Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean-variance relationship of the log-counts-per-million using 'voom'. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source 'limma' package.

The lung presents a highly oxidative environment, which is tolerated through engagement of tightly controlled stress response pathways. A critical stress response mediator is the transcription factor nuclear factor erythroid-2-related factor 2 (NFE2L2/NRF2), which is negatively regulated by Kelch-like ECH-associated protein 1 (KEAP1). Alterations in the KEAP1/NRF2 pathway have been identified in 23% of lung adenocarcinomas, suggesting that deregulation of the pathway is a major cancer driver. We demonstrate that inactivation of Keap1 and Pten in the mouse lung promotes adenocarcinoma formation. Notably, metabolites identified in the plasma of Keap1f/f/Ptenf/f tumor-bearing mice indicate that tumorigenesis is associated with reprogramming of the pentose phosphate pathway. Furthermore, the immune milieu was dramatically changed by Keap1 and Pten deletion, and tumor regression was achieved utilizing immune checkpoint inhibition. Thus, our study highlights the ability to exploit both metabolic and immune characteristics in the detection and treatment of lung tumors harboring KEAP1/NRF2 pathway alterations.

Pooled library sequencing screens that perturb gene function in a high-throughput manner are becoming increasingly popular in functional genomics research. Irrespective of the mechanism by which loss of function is achieved, via either RNA interference using short hairpin RNAs (shRNAs) or genetic mutation using single guide RNAs (sgRNAs) with the CRISPR-Cas9 system, there is a need to establish optimal analysis tools to handle such data. Our open-source processing pipeline in edgeR provides a complete analysis solution for screen data, that begins with the raw sequence reads and ends with a ranked list of candidate genes for downstream biological validation. We first summarize the raw data contained in a fastq file into a matrix of counts (samples in the columns, genes in the rows) with options for allowing mismatches and small shifts in sequence position. Diagnostic plots, normalization and differential representation analysis can then be performed using established methods to prioritize results in a statistically rigorous way, with the choice of either the classic exact testing methodology or generalized linear modeling that can handle complex experimental designs. A detailed users' guide that demonstrates how to analyze screen data in edgeR along with a point-and-click implementation of this workflow in Galaxy are also provided. The edgeR package is freely available from http://www.bioconductor.org.

SNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Existing SNP calling algorithms have difficulty dealing with these low frequency variants, as the underlying models rely on each genotype having a reasonable number of observations to ensure accurate clustering.

Carefully designed control experiments provide a gold standard for benchmarking different genomics research tools. A shortcoming of many gene expression control studies is that replication involves profiling the same reference RNA sample multiple times. This leads to low, pure technical noise that is atypical of regular studies. To achieve a more realistic noise structure, we generated a RNA-sequencing mixture experiment using two cell lines of the same cancer type. Variability was added by extracting RNA from independent cell cultures and degrading particular samples. The systematic gene expression changes induced by this design allowed benchmarking of different library preparation kits (standard poly-A versus total RNA with Ribozero depletion) and analysis pipelines. Data generated using the total RNA kit had more signal for introns and various RNA classes (ncRNA, snRNA, snoRNA) and less variability after degradation. For differential expression analysis, voom with quality weights marginally outperformed other popular methods, while for differential splicing, DEXSeq was simultaneously the most sensitive and the most inconsistent method. For sample deconvolution analysis, DeMix outperformed IsoPure convincingly. Our RNA-sequencing data set provides a valuable resource for benchmarking different protocols and data pre-processing workflows. The extra noise mimics routine lab experiments more closely, ensuring any conclusions are widely applicable.

Illumina whole-genome expression BeadArrays are a popular choice in gene profiling studies. Aside from the vendor-provided software tools for analyzing BeadArray expression data (GenomeStudio/BeadStudio), there exists a comprehensive set of open-source analysis tools in the Bioconductor project, many of which have been tailored to exploit the unique properties of this platform. In this article, we explore a number of these software packages and demonstrate how to perform a complete analysis of BeadArray data in various formats. The key steps of importing data, performing quality assessments, preprocessing, and annotation in the common setting of assessing differential expression in designed experiments will be covered.

Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or "filtered" from further analysis to avoid misleading results.

Genomic imprinting establishes parental allele-biased expression of a suite of mammalian genes based on parent-of-origin specific epigenetic marks. These marks are under the control of maternal effect proteins supplied in the oocyte. Here we report epigenetic repressor Smchd1 as a novel maternal effect gene that regulates the imprinted expression of ten genes in mice. We also found zygotic SMCHD1 had a dose-dependent effect on the imprinted expression of seven genes. Together, zygotic and maternal SMCHD1 regulate three classic imprinted clusters and eight other genes, including non-canonical imprinted genes. Interestingly, the loss of maternal SMCHD1 does not alter germline DNA methylation imprints pre-implantation or later in gestation. Instead, what appears to unite most imprinted genes sensitive to SMCHD1 is their reliance on polycomb-mediated methylation as germline or secondary imprints, therefore we propose that SMCHD1 acts downstream of polycomb imprints to mediate its function.

Archetypal human pluripotent stem cells (hPSC) are widely considered to be equivalent in developmental status to mouse epiblast stem cells, which correspond to pluripotent cells at a late post-implantation stage of embryogenesis. Heterogeneity within hPSC cultures complicates this interspecies comparison. Here we show that a subpopulation of archetypal hPSC enriched for high self-renewal capacity (ESR) has distinct properties relative to the bulk of the population, including a cell cycle with a very low G1 fraction and a metabolomic profile that reflects a combination of oxidative phosphorylation and glycolysis. ESR cells are pluripotent and capable of differentiation into primordial germ cell-like cells. Global DNA methylation levels in the ESR subpopulation are lower than those in mouse epiblast stem cells. Chromatin accessibility analysis revealed a unique set of open chromatin sites in ESR cells. RNA-seq at the subpopulation and single cell levels shows that, unlike mouse epiblast stem cells, the ESR subset of hPSC displays no lineage priming, and that it can be clearly distinguished from gastrulating and extraembryonic cell populations in the primate embryo. ESR hPSC correspond to an earlier stage of post-implantation development than mouse epiblast stem cells.

The KRAS oncoprotein, a critical driver in 33% of lung adenocarcinoma (LUAD), has remained an elusive clinical target due to its perceived undruggable nature. The identification of dependencies borne through common co-occurring mutations are sought to more effectively target KRAS-mutant lung cancer. Approximately 20% of KRAS-mutant LUAD carry loss-of-function mutations in KEAP1, a negative regulator of the antioxidant response transcription factor NFE2L2/NRF2. We demonstrate that Keap1-deficient KrasG12D lung tumors arise from a bronchiolar cell-of-origin, lacking pro-tumorigenic macrophages observed in tumors originating from alveolar cells. Keap1 loss activates the pentose phosphate pathway, inhibition of which, using 6-AN, abrogated tumor growth. These studies highlight alternative therapeutic approaches to specifically target this unique subset of KRAS-mutant LUAD cancers.

The data produced by long-read third-generation sequencers have unique characteristics compared to short-read sequencing data, often requiring tailored analysis tools for tasks ranging from quality control to downstream processing. The rapid growth in software that addresses these challenges for different genomics applications is difficult to keep track of, which makes it hard for users to choose the most appropriate tool for their analysis goal and for developers to identify areas of need and existing solutions to benchmark against.

Smac mimetics target inhibitor of apoptosis (IAP) proteins, thereby suppressing their function to facilitate tumor cell death. Here we have evaluated the efficacy of the preclinical Smac-mimetic compound A and the clinical lead birinapant on breast cancer cells. Both exhibited potent in vitro activity in triple-negative breast cancer (TNBC) cells, including those from patient-derived xenograft (PDX) models. Birinapant was further studied using in vivo PDX models of TNBC and estrogen receptor-positive (ER+) breast cancer. Birinapant exhibited single agent activity in all TNBC PDX models and augmented response to docetaxel, the latter through induction of TNF. Transcriptomic analysis of TCGA datasets revealed that genes encoding mediators of Smac-mimetic-induced cell death were expressed at higher levels in TNBC compared with ER+ breast cancer, resulting in a molecular signature associated with responsiveness to Smac mimetics. In addition, the cell death complex was preferentially formed in TNBCs versus ER+ cells in response to Smac mimetics. Taken together, our findings provide a rationale for prospectively selecting patients whose breast tumors contain a competent death receptor signaling pathway for the further evaluation of birinapant in the clinic.

Despite their common use in research, monoclonal antibodies are currently not systematically sequenced. This can lead to issues with reproducibility and the occasional loss of antibodies with loss of cell lines. Hybridoma cell lines have been the primary means of generating monoclonal antibodies from immunized animals, including mice, rats, rabbits, and alpacas. Excluding therapeutic antibodies, few hybridoma-derived antibody sequences are known. Sanger sequencing has been "the gold standard" for antibody gene sequencing, but this method relies on the availability of species-specific degenerate primer sets for amplification of light and heavy antibody genes and it requires lengthy and expensive cDNA preparation. Here, we leveraged recent improvements in long-read Oxford Nanopore Technologies (ONT) sequencing to develop Nanopore Antibody sequencing (NAb-seq): a three-day, species-independent, and cost-effective workflow to characterize paired full-length immunoglobulin light- and heavy-chain genes from hybridoma cell lines. When compared to Sanger sequencing of two hybridoma cell lines, long-read ONT sequencing was highly accurate, reliable, and amenable to high throughput. We further show that the method is applicable to single cells, allowing efficient antibody discovery in rare populations such as memory B cells. In summary, NAb-seq promises to accelerate identification and validation of hybridoma antibodies as well as antibodies from single B cells used in research, diagnostics, and therapeutics.

No abstract available

Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.

Illumina Sentrix-6 Whole-Genome Expression BeadChips are relatively new microarray platforms which have been used in many microarray studies in the past few years. These Chips have a unique design in which each Chip contains six microarrays and each microarray consists of two separate physical strips, posing special challenges for precise between-array normalization of expression values.

Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants.

Down syndrome (DS) individuals suffer mental retardation with further cognitive decline and early onset Alzheimer's disease.

The RUNX1 transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML). The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia.

RNA-seq datasets can contain millions of intron reads per library that are typically removed from downstream analysis. Only reads overlapping annotated exons are considered to be informative since mature mRNA is assumed to be the major component sequenced, especially for poly(A) RNA libraries. In this study, we show that intron reads are informative, and through exploratory data analysis of read coverage that intron signal is representative of both pre-mRNAs and intron retention. We demonstrate how intron reads can be utilized in differential expression analysis using our index method where a unique set of differentially expressed genes can be detected using intron counts. In exploring read coverage, we also developed the superintronic software that quickly and robustly calculates user-defined summary statistics for exonic and intronic regions. Across multiple datasets, superintronic enabled us to identify several genes with distinctly retained introns that had similar coverage levels to that of neighbouring exons. The work and ideas presented in this paper is the first of its kind to consider multiple biological sources for intron reads through exploratory data analysis, minimizing bias in discovery and interpretation of results. Our findings open up possibilities for further methods development for intron reads and RNA-seq data in general.