Evaluation of RNA velocity as a signal for the reconstruction of gene regulatory networks.

openSingle-cell sequencing techniques are becoming more and more used. Methods to compute RNA velocity are improving. In this work, the main purpose is to study in deep the relation between RNA velocity and the interaction of genes, in order to reconstruct the corresponding Gene Regulatory Network.Single-cell sequencing techniques are becoming more and more used. Methods to compute RNA velocity are improving. In this work, the main purpose is to study in deep the relation between RNA velocity and the interaction of genes, in order to reconstruct the corresponding Gene Regulatory Network

Creation of functional viruses from non-functional cDNA clones obtained from an RNA virus population by the use of ancestral reconstruction

RNA viruses have the highest known mutation rates. Consequently it is likely that a high proportion of individual RNA virus genomes, isolated from an infected host, will contain lethal mutations and be non-functional. This is problematic if the aim is to clone and investigate high-fitness, functional cDNAs and may also pose problems for sequence-based analysis of viral evolution. To address these challenges we have performed a study of the evolution of classical swine fever virus (CSFV) using deep sequencing and analysis of 84 full-length cDNA clones, each representing individual genomes from a moderately virulent isolate. In addition to here being used as a model for RNA viruses generally, CSFV has high socioeconomic importance and remains a threat to animal welfare and pig production. We find that the majority of the investigated genomes are non-functional and only 12% produced infectious RNA transcripts. Full length sequencing of cDNA clones and deep sequencing of the parental population identified substitutions important for the observed phenotypes. The investigated cDNA clones were furthermore used as the basis for inferring the sequence of functional viruses. Since each unique clone must necessarily be the descendant of a functional ancestor, we hypothesized that it should be possible to produce functional clones by reconstructing ancestral sequences. To test this we used phylogenetic methods to infer two ancestral sequences, which were then reconstructed as cDNA clones. Viruses rescued from the reconstructed cDNAs were tested in cell culture and pigs. Both reconstructed ancestral genomes proved functional, and displayed distinct phenotypes in vitro and in vivo. We suggest that reconstruction of ancestral viruses is a useful tool for experimental and computational investigations of virulence and viral evolution. Importantly, ancestral reconstruction can be done even on the basis of a set of sequences that all correspond to non-functional variants

A detailed protocol for subcellular RNA sequencing (subRNA-seq)

In eukaryotic cells, RNAs at various maturation and processing levels are distributed across cellular compartments. The standard approach to determine transcript abundance and identity in vivo is RNA sequencing (RNA-seq). RNA-seq relies on RNA isolation from whole-cell lysates and thus mainly captures fully processed, stable, and more abundant cytoplasmic RNAs over nascent, unstable, and nuclear RNAs. Here, we provide a step-by-step protocol for subcellular RNA-seq (subRNA-seq). subRNA-seq allows the quantitative measurement of RNA polymerase II-generated RNAs from the chromatin, nucleoplasm, and cytoplasm of mammalian cells. This approach relies on cell fractionation prior to RNA isolation and sequencing library preparation. High-throughput sequencing of the subcellular RNAs can then be used to reveal the identity, abundance, and subcellular distribution of transcripts, thus providing insights into RNA processing and maturation. Deep sequencing of the chromatin-associated RNAs further offers the opportunity to study nascent RNAs. Subcellular RNA-seq libraries are obtained within 5 day

Rapid creation and quantitative monitoring of high coverage shRNA libraries.

Short hairpin RNA libraries are limited by low efficacy of many shRNAs and by off-target effects, which give rise to false negatives and false positives, respectively. Here we present a strategy for rapidly creating expanded shRNA pools (approximately 30 shRNAs per gene) that are analyzed by deep sequencing (EXPAND). This approach enables identification of multiple effective target-specific shRNAs from a complex pool, allowing a rigorous statistical evaluation of true hits

Genome-wide profiling of human cap-independent translation-enhancing elements.

We report an in vitro selection strategy to identify RNA sequences that mediate cap-independent initiation of translation. This method entails mRNA display of trillions of genomic fragments, selection for initiation of translation and high-throughput deep sequencing. We identified >12,000 translation-enhancing elements (TEEs) in the human genome, generated a high-resolution map of human TEE-bearing regions (TBRs), and validated the function of a subset of sequences in vitro and in cultured cells

An optimized kit-free method for making strand-specific deep sequencing libraries from RNA fragments

Deep sequencing of strand-specific cDNA libraries is now a ubiquitous tool for identifying and quantifying RNAs in diverse sample types. The accuracy of conclusions drawn from these analyses depends on precise and quantitative conversion of the RNA sample into a DNA library suitable for sequencing. Here, we describe an optimized method of preparing strand-specific RNA deep sequencing libraries from small RNAs and variably sized RNA fragments obtained from ribonucleoprotein particle footprinting experiments or fragmentation of long RNAs. Our approach works across a wide range of input amounts (400 pg to 200 ng), is easy to follow and produces a library in 2-3 days at relatively low reagent cost, all while giving the user complete control over every step. Because all enzymatic reactions were optimized and driven to apparent completion, sequence diversity and species abundance in the input sample are well preserved

VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research

Polymorphism identification and improved genome annotation of Brassica rapa through Deep RNA sequencing.

The mapping and functional analysis of quantitative traits in Brassica rapa can be greatly improved with the availability of physically positioned, gene-based genetic markers and accurate genome annotation. In this study, deep transcriptome RNA sequencing (RNA-Seq) of Brassica rapa was undertaken with two objectives: SNP detection and improved transcriptome annotation. We performed SNP detection on two varieties that are parents of a mapping population to aid in development of a marker system for this population and subsequent development of high-resolution genetic map. An improved Brassica rapa transcriptome was constructed to detect novel transcripts and to improve the current genome annotation. This is useful for accurate mRNA abundance and detection of expression QTL (eQTLs) in mapping populations. Deep RNA-Seq of two Brassica rapa genotypes-R500 (var. trilocularis, Yellow Sarson) and IMB211 (a rapid cycling variety)-using eight different tissues (root, internode, leaf, petiole, apical meristem, floral meristem, silique, and seedling) grown across three different environments (growth chamber, greenhouse and field) and under two different treatments (simulated sun and simulated shade) generated 2.3 billion high-quality Illumina reads. A total of 330,995 SNPs were identified in transcribed regions between the two genotypes with an average frequency of one SNP in every 200 bases. The deep RNA-Seq reassembled Brassica rapa transcriptome identified 44,239 protein-coding genes. Compared with current gene models of B. rapa, we detected 3537 novel transcripts, 23,754 gene models had structural modifications, and 3655 annotated proteins changed. Gaps in the current genome assembly of B. rapa are highlighted by our identification of 780 unmapped transcripts. All the SNPs, annotations, and predicted transcripts can be viewed at http://phytonetworks.ucdavis.edu/

Legislative and Administrative Processes. By Hans A. Linde and George Bunn; Introduction to the American Public Law System: Cases and Materials. By Jerry L. Mashaw and Richard A. Merrill

Background: Genome and transcriptome sequencing applications that rely on variation in sequence depth can be negatively affected if there are systematic biases in coverage. We have investigated patterns of local variation in sequencing coverage by utilising ultra-deep sequencing (>100,000X) of mtDNA obtained during sequencing of two vertebrate genomes, wolverine (Gulo gulo) and collared flycatcher (Ficedula albicollis). With such extreme depth, stochastic variation in coverage should be negligible, which allows us to provide a very detailed, fine-scale picture of sequence dependent coverage variation and sequencing error rates. Results: Sequencing coverage showed up to six-fold variation across the complete mtDNA and this variation was highly repeatable in sequencing of multiple individuals of the same species. Moreover, coverage in orthologous regions was correlated between the two species and was negatively correlated with GC content. We also found a negative correlation between the site-specific sequencing error rate and coverage, with certain sequence motifs "CCNGCC" being particularly prone to high rates of error and low coverage. Conclusions: Our results demonstrate that inherent sequence characteristics govern variation in coverage and suggest that some of this variation, like GC content, should be controlled for in, for example, RNA-Seq and detection of copy number variation