jackalope: A swift,versatile phylogenomic and high‐throughput sequencing simulator

High‐throughput sequencing (HTS) is central to the study of population genomics and has an increasingly important role in constructing phylogenies. Choices in research design for sequencing projects can include a wide range of factors, such as sequencing platform, depth of coverage and bioinformatic tools. Simulating HTS data better informs these decisions, as users can validate software by comparing output to the known simulation parameters. However, current standalone HTS simulators cannot generate variant haplotypes under even somewhat complex evolutionary scenarios, such as recombination or demographic change. This greatly reduces their usefulness for fields such as population genomics and phylogenomics. Here I present the R package jackalope that simply and efficiently simulates (i) sets of variant haplotypes from a reference genome and (ii) reads from both Illumina and Pacific Biosciences platforms. Haplotypes can be simulated using phylogenies, gene trees, coalescent‐simulation output, population‐genomic summary statistics, and Variant Call Format (VCF) files. jackalope can simulate single, paired‐end or mate‐pair Illumina reads, as well as reads from Pacific Biosciences. These simulations include sequencing errors, mapping qualities, multiplexing and optical/PCR duplicates. It can read reference genomes from fasta files and can simulate new ones, and all outputs can be written to standard file formats. jackalope is available for Mac, Windows and Linux systems.     

Prevention,diagnosis and treatment of high‐throughput sequencing data pathologies

High‐throughput sequencing (HTS) technologies generate millions of sequence reads from DNA/RNA molecules rapidly and cost‐effectively, enabling single investigator laboratories to address a variety of ‘omics’ questions in nonmodel organisms, fundamentally changing the way genomic approaches are used to advance biological research. One major challenge posed by HTS is the complexity and difficulty of data quality control (QC). While QC issues associated with sample isolation, library preparation and sequencing are well known and protocols for their handling are widely available, the QC of the actual sequence reads generated by HTS is often overlooked. HTS‐generated sequence reads can contain various errors, biases and artefacts whose identification and amelioration can greatly impact subsequent data analysis. However, a systematic survey on QC procedures for HTS data is still lacking. In this review, we begin by presenting standard ‘health check‐up’ QC procedures recommended for HTS data sets and establishing what ‘healthy’ HTS data look like. We next proceed by classifying errors, biases and artefacts present in HTS data into three major types of ‘pathologies’, discussing their causes and symptoms and illustrating with examples their diagnosis and impact on downstream analyses. We conclude this review by offering examples of successful ‘treatment’ protocols and recommendations on standard practices and treatment options. Notwithstanding the speed with which HTS technologies – and consequently their pathologies – change, we argue that careful QC of HTS data is an important – yet often neglected – aspect of their application in molecular ecology, and lay the groundwork for developing a HTS data QC ‘best practices’ guide.     

Pan‐cancer RNA‐seq data stratifies tumours by some hallmarks of cancer

Numerous genetic and epigenetic alterations cause functional changes in cell biology underlying cancer. These hallmark functional changes constitute potentially tissue‐independent anticancer therapeutic targets. We hypothesized that RNA‐Seq identifies gene expression changes that underly those hallmarks, and thereby defines relevant therapeutic targets. To test this hypothesis, we analysed the publicly available TCGA‐TARGET‐GTEx gene expression data set from the University of California Santa CruzToil recompute project using WGCNA to delineate co‐correlated ‘modules’ from tumour gene expression profiles and functional enrichment of these modules to hierarchically cluster tumours. This stratified tumours according to T cell activation, NK‐cell activation, complement cascade, ATM, Rb, angiogenic, MAPK, ECM receptor and histone modification signalling. These correspond to the cancer hallmarks of avoiding immune destruction, tumour‐promoting inflammation, evading growth suppressors, inducing angiogenesis, sustained proliferative signalling, activating invasion and metastasis, and genome instability and mutation. This approach did not detect pathways corresponding to the cancer enabling replicative immortality, resisting cell death or deregulating cellular energetics hallmarks. We conclude that RNA‐Seq stratifies tumours along some, but not all, hallmarks of cancer and, therefore, could be used in conjunction with other analyses collectively to inform precision therapy.     

Allelic imbalance metre (Allim), a new tool for measuring allele‐specific gene expression with RNA‐seq data

Estimating differences in gene expression among alleles is of high interest for many areas in biology and medicine. Here, we present a user‐friendly software tool, Allim, to estimate allele‐specific gene expression. Because mapping bias is a major problem for reliable estimates of allele‐specific gene expression using RNA‐seq, Allim combines two different strategies to account for the mapping biases. In order to reduce the mapping bias, Allim first generates a polymorphism‐aware reference genome that accounts for the sequence variation between the alleles. Then, a sequence‐specific simulation tool estimates the residual mapping bias. Statistical tests for allelic imbalance are provided that can be used with the bias corrected RNA‐seq data.     

Single cell RNA‐seq in the sea urchin embryo show marked cell‐type specificity in the Delta/Notch pathway

Sea urchin embryos are excellent for in vivo functional studies because of their transparency and tractability in manipulation. They are also favorites for pharmacological approaches since they develop in an aquatic environment and addition of test substances is straightforward. A concern in many pharmacological tests though is the potential for pleiotropic effects that confound the conclusions drawn from the results. Precise cellular interpretations are often not feasible because the impact of the perturbant is not known. Here we use single‐cell mRNA (messenger RNA) sequencing as a metric of cell types in the embryo and to determine the selectivity of two commonly used inhibitors, one each for the Wnt and the Delta‐Notch pathways, on these nascent cell types. We identified 11 distinct cell types based on mRNA profiling, and that the cell lineages affected by Wnt and Delta/Notch inhibition were distinct from each other. These data support specificity and distinct effects of these signaling pathways in the embryo and illuminate how these conserved pathways selectively regulate cell lineages at a single cell level. Overall, we conclude that single cell RNA‐seq analysis in this embryo is revealing of the cell types present during development, of the changes in the gene regulatory network resulting from inhibition of various signaling pathways, and of the selectivity of these pathways in influencing developmental trajectories.     

RNA‐sequencing profiles hippocampal gene expression in a validated model of cancer‐induced depression

To investigate the pathophysiology of cancer‐induced depression (CID), we have recently developed a validated CID mouse model. Given that the efficacy of antidepressants in cancer patients is controversial, it remains unclear whether CID is a biologically distinct form of depression. We used RNA‐sequencing (RNA‐seq) to investigate differentially expressed genes (DEGs) in hippocampi of animals from our CID model relative a positive control model of depressive‐like behavior induced with chronic corticosterone (CORT). To validate RNA‐seq results, we performed quantitative real‐time RT‐PCR (qRT‐PCR) on a subset of DEGs. Enrichment analysis using DAVID was performed on DEGs to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and biological process gene ontologies (GO:BP). qRT‐PCR results significantly predicted RNA‐seq results. RNA‐seq revealed that most DEGs identified in the CORT model overlapped with the CID model. Enrichment analyses identified KEGG pathways and GO:BP terms associated with ion homeostasis and neuronal communication for both the CORT and CID model. In addition, CID DEGs were enriched in pathways and terms relating to neuronal development, intracellular signaling, learning and memory. This study is the first to investigate CID at the mRNA level. We have shown that most hippocampal mRNA changes that are associated with a depressive‐like state are also associated with cancer. Several other changes occur at the mRNA level in cancer, suggesting that the CID model may represent a biologically distinct form of a depressive‐like state.     

PipeCraft: Flexible open‐source toolkit for bioinformatics analysis of custom high‐throughput amplicon sequencing data

High‐throughput sequencing methods have become a routine analysis tool in environmental sciences as well as in public and private sector. These methods provide vast amount of data, which need to be analysed in several steps. Although the bioinformatics may be applied using several public tools, many analytical pipelines allow too few options for the optimal analysis for more complicated or customized designs. Here, we introduce PipeCraft, a flexible and handy bioinformatics pipeline with a user‐friendly graphical interface that links several public tools for analysing amplicon sequencing data. Users are able to customize the pipeline by selecting the most suitable tools and options to process raw sequences from Illumina, Pacific Biosciences, Ion Torrent and Roche 454 sequencing platforms. We described the design and options of PipeCraft and evaluated its performance by analysing the data sets from three different sequencing platforms. We demonstrated that PipeCraft is able to process large data sets within 24 hr. The graphical user interface and the automated links between various bioinformatics tools enable easy customization of the workflow. All analytical steps and options are recorded in log files and are easily traceable.