and antibiotic production Streptomyces coelicolor . In addition, we predicted 3,597 ncRNAs validated 13 them experimentally, including several that are differentially expressed bacterial hormone-defective mutants.

Abstract Background Non-coding RNAs (ncRNAs) are an emerging focus for both computational analysis and experimental research, resulting in a growing number of novel, non-protein coding transcripts with often unknown functions. Whole genome screens higher eukaryotes, example, provided evidence surprisingly large ncRNAs. To supplement these searches, we performed seven yeast species searched new ncRNAs RNA motifs. Results A comparative the genomes yielded roughly 2800 genomic loci that showed...

Natural antisense transcripts are reported from all kingdoms of life and several recent reports genomewide screens indicate that they widely distributed. These seem to be involved in various biological functions may govern the expression their respective sense partner. Very little, however, is known about degree evolutionary conservation transcripts. Furthermore, none earlier analyses has studied whether relationships solely dual or more complex relationships. Here we present a systematic...

Background and Purpose Target engagement dynamics can influence drugs' pharmacological effects. Kinetic parameters for drug:target interactions are often quantified by evaluating competition association experiments—measuring simultaneous protein binding of labelled tracers unlabelled test compounds over time—with Motulsky–Mahan's “kinetics competitive binding” model. Despite recent technical improvements, the current assay formats impose practical limitations to this approach. This study...

Deep convolutional neural networks show outstanding performance in image-based phenotype classification given that all existing phenotypes are presented during the training of network. However, real-world high-content screening (HCS) experiments, it is often impossible to know advance. Moreover, novel discovery itself can be an HCS outcome interest. This aspect not yet covered by classical deep learning approaches. When presenting image with a trained network, fails indicate novelty but...

Biopharmaceutical drug discovery, as of today is a highly automated, high throughput endeavor, where many screening technologies produce high-dimensional measurement per sample. A striking example High Content Screening (HCS), which utilizes automated microscopy to systematically access the wealth information contained in biological assays. Exploiting HCS its full potential traditionally requires extracting number features from images capture much possible, then performing algorithmic...

Abstract Deep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of major technological innovation areas, predicted to replace lots repetitive, but complex tasks human labor within next decade. It also expected be ‘game changing’ for research activities in pharma life sciences, where large sets similar yet data samples are systematically analyzed. learning currently conquering formerly expert domains especially areas requiring perception, previously not...

Drug discovery programs are moving increasingly toward phenotypic imaging assays to model disease-relevant pathways and phenotypes in vitro. These offer richer information than target-optimized by investigating multiple cellular simultaneously producing multiplexed readouts. However, extracting the desired from complex image data poses significant challenges, preventing broad adoption of more sophisticated assays. Deep learning-based analysis can address these challenges reducing effort...

Abstract Given a set S of n locally aligned sequences, it is needed prerequisite to partition into groups very similar sequences facilitate subsequent computations, such as the generation phylogenetic tree. This article introduces new method clustering which partitions subsets that overlap each pair within subset at least given percentage c lengths two sequences. We show this problem can be reduced finding all maximal cliques in special kind max-tolerance graph we call c-max-tolerance graph....

Surface plasmon resonance (SPR) is a powerful method for obtaining detailed molecular interaction parameters. Modern instrumentation with its increased throughput has enabled routine screening by SPR in hit-to-lead and lead optimization programs, become mainstream drug discovery technology. However, the processing reporting of data are typically performed manually, which both time-consuming tedious. Here, we present workflow concept, design experiences software module relying on single,...

Recent efforts for increasing the success in drug discovery focus on an early, massive, and routine mechanistic and/or kinetic characterization of drug-target engagement as part a design-make-test-analyze strategy. From experimental perspective, many assays can be translated into scalable format automation platforms thereby enable hundreds or thousands compounds. However, now limiting factor to achieve such in-depth at high-throughput becomes quality-driven data analysis, sheer scale which...

In drug development, image-based bioassays are commonplace, typically run in high throughput on automated microscopes. The resulting cell imaging data comes from multiple instruments and has been acquired at different time points, leading to technical biological variation the data, potentially hampering quantitative analysis across an assay campaign. this work, we analyze robustness of a novel concept called Vision Transformers with respect variations. We compare their performance recent...

The Cellular Thermal Shift Assay (CETSA) enables the study of protein-ligand interactions in a cellular context. It provides valuable information on binding affinity and specificity both small large molecule ligands relevant physiological context, hence forming unique tool drug discovery. Though high-throughput lab protocols exist for scaling up CETSA, subsequent data analysis quality control remain laborious limit experimental throughput. Here, we introduce scalable robust workflow which...

Deep Neural Networks (DNNs) have shown remarkable success in various computer vision tasks. However, their black-box nature often leads to difficulty interpreting decisions, creating an unfilled need for methods explain the and ultimately forming a barrier wide acceptance especially biomedical applications. This work introduces novel method, Pixel-wise Channel Isolation Mixing (PCIM), calculate pixel attribution maps, highlighting image parts most crucial classification decision but without...

Discriminative neural networks eventually fail when confronted with data that was not part of the distribution training samples. Moreover, out (OOD) points are frequently ill-predicted, which can lead to misleading results and interpretation, especially in real-world applications such decision systems. Recently, several methods had been developed tackle this problem. These studies often rely on quality parameters, deduced by theoretical considerations, rather than real world challenges. In...

Ion channels are drug targets for neurologic, cardiac, and immunologic diseases. Many disease-associated mutations drugs modulate voltage-gated ion channel activation inactivation, suggesting that characterizing state-dependent effects of test compounds at an early stage development can be great benefit. Historically, the on biophysical properties voltage-dependent activation/inactivation could only assessed by using low-throughput, manual patch clamp recording techniques. In recent years,...

Uncovering novel drug candidates for treating complex diseases remain one of the most challenging tasks in early discovery research. To tackle this challenge, biopharma research established a standardized high content imaging protocol that tags different cellular compartments per image channel. In order to judge experimental outcome, scientist requires knowledge about channel importance with respect certain phenotype decoding underlying biology. contrast traditional analysis approaches, such...