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With long-read sequencing we have entered an era where individual genomes are routinely assembled to near-completion and where complex genetic variation can efficiently be resolved. Here we demonstrate that long reads can be applied also to study the genomic architecture of individual human cells. Clonally expanded CD8+ T-cells from a human donor were used as starting material for a droplet-based multiple displacement amplification (dMDA) method designed to ensure long molecule lengths and minimal amplification bias. Sequencing of two single cells was performed on the PacBio Sequel II system, generating over 2.5 million reads and ~20Gb HiFi data (>QV20) per cell, achieving up to 40% genome coverage. This data allowed for single nucleotide variant (SNV) detection, including in genomic regions inaccessible by short reads. Over 1000 high-confidence structural variants (SVs) per cell were discovered in the PacBio data, which is four times more than the number of SVs detected in Illumina dMDA data from clonally related cells. In addition, several putative clone-specific somatic SV events could be identified. Single-cell de novo assembly resulted in 454-598 Mb assembly sizes and 35-42 kb contig N50 values. 1762 (12.8%) of expected gene models were found to be complete in the best single-cell assembly. The de novo constructed mitochondrial genomes were 100% identical for the two single cells subjected to PacBio sequencing, although mitochondrial heteroplasmy was also observed. In summary, the work presented here demonstrates the utility of long-read sequencing towards understanding the extent and distribution of complex genetic variation at the single cell level.

The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

Technological advances in the last decade have enabled biologists to produce increasing amounts of information for the transcriptome, proteome, interactome, and other -omics data sets in many model organisms. A major challenge is integration and biological interpretation of these massive data sets in order to generate testable hypotheses about gene regulatory networks or molecular mechanisms that govern system behaviors. Constructing gene networks requires bioinformatics skills to adequately manage, integrate, analyze and productively use the data to generate biological insights. In this chapter, we provide detailed methods for users without prior knowledge of bioinformatics to construct gene networks and derive hypotheses that can be experimentally verified. Step-by-step instructions for acquiring, integrating, analyzing, and visualizing genome-wide data are provided for two widely used open source platforms, R and Cytoscape platforms. The examples provided are based on Arabidopsis data, but the protocols presented should be readily applicable to any organism for which similar data can be obtained.

International audience; Nitrate acts as a potent signal to control global gene expression in Arabidopsis. Using an integrative bioinformatics approach we identified TGA1 and TGA4 as putative regulatory factors that mediate nitrate responses in Arabidopsis roots. We showed that both TGA1 and TGA4 mRNAs accumulate strongly after nitrate treatments in roots. Global gene expression analysis revealed 97% of the genes with altered expression in tga1 tga4 double mutant plants respond to nitrate treatments, indicating that these transcription factors have a specific role in nitrate responses in Arabidopsis root organs. We found TGA1 and TGA4 regulate the expression of nitrate transporter genes NRT2.1 and NRT2.2. Specific binding of TGA1 to its cognate DNA sequence on NRT2.1 and NRT2.2 promoters was confirmed by chromatin immunoprecipitation assays. The tga1 tga4 double mutant plants exhibit nitrate-dependent lateral and primary root phenotypes. Lateral root initiation is affected in both tga1 tga4 and nrt1.2 nrt2.2 double mutants, suggesting TGA1 and TGA4 regulate lateral root development at least partly via NRT2.1 and NRT2.2. Additional root phenotypes of tga1 tga4 double mutants indicate that these transcription factors play an important role in root developmental responses to nitrate. These results identify TGA1 and TGA4 as important regulatory factors of the nitrate response in Arabidopsis roots.