Notes

Erradication involving Gremlin-2 adjusts estrous cyclicity and also interferes with women male fertility within these animals.
DNAModAnnot is distributed as a R package available via GitHub (https//github.com/AlexisHardy/DNAModAnnot).

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
The knowledge of potentially druggable binding sites on proteins is an important preliminary step towards the discovery of novel drugs. The computational prediction of such areas can be boosted by following the recent major advances in the deep learning field and by exploiting the increasing availability of proper data.

In this paper, a novel computational method for the prediction of potential binding sites is proposed, called DeepSurf. DeepSurf combines a surface-based representation, where a number of 3 D voxelized grids are placed on the protein's surface, with state-of-the-art deep learning architectures. After being trained on the large database of scPDB, DeepSurf demonstrates superior results on three diverse testing datasets, by surpassing all its main deep learning-based competitors, while attaining competitive performance to a set of traditional non-data-driven approaches.

The source code of the method along with trained models are freely available at https//github.com/stemylonas/DeepSurf.git.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
High-throughput sequencing technologies are used increasingly, not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence, and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations.

To support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape.

V-pipe is freely available at https//github.com/cbg-ethz/V-pipe.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.Lipid metabolism reprogramming is now accepted as a new hallmark of cancer. Hence, targeting the lipogenesis pathway may be a potential avenue for cancer treatment. Valproic acid (VPA) emerges as a promising drug for cancer therapy; however, the underlying mechanisms are not yet fully understood. selleck compound In this study, we aimed to investigate the effects and mechanisms of VPA on cell viability, lipogenesis, and apoptosis in human prostate cancer PC-3 and LNCaP cells. The results showed that VPA significantly reduced lipid accumulation and induced apoptosis of PC-3 and LNCaP cells. Moreover, the expression of CCAAT/enhancer-binding protein α (C/EBPα), as well as sterol regulatory element-binding protein 1 (SREBP-1) and its downstream effectors, including fatty acid synthase (FASN), acetyl CoA carboxylase 1 (ACC1), and anti-apoptotic B-cell lymphoma 2 (Bcl-2), was markedly decreased in PC-3 and LNCaP cells after VPA administration. Mechanistically, the overexpression of C/EBPα rescued the levels of SREBP-1, FASN, ACC1, and Bcl-2, enhanced lipid accumulation, and attenuated apoptosis of VPA-treated PC-3 cells. Conversely, knockdown of C/EBPα by siRNA further decreased lipid accumulation, enhanced apoptosis, and reduced the levels of SREBP-1, FASN, ACC1, and Bcl-2. In addition, SREBP-1a and 1c enhanced the expressions of FASN and ACC1, but only SREBP-1a had a significant effect on Bcl-2 expression in VPA-treated PC-3 cells. Based on the results, we concluded that VPA significantly inhibits cell viability via decreasing lipogenesis and inducing apoptosis via the C/EBPα/SREBP-1 pathway in prostate cancer cells. Therefore, VPA that targets lipid metabolism and apoptosis is a promising candidate for PCa chemotherapy.
Alignment-free distance and similarity functions (AF functions, for short) are a well established alternative to pairwise and multiple sequence alignments for many genomic, metagenomic and epigenomic tasks. Due to data-intensive applications, the computation of AF functions is a Big Data problem, with the recent literature indicating that the development of fast and scalable algorithms computing AF functions is a high-priority task. Somewhat surprisingly, despite the increasing popularity of Big Data technologies in computational biology, the development of a Big Data platform for those tasks has not been pursued, possibly due to its complexity.

We fill this important gap by introducing FADE, the first extensible, efficient and scalable Spark platform for alignment-free genomic analysis. It supports natively eighteen of the best performing AF functions coming out of a recent hallmark benchmarking study. FADE development and potential impact comprises novel aspects of interest. Namely, (a) a considerable effort of distributed algorithms, the most tangible result being a much faster execution time of reference methods like MASH and FSWM; (b) a software design that makes FADE user-friendly and easily extendable by Spark non-specialists; (c) its ability to support data- and compute-intensive tasks. About this, we provide a novel and much needed analysis of how informative and robust AF functions are, in terms of the statistical significance of their output. Our findings naturally extend the ones of the highly regarded benchmarking study, since the functions that can really be used are reduced to a handful of the eighteen included in FADE.

The software and the datasets are available at https//github.com/fpalini/fade.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
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