The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and ...development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
We present a new de novo transcriptome assembler, Bridger, which takes advantage of techniques employed in Cufflinks to overcome limitations of the existing de novo assemblers. When tested on dog, ...human, and mouse RNA-seq data, Bridger assembled more full-length reference transcripts while reporting considerably fewer candidate transcripts, hence greatly reducing false positive transcripts in comparison with the state-of-the-art assemblers. It runs substantially faster and requires much less memory space than most assemblers. More interestingly, Bridger reaches a comparable level of sensitivity and accuracy with Cufflinks. Bridger is available at https://sourceforge.net/projects/rnaseqassembly/files/?source=navbar.
Organ homeostasis is orchestrated by time- and spatially restricted cell proliferation. Studies identifying cells with superior proliferative capacities often rely on the lineage tracing of a subset ...of cell populations, which introduces a potential selective bias. In this work, we developed a genetic system proliferation tracer (ProTracer) by incorporating dual recombinases to seamlessly record the proliferation events of entire cell populations over time in multiple organs. In the mouse liver, ProTracer revealed more hepatocyte proliferation in distinct zones during liver homeostasis, injury repair, and regrowth. Clonal analysis showed that most of the hepatocytes labeled by ProTracer had undergone cell division. By genetically recording proliferation events of entire cell populations, ProTracer enables the unbiased detection of specific cellular compartments with enhanced regenerative capacities.
The Cre-loxP recombination system is the most widely used technology for in vivo tracing of stem or progenitor cell lineages. The precision of this genetic system largely depends on the specificity ...of Cre recombinase expression in targeted stem or progenitor cells. However, Cre expression in nontargeted cell types can complicate the interpretation of lineage-tracing studies and has caused controversy in many previous studies. Here we describe a new genetic lineage tracing system that incorporates the Dre-rox recombination system to enhance the precision of conventional Cre-loxP-mediated lineage tracing. The Dre-rox system permits rigorous control of Cre-loxP recombination in lineage tracing, effectively circumventing potential uncertainty of the cell-type specificity of Cre expression. Using this new system we investigated two topics of recent debates-the contribution of c-Kit
cardiac stem cells to cardiomyocytes in the heart and the contribution of Sox9
hepatic progenitor cells to hepatocytes in the liver. By overcoming the technical hurdle of nonspecific Cre-loxP-mediated recombination, this new technology provides more precise analysis of cell lineage and fate decisions and facilitates the in vivo study of stem and progenitor cell plasticity in disease and regeneration.
The use of the dual recombinase-mediated intersectional genetic approach involving Cre-loxP and Dre-rox has significantly enhanced the precision of in vivo lineage tracing, as well as gene ...manipulation. However, this approach is limited by the small number of Dre recombinase driver constructs available. Here, we developed more than 70 new intersectional drivers to better target diverse cell lineages. To highlight their applicability, we used these new tools to study the in vivo adipogenic fate of perivascular progenitors, which revealed that PDGFRa+ but not PDGFRa–PDGFRb+ perivascular cells are the endogenous progenitors of adult adipocytes. In addition to lineage tracing, we used members of this new suite of drivers to more specifically knock out genes in complex tissues, such as white adipocytes and lymphatic vessels, that heretofore cannot be selectively targeted by conventional Cre drivers alone. In summary, these new transgenic tools expand the intersectional genetic approach while enhancing its precision.
Display omitted
•More than 70 new Dre driver lines are provided as a resource for intersectional genetics•PDGFRa+PDGFRb+ or PDGFRa+ perivascular cells contribute to de novo adipocytes•“Exclusion” dual recombinase enables gene deletion in white but not brown adipocytes•Sequential dual recombinase enables gene deletion in lymphatic endothelial cells
The combinatory use of Dre and Cre recombinase-mediated intersectional genetics significantly enhances the precision of in vivo lineage tracing and gene targeting. Han et al. developed more than 70 new intersectional drivers to target diverse cell lineages. Highlighting their application, Han et al. used these new tools to study perivascular progenitors of adipocytes and performed gene knockout in white adipocytes (WAs) and lymphatic endothelial cells (LECs).
High-throughput RNA-seq technology has provided an unprecedented opportunity to reveal the very complex structures of transcriptomes. However, it is an important and highly challenging task to ...assemble vast amounts of short RNA-seq reads into transcriptomes with alternative splicing isoforms. In this study, we present a novel de novo assembler, BinPacker, by modeling the transcriptome assembly problem as tracking a set of trajectories of items with their sizes representing coverage of their corresponding isoforms by solving a series of bin-packing problems. This approach, which subtly integrates coverage information into the procedure, has two exclusive features: 1) only splicing junctions are involved in the assembling procedure; 2) massive pell-mell reads are assembled seemingly by moving a comb along junction edges on a splicing graph. Being tested on both real and simulated RNA-seq datasets, it outperforms almost all the existing de novo assemblers on all the tested datasets, and even outperforms those ab initio assemblers on the real dog dataset. In addition, it runs substantially faster and requires less memory space than most of the assemblers. BinPacker is published under GNU GENERAL PUBLIC LICENSE and the source is available from: http://sourceforge.net/projects/transcriptomeassembly/files/BinPacker_1.0.tar.gz/download. Quick installation version is available from: http://sourceforge.net/projects/transcriptomeassembly/files/BinPacker_binary.tar.gz/download.
Coronary arteries bring blood flow to the heart muscle. Understanding the developmental program of the coronary arteries provides insights into the treatment of coronary artery diseases. Multiple ...sources have been described as contributing to coronary arteries including the proepicardium, sinus venosus （SV）, and endocardium. However, the developmental origins of coronary vessels are still under intense study. We have produced a new genetic tool for studying coronary development, an AplnCreER mouse line, which expresses an inducible Cre recombinase specifically in developing coronary vessels. Quantitative analysis of coronary development and timed induction of AplnCreER fate tracing showed that the progenies of subepicardial endothelial cells （ECs） both invade the compact myocardium to form coronary arteries and remain on the surface to produce veins. We found that these subepicardial ECs are the major sources of intramyocardial coronary vessels in the developing heart. In vitro explant assays indicate that the majority of these subepicardial ECs arise from endocardium of the SV and atrium, but not from ventricular endocardium. Clonal analysis of Apln-positive cells indicates that a single subepicardial EC contributes equally to both coronary arteries and veins. Collectively, these data suggested that subepieardial ECs are the major source of intramyocardial coronary arteries in the ventricle wall, and that coronary arteries and veins have a common origin in the developing heart.
Abstract
Motivation
Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art ...methods are incapable of discovering with high accuracy different patterns of high biological relevance.
Results
In this paper, a novel biclustering algorithm based on evolutionary computation, a sub-field of artificial intelligence, is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units. We demonstrate that EBIC greatly outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms.
Availability and implementation
EBIC source code is available on GitHub at https://github.com/EpistasisLab/ebic.
Supplementary information
Supplementary data are available at Bioinformatics online.
The postnatal coronary vessels have been viewed as developing through
expansion of vessels formed during the fetal period. Using genetic
lineage tracing, we found that a substantial portion of ...postnatal
coronary vessels arise de novo in the neonatal mouse heart, rather than
expanding from preexisting embryonic vasculature. Our data show that
lineage conversion of neonatal endocardial cells during trabecular
compaction generates a distinct compartment of the coronary circulation
located within the inner half of the ventricular wall. This lineage
conversion occurs within a brief period after birth and provides an
efficient means of rapidly augmenting the coronary vasculature. This
mechanism of postnatal coronary vascular growth provides avenues for
understanding and stimulating cardiovascular regeneration following
injury and disease.
PDF
