Noninvasive prenatal diagnosis (NIPD) has become a common, safe, and effective procedure for detection of inherited diseases early in pregnancy. It is based on the analysis of fetal cell-free DNA (cffDNA) derived from the placenta, circulating in the maternal plasma. De novo mutations, although rare, cause a considerable number of dominant genetic disorders. Due to the sparse representation of fetal-derived sequences in the blood, the challenge of detecting low frequency fetal de novo mutations becomes preponderant. Hence, this detection type requires deep genome-wide sequencing of cffDNA from maternal plasma and a unique analysis approach. Here we suggest and discuss a method for identifying de novo mutations based on whole genome sequencing (WGS) of cell-free DNA (cfDNA) from maternal plasma samples. Our method consists of an augmented pipeline for analysis of de novo mutation candidates. It begins with an enhanced noninvasive fetal variant calling step, followed by a candidate de novo mutation filtration, and then finally, a supervised machine learning approach is utilized for reduction of false positive rates. Overall, this study provides a basis for genome-wide de novo mutation analysis in NIPD procedures, which could be used in any procedure where rare de novo mutations should be carefully picked out of a sea of data.Noninvasive prenatal diagnosis (NIPD) is an emerging field, that enables testing for diseases in the fetus with no risk to the pregnancy, compared to invasive methods (e.g., amniocentesis). The procedure is based on the presence of fetal DNA within the mother's plasma cell-free DNA (cfDNA). Today, NIPD is performed for chromosomal abnormalities (e.g., Down syndrome) and some large deletions/duplications. It is also available for point mutations but is limited for one mutation or up to several genes simultaneously. Genome-wide detection of fetal point mutations was presented in a few studies, and the first software tool for this task, Hoobari, has recently become available. Here we describe the necessary steps in genome-wide noninvasive fetal genotyping, including examples using the Hoobari software. We discuss the various materials, software, computational infrastructure, and samples required for this analysis. Genome-wide analysis of point mutations in the fetus is not widely studied, albeit much space for algorithmic improvements exists. Here we suggest practical solutions for challenges along the process. Our work assists bioinformaticians in accessing NIPD data analysis and can eventually be utilized for other cfDNA-related fields.The ATAC-seq assay has emerged as the most useful, versatile, and widely adaptable method for profiling accessible chromatin regions and tracking the activity of cis-regulatory elements (cREs) in eukaryotes. Thanks to its great utility, it is now being applied to map active chromatin in the context of a very wide diversity of biological systems and questions. In the course of these studies, considerable experience working with ATAC-seq data has accumulated and a standard set of computational tasks that need to be carried for most ATAC-seq analyses has emerged. Here, we review and provide examples of common such analytical procedures (including data processing, quality control, peak calling, identifying differentially accessible open chromatin regions, and variable transcription factor (TF) motif accessibility) and discuss recommended optimal practices.Deep learning is defined as the group of computational techniques allowing for the discovery of latent information within large amounts of data. Recently, many fields have seen the immense potential of deep learning to solve various tasks in ways which outperformed many other traditional methods. Genomic research could be the next frontier to take advantage of deep learning, as it has the perfect combination of vast amounts of data and diverse tasks. Here we present the platform we generated to combine deep learning and genomic sequencing data. We tested the platform on publicly available sequencing data from the gut microbiome of cancer patients. We showed that our platform is capable of classifying patients with higher accuracy than other methods, with some caveats. Overall, we believe genomic research is the next frontline for deep learning as there are exciting avenues waiting to be explored. We think that our platform, presented here, could serve as the basis for such future research.RNA-Seq is nowadays an indispensable approach for comparative transcriptome profiling in model and nonmodel organisms. Analyzing RNA-Seq data from nonmodel organisms poses unique challenges, due to unavailability of a high-quality genome reference and to relative sparsity of tools for downstream functional analyses. In this chapter, we provide an overview of the analysis steps in RNA-Seq projects of nonmodel organisms, while elaborating on aspects that are unique to this analysis. These will include (1) strategic decisions that have to be made in advance, regarding sequencing technology and reference to use; (2) how to search for available draft genomes, and, if necessary, how to improve their gene prediction and annotation; (3) how to clean raw reads before de novo assembly; (4) how to separate the reads in RNA-Seq projects of symbiont organisms; (5) how to design and carry out a de novo transcriptome assembly that will be comprehensive and reliable; (6) how to assess transcriptome quality; (7) when and how to reduce redundancy in the transcriptome; (8) techniques and considerations in transcriptome functional annotation; (9) quantitating transcript abundance in the face of high transcriptome redundancy; and, most importantly, (10) how to achieve functional enrichment testing using available tools which either support a large range of species or enable a universal, non-species-specific analysis.Throughout the chapter, we will refer to a variety of useful software tools. https://www.selleckchem.com/products/7acc2.html For the initial analysis steps involving high-volume data, these will include Linux-based programs. For the later steps, we will describe both Linux and R packages for advanced users, as well as many user-friendly tools for nonprogrammers. Finally, we will present a full workflow for RNA-Seq analysis of nonmodel organisms using the NeatSeq-Flow platform, which can be used locally through a user-friendly interface.