Systems Biology

The KAS-ATAC assay provides a method to capture genomic DNA fragments that are simultaneously physically accessible and contain single-stranded DNA (ssDNA) bubbles. These are characteristic features of two of the key processes involved in regulating and expressing genes—on one hand, the activity of cis-regulatory elements (cREs), which are typically devoid of nucleosomes when active and occupied by transcription factors, and on the other, the association of RNA polymerases with DNA, which results in the presence of ssDNA structures. Here, we present a detailed protocol for carrying out KAS-ATAC as well as basic processing of KAS-ATAC datasets and discuss the key considerations for its successful application.

This manuscript details two modified protocols for the isolation of long-stranded or high molecular weight (HMW) DNA from Magnaporthaceae (Ascomycota) fungal mycelium intended for whole genome sequencing. The Cytiva Nucleon PhytoPure and the Macherey-Nagel NucleoBond HMW DNA kits were selected because the former requires lower amounts of starting material and the latter utilizes gentler methods to maximize DNA length, albeit at a higher requirement for input material. The Cytiva Nucleon PhytoPure kit successfully recovered HMW DNA for half of our fungal species by increasing the amount of RNase A treatment and adding in a proteinase K treatment. To reduce the impact of pigmentation development, which occurs toward later stages of culturing, extractions were run in quadruplicate to increase overall DNA concentration. We also adapted the Macherey-Nagel NucleoBond HMW DNA kit for high-quality HMW DNA by grinding the sample to a fine powder, overnight lysis, and splitting the sample before washing the precipitated DNA. For both kits, precipitated DNA was spooled out pre-washing, ensuring a higher percentage of high-integrity long strands. The Macherey-Nagel protocol offers advantages over the first through the utilization of gravity columns that provide gentler treatment, yielding >50% of high-purity DNA strands exceeding 40 kbp. The limitation of this method is the requirement for a large quantity of starting material (1 g). By triaging samples based on the rate of growth relative to the accumulation of secondary metabolites, our methodologies hold promise for yielding reliable and high-quality HMW DNA from a variety of fungal samples, improving sequencing outcomes.

Rapid development in single-cell chromosome conformation capture technologies has provided valuable insights into the importance of spatial genome architecture for gene regulation. However, a long-standing technical gap remains in the simultaneous characterization of three-dimensional genomes and transcriptomes in the same cell. We have described an assay named Hi-C and RNA-seq employed simultaneously (HiRES), which integrates in situ reverse transcription and chromosome conformation capture (3C) for the parallel analysis of chromatin organization and gene expression. Here, we provide a detailed implementation of the assay, using mouse embryos and cerebral cortices as examples. The versatility of this method extends beyond these two samples, with the potential to be used in various other cell types.

Key features

• A multi-omics sequencing approach to profile 3D genome structure and gene expression simultaneously in single cells.

• Compatible with animal tissues.

• One-tube amplification of both DNA and RNA components.

• Requires three days to complete.

Graphical overview

Schematic illustration for the Hi-C and RNA-seq employed simultaneously (HiRES) workflow

Many single nucleotide polymorphisms (SNPs) identified by genome-wide association studies exert their effects on disease risk as expression quantitative trait loci (eQTL) via allele-specific expression (ASE). While databases for probing eQTLs in tissues from normal individuals exist, one may wish to ascertain eQTLs or ASE in specific tissues or disease-states not characterized in these databases. Here, we present a protocol to assess ASE of two possible target genes (GPNMB and KLHL7) of a known genome-wide association study (GWAS) Parkinson’s disease (PD) risk locus in postmortem human brain tissue from PD and neurologically normal individuals. This was done using a sequence of RNA isolation, cDNA library generation, enrichment for transcripts of interest using customizable cDNA capture probes, paired-end RNA sequencing, and subsequent analysis. This method provides increased sensitivity relative to traditional bulk RNAseq-based and a blueprint that can be extended to the study of other genes, tissues, and disease states.

Key features

• Analysis of GPNMB allele-specific expression (ASE) in brain lysates from cognitively normal controls (NC) and Parkinson’s disease (PD) individuals.

• Builds on the ASE protocol of Mayba et al. (2014) and extends application from cells to human tissue.

• Increased sensitivity by enrichment for desired transcript via RNA CaptureSeq (Mercer et al., 2014).

• Optimized for human brain lysates from cingulate gyrus, caudate nucleus, and cerebellum.

Graphical overview

Individual nucleotide resolution UV cross-linking and immunoprecipitation followed by high-throughput sequencing (iCLIP-seq) is a powerful technique that is used to identify RNA-binding proteins’ (RBP) binding sites on target RNAs and to characterize the molecular basis of posttranscriptional regulatory pathways. Several variants of CLIP have been developed to improve its efficiency and simplify the protocol [e.g., iCLIP2 and enhanced CLIP (eCLIP)]. We have recently reported that transcription factor SP1 functions in the regulation of alternative cleavage and polyadenylation through direct RNA binding. We utilized a modified iCLIP method to identify RNA-binding sites for SP1 and several of the cleavage and polyadenylation complex subunits, including CFIm25, CPSF7, CPSF100, CPSF2, and Fip1. Our revised protocol takes advantage of several features of the eCLIP procedure and also improves on certain steps of the original iCLIP method, including optimization of circularization of cDNA. Herein, we describe a step-by-step procedure for our revised iCLIP-seq protocol, that we designate as iCLIP-1.5, and provide alternative approaches for certain difficult-to-CLIP proteins.

Key features

• Identification of RNA-binding sites of RNA-binding proteins (RBPs) at nucleotide resolution.

• iCLIP-seq provides precise positional and quantitative information on the RNA-binding sites of RBPs in living cells.

• iCLIP facilitates the identification of sequence motifs recognized by RBPs.

• Allows quantitative analysis of genome-wide changes in protein-RNA interactions.

• Revised iCLIP-1.5 protocol is more efficient and highly robust; it provides higher coverage even for low-input samples.

Graphical overview

Genome-wide sequencing of RNA (RNA-seq) has become an inexpensive tool to gain key insights into cellular and disease mechanisms. Sample preparation and sequencing are streamlined and allow the acquisition of hundreds of gene expression profiles in a few days; however, in particular, data processing, curation, and analysis involve numerous steps that can be overwhelming to non-experts. Here, the sample preparation, sequencing, and data processing workflow for RNA-seq expression analysis in yeast is described. While this protocol covers only a small portion of the RNA-seq landscape, the principal workflow common to such experiments is described, allowing the reader to adapt the protocol where necessary.

Graphic abstract:

Basic workflow of RNA-seq expression analysis.

Association mapping is the process of linking phenotypes with genotypes. In genome wide association studies (GWAS), individuals are first genotyped using microarrays or by aligning sequenced reads to reference genomes. However, both these approaches rely on reference genomes which limits their application to organisms with no or incomplete reference genomes. To address this, reference free association mapping methods have been developed. Here we present the protocol of an alignment free method for association studies which is based on counting k-mers in sequenced reads, testing for associations between k-mers and the phenotype of interest, and local assembly of the k-mers of statistical significance. The method can map associations of categorical phenotypes to sequence and structural variations without requiring prior sequencing of reference genomes.

Gene transcription in bacteria often starts some nucleotides upstream of the start codon. Identifying the specific Transcriptional Start Site (TSS) is essential for genetic manipulation, as in many cases upstream of the start codon there are sequence elements that are involved in gene expression regulation. Taken into account the classical gene structure, we are able to identify two kinds of transcriptional start site: primary and secondary. A primary transcriptional start site is located some nucleotides upstream of the translational start site, while a secondary transcriptional start site is located within the gene encoding sequence.

Here, we present a step by step protocol for genome-wide transcriptional start sites determination by differential RNA-sequencing (dRNA-seq) using the enteric pathogen Shigella flexneri serotype 5a strain M90T as model. However, this method can be employed in any other bacterial species of choice. In the first steps, total RNA is purified from bacterial cultures using the hot phenol method. Ribosomal RNA (rRNA) is specifically depleted via hybridization probes using a commercial kit. A 5′-monophosphate-dependent exonuclease (TEX)-treated RNA library enriched in primary transcripts is then prepared for comparison with a library that has not undergone TEX-treatment, followed by ligation of an RNA linker adaptor of known sequence allowing the determination of TSS with single nucleotide precision. Finally, the RNA is processed for Illumina sequencing library preparation and sequenced as purchased service. TSS are identified by in-house bioinformatic analysis.

Our protocol is cost-effective as it minimizes the use of commercial kits and employs freely available software.

Plant roots associate with a wide diversity of bacteria and archaea across the root-soil spectrum. The rhizosphere microbiota, the communities of microbes in the soil adjacent to the root, can contain up to 10 billion bacterial cells per gram of soil (Raynaud and Nunan, 2014) and can play important roles for the fitness of the host plant. Subsets of the rhizospheric microbiota can colonize the root surface (rhizoplane) and the root interior (endosphere), forming an intimate relationship with the host plant. Compositional analysis of these communities is important to develop tools in order to manipulate root-associated microbiota for increased crop productivity. Due to the reduced cost and increasing throughput of next-generation sequencing, major advances in deciphering these communities have recently been achieved, mainly through the use of amplicon sequencing of the 16S rRNA gene. Here we first present a protocol for dissecting the microbiota from various root compartments, developed using rice as a model. We next present a method for amplifying fragments of the 16S rRNA gene using a dual index approach. Finally, we present a simple workflow for analyzing the resulting sequencing data to make ecological inferences.

Next-generation sequencing (NGS) offers unparalleled resolution for untargeted organism detection and characterization. However, the majority of NGS analysis programs require users to be proficient in programming and command-line interfaces. EDGE bioinformatics was developed to offer scientists with little to no bioinformatics expertise a point-and-click platform for analyzing sequencing data in a rapid and reproducible manner. EDGE (Empowering the Development of Genomics Expertise) v1.0 released in January 2017, is an intuitive web-based bioinformatics platform engineered for the analysis of microbial and metagenomic NGS-based data (Li et al., 2017). The EDGE bioinformatics suite combines vetted publicly available tools, and tracks settings to ensure reliable and reproducible analysis workflows. To execute the EDGE workflow, only raw sequencing reads and a project ID are necessary. Users can access in-house data, or run analyses on samples deposited in Sequence Read Archive. Default settings offer a robust first-glance and are often sufficient for novice users. All analyses are modular; users can easily turn workflows on/off, and modify parameters to cater to project needs. Results are compiled and available for download in a PDF-formatted report containing publication quality figures. We caution that interpreting results still requires in-depth scientific understanding, however report visuals are often informative, even to novice users.