Bioinformatics Knowledge Research Articles

Fifty years of the BTS-some reflections.

As we celebrate 50 years of the BTS, it is timely to consider what topics and challenges dominated our thinking in toxicology 50 years ago and how things have evolved to the current day. To gain insight into this, we sought the views of several senior toxicologists who offered their observations and reflections on their careers in toxicology. One notable trend is the changing skill sets that might be considered essential for a career in toxicology. Fifty years ago, experience of in vivo methodologies was expected whereas today’s recruits into toxicology rarely have this skill set. In contrast, in silico “and” in vitro (ISIV) methods such as molecular biology and computational methods dominate our approaches; a working knowledge of bioinformatics and computational approaches would be expected today but unheard of 50 years ago (Fig. 1). Another emerging theme is the need to make decisions based on limited data. This is well illustrated by experiences shared by Dr Diane Benford. “Whether you are working on naturally occurring compounds and toxins or those arising from human activity, you have to make decisions on limited data, with no possibility of asking a manufacturer to perform more studies.” Indeed, regulatory toxicology studies conducted according to internationally agreed guidelines are commonly not available and it would often be hard to justify conducting or repeating such work. In the early days of her research, Dr Sue Barlow’s research topic was potential changes in brain function after prenatal exposure to drugs. “The challenge was to work out if any of the animal cognitive behaviour techniques could be applied to very young animals, and how to interpret the results,” she explained. Professor Faith Williams also pointed out that today we often rely on data that could have been generated 40 or 50 years ago “A good example of this is our dependency on legacy data on skin absorption, requiring us to have robust methodologies for extrapolation.”

The prognostic value and clinical significance of mitophagy-related genes in hepatocellular carcinoma.

Background: Mitophagy has been found to play a significant part in the cancer process in a growing number of studies in recent years. However, there is still a lack of study on mitophagy-related genes’ (MRGs) prognostic potential and clinical significance in hepatocellular carcinoma (HCC). Methods: We employed bioinformatics and statistical knowledge to examine the transcriptome data of HCC patients in the TCGA and GEO databases, with the goal of constructing a multigene predictive model. Then, we separated the patients into high- and low-risk groups based on the score. The model’s dependability was determined using principal components analysis (PCA), survival analysis, independent prognostic analysis, and receiver operating characteristic (ROC) analysis. Following that, we examined the clinical correlations, pharmacological treatment sensitivity, immune checkpoint expression, and immunological correlations between patients in high and low risk groups. Finally, we evaluated the variations in gene expression between high- and low-risk groups and further analyzed the network core genes using protein-protein interaction network analysis. Results: Prognostic models were built using eight genes (OPTN, ATG12, CSNK2A2, MFN1, PGAM5, SQSTM1, TOMM22, TOMM5). During validation, the prognostic model demonstrated high reliability, indicating that it could accurately predict the prognosis of HCC patients. Additionally, we discovered that typical HCC treatment medicines had varying impacts on patients classified as high or low risk, and that individuals classified as high risk are more likely to fail immunotherapy. Additionally, the high-risk group expressed more immunological checkpoints. The immunological status of patients in different risk categories varies as well, and patients with a high-risk score have a diminished ability to fight cancer. Finally, PPI analysis identified ten related genes with potential for research. Conclusion: Our prognostic model had good and reliable predictive ability, as well as clinical diagnosis and treatment guiding significance. Eight prognostic MRGs and ten network core genes merited further investigation.

NanoForms: an integrated server for processing, analysis and assembly of raw sequencing data of microbial genomes, from Oxford Nanopore technology.

BackgroundNext Generation Sequencing (NGS) techniques dominate today’s landscape of genetics and genomics research. Though Illumina still dominates worldwide sequencing, Oxford Nanopore is one of the leading technologies currently being used by biologists, medics and geneticists across various applications. Oxford Nanopore is automated and relatively simple for conducting experiments, but generates gigabytes of raw data, to be processed by often ambiguous set of alternative bioinformatics command-line tools, and genomics frameworks which require a knowledge of bioinformatics to run.ResultsWe established an inter-collegiate collaboration across experimentalists and bioinformaticians in order to provide a novel bioinformatics tool, free for academics. This tool allows people without extensive bioinformatics knowledge to simply process their raw genome sequencing data. Currently, due to ICT resources’ maintenance reasons, our server is only capable of handling small genomes (up to 15 Mb). In this paper, we introduce our tool, NanoForms: an intuitive and integrated web server for the processing and analysis of raw prokaryotic genome data, coming from Oxford Nanopore. NanoForms is freely available for academics at the following locations: http://nanoforms.tech (webserver) and https://github.com/czmilanna/nanoforms (GitHub source repository).

Bioinformatics Method Was Used to Analyze the Highly Expressed Gene FAM83A of Breast Cancer in Young Women.

Objectives Preliminary analysis of breast cancer related to unknown functional gene FAM83A through bioinformatics knowledge to inform further experimental studies. Select high expression genes for breast cancer and use bioinformatics methods to predict the biological function of FAM83A. Methods Genes with significant differences in expression between breast tumors and normal breast tissue libraries were selected from CGAP's SAGE Digital Gene Expression Displayer (DGED) database. An unknown functional gene, FAM83A, which is highly expressed in breast cancer, was screened. We performed an analysis of the gene structure, subcellular localization, physicochemical properties of the encoding products, functional sites, protein structure, and functional domains. Results Through SAGE DGED, a total of 185 genes with expression differences were found. The structure and function of FAM83A have ideal predictions, and it is generally determined that this gene encodes a nuclear protein with a nucleoprotein. The active site of PLDc and the functional domain of DUF1669 can be involved in signal transduction and gene expression regulation in tumorigenesis and metastasis. Digital gene representation of the Tumor Genome Project Data Library was used to select differentially expressed genes in breast cancer tissue and breast benign tumor tissue. Conclusion Studies show that FAM83A is a potential research target associated with tumorigenesis and metastasis. Initial tests confirmed the expression of this gene. Lay a solid foundation for further research learning. FAM83A is a highly expressed gene in breast cancer and can serve as a target for studying molecular mechanisms in breast cancer.

The Easy-to-Use SARS-CoV-2 Assembler for Genome Sequencing: Development Study

BackgroundEarly sequencing and quick analysis of the SARS-CoV-2 genome have contributed to the understanding of the dynamics of COVID-19 epidemics and in designing countermeasures at a global level.ObjectiveAmplicon-based next-generation sequencing (NGS) methods are widely used to sequence the SARS-CoV-2 genome and to identify novel variants that are emerging in rapid succession as well as harboring multiple deletions and amino acid–changing mutations.MethodsTo facilitate the analysis of NGS sequencing data obtained from amplicon-based sequencing methods, here, we propose an easy-to-use SARS-CoV-2 genome assembler: the Easy-to-use SARS-CoV-2 Assembler (ESCA) pipeline.ResultsOur results have shown that ESCA could perform high-quality genome assembly from Ion Torrent and Illumina raw data and help the user in easily correct low-coverage regions. Moreover, ESCA includes the possibility of comparing assembled genomes of multisample runs through an easy table format.ConclusionsIn conclusion, ESCA automatically furnished a variant table output file, fundamental to rapidly recognizing variants of interest. Our pipeline could be a useful method for obtaining a complete, rapid, and accurate analysis even with minimal knowledge in bioinformatics.

Molecular bioprospecting of plant extracts: Experience report of the BIOPROS/UFV group in the search for antitumor compounds

This manuscript discusses the experience of the research group BIOPROS (Molecular Bioprospecting in the Sustainable Use of Biodiversity) from the Federal University of Viçosa in the field of bioprospecting. We describe our experience on the search for antitumor compounds from the collection of extracts of native tree species from the Atlantic Forest biome. Presenting an interdisciplinary approach, integrating knowledge of forestry engineering, bioinformatics and natural products chemistry, the bioprospecting research of the BIOPROS group has innovated in the generation of pharmacochemical knowledge of native species of the Atlantic Forest. For the composition of the extract library a total of 220 plant species distributed in 57 botanical families and 140 genera were identified. 196 extracts from 49 plant species were produced; all of them evaluated for cytotoxic activity. By showing the process of obtaining a promising antitumor activity withanolides compounds from Athenaea velutina, a species hitherto little known to science, this manuscript shows our sequence of methodological steps used to unravel bioactive natural products from fragments of Atlantic Forest. The research follows the premises of the Convention on Biological Diversity, regarding the creation of strategies for the sustainable use of biodiversity.

Fast and Easy Nanopore Sequencing Workflow for Rapid Genetic Testing of Familial Hypercholesterolemia.

Familial hypercholesterolemia (FH) is an autosomal dominant lipid metabolism disorder characterized by severely elevated plasma low-density lipoprotein cholesterol levels. The disease is caused by mutations in 3 genes (LDLR, APOB and PCSK9) while over 90% of the mutations are located within the LDLR gene. Thus, genetic analysis of the LDLR gene is the first step in the genetic diagnosis of FH. However, conventional methods like Sanger and NextGen sequencing are still costly and time-consuming. In contrast, Oxford Nanopore technology sequencing is an emerging third-generation sequencing technology featured by easy operability, low cost, small size and the capability of parallel sample sequencing. Here, we present an easy Nanopore-sequencing-based workflow for the rapid genetic testing of FH taking only 3 days and costing less than $50 per sample without the requirement for deep bioinformatic knowledge. Using our workflow, we were able to identify the underlying pathogenic variants of 10 FH patients including one novel, not yet recorded pathogenic variants. Our workflow allows the rapid evaluation of the pathogenic variants by utilizing detailed variant information from Ensembl. Additionally, our workflow is not restricted to sequencing the LDLR gene alone but can be easily adapted to the other FH-causing genes and more importantly, to any desired gene contributing to any hereditary disease. Therefore, our workflow is an attractive opportunity for every diagnostic laboratory to offer fast and easy in-house genetic diagnostics.

TITAN: An end-to-end data analysis environment for the Hyperion™ imaging system.

Imaging Mass Cytometry (IMC) is a powerful high-throughput technique enabling resolution of up to 37 markers in a single fixed tissue section while also preserving in situ spatial relationships. Currently, IMC processing and analysis necessitates the use of multiple different software, labour-intensive pipeline development, different operating systems and knowledge of bioinformatics, all of which are a barrier to many potential users. Here we present TITAN - an open-source, single environment, end-to-end pipeline that can be utilized for image visualization, segmentation, analysis and export of IMC data. TITAN is implemented as an extension within the publicly available 3D Slicer software. We demonstrate the utility, application, reliability and comparability of TITAN using publicly available IMC data from recently-published breast cancer and COVID-19 lung injury studies. Compared with current IMC analysis methods, TITAN provides a user-friendly, efficient single environment to accurately visualize, segment, and analyze IMC data for all users.

PyProtModel: An easy to use GUI for comparative protein modeling

A deep insight into the 3D structures of the proteins is essential to understand their functions and will be helpful in drug design and making decisions for the treatment of diseases. The 3D structure for less than one-thousandth of the protein sequences has been determined so far due to the slow structure elucidation procedures with experimental methods, such as X-ray. For this reason, different computational methods were used to determine the structure of the proteins, including template-based and de novo approaches. Much software has been developed to facilitate the computational approaches in protein modeling, some of which need user expertise and extensive knowledge in bioinformatics. The present study attempted to provide a user-friendly environment to motivate the researchers in computational biology, a Python-based interface based on MODELLER software. PyProtModel can be used as a toolbox in structural bioinformatics for protein modeling from sequence to the prediction of 3D structure. In addition, the users can take benefits of PyProtModel in order to prepare and evaluate protein PDB files prior to other in silico experiments. PyProtModel allows the user to apply different MODELLER options, automates, and speeds up time-consuming homology modeling steps. Different aspects of PyProtModel and a comprehensive view of the application will be discussed in this paper. The application is available for download and use at: https://github.com/msisakht/pyprotmodel.git.

Systems biology–the transformative approach to integrate sciences across disciplines

Abstract Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.

High diagnostic rate of trio exome sequencing in consanguineous families with neurogenetic diseases.

Consanguineous marriages have a prevalence rate of 24% in Turkey. These carry an increased risk of autosomal recessive genetic conditions, leading to severe disability or premature death, with a significant health and economic burden. A definitive molecular diagnosis could not be achieved in these children previously, as infrastructures and access to sophisticated diagnostic options were limited. We studied the cause of neurogenetic disease in 246 children from 190 consanguineous families recruited in three Turkish hospitals between 2016 and 2020. All patients underwent deep phenotyping and trio whole exome sequencing, and data were integrated in advanced international bioinformatics platforms.We detected causative variants in 119 known disease genes in 72% of families. Due to overlapping phenotypes 52% of the confirmed genetic diagnoses would have been missed on targeted diagnostic gene panels. Likely pathogenic variants in 27 novel genes in 14% of the families increased the diagnostic yield to 86%. Eighty-two per cent of causative variants (141/172) were homozygous, 11 of which were detected in genes previously only associated with autosomal dominant inheritance. Eight families carried two pathogenic variants in different disease genes. De novo (9.3%), X-linked recessive (5.2%) and compound heterozygous (3.5%) variants were less frequent compared to non-consanguineous populations.This cohort provided a unique opportunity to better understand the genetic characteristics of neurogenetic diseases in a consanguineous population. Contrary to what may be expected, causative variants were often not on the longest run of homozygosity and the diagnostic yield was lower in families with the highest degree of consanguinity, due to the high number of homozygous variants in these patients. Pathway analysis highlighted that protein synthesis/degradation defects and metabolic diseases are the most common pathways underlying paediatric neurogenetic disease. In our cohort 164 families (86%) received a diagnosis, enabling prevention of transmission and targeted treatments in 24 patients (10%).We generated an important body of genomic data with lasting impacts on the health and wellbeing of consanguineous families and economic benefit for the healthcare system in Turkey and elsewhere. We demonstrate that an untargeted next generation sequencing approach is far superior to a more targeted gene panel approach, and can be performed without specialized bioinformatics knowledge by clinicians using established pipelines in populations with high rates of consanguinity.

Et Özgüllüğünün Belirlenmesinde Primer Setlerinin Tasarımına Yönelik Biyoinformatik Tabanlı Bir Yaklaşım

Polymerase chain reaction (PCR) and its derivatives are one of the most widely used DNA-based methods in species determination studies in meat and meat products. Chromosomal or mitochondrial genes of the species can be targeted in PCR-based analyzes used in species detection studies. Many researchers are able to realize oligonucleotide differences between species through online alignment programs on mitochondrial DNA. Using chromosomal DNA would provide more concise results in quantification studies. However, determining the marker regions for genomic DNA is challenging due to the large size of the chromosomes. Bioinformatics approaches are available for selected applications. However, using those approaches requires intensive knowledge of computer science, molecular biology, and bioinformatics in addition to high computational power. In this study, a pipeline is presented that will provide a user-friendly approach to be adopted by facilities where contamination analyzes are routinely performed.

The Development of a Sustainable Bioinformatics Training Environment Within the H3Africa Bioinformatics Network (H3ABioNet)

Bioinformatics training programs have been developed independently around the world based on the perceived needs of the local and global academic communities. The field of bioinformatics is complicated by the need to train audiences from diverse backgrounds in a variety of topics to various levels of competencies. While there have been several attempts to develop standardised approaches to provide bioinformatics training globally, the challenges encountered in resource limited settings hinder the adaptation of these global approaches. H3ABioNet, a Pan-African Bioinformatics Network with 27 nodes in 16 African countries, has realised that there is no single simple solution to this challenge and has rather, over the years, evolved and adapted training approaches to create a sustainable training environment, with several components that allow for the successful dissemination of bioinformatics knowledge to diverse audiences. This has been achieved through the implementation of a combination of training modalities and sharing of high quality training material and experiences. The results highlight the success of implementing this multi-pronged approach to training, to reach audiences from different backgrounds and provide training in a variety of different areas of expertise. While face-to-face training was initially required and successful, the mixed-model teaching approach allowed for an increased reach, providing training in advanced analysis topics to reach large audiences across the continent with minimal teaching resources. The transition to hackathons provided an environment to allow the progression of skills, once basic skills had been developed, together with the development of real-world solutions to bioinformatics problems. Ensuring our training materials are FAIR, and through synergistic collaborations with global training partners, the reach of our training materials extends beyond H3ABioNet. Coupled with the opportunity to develop additional career building soft skills, such as scientific communication, H3ABioNet has created a flexible, sustainable and high quality bioinformatics training environment that has successfully been implemented to train several highly skilled African bioinformaticians on the continent.

BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data.

BackgroundGenome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples.ResultsHere we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs.ConclusionsResearchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.

A SARS-CoV-2 sequence submission tool for the European Nucleotide Archive.

SummaryMany aspects of the global response to the COVID-19 pandemic are enabled by the fast and open publication of SARS-CoV-2 genetic sequence data. The European Nucleotide Archive (ENA) is the European recommended open repository for genetic sequences. In this work, we present a tool for submitting raw sequencing reads of SARS-CoV-2 to ENA. The tool features a single-step submission process, a graphical user interface, tabular-formatted metadata and the possibility to remove human reads prior to submission. A Galaxy wrap of the tool allows users with little or no bioinformatics knowledge to do bulk sequencing read submissions. The tool is also packed in a Docker container to ease deployment.Availability and implementationCLI ENA upload tool is available at github.com/usegalaxy-eu/ena-upload-cli (DOI 10.5281/zenodo.4537621); Galaxy ENA upload tool at toolshed.g2.bx.psu.edu/view/iuc/ena_upload/382518f24d6d and github.com/galaxyproject/tools-iuc/tree/master/tools/ena_upload (development); and ENA upload Galaxy container at github.com/ELIXIR-Belgium/ena-upload-container (DOI 10.5281/zenodo.4730785).

Genome-resolved metagenomics using environmental and clinical samples.

Recent advances in high-throughput sequencing technologies and computational methods have added a new dimension to metagenomic data analysis i.e. genome-resolved metagenomics. In general terms, it refers to the recovery of draft or high-quality microbial genomes and their taxonomic classification and functional annotation. In recent years, several studies have utilized the genome-resolved metagenome analysis approach and identified previously unknown microbial species from human and environmental metagenomes. In this review, we describe genome-resolved metagenome analysis as a series of four necessary steps: (i) preprocessing of the sequencing reads, (ii) de novo metagenome assembly, (iii) genome binning and (iv) taxonomic and functional analysis of the recovered genomes. For each of these four steps, we discuss the most commonly used tools and the currently available pipelines to guide the scientific community in the recovery and subsequent analyses of genomes from any metagenome sample. Furthermore, we also discuss the tools required for validation of assembly quality as well as for improving quality of the recovered genomes. We also highlight the currently available pipelines that can be used to automate the whole analysis without having advanced bioinformatics knowledge. Finally, we will highlight the most widely adapted and actively maintained tools and pipelines that can be helpful to the scientific community in decision making before they commence the analysis.

IsomiR_Window: a system for analyzing small-RNA-seq data in an integrative and user-friendly manner

BackgroundIsomiRs are miRNA variants that vary in length and/or sequence when compared to their canonical forms. These variants display differences in length and/or sequence, including additions or deletions of one or more nucleotides (nts) at the 5′ and/or 3′ end, internal editings or untemplated 3′ end additions. Most available tools for small RNA-seq data analysis do not allow the identification of isomiRs and often require advanced knowledge of bioinformatics. To overcome this, we have developed IsomiR Window, a platform that supports the systematic identification, quantification and functional exploration of isomiR expression in small RNA-seq datasets, accessible to users with no computational skills.MethodsIsomiR Window enables the discovery of isomiRs and identification of all annotated non-coding RNAs in RNA-seq datasets from animals and plants. It comprises two main components: the IsomiR Window pipeline for data processing; and the IsomiR Window Browser interface. It integrates over ten third-party softwares for the analysis of small-RNA-seq data and holds a new algorithm that allows the detection of all possible types of isomiRs. These include 3′ and 5′end isomiRs, 3′ end tailings, isomiRs with single nucleotide polymorphisms (SNPs) or potential RNA editings, as well as all possible fuzzy combinations. IsomiR Window includes all required databases for analysis and annotation, and is freely distributed as a Linux virtual machine, including all required software.ResultsIsomiR Window processes several datasets in an automated manner, without restrictions of input file size. It generates high quality interactive figures and tables which can be exported into different formats. The performance of isomiR detection and quantification was assessed using simulated small-RNA-seq data. For correctly mapped reads, it identified different types of isomiRs with high confidence and 100% accuracy. The analysis of a small RNA-seq data from Basal Cell Carcinomas (BCCs) using isomiR Window confirmed that miR-183-5p is up-regulated in Nodular BCCs, but revealed that this effect was predominantly due to a novel 5′end variant. This variant displays a different seed region motif and 1756 isoform-exclusive mRNA targets that are significantly associated with disease pathways, underscoring the biological relevance of isomiR-focused analysis. IsomiR Window is available at https://isomir.fc.ul.pt/.

Gene Annotation in High Schools: Successful Student Pipeline and Teacher Professional Development in Bioscience Using GENI-ACT.

Knowledge of genomics is an essential component of science for high school student health literacy. However, few high school teachers have received genomics training or any guidance on how to teach the subject to their students. This project explored the impact of a genomics and bioinformatics research pipeline for high school teachers and students using an introduction to genome annotation research as the catalyst. The Western New York-based project had three major components: (1) a summer teacher professional development workshop to introduce genome annotation research, (2) teacher-guided student genome annotation group projects during the school year, (3) with an end of the academic year capstone symposium to showcase student work in a poster session. Both teachers and students performed manual gene annotations using an online annotation toolkit known as Genomics Education National Initiative-Annotation Collaboration Toolkit (GENI-ACT), originally developed for use in a college undergraduate teaching environment. During the school year, students were asked to evaluate the data they had collected, formulate a hypothesis about the correctness of the computer pipeline annotation, and present the data to support their conclusions in poster form at the symposium. Evaluation of the project documented increased content knowledge in basic genomics and bioinformatics as well as increased confidence in using tools and the scientific process using GENI-ACT, thus demonstrating that high school students are capable of using the same tools as scientists to conduct a real-world research task.

ATACgraph: Profiling Genome-Wide Chromatin Accessibility From ATAC-seq.

Assay for transposase-accessible chromatin using sequencing data (ATAC-seq) is an efficient and precise method for revealing chromatin accessibility across the genome. Most of the current ATAC-seq tools follow chromatin immunoprecipitation sequencing (ChIP-seq) strategies that do not consider ATAC-seq-specific properties. To incorporate specific ATAC-seq quality control and the underlying biology of chromatin accessibility, we developed a bioinformatics software named ATACgraph for analyzing and visualizing ATAC-seq data. ATACgraph profiles accessible chromatin regions and provides ATAC-seq-specific information including definitions of nucleosome-free regions (NFRs) and nucleosome-occupied regions. ATACgraph also allows identification of differentially accessible regions between two ATAC-seq datasets. ATACgraph incorporates the docker image with the Galaxy platform to provide an intuitive user experience via the graphical interface. Without tedious installation processes on a local machine or cloud, users can analyze data through activated websites using pre-designed workflows or customized pipelines composed of ATACgraph modules. Overall, ATACgraph is an effective tool designed for ATAC-seq for biologists with minimal bioinformatics knowledge to analyze chromatin accessibility. ATACgraph can be run on any ATAC-seq data with no limit to specific genomes. As validation, we demonstrated ATACgraph on human genome to showcase its functions for ATAC-seq interpretation. This software is publicly accessible and can be downloaded at https://github.com/RitataLU/ATACgraph.

Methodologies for Discovery and Quantitative Profiling of sRNAs in Potato.

Small RNAs (sRNAs) are short noncoding RNAs involved in the regulation of a wide range of biological processes in plants. Advances in high-throughput sequencing and development of new computational tools had facilitated the discovery of different classes of sRNAs, their quantification, and elucidation of their functional role in gene expression regulation by target transcript predictions. The workflow presented here allows identification of different sRNA species: known and novel potato miRNAs, and their sequence variants (isomiRs), as well as identification of phased small interfering RNAs (phasiRNAs). Moreover, it includes steps for differential expression analysis to search for regulated sRNAs across different tested biological conditions. In addition, it describes two different methods for predicting sRNA targets, in silico prediction, and degradome sequencing data analysis. All steps of the workflow are written in a clear and user-friendly way; thus they can be followed also by the users with minimal bioinformatics knowledge. We also included several in-house scripts together with valuable notes to facilitate data (pre)processing steps and to reduce the analysis time.